Development of an Adaptive Fuzzy Logic System for Energy Management in Residential Buildings

Transcription

1 Development of an Adaptive Fuzzy Logic System for Energy Management in Residential Buildings by Abdolazim Keshtkar M.Sc., Iran University of Science and Technology, 2006 B.Sc., University of Guilan, 1999 Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in the School of Mechatronic Systems Engineering Faculty of Applied Sciences Abdolazim Keshtkar 2015 SIMON FRASER UNIVERSITY Summer 2015

2 Approval Name: Degree: Title: Examining Committee: Abdolazim Keshtkar Doctor of Philosophy Development of an Adaptive Fuzzy Logic System for Energy Management in Residential Buildings Chair: Kevin Oldknow Lecturer Siamak Arzanpour Senior Supervisor Associate Professor Mehrdad Moallem Supervisor Professor Jiacheng Wang Supervisor Assistant Professor Krishna Vijayaraghavan Internal Examiner Assistant Professor Hamidreza Zareipour External Examiner Professor Department of Electrical and Computer Engineering University of Calgary Date Defended/Approved: August 26, 2015 ii

3 Abstract Heating, Ventilation, and Air Conditioning (HVAC) systems are the main target for energy and load management in residential buildings due to their high energy consumption. The role of Thermostats is to automatically control the HVAC systems while users accommodate their everyday schedules and preferences. The initiatives such as demand response (DR) programs, Time of Use (TOU) rates, and real-time pricing (RTP) are often applied by smart grids to encourage customers in order to reduce consumption during peak demand periods. However, it is often a hassle for residential users to manually reprogram their thermostats in response to smart grid initiatives and/or environmental conditions that vary over time. In this thesis, the research endeavors are dedicated to bring forward a novel autonomous and adaptable system for control of residential HVAC systems which results in an Adaptive Smart Thermostat. To do so, a House Simulator is developed in MATLAB-GUI with thoughtful consideration as a tool to facilitate the study of energy management for residential HVAC systems in smart grids. The simulator also assists in the implementation and verification of our proposed techniques under different scenarios such as RTP, various user schedules, and different environmental conditions. Furthermore, a new algorithm using rule-based fuzzy logic and wireless sensors capabilities for residential demand-side management is developed. The algorithm is augmented into existing programmable communicating thermostats (PCT) in order to enhance the learning capability of PCTs during participation in DR programs. The conducted results show that the PCT equipped with our approach, versus existing PCTs, performs better with respect to energy and cost saving, while maintaining user thermal comfort. The achieved results led us to develop a novel Autonomous Smart Thermostat that is the result of a synergy of supervised fuzzy logic learning, wireless sensor capabilities, and smart grid incentives. The results demonstrate that the developed thermostat autonomously adjusts the set point temperatures in ASHRAE thermal comfort-zone, while not ignoring the energy conservation aspects. However, in cases that the user overrides the decision(s) made by autonomous system, a novel Adaptive Fuzzy Learning Model utilizing wireless sensor capabilities is developed in order to learn and adapt to user new preference and schedule changes based on rulebased fuzzy logic learning. The results show that the developed system is adaptable, smart, and capable of intelligent zoning control while it improves energy management in residential buildings without jeopardizing thermal comfort. Keywords: Fuzzy Logic Learning; Adaptive Systems; Wireless Sensors; Smart Grid Initiatives; Residential Energy Management; Smart Thermostats iii

4 Dedication To my parents Mohammad Hossein and Kobra and To my wife, Foroogh, and to our lovely angel Narmella iv

5 Acknowledgments I would like to express my gratitude to my senior supervisor Dr. Siamak Arzanpour for his support, patience, and encouragement. His guidance and comments assisted me in all the time of research and writing of this thesis. I am grateful to my supervisors: Dr. Mehrdad Moallem and Dr. Jiacheng Wang for their helpful comments during my research. I am also profoundly thankful to Mr. Reza Baraty from Surround Technology Ltd. for his encouragement. I would like to thank Dr. Krishna Vijayaraghavan for his time and comments as my internal examiner. I would also like to express my appreciation to Dr. Kevin Oldknow for his time and management as the chair of my defense session. My deep gratitude goes to Dr. Hamidreza Zareipour for reading my thesis as external examiner and his precious and insightful comments. My deep appreciations go to staff in the School of Mechatronic Systems Engineering, Mrs. Jennifer Coffey, Mr. Taha Al-khudairi, Mrs. Jennifer Leone, Mrs. Carol Jang, Mr. Zian Khanzadeh, and Mrs. Julibeth Fernandez for their time, help, and patience. I would also like to thank my Friends: Dr. Sadegh HajHashemi, Dr. Pouria Ahmadi, Dr. Vahid Zakeri, Kian Davoudi Rad, Sina Doroudgar, Soheil Sadeghi, Moein Manbachi, and Shahab Azimi for their help and time. I had amazing times with you guys. I am deeply grateful to my parents Mohammad Hossein and Kobra for raising and taking care of me with love, and to my brothers and sisters for their unconditional supports, and special thanks to my elder brother Dr. Fazel Keshtkar for teaching me artificial intelligence and his comments. My sincere appreciations go to my wife Foroogh for her love, moral support, patience, and understanding during my study, and to my beloved daughter Narmella who enlightened our life. Foroogh and Narmella!!! I am not able to turn back time; however, I will do my best to make up the times that I have missed spending with you. v

6 Table of Contents Approval... ii Abstract... iii Dedication... iv Acknowledgments... v Table of Contents... vi List of Tables... ix List of Figures... x List of Acronyms... xiii Chapter 1. Introduction Background Motivation and Objective Research Description Summary of Contribution Thesis Outline Chapter 2. Literature Review Introduction Residential HVAC Systems and Role of Thermostats Residential Thermostats Programmable Thermostats (PTs) Programmable Communicating Thermostats (PCTs) Intelligent Thermostats Thermal Comfort Residential Energy Management and the Role of Smart Grid Initiatives: Methods and Programs Demand-side Management (DSM) Programs Smart Grid Technology Smart Grid initiatives and Consumer Engagement Smart Meters and Their Benefits for Consumers Dynamic Pricing Incentives Demand Response (DR) Programs In-home Displays and Energy Information Tools Smart Home Energy management Systems and Home Area Network (HAN) Wireless Sensor Networks (WSN) Fuzzy Logic Expert Systems Rule-based Expert Systems Fuzzy Expert Systems Application of Artificial Intelligence, Wireless Sensors, Smart Grid initiatives for Energy management: a Review vi

7 Chapter 3. Development of a House Heating-Cooling Simulator with the Application in Smart Grids Introduction Model of a Residential Heating-Cooling System House Thermodynamic Model for the Simulator Design of House Simulator in MATLAB-GUI Components of the Simulator Simulation results and Discussion Chapter 4. Smart Demand-side Management (DSM) in Smart Grids Introduction Fuzzy Logic System and Description of DSM in a House Platform Input and Output Parameters and Their Associate Membership Functions Outdoor Temperature (T out ) Electricity Price (P E ) Occupant Presence (P O ) Initialized Set Point (S i ) System Output or Load Reduction (L R ) Fuzzy Logic Decision-Making Algorithm Simulation Results and Discussion Comparison of FLA with Conventional Thermostat, PT, and PCT under TOU Programs Response of the FLA to WSN information under RTP The Role of FLA, WSN, and Smart Grid Incentives in Residential Energy Management Chapter 5. Implementation of Adaptive Fuzzy Logic Learning Introduction Synopsis of the Solution Autonomous Smart Thermostat Using Fuzzy Logic Input and Output Variables and Fuzzy Control Rule-based Fuzzy Control Rules for Autonomous Smart Thermostat Simulation Results and Performance of SFLL Adaptive Fuzzy Logic Learning System Methodology of Adaptation Adaptive Learning Model (ALM) Application of ALM for Adaptation of Autonomous Thermostat Input of ALM and Their Membership Functions System Outputs for Adaptation Fuzzy Logic Decision-making for Adaptation Description of the Adaptive Fuzzy Logic Algorithm Overview Implementation Steps and Routines of the AFL Intelligent Zone Control (ZC) Simulation Results and Performance of AFL Simulation of Intelligent Zone Control (ZC) vii

8 The Role of Adapting to Occupant s Patterns in Residential Energy Management Analysis of Adapting to User s Pattern Changes Chapter 6. Conclusion and Future Works Conclusion Future Works References Appendix A. Fuzzy Confidence Interval (FCI) Theory Application of FCI in Our Research Appendix B. Preliminary Works and Experiments Implementation of Zigbee-based Thermostat for Controlling an Air Conditioner (AC) System Arduino UNO Microcontroller Input and Output Pins Communication Programming Implementation of a wireless rule-based thermostat for an AC System viii

9 List of Tables Table 2.1. Acceptable temperature ranges based on ASHRAE Standard Table 3.1. User schedules for weekdays Table 3.2. User schedules for weekends Table 3.3. List of House Simulation Parameters Table 3.4. TOU rates for winter season, Hydro one, Ontario, Canada Table 4.1. Some of Fuzzy logic decision-making rules Table 4.2. User schedules for weekdays Table 5.1. Some of fuzzy rules for adjusting set points for Comfort Mode Table 5.2. Ten different scenarios for verification of the performance of SFLL Table 5.3. Fuzzy rules for adapting to pattern changes Table 5.4. Some of Fuzzy rules for intelligent zone-control Table 5.5. Daily intervals and their associated set points Table 5.6. Designed scenarios for assessment of intelligent zone control Table 5.7. Results of AFL (with and without ZC and Adaptation) Table 5.8. User schedules for weekdays Table 5.9. Adapted Values for different changes Table Adapted values for different changes ix

10 List of Figures Figure 1.1. Conceptual Design of the Proposed Smart Thermostat in the House... 9 Figure 2.1. Distribution of energy use by sectors Figure 2.2. Energy consumption by end-use devices in Canada Figure 2.3. Residential central forced-air HVAC system Figure 2.4. Block diagram for heating system Figure 2.5. The relationship between PMV and PPD Figure 2.6. Load control strategies Figure 2.7. TOU and RTP pricing mechanisms Figure 2.8. Platform of sensor nodes Figure 2.9. Telos ultra-low power wireless module (sensor node) Figure Basic structure of a rule-based expert system Figure Structure of Fuzzy Logic System Figure An example for triangular membership function Figure The centroid method of Defuzzification Figure 3.1. Concept of House Heating-Cooling System Model Figure 3.2. Heat transfer based on electrical module Figure 3.3. A typical two storey house Figure 3.4. Simulator Interface at Work for one day Figure 3.5. Schedule for setting the daily/weekly set points Figure 3.6. Demand Response Feature for Simulator Figure 3.7. House and HVAC Parameters in Simulator Figure 3.8. Simulator Price Control Figure 3.9. Different components of the simulator Figure Response time of the system for different outdoor temperature Figure Energy consumption and cost for schedule and non-schedule set points Figure A scenario of participating in DR based on TOU rates Figure Energy consumption and associated cost in Zone-controlled environment Figure 4.1. A simplified illustration of smart DSM applied to a residential HVAC system x

11 Figure 4.2. Membership functions of outdoor temperature Figure 4.3. Membership functions of electricity price Figure 4.4. Membership functions of occupancy Figure 4.5. Membership functions of set points initialized by user Figure 4.6. Membership functions of system output (Load Reduction) Figure 4.7. Flowchart of fuzzy logic decision-making algorithm Figure 4.8. Outdoor temperature, occupant s presence, and TOU prices Figure 4.9. Simulator as Conventional Thermostat with DR Enabled Figure Simulator as PT with DR Enabled Figure Simulator as PCT with DR enabled Figure Simulator as PCT Equipped with FLA and DR enabled Figure Comparison of PCT and FLA with DR enabled for fixed set point Figure RTP for residential customers, Ameren Illinois Power Co., Jan Figure Response of FLA to WSN information under RTP Figure Energy consumption with/without enabling different sensor nodes Figure 5.1. Main controller unit for adapting and intelligent energy management Figure 5.2. Membership function of residential hourly demand Figure 5.3. Fuzzy membership functions of system output (Set Point) Figure 5.4. Adjusted set points by autonomous smart thermostat: Comfort Mode Figure 5.5. Adjusted set points for economy and comfort modes Figure 5.6. Conceptual design of ALM Figure 5.7. Daily cluster Figure 5.8. Membership function of override flag Figure 5.9. Membership functions of Start Time and End Time Figure Membership functions of weights (Heat-Cool Set Points) Figure Membership function of start time-end time for adaptation as output Figure Membership function of heat-cool set point for adaptation as output Figure Implementation of AFL Figure Membership functions for difference between indoor and outdoor temperature xi

12 Figure Membership function of airflow rate Figure Ontario Hourly Demand for one day taken from [140] Figure Hourly electricity price taken from [140] Figure Comparison of adjusting set points with and without intelligent zone control Figure Results of AFL (Entire house vs. zone controlled) Figure One day simulation for the system under training Figure Heat Set Point for Different Days of the Second Week xii

13 List of Acronyms AC Air Conditioner ADC Analog-to-Digital Conversion AFL Adaptive Fuzzy Logic AHAM Association of Home Appliance Manufacturers AI Artificial Intelligence ALM Adaptive Learning Model AMI Advanced Metering Infrastructure ANN Artificial Neural Networks ASHRAE American Society of Heating, Refrigerating and Air-Conditioning Engineers BTU British Thermal Unit CAHEM Computer-Aided Home Energy Management COG Center of Gravity DLC Direct Load Control DR Demand Response DSM Demand-side Management EE Energy Efficiency EGU Electricity Generating Utilities EIA Energy Information Administration EMS Energy management Systems EV Electric Vehicle FLA Fuzzy Logic Approach FLC Fuzzy Logic Controller GA Genetic Algorithm GHG Greenhouse Gases GUI Graphical User Interface HAN Home Area Network HV Hybrid Electric Vehicle HMM Hidden Markov Model HVAC Heating, Ventilation, and Air Conditioning ICT Information and Communications Technologies KB Knowledge Base xiii

14 kw kwh LCD MOM TOD TOU PAR PC PCT PHEV PIR PLC PMV PPD PT PV RECS RFID RH RTP SFLL SP SSR WSN ZC Kilowatt Kilowatt hour Liquid Crystal Display Mean of Maxima Time of Day Time of Use Peak-to-Average Ratio Personal Computer Programmable Communicating Thermostat Plug-in Hybrid Electric Vehicle Passive Infrared Power Line Communication Predicted Mean Vote Predicted Percent Dissatisfied Programmable Thermostat Photo Voltaic Residential Energy Consumption Survey Radio Frequency Identification Tags Relative Humidity Real-time Pricing Supervised Fuzzy Logic Learning Set Point Solid State Relay Wireless Sensor Networks Zone Control xiv

15 Chapter 1. Introduction 1.1. Background The world has confronted some interrelated challenges such as management of electric consumption, energy efficiency, and global warming. In order to manage these challenges, many governments and electric utilities have recently renewed their policies. The policies include the conservation and energy efficiency programs, demand response programs, changing electricity pricing mechanisms, making new energy management systems, residential or commercial load management programs, and fuel substitution programs [1], [2], [3], [4], [5], [6]. These programs result in using the available energy resources more efficiently, reducing regional electrical outages, avoiding interruption in supply systems, reduction in CO 2 and greenhouse gases emissions, and risk management [4], [5], [6]. Among five sectors of the economy, which are Industrial, Transportation, Residential, Commercial, and Agriculture, residential buildings have been the third place in terms of consuming energy [7], [8]. They account for approximately 17% and 14% of total energy consumption in Canada and the U.S. respectively. In addition, the residential electricity demand is predicted to increase by 24% in the following two decades [9]. Among all appliances in residential sector; heating, ventilation, and air conditioning (HVAC) systems are the main target for energy and load management because they constitute a significant part of the total energy consumption in the world and particularly in the North America [7], [9]. HVAC systems are also the main electrical load during peak load periods. Moreover, electricity use for HVAC systems is rapidly increasing due to population growth in hot/cold climates, changing lifestyle, and greater demand for comfort. The amount of energy to heat up or cool down homes comprised of 1

16 approximately 64% and 54% of the total residential energy in Canada and the U.S. respectively [7]. Nowadays, a set of initiatives and incentives such as Time of Use (TOU) rates, Real-time Pricing (RTP), Energy Efficiency (EE) programs, and Demand Response (DR) programs, Home Area Networks (HAN) are introduced by electric utilities and smart grids to encourage residential customers to participate in load management programs [4], [10], [11]. The aim of residential load management programs usually consists of the following design objectives: shedding consumption and/or shifting consumption [3], [12]. However, responding to smart grid initiatives such as DR and TOU strongly depends on people acceptance and participation; and in some cases, such as building fully home automation systems for load management, are not cost-effective [5], [12], [13], [14]. Nevertheless, residential energy conservation and load management can significantly be facilitated through the devices that allow their users to schedule and program their usage automatically such as programmable thermostats, dishwasher, washing machines, pool pumps, and electric water heater [15], [16], [17]. On the other hand, changing needs, schedules, lifestyle, and rising electricity prices (e.g., transition from fixed price to dynamic pricing) impact the consumers whose energy bills significantly relate to HVAC systems. Programmable thermostats (PTs) are being used widely for automatic control of HVAC systems to provide satisfactory thermal comfort and save energy [15]. The main feature of the PTs is programmability. This feature enables users to change the set point (SP) temperatures on a schedule based on their preferences and needs. Since occupants needs, daily schedules, habits, and preferences are totally different; most of the times conserving energy, saving cost, and providing thermal comfort depend on how PTs are programmed and controlled by users. Hence, PTs require constant interaction and programming for their best performance. In addition, one of the biggest problems of using PTs is that users often mistake to utilize these devices effectively or forget to setback the initialized SP temperatures. Several studies have also shown insignificant savings in users with PTs compared to users using conventional thermostats. Other shortcomings of PTs are: relying on a single indoor temperature sensor and lack of communication with new smart grid technologies (i.e., smart meters), lack of multiple 2

17 zone temperature control, and lack of learning and adapting to users schedule changes [15], [18], [19], [20]. Advanced Metering Infrastructure (AMI) systems are now being deployed as a part of growing smart grid initiatives to provide foundational platforms for engaging consumers to Demand-Side Management (DSM) programs such as DR [4], [10], [21]. Many utilities ensure that the deployed AMIs supports DR functionality through communicating with the home devices via smart meters. The smart meters and two-way AMI networks can enable the measurement and verification capabilities that allow the utility to verify which controlling devices participate in a DR event and how much load is reduced. Furthermore, recent advances in ultra-low power wireless sensor networks (WSN) technology have enabled unprecedented monitoring and control capabilities at less cost than traditional wired sensing [22]. WSNs can enable the disaggregation of the conventional thermostat into the functional components: sensing, computation, and actuation. The dramatic cost reduction in acquiring significantly more sensor information presents new opportunities for residential demand-side energy management [23]. For example, WSNs can potentially enable inexpensive room-by-room monitoring of environmental conditions, such as temperature, humidity, and even occupancy. Nowadays, the capabilities of wireless sensor nodes to measure different variables of interest (i.e., temperature, occupant activity, humidity, pressure) can help both supply-side and demand-side to manage and conserve energy. Capabilities of this technology can even enhance the limitations of the existing energy management devices such as thermostats [24]. In addition, the integration and utilization of different wireless sensor nodes and intelligent approaches such as fuzzy logic, neural networks, and artificial intelligence techniques are investigated and tested in home devices, HAVC and lighting systems to save energy and provide user comfort [25], [26]. With the advancement in wireless sensors, communications networks, and popularity of using smart meters, nowadays, PTs have been extended into programmable communicating thermostats (PCTs) [18]. Some of PT s shortcomings such as lack of communication with new smart grid technologies are addressed in 3

18 existing PCTs. The main feature of PCTs is their capability to participate in DR programs and TOU rates at user discretion. In this way, PCTs can potentially reduce the residential HVAC load by decreasing or increasing the initialized SP temperatures when the prices vary over the time via communicating with deployed smart meters [14], [18]. However, in most cases the users with PCTs experience a significant thermal dissatisfaction during participation in DR events particularly in cold winters and hot summers [19]. In these cases the PCT requires constant interaction from its user to modify the initialized set points. This often causes inconvenience for users. In addition, the users with PCTs face a major barrier in the regions that the electricity pricing mechanism is flat-rate since the PCT only operates as a simple PT. Besides, in many cases, the users with PCTs have similar problems that the users with PTs [19]. Furthermore, human behavior is one of the important parameters that may impact the operations of thermostats. According to Energy Information Administration (EIA) in the U.S., during the heating seasons, in the houses equipped with PTs; 60% of consumers used these devices to decrease the temperature during the night time but only 45% decrease it during the day time. Additionally, during the cooling season, only 55% of users with PTs set their thermostats to increase temperature at night as well as during the day. Therefore, using a PT by itself does not guarantee reduction in energy consumption and most of the times it depends on how the device is programmed and controlled by the users. On the other hand, about 35% to 50% of the U.S. households utilize the PTs as a non-programmable thermostat (on/off switch) [18], [19]. In addition to PTs, as explained in [18] the smart thermostats are being used in many residential buildings across the U.S. where they attempt to make more automated decision than PTs. These devices have a user advanced interface and for the first few weeks of operation; the users initialize their schedules and preferences for different times of the day to maintain thermal comfort. The thermostat then learns occupant s schedules for the different days of the week. Finally, after learning occupant s schedules and preferences it reuses this information for next daily/weekly schedules. However, it does not address the problem of learning schedule and pattern changes. Therefore, they seem to be a PT with capability of memorizing and remembering the occupant preferred SP temperatures or daily schedules. As discussed in [13], demand response thermostats are also called and being used as smart thermostats with the aim to 4

19 decrease/increase the SP temperatures in response to pricing signals sent from the utilities. However, these kinds of thermostats are not able to adapt to occupant s pattern and schedule changes. In addition, they do not provide thermal comfort during sudden rise or drop in outdoor temperature due to large shifting (offsets) from the initialized SP during high electricity rates. As a result, existing smart thermostats are not really smart as they do not reflect the learning and adapting to user schedule changes [18]. Hence, designing a Smart Thermostat which does not require to be constantly programmed by its user could address these problems. Moreover, smart gird initiatives are designed and applied by utilities to encourage consumers to reduce their electricity usage during peak load demands or high electricity prices by shedding the loads during on peak hours or shifting loads to off-peak hours. Seven reasons are summarized in [18] to demonstrate the importance and the role of residential thermostats in reducing peak load demand. For example, they concluded that by reducing the set point temperatures in winter during DR events or high electricity prices the thermostats can contribute to shave peak load demand. Thus, the DSM programs for residential devices such as HVAC systems can potentially benefit both consumers and utilities. However, consumers knowledge regarding DR, DSM programs and even their impacts on electricity bill and energy management is fairly limited [21]. In addition, the lack of knowledge among the residential customers regarding how to respond to RTP and DR programs and the lack of intelligence in residential energy management systems (i.e., PTs and PCTs) are two major obstacles for optimally utilizing the advantages of smart grid incentives. For example, it has been shown in [27] that although the electricity prices have been accessible via telephone and the Internet for residential customers as a RTP program in Chicago, households rarely check prices to respond properly because it has been difficult for them to constantly follow the hourly prices. Therefore, the need for adaptable smart energy management systems such as thermostats capable of making intelligent decisions based on smart prices, while maintaining occupant s comfort is necessary. Recently, several sophisticated appliance scheduling schemes for residential energy management have been proposed in [28], [29], [30], [31], [32], [33]. They have suggested various methods to schedule appliances in order to provide an optimum 5

20 saving in energy and cost using the techniques such as particle optimization, neural network-based prediction approach, and game theory. In some cases such as [29] and [30] the proposed scheduling approaches increase the peak-to-average ratio (PAR). This (PAR) is a major problem when residential customers shift or schedule the operation of their appliances to off-peak hours. Although aforementioned approaches are able to provide energy savings, individual preferences of consumers are not considered in these methods. Besides, these approaches are used for residential appliances to only operate autonomously, however they have not considered the alternative solutions if the users override the made decisions (i.e., lack of adapting to user schedule and preference changes). Furthermore, with the advancement and broad applications of Artificial Intelligence (AI) models in technology, the emergence of adaptive learning systems in energy management systems is obvious [34]. Among all AI techniques, the fuzzy rulebased learning systems are widely used in various areas such as robotic and home appliances and considered to be the best option for building knowledge-based systems [35], [36]. Fuzzy Logic is also used in cases of machine learning endeavors, such as the unsupervised learning systems and optimization problems [37], [38]. The fuzzy logic only uses fuzzy variables for computing its output. Input and output sets are connected through a set of IF-THEN rules in order to obtain the corresponding output(s). The main advantage of fuzzy logic controllers compared to conventional ones is based on the fact that no mathematical modeling is required for controller design. In addition, Knowledge Base (KB) is the essential part of a fuzzy logic controller. The KB consists of IF-THEN rules, membership functions, and a database designed based on knowledge from an expert. KB is also based on learning methods which do not require a mathematical model of the system Motivation and Objective Nowadays, managing the peak load problems is being shifted more towards the demand-side rather than supply-side. However, there are different users with various interests when discussing peak load management in demand-side. These require making smart and more independent energy management systems such as thermostats 6

21 in demand-side rather than installing generators and building infrastructures in supplyside. In addition, the incentives such as DR initiatives, TOU rates, and RTP are offered by Electricity Generating Utilities (EGUs) to encourage consumers to shed or shift their electricity usage during peak load demand. Utilizing these initiatives in residential energy management systems such as thermostats will be beneficial for both utilities during peak load demand and consumers during high electricity prices. However, it is often confusing and a hassle for residential customers to manually respond and schedule their thermostats based on smart grid initiatives. Hence, in order to optimally utilize the benefits of such initiatives; the need for smart in-home energy management systems such as thermostats capable of responding to smart prices, while saving energy without jeopardizing user comfort is necessary. To date, researches on smart energy management systems for residential buildings is in infancy and in the most cases they have been concentrated on designing autonomous systems for energy management based on time-varying prices to shift the residential load to off-peak hours. This mostly increases peak-to-average ratio (PAR) when households shift the operation of their home appliances to off-peak hours. In addition, these approaches require constant interactions from users, while are not able to adapt to user pattern and preference changes. The main objective of this research is to develop a smart thermostat to address the above issues by utilizing Adaptive Fuzzy Learning Model and providing an autonomous, smart, and adaptable energy management system for residential buildings. The developed adaptive model can either be embedded into existing PCTs to extend them into a Smart Thermostat or can operate by itself as an Adaptable Smart Thermostat. The adaptive system creates an intelligent system, which utilizes Fuzzy Logic rule-based techniques, wireless sensors, and smart grid initiatives (i.e., dynamic electricity pricing) to learn and adapt, where resulting in energy and cost saving. By using the developed Supervised Fuzzy Logic Learning approach; the smart thermostat does not require to be constantly modified by user(s). In the cases that the user changes or overrides the made decisions, the smart thermostat detects and learns; then adapts to occupants preference and schedule changes utilizing the proposed fuzzy logic algorithm and adaptive learning principles without eliminating the existing knowledge. The 7

22 proposed Smart Thermostat can offer intelligent zone-control solution as well. Furthermore, it has the capability to participate in smart grid incentives to automatically respond to TOU rates or RTP programs without jeopardizing occupant s thermal comfort. As a result, the proposed approaches improve the comfort and energy management in residential buildings, and simultaneously offer new capacities and solutions that can be collaborated towards the development and integration of lighting systems, renewable energy storage systems such as Battery Storage Systems and Plug-in Hybrid Electric Vehicles into smart grid Research Description This research investigates the integration of wireless sensors capabilities and smart grid initiatives with rule-based fuzzy logic techniques and adaptive learning principles in order to develop a novel Adaptive Fuzzy Logic Learning System for a main controller unit, called Smart Thermostat. It offers intelligent energy management and conservation for residential buildings. There are many parameters that can be taken into account for residential energy management; however our fuzzy logic approach is developed for the parameters that directly relate to energy management and occupant s thermal comfort. The parameters are user activity and indoor and outdoor temperatures, electricity prices, and current load demand of the house or a region. As shown in Figure 1.1, the Smart Thermostat, as a main controller, constantly receives the information from the distributed wireless sensor nodes deployed inside and outside of the house. These sensors are used to measure or detect indoor and outdoor temperatures, and occupant activities in the house. It also communicates with deployed smart meter to read the price signals applied by utilities and current load demand of the house. As depicted in Figure 1.1 the smart thermostat utilizes fuzzy rule-based algorithms to make decision in order to adjust new set point temperatures based on the new information received from sensors and smart grid. It finally sends the control signals or actuation commands to turn on or off the residential HVAC system via actuator(s) according to new adjusted set points. The information received from sensors and smart grid is also used to adjust the air dampers for zone-control environment solution. In addition, the smart thermostat adapts to occupant s pattern and schedule changes using 8

