Guidelines for the Evaluation of Building Performance

Transcription

1 Guidelines for the Evaluation of Building Performance Editors: Christian Neumann Dirk Jacob Fraunhofer Institute for Solar Energy Systems Freiburg, Germany

2 Building EQ Tools and methods for linking EPDB and continuous commissioning is supported by the European Commission in the programme Intelligent Energy Europe (IEE). Key action: SAVE Agreement N : EIE/06/038/SI This report was prepared as a deliverable of Workpackage 3 of Building EQ. February 2008 For more information visit us at: Disclaimer The sole responsibility for the content of this report lies with the authors. It does not necessarily reflect the opinion of the European Communities. The European Commission is not responsible for any use that may be made of the information contained therein. Fraunhofer ISE I

3 CONTENT Abstract Introduction Guidelines The general outline Ongoing Commissioning The Building EQ approach (4-step approach) The Minimal Data Set Guidelines The Details of the steps Step 1: Benchmarking (Operational Rating) General Description Flow Chart Stock Data Measurements Performance Metrics & Evaluation Techniques Outcomes / aims of this step Step 2: Certification (Asset rating) General Description Flow chart Stock Data Measurements Performance Metrics & Evaluation Techniques Further actions Outcomes / aims of this step Step 3: Optimisation Overview General description Outcomes / aims of the step Step 3a: Standard analysis (measurement based) General description Flow chart Stock Data Measurements Performance Metrics & Evaluation Techniques...27 Fraunhofer ISE II

4 3.4.6 Further actions Step 3b: Standard analysis (model based) General description Flow chart Stock data Measurements Performance Metrics & Evaluation Techniques Step 4: Regular Inspection General Description Flow Chart Stock Data Measurements Performance Metrics & Evaluation Techniques Outcomes / aims of this step Measurement and verification / Definition of Baselines Baseline Regression models (IPMVP Option C) Measurement equipment and data transfer Technical issues Data transfer Cost National approaches Germany How the 4 step procedure is realized Availability of stock data Availability of measured data Tools for step 3 (analysis and optimization) What barriers were identified / are expected in the course of the 4 step procedure? What are the possible links between the national implementation of the EPBD and CC? Sweden How the 4 step procedure is realized...61 Fraunhofer ISE III

5 6.2.2 What barriers were identified / are expected in the course of the 4 step procedure? Cost for additional measurement equipment What are the possible links between the national implementation of the EPBD and CC (including prescribed maintenance procedures on national level)? Italy How the four step procedure is realized Availability of stock data Availability of measured data Tool for step 3 (analysis and optimization) What barriers were identified / are expected in the course of the 4 step procedure? What are the possible links between the national implementation of the EPBD and CC? Finland How the 4 step procedure is realized Availability of stock data Availability of measured data Tools for step What barriers were identified / are expected in the course of the 4 step procedure What are the possible links between the national implementation of the EPBD and CC? Possibilities for further analysis Stock Data Measurements Performance Metrics & Evaluation Techniques Outcomes / aims of further analysis ANNEX 103 ANNEX 1 CHECKLIST OPERATIONAL RATING 104 ANNEX 2 EVALUATION OF QUESTIONNAIRE CERTIFICATION DATA 113 Fraunhofer ISE IV

6 Abstract These Guidelines are based on the results of the report The EPBD and Commissioning /1/. They describe a 4-step procedure for the cost effective performance analysis of buildings that follows a general top-down approach and which tries to combine the outcomes of the certification process according to EPBD with CC. The idea of this top-down approach is to put effort in form of measurements and analysis only where and when necessary. The transition from one step to the next should only be performed if certain criteria are fulfilled. Flow charts are presented for each step that guides the analyst through the process in order to standardized the analysis. Steps 1. Benchmarking (Operational Rating) 2. Certification (Asset Rating) 3. Optimisation 4. Regular Inspection Benchmark (Operational Rating) Certification (Asset Rating) + Availability of hourly data Operation without faults and optimized & Savings calculated continuous commissioning Time Figure 1 Scheme of the 4-step procedure on a time scale Furthermore, these guidelines are based on the following assumptions: Persistence of energy efficient operation of a non-residential building can only be achieved by ongoing commissioning An ongoing monitoring (based on hourly or sub hourly measurements) is therefore crucial The installation of additional measurement equipment or the is carried out only if necessary for further analysis. All analysis should be based on a predefined minimal data set. The predefined minimal data set plays an important role in the process as a major part of the analysis is based on it. Generally, the availability of measured data with sufficient quality in existing buildings is low. At the same time the monitoring of all components of a system usually requires a considerable budget for additional measurements and is not feasible. Considering this situation the analyst has to de- Fraunhofer ISE

7 cide upon a minimal data set that is able to reveal the characteristics of the performance without demanding to much budget. The minimal data set according to these guidelines was consciously chosen. It is believed to be the minimal amount of measured data that is necessary to facilitate a rough overall assessment of the performance of the system. The Guidelines also gives an overview over the individual situation in the countries of the consortium. Once more it becomes clear that the implementation of the EPBD is very different in the different Member states. Concerning the availability of stock data and measured data for the demonstration buildings similar situations are reported. Generally, the availability of high-quality stock and measured data is critical especially for older buildings. Finland seems to be an exception as the situation is significantly better than in the other countries. The effort for gathering detailed stock data ranges from less than 1 day up to days. Besides technical difficulties and missing documentation, administrational problems (locating responsibilities, contractual and security issues) may play a major role and slow down the data acquisition process. Looking at measured data it can be stated that for the minimal data set (except in Finland) additional measurement equipment has to installed and the data logging and transfer had to be arranged. Even if a BAS is existing it is not designed for serious data analysis. The cost for acquiring the minimal data set that was experienced in the Building EQ project is in the order of to EUR per building. However, the cost depends very much on the actual state of the system (BAS available?, metres available, etc.) and not so much on the building size. General rules can not be given but compared to the yearly energy costs of the buildings a static pay back time of less than 2 years appears reasonable - even if only 10% energy/cost savings are achieved. yearly energy cost / [ /a] heat/gas electricity static payback MAX static payback MIN Germany_bui1 Germany_bui2 Germany_bui3 Germany_bui4 Finland_bui1 Finland_bui2 Finland_bui3 Finland_bui4 static payback for monitoring / [a] Figure 2 Estimated static payback of monitoring in demonstration buildings based on real yearly energy cost. Assumption: energy savings through Continuous Commissioning=10% Cost for data acquisition = (MIN) / (MAX) Fraunhofer ISE

