ENERGY CONSERVATION IN BUILDINGS AND COMMUNITY SYSTEMS. Technical Report. P. Michel & M. El Mankibi ENTPE DGCB LASH France

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1 IEA INTERNATIONAL ENERGY AGENCY ENERGY CONSERVATION IN BUILDINGS AND COMMUNITY SYSTEMS Technical Report ADVANCED CONTROL STRATEGY P. Michel & M. El Mankibi ENTPE DGCB LASH France Annex35 HybVent Hybrid Ventilation in New and Retrofitted Office Buildings

2 Abstract strategy is one of the key elements of the performance of a ventilation system. Classical strategies control parameters one by one and are often based on On-Off or PID control. More advanced and complex strategies exist, which can control several parameters at a time. Some of these techniques are presented here and illustrated by examples. Are also defined the key role of the tuning process for a complex strategy, as well as the integration in a building energy management system. strategies A control loop is basically composed of three main items, the quality of which determines the quality of the overall control process. The sensors provide to the control system (dedicated controller, outstation as a part of a BEMS ) information about either the controlled system (closed loop) or the external s (open loop). Temperature, CO 2 concentration, relative humidity, wind and air speed, pressure drop are some of the parameters which may be used in a control strategy of a hybrid ventilation system. Figure illustrates the basic principle of a closed loop (left) and an open loop (right). ler External s ler Set point Actuator Actuator Error device Sensor device Sensor led system led led system led Fig. : Closed loop (left) and open loop (right) The actuators (motors, dampers ) control the equipment (openings, fans ) through control s the value of which is defined by the control strategy implemented in the controller. Figure 2 below presents, from left to right examples of actuators for natural ventilation (chain and piston for windows) and for mechanical ventilation (damper motor). Fig. 2 : Actuators for natural ventilation (left and centre)and for mechanical ventilation (right) Advanced Strategies 2 /

3 The performance of a control strategy is particularly sensitive to : The quality of the information provided by sensors The efficiency of the actuators The adequacy of the control strategy to the controlled system and to the targets to be reached A On-Off control may be efficient if well tuned : dead band value and minimum service x of the control, for example a minimum ventilation rate (figure 3). strategy has also to be evaluated through automation s : dead time, time constant (figure 4). % led Time constant λ = td τe x % Set point led td τe time Dead band time Fig.3 : Basic principle and parameters of a On-Off controller Fig. 4 : Dynamics of a controlled system As a combination of natural and mechanical ventilation, hybrid ventilation implies the control system may : natural ventilation mechanical ventilation switch between natural and mechanical ventilation fan assisted natural ventilation night cooling through natural or mechanical ventilation Basic control techniques may be implemented in stacked controllers for the management of indoor air quality, indoor temperature, energy consumption These techniques control only one parameter and may lead to contradictory strategies. Advanced techniques on the contrary can control several parameters through an optimised strategy. They required a set of sensors and actuators and are to be tuned for an optimum result. Advanced control techniques Rule-based control The control of HVAC systems or building components is in this case based on rules such as : If CONDITION Then ACTION The item [CONDITION] can take only two values (True or False). It suppose to use Boolean or comparison operators (AND, OR, =, > ). This item can be : Simple : If (T T ) Then Composed : If (T T) And (HR<HRMAX) Then This kind of control strategy is frequently implemented in Building Energy Management Systems (BEMS) ; it can simultaneously browse and control several parameters. The main issue is thus the definition of the rules, which cant be the result of an automatic procedure and implies an expert kwledge of the systems to be managed. This technique is however a good opportunity for the combined control of HVAC systems and passive components of the building envelope. Advanced Strategies 3 /