23 the adaptive learning principles, if any change(s) in user schedules and preferences are being detected by sensors. Figure 1.1. Conceptual Design of the Proposed Smart Thermostat in the House As a result, the expectations of an actual Smart Thermostat that is equipped with the proposed "adaptive fuzzy logic algorithm" are as follows: An autonomous and smart system capable of operating in a correct and accurate way without constant interaction in order to save energy and provide thermal comfort (Reliability). A device that must be able to learn occupant s patterns via sensors and adapt to occupant s pattern and schedule changes without constant programming (Adaptability). The thermostat equipped with the proposed algorithm has to handle existing and smart grid initiatives such as TOU rates and RTP, demand-side management capability, and DR that result in energy management and conservation (The main goals of smart grids). A system with capability to control multiple zones of a house. This device has to be user friendly such that everyone can work and interact with it if needed (Understandability). 9

24 1.4. Summary of Contribution In order to implement the features mentioned in section 1.2, the initial system that is a programmable thermostat, needs to evolve by adding intelligence optimal solution techniques. The main contributions of this research are as follows: Implementing a prototype of Home Area Network (HAN) for energy management in residential buildings based on price-index utilizing Wireless Zigbee Sensors and a Microcontroller. This prototype is built in order to assure the combination of wireless sensors capabilities and conventional rule-based techniques will help to build and control in-home energy devices to save power and money via experiment (Appendix B). Creating a House Simulator for house heating/cooling model in Matlab-GUI with thoughtful consideration for using it as an expert system shell to evaluate the performance of our proposed approaches. Analytical model that consists of house thermodynamic equations to emulate indoor temperature sensor is derived and being embedded into simulator. House Simulator is designed and implemented in order to demonstrate the future possibility concepts of: o Supervised Fuzzy Logic Rule-based System using wireless sensors. o Adaptive Learning Model using wireless sensors and Fuzzy Logic and its use for evaluation, implementation, and verification of new adaptive fuzzy learning techniques for future Smart Thermostats. Proposing a novel fuzzy logic approach for Smart Demand-side Management in residential buildings; embedded into existing PCTs. Presenting a novel Supervised Fuzzy Logic Learning for designing an Autonomous Smart Thermostats utilizing wireless sensors and smart grid initiatives. 10

25 Developing a novel Adaptive Learning Model and utilizing wireless sensors and rule-based fuzzy logic for existing PCTs and proposed Autonomous Smart thermostats to learn and adapt to occupant s pattern changes. A novel knowledge base approach for intelligent zone-controlled environment using rule-based fuzzy logic and wireless sensors is investigated as a significant part of the system for energy conservation Thesis Outline The objective of this research is to create an Autonomous and Adaptable Fuzzy Logic Learning System which enables intelligent power management without sacrificing thermal comfort in residential buildings. Our proposed solution is extended beyond a programmable thermostat or even a programmable communicating thermostat by developing an autonomous and adaptive intelligent system. This thesis is organized by the following subsequent: a literature review is provided in Chapter 2. Chapter 3 describes and demonstrates house thermodynamic model, the designed house simulator engine in MATLAB-GUI, design architecture, and its objective to be used as a smart system shell for implementation of further stages of research. Details and initial results and discussion of the designed simulator are presented in this section as well. A novel demand-side management approach using fuzzy logic, wireless sensors, and smart grid incentives to enhance the learning capability of PCTs is discussed in Chapter 4. A novel Autonomous and Adaptable system for control of residential HVAC systems which results in an Adaptive Smart Thermostat is presented in Chapter 5. The proposed Adaptive Smart Thermostat is evaluated under different scenarios such as various pricing programs (i.e., RTP, TOU), changes in user preferences and schedules, etc. To verify the results statically; a fuzzy confidence interval is used. In addition, a simple but useful approach using rule-based fuzzy logic is added to the designed thermostat for zone-control environment solution. In Chapter 6 the concluding remarks and findings of the research efforts are presented. Recommendations for future research are also discussed in Chapter 6. In appendix A, the fuzzy logic confidence interval is discussed. In Appendix B, a few potential features of a cooler system by experiment utilizing Xbee wireless sensors, microcontroller, and the designed electric circuits, which could be beneficial for the readers is presented. 11

26 Chapter 2. Literature Review 2.1. Introduction After the energy crisis in 1970s, energy efficiency and management of electric power consumption have been considered as two major programs in the various sectors of the economy such as residential, commercial, and industrial. Many governments and utilities (Producers, National Grid Operator, Network Companies and Electric Suppliers) have recently renewed their policies to re-regulate and amend energy efficiency programs, electricity prices mechanisms, load control strategies, and consumer productivity. These policies would result in better energy management, reducing widespread regional electrical power outages, and making more automated decisions for demand load reduction, and risk management. According to the Natural Resources Canada s Office of Energy Efficiency, Canadians have spent $152 billion on energy in This amount has been spent for various purposes such as heating and cooling of residential, industrial, and institutional buildings as well as operating appliances, cars, and industrial processes. Figure 2.1shows the distribution of energy use by five sectors of the economy, i.e. residential, industrial, commercial/institutional, transportation and agriculture. Natural gas (28%) and electricity (21%) are used by all sectors of the economy and almost accounted for half of the total consumed energy in Canada [7]. As shown in Figure 2.1, residential sector is the third sector in terms of energy consumption and accounts for 17% of the total energy use in Canada. It is worth to be mentioned around 21% of the total energy is consumed by residential buildings in U.S. In terms of the amount of electricity use in residential buildings, it contributes 36% and 40% of total electricity consumption in U.S. and Canada respectively. In addition, the 12

27 average annual electricity consumption for a U.S. residential utility customer was 10,837 kwh, an average of 903 kwh per month in This amount for Canadian households was around kwh per year in Furthermore, according to the recent Annual Energy Outlook report of the U.S. Energy Information Administration, (EIA) residential electricity demand is forecasted to increase by 24% within the next two decades, while the global electricity consumption trend is also reported to be increasing continuously. The negative impacts of rising consumption are becoming more evident with reduction in fossil fuels and accumulating greenhouse gases (GHG) [7], [8], [9]. 40% 35% 30% 30% 37% Distribution of energy use by sectors in Canada 25% 20% 15% 17% 14% 10% 5% 0% Figure 2.1. Transportation Industrial Residential Commercial Agriculture Distribution of energy use by sectors 2% Furthermore, the amount of energy use in the home depends on a number of factors that may change over time such as inhabitants habits, needs, and preferences and even it varies between different areas a city. For example, differences in climate and the size of houses will impact the amount of energy required for heating and cooling in residential buildings. Among all devices in commercial and residential buildings; Heating, Ventilation, and Air Conditioning (HVAC) systems represent a significant portion of the primary energy consumption in the world. Figure 2.2 shows the distribution of energy use by end-use devices in Canada in This indicates 64% of residential energy is consumed by HVAC systems [7]. The population growth, changing lifestyle, electrification, and rise in the number of households have caused the use of electricity for heating or cooling in residential buildings to nearly double from 1985 to 2012 in U.S. 13

28 and Canada. Approximately 49% and 42% of total residential electrical energy were used for HVAC systems in Canada and U.S. respectively in 2012 [9]. Therefore, load management of residential HVAC systems poses challenges because they are the main electrical loads during critical peak load periods. From utilityside increasing residential HVAC load during peak periods can lead to regional electrical power outages or interruption in energy demanded. For example, the large spikes in electrical power consumption during the middle of the year result from air conditioning during hot summer days in California, United States in Specifically, residential HVAC has also been shown to contribute approximately half of the additional critical peak electrical power consumption in California [39]. 70% 60% 50% 40% 64% Energy consumption by end-use in Canada 30% 20% 10% 0% Space Heating and cooling 17% 15% 4% Water Heating Appliances Lighting Figure 2.2. Energy consumption by end-use devices in Canada In grid-based energy supply systems, such as electricity and natural gas, peak load demand situations are the particular challenges for the generation and transmission of the energy demanded. Hence, the supply systems have to be designed to give uninterrupted service to consumers within the terms of the particular agreements and tariffs chosen. In the past, insufficient generation of electrical power and its potential problems in the grid were often addressed at the supply or utility side. Nowadays, the management of the peak load problems is being shifted more towards the demand-side. Nowadays, many governments and utilities are interested in finding new solutions to manage the ever-increasing energy demand, electricity costs and environmental impacts. The programs such as DR, TOU rates, RTP are often offered by utilities and as 14

29 smart grids incentives to encourage customers such as residential users to reduce their usage during peak load periods [4], [11], [39]. On the other hand, with the advancement in embedded systems and wireless sensors a suitable ground has been provided for designing and building new advanced wireless-based devices [40]. The combination of wireless sensors and smart grid initiatives can be used to improve and enhance the limitations of the existing energy management systems [41], [42], [43]. Furthermore, among vast applications of Artificial intelligence (AI) techniques in many facets of our lives, residential energy management has recently been an interesting and ever-growing area of research for applying AI approaches such as fuzzy logic [43], [44], [45], [46], [47]. In summary, the big picture of both energy management and peak load management can be integrated using above mentioned technologies and initiatives by control of HVAC systems as the residential main load. The smart thermostats for control of residential HVAC systems utilizing WSN, smart grid initiatives, and advanced control approaches as key elements can be achieved by attempting to make them more adaptable, autonomous and aware of our environment Residential HVAC Systems and Role of Thermostats Two types of heating systems are most common in the houses: forced-air or radiant. However, the forced-air-based systems are being used in the majority of the houses in the North America. The heat source is either a furnace, which burns a gas, or an electric heat pump. Furnaces are generally installed with central air conditioners. Heat pumps provide both heating and cooling. These systems use a central furnace plus an air conditioner, or a heat pump [48]. Figure 2.3 shows the components of a ventral forced-air HVAC system. The basic HVAC components are: fans (blowers), furnace or heating unit, filters, compressor, condensing units, evaporator, control system, and air distribution systems. As shown in Figure 2.3, forced-air systems utilize a series of ducts to distribute the conditioned heated or cooled air throughout the home. A blower, located in a unit called an air handler, forces the conditioned air through the ducts. As depicted in Figure 2.3, 15

30 the major components of a central forced-air system include the heat exchanger, air distribution system, and thermostat. The heat exchanging unit consists of a heating unit and a cooling unit. A split system consisting of an indoor natural gas furnace and outdoor electric air-conditioning unit is typical. The capacity of the heat exchanging units is commonly expressed in tons (1 ton = 12,000 Btu/hr) of cooling and British thermal unit (Btu) per hour for heating. The air handling unit refers to the combination of furnace, air blower, and air filter. Air handling units are characterized by flow rate, often measured in cubic feet per minute (cfm) of air flow. Actuation is single-stage with finite actuation periods where the system is on or off, often referred to a two-position system. Multi-stage and multi-zone systems are increasingly found in larger residences, but constitute a small portion of the existing base central-forced air system. Figure 2.3. Residential central forced-air HVAC system The air distribution system refers to the ducts, dampers, diffusers, grilles, and registers that deliver conditioned supply air to locations within the building. Dampers 16

31 within the ducts facilitate pressure balancing and flow control. Diffusers, grilles, and registers are various types of duct entrances that provide unique air delivery characteristics. In particular, registers are characterized by an adjustable damper. Generally several air supply outlets are located throughout a residential building with a fewer number of air return intakes. Franklin et al. [49] demonstrate that residential HVAC systems can be modeled as a closed-loop feedback control system. Residential HVAC design is generally regarded as a heat transfer control problem. The major elements are modeled as a controller, plant, and sensor. Residential HVAC systems are generally classified as a thermostatically controlled load. In Figure 2.4, the thermostat consists of both controller and sensor. The actuator is the mechanical conditioning devices and the distribution system. The plant actually refers to the house and associated thermodynamic behavior, where disturbances include external heat gains or losses not associated with the mechanical conditioning devices. Figure 2.4. Block diagram for heating system Generally speaking, the basic function of a thermostat can be described as following: users specify a set point temperature indicating their comfort preference(s) during a day. The thermostat uses a local temperature measurement (indoor temperature) in level-crossing control logic with the specified set point temperature. The thermostat requests cooling if the measured temperature is greater than the specified temperature. Conversely, the thermostat requests heating if the measured temperature is less than the specified temperature. 17

32 2.3. Residential Thermostats Residential thermostats have been a key element in controlling HVAC systems for over sixty years. Nowadays, thermostats have evolved and many capabilities such as touchscreens, color displays, internet connections, Home Area Networks (HAN) connections, remote controls (i.e., through cell phone or internet), voice controls, external communication (i.e., through radio-link to utility), ventilation, humidity controls, and zonal controls been added to them. These capabilities were added to thermostats in order to be more user-friendly and easy to use and more importantly to provide both thermal satisfaction and energy conservation aspects. However, there are four types of thermostats with various interfaces and capabilities that being used widely among residential customers in the North America. They can be classified as Conventional Thermostats (fixed set point), Programmable Thermostats (PT), Programmable Communicating Thermostats (PCT), and Smart or Intelligent Thermostats [19] Programmable Thermostats (PTs) Nowadays, Programmable Thermostats (PTs) are being used widely by most of U.S. and Canadian households to control their HVAC systems. In California, the 2005 Residential Energy Consumption Survey (RECS) reported 37% had a conventional thermostat and 44% had a PT. According to [50], 47% of Canadian households use PT to control their heating or cooling systems. U.S. and Canada government s policies (Federals and Provincials) as well as higher energy costs certainly encouraged consumers to install more automated thermostats such as PTs. The main feature of PTs is its programmability. They can be programmed to change set point temperatures on a schedule based on user preferences and comfort but perhaps half of households actually use PTs in this way [19]. However, most of PTs remain a sensor-limited device. This causes that residential single-zone central air-force systems are relayed on a single temperature sensor to determine the comfort temperature of all rooms in a house (lack of zonal control).in addition, several field studies show no significant savings in households using 18

33 PTs compared to households using non-pts [15], [51], [52]. Two other studies argued that homes relying on PTs consumed more energy than those where the occupants set the thermostats manually [19], [53]. According to these studies, a PT itself does not guarantee reduction in energy consumption, but instead depends on how the PT is programmed and controlled by the household. More recently, part of the 2008 California Building Energy Efficiency Standards, commonly referred to as Title 24, requires that PTs have the ability to set temperature preferences for at least four different time periods per day [54]. However, one of the barriers of using PTs to save energy is that users often fail to use these devices as they are designed. People find PTs difficult to program and to understand [55], [56]. In addition, Rudge et al. [57] reports room-to-room temperature differences are greater than 3 C in many buildings that use PTs or Conventional thermostats in U.S. These are the reasons that highlight the needs for distributed wireless temperature sensors in residential buildings for providing thermal comfort and zonal control is a must Programmable Communicating Thermostats (PCTs) The advancement in communication networks and wireless sensors has smoothed the path for researchers to utilize the capabilities of this technology in their field of interests [22]. The potential of using communication networks to the residential sector was extended by National Grid Operators, Network Companies, and Electric Suppliers to build Advanced Metering Infrastructure (AMI) [4], [58]. The purpose of deploying AMI technologies such as smart meters is to assist utilities to meet their energy needs, by introducing some electricity pricing mechanisms such as TOU rates with the aim to encourage consumers to shift part of their electricity use to off-peak hours or shed their electricity usage during on-peak hours (reducing set point temperature during heating operation).other objectives for employing AMI are to improve monitoring of energy distribution and transmission related energy losses, quickly identify power outages. Thus, allowing the customers (i.e., residential) to conserve and save energy (on high electricity costs), and assist utilities to better manage the peak load demands. One general approach for load reduction is direct load control (DLC) [16], [59], [60]. In DLC programs, based on an agreement between the utility company and the 19

34 customers, the utility or an aggregator, which is managed by the utility, can remotely control the operations and energy consumption of certain appliances in a household. For example, it may control lighting, thermal equipment (i.e., HVAC systems), refrigerators, and pumps. However, when it comes to residential load control and home automation, users privacy can be a major concern and even a barrier in implementing DLC programs [61]. An alternative for DLC is smart pricing, where users are encouraged to individually and voluntarily manage their loads, e.g., by reducing their consumption at peak hours [13], [14], [39], [62]. Therefore, the need for devices in demand-side to automatically respond to utilities initiatives such as smart pricing (TOU rates) is apparent. As defined by Lafferty et al. [2] Demand responsive control systems should integrate the controls for the distributed energy system with electronic communication and metering technology to facilitate one-way or two-way communication between utility and customer equipment. These technologies are used to reduce energy use (by dimming lights, raising air-conditioning set points, etc.) in response to peak electricity demand emergencies and/or prices. The above-mentioned programs and technologies provided and offered by utilities led thermostat manufacturers to improve PTs to PCTs that have potential for two-way communication with utilities via deployed smart meters [63]. These devices are equipped with LCD user interfaces, and wireless interface, for communications and network capability to a multitude of sensors/actuators, offering a variety of options for controlling thermal and humidity comfort in commercial and residential buildings. The California Energy Commission is planning to mandate PCTs for all central heating or cooling units through building standards, appliance standards, load management standards, or some combination thereof [18]. This effort is being undertaken to reduce or eliminate the need for rotating outages through temporary reductions in air-conditioning services during emergency events. In addition, customers could use the same technology to voluntarily and automatically reduce load in response to dynamic rates and incentive programs. An important aspect of this goal is that the PCTs be compatible statewide such that customers can purchase equipment irrespective of utility service territory [18], [63]. 20

35 Nowadays, with the advancement in communication networks and wireless sensors most of PCTs can potentially communicate with other wireless appliances in the home to help both consumers and utility in peak load demand periods. As discussed in [63], a 2.2 C step up in thermostat setting for PCT has the potential to save about 450 Watt for each installed thermostat and more than 10% of peak residential air conditioning load. Since these kinds of thermostats can potentially respond to smart prices and utility events; in some cases they are so-called smart thermostat as well. However, these thermostats have not capability to learn and adapt to users schedule and pattern changes [19] Intelligent Thermostats As described in [19], intelligent thermostats attempt to make control even more automated than just using clocks and thermal and humidity sensors. Some rely on a user interface where for the first few days/weeks of operation the occupant makes adjustments to maintain comfort. The thermostat then learns things such as occupancy patterns for different days of the week and the variation of desired temperatures at different times of day, and the response time of the home. It uses this information to essentially self-program itself to reproduce the temporal changes in temperature desired by the occupant. In addition, as mentioned in [18], the concepts of Smart Thermostats are being investigated in order to come up with systemic solutions which are adaptable, energy aware, and easy to use. However, these kinds of intelligent thermostat described in [19] can be regarded as a PT capable of memorizing user schedules for different times of the day. follows: As a result, a list of disadvantages of existing thermostats in today s market is as Programmable Thermostats (PTs) Depending on how PTs are programmed and controlled by users. Lack of communication with smart meters. Constant interaction and programming. Occupants often mistake or forget to setback. Lack of learning capability. 21

36 Programmable Communicating Thermostats (PCTs) They act like a PT in the region where the price mechanism is flat-rate. Lack of Capability to provide user thermal comfort in DR events. Require constant interaction from its user to modify their schedules and preferences. Lack of learning and adapting to user schedule changes. Intelligent Thermostats Unable to adapt to user pattern changes. Lack of Zone-Control capabilities. Represent a PCT capable of remembering the user preferred set points (store in memory). Nest Thermostat ( ) [64] Need user constant interaction to adjust their preferences Lack of Intelligent Zone-Control Environment Acts as Smart Thermostat based on only Occupancy Sensors for saving energy Thermal Comfort Based on the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE), Standard , the thermal comfort is a state of mind and feeling where a person describes satisfaction with the thermal environment [65]. The purpose of ASHRAE Standard 55, Thermal Environmental Conditions for Human Occupancy, is to specify the combinations of indoor space environment and personal factors that will produce thermal environmental conditions acceptable to 80% or more of the occupants within a space (ASHRAE 2004). While acceptability is never precisely defined by the standard, it is commonly accepted within the thermal comfort research community that acceptable is synonymous with satisfaction, and that satisfaction is indirectly associated with thermal sensations of slightly warm, neutral, and slightly cool, and that thermal sensation is the question most commonly asked in both laboratory and field studies of thermal comfort. 22

37 ASHRAE Standard 55 is currently based on the heat balance model of the human body, which predicts that thermal sensation is exclusively influenced by environmental factors (temperature, thermal radiation, humidity and air speed), and personal factors (activity and clothing). An alternative theory of thermal perception is the adaptive model, which states that factors beyond fundamental physics and physiology play an important role in impacting people s expectations and thermal preferences [66]. They believe that thermal sensations, satisfaction, and acceptability are all influenced by the match between one s expectations about the indoor climate in a particular context, and what actually exists. They mention that while the heat balance model is able to account for some degrees of behavioral adaptation (such as changing one s clothing or adjusting local air velocity), it is not able to account for the psychological dimension of adaptation, which may be particularly important in contexts where people s interactions with the environment (i.e., personal thermal control), or diverse thermal experiences, may alter their expectations, and thus their thermal sensation and satisfaction. One context where these factors play a particularly important role is naturally ventilated buildings. Humphreys [67] found that the comfort temperature differed between groups of people feeling thermally comfortable. According to Humphreys, the mean comfort rating changed less with indoor temperature from climate to climate than might be expected. In addition, based on ASHRAE standard, thermal comfort temperature for a particular location helps in estimating the heating requirements and in the calculation of heating degree-days. Furthermore, thermal comfort is one of the main objectives for designing an HVAC system (ASHRAE 1997). For this reason, energy conservation and power management approaches for residential HVAC should incorporate thermal comfort. The HVAC systems engineers often design solar energy systems by taking the standard thermal comfort temperature as 22 C. However, this number is totally depends on the climate and other parameters such as clothing in that region. A wide range of research exists about measuring, modeling, and predicting thermal comfort. Federspiel in [68] and Charles in [69] review a number of comfort models. Models for satisfaction and sensation are typically a combination of empirical studies as well as analytical calculations. Fanger in [70] develops the Predicted Mean 23

38 Vote (PMV) and Predicted Percent Dissatisfied (PPD) joint model which quantifies thermal sensation and correlates satisfaction to sensation for steady state conditions. Fanger s PMV-PPD joint model is commonly accepted in research and practice (ASHRAE 1997) (ISO 1994). However, we choose PMV-PPD joint model for thermal comfort in this thesis. PMV describes thermal sensation on a 7-point scale that ranges from -3 indicating cold to +3 indicating hot. A PMV score of zero indicates thermally neutral conditions. The model is an empirically-derived relationship between thermal sensation and six parameters: air temperature, mean radiant temperature, relative humidity, air velocity, the person s metabolic rate, and the person s clothing level. More generally, PMV is a function of the occupant s metabolic rate of heat production and thermal load on the body from external sources. The relation between PPD and PMV is expressed in equation 2.1 as: = (.. ) (2.1) Figure 2.5. The relationship between PMV and PPD The PPD states the percentage of a group of people that are likely to be dissatisfied in an environment with a particular PMV score. The relationship between PMV and PPD is shown in Figure 2.6 [70]. The minimum PPD is 5%, which means that a 24

39 few people are always likely to be dissatisfied with the thermal conditions. PPD is, in fact, the combination of two distribution curves centered on ± 1.5 PMV Residential Energy Management and the Role of Smart Grid Initiatives: Methods and Programs In this section we investigate the programs proposed for reducing residential consumption and the role of recent smart grid initiatives in energy management. Peak load demand situations in grid-based energy supply systems such as electricity and natural gas present particular challenges for the generation and transmission of the energy demanded. Each system has to be designed to give uninterrupted service to consumers within the terms of the particular agreements and tariffs chosen [4], [39]. Management of load demand for reducing the consumption is a sensitive factor in the electricity supply system. Demand (consumption) and supply (production) should be constantly balanced in order to avoid supply interruptions with all their negative technical, economic and social consequences. In the literature and within energy research community there are various terms such as load management, demand response, and energy efficiency in order to manage the peak load demand. All these terms and methods belong to Demand-side Management programs. They have similarities and differences. All of them are used to reduce the consumption. However, it is important to classify what kinds of consumption exist before considering where and how these approaches and programs are implemented to reduce consumption Demand-side Management (DSM) Programs Generally speaking, demand-side management (DSM) refers to programs applied by utility companies to control the energy consumption at the customer side of the meter [71]. In the most cases, these programs are employed to use the available energy more efficiently without installing new generation and transmission infrastructure. DSM programs include conservation and energy efficiency programs, fuel substitution programs, demand response programs, and residential or commercial load management 25

40 programs [5], [10], [21]. Load demand can generally be reduced in different ways, depending on the existing technical and economic conditions. Killicote et al. [72] have summarized the demand side strategies that are able to influence load demand. They emphasized on three strategies as following: peak load management, demand response and energy efficiency for reducing the residential load demand. These three strategies as residential load management programs usually aim at one or both of the following design objectives: reducing (shedding) consumption and shifting consumption [3]. Koomey and Brown in [12] provide several good definitions to distinguish energy, power, and the related methods for reducing consumption. Energy consumption is typically described in terms of annual consumption. Instantaneous load or demand refers to electric power consumption. Peak load is the maximum simultaneous electricity demand for some portion of the electrical system, typically averaged over an hour. Load reduction strategies are those that reduce service demands without affecting the economic benefit derived from that energy use. Load shifting strategies are those that involve shifting loads to off peak periods using energy storage or smart controls. Both of the load management approaches directly state the methods do not reduce the benefit to the consumer. Figure 2.7 visualizes three strategies for demandside management. Energy efficiency and load management methods as a part of demand-side management programs can target the three major components of the residential HVAC system as following: heat exchanger, air distribution system, and thermostat. Nowadays, the thermostats as a central unit for control of residential HVAC systems can aim at demand response programs. Furthermore, it is useful to distinguish load management and demand response. Both demand response and load management focus on timing of electricity use on the demand side of the electricity market. The two concepts however differ in the main target: demand response aims at changing prices and reducing total electricity use, while load management mainly focuses on a constant ability of the supply system to meet demand [73]. 26

41 Figure 2.6. Load control strategies The main focus of demand response and load management is not on end-use energy savings but rather on total energy consumption in the whole system [74]. From technical point of view, demand response essentially is a way to ensure, or help to maintain, stability in the grid and optimal operation of generation units. Demand response and load management could result in decreases in total energy needs of the system if they result in spreading of peaks in load demand. In the other words, demand response is a type of load management that attempts to reduce and modify the power demand (reducing Kilowatt or kw) at any particular time. It is also imperative to recognize energy efficiency and demand response. There are both similarities and differences between these programs. As explained in [75], both of them affect consumer energy use. However, their ultimate objectives differ. Demand response programs tend to reduce peak demand or kilowatts (kw) during specific times based on system reliability or electricity prices. Energy efficiency programs, however, tend to focus on cost-effective reductions in overall kilowatt-hours (kwh) such as replacing the lighting systems with energy-efficient ones. Technically speaking, demand response programs attempt to curtail the demand of load or energy use such as reducing the set point temperatures in HVAC systems or shifting energy consumption to off-peak hours. On the other hand, energy efficiency programs reduce consumption through ongoing measures employed at all times such as replacing lighting systems with more energy-efficient ones. 27

42 Smart Grid Technology Many national governments are encouraging smart grid technology as a costeffective way to modernize their power system infrastructure while enabling the integration of low-carbon energy resources. In many countries, development of the smart grid is also regarded as an important economic, commercial, and environmental opportunity to develop new products and services [76], [77], [78]. Since about 2005, many of North American electric utilities have been increasing to deploy smart grid technologies. The main aims of deploying such technologies are as follows [4], [10], [79], [80]: For electric utilities and electricity system operators, they provide tools to address peak load demand, to improve system reliability, and to manage distributed generation and energy storage technologies, and reducing greenhouse gases and CO 2 emissions. For consumers and particularly residential and commercial customers, they offer new opportunities to manage their electricity use and participating in saving resources. They can also connect their energy storage systems to the grid, improve power quality, prevent and reduce the power line outages, and create new opportunities for consumers who like to save environment and sustainability. As described in [76], [81], [82], to fulfill the different requirements of the smart grid, the following enabling technologies must be developed and implemented: (a) Information and communications technologies (ICT) such as Two-way communication technologies to provide connectivity between different components in the power system and loads; (b) the necessary software and hardware to provide customers with greater information, enable customers to trade in energy markets and enable customers to provide demand-side response; 28

43 (c) Software to ensure and maintain the security of information and standards to provides capability and interoperability of information and communication systems; (d) Integrated sensors, measurements, control and automation systems and information and communication technologies to provide rapid diagnosis and timely response to any event in different parts of the power system, smart appliances, communication, controls and monitors to maximize safety, comfort, convenience, and energy savings of homes. (e) Smart meters, communication, displays and associated software to allow customers to have greater choice and control over electricity and gas use. They will provide consumers with accurate bills, along with faster and easier supplier switching, to give consumers accurate real-time information on their electricity and gas use and other related information and to enable demand management and demand-side participation. (f) Different power electronic interfaces and power electronic supporting devices to provide efficient connection of renewable energy sources and energy storage devices. Based on abovementioned requirements, however in order to fully utilize the benefits of smart grid technology in demand-side, making smart devices such as thermostat to automatically respond to smart grid initiatives is apparent [83]. On the other hand, regardless of advantages of smart grid technologies, in some cases the utilities have experienced consumer resistance for deploying these technologies [84], [85]. As a result of these recent experiences, utilities have recognized the importance of pairing smart grid technology deployment with effective consumer engagement in order to promote awareness and support, to minimize consumer complaints, and to increase the perception and the use of these technologies Smart Grid initiatives and Consumer Engagement The Smart Grid initiatives aim at giving much greater visibility to lower voltage networks and to enable the participation and engagement of customers in the operation 29