8 1. Introduction The building sector is responsible for more than 40 percent of the European energy consumption. At the same time, the potential to save energy by appropriate building operation management, i.e. by taking measures involving very low or no investment costs, ranges from 5 30%. This applies particularly to the nonresidential building stock. At present, however, technical systems in buildings are not usually monitored to guarantee their performance or to check the energy efficiency of their operation. Maintenance is limited to ensuring that the primary functional aim is fulfilled e.g. warm or cool rooms. Even in new buildings, an energy optimised operation is often not achieved. Often technical systems in buildings operate far below their energetic/economic optimum. At the same time, the system owner or operator lacks the technical know-how and/or capital necessary to make any improvements. The Energy Performance of Buildings Directive (Directive 2002/91/EC) which prescribes energy certificates for new and existing buildings might offer some opportunities in this field. With the increasing dissemination of energy certificates the awareness of building owners concerning energy efficiency will rise. Furthermore the EPBD is considering the building envelope and the HVAC systems as parts of the same entity and could thereby establish a basis for global optimisation of building performance. Another quite new approach, that first was established in USA, is ongoing commissioning. The term denotes an ongoing process for the quality assurance of building performance. It is designed to develop targets and to verify and document their achievement. Continuous commissioning is seen as a prerequisite for an energy efficient long term operation of buildings. In general, the aims and the holistic approach of ongoing commissioning are the same as the ones given in the European Performance of Buildings Directive (EPBD). Therefore it should be possible and worthwhile to find a linkage between them that leads to synergies. The report The EPBD and Continuous Commissioning /1/ describes potential links between CC and the EPBD by evaluating different assessment technologies for the performance of buildings (that can be used for CC) with respect to their practical application and potential connections to the EPBD. Measurement based techniques are considered as well as model based techniques and functional performance tests. As a result, it can be stated that - if asset ratings are applied - the certification could deliver the actual state of the building and a theoretical target value for energy performance. However, asset ratings for existing buildings which are investigated in Building EQ are only prescribed in a few countries. Most Member States will have operational ratings for existing buildings which in their present definition are not suited for any kind of detailed analysis. One major drawback (at present state) is the diversity of the different national implementations in the Member States. That is, there will be no common data set for all Member States that can be exploited for performance analysis. Fraunhofer ISE

9 On the other hand, there exist a lot of valuable assessment techniques like: Benchmarking, Visualisation, model based techniques and Functional Performance Tests (FPT) that can be applied for ongoing commissioning. These Guidelines are build on the results of the above mentioned report. Within the Guidelines a general procedure for the evaluation of building performance is presented that combines the certification with an introduction of a continuous commissioning process. The general structure of the Guidelines is as follows: Chapter 2 General outline of the 4-step approach developed for Building EQ and overview over the CC process. Chapter 3 Details of the single steps of the 4-step approach. Chapter 4 Description of different approaches for the calculation of savings depending on the availability of historical consumption data. Chapter 5 Technical and financial details on data acquisition and transfer. Chapter 6 Experiences gathered by the partners in the consortium and specific national approaches. Chapter 7 Possibilities for further and more detailed analysis. Fraunhofer ISE

10 2. Guidelines The general outline This chapter describes the general structure of the process developed for evaluation of building performance in the framework of Building EQ. The general idea is to exploit or include the results from the EPBD-certification as much as possible in an ongoing commissioning process Ongoing Commissioning The Continuous Commissioning Guidebook of the FEMP /3/ gives the following definition for continuous commissioning: Continuous Commissioning is an ongoing process to resolve operating problems, improve comfort, optimize energy use and identify retrofits for existing commercial and institutional buildings and central plant facilities. It is presumed that the CC is performed and managed by a professional Commissioning Provider that is usually a contractor of the building owner. Furthermore, the CC process is split in two phases: Phase1: Project Development This phase comprises the identification of buildings to undergo the CC process and a first pre-scanning. The pre-scanning includes a check of design documents and available energy measurements on whole building level. Furthermore, the owners requirements are defined and the availability of inhouse staff is checked. Phase 2: Implementation and Verification Phase can be further split into six steps: o o o o o Develop the CC plan and form the project team A detailed plan with the major tasks concerning measurements, analysis and a time schedule are developed. The in-house staff or owners representative involved in the project must be identified. Develop performance baselines Document existing energy performance, system conditions and all known comfort problems. Development of a metering plan. Conduct system measurements and develop CC measures Identify current operation schedules, set points and problems, develop solutions to existing problems, Develop improved operation and control schedules and set points, identify potential costeffective energy retrofit measures. Implement CC measures Implement solutions for existing operational and comfort problems, implement and refine improved operation and control schedules. Document comfort improvements and energy savings Document all achieved improvements Fraunhofer ISE

11 o Keep commissioning continuous Maintain achieved improvements and provide measured annual energy savings. The practical implementation of continuous commissioning is often constrained by the following: Lack of awareness of building owner / operation staff Often, the need for continuous commissioning is not appreciated by the building owner or the operation staff. The cost-benefit relation of such an procedure is perceived as high. Lack of data Especially for existing buildings there often is a lack of data. Stock data might be not available at all, distributed and difficult to access or just wrong due to erroneous or not updated documentation. Metering data normally is reduced to a minimum necessary for the energy billing. Budget Although it is possible to utilize very detailed building models and/or a large set of measured data for analysis, the cost of such an approach is far to high to adopt it as a standard procedure. The budget is a strong constraint for the measurement equipment as well as for the effort put into the acquisition of the stock data during the audit. In this context, The EPBD can be helpful in the following ways: Increased awareness of buildings owners The certification requested by the EPBD will help to make building owners aware of the energetic performance of their building. Operational rating Operational ratings are based on the actual energy consumption of the building and can therefore provide a first classification and a baseline for the annual energy consumption. In fact, operational ratings can be seen as a simple benchmarking. Asset ratings Asset ratings requires a quite detailed model of the building (envelope as well as systems). Once these data is available it can be utilized in different ways. Firstly, a target value for the energy consumption of the building can be calculated. Furthermore, the major energy consumers in the building or system respectively can be identified. The model can also be used for parametric studies in order to identify major saving potentials. Fraunhofer ISE