4 Figure 5 below presents an basic example of what could be the rule-based control of indoor air temperature. When this temperature is out the comfort zone, T air is either higher than a maximum value (upper part of the scheme) or lower than a minimum value (lower part of the scheme). The controller will then try (in this order) to : Avoid discomfort (and overconsumption) due to HVAC systems : switch off heating (T air > T max ) or cooling (T air < T min ). Use the thermal mass of the envelope by controlling openings, shading devices and ventilation (for heating or cooling). Use HVAC (active) systems, while consuming energy, to come back to comfort conditions. Only simple rules are implemented in this strategy which controls only one parameter. More complex but similar strategies could for example control both indoor temperature and CO 2 concentration with combined rules and by introducing other control parameters (wind speed, outdoor pollution ). STOP HEATING SHADING DOWN ACTIVE COOLING ON Heating on? Φ sol > Φ limit?? MEASURE T air T air in COMFORT ZONE? T air > T max? T air > T ext? n OPEN WINDOW Active cooling on? T air > T ext? MECH. VENTILATION ON Window open? CLOSE WINDOW Φ sol > Φ limit? SHADING UP NO CHANGE ACTIVE COOLING OFF HEATING ON Optimum and predictive control Fig. 5 : Rule-based control of indoor air temperature Important research have been conducted on optimum and predictive control strategies during the 8 s and 9 s but industrial development has followed these scientific works, especially because of the implementation issues. The principle of optimum control is the definition of a strategy based on a model of the system to be controlled, obtained for example thanks to an identification process, such as : X Ý ()= t X t = A Xt ()+ B Rt ()+ C W () t J = () Yt ()= D Xt t F t () ( Yt ) 2 dt + P F 2 () t Rt () dt () Y t Advanced Strategies 4 /

5 Where X represents the state internal s (indoor temperatures ), R represents the inputs (control s), W represents the external s (outdoor temperature, solar radiation, wind speed ) and Y represents the outputs (controlled s). At each step, the optimum value of the R vector by minimising a cost function J taking into account a balance between energy and comfort. Predictive control includes a provisional model of external s (outdoor temperature, solar radiation ) for the next hours or the next day. Such a model is frequently based on statistical analysis of meteorological data of the site or of stations close to it. Such models are efficient for the control of high inertia or massive systems. The control of hybrid ventilation systems could thus take advantage of such techniques especially to improve night cooling or to avoid overheating. Neural networks Neural control has been developed since the end of the 8 s by introducing an analogy with human brain. Neural controller is defined as a combination of several layers, the first one being the input one and the last one the output one. Each layer is built with neurons linked to neurons of adjacent layers by connections called synapses. Initially, each synapse has a weight between and, and first layer neurons receive the controller input values. At each step, the controller determines the values of the internal layer neurons using a relation such as : V n i = f N n n C ji V j j = where V i n value of neuron i of layer n, C ji weight of connection between neuron j of layer (n-) and neuron i of layer n, N n- number of neurons of layer (n-), and f a function such as : f : x a + e x Once the number of layers and the number of neurons per layer are defined, the tunig process consists in adjusting the connection weights through off line learning or auto-adaptive techniques. Such a tuning process is difficult and very time consuming,, especially in on line tuning.. Fuzzy logic Fuzzy control is similar in principle to rule based control ; it is based on fuzzy (linguistic) rules such as : If CONCENTRATION DIFFERENCE LARGE POSITIVE Then FAN SPEED VERY HIGH The semantic concepts Large Positive and Very High refer to fuzzy sets, shaped as triangles, trapeziums or as gaussian curves. The belonging of a to such a fuzzy set is t binary but is quantified through a membership function varying continuously from to. Fig. 6 : Examples of membership function The definition of control s using controlled is based on a four step procedure illustrated by figure 7 : Fuzzyfication. Numeric (measured) values are transformed into fuzzy s VarIn i using most of the time the singleton method. Inference. Fuzzy rules are applied to each fuzzy, after defining their membership function to the different fuzzy sets A i et B i. The output fuzzy set for one rule is defined by using an operator (most of the time Minimum). Composition. A unique output fuzzy set for the output VarOut is obtained by aggregation of the different output fuzzy sets, using ather operator (frequently Maximum). Defuzzyfication. The value of the output (control), to be sent to an actuator, is numerically determined, using for example the centre of gravity method. Advanced Strategies 5 /

6 Rule R A A2 A3 B B2 B3 C C2 C3 min VarIn VarIn2 VarOut Rule R2 A A2 A3 B B2 B3 C C2 C3 VarIn VarIn2 VarOut Input Input 2 R : If VarIn is A and VarIn2 is B then VarOut is C max R2 : If VarIn is A2 and VarIn2 is B2 then VarOut is C2 Output VarOut Fig. 7 : Basic principle of a two input one output fuzzy controller Fuzzy control can easily combine several input and output s, taking into account uncertainty and inaccuracy. Moreover, the design of linguistic rules easily translates an expert kwledge of the system to be controlled. However, multiplying the number of s quickly leads to a huge number of rules. The architecture of the controller has to be designed in order to reduce this number, decreasing in the same way the accuracy of the control strategy. A hierarchical architecture is an example of such structure : the global fuzzy controller is in this case composed of various two input one output fuzzy controllers. Intermediate controllers produce fuzzy outputs which act as fuzzy inputs for the leading controllers. Tsupply-Tin PMV(t) dpmv(t)/dt a Thermal Demands 2a Required Heating Cooling System Status Req. Heating/cooling Operating mode Tout - Tin Electric power 2c Energy preference 2d HVAC System status Fan speed CO 2 (t) CO 2 (t-) Fan Speed b Air Quality Demands Layer : System Demands 2b Required Ventilation System Status Layer 2 HVAC status Fig. 8 : Hierarchical architecture of a fuzzy controller for ventilation / cooling in summer conditions Advanced Strategies 6 /