44 of the power system, particularly through Smart Meters, Smart Energy Management Systems, and Smart Homes [12], [83]. The smart grid will support improved energy efficiency and allow a much greater utilization of renewable resources. The smart grid concept combines a number of technologies, end-user solutions and addresses a number of policy and regulatory drivers. However, smart grid does not have a single clear definition. The European Technology Platform defines the Smart Grid as [86]: A Smart Grid is an electricity network that can intelligently integrate the actions of all users connected to it generators, consumers and those that do both in order to efficiently deliver sustainable, economic and secure electricity supplies. According to the U.S. Department of Energy [87]: A smart grid uses digital technology to improve reliability, security, and efficiency (both economic and energy) of the electric system from large generation, through the delivery systems to electricity consumers and a growing number of distributedgeneration and storage resources. And finally according to Canadian Electricity Association in 2012 [77]: A smart grid is the addition of two-way communications, control, and automation capabilities to the existing power grid to make it more reliable, flexible, and efficient, intelligence, clean, safe, and consumer-friendly. Generally speaking, the smart grid technologies and tools can enable consumers to understand their electricity use, manage their electricity bills, and sell power back to the grid during high electricity demand. According to the U.S. Department of Energy [87]: In the smart grid, consumers will be an integral part of the electric power system. They will balance supply and demand and ensure reliability by modifying the ways they use and purchase the electricity. From demand-side, the smart grid initiatives which have direct impact on consumers to meet smart grid goals and enable consumers to become active participants in the electricity system are as follows [39], [74], [77], [87]: 30

45 1) Smart Meters and Advance Metering Infrastructure (AMI), 2) Dynamic Pricing Incentives, 3) In-home Displays and Energy Information Tools, 4) Demand Response Initiatives, 5) Smart Home Energy Management Systems or Home Area Networks, 6) Facilitation of Distributed Generation, 7) Facilitation of Electric Vehicles and Energy Storage Technologies. Since the objective of our research is designing a Smart Thermostat for control of residential HVAC systems we investigate the components and initiatives proposed in 1 to 5 due to their direct relevant to our problem Smart Meters and Their Benefits for Consumers Smart metering refers to systems that measure, collect, analyses, and manage energy use using advanced Information and Communication Technology (ICT). The concept includes two-way communication networks between smart meters and various actors in the energy supply system. Smart meters enable visibility of electricity use and provide strategically valuable information both for supply and demand side. In addition, the smart meter is seen to facilitate demand-side management through providing realtime information exchange and advanced control capabilities. In recent years, residential, industrial, and commercial consumers have increasingly been using more advanced meters, which record energy use over short intervals, typically every half hour. This allows the energy suppliers to design tariffs and charging structures that reflect wholesale prices and helps the customers understand and manage their pattern of electricity demand. Smart meters are even more sophisticated as they have two-way communications and provide a real-time display of energy use and pricing information, dynamic tariffs and facilitate the automatic control of 31

46 electrical appliances. The most important incentives that are often offered or applied by smart grids through employed smart meters are as follows: Dynamic Pricing Incentives DSM programs can be based on regulations or economic measures. Different tariffs and pricing mechanisms are introduced by utilities in order to encourage customer to reduce the load demand in peak periods or shift the load to off-peak hours. Most common examples for residential customers are Time-Of-Use (TOU) and Real-time pricing (RTP). Figure 2.8 visualizes TOU and RTP pricing mechanisms. In this research we will use TOU and RTP for evaluation of our approaches. These pricing mechanisms can be defined as follows: 1) Time Of Use (TOU) TOU pricing is designed to reflect the utility cost structure where rates are more expensive during peak periods and cheaper during off-peak periods. Both the supplier and the end-user benefit from successfully designed TOU rates. This mechanism is applied in Ontario, Canada [88]. 2) Real-time Pricing (RTP) The principle of RTP is that the consumer (end-user) price is linked to the whole sale market price. The principle feature is that timing and prices are not set in advance. Figure 2.7. TOU and RTP pricing mechanisms 32

47 As defined by Barbose and Goldman in [89], under real time pricing (RTP) tariffs, retail electricity consumers are charged prices that vary over short time intervals(typically hourly) and are quoted one day or less in advance, to reflect contemporaneous marginal supply costs. These tariffs differ significantly from those typically used by electric utilities, which are based on prices that are fixed for months or years at a time to reflect average, embedded supply costs, with little or no differentiation with respect to the timing of consumption Demand Response (DR) Programs The concept of DR is nowadays used by various agents in energy markets with different interpretations. In some cases the concept is used as an umbrella to cover a multitude of actions [10]. In other cases it simply defines a specific load control action. For example Laurita in [90] defines DR as follows: Customers reducing their electricity consumption in response to either high wholesale electricity prices or system reliability events. Customers being paid for performance based on wholesale market prices. Demand Response Coordinating Committee in U.S. defines DR as follows [91]: Providing electricity customers in both retail and wholesale electricity markets with a choice whereby they can respond to dynamic or time-based prices or other types of incentives by reducing and/or shifting usage, particularly during peak periods, such that these demand modifications can address issues such as pricing, reliability, emergency response, and infrastructure planning, operation, and deferral. According to aforementioned definitions; DR can be a significant improvement to apply smart demand-side management techniques through design and implementation of smart devices such as thermostats. We believe in our case there are two principle points of view (leading to specific interests) when describing DR as a process for demand-side management: 33

48 Consumer-side: (i) automated response and load control systems (making processes more automated), (ii) energy supply without significant interruptions or loss of comfort, (iii) saved money Utility-side: (i) effective pricing mechanisms, (ii) automatic load reduction, (iii) risk management. Furthermore, participation in DR has been found to often lead to greater adoption of Energy Efficiency (EE) because DR can increase a customer s awareness of its energy usage [75]. Another key opportunity for reducing EE and DR delivery costs is the ability to use advanced technologies to enable EE and DR at a customer s facility simultaneously [72]. Providing customers with integrated demand-side management leads to more efficient or smarter devices and facility operations. With the advancements in today s metering and smart grid technologies, there is also a significant opportunity to realize both EE and DR together through a shared technology platform (smart meters) [74] In-home Displays and Energy Information Tools These devices allow residential customers to track their electricity use in detail and to understand their energy bills. They can also help consumers to manage energy use and providing opportunities to reduce their electricity consumption and costs. In some cases, these devices provide near real-time information, and can also include charts, energy efficiency tips, and analytical tools to compare their electricity usage with respect to previous days and other households. In our case, the designed smart thermostat can wirelessly read the current demand of the house as one of important parameters for shedding the load (reducing set point temperatures) during the day Smart Home Energy management Systems and Home Area Network (HAN) Households want to manage effectively their electric energy, gas and water consumption to reduce expenditures and, at the same time, to be friendlier to the environment. HAN can offer these capabilities and also offer support to aging people, allowing them to stay in their homes, be more autonomous and increase their quality of 34

49 life [43]. In addition, the trend of future smart home energy management systems development is to achieve system integration, control, network bridging, and the design of communication chip. This is because the future smart home appliances should be networked, and allowed users to control and monitor through PC or internet with the communication chip such as ZigBee, or Ethernet [41]. In order to bring more value to these functions, for example a smart thermostat, which aims at connecting (integration) the residential HVAC systems into smart grid, must be able to respond to price signals received from utilities via smart meters and optimize electricity use [63], [83]. Such integration will help consumers to optimally utilize the advantages of smart grid programs.in addition, a large percentage of the installed smart meters will have the capability to connect the AMI meter to a Home Area Network (HAN), allowing communication of cost, consumption, and other data within the home. The HAN will enable consumers to install products, including real-time displays, home energy management systems, and smart devices, such as appliances, HVAC thermostats, hot water heaters, pool pumps, and Electric Vehicle (EV) charging stations that are capable of responding to the signals from the smart meter [92], [93]. As a result, the underlying technology that enables and provides initial infrastructures to make residential customers to become active participants in the electricity system is availability of HAN or smart energy management devices and communication systems. To be effective and easily deployed, the HAN communication network and smart energy management systems must be based on a network technology that utilizes open data architecture that is low-cost, consumes a minimum amount of energy, and does not require extensive new infrastructure. Therefore, this technology that is so-called Wireless Sensor Networks (WSN) as a whole can potentially provide enough functionality to manage energy consumption in residential buildings. Hence, deploying different wireless sensor nodes such as temperature and occupancy sensors to measure or detect different variables of interest can help manufacturers make smart energy management systems in order to meet both consumers and smart grid goals. 35

50 2.6. Wireless Sensor Networks (WSN) Recent advances in micro-electro-mechanical systems and in low-power wireless network technology have created the technical conditions to build multi-functional tiny sensor devices, which can be used to measure and detect according to physical phenomena of their surrounding environment [22]. Wireless sensor nodes are low-power devices equipped with processor, storage, a power supply, a transceiver, and one or more sensors, and in some cases, with an actuator. Several types of sensors can be attached to wireless sensor nodes, such as thermal, chemical, optical, and biological. These wireless sensor devices are small and they are cheaper than the regular sensor devices. From communication point of view, the wireless sensor nodes can automatically organize themselves to form an ad-hoc multi hop network so-called Wireless Sensor Networks (WSN). WSN may be comprised by hundreds or maybe thousands of ad-hoc sensor node devices, working together to accomplish a common task. Self-organizing, self-optimizing, and fault-tolerant are the main characteristics of this type of network [40]. Widespread networks of inexpensive wireless sensor devices offer a substantial opportunity to monitor and measure more accurately the surrounding physical phenomena s when compared to wired sensor methods. Generally speaking, WSNs are the convergence of sensing, computation, and communication capabilities into networked cubic centimeter sized devices. WSNs are often referred as the next class of computing in which every device contains embedded computing capabilities and connects to the Internet [94] to enable ubiquitous and proactive computing [95]. In terms of technology, WSNs fall between radio frequency identification tags (RFID) and mobile platforms (cellphones, PDA s) as a class of devices defined by device size, computational ability, power consumption, and communication range. Estrin, Culler et al. [96] propose taxonomy to classify WSN challenges as well as applications involves dimensions of scale, variability, and autonomy, where each dimension consists of temporal and spatial aspects. In this taxonomy, the authors acknowledge the intimate relationship between the technology and the physical world. 36

51 Sensor nodes are the initial components of any WSN. They provide the basic functionalities as follows [22], [97]: i) Signal conditioning and data acquisition for different sensors. ii) iii) iv) Temporary storage of the acquired data. Data processing. Analysis of processed data for diagnosis of and, potentially alert generation. v) Self-monitoring for example to supply voltage. vi) vii) viii) ix) Scheduling and execution of the measurement tasks. Management of the sensor node configuration. Reception, transmission, and forwarding of data packets. Coordination and management of communications and networking. In order to provide abovementioned capabilities, as shown in Figure 2.9 a sensor node consists of one or more sensors and even actuators, a signal conditioning unit, an analog-to-digital conversion module (ADC), a radio transceiver, energy supply unit, and a central processing unit (CPU). Depending on the deployment environment, it can be important to protect the sensor hardware from mechanical and chemical aggressions with an appropriate shield. In Figure 2.9, the Transceiver is a communication device that consists of a transmitter and a receiver, which enables the two-way wireless communications among sensor nodes. The essential task performed by the transceiver in sensor nodes is able to receive and transmit data via radio waves at a specific frequency (e.g. wireless sensor networks typically use frequencies between 433 MHz and 2.4 GHz). The main parts of the transceiver are the radio frequency building block (performs analog signal processing) and the baseband building block (performs the digital signal processing). 37

52 Figure 2.8. Platform of sensor nodes For applications in residential energy management, the Telos platform shown in Figure 2.10 has a concurrently designed operating system and radio communication called TinyOS is a suitable platform. Telos platform is the evolution of several prior wireless sensor node platforms [82]. It provides a suitable platform for evaluating multisensor residential HVAC strategies because it contains the necessary suite of onboard sensor (e.g. temperature, humidity). Moreover, the potential growth and impact of the smart grid, particularly with respect to the widespread use of smart appliances and other devices, will have a major impact on the cost and reliability of electric power. The aim of deploying smart grids is to enhance the integration of renewable power resources, reduce peak loads on utilities, encourage energy efficiency, and reduce costs for both utilities and consumers. To realize the benefits of the smart grid, it is critical that the underlying communications technology which is necessary to make the grid smart consume as little power as possible [29]. Because of the widespread use of these communication technologies at the residential level for HAN and in the smart devices, the choice of technologies is very important [61]. 38

53 Figure 2.9. Telos ultra-low power wireless module (sensor node) It is also expected that the use of smart appliances and energy management systems will allow consumers to manage and reduce their energy bills and overall consumption. Therefore, it is essential that the communication and control hardware designed to perform these management tasks consume the minimum amount of energy possible [86]. The three logical candidates for HAN/smart-product applications are: Bluetooth (based on IEEE ), ZigBee (based on IEEE ), and Wi-Fi (based on IEEE ) [98]. The Association of Home Appliance Manufacturers (AHAM) has also performed a screening study of possible communications technologies for using in home appliances [99]. The AHAM study reached a similar conclusion; based on the current maturity of these technologies, ZigBee, Wi-Fi, Bluetooth, and PLC meet the requirements for smart appliances. The lowest power networking technology that is widely available is the (ZigBee) standard [40]. ZigBee is also optimized to minimize power consumption and cost as well. These characteristics make it well suited to the HAN applications Fuzzy Logic Expert Systems The goal of artificial intelligence (AI) as a science is to make machines do things that would require intelligence if done by humans. One of the major paradigm shifts in AI field was its change of focus from general purpose, weak methods to domain specific methods, which initially lead to the development of expert systems. In addition to the expert systems, AI field was further enriched by introduction of techniques such as fuzzy 39

54 logic, biologically inspired techniques such as artificial neural networks (ANN) [100], Adaptive Resonance Theory (ART) [101], clustering algorithms, evolutionary computations genetic algorithm (GA), simulated annealing, intelligent agents [102] etc.; hence, providing vast methods for building intelligent systems with supervised and/or unsupervised learning capabilities [103]. Furthermore, in most cases, the integration of expert systems and ANNs, and fuzzy logic and ANNs improve the adaptability, fault tolerance and speed of knowledge-based systems Rule-based Expert Systems The rule-based and/or frame-based expert systems are a typical approach taken to represent and build knowledge-based systems [100], [104], [105]. The rule-based expert system uses IF-THEN rules, while the frame-based expert system uses frames (i.e. objects and/or structures) to represent the knowledge. As shown in Figure 2.11, a rule-based expert system has five components: the knowledge base, the database, the inference engine, the explanation facilities, and the user interface. These five components are essential for any rule-based expert system [100]. The knowledge base contains the domain knowledge useful for problem solving. In a rule-based expert system, the knowledge is represented as a set of rules. Each rule specifies a relation, recommendation, directive, strategy or heuristic and has the IF (condition) THEN (action) structure. When the condition part of a rule is satisfied, the rule is said to fire and the action part is executed. The database includes a set of facts used to match against the IF (condition) parts of rules stored in the knowledge base. The inference engine carries out the reasoning whereby the expert system reaches a solution. It links the rules given in the knowledge base with the facts provided in the database. An expert system must be able to explain its reasoning and justify its advice, analysis or conclusion. The user interface is the means of communication between a user seeking a solution to the problem and an expert system. The communication should be as meaningful and friendly as possible. A rule-based expert system consists of if-then rules (conditions and actions) and can have multiple conditions to represent the knowledge needed to solve a problem in a particular domain of study. 40

55 Figure Basic structure of a rule-based expert system If x 1, x 2 x n represent the conditions for a particular problem; and y 1, y 2 y n represent the actions to be taken if a particular condition(s) is true, the related rules can be expressed as shown in examples below: A simple IF-THEN rule: IF (x 1 ) THEN (y 1 ) A rule with multiple conditions: IF (x 1 AND x 2 AND x 3 AND x n ) THEN (y 2 ) A rule with multiple mixed (AND/OR) conditions: IF (x 1 AND x 2 OR x 3 OR x 4 AND x n ) THEN (y n ). 41

56 Fuzzy Expert Systems Fuzzy logic or fuzzy set theory was introduced by Professor Lotfi Zadeh, Berkeley s electrical engineering department chairman, in 1965 [106]. Since then, numerous fields have benefited from its advantages [107]. Fuzzy logic provides a means to compute with words. It concentrates on the use of fuzzy values that capture the meaning of words, human reasoning and decision making, and provides a way of breaking through the computational burden of traditional expert systems [108]. In addition, since the inputs and outputs of fuzzy logic controllers are real variables mapped with a nonlinear function, they are suitable for various engineering applications. The main advantage of fuzzy logic controllers compared to conventional controllers is based on the fact that no mathematical modeling is required for the design of the controller [109]. The essential part of a fuzzy controller is a Knowledge Base (KB). The KB consists of IF-THEN rules, membership functions and a Data Base designed based on knowledge from a human expert or based on learning methods which do not require a mathematical model of the system [108]. The structure of a general fuzzy logic system is shown in Figure 2.12.The steps executed by the fuzzy system are: Fuzzification of input variables, rule evaluation, aggregation of the rule outputs, and Defuzzification as depicted in Figure The components of a fuzzy logic system can be described as follows: Fuzzification The Fuzzy Logic only uses fuzzy variables for computing its output. Therefore, in a fuzzy logic system all real-valued inputs x ϵ X, where X is the set of possible input variables, is first fuzzified. It is performed by statically defined membership functions (linguistic variables). Zadeh defines linguistic variables as variables whose values are not numbers but words or sentences in a natural or artificial language. A membership function assigns a truth value between 0 and 1 to each point in the fuzzy set s domain. A fuzzy set can be simply defined as a set with fuzzy boundaries. 42

57 Figure Structure of Fuzzy Logic System Let X be the universe of discourse and its elements be denoted as x. In the fuzzy theory, fuzzy set A of universe X is defined by function ( ) called the membership function of set A ( ): [0,1], Where ( ) = 1, ; ( ) = 0, ; 0 < ( ) < 1,. This set allows a continuum of possible choices. For any element x of universe X, membership function ( ) equals the degree to which x is an element of set A. This degree, a value between 0 and 1, represents the degree of membership, also called membership value, of element x in set A. It is worth to be mentioned that there exists several membership functions with different attributes such as triangular, trapezoidal, bell shape, etc. As an example, the triangular membership function µ(x), d 1, d 2, d 3 : [0,1] is specified by three parameters(,, ) with d 1 <d 2 <d 3 as expressed in (2.2): 43

58 ( ) = 0 < < > 3 (2.2) The parameter (,, ) with < < determine the coordinates of three corners of the underlying triangular membership function. Figure 2.13 shows the triangular membership function with d 1, d 2 and d 3 parameter. Figure An example for triangular membership function Determining the membership function is the first question that has to be tackled in fuzzy logic systems. The successfulness of fuzzy application depends on a number of parameters such as fuzzy membership function, fuzzy rule and also the significant input factors in the model Esmin et al. [110]. However, the conventional fuzzy modeling has a drawback that fuzzy rules and the fuzzy membership function are determined by experienced knowledge or trial and error [111]. This approach is time consuming since it uses trial and error to find good fuzzy rule and membership function, and can also not guarantee to find optimal or near optimal fuzzy rules and membership function. Generally speaking, there is no standard method for transformation of the human knowledge or experience into the rule base of a fuzzy inference system, and no general procedure for choosing the optimal number of rules. Hence, depending on the application, there is a need for a good method such as statistical approach for tuning the membership functions in order to minimize the output error measure or a maximize model performance. 44

59 Fuzzy Logic Rules and Decision Making In a fuzzy logic system the idea is to specify the input parameters in natural language and, with the help of a fuzzy-rule set, define the relationship among different inputs with the output(s). Input and output sets are combined through a set of rules in order to obtain the corresponding output(s). In Figure 2.14, the rule-based represents the knowledge of the outside world (i.e., information and data from wireless sensors) and specifies how to react to input signals as well. The system constantly evaluates the available inputs and makes decisions about the output(s) of the system according to the defined rules. A rule-base consists of a set of linguistic statements, called rules. These rules are of the form of IF premise, THEN consequent where the premise is composed of fuzzy input variables connected by logical functions (e.g. AND, OR, NOT) and the consequent is a fuzzy output variable. Consider a t-input 1-output fuzzy logic system with rules of the form: : When input = (,,,, ) is applied, the degree of firing of some rule can be computed as: ( ) ( ) ( ) = ( ) Here µ represents the membership function and both S and T indicate the chosen triangular norm. A triangular norm is a binary operation such as AND/OR applied to the fuzzy sets provided by the membership functions [108]. Aggregation of the Rule Outputs Aggregation is the process of unification of the outputs of all rules. In other words, we take the membership functions of all rule consequents previously clipped or scaled and combine them into a single fuzzy set. Thus, the input of the aggregation process is the list of clipped or scaled consequent membership functions, and the output is one fuzzy set for each output variable. 45

60 Defuzzification The last step in the fuzzy inference process is Defuzzification. Fuzziness helps us to evaluate the rules, but the final output of a fuzzy system has to be a crisp number. The input for the Defuzzification process is the aggregate output fuzzy set and the output is a single number. There are several Defuzzification methods [107], but probably the most popular one is the centroid technique. It finds the point where a vertical line would slice the aggregate set into two equal masses. Mathematically this center of gravity (COG) can be expressed as following: = ( ) ( ) (2.3) Figure The centroid method of Defuzzification As Figure 2.14 shows, a centroid Defuzzification method finds a point representing the COG of the fuzzy set, A, on the interval, ab. In theory, the COG is calculated over a continuum of points in the aggregate output membership function, but in practice, a reasonable estimate can be obtained by calculating it over a sample of points, as shown in Figure

61 2.8. Application of Artificial Intelligence, Wireless Sensors, Smart Grid initiatives for Energy management: a Review The emergence of powerful embedded micro-computer systems, and WSN, provides a good ground for in-depth research and adaptation of existing intelligent technologies and concepts, while exploring the new ones. These initiatives have motivated and led to many scientific endeavors and contributions to our society, where the existing state-of-the-art intelligent technologies and concepts are used in integration of many intelligent systems towards a new era of Smart Homes and Smart Energy Management Systems [112], [113], [114]. The leading edge technology in the area of intelligent systems and smart sensor networks are an essential part of our everyday life. Their evolution will help us to better utilize our energy resources (i.e. energy saving initiatives), and enhance our way of living. Indeed, many governments and utilities are interested to better utilize electricity, and encourage initiatives leading towards the development of intelligent systems. Thus, numerous research groups are closely involved in bringing forward efficient energy management systems for our living environments, furthering the research in intelligent and automatic control systems. The capabilities of sensor nodes to observe or monitor different variables (i.e., temperature, occupant activity, humidity) can help to enhance the limitations of the existing energy management devices [41]. Therefore, wireless sensors nodes can bring forward cost-effective mechanisms for monitoring, load control and energy management systems [43]. An application of occupancy sensors to save energy is presented in [115], where the deployed occupancy sensors detect occupant s activities and sleep patterns in a home and use these patterns to save energy by turning on/off the home s HVAC system accordingly. They use wireless motion sensors, Passive Infrared sensor (PIR) and door sensors, which are cheap and easy to deploy. Their system which is called smart thermostat uses sensors information to infer when residents are away, home, or sleep and turns the home HVAC system off as much as possible without losing occupant comfort. In order to estimate the probability occupant s states (away, home, or sleep) at home, the smart thermostat uses a Hidden Markov Model (HMM): (a) Away when the 47

62 home is unoccupied, (b) Active when the home is occupied and at least one resident is awake, and (c) Sleep when all the residents in the home are sleeping. Once the system detects a state transition with high probability, it responds by switching the temperature set point appropriately. However, they have not taken into consideration controller reactions if any change happens to the electricity rates during a day or if user changes the preferences or schedules. Lin et al. [116] present the application of multiple sensors to control a single actuator of a thermal system with PI controller. First they simulate a commercial building consists of four rooms to compare a single-sensor to multi-sensor techniques. Then they evaluate controlling of the averaging of all indoor temperatures, controlling the average of only the hottest and coldest temperatures, and controlling the temperature that is furthest from the temperature set point. The authors also evaluate controlling comfort rather than temperature by using a discomfort penalty function dependent on temperature which is based on Fanger s PMV-PPD model. The authors evaluate optimizing the comfort of rooms outside a predefined comfort zone and optimizing for energy consumption of rooms inside a predefined comfort zone. They further focus on the effect of occupancies per area since they observe that internal loads are the main source of temperature difference between the four simulated rooms. They conclude that multi-sensor strategies can simultaneously reduce energy consumption and improve comfort, but not all multi-sensor strategies necessarily outperform the single sensor strategy. A context sensitive and adaptive fuzzy control system to control the lighting system is discussed in [25]. The authors simulate and implement their approach in a smart home laboratory that has been built for these kinds of research at Tamper University of Technology, in Finland. Their fuzzy control system uses seven input variables to control two output variables. They take into account outdoor illumination level, user activity and time as main input variables. Besides, the states of the two outputs and their corresponding override flags are considered as input variables as well. The ceiling lighting power and Venetian blinds position are used as two linguistic output variables. The fuzzy control system monitors the context of the home with input devices and, changes the environment using its actuators according to learned rules. 48

63 Occupants don t need to interfere with the control system at all, but can override the actuators if needed. They also provide two control modes, autonomous control and event-based control to better the system function for different scenarios. In fact, the system utilizes a principle of continuous learning so that it does not require any training prior to use. They conclude that designed controller can adapt to changing conditions of occupants. However, in this method they just focus on basic function of lighting system to find out the issues and problems of the proposed algorithm from initial tests. For example their controller is not able to handle lighting control when the user forgets to turn it off after leaving the rooms. Another problem with this approach is the number of rules that make the system computationally expensive. In [117], a method based on fuzzy logic rule-based is proposed and being implemented on an air conditioner (AC) system using Zigbee nodes in order to reduce the on/off frequency of the AC. They use the temperature, humidity, fan speed, and engine speed as input variables. Their approach is based on designing a supervised fuzzy logic learning system. They define 72 rules to control system outputs. Their experiments show promising results compared to a traditional control system. However, this method does not consider smart grid incentives, user presence, and is not able to accommodate to user preference changes. In the context of Artificial Intelligence (AI), an agent is a device that senses or receives the environment information via sensors and acts upon sensory inputs via actuators [102]. Adding features based on the user needs or patterns to an agent could increase its capabilities to make more intelligent decision about certain operation(s) and lead us to approach smart agent concept (i.e. smart PCT can act as a central agent for HVAC systems). The agent function defines the actions of a smart agent related to sensory data inputs, whereas agent program must be very implementable [47]. An agent program is also an implementation of the code. For instance, wireless sensor/actuator nodes which percept via sensor(s), execute a matching rule, and takes action via actuator(s). The learning element is one essential component of the learning agent, which is responsible for potential agent s improvements, such as to perform better in the future. 49

64 This is achieved via a feedback element of the learning agent which decides how well the agent is performing based on some defined criteria [24]. There have been numerous agent-based research initiatives on energy conservation using wireless sensors and intelligent approaches. Page et al. [118] modeled the occupancy using a Markov chain and developed a time series model of an occupant in particular zones of the building. The generated model, along with user behavior models, has significant influence on building energy consumption. Authors in [119] and [120] attempted to create statistical occupancy time-series model based on occupancy survey of the people on a regular day. A rule based technique is applied on motion sensor data in [121] and achieved an accuracy of 83%. They learned the rules with statistical methods in the context of single occupant in a room. A smart sensing and adaptive energy management software is used in [122] to decrease the energy usage of HVAC systems in an institutional building. The approach is applied to shut off the HVAC systems when the rooms are unoccupied. To do so, they implement a multi-modal sensor unit that is nonintrusive and low-cost, combining information such as motion detection, CO 2 reading, sound level, ambient light, and door state sensing. They show that in the provided live test-bed, the sensors readings can be used to accurately estimate the number of occupants in each room using machine learning techniques. These techniques are also applied to predict future occupancy by creating agent models of the occupants. These predictions enable the HVAC system to increase efficiency by continuously adapting to occupancy forecasts of each room. Their results show 20% reduction in energy consumption. They believe that their predictive models can be deployed to a variety of offices, labs, and classrooms throughout campus buildings, and is adaptive enough to quickly learn occupant behaviors when deployed in the field. Warner et al [123] discuss an agent-based controller for residential comfort management that attempts to create a balance between occupant cost and comfort preferences. The system utilizes user preferences, environmental information from the internet, electricity price schedules, and building conditions to provide preheating during 50