12 2.2. The Building EQ approach (4-step approach) Generally continuous commissioning is can be described as a top-down-approach that starts on the building level and goes down to selected single subsystems or components if necessary, i.e. if problems were observed in this subsystem or component. In the framework of the Building EQ project a 4-step procedure was developed that follows also a general top-down approach and which tries to combine the outcomes of the certification process according to EPBD with CC. The idea of this top-down approach is to put effort in form of measurements and analysis only where and when necessary. The transition from one step to the next should only be performed if certain criteria are fulfilled. Furthermore, these guidelines are based on the following assumptions: Persistence of energy efficient operation of a non-residential building can only be achieved by ongoing commissioning An ongoing monitoring (based on hourly or sub hourly measurements) is therefore crucial However, the installation of the measurement equipment is carried out only if necessary for further analysis. All analysis should be based on a predefined minimal data set. Table1 gives an simplified overview over the single steps. Table1 Step No. Overview over 4-step-procedure name description 1 Benchmarking (Operational Rating) 2 Certification (Asset rating) Gather basic consumption and stock data to perform an operational rating. Derive a first classification / baseline of the building performance Calculate theoretical target value for consumption with asset rating (based on a building and system model). Identify saving potentials 3 Optimisation Refinement of baseline. Introduction of energy saving measures: Fault Detection and Diagnosis (FDD) + Optimisation Calculate and document energy savings 4 Regular Inspection Introduce an ongoing monitoring to maintain an efficient operation. Fraunhofer ISE

13 In order to arrive at a systematisation, for each step the following items are to be defined: A flow diagram that shows how the step is applied for different boundary conditions and when the next step is to be applied. Required stock data Required measured data Performance Metrics / Evaluation Techniques Outcomes / aims of the step By providing these definitions the task of developing a CC plan and energy saving measures should be standardized at least at the whole building level. In chapter 3, you will find a detailed definition for each step. Figure 3 shows a simplified scheme of the 4-step procedure on a time scale as it is applied to the demonstration buildings within the Building EQ project. Steps 1. Benchmarking (Operational Rating) 2. Certification (Asset Rating) 3. Optimisation 4. Regular Inspection Benchmark (Operational Rating) Certification (Asset Rating) + Availability of hourly data Operation without faults and optimized & Savings calculated continuous commissioning Time Figure 3 Scheme of the 4-step procedure on a time scale It is important to notice that the continuous commissioning approach can be introduced right after step 1 as a classification of the building is available already at that stage. While step 1+2 are principally defined by the national implementation of the EPBD in most Member States, step 3 and 4 are not covered by the EPBD. However, the report The EPBD and Continuous Commissioning /1/ shows that due to the diversity of the different national implementations, even for step 1+2 Fraunhofer ISE

14 there will be no common data set for all Member States that can be exploited for performance analysis. Two different kind of data has to be distinguished considering the performance analysis of buildings: Stock data Stock data comprises information about the structure and properties of the building envelope (e.g.: U-values and areas) and the HVAC system (e.g.: kind and capacity of heat generators). Measured data Measured data comprises all measurements of process and state variables in the building. Consequently, for each step a set stock data was developed if necessary. Furthermore a minimal data set of measured data was developed that is described in the next chapter and that applies to the whole procedure The Minimal Data Set In order to evaluate the performance of a building measured data at least of the energy consumption is necessary. Generally, the availability of measured data with sufficient quality in existing buildings is low. At the same time the monitoring of all components of a system usually requires a considerable budget for additional measurements and is not feasible. Considering this situation the analyst has to decide upon a minimal data set that is able to reveal the characteristics of the performance without demanding to much budget. In the framework of Building EQ a minimal set of measured data was consciously chosen. It is believed to be the minimal amount of measured data that is necessary to facilitate a rough overall assessment of the performance of the system. The minimal data set is shown in Table 2. Fraunhofer ISE

15 Table2 Minimal data set of measured data item Measured value unit min. time resolution* remarks consumption total consumption of fuels kwh h e.g. gas, oil, biomass total consumption of district heat kwh h total consumption of district cold kwh h total consumption of electricity kwh h total consumption of water m³ h weather outdoor air temperature C h own weather station or from weather data provider outdoor rel. humidity % h own weather station or from weather data provider global irradiation W/m² h own weather station or from weather data provider indoor conditions indoor temperature C h choose one or more reference zones for that measurement indoor relative humidity C h choose one or more reference zones for that measurement system *h= hourly Flow / return Temperatures of main water circuits supply air temperature of main AHUs supply air relative humidity of main AHUs C h main heat/cold distribution. Main in this context refers to the distribution in the building and not to a primary distribution such as a district heating system. C h only if supply air is thermodynamically treated % h only if supply air is humidified / dehumidified The rationale for this data set is given below: Weather data In order to identify the weather dependent part of the load the outdoor air temperature, humidity and insolation must be measured. Indoor climate As indoor climate (temperature and humidity) is the control variable for the HVAC system, it is important to measure at least some reference zones. System Data (water based) The supply and return temperatures of the main water circuits help to understand how the load is met. Fraunhofer ISE

16 Even though the mass flow and/or the control signal of the pumps would be also of high interest in this context, these variables are not part of the minimal data set as their installation is quite expensive. However, if either of these variables are available over the BAS, it should be recorded. System Data (air based) The Supply air temperature and moisture are recorded for the minimal data set given that the supply air is thermodynamically handled. For the air flow and the control signals of fans and dampers the same rationale as for water based systems apply. This data set is recorded at least hourly. In chapter 5 practical and financial issues of recording the data will be discussed. Fraunhofer ISE

17 3. Guidelines The Details of the steps Table3 gives an overview over the details of the 4-step procedure. The following chapters will describe every step in detail. Table3 Overview 4-step procedure Step 1 Step 2 Step 3 (a+b) Step 4 Name Benchmarking (Operational rating) Certification (Asset Rating) Optimization Regular Inspection Description Gather basic consumption and stock data and first classification / baseline of the building performance Asset rating according to national implementation of the EPBD, if applicable. Analysis of the building performance, identification and implementation of energy saving measures and optimization of performance Maintain optimized performance by ongoing (minimal) monitoring Stock Data minimal building description Depending on national implementation, if applicable (otherwise see step 3) 3a: Only basic data (step 1) 3b: Data of building and HVAC system for simplified model No additional stock data needed Additional stock data according to individual needs Measured Data Utility bills / own meter readings (yearly / monthly) None Minimal data set according to 2.3 Additional measurements according to individual needs Reduce to minimum Performance metrics Evaluation techniques specific energy consumption / signatures Depending on national implementation 3a: standard analysis (measurement based) 3b: standard analysis (model based) Energy consumption as major metric individual approaches Further Actions (only if required: Installation of data acquisition) (only if required: Installation of data acquisition) Installation of data acquisition (if not yet available) Implementation of energy saving measures None Outcomes First classification + baseline (yearly / monthly) Theoretical benchmark Deep insight in system Identification of major energy consumers Faultless / optimized operation Energy saving measurements introduced Documentation of energy savings Persistence of energy efficient performance Fraunhofer ISE