7 Figure 8 represents a hierarchical architecture for a fuzzy controller of a ventilation / cooling system in summer conditions. Comfort conditions are evaluated through PMV, CO 2 concentration and their derivates. The outputs of the controller are the fan speed and the operating mode i.e. ventilation vs cooling. Each module represents a simple fuzzy controller. The difference between the outdoor temperature and the indoor temperature assesses the cooling power of the outside air. In an energy preference configuration, this cooling power is used to maintain thermal comfort according to thermal demand (and trend). CO 2 concentration (and trend) is in the same way used to define the ventilation rate required. The last module thus defines the fan speed (in order to assess the ventilation needs) and the operating mode, taking into account comfort requirements and energy saving potential. Fuzzy control technique is based on an expert kwledge of the controlled process, avoiding the development of a mathematical model. However, fuzzy sets and fuzzy rules constitute such a model and have thus to be validate in each case. The present scientific developments focus on automatic tuning techniques to optimise both fuzzy sets and fuzzy rules. Tuning process The tuning process of a controller has a direct and strong influence on the efficiency of this controller, whatever the strategy or the control technique could be. The more complex the control strategy is, the more difficult and time consuming the tuning process is. The purpose of this tuning process is to optimise the control strategy regarding the building, the components and the systems to be controlled. Tuning the On-Off controllers of a hybrid ventilation system (for example a fan assisted natural ventilation system) vs CO 2 concentration is done by adjusting the dead bands (and the minimum opening / fan speed). Frequently, industrial On-Off control products have a fixed dead band. Tuning a PID controller, the behaviour of which is described by the equation below, supposed to adjust three parameters, i.e. K p, T i and T d. This can be done by installers using predefined protocols and methods (Broïda, Ziegler and Nichols ). U(t) = K p E(t) + t E(t) + T d de(t) T i dt Figure 9 shows the result of a bad PID controller tuning. This could thus result in permanent error, high time response, as well as strong instability of the control strategy, with impacts on energy consumption, components lifetime Correct PID tuning time Wrong PID tuning time Fig. 9 : Results of a PID controller tuning : correct (upper) and wrong (lower) tuning The tuning process of a multicriteria controller based on advanced techniques is much more complicated. Tuning a fuzzy controller supposes in theory to optimise all fuzzy sets and fuzzy rules. Present tuning methods are based on artificial intelligence techniques and frequently tune firstly fuzzy sets. Although time consuming, the tuning process of such a controller has a real impact on the efficiency of the control strategy. Advanced Strategies 7 /

8 Fitness function A fitness function may be used to evaluate the performance of a control strategy (compared to others) or the efficiency of a tuning process. The definition of this fitness is thus of major importance. Considering basic strategies, for example a On-Off control of the CO 2 concentration, by switch a fan or by opening / closing a grill, this function could be rather simple : cumulative time the concentration is over limit, maximum concentration For advanced strategies, the structure of the fitness function has a strong impact and has to in accordance with targets of the control strategy, to be defined by the manager of the building. Hybrid ventilation systems dealing at least with IAQ, thermal comfort and energy consumption, a fitness function could be defined as : Φ= a i C i i where C i are performance criteria and a i are weights. For a hybrid ventilation system, the resulting fitness function could be : Φ=a T air T set + DB + a 2 2 max( ; ( CO 2 CO 2max ))+ a 3 C energy where DB is a temperature dead band, T set is the temperature set point and C energy the energy consumption. Other parameters could of course be taken into account, measuring for example the lifetime impact on mechanical elements (ventilation fan, openings actuators ). Weights have to be defined according to manager s targets (energy efficiency, staff productivity, technical management ) and have to standardise the different criteria (for example through a financial evaluation). The fitness function (i.e. the controller performance) is thus strongly sensitive to the definition of these weights. Integrated control systems The main purpose of a Building Energy Management System (BEMS) is to control, in an efficient and rational way, HVAC systems and building components. Building management has been in the last years transformed thanks to digital techlogies and decentralised architecture in which advanced control strategies are implemented in outstations. A general BEMS architecture can be described through four main levels (figure ) : Field level is concerned with sensors and actuators and is the interface with building equipment and components. Outstations constitute the automation level, collecting data and controlling systems. Management level offers the opportunity to tune and manage the systems, to store and analyse data, and to communicate with external systems and operators. Users represent the fourth level including both technical managers and final users. User level Management level Outstation Outstation Outstation Automation level Sensors Actuators Sensors Sensors Actuators Actuators Field level Fig. : General architecture of a Building Energy Management System Advanced Strategies 8 /