65 the winter season. In this method the users have to interact with the agent constantly to set their daily schedules and preferences. Sharples et al. [124] propose a multi-agent structure where individual agents are in charge of learning the characteristics of individual rooms. They observe the definition of intelligent building differs between the building industry and computer scientists. Then, Hagras and Callaghan et al. [47] present using embedded agents for smart buildings, utilizing fuzzy logic to obtain knowledge and genetic algorithms to decrease the time for developing fuzzy rules. They design a two-tiered system using a highbandwidth wired internet protocol (IP) backbone to connect low-speed wired subnetworks within room. Each room has a room agent, consisting of a hardware device and software platform. Each room as an agent uses four sensors (indoor temperature, outdoor temperature, indoor illumination, and outdoor illumination), where fuzzy rules classify sensor levels into low, medium, and high values. While the room agents share the information from sensor and actuator, different room agents do not explicitly coordinate actuation. Rather the shared information from room agents facilitates each agent to determine its own behavior. Furthermore, the integration of wireless sensors with intelligent and soft computing techniques such as fuzzy logic and neural networks can play a significant role in development of future smart grid and in-home energy management systems. They can be integrated to collect and evaluate different environmental conditions in order to learn user patterns, save energy, provide comfort, and reduce the peak load demand. An Adaptive learning system that provides a smart and adaptable energy management systemic solution for smart buildings is discussed in [125]. They embedded more intelligence to existing PCT to convert them into a smart thermostat for optimal energy management in smart buildings. In fact, the proposed approach is a hybrid intelligent system, which uses WSN information and artificial intelligence techniques to learn and adapt to user patterns. They have used numerous intelligent agents and a central controller unit to control residential HVAC system. The thermostat as central agent offers intelligent energy management for smart buildings by using the proposed rule-based expert system and adaptive learning principles. However, the method is very user interaction-based as the user has to interact with the thermostat to adjust the 51

66 preferences from the beginning. In addition, the suggested approach for participating in DR programs is based on the TOU rates which will be a significant problem in the regions that the electricity pricing mechanisms is flat rate or RTP. They have not also considered occupant thermal comfort during participates in DR when the home is occupied. In most cases, such participation will suffer the occupants when the outdoor temperature is very cold or very hot. Liang et al. [126] investigate the design of an intelligent comfort control system by combining the human learning and minimum power control strategies for an HVAC system. In their work, a minimum power control strategy including both balancing the input power of HVAC devices and reducing energy consumption is used. Based on the PMV model a human learning strategy was designed to tune the user s comfort zone by learning the specific user s comfort preference. Rafael et al. [127] propose the use of genetic algorithms to develop smartly tuned fuzzy logic controllers for the control of commercial HVAC systems, where concerning energy performance and indoor comfort requirements. They find the problem has some specific restrictions that make it very particular and complex because of the large time requirements existing due to the need of considering multiple criteria and to the long computation time models require to assess the accuracy of each individual. To solve these restrictions, a genetic tuning strategy considering an efficient multi-criteria approach is proposed. Several fuzzy logic controllers are produced and tested in laboratory experiments in order to check the adequacy of such control and tuning technique. The proposed technique has yielded much better results than the classical On-Off controller showing the good behavior that FLCs can achieve on these kinds of complex multi-criteria problems. Li Ping et al. [128] present a RTP mechanism that decreases the load ratio from high-peak to mid-peak through demand response management in smart grids. The proposed method is based on a two-stage optimization problem. In one side, each customer reacts to prices offered by the supplier and maximizes its payoff, which is the difference between its quality-of-usage and the payment to the supplier. In the other side, the supplier designs the RTPs in response to the predicted user reactions to maximize its profit. In fact, each user computes its optimal energy consumption either in 52

67 closed forms or through an efficient iterative algorithm as a function of the prices. In supplier side, authors develop a Simulated-Annealing-based Price Control algorithm to solve the non-convex price optimization problem. In terms of practical implementation, the customers and the supplier interact with each other via a limited number of message exchanges to find the optimal prices. By doing so, the supplier can overcome the uncertainty of customers responses, and customers can determine their energy usage based on the actual prices. Their simulation results show that the proposed RTP mechanism can significantly flatten the energy usage peaks, decrease the supplier s cost, and improve the payoffs of the users. The implementation of wireless sensors to control the load of commercial cool room refrigeration and commercial HVAC system is presented in [129]. The authors demonstrated that provided intelligent agents with wireless sensors can enable costeffective dynamic electricity pricing directly to individual devices within a building. The authors note that multi-agent system architectures enable consistent descriptions of component functionality despite implementation specific details. However, well defined coordination and communication protocols between agents are required for system efficiency. The system is a combination of wired and wireless sensors including temperature sensors, door sensors, occupancy sensors, relays to activate the fan, and a current shunt meters for the compressor and fan. The system utilizes several communication protocols including IEEE , Bluetooth, X10, and GPRS. An unsupervised multi-agent system for learning occupant preferences to control lighting system of a commercial building is proposed by Rutishauser et al. [42]. They choose cluster, rather than room, that consists of clusters of sensors, actuators, and user feedback devices. A controller agent utilizes sensor information to learn occupant preferences, form via defined fuzzy rules, and provides control signals to field bus actuators deployed throughout the building. The performance of the proposed architecture has been evaluated by considering user comfort and postponed its effect on energy consumption. An Intelligent Metering/Trading/Billing System and its implementation in Demand Side Load Management of smart grids and smart distribution systems are presented in [130]. The intelligent system provides real-time price data to users via communication 53

68 networks. Consumers can set their demands through specifying the operating time of some of the home devices such as heating systems based on the offered real-time prices to shift their consumptions and save money. Customers can also opt in direct load control program in control center of a micro grid to shift their air condition demands by turning it on and off based on the real-time prices and outdoor temperature variations to save energy and shift the high-peak load demand to off-peak load demand. In [131], a Computer-Aided Home Energy Management (CAHEM) system is presented. The system integrates user schedules, information about real-time electricity prices, loads, and weather information into a load-shifting algorithm that looks for an optimal trade-off scheme between residential electricity consumer satisfaction and peak load reduction using a rule-based fuzzy controller. They use fuzzy logic to facilitate residential consumer demand responsiveness to price, load, and weather. The approach is intended to be inexpensive and simple to use, and to both shave and shift electricity usage away from peak periods without requiring significant consumer discomfort or effort. In order to evaluate the effects of the CAHEM system on system load; they have used data from 1999, and then use it to create a model of consumer response that they embed in a larger multi-agent simulation (MAS) of the electricity market. The provided computerized system is composed of three units: load models, user interface software, and an algorithm for load shifting. They have concluded that their approach can indeed mitigate high prices, improve system reliability, and reduce price volatility faced by load serving entities; however its benefits may be reduced if retailers (sellers) are able to respond strategically. In [132], the authors evaluate the performance of an in-home energy management application. The performance of the proposed approach is compared with an optimization-based residential energy management scheme whose objective is to minimize the energy expenses of the consumers. For this purpose, they developed an optimization-based residential energy management scheme, which aims to minimize the energy expenses of the consumers by scheduling appliances to less expensive hours according to the TOU tariff. They assume that one day is divided into equal length consecutive timeslots which have varying prices for electricity consumption similar to TOU tariff. The objective function minimizes the total energy expenses by scheduling the 54

69 appliances in the appropriate timeslots. In the proposed model, consumer requests are given as an input and an optimum scheduling is achieved at the output. They show that the suggested technique decreases energy expenses, reduces the contribution of the consumers to the peak load, and reduces the carbon emissions of the household. They believe that the in-home energy management application is more flexible as it allows communication between the controller and the consumer utilizing the wireless sensor home area network. They also evaluate the performance of in-home energy management application under the presence of local energy generation capability and for real-time pricing. However, this approach requires constant interaction from users, and the proposed shifting schedule scheme increases the PAR during off-peak hours. The impact of demand-side management on residential customers is discussed in [133]. They implement a simple DSM control strategy to study its effect on domestic loads. In fact, the aim is to reduce the coincident peak produced at the low voltage feeder level by high power appliances, without affecting the other loads. This is achieved by temporarily inhibiting the turn-on of by consumers of high power appliances that are not already in use. This control measure is activated when the total load on the low voltage feeder exceeds a present value. The DSM controller then coordinates the temporary inhibition of certain high power appliances in each house. By shifting the sharp jumps caused by the various consumers, this DSM scheme thus reduces the number of coincident peaks. Aggregated residential demand response programs have been considered in [10]. They explore the main industry drivers of smart grid and the different facets of distributed energy resources under the smart grid paradigm. Authors then concentrate on DR and summarize the existing and evolving programs at different independent system operators or regional transmission organizations and the product markets they can participate in. Finally, they conclude DR is an important ingredient of smart grid, promoting both market efficiency and operational reliability. They also conclude that if DR is implemented correctly, it can help protect supply market power against scarcity conditions, and improve operational reliability against profusion and variable generation. A residential load control scheme that is suitable for grids with real-time pricing is proposed in [28]. The authors focus on an automatic controller that is able to predict the 55

70 price of electricity during the scheduling horizon and schedule appliances to provide an optimum cost and waiting time within that horizon. To do so, they propose an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates. Our design is based on simple linear programming computations. Moreover, they believe that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. However, this is particularly true if the utility companies provide price information only one or two hours ahead of time. Their results show that the combination of their proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. However, their approach can only be applied to real-time pricing and does not consider other price mechanisms such as TOU and current demand of the house. In addition this approach has not learning capability. It also is based on demand shifting whereas our method is based on demand shedding that will reduce the PAR under any conditions. In [29], the authors propose a decision-support tool for smart homes. The proposed approach enables end users to first assign values to desired energy services, and then scheduling their available distributed energy resources to maximize net benefits. To do so, they use the particle swarm optimization technique. They improve the basic formulation of cooperative particle swarm optimization by introducing stochastic repulsion among the particles. They use a PHEV, space heater; water heater, pool pump, and a PV system are scheduled based on various TOU tariffs to apply their approach. However, it is just an autonomous system and the communication among the distributed sensor nodes and consumer has not been considered, whereas in our scheme, the controller and the user communicate through thermostat interfaces and can handle all pricing programs. In [33], several management and control schemes are proposed for micro-grids and for single houses. The authors use a neural network-based prediction approach to predict the day-ahead demand. According to the predicted demand, the schedule of the microchp device in each house is optimized. In addition, local appliances are controlled 56

71 to optimize electricity import/export of the home. Our energy management is different than [33] since we aim to minimize the cost of electricity based on TOU rates. Our work relies on demand shedding rather than scheduling generation and consumption to attain a balance. Moreover, in our research, we assume that each or user house makes independent decisions unlike a set of houses being controlled by a steering signal from a global controller. Mohsenian-rad et al. [32] focus on reducing the peak-to-average electricity usage ratio by finding an optimal consumption schedule for the subscribers in a neighborhood. The authors use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user s individual daily electricity charges. However, the proposed system operates autonomously and is not able to adapt to occupant s schedule changes. This system just responds to RTP programs and does not work for flat-rate mechanism. The approach is based on shifting consumption which usually increases the individual PAR. In addition, their approach needs to constantly be modified by users, if they want to use multiple energy resources. In [134], the authors propose an energy management protocol which allows consumers to set a maximum consumption value and the residential gateway is able to turn off the appliances that are in standby mode, or overwriting the user defined programs with less energy consuming ones. However, defining a maximum value for consumption is not practical and overwriting consumer settings may result in discomfort of the inhabitants. Our ihem application interacts with the consumers via appliance 57

72 interfaces, and consumers negotiate with the controller using a three-way handshake protocol. A dynamic demand response controller based on RTP for residential buildings is discussed by J. Hoon et al. [135]. This paper focuses on developing a control strategy for the HVAC systems to respond to real-time prices for peak load reduction. A proposed dynamic demand response controller changes the set-point temperature to control HVAC loads depending on electricity retail price published every 15 minutes and partially shifts some of this load away from the peak. The advantages of the proposed control strategy are that DDRC has a detailed scheduling function and compares the RTP of electricity with a threshold price that customers set by their preference in order to control HVAC loads considering energy cost. The controller reduces the initialized set point for 3 C if the price is high weight with respect to threshold price; 2 C for medium, and 1 C for low differences. The assumed thermal comfort zone in this research for heating is between 19 to 22 C. However, two major problems are raised by using this approach. First, they just set the set point on a fixed value (22 C). They didn t consider if the user schedules and initialized set points are different during a day, i.e., such as a PT. Second, they have not considered how the controller can manage user thermal comfort during high prices when the initialized set point is close to lower boundaries of ASHRAE comfort zone (e.g., 19 C). Furthermore, the factors such as air temperature, relative humidity, and solar radiation that are related to outdoor thermal environment can affect the evaluations of people thermal comfort. ASHRAE has defined the thermal comfort as following: the thermal comfort is a state of mind where a person expresses satisfaction with the thermal environment (ASHRAE Standard ). Generally, describing thermal comfort involves determining thermal sensation (e.g. hot, cold) and the relationship between satisfaction and sensation. Thermal comfort is one the main objectives for an HVAC system. For this reason, energy conservation and power management approaches for residential HVAC should incorporate thermal comfort. Models for satisfaction and sensation are typically a combination of empirical studies as well as analytical calculations. Several indices integrating thermal environmental factors and heat balance of the human body are applied for accessing thermal comfort. However, 58

73 predicted mean vote (PMV), standard effective temperature (SET) indices have a solid basis for indoor comfort use. Fanger [70], develops the Predicted Mean Vote (PMV) and Predicted Percent Dissatisfied (PPD) joint model which quantifies thermal sensation and correlates satisfaction to sensation for steady state conditions. ASHRAE also recommends that relative humidity (RH) being maintained below 60%. The RH should be bigger than 30% as well. Table 2.1 shows ASHRAE (2004) acceptable ranges of operative temperature for RH levels of 30% and 60%. If the RH is very high, the heat loss by evaporation will be much greater, so raising the temperature is one practical way to compensate for the extra loss by evaporation. When temperatures are within the comfort range (19 23 C) the RH has a little effect on comfort provided that it is within the range of 40 70%. And in addition, influence of the humidity is not great for the people with very light or sedentary activities. ASHRAE comfort zone (19 23 C), (18 22 C), or (19 22 C) is commonly accepted in research and practice. The lower zone considerably helps us in saving our energy resources [67]. Table 2.1. Acceptable temperature ranges based on ASHRAE Standard-55 Conditions Acceptable Ranges ( C) Summer Season (Clothing Insulation = 0.5 Clo) Relative humidity 30% Relative humidity 60% Winter Season (clothing insulation = 1.0 clo) Relative humidity 30% Relative humidity 60% In [136], the authors propose the use of a fuzzy adaptive network to model the thermal comfort system. To do so, the actual experimental data are used to train the network and to assess an individual s thermal comfort. Although only very simple examples are used in this approach, the results show the usefulness of the proposed approach than regression method. A novel system which is capable of adapting to the user s thermal preferences without any prior knowledge, and measuring the comfort level by aggregating several 59

74 thermal parameters into one single thermal index is discussed in [137]. This single value is used in a static set of fuzzy rules that easily understood by the user. The labels used in the rules are dynamically adapted to the estimated preferences of the user. In addition, to collect environmental conditions inside and outside of the house they use wireless sensor nodes to measure indoor/outdoor temperatures, and indoor/outdoor relative humidity. Experimental simulation shows that our proposal is capable of learning on-line the optimal thermal feeling for the user, and anticipating the necessary actions to obtain such thermal comfort in an indoor environment. 60

75 Chapter 3. Development of a House Heating-Cooling Simulator with the Application in Smart Grids 3.1. Introduction One of the main targets of future smart grids and energy management system manufacturers is integrating residential appliances and renewable energy resources into grid. This integration will help both users and utilities to benefit from smart grid initiatives. The users can save energy and cost on high electricity prices and utilities can avoid operation of peaking units such as low efficiency gas turbine or manage peak load periods. Smart control of residential devices such as HVAC systems while considering smart grid initiatives can potentially play an important role in reducing the peak demand and energy consumption. These can be performed by designing smart thermostat for HVAC systems or building smart homes with automation systems to control residential loads. Hence, in order to consider different scenarios related to energy management and cost in residential HVAC systems, there is a need to develop a user-friendly simulator. The simulator can help analyze how residential energy consumption and its associated costs can be influenced by some interrelated energy management factors such as electricity prices, occupancy, house parameters (i.e., wall thickness and number of windows). The proposed simulator is implemented using MATLAB with Graphical User Interface (GUI). It will assist us to simulate and evaluate energy consumption and associated costs for various parameters that are directly related to energy management in residential buildings. More importantly, the simulator will be used as an expert shell to help us in improvements of existing thermostats and implementation of the advanced intelligent techniques for future Smart Thermostats, such as our Supervised Fuzzy 61

76 Logic Learning (SFLL), Adaptive Fuzzy Logic (AFL) model, and Fuzzy Zone Controlled environment approach for residential buildings. The simulator will assist to investigate the impact of smart grid initiatives such as TOU rates on energy conservation. Besides, the benefits of multi-zone control versus controlling home heating system as a single zone unit will be considered by the simulator. Furthermore, it assists in proof of concept development and implementation of the proposed Adaptive Learning Model described in Chapter Model of a Residential Heating-Cooling System The model that is embedded into the simulator relies on fundamental principles of heat transfer and house thermodynamics equations. As shown in Figure 3.1, it consists of outdoor temperature (T out ), indoor temperature (T in ), temperature sensor or transducer, HVAC system, and a Thermostat unit. Since the aim of the simulator is utilizing wireless sensors to manage energy consumption, we assume that the -ambient conditions such as outdoor/indoor temperatures are being received from sensors. The outdoor temperature data is recorded offline and then loaded to the simulator from an excel file to provide the hourly temperature data to the Thermostat. Figure 3.1. Concept of House Heating-Cooling System Model The role of transducer in Figure 3.1 is sensing the heat losses of the house based on the outdoor and indoor temperature inputs and the house thermal characteristics. Residential HVAC system receives the inputs (actuation signals) from the Thermostat and reacts by applying the control signal to turn on/off the heater or 62

77 cooler. It provides the heated/cooled air with the constant air flow and temperature to the house. Similar principles can be applied to a HVAC system with multiple stages for zone control capability, where multiple parameters are needed to control the heater/cooler stages and adjust the heated/cooled air flow, based on the user preferences. As shown in Figure 3.1 the Thermostat as the central unit for residential HVAC systems consists of the indoor temperature sensor and a controller. The indoor temperature sensor module receives the inputs from the HVAC system (I.e., heat gain) and transducer, and computes the room temperature accordingly. It has to be mentioned that the heat is generated by the heater as well as the loss computed based on the house thermodynamics model. Finally, the controller part of the Thermostat reacts when indoor temperature value received from the indoor temperature sensor is different than the set point temperature(s) of a day adjusted by the user House Thermodynamic Model for the Simulator A thermodynamic model of a house establishes the relationship between generated heat and cool as the input, losses as the disturbances, and indoor temperature as the output. There are several factors that may influence the thermodynamic model of a house. The material properties of buildings contribute to the thermal performance and energy consumption. The walls, floor, roof, and windows have thermal conductivity, and allow heat and cool energy loss. One of the main challenges to model the thermal characteristics of a house is the effect of weather on the indoor temperature. For instance, wind, sun, and outdoor temperature have greater influence on indoor temperature than any air vent register. However, it is difficult to build a model that completely captures the effect of weather on indoor temperatures because outdoor weather conditions constantly change and rarely repeat. Furthermore, heat transfer takes place via the mechanisms of conduction, convection, and radiation. The term conduction refers to heat transfer that occurs across the medium layer. The conduction can be regarded as the transfer of energy from high to low energy parts of a substance because of the interactions between particles. The convection heat transfer mode as energy transfer occurs within a fluid due to the combined effects of conduction and bulk fluid motion. Finally, thermal radiation is the 63

78 energy emitted by matter that is at a finite temperature. The energy of the radiation field is transported by electromagnetic waves or photons. While the transfer of energy by conduction or convection requires the presence of a material medium. In fact, radiation transfer occurs most efficiently in a vacuum. In this research we only consider the conduction mechanism to derive the thermodynamic model of the house. This model is utilized in order to verify and analyze our proposed approaches with respect to different energy management parameters. That is also used to enable planning for implementation of our developed techniques in the next chapters. The simplified model of the house using the conduction mechanism can be derived in two ways: (1) an electrical module or circuit, and (2) thermodynamic techniques. From electrical circuit point of view, a house can be a network of rooms with first-order systems. Figure 3.2 shows a single room that is represented by an electrical circuit [138]. In this Figure, T amb is the outside ambient temperature; the elements for thermal characteristics of a room are thermal resistance of walls R w, thermal resistance of windows R c, thermal capacitance of the wall C w to store heat, and indoor air C i. Figure 3.2. Heat transfer based on electrical module The air conditioner (AC) and furnace system (heat-cool) are represented by the Q ac-ht as thermal source. Using this model, the room s temperature, the power consumption in the room, and the corresponding cost of consumed energy can be computed. The following differential equations can be obtained from Figure 3.2: = = ( ) ( ) + + (3.1) 64

79 Where, S operates as a binary variable showing the state of HVAC system ON or OFF. The second way for deriving the house thermal model is based on the fundamental principles of thermodynamics and ASHRAE recommendations. In the context of buildings, conduction happens through solid walls that have not thermal equilibrium. It is affected by the difference between indoor and outdoor temperature T, thermal resistivity of materials used in the house k, wall thickness L and area A, as shown below. The heat flow is then = = (3.2) Using the principles that mentioned above, I will derive the governing heat transfer equations for the temperature distribution in walls and rooms of a typical two storey house in Canada as shown in Figure 3.4 [3]. In addition, the house model is derived based on assumptions below: The air temperature in a room has one temperature across its volume. A more accurate model of temperature is significantly more complex and it does not have that effect on residential energy management. The specific heat of air, c p, is constant. In reality, c p is at 250 K and at 300 K, so our assumption is accurate to within 0.1% error over the range of temperatures that would occur in a building. All rooms are at the same pressure used in the heating and cooling ducts. Air exchange between rooms is then isobaric, so the air mass in the room will not change in the process. Neglecting radiative heating. In a real building, the changing position of the sun through the day, and variations in atmospheric attenuation, will affect the radiation. Here due to lack of exact data for the intensity of irradiation from the sun for a given time in a day, we ignore the sun irradiation. 65

80 Neglecting radiative coupling between inner building walls; as the temperature difference between pairs of walls should be small, the effects of interior radiative coupling are likely to be minimal. Figure 3.3. A typical two storey house For a single room, the amount of the supplied heated/cooled air into the room from our assumptions is, =.. (3.3) Where T is the difference between indoor and outdoor temperature, isthe heated/cooled airflow, and c is the specific air capacity (J/kg K). The heat/cool losses and gains of a house are equal to: =, R = (3.4) = (T T ). M. (3.5) 66

81 In the equations 3.4 and 3.5, the parameters are being introduced as following: R eq : Equivalent thermal resistance of the house l: Wall Thickness (m) λ: Thermal conductivity of (w/m.k) for air at 20 C λ = (w/m.k) T indoor : Indoor temperature measured by wireless temperature sensors ( C) T outdoor : Outdoor temperature measured by outdoor wireless temperature sensor ( C) : Heat flow into the house M f : Air mass flow rate through the heater (kg/sec) : Specific heat capacity of the air at constant pressure (J/kg K) In addition, the dynamic response of indoor temperature in heating operation that represents data of indoor temperature sensor is computed as following: =. [ ] (3.6) Mair = d.v (3.7) Where and are thermal model for the rate of heat/cool losses and gains of a house and the initial model of a heated air supplied to the house respectively. : Rate of indoor temperature change inside the house M air : Mass of air (kg) V: Volume of the house (m 3 ) The parameter d is density of the air; and its value is 1.21 Kg/m 3. The equation 3.6 implements the thermodynamic energy equation in order to describe a simple model of temperature variations inside the house. After applying the boundaries for computing the heat-cool gain and losses, we have 67

82 dt = Q = M. c. T. (t t ) (3.8) Q = dt =. T. (t t ) (3.9) T = (Q. Q ) (3.10) dt = Q = M. c. T. (t t ) (3.11) dt = Q =. T. (t t ) (3.12) T = (Q. Q ) (3.13) Where, T = T T T = T T T = T T (3.14) T = T T t, t Upper and lower bound of the simulation time. In addition, the thermal resistance in a house is also calculated as following: R =. (3.15) R =. (3.16) R =. (3.17) 68

83 is equivalent thermal resistance of the house(. ), and are thermal resistance of walls and windows respectively (. ). K, L, and A in equation 3.11 are also thermal coefficient, thickness, and area respectively. The heat/cool cost is computed by the following equation: = [. ]dt (3.18) Where and p are heat/cool flow rate through the cooler into the room / and the electricity price (cents/ kwh) respectively Design of House Simulator in MATLAB-GUI The simulator is implemented using MATLAB-GUI. In order to design a userfriendly interface and facilitate evaluating different parameters related to residential energy management; the specifications of existing thermostats, smart grid initiatives, and energy-saving opportunities are taken into consideration. These specifications can be regarded from two aspects: simulator s interface and feedback programs. Most of residential users still prefer to use PTs because of the complexity of a user interface in PCT. Therefore, designing a user-friendly interface is important for developing a simulator. The simulator interface is shown in Figure 3.4 in which the main elements of the simulator are captured. The interface is designed to enable the customer to enter choices for controller operation. It also provides feedback to the consumer about thermostat settings and other data and information profile. Furthermore, the thoughtful considerations for designing the simulator as a Smart Thermostat are the ways that the system can provide potentials to save energy. For example, real-time and direct feedback provides a wide range of knowledge to consumers to realize about their daily or monthly usage through taking feedback from the provided information on the screen. Hence, in-home energy management systems 69

84 such as Smart Thermostats should provide the potential for learning by doing when the consumer turns them on/off or decreases/increases the set point temperatures. In this way, the consumers can receive instantaneous feedback from thermostat that allows them to know their energy consumption and the effect of other parameters such as electricity prices and outdoor temperature on their bills. Therefore, displaying the information and data such as indoor and outdoor temperatures, daily consumption (kwh), total consumption, plotting hourly consumption, the associated HVAC daily and monthly costs, current adjusted set point, and current effective electricity rate are the parameters that provide potential for the consumers to reduce their energy consumption and save on their bills. Figure 3.4. Simulator Interface at Work for one day The left hand side of the Figure 3.4 shows the indoor and outdoor temperature profiles. The outdoor temperature plot is read from an excel file by the simulator (left bottom side: load temp. key), and represent the real weather data taken from the Canada s National Climate Organization or can be defined by user. The indoor 70

85 temperature profile shows the dynamic response of the house HVAC system which takes into account factors such as the thermal model of a house, heating loss and gain. The bottom left side of the Figure 3.4 is the hourly consumption graph for one day. This can help users to monitor their hourly consumption during a day and change their usage patterns for saving energy. On the bottom right side of the Figure 3.4 is Thermostat interface, which displays the data as simulation progresses, such as the indoor/outdoor temperatures, total energy consumed (kwh), total cost ($), heat/cool set points at different levels of simulation, TOU or RTP Rates at different points of simulation and the mode of operation (Heat, Cool, Auto, Off). In addition, as shown on the top of Figure 3.4, there are four more tabs namely Schedule, Demand Response, Prices, and House Parameters. The operation of each tab is as follows: Schedule The schedule control option in Figure 3.5 enables consumers to choose different daily schedules, including set points and time intervals during the simulation process. More recently, part of the 2008 California Building Energy Efficiency Standards requires that programmable thermostats have the ability to set temperature preferences for at least four different time periods per day. Therefore, as shown in Figure 3.5 for the designed simulator (smart thermostat) we assume six set points intervals. In fact, this feature enables users to adjust heat and cool set points at different times of the day/week based on their preferences and working schedule. The fix set point option in Figure 3.5 is used when the user want to adjust the Set Points (SP) on constant value for entire day, similar to a conventional thermostats. Besides, the offsets option in this figure allows users to decrease or increase the adjusted SP by defining the offset values for heating and cooling. Demand Response Figure 3.6 depicts the simulator DR control which enables one to participate (Opt-in or Opt-out) in utility DR programs. In the past, residential consumers used energy in a care-free manner because of low electricity prices. Nowadays, residential 71

86 consumers have become increasingly aware of their energy consumption. Utilities have initiated programs to encourage residential consumers to reduce energy consumption during critical times of the year to avoid blackouts. Figure 3.5. Schedule for setting the daily/weekly set points Figure 3.6. Demand Response Feature for Simulator As a result, the capability of participating in DR programs must be embedded into future in-home energy management systems. Nowadays, this feature is the most important feature of existing PCTs. This enables incentives for 72

87 the users to reduce cost and energy, and helps utilities during peak load demands. The user can enter different TOU rates that offered by the utilities as On Peak, Mid Peak, and Off Peak to participate in DR events at their discretion. The user can also shift his/her load from on-peak periods to off-peak by adjusting the option off provided in the bottom of this Figure. Parameters Figure 3.7 depicts the details of this feature of simulator. This tab enables user to enter different house and HVAC parameters such as wall and windows thickness, volume of HVAC s airflow, etc. It enables users to observe energy consumption and its associated cost and saving when they change house and HVAC parameters. Figure 3.7. House and HVAC Parameters in Simulator Prices As mentioned in chapter 2, smart grid initiatives are applied in order to encourage customers to reduce their electricity usage during on-peak demand and high electricity prices. These initiatives will compel home appliances manufacturers to build home energy management systems such as thermostats capable of making intelligent decisions based on smart prices. The designed simulator can potentially communicate with smart meters to receive price signals. To emulate such process an excel file 73