18 3.1. Step 1: Benchmarking (Operational Rating) General Description The purpose of step 1 is to gather the most basic information about the building and its energetic performance. It relies only on data which in most cases is readily available from the building owner. The data should provide a first classification of the building and a simple baseline Flow Chart Figure 4 on the next page shows the flow chart for Step 1. Depending on the availability and time resolution of historical consumption data, different analysis and /or actions are applied. Meters are not installed at all This situation might occur e.g. on a campus with many buildings but only with one metre. If - nevertheless - the performance of a building is to be evaluated, the meters (at least for consumption according to 2.3) have to be installed. An operational rating can only be done after one year of data is recorded. During that time Step 3a can be applied for the identification of saving potentials. No historical consumption data available The same steps than above apply. Annual historical consumption data available An operational rating can be performed and the actual performance of the building can be compared to a reference building. Values for reference buildings are normally available from national data bases. If the actual consumption appears high, step 2 is introduced. Monthly historical consumption data available If also weather data for the months is available so called signatures can be developed which show the weather dependent and independent part of the consumption. These signatures provide also a more sophisticated baseline. Hourly historical consumption data available Additionally. Step 3a can be introduced in order to identify saving potentials. Fraunhofer ISE

19 1. Start Main sensors correctly installed? yes Annual historical consumption data available? yes Monthly historical consumption data available for at least 9 months? yes Hourly historical consumption data available for at least 2 months? yes 2. Analysis no no no no Perform OR with weather correction ( annual baseline) yes Perform OR with weather correction ( annual baseline) Perform OR with weather correction ( annual baseline) Actual consumption lower than reference building? Actual consumption lower than reference building? yes Actual consumption lower than reference building? yes no Create Signatures from monthly consumption and weather data ( monthly baseline) Create Signatures from monthly consumption and weather data ( monthly baseline) Is shape of Signature normal? Is shape of Signature normal? no no 3. Actions Install measurement equipment for minimal data set Record subhourly data (e.g. 10 minute values) (After 12 months) 4. Next Steps Introduce Step 3a Introduce Step 2 Introduce Step 4 Introduce Step 2 Introduce Step 4 Introduce Step 2 Introduce Step 4 Involved parties: building owner / in-house staff Introduce Step 3a Contractor + building owner / in-house staff Contractor Figure 4 Flowchart for Step 1: Benchmarking (Operational Rating) Fraunhofer ISE

20 3.1.3 Stock Data The different national implementations of the EPBD ask for different kind of stock data for the operational rating. However, in order to classify the building and to be able to calculate specific values of the energy consumption, the data shown in Table1 must be compiled: Table4 step 1: stock data data General data Area / reference Values Energy consumption Water consumption Main utilization Tariffs (optional) remarks e.g. location and year of construction Reference values for calculation of specific consumption, e.g. useful floor area, gross volume, etc. Annual consumption and utilization of every energy carrier delivered to the building Annual consumption and utilization of water delivered to the building main utilization of the building or major building zones respectively tariffs for every energy carrier and water For the Building EQ project a checklist was developed for the collection of these data which is shown in Annex 1. Fraunhofer ISE

21 3.1.4 Measurements For step 1 historical consumption data on the whole building level is required. The total amount of energy and water delivered to the building by the utility should be listed if possible on a monthly basis. Besides the utility bills, manual metre readings by the operation staff might be available. But these should only be used if the exact date of reading is recorded. Time specifications only mentioning the month of the reading are not sufficient. If more detailed metering data is available (e.g. sub metering for electricity or heat or data with a higher time resolution) this will be subject to step 3a. Table5 Step 1: measurements (if applicable) Measured value unit time resolution* remarks total consumption of fuels kwh m / a e.g. gas, oil, biomass total consumption of district heat kwh m / a total consumption of district cold kwh m / a total consumption of electricity kwh m / a total consumption of water m³ m / a *m = monthly, a= yearly Performance Metrics & Evaluation Techniques The performance indicators for step one are specific values for the energy consumption that might be displayed as specific consumption values or as characteristic energy signature. Both can be utilized as a pre-retrofit baseline. Table 6 Step 1: performance indicators Performance Metric unit Evaluation technique Annual specific consumption (e.g. specific energy consumption per square meter of net useful area or net useful volume) kwh/m² or kwh/m³ per year or month Calculate the specific consumption values from the measured consumption and e.g. the gross conditioned area. This can be compared to statistically derived values for similar buildings of the building stock (if available), to values from previous years or to values from similar buildings nearby. Typically a weather correction will be performed for the comparison. Energy signature (dependency of consumption on weather + other variables) - if at least 9-12 month of monthly metre readings and weather data for the respective months are available, a preliminary baseline can be developed as an energy signature (regression model, see chapter 4) Also cost data can be utilised for equivalent performance metrics. Fraunhofer ISE

22 3.1.6 Outcomes / aims of this step First classification of building performance if monthly metre readings and weather data are available: baseline (regression model) Rough insight in possible saving potentials Fraunhofer ISE

23 3.2. Step 2: Certification (Asset rating) General Description Step 2 comprises the asset rating according to the national implementation of the EPBD. This will be a more or less detailed theoretical calculation of the energy demand of the building. Therefore it is necessary to collect stock data of the building envelope and the HVAC system. Accordingly, this step will deliver deeper insight in the system and an identification of the main energy consumers Flow chart Figure 5 on the next page shows the flowchart for Step 2. The different starting points are characterized by different availability of measured data. But in any case the analysis in step 2 starts with the acquisition of stock data and the calculation according to the national implementation of asset ratings. Considering the different national implementations the following situation may arise: The implementation is suited for modelling the actual building and system and all parameters and boundary conditions are known. In this case the calculated target value is compared to the actual performance of the building. If the target value is well below the actual performance further analysis is introduced with step 3b. Even though the implementation of the asset ratings is in principal suited for modeling the actual building and system, some parameters or boundary conditions are not sufficiently well known (e.g. operating schedules). In this case it is recommended to install the measurement equipment for the minimal data set according to 2.3 (if not already installed) in order to gather these information. The national implementation doesn t include an asset rating. Or it is not suited for modelling and calculating the actual building and system correctly or completely because certain parts of the real system are not covered by the implementation (e.g. bore hole heat exchangers). Consequently an asset rating might not be possible or reasonable. The Building EQ team expects that this situation will occur quite often. In this case step 3b should be introduced which provides a simplified but universal model that can be used instead. Note that in this case the installation of measurement equipment for the minimal data set according to 2.3 is also necessary. Fraunhofer ISE