9 Outstations are programmable digital controllers which on the one hand control building equipment and components and on the other hand acquire, store and transmit to supervisor control and controlled parameters. Outstations are frequently dedicated to the control of one function or one type of equipment : ventilation, lighting Various functions implemented in ROM allow the designer to build advanced control strategies : control functions (On-Off, PID, cutting off ), Boolean functions and comparators, time functions Transmission of data through the BEMS is done via BUS (field BUS between sensors, actuators and outstations, data BUS between outstations and supervisor). BEMS offer a great opportunity to control hybrid ventilation systems in buildings : Basic control strategies, such as occupancy-based control (figure ), may be easily defined and tuned, as well as linked to other management systems (security, staff administration ). Fig. : Occupancy-based control strategy for ventilation Hybrid ventilation control may be included in an advanced overall building strategy (i.e. rule-based or fuzzy one), including for example solar shading, night cooling or energy conservation at a building level. strategy of hybrid ventilation can easily be tuned thanks to the management tools implemented in the supervisor. Set points, occupation profiles, conditions for natural / mechanical ventilation switch or for fan assistance Recent developments in BEMS offer the opportunity to have more efficient relationships with final users, especially in office buildings where phones and PC constitute particular interfaces. These ones may receive from or send to the control system information on indoor climatic conditions. In some cases, they also can interact with the control system through their interface (PC screen, telephone). Unlike hardware interfaces, these ones may be easily changed and adapted to the control strategies, the controlled systems and the final users specifically. This is particularly important for hybrid ventilation systems for which final users are key elements of the control strategy and other parameters, such as ise, can play a key role. An experimental device for control strategies of hybrid ventilation In order to validate advanced control strategies of hybrid ventilation techniques, an experimental device called HYBCELL has been designed at ENTPE. This cell, 5. m long, 3.5 m wide and 2.9 m high, is representative of a large office room or a small meeting room (figure 2). It has been fully equipped with various sensors for the measurement of physical parameters (temperature, humidity, C 2, COV, pressure) inside and outside the cell. Moreover, wind direction, wind velocity and solar radiation can be collected from a local meteo station close to the cell. The slopped façade is equipped with two symmetrical series of sash windows. One of these two series has been equipped with step by step engines to control the automatic opening of the windows from to % (figure 3). The actuators are connected to a control system which also acquires all physical parameters (figure 4). This system also controls a speed extraction fan which ensures the mechanical ventilation of the cell. Concrete paves can be added on the floor of the cell in order to increase the thermal mass of the cell (figure 5). This is of particular importance in summer conditions in order to test the impact of sun and the role of night cooling. Up to three virtual occupants can be present in the cell (figure 7). The occupancy can be controlled by the acquisition system which defines the level of occupancy (from to 3 occupants) and the level of activity ( met or.2 met), i.e. the injection of sensible heat and CO 2 (figure 6). Basic and advanced control strategies can be directly implemented in the control system in order to be tested. Natural ventilation, combining any of the six windows, mechanical ventilation and any configuration of hybrid ventilation can thus Advanced Strategies 9 /

10 be tested in this cell, including fan assisted natural ventilation, night cooling, switching mechanical and natural ventilation, or switching between seasonal strategies. Fig. 2 : External (left) and internal (right) view of HYBCELL Fig. 3 : Driving system Fig. 4 : system Advanced Strategies /

11 Fig. 5 : Removable concrete paves Fig. 6 : CO 2 injector Fig. 7 : Virtual occupant of the cell Advanced Strategies /