88 consists of electricity prices (RTP or TOU) is loaded into simulator. The simulator can handle flat-rate, TOU rates, and RTP. Figure 3.8 shows the price control tab that the user can enable one of them depending on the electricity rates offered by utilities in that region. Figure 3.8. Simulator Price Control 3.4. Components of the Simulator The thermal model used in the simulator as heart of simulator engine emulates a house heat and cool gain/losses in order to monitor and analyze its response under various scenarios. It enables us for a feasible implementation of a Smart Thermostat and additionally implementation of our approaches such as supervised fuzzy logic, smart load reduction, and adaptive learning system techniques. As described in Section 3.1, heat flow through a house as well as home energy management depend on many parameters, such as the difference between inside and outside temperature, conductivity of building materials, occupancy, responding to smart grid initiatives, thickness of walls, windows, etc. These different but interrelated parameters require us to classify them in order to simplify the implementation of the simulator. Based on the classification of parameters the conceptual model of simulator was rendered and precisely programmed in different classes in MATLAB as shown in Figure 3.9. As it can be observed in Figure 3.9, the Thermostat represents the main class of the 74

89 simulator, where the control techniques are located. In fact, as it will be observed later, implementation of our approaches using this simulator that has an environment similar to an actual thermostat is essential for the effective proof of our concepts The classes House parameters and equations, Schedules, Smart Grid Incentives, Sensors Information, Demand of House, and Time are the other classes which have a relationship with the main class. They are introduced within main application with default system values, and properties. These values can be set for different houses and HVAC models. Figure 3.9. Different components of the simulator The main advantage in the implementation of the above design strategy is to enable users to simply change any class parameters (i.e. simulation step size, interval, initial conditions, schedules, smart grid initiatives, sensors information, etc.). It also provides a user-friendly and flexible house model for implementation and simulation under different conditions. Finally, it can be used as an expert shell, to explore and implement our new concepts and techniques. The feature and contents of each class is summarized as follows: Smart Grid Initiatives Provides the electricity prices and DR events at different times of day based on the fixed price, RTP, and TOU rates sat by the Utility. 75

90 Indoor and Outdoor Temperature Sensor -Provides information about the indoor and outdoor temperatures to the simulator. Occupancy Provides information about user presence at home and different zones. House Demand Provides the current demand of a house. Time -Provides the necessary info for the simulation process, including simulation step size, speed, time of the day, set points at different instances of a day, etc. HVAC System Parameters - React based on the heater and cooler state, temperature profiles, heating gain/loss, and other house system parameters. Graph and reports - Plots 24 hour daily outside temperature from the weather data; plots the indoor room temperatures at different time intervals, plot hourly consumption, (based on point to point of simulation step sizes).for each step of simulation, data is saved into a log file, which includes heat loss, heat gain, total energy consumption, energy consumption for different TOU rates, RTP, total cost for each step and the total cost for the entire simulation period. Schedule and preferences Enables a user to choose and adjust the desired heat and cool set points for different times of a day (hour: min) for each week day and weekends. House System Parameters - enables a user to select different house parameters, including house volume, wall and window area, wall and window thickness and thermal coefficients, number of windows, etc. Thermostat & Control Techniques provides the main control unit of the heatingcooling system, by evaluating inputs, and matching and directing the input output relationships, computing the main values based on the parameters introduced to the simulator (i.e. equivalent thermal resistance of a house, heat-cool set points, air flow rate, heater capabilities, indoor temperature, etc.). More features such as use of Supervised Fuzzy Logic System, Adaptive Fuzzy Logic, and Fuzzy Zone Control will 76

91 be embedded into the simulator as new control techniques to develop an Adaptive Smart Thermostat Simulation results and Discussion In order to analyze and verify the performance of the simulator as a thermostat, first we need to embed some data such as house parameters, outdoor temperature, HVAC parameters, electricity rates (e.g., fixed rate, TOU, and RTP) in the simulator. I considered the thermostat responses with respect to energy savings and cost for several user preferences, such as scheduling usage versus fixed set points, and using fixed price, TOU rates, and DR programs for evaluating the impact of smart grid incentives on energy conservation and cost for our future research in next chapters. The parameters and other variables needed for the simulation such as user schedules for weekdays and weekends, and house parameters are listed in Tables 3.1, 3.2, and 3.3 respectively. Table 3.1. User schedules for weekdays Time Of Day Heat Set Point ( C) User Status 00:00 to 06:00 21 Sleep 06:00 to 08:00 22 Awake 08:00 to 11:00 17 Away 11:00 to 17:00 18 Away 17:00 to 19:00 20 Home 19:00 to 24:00 23 Home TOU rates shown in Table 3.4 are taken from Hydro One utility in Ontario, Canada and are in effect in 2014 for winter season. The weather data for outdoor temperature is from the Canada s National Climate Archive. We also assume that a single forced-air central heating and cooling HVAC system operates in the house. The heater and cooler independently provided BTU per hour equal to 8.8 kwh via three states: heat, cool, off. 77

92 A constant-volume supply fan distributes conditioned air through one register in each room, where air distribution is proportional to room area. The temperature of air supply into house or rooms is 45 C. It is worth to mention that in our simulation, the response time of the system based on the parameters listed in the Table 3.3, for different average outdoor temperatures (-5 C, 0 C, 5 C) and a fixed set point of 20 C is computed. The initial inside house temperature is also assumed 0 C. Figure 3.10 shows the response of the system for step size of 5 minutes. As it can be observed from Figure 3.10, the response time of the system to reach the desired set point (20 C) corresponding to average outside temperatures (-5 C, 0 C, 5 C) is approximately 165, 115, and 95 min, respectively. Table 3.2. User schedules for weekends Time Of Day Heat Set Point ( C) User Status 00:00 to 08:00 20 Sleep 08:00 to 15:00 22 Home 15:00 to 19:00 17 Away 19:00 to 24:00 23 Home Table 3.3. List of House Simulation Parameters Parameters Values Unit House Length 15 m House Width 8 m House Altitude 5.5 m Number of windows 6 Window length 1.5 m Window height 1 m Windows thickness 0.01 m Walls thickness 0.3 m Wall Thermal coefficient W/m.K Window Thermal Coefficient 0.78 W/m.K Initial House Temperature 0 C 78

93 Table 3.4. TOU rates for winter season, Hydro one, Ontario, Canada Time Of Day Price ($) Description 00:00 to 07: Off-peak 07:00 to 11: On-peak 11:00 to 17: Mid-peak 17:00 to 19: On-peak 19:00 to 24: Off-peak In this case, the actual set point has an offset of ±1 C. This is because the step size (minimum on/off time of HVAC system is 5 min) the average offset (dead-band of the system) is chosen ±1 C. It is worth to mention that the step responses shown in Figure 3.10 prove that the simplified thermal model of the house derived in Section 3.1 is similar to corresponding electric circuit model proposed in [138]. For three months simulation; the initial indoor temperature of a house is set on 0 C. Since the simulation is run for 90 consecutive days, the indoor temperature of the next day is equal to the previous day at 23:59. Figure Response time of the system for different outdoor temperature In order to demonstrate the significance of scheduling the preferences based on smart grid incentives (e.g., TOU rates); a three-month simulation is run. Figure 3.11 depicts the energy consumption and their associated cost for simulation of a typical 79

94 weekly schedule of set points versus fixed set points of 20 C, 22 C, 24 C and 26 C, based on the different TOU rates. Weather data for outdoor temperatures, used for simulation is for months of December 2013, January and February 2014, in Ontario, Canada. It can be observed from Figure 3.11 that the potential savings in energy consumption is possible to be achieved with weekly schedules instead of fixed set point. As shown in Figure 3.11, a user can potentially save 894 kwh of energy by scheduling his/her usage and preferences compared to keeping the heat SP only on 20 C for three months. In addition, the cost associated with maintaining the temperature on 20 C is about $1416, while using a simple schedule can reduce it to $1327 ($89 saving in cost). Furthermore, these results show if the households can reduce the SP temperatures during on-peak price, they can decrease the load demand on peak load periods (i.e., the main objective of smart grid incentives). Therefore, the role of participating in DR and TOU programs to save energy is apparent. Energy Consumption (kwh) for 3 months for different SP and their associated costs based on TOU rates Cost ($) Consumption (kwh) $ $ $ $ $ Scheduled 20 C 22 C 24 C 26 C Consumption kwh (on-peak) Consumption kwh (mid-peak) Consumption kwh (off-peak) Total Cost ($) Figure Energy consumption and cost for schedule and non-schedule set points Furthermore, Figure 3.12 shows a scenario of one day when a user participates in DR events where simulator operates as a PCT and set point temperature is set on 21 80

95 C. The offset values sat by user for participating in DR (load shedding) for on-peak prices, mid-peak prices, and off-peak prices are 4, 3, and 1 C respectively. The Thermostat constantly communicates with smart meter to read the price signals in order to apply particular offset value based on the TOU rates. However, in most cases this participation jeopardizes occupant thermal comfort particularly in cold winters or hot summers. Therefore, making this participation more automated and intelligence by considering wireless sensor capabilities, while maintaining thermal comfort is necessary for future thermostats. Such smart thermostat will integrate residential HVAC systems into smart grid that is the main goal of future smart grid and energy systems. In the next chapter we will go through designing smart demand-side management for HVAC systems, where we propose a fuzzy logic approach versus existing DR approaches that are being used in existing PCTs. Figure A scenario of participating in DR based on TOU rates In addition, Figure 3.13 shows the simulation results for zoning control of the house which provides a better performance with respect to energy conservation. It also reflects more cost savings for a zone controlled house. 81

96 Figure Energy consumption and associated cost in Zone-controlled environment As a result, the effect of zoning control environment can be taken into account as a very important step for better energy management. In this case, the role of sensor nodes such as occupancy sensors is apparent particularly for a Smart Thermostat, that could be used to control the indoor temperatures in different zones of a house. In addition to the lack of intelligence in existing PTs and PCTs, another important factor which impacts their performance, is the lack of multi-zone control capability that is due to being equipped with only one or two sensor nodes. 82

97 Chapter 4. Smart Demand-side Management (DSM) in Smart Grids 4.1. Introduction In future smart grids, residential customers will be an integral part of the electric power system. They will balance supply and demand and ensure reliability by modifying the ways they use and purchase the electricity. As stated in [15] and [19] the users with PCTs were faced a significant thermal dissatisfaction during participation in DR events particularly in cold winters and hot summers. Therefore, the need for developing smart algorithms for demand-side management in residential buildings is apparent. Integration of artificial intelligence (AI) techniques such as fuzzy logic and Wireless Sensor Networks will play an important role in the extension of smart grids and in-home Energy Management Systems (EMS) such as thermostats. A set of incentives such as DR programs, TOU rates or RTP are applied by utilities to encourage customers to reduce their load during peak load demand and/or high electricity prices. However, responding manually to dynamic electricity price is often a hassle and confusing for residential customers to manually respond to electricity prices that vary over time. Hence, the need for smart in-home EMS such as thermostats which are capable of reducing the load demand by learning through information provided by distributed wireless sensors and implementation of smart grid incentives is necessary in future smart grid. The role of PCTs is to control residential HVAC systems in order to provide consumer with a means to manage and reduce energy use, while accommodating their everyday schedules, preferences and needs. However, lack of learning capability and jeopardizing occupant s thermal comfort during participation in DR programs are the 83

98 major drawbacks of existing PCTs. In this chapter, the development of a Fuzzy Logic Approach (FLA) which utilizes wireless sensors and smart grid incentives for smart load reduction in residential HVAC systems is presented leading to smart demand-side management. The proposed FLA is embedded into existing PCTs in order to augment more intelligence to them, while maintaining occupant s thermal comfort. In order to emulate an actual thermostat, the designed simulator, presented in Chapter 3, is used as a PCT which is capable of handling both TOU and RTP. Several different scenarios are implemented to evaluate the performance of FLA and verify its capabilities in smart load reduction, energy saving, and occupant s thermal comfort. The proposed smart load reduction is performed by applying specific fuzzy rules through evaluating the available inputs received from wireless sensors and smart grid Fuzzy Logic System and Description of DSM in a House Platform Figure 4.1 shows a simplified illustration of utilizing fuzzy logic, wireless sensors, and smart grid incentives for demand-side management in residential buildings. Any kind of management needs information about the environment that is being controlled. As pointed out in Chapter 2, Section 2.6 the wireless sensors offer the freedom to install different and/or similar sensors in any part of the building without the need of wires. Therefore, they can provide plenty of measured and/or monitored information for the energy management within a building. In Figure 4.1, it is assumed that input variables such as outdoor temperature and occupant s activity are received from wireless sensors and the electricity price taken from smart meter. Furthermore, modifying operation of home devices such as PCTs manually in a dynamic environment is difficult. This is because of continuous variations in input variables (shown in Figure 4.1) as well as the time and effort required to constantly override the initialized schedules and preferences. Hence, augmenting capability of learning continually by itself is necessary for existing PCTs. Therefore, applying fuzzy logic techniques such as supervised fuzzy logic learning can be practical and feasible in these cases. 84

99 Figure 4.1. A simplified illustration of smart DSM applied to a residential HVAC system As it can be observed in Figure 4.1 and recall from Chapter 2, Section 2.7, the steps executed by the fuzzy logic system are: Fuzzification of input variables, Rule evaluation, Aggregation of the rule outputs, and Defuzzification. The PCT equipped with FLA constantly utilizes available inputs to compute the amount of load reduction. This operation is performed by applying specific rule(s) associated with input values (i.e., wireless sensors information). The fuzzy rules are tuned somehow to shed the load without jeopardizing occupant s thermal comfort. As shown in Figure 4.1, the defuzzified value that represents the amount of HVAC load reduction ( C) is reduced either from the initialized set point for that time of a day or added to them depending on the operation modes (heat/cool). In addition, the main advantage of fuzzy logic controllers compared to conventional controllers is based on the fact that no mathematical modeling is required for controller design. In addition, Knowledge Base (KB) is the essential part of a fuzzy logic controller. The KB consists of IF-THEN rules, membership functions, and a data base designed based on knowledge from a human expert or based on learning methods which do not require a mathematical model of the system Input and Output Parameters and Their Associate Membership Functions The capabilities of sensor nodes to sense, measure, or detect different variables of interest, such as temperature, pressure, illumination, and occupancy can significantly 85

100 enhance the capability of existing in-home EMS such as PCTs. They can enable a better energy management by using the sensors data to control HVAC system(s) more effectively. The aim of FLA is participating in DR program with user choice for smart load reduction while thermal comfort is not compromised. The successfulness of fuzzy techniques depends on the right selection of parameters and defining proper membership functions and fuzzy rules in the model. Here, many different parameters can be used as input parameters of the fuzzy system. However, we prefer to consider the parameters that directly relate to energy management in residential buildings. In this research, four important parameters that influence increasing electricity demand in residential buildings are taken into account as system inputs. In addition, they can impact on demand-side management, cost, and thermal comfort. These parameters are current outdoor temperature (T out ), current electricity price (P E ), occupant presence (P O ), and current initialized set point (S i ). These inputs are totally interrelated and fuzzy logic can be the best choice in order to compromise among them. In this research, the geometric pattern of triangle is used to define membership functions of input and output variables. A membership function assigns a value between 0 and 1 to each point in the fuzzy set s domain. Input and output sets are connected through a set of IF-THEN rules in order to obtain the corresponding output(s) as shown in Figure 4.1. Here, the only output of the system is Load Reduction (L R ). Indeed, the L R is the amount of temperature that is reduced from current initialized set point in order to shed the residential demand. The role of each parameter in residential energy management and their membership functions are described as follows: Outdoor Temperature (T out ) Loads are usually affected by many dynamic and stochastic variables such as outdoor temperature, energy prices, etc. Load prediction becomes difficult when the behavior of the dynamic variables is not well known. There is a strong relationship between climate change and energy demand. Temperature can also exhibit strong fluctuations within a day in many countries such as Canada. This makes residential HVAC systems a highly variable load. Therefore, demand of load significantly depends on the temperature of the day in cold countries such as Canada. Normally, when the 86

101 temperature is very cold/hot; the demand of electricity is high. However, capturing the relationship between outdoor temperature and house load demand is difficult. Fuzzy logic can overcome such difficulties and uncertainties. In this research, we assume that the outdoor temperature can be taken from outdoor wireless temperature sensor. The outdoor temperature is collected hourly. Figure 4.2 shows the membership functions of outdoor temperature. The values received from outdoor wireless temperature sensor are first fuzzified by the membership function depicted in Figure 4.2. Figure 4.2. Membership functions of outdoor temperature The mathematical equations of the membership function shown in Figure 4.2 can be stated as follows: 1 0 ( ) = < < 5 ( ) = < < 10 ( ) = < < 20 ( ) = 1 20 (4.1) 87

102 Electricity Price (P E ) Smart meters hold the potential of providing energy consumers with real-time energy consumption data, energy cost information, and empowering consumers to effectively manage their energy consumption. As shown in Chapter 3, reducing and scheduling usage during on-peak price, could result in decreasing the load demand on peak load periods (i.e., the main objective of DR and TOU rates). Nowadays, with the advancements in metering and smart grid technologies, there is a great opportunity to realize both energy efficiency and DR together through a shared technology platform (i.e., smart meters). In this research, in order to emulate two-way communication between the thermostat and smart meter; an excel file contains TOU rates or RTP is used. Figure 4.3 shows the membership functions of electricity price read from smart meter. However, the range of membership function can simply change depending on the electricity prices applied by utilities in that region. Figure 4.3. Membership functions of electricity price Occupant Presence (P O ) For the controller to consider the effect of occupancy it is essential to know whether or not someone is in the controlled space (rooms or house). This is because the 88

103 control system must act differently when a person is present for providing thermal comfort. Using occupancy sensors for energy and cost saving can be beneficial, particularly in the regions that the price mechanism is flat-rate. In these regions such as British Columbia, Canada, DR programs based on TOU rates do not work and the employed PCT operates as a PT. Figure 4.4. Membership functions of occupancy Technically speaking, occupant activity can be detected by occupancy sensors, door sensors, motion sensors or a combination of these kinds of sensors which are inexpensive and easy to install. A significant part of fuzzy logic decision-making depends on the information received from wireless occupancy sensors in order to infer whether the inhabitant is home or away. It is assumed that the occupancy sensors give a real output value of 0 or 255 based on binary information. As depicted in Figure 4.4, this interval is fuzzified into two different values of the linguistic variable Present and Absent. It is clear from the Figure 4.4 that only one state can be received at a time Initialized Set Point (S i ) In order to provide occupant thermal comfort it is important to take into consideration the initialized set points as one of the inputs., where the proposed fuzzy logic decision-making is executed to set the amount of L R (output). By doing so, the system constantly checks the current initialized set point along with other information received from sensors and smart grid in order to apply the appropriate fuzzy rule(s). 89

104 Therefore, this assists us to maintain the new adjusted set point in ASHRAE comfort zone properly (18 C to 22 C). Recall from Chapter 2, the ASHRAE zone can be simply changed to other boundaries such as (19 C to 23 C) depending on the relative humidity in that region. Figure 4.5 shows the membership function of the initialized set point. Figure 4.5. Membership functions of set points initialized by user System Output or Load Reduction (L R ) As mentioned in Section 4.2., load reduction is the only output of the system. Figure 4.6 depicts the membership function of the system output. The defuzzified value of the output specifies the amount of temperature that has to be reduced from current initialized set point based on the aggregation of outputs from all the defined rules. The aggregated fuzzy sets must be transformed into crisp values for the control variables. This is the goal of the Defuzzification interface. There are two types of Defuzzification approaches based on the way in which the individual fuzzy sets are aggregated through connectives as follows [127]: Type A: Aggregation first, Defuzzification after The Defuzzification Interface performs the aggregation of the individual fuzzy sets inferred, to obtain the final output of fuzzy set. Usually; in this case the aggregation 90

105 operator modeling is the minimum or the maximum. After that, the fuzzy set is defuzzified using any strategy technique, like the Mean of Maxima (MOM), or the Center of Gravity (COG). Figure 4.6. Membership functions of system output (Load Reduction) Type B: Defuzzification first, Aggregation after It avoids the computation of the final fuzzy set by considering the contribution of each rule output individually, obtaining the final control action by taking a calculation (an average, a weighted sum or a selection of one of them) of a concrete crisp characteristic value associated to each of them. However, we use Type B for Defuzzification because Type A is computationally expensive and hard to implement. Here, Mamdani technique is employed for Defuzzification of output because it provides a natural framework to include expert knowledge in the form of linguistic rules which is very important in our problem. The COG approach described in equation 4.2 is used for Defuzzification, where ( ) membership function of fuzzy set A and m is the number of rules applied to the controller. = ( ). ( ) (4.2) 91

106 4.4. Fuzzy Logic Decision-Making Algorithm In building automation in which a HVAC system is the primary energy consumer, the aim of a controller is to maintain the indoor temperature between the desired intervals. In our case, the objective is to maintain indoor temperature within the ASHRAE comfort-zone based on information received from WSN and smart grid incentives by controlling the set points that are already initialized by a user. Furthermore, other important objectives must be considered, e.g., energy savings, thermal comfort, etc. several factors have to be considered in order to achieve these objectives. Here, the proposed FLA has to constantly be tuned to balance the new set point in order to achieve the objectives. For this purpose, the system invokes the fuzzy rules to compute the amount of L R. Table 4.1. Some of Fuzzy logic decision-making rules Rules T out P E P U S i Output Rules T out P E P U S i Output (L R) 1 Very Cold H P L L 9 Cool H P M M 2 Very Cold H A L M 10 Cool H A H H 3 Very Cold M P H M 11 Cool M P H H 4 Very Cold L P M L 12 Cool L P L L 5 Cold H P M M 13 Natural H P H H 6 Cold H A H H 14 Natural H A H H 7 Cold M P M M 15 Natural H P M M 8 Cold L P L L 16 Natural H A M H Simultaneously, other goal of the fuzzy rules is to work as a smart agent to determine the best indoor temperature in order to provide thermal comfort when the home is occupied. All these procedures are performed by the defined and tuned fuzzy rules. Some rules are tabulated in Table 4.1. In that Table, H stands for High, M indicates Medium, L denotes Low, P shows Present and A stands for Absent. The rules are structured as follows: IF current T out is Cold AND P E is High AND P U is Present AND S i is High Load Reduction (L R ) is High. 92

107 The sequence of events taking place during the decision making process is described in Figure 4.7. The main steps of fuzzy logic algorithm shown in Figure 4.7 are described below: Figure 4.7. Flowchart of fuzzy logic decision-making algorithm 1. In this step the FLA creates and initializes the weekdays, user schedules and their associated interval times, and set point temperatures for each day of week, TOU rates, RTP, etc. 2. Initialize the membership function of input/output variables, and then initialize the initial values of inputs. 93

108 3. The environment is constantly monitored via wireless sensors which are capable of detecting the temperature changes, electricity price changes, and the activity of occupants. This allows thermostat to detect when the occupant s schedules and/or systems inputs are changing. 4. The system keeps comparing the initialized inputs with new information taken from wireless sensors and smart meter. If there is no change, it goes to step 3 to monitor/detect the new inputs, if any change(s) occurs, it goes to step In this step, the user preference to participate in DR will be checked. For this purpose, we have defined a DR flag in our code. If DR flag is enabled, it goes to step 6, otherwise it goes to step The initial sensor values are replaced and updated with new values. This assists us to use this new information for next hours in order to compare with further values taken from sensors, if any change occurs. Finally in this step, new value(s) is fuzzified by defined membership functions. 7. The fuzzy rule-based decision(s) shown in Table 4.1 is applied accordingly to compute the amount of L R. 8. The aggregation of outputs from all defined rules is defuzzified by COG stated in equation Finally, the defuzzified value is assigned to L R that represents the amount temperature ( C) that has to be deduced from initialized set points for that specific time of day. 10. As long as DR option is enabled, the participation will progress and it will go to the step 3 and waits for next information from wireless sensors and smart grid incentives. 94

109 4.5. Simulation Results and Discussion The proposed smart residential load reduction framework in this paper can be extended in various directions. In this section, we consider a number of different scenarios that regularly occur during a day or week. These scenarios are defined by having different daily or weekly schedules, initialized set points, electricity price programs, outdoor temperatures, and occupancy cases. The performance of the proposed FLA is evaluated by energy saving, thermal comfort. These evaluations are conducted in TOU and RTP environments Comparison of FLA with Conventional Thermostat, PT, and PCT under TOU Programs In order to compare and validate the FLA, first it is embedded and implemented into the house simulator engine. The FLA s responses are compared with conventional thermostats (fixed set point), existing PTs, and PCTs. The simulations are implemented for the conditions shown in Figure 4.8 and schedules listed in Table 4.2. Figure 4.8 shows different scenarios for outdoor temperature and user presence which are used to validate FLA in terms of saving energy and thermal comfort, where it is compared with existing PTs and PCTs during their engagement in DR programs. TOU rates for different times of day are tabulated in Table 3.4 (refer to Chapter 3, Section 3.5). House parameters are already listed in Table 3.3 in Chapter 3, Section 3.5. Table 4.2. User schedules for weekdays Time Of Day Heat Set Point ( C) User Status 00:00 to 08:00 21 Home 08:00 to 11:00 18 Away 11:00 to 17:00 19 Away 17:00 to 19:00 22 Home 19:00 to 24:00 23 Home Four different roles of the simulator after participating in DR events based on TOU rates are shown in Figures 4.9, 4.10, 4.11, and These Figures reveal the response and reaction of the house simulator engine when we enable it to operate as conventional thermostat, PT, PCT, and FLA respectively. 95

110 Figure 4.8. Outdoor temperature, occupant s presence, and TOU prices Figure 4.9. Simulator as Conventional Thermostat with DR Enabled 96

111 Figure Simulator as PT with DR Enabled As pointed out in section 2.3, conventional thermostats and PTs are isolated devices with respect to communicating with environment (i.e., smart meters), therefore, as it can be observed from Figures 4.9 and 4.10, both conventional thermostat and PT cannot shed the load when the wireless sensors information and/or electricity price vary over the time (lack of communicating with smart meters). Therefore, the indoor temperature only follows the initialized set points (red line). The energy consumption for the scenarios shown in Figure 4.8 for conventional thermostat and PT is kwh and kwh respectively and their corresponding costs equal to $12.21 and $ Figures 4.11 and 4.12 show the reaction of system, where the simulator acts as a PCT that exists in today s market and a PCT that is equipped with our FLA respectively. In both Figures, we have enabled the DR option. As shown in Figure 4.11, by enabling DR based on TOU rates, the PCT decreases the initialized set points listed in Table 4.2 for 1 C, 3 C, and 5 C (i.e., offset values) associated with off-peak, mid-peak, and onpeak rates respectively. In fact, when the DR option is being enabled with user choice, the PCT communicates with the installed smart meter to read the price signals in order to reduce the initialized set points based on offset values entered by user. As it can be observed in Figure 4.11, between 12:00 AM to 7:00 AM and 19:00 to 24:00, where the price is off-peak; the PCT only sheds the load (initialized set point) for 1 C. However, 97

112 insufficient energy saving arises between 21:00 to 24:00, where the outdoor temperature is 16 C, user is home, and initialized set point is 23 C. In that case, since the outdoor temperature is16 C ( natural ) and initialized set point is high (23 C), adding intelligence to PCT can shed the residential load more than 1 C, while still maintaining thermal comfort. This issue has been addressed using FLA as shown in Figure As it can be seen, although the price is off-peak, FLA constantly evaluates other available inputs (i.e., information received from wireless sensors and initialized set points) and adjusts the new set point on 18 C. Therefore, FLA saves energy and cost without sacrificing thermal comfort (lower band of ASHRAE comfort zone). Figure Simulator as PCT with DR enabled Other scenario for comparison of FLA versus existing PCTs is considered from 7:00 AM until 11:00 AM when the price is on-peak. First we consider the scenario from 7:00 AM to 8:00 AM when the home is occupied, outdoor temperature is -2 C (very cold), and initialized set point is 21 C. By using PCT as shown in Figure 4.11, the initialized set point is decreased by 5 C and adjusted on 16 C. This new set point makes discomfort for occupants because of the outdoor temperature which is -2 C (sacrificing thermal comfort). This is the major problem that households have with existing PCTs. This problem is solved utilizing our proposed FLA. 98

113 Figure Simulator as PCT Equipped with FLA and DR enabled As shown in Figure 4.12, FLA puts the new SP on 18 C, where it both saves energy sufficiently and provides thermal comfort. This is because the FLA compromises between saving energy and comfort by applying particular rules. In Figure 4.12, FLA detects the occupants at 16:00 and increases the SP from 16 C to 19 C until 17:00. In this case, FLA reduces the initialized set point from 22 C to 19 C. After that, at 17:00, FLA receives high electricity price (on-peak) that lasts until 19:00. As it is shown in Figure 4.12, FLA decreases the initialized set point from 17:00 until 19:00, where the price becomes off-peak. In this case, although FLA reduces the set point, it still keeps the new set point in comfort zone. After 19:00 the thermostat equipped with FLA starts increasing the set point gradually because we have passed the on-peak rates. This shows that the FLA can manage participating in DR better versus a PCT, while it provides thermal comfort. In addition, the energy consumption for the scenario shown in Figure 4.11 for PCT and FLA is kwh and kwh respectively and their corresponding costs are $9.9 and $