24 1. Start from (annual data and baseline available) from (monthly data and baseline available) From -> (monthly / hourly data and monthly baseline available) 2. Analysis Acquire stock data Perform AR according to national implementation of EPBD. All parameters and boundary conditions are available? yes Check Target Value from AR. Actual consumption lower than target value? yes no no 3. Actions If start = Install measurement equipment for minimal data set Record subhourly data (e.g. 10 minute values) 4. Next Steps Introduce Step 3a Introduce Step 3b Introduce Step 4 Involved parties: building owner / in-house staff Contractor + building owner / in-house staff Contractor Figure 5 Flowchart for Step 2: Asset rating Fraunhofer ISE

25 3.2.3 Stock Data The amount of data that will be available after the certification strongly depends on the kind of the national implementation. As a survey among the partners in the Building EQ project showed, these data can be quite different. In fact the data the 4 countries in the consortium have in common is very few (less than 10%, see Annex 2). A more general approach is given in step 3b Measurements If necessary (see flowchart) the measurement equipment for the minimal data set according to 2.3 is installed Performance Metrics & Evaluation Techniques The performance metrics that are used according to the standard certification process for asset ratings in the Member States. Probably the most common performance metric will be specific end-energy or primary energy demand with respect to the conditioned floor area or building volume. Table7 shows examples for the countries of the Building EQ team members. Table7 Overview performance metrics in certification in different countries Germany Italy Sweden Finland Asset rating Total primary energy for the whole building (for Heating, DHW, Cooling, Ventilation, Aircon. Light) compared to a reference building with same characteristics [kwh/m²a] Heat transmission value of the building envelope [W/m²K] End-Energy for the subsystems: heating, domestic hot water, lighting, ventilation, cooling [kwh/m²a]* and separated for the different energy sources (gas, oil, electricity) Asset rating Total primary energy for heating, ventilation and DHW (not included: cooling, air conditioning in summer) [kwh/m³a] Classification from A (very good) to G (very bad) for the winter heating consumption (regionally) Heating system performance: ηg as ratio between Building Heat Requirement and Total primary energy consumption Operational rating (Sweden has only OR) Measured annual energy less the tentants' (or users') energy [kwh/m²a] Energy: Measured total annual energy use for heating of the building and electricity for operational purposes controlled by the building owner. Energy supplied to the cooling is assumed to be measured and then added to the benchmark. Upper and a lower benchmark for each building category Asset rating (only for new and renovated buildings + small residential buildings) Total end-use energy, including heating and cooling energy, electricity without socket load and occupant use (kwh/m²,a). Classification from A (very good) to G (very poor). Specific energy use upper and lower limit values for each grade (A to G) are given for ten different building types. Standardized calculation method for small residential buildings only. Fraunhofer ISE

26 3.2.6 Further actions As shown in the flow chart the installation of measurement equipment might be necessary Outcomes / aims of this step Asset rating provides theoretical target value for consumption Deeper insight in system Identification of major energy consumers Fraunhofer ISE

27 3.3. Step 3: Optimisation Overview Actually, step 3 is divided into two sub-steps (3a and 3b) which will be described in detail in chapter 3.4 and 3.5. This chapter gives an overview General description Step 3 is the crucial part of the process as it includes the analysis of the building performance, the identification and implementation of energy saving measures and the optimisation of operation. Generally, this procedure is called fault detection and diagnosis (FDD) and Optimization. While faults can be described as an unintentional worsening in the scheduled operation, optimization is characterized as targeted improvements of the scheduled operation or its adjustment to the currently imposed boundary conditions. In order to be able to optimise the building performance there should be no gross faults in the operation. Therefore, prior to the optimisation fault detection and diagnosis must be performed (see /1/) Typical problems addressed by FDD and Optimization in existing buildings according to /18/ are, e.g.: Scheduling problems Drives like pumps and fans are operated during the entire day and on the weekend, even when they are not required and even without the operator`s knowledge. Simultaneous heating and cooling Due to incorrect set points, the same zone is simultaneously supplied with heating and cooling energy, thereby increasing the energy consumption Faulty controls The desired comfort or planned energy efficiency is not reached due to programming mistakes in the system control, despite correct specification, or the sensors or actuators are not positioned correctly. Deactivated or falsely set controls When problems appear, the controls are often taken out of operation or rudely adjusted, in order to compensate for other defects in the system. Calibration is lacking Sensors which are used for controlling systems give invalid values due to lack of calibration or calibration that was falsely performed. As a result, these values negatively influence the indoor climate and/or energy consumption. Lack of maintenance: Due to lack of maintenance, the function or efficiency of the components is limited. Lacking hydraulic balancing Pipe and duct systems are often not hydraulically balanced, especially after reconstructions or changes in use. Generally this results in increased energy consumption and/or decreased comfort. Fraunhofer ISE

28 Setpoints / resets Settings (e.g. temperatures, flow rates) are often adjusted over time based on personal preferences, to compensate for inadequate system operation. In addition, sensors require periodic recalibration. staging / sequence of most efficient generators Equipment often is not operated in the most efficient combination of chillers, boilers, and fans at varying load conditions. Malfunction of dampers and valves e.g. fully or partly closed dampers or valves might result in poor performance and reduced comfort. oversizing / undersizing Many systems show over dimensioning and might therefore have poor performance. The analysis performed in step 3 aims at identifying this kind of faults and saving potentials in a systematic approach by using measured data according to chapter 2.3 and eventually a model derived from an asset rating. Step 3 is divided into two sub-steps which are shown in Table 8. The main difference between the sub steps is that step 3a solely relies on measured data and general rules for FDD, while step 3b uses measured data and models for FDD and Optimization. These steps are called standard analysis in order to stress their general character and to distinguish them from any other kind of further analysis. Table 8 Step 3: Overview over Sub-steps of standard analysis. Step 3a: standard analysis measurement based Step 3b: standard analysis model based Description Rule based FDD Model based FDD and optimization Stock Data None Minimal set Measured Data Minimal data set Minimal data set Performance metrics Evaluation techniques Strength Weakness Pre-defined visualization (with guidance for interpretation) rule based analysis Easy to apply Can be applied in any building without adjustment. Delivers no theoretical reference value Energy conservation opportunities might be hard to identify without detailed system knowledge Weak link to EPBD Calculate building specific benchmark based on model of building + HVAC system (monthly values) Building specific reference value can be provided Strong link to EPBD Deeper insight in building and systems Requires more effort as model has to be created according to specifics of the building. Calibration of model might be difficult Fraunhofer ISE

29 3.3.2 Outcomes / aims of the step Identification of energy conservation opportunities Energy saving measures introduced Faultless / optimized operation Documentation of energy savings Fraunhofer ISE