114 Figure Comparison of PCT and FLA with DR enabled for fixed set point Comparison of between PCT and FLA during participation in DR under TOU rates when the thermostat is set on initialized set point listed in table 4.2. It can be concluded that by utilizing FLA, we brought intelligence to a PCT that saves energy, while maintaining thermal comfort. Besides, FLA can help both utility in peak load curtailment and households during high electricity rates Response of the FLA to WSN information under RTP The lack of knowledge among the residential customers regarding how to respond to smart grid incentives such as RTP and the lack of intelligence in residential EMS (i.e., PTs and PCTs) are two major obstacles for optimally benefiting from the advantages of RTP and TOU. For example, most households use PTs or conventional thermostats at home that require to be programmed manually. This should be difficult for residential users to optimally schedule or change the operation of their HVAC systems through their thermostats in response to time-varying prices that they receive from smart grids in a RTP program. Hence, in this section we evaluate the performance of FLA in response to hourly RTP. For this purpose, the residential RTP tariff shown in Figure

115 is taken from [139]. We put this RTP in the membership functions form that already shown in Figure 4.3 in order to fuzzify it. To do so, we have simply changed the range of prices in Figure 4.3. We ran the FLA for RTP depicted in Figure 4.15 and different schedules shown in Table 4.2. Figure 4.15 shows the dynamic response of FLA to actual RTP rates, where the FLA simultaneously takes into account the outdoor temperature, current initialized set points, and occupancy. The hourly outdoor temperature profile that is used for evaluation of FLA has also been plotted in Figure 4.15 (green line). Figure RTP for residential customers, Ameren Illinois Power Co., Jan As it can be observed on the top left corner of Figure 4.16, between 12:00 AM to 4:00 AM, where the price is low (see Figure 4.15), home is occupied, and outdoor temperature is very cold, FLA reduces the initialized SP by applying the particular fuzzy rules associated with the information received from sensors. Between 5:00 AM to 7:00 AM that the price is rising from low to medium, and finally high, FLA decreases the initialized set point for 2.5 C and adjusts it on 18.5 C to reduce the residential load without jeopardizing thermal comfort since the home is occupied. Similarly, between 7:00 AM to 8:00 AM where the initialized set point is still 21 C since the price is approaching to medium and high the new set point increased to19 C. Furthermore, no one is detected by occupancy sensors from 8:00 AM to 15:00. Thus, in the period of 8:00 AM to 12:00 that the price is medium the initialized set point decreased to around 16 C (keep in your mind the initialized set point is 18 C at this 101

116 period). From 12:00 to 15:00 where the price is low and no one has been detected by occupancy sensors, the set point is still set on 16 C (at this period initialized set point was 19 C). At 15:00 an activity was detected by occupancy sensors and the outdoor wireless temperature sensor reports -2 C, therefore FLA raises the current set point (16 C) to around 21 C to provide thermal comfort (the initialized set point is 22 C at this time). There is a high rise in electricity price detected by FLA through communicating with deployed smart meter at 16:00 which lasts until 18:00. At this period, the purpose of smart grid incentive in raising the price is to encourage consumers to reduce or shift their load demand. Although user has adjusted the initial set point on 22 C, FLA reduces it to 19 C to meet smart grid objectives. This new set point (19 C) also prevents discomfort based on ASHRAE standard. Figure Response of FLA to WSN information under RTP In addition, as it can be observed in Figure 4.16, after 18:00 the price is medium and then low until 24:00. The FLA detects changes in electricity rates by constant communicating with smart meter. Consequently, as shown in Figure 4.16, between 18:00 until 20:00; FLA reduces the initialized set point for 2 C, and after that from 20:00 to 24:00, where the initialized set point is high (23 C), outdoor temperature very cold, the new set point is 20.5 C. As a result, the FLA acts proactively to shed the residential load based on RTP as well. By doing so, our approach potentially contributes in reducing 102

117 the peak-to-average ratio (PAR) that is a major problem when residential customers shift their load to off-peak hours The Role of FLA, WSN, and Smart Grid Incentives in Residential Energy Management To find energy saving utilizing fuzzy logic, wireless sensors, and smart grid initiatives in residential EMS, a one month simulation similar to conditions shown in section was run. For this purpose, the energy consumption between PTs, PCTs, and the PCT equipped with FLA was compared. The simulation was run for schedules listed in Tables 4.2 and 4.3, and the outdoor temperature taken from the Canada s National Climate Archive for months of December Figure 4.16 depicts the energy consumption by applying identical conditions (e.g., similar SPs, schedules, outdoor temperature, etc.) with and without enabling different wireless sensor nodes. The first bar in the left of Figure 4.16 is one month energy consumption using FLA, where all sensors and smart grid incentives are enabled. As it can be observed, there is a potential savings about 478 kwh for similar conditions compared to PTs (please refer to Figure 4.16 first right bar). In addition, the second left bar in Figure 4.16 shows the energy consumption when the occupancy sensors are enabled, while smart grid incentive (i.e., electricity price) is disabled. There is an increase in energy consumption about 171 kwh compared to FLA. Indeed, in the first left bar, the system constantly monitors the environment and applies particular rule(s) based on the information received from wireless occupancy sensor node installed in the house. This proves the importance of using occupancy sensors for saving energy in residential EMS. As mentioned in section 4.3.2, the price taken from smart meters is a significant parameter to enhance capability of energy management systems such as PCTs. The second right bar shows the energy consumption when occupancy sensors and outdoor temperature are disabled, and the price information enabled. This bar represents only a PCT capable of communicating with smart meter. Therefore, there is potential saving about 253 kwh with respect to PT. As a result, Figure 4.16 demonstrate that by incorporating a simple sensing technology to automatically detect occupancy in a home, 103

118 an outdoor wireless temperature sensor to measure outside temperature, small communication chips such as Zigbee to communicate with smart meter to read electricity price signals, and implementing the proposed FLA to automatically turn on/off the residential HVAC system accordingly; we can effectively manage energy consumption without sacrificing thermal comfort One month energy consumptionwithand without enabling different sensor nodes (kwh) All Sensors Enabled (FLA) FLA with Occupancy Enabled and Price Disabled Price Enabled and Occupancy and Outdoor Temp. Disabled (PCT) All Sensors Disabled (PT) Figure Energy consumption with/without enabling different sensor nodes 104

119 Chapter 5. Implementation of Adaptive Fuzzy Logic Learning 5.1. Introduction In the most research related to energy management systems there is a manual interaction between human (user) and machine (device) to adjust preferences and schedules in order to save energy and cost and provide comfort. This interaction is often a hassle and in some cases irritates inconvenience to users if they continually modify their preferences and schedules particularly with respect to the factors such as dynamic electricity prices, outdoor temperature, and energy demand that vary over time. The role of Thermostats is automatic control of HVAC systems while the users manually accommodate their everyday schedules and preferences (manual interacting with thermostat). However, the lack of learning and adapting to user schedule and preference changes (i.e., existing PTs and PCTs), and being an interaction-based device (i.e., smart thermostats such as NEST) are the major barriers of existing Thermostats in today s market. Other shortcomings such as lack of responding to DR programs and time-varying prices, lack of zoning control still exist in the most of Thermostats. As demonstrated in Chapter 4, a synergy of artificial intelligence approaches such as fuzzy logic with wireless sensors capabilities and smart grid initiatives could assist both residential customers on saving energy and cost, and utilities during peak load demand. As mentioned in Chapter 2, Section 2.7 several methods based on neural networks and genetic algorithm technologies have been used in various applications to search numerical data for fuzzy rules. However, in most cases the existing knowledge (initial fuzzy rules) is removed and replaced with new rules when the initial rules are modified and overridden by user. In our research, we propose an Adaptive Fuzzy 105

120 Learning (AFL) system that can find new fuzzy rules or modify and tune existing rules based on the data received from wireless sensors without eliminating the existing fuzzy rules. In this chapter, the concept of Adaptable Thermostat is investigated as a systemic approach that is adaptable to user preference changes and at the same time can address energy consumption and thermal comfort concerns. The proposed thermostat is also easy to use compared to existing PCTs and intelligent thermostats. This chapter demonstrates the actual implementation of an Adaptive Learning Model (ALM) that is an integration of wireless sensors, smart grid initiatives, and fuzzy logic techniques. Prior to designing AFL, an autonomous control system based on Supervised Fuzzy Logic Learning (SFLL) techniques for better energy management and conservation without any user interaction is proposed. Moreover, as described earlier in Chapters 3 and 4, the simulator tool was designed in order to be used as an expert shell to assist in development, implementation and verification of SFLL, and ALM via AFL Synopsis of the Solution Figure 5.1 depicts the design of the main controller (thermostat) to learn using SFLL and adapt to user actions using ALM. As shown in this Figure, the main controller unit that represents an Adaptive Smart Thermostat is an adaptive environmental control system which includes system inputs (information from wireless sensor nodes and smart grid initiatives), a fuzzy rule-based system (SFLL), and an adaptive learning system in which the user can make action over the made decision. The wireless sensor nodes also consist of various sensors and actuator node(s) such as occupancy, temperature, and zigbee chips for communicating with smart meter. The central controller unit uses fuzzy rule-based techniques in conjunction with learning and adapting principles. It utilizes the knowledge related to the user schedules, preferences, and rates of heating/cooling of different zones to make decisions for energy saving and cost. 106

121 The smart thermostat as the main controller unit should represent a device with wireless interface that technically is more advance than a PCT or existing smart thermostats. In fact, it should reflect an adaptable energy management system with two way communication and network capability, interface to multiple sensors and actuators, and offer a variety of options to provide energy saving and thermal comfort without user interaction. Figure 5.1. Main controller unit for adapting and intelligent energy management The smart thermostat should be able to communicate with smart meters to read electricity price signals or smart grid initiatives in order to help utilities in the peak load demand. The main controller system should also have the ability to access and retrieve in real-time information from the sensor nodes distributed in many possible and essential parts of the house to manage the residential HVAC system more effectively. To achieve such capabilities, the use of only one intelligent model or technique would not be sufficient. Hence, we need to utilize a synergy of different techniques (at least two techniques), one for operating autonomously and another for adapting to changes to approach our objectives as an Adaptive Smart Thermostat. 107

122 5.3. Autonomous Smart Thermostat Using Fuzzy Logic Generally speaking, an autonomous system is self-ruling and independent system based on the learned information. In fact, the user is not required to focus on controlling the system. Nowadays, with transition from flat-rate electricity prices to dynamic pricing; self-adjusting and self-scheduling of the operation of in-home devices are gaining increasing attention due to the inherent difficulties in their manual adjusting and control. It is being more difficult particularly when the relevant energy management factors such as electricity prices and energy demand vary during a day. As mentioned in Chapter 2, Section 2.7, many techniques for shifting the house demand to off-peak hours through scheduling the in-home devices have been addressed. To date, there is no technique for autonomous adjusting of Thermostat set point temperatures without user interaction or intervention. One of the main problems with existing smart thermostats and PCTs [15] and [19], and even the most current smart thermostat NEST is their dependency on user interaction and constant programming. Users should adjust the set point temperatures based on their working schedules, preferences, and needs by considering the environmental conditions, electricity prices, and DR programs. In most cases, the adjusted set points at different intervals of a day/week are not the certain values for those specific intervals. For example, during a day/week we have variations in load demand because of sudden rises/drops in outdoor temperature. Users usually forget to change their set point temperatures according to the variations in the load demand, electricity price, and/or outdoor temperature. Hence, the need for a system to work autonomously while maintaining user comfort is apparent. The autonomous smart thermostat proposed in this chapter is a thermostat that controls the Residential HVAC systems through adjusting the set point temperatures based on available inputs and the learned information without user intervention. The main objective of the proposed autonomous smart thermostat system is to integrate residential HVAC systems into smart grids. The autonomous system will bring forward a smart system for better energy management while maintaining occupant thermal comfort in residential buildings. The autonomous smart thermostat employs fuzzy logic rulebased techniques to learn and utilizes wireless sensors and smart grid initiatives to 108

123 monitor/detect, and actuators to control. It does not require to be programmed by the occupant(s). It will also learn and adapt to the occupant s preferences changes, and provides intelligent zone-controlled solution that will explained in the following sections. In this research, the aim of the autonomous control mode is enabling the system to operate fully in background with little effort from its users to modify preferences if needed. The system utilizes a principle of continuous learning using SFLL so that it does not require any training prior to use. The reason that the learning process should be continuous is due to the fact that user s preferences and routines vary over time Input and Output Variables and Fuzzy Control Rule-based In order to design the autonomous system based on SFLL, we need to specify input and output variables of the system. Although there are many parameters that could be used as system inputs for energy management in residential buildings and HVAC systems, we use four important factors that influence energy management and conservation as well as thermal comfort. Those inputs, as elaborated in Chapter 4, are outdoor temperature, occupancy, and electricity prices with the same membership functions as already shown in Figures 4.2, 4.3, and 4.4 respectively. We also use Hourly house/region electricity demand as a new input to the system. This variable contributes to residential/regional load demand reduction when other home appliances are operating. This variable is more important particularly when users shift their load to off-peak hours. In many cases, shifting loads to off-peak times will increase the PAR. Therefore, at these times the SFLL autonomously sheds the residential HVAC load as the largest contributor to energy consumption by reducing set point temperatures. In addition, this parameter can contribute to the idea of thinking globally and acting locally. Figure 5.2 shows the membership functions of Hourly electricity demand ( ) of a house. It is assumed that in Canada, the average of the highest hourly demand for a residential building is equal to 1.80 kw, the medium is 1.20 kw, and the lowest is 0.60 kw [3]. Based on these values we have defined the membership functions of. The values (intervals) of the membership functions can simply be changed, when we take into account the regional hourly demand instead without changing the fuzzy rules. 109

124 Figure 5.2. Membership function of residential hourly demand The only output of the system is the set points. The system autonomously adjusts output using available inputs received from wireless sensors and the electricity prices as well as load demand taken from smart meter. The triangular membership functions of the output are shown in Figure 5.3. Figure 5.3. Fuzzy membership functions of system output (Set Point) 110

125 As shown in Figure 5.3, we have divided the system output into 9 different zones. The first zone (SP 1 ) covers 3 C. Most of times, this zone is tuned during unoccupied state. It is often recommended to keep the temperature of an empty house between 14 to 16 C [3]. Other zones cover 2 C of the total range (16-24 C). Each of these set point zones overlaps adjacent set point zones or membership functions by half as shown in Figure 5.3. The spacing of set point zones is hence 1 C. The used spaces are chosen to achieve accurate indoor temperature. In addition, the selected zones and spaces can give us such a freedom to change the set point temperatures to tune the fuzzy rules in order to save energy and cost Fuzzy Control Rules for Autonomous Smart Thermostat The Thermostat equipped with SFLL keeps monitoring the house conditions via sensors and acts according to learned rules. Users are not required to interfere with the control system at all, but they can override the made decision(s) if needed. The proposed SFLL constantly tune the set point temperature (system output) in ASHRAE comfort-zone in order to maintain thermal comfort. ASHRAE recommends that relative humidity (RH) being maintained below 60%. The RH should be bigger than 30% as well. However, influence of the humidity is not great for the people with very light or sedentary activities. ASHRAE comfort-zone (19 23 C) for cold regions and (18 22 C) for cool regions is commonly accepted in research and practice in HVAC heating operation. The lower zone considerably helps us in saving energy and cost. Moreover, the autonomous smart thermostat acts proactively by applying defined fuzzy rules. Some of fuzzy rules in order to autonomously adjust the set point temperature in comfort-zone are depicted in Table 5.1. For example, rule 2 is described and applied to compute output as following: IF outdoor temperature (T out ) is very cold AND electricity price (P h ) is High AND house demand (D h ) is high AND user (P o ) is present THEN Output is SP7. In the statement above, the output (SP7) can vary between 21 to 23 C (refer to membership function of SP7 in Figure 5.3) based on the values of inputs. In addition, to 111

126 maintain the set point temperatures in the ASHRAE comfort-zone, the system constantly invokes the fuzzy rules to compute the amount of set point (Output) while considering the energy conservation aspects. This happens if any change is detected in wireless sensor nodes, electricity price, and/or demand. Finally, the control signal is sent to actuate an on/off relay which results in turning on/off a residential HVAC system. Table 5.1. Some of fuzzy rules for adjusting set points for Comfort Mode # Rule T out P E D h P o Output (Set Point) 1 Very Cold L L P SP8 2 Very Cold H H P SP7 3 Very Cold H H A SP2 4 Very Cold H L P SP7 5 Cold H H P SP6 6 Cold L H P SP7 7 Cold L L A SP1 8 Cold M H P SP7 9 Cool H H P SP5 10 Cool H L P SP6 11 Natural H H P SP3 12 Natural L H P SP Simulation Results and Performance of SFLL The proposed autonomous system using SFLL is embedded and implemented in the simulator engine. The thermostat reactions with respect to changes in electricity prices (TOU and RTP), load demand, occupancy, and outdoor temperature are monitored. In order to facilitate the use of the residential HVAC model at an aggregate level study, these parameters are predefined. To do so, we planned ten different scenarios during a day as depicted in Table 5.2. It is planned, for instance, the scenario 1 in which outdoor temperature is 2 C, house demand is 0.5 kw, electricity price is 7.2 cents, and user is home occur between 12:00 AM to 2:00 AM. The rest of scenarios planned at other times of the day (refer to column Time of Day in Table 5.2). It has to be mentioned that the electricity prices are taken from Hydro one, Ontario, Canada for winter season. For each interval (Time of Day), the thermostat evaluates the values of 112

127 inputs and autonomously adjusts the set point temperature (output) by applying specific rule(s) associated with those values of inputs. In the proposed SFLL, we have defined two different modes namely Economy Mode and Comfort Mode. We have a small shift in load reduction in Comfort Mode and a large load reduction for Economy Mode. Maximizing comfort implies minimizing load reduction benefits and vice versa. In Economy Mode the comfort zone is (18-22 C), and for Comfort Mode the range (19-23 C) is selected. Table 5.2. Ten different scenarios for verification of the performance of SFLL Description Time of Day T Out ( C) D h (kw) P E (cents) P u Output ( C) Scenario 1 0:00 to 2: Home 22.5 Scenario 2 2:00 to 6: Home 23 Scenario 3 6:00 to 7: Home 22.8 Scenario 4 7:00 to 8: Home 19.2 Scenario 5 8:00 to 11: Home 19 Scenario 6 11:00 to 14: Away 15.5 Scenario 7 14:00 to 17: Away 15.5 Scenario 8 17:00 to 19: Home 18.3 Scenario 9 19:00 to 21: Home 20 Scenario 10 21:00 to 24: Home 17.2 Figure 5.4 shows the reaction of thermostat which adjusts the set points autonomously based on information listed in Table 5.2 while the Comfort Mode is chosen as user preferred mode. In Figure 5.4, the response of thermostat to changing in electricity price can be observed from 7:00 AM to 11:00 AM. At this interval (refer to Table 5.2 scenarios 4 and 5) the price increases from 7.2 cents (low) to 12.9 cents (high) and the thermostat autonomously reduces the set point to save cost and energy. Although the system still saves energy and cost using Comfort Mode, the energy consumption can be managed better when we use Economy Mode. Furthermore, in order to observe that how the set point temperatures are set using SFLL; a comparison between Comfort Mode and Economy Mode based on information listed in Table 5.2 is shown in Figure 5.5. As it can be observed from Figure 5.5, the reduction in residential HVAC load demand using Economy Mode is larger 113

128 particularly during high electricity prices. In fact, the proposed approach proactively responds to time-varying prices and the residential HVAC system is integrated into smart grid without any interaction from its user. Figure 5.4. Adjusted set points by autonomous smart thermostat: Comfort Mode In Economy Mode (refer to Figure 5.5), the thermostat keeps attempting to adjust the set points (output) between C. From 21:00 to 24:00 when the outdoor temperature is 14 C (natural), demand is low (0.6 kw), and the price is off-peak (low); the thermostat adjusts the set point on 16 C while it is 17.6 C using Comfort Mode (refer to Figure 5.5, S 10 for scenario 10). In this case there is no need to keep the house warm when the outdoor temperature is natural. In addition, in order to verify the importance of current house demand (kw) as one of inputs; the response of thermostat to two similar scenarios (refer to scenarios 2 and 9 in Table 5.2) with different demands is considered. In scenario 2, where the demand is low (0.4 kw) the thermostat puts the set point temperature on 22 C (S 2 ). While in scenario 9, where the demand is high (1.8 kw); the set point is autonomously set on 20 C (S 9 ). In this case, although the thermostat reduces the set point (S 9 ), it is still in ASHRAE comfort-zone in order to maintain occupant s thermal comfort. In this case, in addition to saving energy; the autonomous smart thermostat can contribute to 114

129 reduce the PAR that is a major problem particularly when residential customers shift the operation of their appliances to off-peak hours. As a result, regardless of what mode is chosen by user as preferred mode, as long as the thermostat detects and monitors any changes in input parameters, the set points are autonomously adjusted in specified comfort zones using tuned fuzzy rules. However, in cases that the user is not satisfied with the made decision(s) there must be a technique behind in order to be able to adapt to user new preference and schedule changes. Hence, in the next section we will develop an Adaptive Fuzzy Logic (AFL) system, where we consider the problem from system perspective. Figure 5.5. Adjusted set points for economy and comfort modes 5.5. Adaptive Fuzzy Logic Learning System As demonstrated in previous section, the thermostat was able to react proactively and appropriately to changes received from wireless sensor nodes and smart grid initiatives based on SFLL rules resulting in setting system output in ASHRAE comfort zone while maintaining thermal comfort and saving energy. However, occupant behavior is one of significant factors that may impact the operations of the designed autonomous thermostat. It can be anticipated that there are 115

130 some cases that users may not feel comfort with respect to the made decision (adjusted set point). Hence, in those cases users should take a corrective action which can be done by manually overriding the made decision (set point value). This leads to changing in that specific rule(s) which being already tuned in SFLL approach. From aforementioned scenarios, two questions arise and will pose challenges: (i) (ii) How to build and update the rule-based model (SFLL) without eliminating the existing knowledge and information (existing rules)? How the thermostat, as main control unit, can differentiate between a corrective interaction (overridden by user) and a normal behavior that is autonomously performed by SFLL. To answer these questions, there must be an adaptive learning principle in background in order to enable the thermostat to learn and adapt itself to new changes as well as to act differently in these situations Methodology of Adaptation To create an Adaptive Learning Model (ALM) for a given problem such as smart thermostat we have to take into account a few principles such as : a): anything that should be related to initial states of the system or any information about the problem (creating a KB subsystem), b): a set of rules for taking actions, and finally c): the condition(s) that indicates that the solution(s) or rule(s) for that particular occurrence(s) is existed or not. Furthermore, in order to automate operations in a smart device, learning user s preferences, schedules, and even a model for device operations is necessary. However, this is not a long-term solution because inhabitants are likely to change their activity and usage patterns over time depending on factors such as weather conditions, electricity prices, etc. As a result, in addition to making autonomous systems, we have to find a solution to adapt to the user pattern changes that may occur over time. This makes us cross the limitations and consider the problem from systems perspective, where the interaction of several subsystems, each with different attributes and specific qualities are considered. In this way, we can maintain the generality of the 116

131 system so that it can be deployed and installed in any house. As shown in Figure 5.6, ALM consists of few subsystems that sharing their knowledge and data to achieve a better outcome. Figure 5.6 shows the conceptual block diagram of ALM and depicts the main blocks of the system. In this Figure the fuzzy logic rule-based provides the decision rules, and keeps comparing the existing knowledge with the new knowledge received from sensor nodes as the system inputs. In addition to the inputs explained in SFLL section, the current state of output (set point temperature) must constantly be compared with existing set point in order to realize whether or not it was overridden by user. The Knowledge base contains information about the HVAC system, number of zones to control, air flow rates, states of air dampers (low, medium, high), thermal characteristics of the house, etc. Figure 5.6. Conceptual design of ALM The role of learning vectors in our approach is to learn preferences based on the user inputs (i.e., overriding the decisions made by SFLL) and sensors. These preferences will be reflected in the weight factor of each element. The adapting vectors exploit information from the learn vectors and adapt to new changes if new preferences and habits are detected based on the defined rules. Since the users schedules and preferences may change during a day or week, first we cluster the days of week, as 117

132 weekday clusters. Therefore, the term cluster in the ALM refers to an initial set of clustered data (i.e., group of data, daily/weekly clusters). In our case they are thermostat daily schedules consisting of temperature set points and their associated time intervals (based on SFLL) for each day of the week, DR, RTP, and TOU price incentives, number of zones to control, etc. These clusters are shown in Figure 5.7. We assign 1 for Monday cluster, 2 for Tuesday cluster, etc. There are seven existing clusters under consideration corresponding to a week. On the other hand, another objective of the learning vectors is maintaining information for each daily cluster. This information contains the set point temperatures for different times of the day, state of set points (e.g., indicating if the value of that element has been overridden), and their associated weights that indicates how much the element s value has changed. Figure 5.7. Daily cluster The elements of learning vectors are updated based on the manual corrective actions being sensed by the sensors. Hence, changes associated with each particular element in learning vector are recorded in the adapting vectors. Based on the changes being detected, the initial weights associated with each particular element are updated. Since the objective is to adapt to user preference 118

133 changes, fuzzy logic rules-based system is utilized to evaluate and decide based on the weights associated with each element, and the existing knowledge. Hence, if the preference change of a user is persistent (i.e., not just a onetime occurrence) it is considered for adaptation. The weights created based on the user preferred tolerances (for any parameter of interest) are used to determine if the change is considered for adaptation or not. Thus, the detected values are applied in order to update the existing elements with the new values. Based on the number of occurrences (i.e. the number of occurrences after which the adaptation takes place), the adapting cluster vectors values are compared and if the values are within the scope of the user s set limits and/or tolerances, a new cluster is created Adaptive Learning Model (ALM) The ALM utilizes wireless sensors and fuzzy logic rule-based techniques in order to provide a novel Adaptive Fuzzy Logic (AFL) system technique for energy management in residential buildings. In order to have an unobtrusive environment, the system must be able to learn the user s habits without actively involving the user in the learning process (i.e., autonomous system). This system must require no change in normal conditions. However, because user s habits and needs are too different and may change over time, the learning process must be continuous. To make this possible, the system must monitor the user s actions and learn through its observations. The main objective of the ALM is to accommodate to the user s preference and/or schedule changes, while considering the energy and cost saving aspects. It has to be mentioned that the ALM guarantees that the existing information and knowledge (i.e., SFLL rules) are not eliminated by the new knowledge when for example a schedule of the user changes. Hence, the ALM verifies whether or not the new knowledge is already available. If the new knowledge is not existed, it is added to the existing KB as new knowledge. In addition, the system must be able to anticipate user needs on the basis of learned data and proactively control the actuators involved. Although the system can work autonomously using SFLL, the user must still have ultimate control over the thermostat. Therefore, the user must be able to override all of the system s decisions. 119

134 In order to model the proposed approach we consider the problem as follows: Let l, l,, l, l represent the elements of the learning vector and w, w,, w, w represent their associated weights respectively. Therefore, we define the vector L as learning vector as follows: L = <,,,,,,,,, > (5.1) In statement 5.1, l, l,, l, l represent the actual values of interest that some of them are pre-defined (initialized) by user and others adjusted by SFLL (i.e., the set point values). The weights are associated with each element of the learning vector. These weights are the error between the pre-defined values and overridden values. In expression 5.1, w to w are the weights associated with the learning elements l to l respectively. Furthermore, let,,, represent the elements that should be adapted if any change happens to occupant schedules and preferences. They are called adapting vector elements. As pointed out, the system adjusts the set points temperatures autonomously using SFLL based on available inputs. However, in order to adapt to occupant s pattern changes the system has to realize whether the output is set based on normal behavior of SFLL (autonomously) or the decision (set point value) has been overridden by user. For this purpose, a separate override flag is assigned to each element of adapting vector. Hence, we define the adapting vector as follows: = <,,,,,,, > (5.2) In statement 5.2,,,, is the override flags associated with elements,,,. Therefore, the vector only includes the values of interest and their associated flags. Hence, for each element l to l in the learning vector, there is a corresponding element of the adapting vector to. We also assume C as weekday clusters that are under observation. In this cluster i shows the day of week, i.e., 1 for Monday, 2 for Tuesday, etc. (refer to Figure 120

135 5.8), and j = 1,2,3,, m represents the number of occurrences within a particular cluster data. For example, C, C,, C shows different clusters from Monday to Sunday and j is the number of occurrences for every event under observation for each day of week. However, the number of occurrences can vary depending on the type of application. Besides, let A represent a set of the corresponding adapting cluster vectors: A = < A, A,, A > (5.3) The vectors A, A,, A represent a set of adapting vectors (daily cluster) under observation for each weekday occurrences. as follows: Moreover, represent a set of learning vectors under observation and is defined = <,,, > (5.4) It is assumed that the initial weight conditions for every learning vector, are = 1 and 1 which -1 indicates that the value of element corresponding to its associated weight has not changed; while, 1 indicates a change of element s value. In addition, we consider the state of override flag as an input to the system. This value is fuzzified to two different linguistic variables off and on as shown in Figure 5.8. The first membership function defines the off state and the second membership function the on state. As shown in Figure 5.8, only one membership function can be received at a time. The conditional checks shown in 5.5 is constantly performed to realize if the changes in adapting elements are due to the applied SFLL rules or the user has taken corrective action. IF ( is on) A = 1 (5.5) else A = 0 121

136 In the cases that the override flag is on, it means the user preferences or the decision made by SFLL has been overridden by user (A = 1). This means user is not satisfied with the made decision and the new value(s) has to be recorded for adapting in future depending on the time of day. If the state of override flag is off, it means the system is on normal status (SFLL). Figure 5.8. Membership function of override flag 5.6. Application of ALM for Adaptation of Autonomous Thermostat As pointed out in Section 5.3, the autonomous system was able to respond and set the system output in ASHRAE comfort zone using SFLL approach. Since, the user is free to perform at any time changes to the actuator in order to achieve a comfort situation, inevitably, in some cases user overrides the decision made by autonomous system for whatever reasons. An override flag that will be explained in Section with conditional checks is used in order to detect when the system swaps from autonomous control mode to event-based control mode. Hence, in event-based control mode it is important to know when (Start Time) a decision has been overridden, until when (End Time) it has been lasted, and finally how much (value of heat/cool set point) has been shifted from values that initialized by user or autonomous system (thermostat). To simplify the expressions, indicates Start Time, stands for End 122

137 Time, and and represent the current heating/cooling set point values for different times of day. The set of learning vectors under observation based on heat/cool set points of a day (,, = 1,2,,24) associated with Zone1 and Zone2 ( = 1, 2) for each active daily cluster ( = 1,2,,7), can be described as follows: = <,,,,,,, > (5.6) In the above expression, the vector elements from 1 to 4 are Heat Set Point, Cool Set Point, Start and End time of Set Points, and from 5 to 8 are their respective weights associated with each element from 1 to 4. In addition, j represents the number of occurrences for learning vectors under observation based on intervals or schedules. The adapting vectors for smart thermostat for zones 1 and 2 ( = 1, 2) are defined as follows: = <,,, > (5.7) Where, each element in represents a time interval for each day of week which has a Start Time and End Time. In turn, they are started from 00:00 AM until 24:00 for each day of week. The users can choose different intervals based on their preferences and working schedules. For example, a user can choose between 00:00 AM and 6:00 AM for one day and another interval for the same element for other days. However, the intervals cannot be less than four and more than 10 for each day. In addition, each timeslots or intervals cannot be less than two hours. Choosing this number of intervals is based on some assumptions. For example, California Building Energy Efficiency Standards requires that programmable thermostats should have at least four set points temperature intervals, where the users can potentially set their schedules at different times of day. Moreover, the intervals allow users to have more flexibility to divide their daily schedules and preferences based on timeslots similar to TOU and RTP for managing electricity consumption. Furthermore, these assumptions can help system s accuracy, and it will also reduce the number of fuzzy rules. 123

138 In statement 5.7, each interval contains set point temperatures for 24 hours a day which are autonomously computed by SFLL based on information received from wireless sensors and smart grid initiatives. Since we assume the information from wireless sensors are received every hour, we have 24 set points as shown in 5.8. These set points are,,,. In 5.8,,,, represent the override flags associated with,,, respectively if any corrective action is taken by user. Therefore, has a structure as follows: <,,,,,,,,, > (5.8) The system automatically assigns each of set points to intervals defined by user for each day of week. For example, the system automatically assigns,, to interval which is defined between 00:00 AM and 6:00 AM. In addition, each element in 5.7 has a structure with respect to 5.8 as follows: <,,, > (5.9) The statement 5.9 is the learning vector when they are without associated weights. However, as long as the override flags are off the smart thermostat keeps operating as an autonomous system based on the proposed SFLL. When the state of override flag associated with each element in 5.8 changes to on at any time the system records this change based on statement 5.6. The structured elements of adapting vector in 5.7 and 5.9 are populated only after comparison with the learning vector data. The learning vectors, in addition to the Heat SP, Cool SP, Start time, and End time, contain values of the weights associated with each element. A fuzzy membership function is assigned to each weight in learning vector if any change is detected. As stated in 5.7 and 5.9, the adapting vectors contain only the values that have to be adapted based on user pattern changes. These elements in adapt vector as it will be discussed later, are being adapted by the fuzzy rule-based algorithm if three consecutive changes occur in each of them. 124

139 Input of ALM and Their Membership Functions When the decision (s) made by SFLL is overridden, the system swaps to evenbased mode. It means a change in the elements of adapting vector has been detected. The overridden is detected by conditional checks that are constantly performed in order to compare the existing (initialized) weights of learning vector elements with the new ones. Figures 5.9 and 5.10 show the membership functions of the weights associated with each element in learning vector if any change occurs. These weights can be assigned to each particular element in the learning and adapting vectors for any daily cluster under observation based on their shifts from actual and/or initial values. In these Figures, the weight High indicates the major shift from initialized values, while the Low signifies a small shift from typical existing value. However, there can be more than three weights or less depending on the case. In our smart thermostat, the user can decide to change the set point values at any time if he/she does not feel comfort with the current indoor temperature. However, the system only takes into account the changes that meet the following statements: 0.5 (5.10) / / 10 (5.11) / / 30 (5.12) The membership functions of Heat Set point and Cool Set point when they are overridden by user three consecutive times shown in Figure In addition, they are defined as a trapezoidal form in order to take into consideration the Predicted Mean Vote (PMV) and Predicted Percent Dissatisfied (PPD) explained in Chapter 2, equation 2.1. In this way, we can maintain PPD around %10, where PMV is [-0.5 to +0.5]. Therefore, when the user overrides the set point value, the system always consider the lower level in heating operation and the upper level in cooling operation. By doing so, we can potentially save more energy and cost. 125

140 Figure 5.9. Membership functions of Start Time and End Time Figure Membership functions of weights (Heat-Cool Set Points) System Outputs for Adaptation The adaptation is performed if an event is repeated for j times. Nevertheless, the number of occurrences in order to adapt to new patterns can vary depending on the application of the proposed ALM. In the Smart Thermostat the number of occurrences that has to occur for a particular element in order to be considered as a new pattern is set to three consecutive occurrences. It is based on some assumptions. For example, consider a scenario when a person increases or decreases the set point value because 126

141 of sedentary or activity at home. Another scenario can be when the occupant changes temporarily the heat set point due to an extremely cold winter day or a sudden drop/rise in outdoor temperature. All these scenarios might be one time occurrence, and not be a preferred permanent set point or a habit. Therefore, the system considers the changes that occur persistently for three times. Based on aforementioned assumptions, we define the membership functions of system outputs for each particular elements of adapting vector. If three consecutive changes happen to these elements, the system has to adapt itself to new changes, while should consider the energy conservation aspects. Both Start Time and End Time are considered as system outputs and they have the same membership functions. As shown in Figure 5.11, triangular membership functions are defined for these outputs. They have been divided into three different zones in order to take into account three possible changes. As it can be observed from Figure 5.12, the membership functions of input and output for start time and end time are identical. We realized that in this way the predicted and adapted start time and end time are more accurate based on defined fuzzy rules that will be presented in the next section. In addition, the membership functions shown in Figure 5.12 are laid evenly and overlap each other which make the decision(s) more precise. In addition to start time and end time as the elements that must be adapted, the heat or cool set point values are other output variables for adaptation if three consecutive changes occur to them. It means the system has to anticipate the user preferred set point for the next day when the set points values are overridden by user. The triangular membership functions of the heat and cool set point as system output is shown in Figure As shown in Figure 5.12, the set point value as one of the elements of adapting vector is similarly divided into 3 different zones in order to take into account three possible changes. Each of these weight zones overlaps adjacent weight zone or membership function by 1.5 C as shown in Figure The overlap zones are chosen to achieve precise adaptation. As a result, based on the changes detected and measured (Figures 5.9 and 5.10), the system will adapt to new the start 127

142 time, end time as well as heat-cool set points by using the fuzzy logic rule-based that will be explained in the next section. Figure Membership function of start time-end time for adaptation as output Figure Membership function of heat-cool set point for adaptation as output Fuzzy Logic Decision-making for Adaptation As pointed out in section 5.6.2, there are three different weights, i.e. High, Medium, and Low, that can be assigned to any daily vector based on the proximity of the actual value to the particular element for three consecutive occurrences of a particular day. The fuzzy logic rule-based decision making according to weights is based on the possible combinations listed in Table 5.3.There are totally 27 combinations for each 128

143 element under observation. In this Table, O represents the first occurrence, O stands for second, and O indicates the third one. In addition, in this Table L, M, and H represent Low, Medium, and High respectively. Table 5.3. Fuzzy rules for adapting to pattern changes #Rule O 1 O 2 O 3 Output #Rule O 1 O 2 O 3 Output 1 L L L L 15 M M H M 2 L L M L 16 M H L M 3 L L H L 17 M H M M 4 L M L L 18 M H H M 5 L M M M 19 H L L L 6 L M H M 20 H L M M 7 L H L L 21 H L H M 8 L H M M 22 H M L M 9 L H H M 23 H M M M 10 M L L L 24 H M H M 11 M L M M 25 H H L M 12 M L H M 26 H H M M 13 M M L M 27 H H H H 14 M M M M The final value (adapted output) returned from the changing weights is based on the weight occurrences. For example, if all three daily/weekly changes have the same weights, the same fuzzy value of three daily/weekly elements is returned as adapted output (refer to Figures 5.9 and 5.10). As another example, if only the first two daily/weekly occurrences of the particular vector elements have high weights while the third occurrence has low weight, the adapted output is medium weight (refer to rule 25). The high weights signify major shifts from existing values; hence the approach is slightly conservative and tends not to make radical changes to the existing schedule or set points. This is because the SFLL always attempts to tune the set point values in ASHRAE comfort zone ranges. Hence, in the cases that major shifts from the existing schedules and/or set points occur, adaptation will take place only after three consecutive occurrences of the high weights (rule number 25). It has to be noted that during Defuzzification, the linguistic values are converted to a real value using center of gravity method explained in Chapter

144 5.7. Description of the Adaptive Fuzzy Logic Algorithm Overview In summary, AFL behaves as follows: observe, measure, and monitor via sensors, acquire and learn from the new information via SFLL (by comparing new knowledge with the existing one), and adapt based on the decision made. In fact, if the new decision is not existed in KB, it adds new clustering knowledge to the dedicated cluster group. As pointed out in Section 5.6 the ALM ensures that the existing knowledge (SFLL rules) is not eliminated by the new knowledge (new rules applied by AFL). Thus, the ALM inquires and verifies whether or not the new knowledge is already available. However, if not, the new decision is added to the existing knowledge-base as user new habit or preference Implementation Steps and Routines of the AFL The main sequences of the AFL algorithm based on the proposed ALM to implement an Adaptive Smart Thermostat are shown in Figure In this flowchart each block has an associated number that are used here to provide additional details as presented in the next page. 1. Load Data file and creates a new Data object which contains read data to emulate sensory information. Thus, the generated file has different occupant s patterns and/or schedule changes, different outdoor temperature, TOU and RTP prices, house and HVAC parameters for the simulation process, in order to mimic different scenarios. Data file which is a CSV file contains fuzzy rules as well. 2. Initialize and fuzzify all inputs loaded from Data file. For example, let p represent the number of occupancy sensors that are installed at different places of a home to detect occupant activities. In our case, we consider the presence or absence in two different zones of a two Storey house as shown in Chapter 3, Figure 3.3. Occupancy sensors can consist of different sensors such as motion sensors, door sensors, and PIR sensors and have different attributes in order to detect the user in the zones. Ultimately, what is reported to the main system (Smart Thermostat) is presence or absence of the 130

145 occupant in that specific zone. Let and represent the states of occupant presence/absence in Zone1 and Zone2 of the house respectively. 3. The learning vectors stated in section 5.6 are created and initialized for each day of week. This is performed by collecting information from wireless sensors in real world. However, this information is gathered from Data file in this research. The Learn vectors contain information based on the interval of schedules of a day and have 8 elements for each zone. We also assumed 6 elements for adapting schedules (refer 5.7). Therefore, they have a size of 8*6 equals to 48 for each occurrence in a particular daily schedule (i.e., number of elements in learning vector multiplied by number of elements in adapting schedules). Thus, for 3 consecutive daily cluster occurrences there will be 48*3 equals to 144 data available for each zone and ultimately 248 for both zones for three occurrences. It has to be noted that if the elements did not change, value of -1 is assigned for that specific element in the Learn vector. 4. Adapting Vectors and their associated override flags based on the learning vectors are created and initialized for each day of week. The value of 0 is assigned to each adaptability vectors and their associated override flags. This value shows no change has happened to the set points in that specific interval. Adapt vectors are populated only after comparison with the Learn vector data and adaptday flag. The system adapts to changes for each day of week; and based on which occurrence of the day it is (first, second or third) it retrieves correct set of elements from Learn vector. The system also has an adaptdaily flag if three consecutive changes occur to each element in adapt vectors in three consecutive days of week. As mentioned before Adapt vector structure is as follows: Heat Set Point, Cool Set Point, Start Time, and Stop Time. Adapt vector is extracted only after comparison with the Learn vector data. Furthermore, we designed the adapting vector for smart thermostat has six intervals for the all active daily cluster. Its structure is Heat SetPoint, Cool SetPoint, Start Time, End Time multiply by six different intervals for the entire active daily cluster. 5. At this stage, the AFL detects the mode of operation. These modes have been initialized in step 1. They are autonomous mode and user adjusting mode. Autonomous mode works based on SFLL and includes economy mode and comfort 131

146 mode. In user adjusting mode all schedules and preferences are entered and defined by user. Figure Implementation of AFL 6. The thermostat equipped with AFL is set by SFLL based on information received from wireless nodes and smart grid initiatives or adjusted by user. However, the received information and the sat schedule and preferences are not permanent and may vary over time or become overridden by user. Therefore, in this step the system is waiting for new information from environment. If any change was detected goes to step

147 7. Upon any change was detected by sensors, the system checks to realize that the occurrence is due to changing in environmental conditions (i.e., outdoor temperature) or a decision initialized by user or SFLL has been overridden. 8. If the override flag is on, it shows the decision made by SFLL or the schedules sat by user (step 5) has been canceled by user, then it goes to step In the event of changing in environmental conditions, AFL measures, fuzzifies, and applies the particular rules associated with these changes using SFLL. 10. From this step until the end the adapting procedure is performed. The adapting procedure is applied for each day of week (1 for Monday, 2 for Tue, etc.) by creating three temporary vectors with a limit of 24 elements (refer to step 4). Therefore, AFL updates those elements that have changed in learning vector with value of 1, and if no change was detected update with value of Verification is performed for each element and specific day occurrence in order to make sure if the change is persistent or not. Therefore, if the number of occurrences related to each element in adapt vector meets the limit, it is marked to be considered for adaptation. If not, come back to step 6 for detecting. 12. The weights of elements that have overridden for three consecutive times are updated. To do so, the active daily cluster file is read; all data added in a Weightread list for future processing. In addition, all the necessary data is stored into a DecisionWeight vector (size of 48 elements). Then, another LearnWeight vector extracts the data from the specific learning vector of the daily cluster for comparison with the DecisionWeight vector. It performs the above processing for all the elements of interest in learning vector. Finally, for each day/week (first, second and third) it performs a weight check, where all the data elements from the DecisionWeight and LearnWeight vector are compared. It has to be mentioned that the initial weights have the value of zero. 13. From ALM description we already defined three different weights which can be allocated to any daily vector based on its shift from the actual value to that specific element for three consecutive occurrences of a particular day. Then, the difference 133

148 between new value and existing value is fuzzified by defined membership functions shown in Figures 5.10 and The fuzzy rule-based decision making according to weights shown in Table 5.3 are applied to adapt to new pattern changes. Each time that the check weight process is done, the result is assigned to that particular element of the daily cluster schedule. 15. The AFL checks the new pattern if it is not in daily cluster, the new pattern is updated as new cluster based on the existing and new knowledge which was adapted. 16. The routine is continued for next daily cluster Intelligent Zone Control (ZC) Most of exiting PCTs do not have zone-control capability or the zonal control in performed manually. On the other hand, our preliminary results in Chapter 3 demonstrated a zone controlled house environment reflects a better gain in terms of energy conservation. Therefore, the effect of a zone controlled environment will be considered for better energy conservation and management. In this case, to achieve better energy management and thermal comfort the role of wireless sensor nodes (occupancy sensors) are apparent for future Smart Thermostats. For this purpose, a rule based algorithm is added to KB subsystem to equip the Smart Thermostat with zone-control capability. The KB is an additional knowledge to the Smart Thermostat which contains information about the house parameters, the air flow rate (low, medium, high), and different stages of HVAC to evaluate the entire house and zone control scenarios. Therefore, a fuzzy Logic rule-based method is embedded into KB to adjust the air flow rate and turn on/off the HVAC system based on the information from occupancy and difference between indoor and outdoor temperature. The KB receives sensors information, the air flow rates, heater temperature, and current zone temperature and inquires its library. The membership functions of difference between indoor and outdoor temperature is shown in Figure

149 In addition, it is essential to know whether the inhabitant is in the controlled zone. This is because that the control system must act differently when a person is present or absent. Therefore, we use the same membership functions for the user presence as shown in Chapter 4, Figure 4.4. The system outputs are the airflow rate (air damper) and set point temperature for each zone. We define these parameters as fuzzy variables for system outputs. The set point membership function has been already defined in Figure 5.3. Figure Membership functions for difference between indoor and outdoor temperature The membership function of airflow rate are shows Figure As it can be observed from Figure 5.15, only one membership function can be active at a time for airflow or air damper. Figure Membership function of airflow rate 135

150 The KB after processing the sensors information, applies the specific fuzzy rules depicted in Table 5.4, and returns the recommended air flow rate, set points, and the necessary active heater stages to be turned on or off. This procedure will result in intelligent zone control. The performance of the approach will be considered with/without enabling intelligent zone control to observe the advantages of integration of fuzzy logic and wireless sensors for energy management and zone control in residential buildings. Table 5.4. Some of Fuzzy rules for intelligent zone-control Rules Inputs Outputs #Rule Price Difference Temp. Occupant Zone SP Airflow 1 High Low Present Z1 =SP4, Z2 = SP3 Low 2 High Low Absent Z1, Z2 = SP1 Low 3 Low Medium Present Z1 =SP5, Z2 = SP4 Medium 4 Medium Medium Absent Z1, Z2 = SP1 Low 5 High High Present Z1 = SP6, Z2 = SP5 High 6 Medium High Absent Z1, Z2 = SP1 Low 5.9. Simulation Results and Performance of AFL Several simulation scenarios are considered in order to verify the performance of AFL. The simulations are run under different conditions such as TOU rates, RTP, and capability of AFL for energy and cost saving as well as adapting to occupant pattern changes. The settings used for the schedules and daily intervals and their associated set points, for week days and weekends for zone 1 and zone 2 are depicted in Tables 5.5. The main house parameters used for the simulation scenarios and TOU rates are already listed in Tables 3.3 and 3.4 in Chapter 3. Table 5.5. Daily intervals and their associated set points Intervals Time of Day Occupancy Associated SP I 1 00:00 to 6:00 Occupied S 1, S 2, S 3, S 4, S 5, S 6 I 2 6:00 to 8:00 Occupied S 7, S 8 I 3 8:00 to 13:00 Unoccupied S 9, S 10, S 11, S 12, S 13 I 4 13:00 to 17:00 Unoccupied S 14, S 15, S 16, S 17 I 5 17:00 to 21:00 Occupied S 18, S 19, S 20, S 21 I 6 21:00 to 24:00 Occupied S 22, S 23, S

151 Figure Ontario Hourly Demand for one day taken from [140] In addition, weather data for outdoor temperatures, used for simulation is for months of December 2014, January and February 2015, Ontario, Canada. Hourly demand (MW) and RTP for months of December 2014, January and February 2015 are taken from [140]. The load profile data and RTP for initial responses for 24 hours is depicted in Figures 5.16, and 5.17 respectively. In this part, instead of the demand of house we consider the demand of the area (province). It is worth to mention that the changes in demand do not affect the defined fuzzy rules. Figure Hourly electricity price taken from [140] 137

152 Simulation of Intelligent Zone Control (ZC) In order to verify the zoning control capability added to knowledge Base subsystem, two different simulation scenarios of the entire house and zone controlled house with AFL enabled are shown and compared in Figure Table 5.6 shows different scenarios for verification on intelligent zone control. For instance, for the scenario 2 that occur at intervals 6:00 AM to 7:00 AM, it assumed that difference between indoor and outdoor temperature is High. The occupant activity in the zone1 is not detected by sensors while the occupant is detected in zone2. For the entire house, the inhabitant is detected Present because the system considers the entire house as one zone. The electricity price can be observed in this Table as well. Based on the available inputs AFL decides which rule(s) listed in Table 5.4 to apply. In this case (zoning control) user sleep and wake patterns can play a significant role in energy and cost reduction. For example, if the temperature of bedrooms occupied by persons is more than 21 C, sleep is disturbed and people feel warm, lethargic, and sleepy. Figure 5.18 shows the adjusted set points with and without enabling the proposed intelligent zone control. This Figure also demonstrates that a zone controlled house environment would reflect a better performance with respect to energy conservation; hence, more cost savings for a zone controlled house. Table 5.6. Designed scenarios for assessment of intelligent zone control Scenario TOD Price Temp. Difference Presence Entire House Z 1 Z :00-6:00 L H P A P 2 6:00-7:00 L H P P A 3 7:00-8:00 H H P P A 4 8:00-17:00 M H A A A 5 17:00-19:00 H H P P P 6 19:00-22:00 L H P P A 7 22:00-24:00 L H P A P In order to verify the functionality of zoning approach with and without AFL a one month simulation was run. The results of simulation are shown in Figure

153 Figure Comparison of adjusting set points with and without intelligent zone control It can be observed from Figure 5.19 that intelligent zone control house with AFL enabled versus the entire house with AFL disabled can potentially provide savings equate to 218 kwh. Hence, an improvement with respect to relative energy consumption with and without algorithm is achievable. Figure Results of AFL (Entire house vs. zone controlled) 139

154 As a result, the effect of a zone controlled environment can be regarded as a very important step for better energy management and cost saving. Also, wireless sensors in residential buildings are necessity for any Smart Thermostat. They can be used to control the indoor temperatures in different zones of a house. In addition, our zoning control technique can be added into existing PCTs and Smart Thermostats as another significant factor which impacts their performances. Therefore, the need for more sensor/actuator nodes is required for an optimal control of a multi-zone controlled environment The Role of Adapting to Occupant s Patterns in Residential Energy Management In this section the performance and capability of AFL with respect to relative energy consumption with/without enabling to adapt to occupant s pattern changes for several different cases are considered. In order to stay on similar conditions, some assumptions are taken. All intervals and user schedules listed in Table 5.5 are fixed during the one month simulation. We assumed the variations of outdoor temperature during one month simulation are similar as shown in Figure 5.20 (green line). The variations of load demand and RTP are assumed to be similar (no change) for all days during the simulation as shown in Figures 5.16 and 5.17 respectively. Based on these assumptions, the system is trained for one week. Figure 5.20 shows one day simulation at work trained under assumptions above. As it can be observed from Table 5.7, for all scenarios the SFLL operates as the core of the system for setting the set points based on data received from wireless sensors and smart grid initiatives. For scenarios 2 to 5, zoning control as an important part of KB subsystem is active. The proposed AFL for adapting to occupant s pattern changes is only active for scenarios 3 and 4 in order to compare the importance of AFL s adapting capability in energy saving. In the first scenario shown in Table 5.7, SFLL algorithm added to PCTs converts them to an Autonomous Thermostat and autonomously adjusts the set points via information received from sensor nodes every hour. In this case, the system is set for 140

155 simulation of entire house, while the zoning control and AFL are not enabled. As it can be observe from Table 5.6, a potential saving of 109 kwh in case 2 is achievable when the zoning control (ZC) is enabled (compared to case 1). In scenario number three, the changes in occupant s patterns are detected or initiated by associated override flags and recorded by AFL for Monday and Tuesday daily clusters patterns. These changes occurred for the start time of S 2 (Set Point 2) and start time of S 21 (Set Point 21) within intervals, respectively (refer to Table 5.5, Intervals and associated SP columns). The start time changed at 2:15 AM within interval for Monday cluster and 21:30 within interval for Tuesday cluster. The occurrences take effect during the second week of simulation (i.e. it has to be noted that first week is dedicated to learning the patterns). Figure One day simulation for the system under training As a result, the actual changes are observed in the main system for Monday and Tuesday cluster files. As shown in Table 5.7 (energy consumption column) the changes took effect, and the occurring total energy consumption due to those changes was met, which validates the AFL s successful adaptation to the change. This demonstrates in comparison to case 2; the updated schedules and patterns (where we reduced both S 2 and S 21 for 2 C) only for two days of a month are resulted in energy saving equates to 9 kwh and in comparison to case 1 the saving is 118 kwh. 141

156 Table 5.7. Results of AFL (with and without ZC and Adaptation) #Scenario SFLL/ZC AFL Enabled Override Flags: Detected Changes Energy Consumption (kwh) 1 Yes/No No No Yes/Yes No No Yes/Yes Yes Yes: Mon., Tue., SP2 and SP Yes/Yes Yes Yes: Mon., Tue., Wed., SP2 and SP Yes/Yes No Yes: Mon., Tue., Wed., SP2 and SP In scenario number 4, the system equipped with AFL (AFL is enabled) detects the states of override flags associated with S 2 and S 21 were converted to on for Wednesday daily cluster again. It now means three consecutive changes occur to S 2 and S 21 based on comparing the recorded data. Similar to Monday and Tuesday daily clusters, the changes happen for start times of I 2 and I 6 for Wednesday daily cluster, where they changed at 2:15 AM and 21:30 respectively. This is measured by comparing the initialized/learned values with new occurrences (start time and end time of each interval, and the values of S 2 and S 21 ). Therefore, adapting to user s new patterns and preferences is taken place on Thursday in the second week of the simulation process until the end of month. As shown in Table 5.7, the potential energy saving related to this scenario is 122 kwh compared to scenario2 as our reference case. The adaptation procedure is explained in the next section. Furthermore, case 5 is presented in order to compare Adaptive Autonomous Thermostat described in case 4 with the Autonomous Thermostat (equipped only with SFLL). This scenario assists us to validate the capability of AFL algorithm versus when it is disabled. Since the adapting capability of AFL is not active in case number 5, the changes only take effect for those particular daily clusters in second week (Monday, Tuesday, and Wednesday). This means the system is not able to adapt to new preference changes. Therefore, the estimated saving in the results of these changes is only 13 kwh in comparison to the reference case 2 during one month simulation. As a result, by considering five different cases elaborated above; it shows very small changes in pattern behavior of the user can provide a significant conservation equals to 231 kwh per month for AFL and ZC enabled. It has to be noted that the 142

157 proposed SFLL considerably assists to reduce the interaction between user and machine (thermostat) as well. Hence, accommodating to schedule and preference changes makes a notable difference in the overall outcomes (comfort, energy, and cost) and proves performance and advantages of AFL and ZC in residential energy management, while helping utilities during peak load demand Analysis of Adapting to User s Pattern Changes Several scenarios for statistical analysis of AFL algorithm are performed in order to validate its performance in adapting to occupant pattern changes. For this purpose, we consider our approach from two aspects. The first method that is called user adjusting is based on user preferred schedules, preferences, and set point temperatures. In this method, similar to existing PCTs, a user can enter and define his/her schedule intervals and their corresponding set point temperatures based on his/her needs such as the one shown in Table 5.8. The objective of considering the problem from this aspect is to demonstrate that AFL can be embedded to existing PCT as well leading to a PCT with capability of learning and adapting to occupant pattern changes (a Smart PCT). As mentioned in section adapting to changes can be week-based or dailybased. However, from user adjusting method we consider adapting based on week days. This means we just compare only the changes related to similar weekdays (Monday to Monday, Tuesday to Tuesday, etc.). In order to detect changes for three consecutive similar weekdays, the system is trained for three weeks. In this way, we have user s habits for three Mondays, three Tuesdays, etc. Table 5.8. User schedules for weekdays SP Time Of Day (Start time to End time) Heat SP ( C) User Status SP 1 00:00 to 06:00 21 Sleep SP 2 06:00 to 08:00 23 Awake SP 3 08:00 to 11:00 17 Away SP 4 11:00 to 17:00 18 Away SP 5 17:00 to 19:00 23 Home SP 6 19:00 to 24:00 21 Home 143