30 3.4. Step 3a: Standard analysis (measurement based) General description Step 3a tries to transform the measured data according to chapter 2.3 in information about the building performance. Furthermore faults and possible saving potentials will be identified. This is done by two methods: Pre-defined intelligent visualization Rule based fault detection As all analysis is based on the minimal data set, this analysis is easy to implement in any building without much knowledge about its properties. However, it requires a general understanding of operation and utilisation to formulate meaningful rules Flow chart Figure 6 on the next page shows the flow chart for step 3a. At least 2 months of sub hourly data should be available for this step. If necessary the measurement equipment and recording must be installed first. The data is visualized and processed in a predefined way that is described in chapter If either a rule or an inspection by an expert detects an unusual behaviour, the data has to be further analysed to find possible saving potentials. In some simple cases this might also be done by rules. In most other cases this will be done by an expert. If it is not possible to identify a saving measure from the minimal data set, the analyst can also decide to do additional analysis or measurements according to chapter 7. If an energy conservation measure is identified and implemented, the savings have to be calculated or measured and the baseline for regular inspection has to be adjusted according to chapter 4. Fraunhofer ISE

31 1. Start from No data available From -> 1.4.7, (sub hourly data available) 2. Analysis Standardized visualization of data Is unusual behavior detected by statistics, rule or by expert? yes Check for possible ECM Could any ECM be identified? yes no no 3. Actions Install measurement equipment for minimal data set Record subhourly data (e.g. 10 minute values) (After at least 2 months of data are available) Further analysis (additional measurements, simulation, etc.) To be decided by the analyst Implement ECM Calculate / measure savings Adjust Baseline 4. Next Steps 3a.4.1 3a.4.2 3a.4.3 Involved parties: building owner / in-house staff Introduce Step 4 Introduce Step 4 Introduce Step 4 Contractor + building owner / in-house staff Contractor Figure 6 Flowchart for Step 3a: Standard analysis (measurement based) Fraunhofer ISE

32 3.4.3 Stock Data Only stock data similar to step 1 is needed (basic information about the building and HVAC system) Measurements The minimal data set according to Performance Metrics & Evaluation Techniques During step 3a visualization and rule based fault detection is performed which are based on the minimal data set. These analysis routines should facilitate the overview and understanding of the characteristics of the energy consumption and the system operation. Furthermore deviations from the expected operation should be detected automatically as far as possible. Visualization For the pre-defined visualization the following chart types will be used (examples will be given in the text below): Time series plot Chronological sequence of measured values. Scatter plots (XY plot) Scatter plots show the dependency of two variables. Additional information can be gained if the values are grouped. Potentially, several scatter plots can be combined to scatter plot matrices to show the interdependency of more than 2 variables. Carpet plots Carpet plots are used to display long time series of a single variable in form of a colour map which often reveals pattern (like weekly operation patterns). Box plots Box plots shows the statistical distribution of a variable for different groups of another variable. In most cases scatter, carpet and box plots will be used for analysis of the data as they deliver characteristic patterns for the energy consumption and the system temperatures e.g. Time series will be used as reference chart, in order to check the time sequence of an unusual behaviour that was detected with one of the other charts. Important tools in visualization are filtering and grouping of data: Filter Filter denotes the creation of a subset of data that satisfies a certain condition (e.g. subset of the measurements of energy consumption below a certain outdoor air temperature). Thus, the behaviour of variables under certain boundary conditions can be studied. Fraunhofer ISE

33 Filtering is also extremely important considering that there are no flow measurements or pump and fan control signals in the minimal data set (see 2.3). Accordingly, whether a water circuit or air handling unit is in operation can only be detected by investigating the system temperature. For water circuits the temperature difference between supply and return pipe will be used for filtering the operation of a circuit. Grouping Data can be grouped according to certain conditions (e.g. heating energy can b grouped for workdays and weekends). Different operating points can thus be compared. Even though the minimal data set will be recorded on an hourly or sub hourly time base, an aggregation to daily or monthly values is reasonable in some cases in order to eliminated dynamic effects. The following examples should illustrate the issues discussed above heat / [kw] Electricity / [kw] Figure 7 Time series plot on hourly basis (heat and electricity consumption). Both figures show a clear difference between the operation on workdays and weekends. They also identify night set back. Finally they identify a minor error in the heating system control which did not consider the holiday on January 6 th. Fraunhofer ISE

34 Wärmeenergieverbrauch VFG mit Witterungsbereinigung über Gradtage ,7% 27,7% 34,9% kwh/a Wärme Figure 8 Time series plot on yearly basis Grouping of annual heating consumption (degree day corrected) for the demo building of the University of Stuttgart. Retrofit of the system took place in December 2004 and January a malfunction of the system took place in 2006 due to the missing of continuous control. An energy saving potential of about 5% seems to be available in Model based control should help to identify appropriate energy conserving measures Figure 9 Scatter plot on daily basis with grouping for workdays (red) and weekends (green) Signature for heating and electricity consumption. Both signatures shows a clear difference between the operation on workdays and weekends. Furthermore the weatherdependent part of the load can be principally identified. Fraunhofer ISE

35 Figure 10 Carpet plot on hourly basis Electricity consumption. The carpet plot shows clear weekly patterns that indicate the difference between night and day operation as well as between weekdays and weekends. Heat on different weekdays Electricity on different weekdays Heat / [W/m²] Electricity/ [W/m²] Weekday Weekday Figure 11 Boxplots on daily basis Heat and Electricity consumption on different weeksdays. The boxplots shows the difference of consumption between workdays and weekends and the distribution on each day. Looking at the examples of visualization above it is obvious that the energy consumption and operation of a building produces typical operation patterns. For the shape of these patterns rules can be formulated. For the daily energy signature for heating (Figure 9 on the right) the following principal rules can be established, e.g.: Fraunhofer ISE

36 The change point (the outdoor temperature at which the heat consumption becomes weather independent) should be located in the range between C. The weather independent load (above the change point) should correspond to the domestic hot water consumption (if there are no other heat consuming processes). For typical office buildings this should be near zero. The slope of the weather dependent part of the signature should correspond to the energetic quality and comfort of the building. If a setback on weekends is scheduled, there should be a clear grouping of day types in the signature. These rules can either be checked by the operation staff, an expert or in an automated way by rules. The Building EQ project will develop sets of such rules for the different operation patterns in a later stage. Fraunhofer ISE