158 A scenario for participating in demand response programs is considered in order to emulate pattern changes of user engagement with respect to the Set Point Start and End times and Heat Set Points of Monday Clusters. For this purpose, the period of the learning and adaptation of system is initialized to three weeks. According to Table 5.8, the set point temperature has been initially adjusted by occupant at 23 C between 17:00 to 19:00, where the price and load demand are normally high at this period of day. In order to predict consumer patterns after three consecutive changes in SP 5, a scenario is emulated as follows: The first day (Monday) the occupant reduces the set point temperature (SP 5 ) from 23 C to 20 C from 16:30 (start time) to 18:30 (end time). The Next Monday SP 5 is reduced to 18 C from 17:30 (start time) to 18:00 (end time). Finally, the set point temperature (SP 5 ) is decreased to 19 C from 18:00 to 19:00 for the third Monday. As shown in Table 5.9, different inputs are applied to start time and end time of Monday Cluster for three consecutive weeks to verify the adaptation capability of AFL. The outputs of AFL that indicate the adapted values after three weeks of learning are shown in Table 5.9. It can be observed from Table 5.9 that the AFL adapts values, for the Monday schedules when the user reduces the SP at 16:30 in first week, and on the second and third week decreases it at 17:30 and 18:00 respectively. Comparing the two last rows of Table 5.8, it can be observed that the AFL adapted values after the third occurrence for start time is different from the crisp/real values of three consecutive occurrences. The adapted start time value using real value is 17:20, while it is 17:08 using AFL. This implies that AFL adapts the value which is closer to frequent occurrences using fuzzification of weights and applying tuned fuzzy rules shown in Table 5.3. Similarly, this occurs for end time and heat set point as well. Recall from Section 5.6.2, the system detects the changes associated with each element in learning and adapting vectors, and then compares them with new values overridden by user, and finally fuzzifies these changes (weights). Since three consecutive occurrences happen to the element(s) of learn vector; AFL applies the corresponding fuzzy rules shown in Table 5.3 to predict new pattern (s), while 144

159 considering energy conservation aspects. In addition, as shown in Table 5.9, in comparison to crisp/real adapting approach with respect to energy conservation; AFL participates in DR programs 12 minutes earlier (start time 17:08) and this engagement lasts 9 minutes longer until 18:40. In addition to start time and end time that is improved by AFL, the Heat SP is 0.4 C lower in comparison to real/crisp approach. As a result, AFL adapts the value which is closer to the typical user patterns observed during the three occurrences, and at the same time leads to energy conservation. Table 5.9. Adapted Values for different changes Occurrence Start Time Value End Time Value Heat SP Value ( C) Initial New Weight (min) Initial New Weight (min) Initial New Weight First Week 17:00 16: :00 18: Second Week 17:00 17: :00 18: Third Week 17:00 18: :00 19: Real Adapted Value AFL Adapted Value 17:20 18: :08 18: Furthermore, other scenarios for verification of AFL are considered in the following when the system is set on Autonomous Mode as elaborated in this Chapter, Section 5.3. The Autonomous Thermostat equipped with SFLL is totally self-ruling and independent. In fact, user does not need to concentrate on controlling the thermostat. In the cases that a user generates an event by overriding the decision(s) (i.e., adjusted set point) the system has to adapt to new pattern changes using AFL. To verify the performance of AFL when the system swaps from autonomous mode to event-based mode, a one month simulation is run. The first weeks is dedicated to training of the system and initializing all information described in AFL algorithm in Section The adjusted set points for different days of the second week based on information received from wireless sensor nodes and smart grid initiatives are shown in Figure 5.21 (S 1 to S 24 distributed within 6 intervals, refer to adapting vectors). As it can be observed from Figure 5.21, in most cases the set point temperatures for each weekday at similar times are different. For example, as shown in Figure 5.21 the 145

160 adjusted set points for all weekdays between 00:00 AM to 5:00 AM are different. This is because the information received from environment conditions are updated every hour and the thermostat proactively sets the output (set point) accordingly. As shown in Table 5.10 multiple changes are applied to decisions made by Autonomous Thermostat (see Figure 5.21) in order to validate and analyze the AFL algorithm statistically. Hence, the decisions are overridden/initialized to emulate pattern changes of user preferences with respect to adapting elements for three consecutive days on the second week (the first week dedicated for training). Figure Heat Set Point for Different Days of the Second Week As mentioned in Section 5.6, the adapting elements are: Start Time and End Time and Heat Set Points, for each day of the week. As depicted in Table 5.10, different set points and their associated start and end times have been overridden for Monday, Tuesday, and Wednesday clusters to see the reaction of AFL for adapting to new changes in the next days (Thursday, Friday, etc.). From Table 5.10 and Figure 5.21 we can observe that the first occurrence is for Monday daily cluster, where the user overrides the made decision (initial value, = 22 C) and reduces to 20 C (overridden value) at 2:30 AM (start time) until 4:00 AM (end time). Since the override flag (F 3 ) associated with S 3 is on, one occurrence is detected. Therefore, the associated weights with adapting and learning elements within interval I 1 146

161 after comparing with initial values are recorded. For example, in this case the weight associated with heat set point is 2 C (, = 2 ). In addition, based on the defined intervals (refer to Table 5.5, intervals and time of day columns) the weights associated with start time and end time are 150 min (2: 30 0: 00 = 2: 30 = 150 ) and 120 min (6: 00 4: 00 = 2: 00 = 120 ) respectively. It has to be noted that the changes have happened in interval I 1 [0: 00. 6: 00. ]. Similar principle is used to compute the weights for other cases. For the second occurrence (Tuesday) the initial value, overridden value and their associated weight are 20.2 C, 18 C, and 2.2 C respectively. While the start time and end time are 1:45 AM and 3:30 AM in turn that are different from Monday cluster. Similarly, the third occurrence is for Wednesday cluster. Table Adapted values for different changes # Weekday Cluster SP No. Overridden Flag No. Initial Value ( C) Overridden Value ( C) Weight ( C) Start Time Monday S 3 F :30 4:00 Tuesday S 2 F :45 3:30 Wednesday S 1 F :30 2:45 Average N/A :25 3:15 Fuzzy Confidence Adapted Values N/A (20.1, 23.2) (18, 20) (1.4, 4.1) (0:11, 2:18) End Time (2:15, 4:00) N/A :06 3:30 We can observe that the AFL adapted value after the third occurrence is not the average value of three consecutive occurrences. The outcomes of AFL adapted values after one week of training are distributed and fall within the limits of the 95 percent Fuzzy Confidence Interval of the sample mean values (for Fuzzy Confidence Interval refer to Appendix A). Fuzzy confidence interval will assure whether or not the AFL adapted values are in the appropriate interval. Thus, implies that AFL adapts the value which is closer to frequent occurrences. Referring to Table 5.10, the AFL adapted value for set point temperature after three times overriding of the made decisions is As it can be observed it is closer to the occurrence of pattern on the second and third day. Hence, AFL does not adapt the average value of three daily occurrences (19 ). In 147

162 this way we can save more energy, while the adapted set point is still in PPD interval ( ). Furthermore, referring to Table 5.9, the AFL adapted value for the next days after Wednesday such as Thursday s occupant pattern changes (start and end time of set point changing), is 1:06 AM which, indeed are closer to the occurrence of pattern on second and third day. Hence, AFL does not adapt the average value of three daily occurrences, which is 1:25 AM. In this way the start time is not affected by a change of pattern on first day of observation 2:30 AM. Instead AFL adapts the value which is closer to the typical user patterns observed during the first and third occurrence, and at the same time leads to energy conservation because it reduces the adapted set point 19 minutes earlier). Besides, the AFL adapted value for end time for next days is 3:30 AM while the average value is 3:15 AM. Therefore, the end time is not affected by a change of preference on the third day 2:45 AM. AFL accommodates to the value that is closer to typical user preferences detected during the first and second occurrence. It results in more energy conservation because the set point remains longer on the adapted value which is lower than initial set points (from Figure 5.21 initial set points are 20.8 and 21.5 between 3:00 AM to 4:00 AM for Thursday and Friday). It has to be noted that in all the aforementioned scenarios, the relations of energy savings and cost to the occupant s comfort are in agreement with the occupant s choices such as preferences of choosing autonomous mode operation which includes Economy Mode and Comfort Mode, preferences of user adjusting Mode such as entering initial schedules, temperature limits and offsets, based on which AFL takes action. One of the advantages of AFL is that the comfort of the occupant is not jeopardized during the process. Instead, occupant s preferences are maintained, while energy savings are achieved. 148

163 Chapter 6. Conclusion and Future Works 6.1. Conclusion HVAC systems were the main target for energy and load management because they constitute a significant portion of the annual total energy consumption in the world and particularly in the North America. In the past, insufficient generation of electrical power and its potential problems in the grid were often addressed at the supply-side. Nowadays, management of peak load problems is being shifted more towards the demand-side. Programmable Thermostats are widely used to control residential HVAC systems with the aim to reduce energy consumption and provide occupant s thermal comfort while occupants accommodate their daily schedules and preferences. The programs such as DR, dynamic pricing (i.e., TOU rates and RTP) are applied by utilities as smart grids initiatives in order to encourage customers such as residential users to reduce their usage during peak load periods. In order to optimally utilize the benefits of such initiatives; the need for smart in-home energy management systems such as Thermostats capable of responding to smart prices, while saving energy and providing user comfort was a must. On the other hand, consumers needs, preferences, schedules, and electricity usage patterns are totally different. Lack of learning capability and adapting to occupant s schedule and preference changes when users change their preferences during the day is the major problems among existing in-home energy management systems such as Programmable Thermostats. Therefore, the need for energy management systems capable of learning and adapting to user schedule and preference changes is apparent. 149

164 To address abovementioned issues, an Adaptive Fuzzy Logic System utilizing wireless sensors and smart grid initiatives with the aim of improving energy management in Residential Buildings and enhancing the learning capabilities of existing thermostats such as PCTs was proposed. In section 1.4, our contributions were briefly stated. Below are described the concluding remarks of the thesis contributions, and research efforts. In Chapter 3, a House Simulator Engine that reflects different types of Thermostats in today s market is implemented using MATLAB with Graphical User Interface (GUI). The simulator enabled us to address the opportunities using scheduling preferences to save energy and cost under different scenarios, DR programs, and TOU applied. More importantly, the simulator was used as an expert shell to help us in development of existing PTs and PCTs and implementation of the advanced intelligent techniques such as our Supervised Fuzzy Logic Learning (SFLL), Adaptive Fuzzy Logic (AFL) model, and Fuzzy Zone Controlled approach for future Smart Thermostats. A period of three months was simulated to demonstrate the approximate energy savings of the scheduled set point settings versus fixed set point (20 ºC). The results showed the user can potentially save 894 kwh in energy by scheduling his/her usage and preferences compared to keeping the heat SP only on 20 C for three months. In addition, the cost saving associated with using a simple schedule was $89 for three months. The recent advancements in WSN technology enabled us to address the opportunities using multiple sensors and smart grid initiatives for energy and comfortcentric control objective in residential HVAC systems in Chapter 4. To do so, a supervised fuzzy logic learning method using wireless sensors and smart grid initiatives was proposed and embedded into existing PCTs in order to add more intelligence to these kinds of Thermostats. Four important parameters that directly relate to energy management in residential HVAC systems namely outdoor temperature, occupant presence/absence in the home, electricity price, and existing initialized set point were chosen as fuzzy input variables. The only output of the system was the amount of temperature that is reduced from set points initialized by user already. A fuzzy 150

165 logic decision-making algorithm was designed to reduce the HVAC load, while maintaining occupant s thermal comfort leading to smart demand-side management. The capabilities of the proposed approach under different scenarios were compared with current PCTs from several directions such as load reduction and thermal comfort. The results showed there was a potential energy saving about 225 kwh with respect to existing PCT for one month simulation. As a result, in Chapter 4 was demonstrated that by the means of simple sensing technology to detect occupancy in a house, outdoor wireless temperature sensor to measure outside temperature, small communication chips such as Zigbee to communicate with smart meter to read electricity price signals, and the proposed supervised fuzzy logic to automatically turn on/off the residential HVAC system based on available inputs; we could significantly manage energy consumption without sacrificing occupant s thermal comfort. In the first part of Chapter 5, an Autonomous System that was a synergy of fuzzy logic techniques, wireless sensors, and smart grid initiatives as a novel approach developed to bring forward a new Autonomous Smart Thermostat. The system autonomously provoked the fuzzy rules based on available inputs in order to adjust the set point temperatures of thermostat without any interaction from its user. In order to protect inhabitant s thermal comfort; the ASHRAE standard comfort-zone range was used to adjust the set points within ASHRAE interval. The autonomous system was implemented based on two operation modes namely comfort and economy. The comfort mode was extended for the users who prefer comfort rather than saving. The economy was developed in order to save more energy and cost. In order to verify the thermostat reaction; ten different scenarios during a day were considered. The results showed the designed Fuzzy Logic Controller for autonomous system could perfectly cope with the changes. A zone-control approach based on fuzzy rule-based algorithm was added to Knowledge Base subsystem to equip the Smart Thermostat with zone-control (ZC) capability. It was added to adjust the air flow rate and turn on/off the HVAC system of a typical two Storey house (refer to Table 5.7). Moreover, in the cases that the user was not satisfied with the made decision(s) adjusted by autonomous system, an Adaptive Fuzzy Logic (AFL) system based on principles of Adaptive Learning Model (ALM) proposed in order to adapt to new preference changes. 151

166 It was shown through emulation of real-world scenarios, that the FLA algorithm is a practical implementation reflecting the main features of the ALM learning technique. In configuration such as zone-control with and without FLA versus entire house control for duration of one month, a potential energy saving about 218 kwh was obtained. Therefore, the effect of a zone controlled environment could be regarded as a very important step for better energy management and cost saving. The role of wireless sensors in residential buildings was apparent necessity for any Smart Thermostat as well. In order to verify the role of adapting to user pattern changes in residential energy management the capability of AFL with respect to relative energy consumption with/without enabling it for five different cases were considered (refer to Table 5.9). The results showed that in the case of without AFL the system was unable to detect occupant s pattern changes, while with AFL active, this was possible, and therefore utilized to adapt and apply the new pattern changes to the current schedule. For scenario number three, the results showed in comparison to case 2 as reference case; the updated schedules and patterns (where we reduced both S 2 and S 21 for 2 C) only for two days of a month were resulted in energy saving equates to 9 kwh and in comparison to case 1 the saving is 118 kwh. In case number 4, the system equipped with AFL (AFL was enabled) detected the states of override flags associated with S 2 and S 21 were converted to on for Wednesday daily cluster again. It indicated that three consecutive changes occurred to S 2 and S 21 based on comparing the recorded data. Similar to Monday and Tuesday daily clusters, the changes happened for start times of I 2 and I 6 for Wednesday daily cluster, where they changed at 2:15 AM and 21:30 respectively. This was measured by comparing the initialized/learned values with new occurrences (start time and end time of each interval I, I and the values of S 2 and S 21 ). Therefore, adapting to user s new patterns and preferences was taken place on Thursday in the second week of the simulation process until the end of month. The potential energy saving related to this scenario was 122 kwh compared to scenario 2 as our reference case. Furthermore, case 5 in Table 5.10 was presented in order to compare Adaptive Autonomous Thermostat described in case 4 with the Autonomous Thermostat (equipped only with SFLL without AFL). This scenario assisted us to validate the capability of AFL algorithm versus when it was unable. Since the adapting 152

167 capability of AFL was not active in case number 5, the changes only took effect for those particular daily clusters in second week (Monday, Tuesday, and Wednesday). This meant the system was not able to adapt to new preference changes. Therefore, the estimated saving in the results of these changes was only 13 kwh in comparison to the reference case 2 during one month simulation. Further experiments were conducted with a multitude of actual Heat Set Points and start and end time changes, and the adapted values taking effect were obvious (as described in detail in Chapter 5, Section 5.9.2). The results of AFL s performance evaluation confirmed improvements of an actual AFL enabled house with respect to energy savings and user thermal comfort, and validated the ALM implementation via AFL. In addition, it also brought to demonstrate the concept of an Adaptive Autonomous Smart Thermostat - a Thermostat with enhanced learning and adapting capabilities (based on the occupant s input pattern changes and other preferences). Moreover, a zone controlled AFL enabled house equipped with sensors, showed further performance improvements with respect to optimized energy conservation and comfort Future Works The synergy of Artificial Intelligence (AI) techniques such as adaptive fuzzy logic, wireless sensors, and smart grid initiatives as a novel approach developed in this thesis provides evidence that this synergy can adapt to occupant pattern changes, while reducing energy consumption and cost without sacrificing comfort and enable Demandside Management functionality Leading to Integration of Residential HVAC systems into Smart Grid which is the main goal of future Smart Grid. This feature of AFL indicates an interesting area to be considered and explored further for numerous applications in residential and commercial buildings as well as power and energy systems. In Author s opinion following areas of opportunity can be investigated. Possible further improvement of AFL to be considered might be the addition of a fuzzy logic approach for potential self-tuning of offsets related to control of airflow rate and heating/cooling stages applied for different zones i.e., within large buildings. 153

168 Investigation of fuzzy logic in conjunction with adaptive neural network application could prove to be an effective approach to be considered in this case. Thermal comfort is very important in any operation environment such as residential and commercial buildings. However, thermal comfort is a very vague and not easily defined term, and it is influenced by both the physical environment and the individual s physiology or psychology. To partially overcome these problems, an integration of fuzzy logic, wireless sensors, and adaptive neural network techniques could be developed to model the thermal comfort. Hence, it can be considered for tuning the set points when SFLL is applied. In order to maintain occupant s thermal comfort and save more energy during a day, it could be important to know occupancy and sleep patterns in a home, and how to use these patterns to save energy by automatically turning on/off the home s HVAC system. This capability can be used in SFLL to have three states (active, sleep, away) for occupancy instead of considering presence or absence. Forecasting the electricity prices in RTP programs for pre-heating and precooling a house without jeopardizing occupant comfort can be conspired and embedded into designed simulator engine. In addition, peak load scenarios, while acting in a harmonized manner within different geographic areas, where demands are not necessarily uniformly distributed, can pose an interesting challenge to address for adjusting the set point temperatures of residential HVAC systems as the main load in residential sector. Furthermore, heat pumps can play an important role in smart grids in the future. Since they are often equipped with hot water tanks and connected to an inert floor heating system, the consumption of power demand for the pump can be shifted in time. This flexibility can avoid bottleneck situations in the grid and help improving the integration of fluctuating power generation resulting especially from renewable energy sources. Therefore, the supervised fuzzy logic approach proposed in Chapter 4 can be developed for Heat Pumps to reduce the temperature of hot water tank based on current house demand and electricity prices. 154

169 In a smart power network, Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs), and energy storage systems can act as either loads or distributed sources of energy. These devices are controlled by Load Control Switches. By developing AFL for Load Control Switches they can autonomously be charged during low electricity demands and/or prices; and power back into smart grid (recharging) at other times resulting in integration of such devices into Smart Grid. In addition, control of lighting systems in commercial and/or residential buildings using Unsupervised Fuzzy Logic Learning Systems and WSNs can poses challenges in order to create an adaptive system which enables intelligent power management. The proposed AFL could be developed for lighting systems as well. In this case, time is an important variable that can be added to the system as one of fuzzy inputs, because the system needs to react differently to the same conditions at different times of day. 155

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181 Appendix A. Fuzzy Confidence Interval (FCI) Theory It has long been recognized that conventional confidence intervals, which we also call crisp" confidence intervals, using a term from fuzzy set theory, can perform poorly for discrete data. The occurrence of fuzzy random variables makes the combination between randomness and fuzziness more persuasive. In this thesis, different set points and/or pattern changes of the user that represent the sample data for different occurrences observed during the simulation. Let be a random variable having a distribution with parameters,,. Let be a fuzzy random variable. Then and are random variables for all h [0,1]. We say that has the same distribution as with fuzzy parameters,, if and have the same distribution as with parameters( ),,( ) and ( ),,( ), respectively. Let,, be independent and identically distributed random variables. Let ( ) and ( ) be two statistics such that ( ) ( ), where =,,. If the random interval [ ( ), ( )] satisfies P ( ) ( ) = 1 Then we say [ ( ), ( )] is a confidence interval for with confidence coefficient1, where x = (,, ) and each is the observed value of for = 1,2,,. Let,, be independent and identically distributed fuzzy random variables with fuzzy parameter. Let be the observed value of for = 1,2,,, where each is a canonical fuzzy number for = 1,2,,. Therefore, it can be seen that ( ) and ( ) are the observed values of ( ) and ( ), respectively for h [0,1]. It is assumed 167

182 that ( ) = and ( ) =. Then by definition, we also see that,, are independent and identically distributed random variables, and,, are independent and identically distributed random variables. It can be written, x = (x,, x ) and x = (x,, x ). Then from two groups of observed values, (x,, x ) and (x,, x ) we can construct a confidence interval [L(x ), U(x )] for θ with confidence coefficient 1 α and a confidence interval [L(x ), U(x )] for θ with confidence coefficient 1 α [141]. Application of FCI in Our Research Let,, be independent and identically distributed from N(μ, σ ). Suppose that σ is known so that μ is the parameter. Let x be the observed value of X for i = 1,2,, n. Let x = x be the mean value. Then the confidence interval for μ with confidence coefficient 1 α is given by x z σ σ n, x +z n Where z is the upper α 2 quantile of N(0, 1) distribution. We also write L(x) = x z and U(x) = x +z Let X,, X be independent and identically distributed fuzzy random variables with fuzzy parameter μ and 1 ( ), where 1 ( ) is a crisp parameter such that (1 ( ) ) = (1 ( ) ) = σ for all h [0,1]. Therefore, based on what has been mentioned in [141], [L(x ), U(x )] and [L(x ), U(x )] are confidence interval for μ and μ, respectively, with confidence coefficient 1 α, where L x = 1 n x z σ n U x = 1 n x +z σ n L x = x z U x = x +z. 168

183 For a triangular fuzzy membership function Assume that the fuzzy observations x for i = 1,, n are triangular fuzzy numbers (x, x, x ), then we can adopt the following notations A = (x x )B = (x x ) C =z. nσ + nr x D = z. nσ + nr x E = z. nσ + nr x F =z. nσ + nr x as follows: Therefore, the lower and upper bands of fuzzy confidence interval are obtained min( C A,D ) if A C and B D B C h = if A C and B < A D if A < B 1 A < < min( E A,F ) if A E and B F B E h = if A E and B < A F if A < B 1 A < < Choosing α = 0.05, yields a 95% confidence interval. A confidence interval of 95% implies that 95 % of all the samples are within the interval that includes µ, and only 5 % of samples would yield erroneous interval. In this thesis, the confidence interval of 95% is used to validate the adapted values for changing patterns and/or user preferences, from the simulation. 169

184 Appendix B. Preliminary Works and Experiments Implementation of Zigbee-based Thermostat for Controlling an Air Conditioner (AC) System Before going through using fuzzy logic rule-based techniques for energy management in residential heating-cooling systems, we performed some experiments on an AC system using different sensors and wireless communication by applying conventional rule-based algorithms. In order to implement and experiment the main ideas, in addition to the Air Conditioner (AC) system that was used and upgraded the following parts and tools were employed as well: Arduino Nano Microcontroller Xbee Modems Xbee Shields One Ethernet Shield Three 1 Channel Relay Module Relay Expansion Board One Solid State Relay (SSR) Digital Temperature and Humidity Sensor (Model DHT11) Current Sensor Two Push buttons LCD Arduino UNO Microcontroller The Arduino Uno is a microcontroller board based on the ATmega328. It has 14 digital input/output pins (of which 6 can be used as PWM outputs), 6 analog inputs, a 16 MHz ceramic resonator, a USB connection, a power jack, an ICSP header, and a reset button. It contains everything needed to support the microcontroller; simply connect it to a computer with a USB cable or power it with an A to D adapter or battery to get started. 170

185 The Uno differs from all preceding boards in that it does not use the FTDI USB-to-serial driver chip. Instead, it features the Atmega16U2 (Atmega8U2 up to version R2) programmed as a USB-to-serial converter. In terms of Memory, the ATmega328 has 32 KB (with 0.5 KB used for the bootloader). It also has 2 KB of SRAM and 1 KB of EEPROM (which can be read and write with the EEPROM library. Input and Output Pins Each of the 14 digital pins on the Uno can be used as an input or output, using pinmode() and digitalread() functions. In addition, some pins have specialized functions: Serial: 0 (RX) and 1 (TX). Used to receive (RX) and transmit (TX) TTL serial data. These pins are connected to the corresponding pins of the ATmega8U2 USB-to- TTL Serial chip. External Interrupts: 2 and 3. These pins can be configured to trigger an interrupt on a low value, a rising or falling edge, or a change in value. PWM: Pins 3, 5, 6, 9, 10, and 11. Provide 8-bit PWM output with the analogwrite() function. SPI: 10 (SS), 11 (MOSI), 12 (MISO), 13 (SCK). These pins support SPI communication using the SPI library ( LED: There is a built-in LED connected to digital pin 13. When the pin is HIGH value, the LED is on, when the pin is LOW, it's off. The Uno has 6 analog inputs, labeled A0 through A5, each of which provide 10 bits of resolution (i.e different values). By default they measure from ground to 5 volts, though is it possible to change the upper end of their range using the AREF pin and the analogreference. 171

186 Communication The Arduino Uno has a number of facilities for communicating with a computer, another Arduino, or other microcontrollers. The ATmega328 provides UART TTL (5V) serial communication, which is available on digital pins 0 (RX) and 1 (TX). An ATmega16U2 on the board channels this serial communication over USB and appears as a virtual com port to software on the computer. The '16U2 firmware uses the standard USB COM drivers, and no external driver is needed. However, on Windows, a.inf file is required. The Arduino software includes a serial monitor which allows simple textual data to be sent to and from the Arduino board. The RX and TX LEDs on the board will flash when data is being transmitted via the USB-to-serial chip and USB connection to the computer (but not for serial communication on pins 0 and 1). A Software Serial library allows for serial communication on any of the Uno's digital pins. Programming The Arduino Uno can be programmed with the Arduino software ( For details, see the reference and tutorials ( The ATmega328 on the Arduino Uno comes pre-burned with a bootloader that allows you to upload new code to it without the use of an external hardware programmer. It communicates using the original STK500 protocol, C header files. You can also bypass the bootloader and program the microcontroller through the ICSP (In-Circuit Serial Programming) header using Arduino ISP or similar. by: The ATmega16U2/8U2 is loaded with a DFU bootloader, which can be activated On Rev1 boards: connecting the solder jumper on the back of the board (near the map of Italy) and then resetting the 8U2. On Rev2 or later boards: there is a resistor that pulling the 8U2/16U2 HWB line to ground, making it easier to put into DFU mode. 172

187 You can then use Atmel's FLIP software (Windows) or the DFU programmer to load a new firmware. Or you can use the ISP header with an external programmer (overwriting the DFU bootloader). Implementation of a wireless rule-based thermostat for an AC System Figures B.1 and B.2 show the upgraded AC and a wireless thermostat that was built in order to control all modes of the AC system (High, Medium, Low, and Compressor) wirelessly based on different environmental conditions. Figure B.3 also shows how they can communicate with each other. To do so, we replaced and added some components such as Arduino Uno Microcontroller, relays, temperature sensors, Solid State Relays, Xbee modems, push buttons to mimic occupancy sensors, and LCD. By doing so, we improve the AC working principles from manual operation to wireless (refer to Figure B.1 and left hand of Figure B.3.) The actuation commands are sent to AC by the implemented thermostat (Figure B.3) Moreover, the implemented systems use microcontrollers to process the proposed rule-based algorithm. There are totally 50 rules. We also built Xbee-based temperature sensors to measure indoor and outdoor temperatures. It also uses Xbee wireless modems for sending/receiving the particular commands and environment information, four relays to turn on/off or change the speed of fan (low, medium, high), and a current sensor to measure the AC electricity consumption. The system gets the sensors information as well as the user schedule in each sensing point and applies the rule-based algorithm to transform all environmental factors into appropriate instructions. It has to be mentioned that to emulate the TOU pricing an excel file is used in order to read the electricity prices. Main tasks of the system for controlling the AC have the following procedures: 1) Read the sensor values and status that representing indoor and outdoor temperatures and occupant presence. 2) Get user schedule and preferences for each day of week, and reading TOU rates for different times of day. needed). 3) Utilizing digital potentiometers to change indoor temperature, and schedule (if 173

188 4) Making decision based on the proposed rule-based algorithm and taking actions accordingly via Xbees. In this experiment the Xbee wireless modems mounted on the implemented thermostat is configured as a coordinator that collects data from other Xbee sensor nodes (indoor and outdoor temperatures) and sends the particular commands to Air Conditioner (AC) system according to the defined rules. The rest of sensor nodes are configured as router or end device that provide environmental information to coordinator. In fact, the control unit (Figure B.3) is wirelessly connected to AC system by deployed Xbee modem and relays on the AC (left side of Figure (B.3)) and sends/receives commands to control the inside temperature by turning the relays on/off. In order to evaluate the implemented method in terms of electric consumption, by deploying a current sensor (Figure (B.1) and (B.3)) the AC consumption from serial monitor of Arduino Microcontroller was separately accumulated. Figure B.1. AC system Upgraded for Wireless Controlling We considered the electricity consumption for three different days. Figure (d) shows the outside temperature for three different days (Modest, Warm and Hot). In this figure the blue line is the variation of the average of indoor temperature for three days under consideration when our approach is applied. The electricity consumption using our 174

189 approach for modest, warm, and hot days is 6.73 kwh, 8.3kWh, and kwh respectively. While based on HOT 2K simulator the average electricity consumption for this AC is kwh. As it can be observed, the consumption for the hottest day is still less than the average consumption of the same residential AC during summer with respect to HOT2K simulator software. We have to point out the user presence for all scenarios are identical. Figure B.2. Wireless Thermostat using Arduino Uno and Xbee Modem Figure B.3. Variations of outdoor temp. for three different days and average of indoor temp. by proposed approach 175