37 Table 9 gives the definition of the visualizations for the minimal data set that is done in step 3a. note that data with different time resolution has to be derived from the original measurements which are hourly or sub hourly. The following details apply for the different time resolutions: Monthly / weekly data: Monthly or weekly values deliver the rough characteristics of the consumption Daily data: Daily data delivers already a much richer information as different daytypes (usually workday / weekend) can be distinguished). Concerning the generation of daily averages for system temperatures of water based circuits the following points have to be observed: The average value for the system temperatures should only include the periods of time in which the corresponding circuit was in operation. As indicator for operation the difference between supply and return temperature can be calculated. (the minimal data set does not - due to cost reasons - contain information on the flow or the control signal of pumps. However, if this information is available it can be utilized for filtering). For the supply air a similar reasoning applies. In this case the difference to the indoor air can be calculated as indicator. Hourly data For hourly data filtering of system temperatures is even more important in order to reduce scattering. Fraunhofer ISE

38 Table 9 Pre-defined visualization for minimal data set (see 2.3) Type of chart Values for display Remarks Time resolution: Time series Scatterplots Time resolution: Time series plot Scatterplots Months / Weeks Consumption and outdoor air temperature / moisture System temperatures and outdoor air temperature Consumption vs. outdoor air temperature ( signatures ) For cold: additionally vs. absolute outdoor air humidity / enthalpy Days Consumption and outdoor air temperature / humidity System temperatures and outdoor air temperature Consumption vs. outdoor air temperature ( signatures ) For cold: additionally vs. absolute outdoor air humidity / enthalpy Grouping: type of day Supply temperatures (water side) vs. outdoor air temperature Grouping: type of day Supply air temperature vs. outdoor air temperature In case of AC system: Supply air humidity vs. outdoor air temperature Grouping: type of day indoor temperature vs. outdoor air temperature In case of AC system: additionally indoor humidity vs. outdoor air humidity Grouping: type of day as reference (can also be done with yearly data) as reference Identification of weather dependent and independent part of consumption and influence of utilization (scatter) as reference as reference Identification of weather dependent and independent part of consumption and influence of utilization (scatter) Identification of setback on basis of days (e.g. on weekends) Identification of control of supply temperatures and potentially different operation modes. Identification of control of supply temperatures and potentially different operation modes. Classification of indoor climate Boxplots Consumption per Weekday Identification of day types: (i.e. days with significantly different loads (normally: workdays <-> weekends) Fraunhofer ISE

39 (continued) Type of chart Values for display Remarks Time resolution: Time series plot Scatterplots Hours Consumption and outdoor air temperature / humidity System temperatures and outdoor air temperature Supply temperatures (water side) vs. outdoor air temperature Filter: Difference of supply- and return temperature must exceed a certain limit (e.g. 2K) Grouping: type of day Supply air temperature vs. outdoor air temperature In case of AC system: Supply air humidity vs. outdoor air temperature Filter: Difference between supply air and indoor air temperature (or humidity respectively) must exceed a certain limit. Grouping: type of day indoor temperature vs. outdoor air temperature In case of AC system: additionally indoor humidity vs. outdoor air humidity Grouping: type of day as reference as reference Identification of control of supply temperatures and potentially different operation modes. Identification of control of supply temperatures and potentially different operation modes. Classification of indoor climate, identification of unusual states Boxplots Consumption per hour of the day Identification of typical consumption profiles for different types of days. Fraunhofer ISE

40 (continued) Type of chart Values for display Remarks Time resolution: Hours Carpetplots Consumption Identification of consumption pattern (daily, weekly, seasonal) Supply temperatures (water side) Filter: Difference between supply and return temperature must exceed a certain limit. Supply air temperature vs. outdoor air temperature Identification of operation patterns (daily, weekly, seasonal) Identification of operation patterns (daily, weekly, seasonal) In case of AC system: Supply air humidity vs. outdoor air temperature Filter: Difference between supply air and indoor air temperature (or humidity respectively) must exceed a certain limit. indoor temperature / humidity outdoor air temperature solar radiation Identification of operation patterns (daily, weekly, seasonal) as reference as reference Rules for automated detection of unusual operation patterns will be further developed in Workpackage 5 of the Building EQ project Further actions If not already available the measurement equipment for recording of the minimal data set has to be installed. Fraunhofer ISE

41 3.5. Step 3b: Standard analysis (model based) General description For step 3b a model of the building and HVAC plant is used for the detailed analysis of saving potentials. The model will be used to calculate monthly energy consumption in dependency of the parameters and actual boundary conditions (like weather or operation schedules) of the building. First, the model has to be calibrated, i.e. the parameters must be adjusted so that the results of the model correspond to the actual operation. Then a parametric study can be performed in order to identify saving potentials. In the ideal case the model can be the model that was created during the asset rating of step 2, producing great synergy thereby. However, the Building EQ team realized that the models provided by the CEN standards and some of the national implementations of the EPBD are not really suited for modeling the real behaviour of buildings. The main reason for that is, tat even though e.g. the CEN gives quite detailed models of the single components of a building and the systems, the structure (i.e. the information about the real interconnections between the components) are not described correctly or completely. Therefore the Building EQ team developed another approach which concentrates on the structure of the system while using very simple component models (oriented at CEN). This approach is supposed to be better suited for the purpose of FDD and Optimization in the framework of Building EQ. A preliminary checklist ( checklist2 ) for data acquisition was developed that is available from the project-website ( The model based approach and the checklist will be further developed in WP 5 (development of tools) Flow chart Figure 12 on the next page shows the flow chart for step 3b. As already described, a the model of the system is created as far as possiblefrom the model (or data) of the asset rating. The model must be calibrated before a parametric study can be performed. It is important to notice that actual measured data for the boundary conditions (weather, schedules) is utilized for this. If a calibration is not possible with the used model (either from the Asset rating or the model developed in Building EQ) further analysis or measurements according to chapter 7 might be necessary to identify saving potentials. If an energy conservation measure is identified and implemented, the savings have to be calculated or measured and the baseline for regular inspection has to be adjusted according to chapter 4. Fraunhofer ISE

42 1. Start 2. Analysis from At least annual data available and measurement equipment installed Create model with data from AR (step 2) Calibrate model with at least 4 months of data yes Perform Parametric study / Optimization yes Calibration successful? Could any ECM be identified? no no 3. Actions Further analysis needed (additional measurements, simulation) To be decided by the analyst Implement ECM Calculate / measure savings Adjust Baseline 4. Next Steps 3b.4.1 3b.4.2 Introduce Step 4 Figure 12 Flowchart for Step 3b: Standard analysis (model based) Introduce Step 4 Involved parties: building owner / in-house staff Contractor + building owner / in-house staff Contractor Fraunhofer ISE

43 3.5.3 Stock data If the model from the asset rating of step 2 can be utilized no further stock data is needed. However, as already mentioned in additional information about certain components or the structure of the HVAC system might be necessary. In the next Building EQ report about tool development, a detailed parameter list for a simplified building and system model with structural information will be given Measurements Minimal data set according to Performance Metrics & Evaluation Techniques After the calibration of the model a parametric study can be performed that varies e.g. operation schedules and set points in a reasonable range that must be discussed with the building owner and operation staff. By using the model the energy consumption for every variation will be calculated. If changing a specific parameter reveals a significant saving potential it might be discussed for implementation. Besides control parameter which in most cases are relatively easy to change (at low cost), there might be other measures that possess a high saving potential but which have significant investment cost (like changes in the pipe or ductwork or exchange of old components). Even if these measures are not primarily addressed by Building EQ, they can be examined and discussed in step 3b. Fraunhofer ISE

44 3.6. Step 4: Regular Inspection General Description After the building performance has been analysed, major faults has been removed and potentially an optimisation has been performed the performance has to be constantly surveyed in order to maintain energy-efficiency Flow Chart Figure 13 on the next page shows the flow chart for step 4. In dependency of time resolution of the measured data and the availability of a model different analysis routines apply that will be described more detailed in chapter Principally the different starting points are: Annual data Monthly data Hourly data Hourly data + model Fraunhofer ISE

45 1. Start from 1.4.3, annual consumption data available from 1.4.5, Monthly consumption data available from 1.4.8, 3a4.1, 3a.4.2, 3a.4.3 Hourly data available from 3b.4.1, 3b.4.2 Hourly data and model available 2. Analysis Check consumption with annual baseline Significant deviation in consumption? no Check consumption with monthly baseline ( signature ) Any outliers detected? no Check consumption on daily / hourly basis by means of an identified patterns Any outliers detected? no Check consumption on daily / hourly basis by means of an identified patterns and in comparison to model Any outliers detected? no yes yes yes yes 4. Next Steps Back to Step 1 Back to Step 1 Back to Step 1 Back to Step 1 Involved parties: building owner / in-house staff Contractor + building owner / in-house staff Contractor Figure 13 Flowchart for Step 3b: Standard analysis (model based) Fraunhofer ISE

46 3.6.3 Stock Data No additional stock data is needed for step Measurements No additional measurements are needed for step Performance Metrics & Evaluation Techniques Again, the kind of evaluation routine will depend on the steps that were performed before introduction of step 4. The previous steps will determine the time resolution of data and the availability of a model. This will also define the kind of baseline used for the regular inspection. Annual data In the case of annual data the actual consumption can be compared to previous years after a weather correction was performed. The weather correction can be done according to national rules. Monthly data In the simplest case the procedure is the same as with annual data. Note that a weather correction is also necessary for such a comparison. That is, at least monthly weather data must be available (at least outdoor air temperature). Note also that weather corrections should only be applied to weather dependent parts of the consumption, e.g. not to the DHW in the case of heating energy. Additionally, signatures for the consumption (energy, water) can be identified as baselines and used for detection of changes. See chapter 4 for an explanation of monthly signatures. Hourly data If hourly data is available it is recommended to use signatures for the daily consumption as baselines for detection of changes. These kind of signatures are multiple linear regression models which parameters are identified from historic data. A day typing should be included in the process in order to account for different operation and occupancy schemes e.g. on workdays and weekends. Furthermore, it might be possible to check the consumption even on an hourly basis (The consumption and operation patterns from step 3a can be used for detection of changes.) Hourly data and model If a calibrated model is available (after performing step 3b) it can naturally be utilized for providing a baseline for the energy consumption. For water consumption still the last bullet point would apply. It has to be observed that the models used in the framework of Buildng EQ will produce monthly values for energy consumption. Nevertheless hourly data is valuable information for determining the boundary conditions, e.g. for the weather. Fraunhofer ISE

47 3.6.6 Outcomes / aims of this step Regular inspection of performance (detection of unusual behaviour or changes in operation). Persistence of energy efficient performance Fraunhofer ISE

48 4. Measurement and verification / Definition of Baselines An important thing concerning the justification of a continuous commissioning approach is the measurement and verification of savings. By measuring or calculating and documenting the achieved saving the cost-benefit of the ongoing performance evaluation can be determined. In order to measure or calculate energy savings, a baseline for the pre-retrofit period must be determined. This can then be compared to post-retrofit energy consumption to determine the savings. The following equation applies: Energy Savings = Baseyear Energy Use - Post-Retrofit Energy Use ± Adjustments The "Adjustments" term in this general equation brings energy use in the two time periods to the same set of conditions. Conditions commonly affecting energy use are weather or occupancy. Adjustments may be positive or negative. The International Performance Measurement and Verification Protocol (IPMVP) describes concepts and options for determining energy and water savings in buildings /4/. The development of the protocol is sponsored by the U.S. Department of Energy (DOE) and an international coalition of facility owners/operators, financiers, contractors and Energy Services Companies (ESCOs). It gives four options for the calculation of energy savings: A: Partially measured retrofit isolation Savings are determined by partial field measurement of the energy use of the system(s) to which an ECM was applied, separate from the energy use of the rest of the facility. Measurements may be either short-term or continuous. Partial measurement means that some but not all parameter(s) may be stipulated. B: Retrofit isolation Savings are determined by field measurement of the energy use of the systems to which the ECM was applied, separate from the energy use of the rest of the facility. Short-term or continuous measurements are taken throughout the post-retrofit period. C: Whole Building Savings are determined by measuring energy use at the whole facility level. Short-term or continuous measurements are taken throughout the postretrofit period. D: Calibrated simulation Savings are determined through simulation of the energy use of components or the whole facility. Simulation routines must be demonstrated to adequately model actual energy performance measured in the facility. This option usually requires considerable skill in calibrated simulation. Fraunhofer ISE

49 Depending on the availability of historical consumption data and the kind of energy saving measure that is to be implemented the different approaches may be chosen. Option A and B deals with single measures that can be separated by (partial) measurements and thereby the saving can be determined. Option C and D refer to the whole building level. Thus, multiple measures can be evaluated with these options. Continuous commissioning in general tries to implement multiple (as much as possible) energy saving measures. Consequently, the following recommendations are given: If at least annual historical consumption data is available, option C is recommended. An adjustment is normally done by a weather correction by taking into account the heating or cooling degree days of the actual and of the base year. Furthermore it has to be observed that weather corrections should only be applied to weather dependent parts of the consumption, e.g. not to the DHW in the case of heating energy. If or monthly historical consumption data for at least one year together with monthly weather data is available, also Option C is recommended. In this case the data can be used to identify consumption signatures by means of multiple linear regression models (see 4.1). If no historical consumption data is available there are the following choices: o o o o If an energy saving measure is identified that is weather independent (e.g. fixed reduction of a constant load, e.g. reduction of air flow of a constant volume fan) the savings could be measured by spot measurements pre- and post-retrofit. Option A or B will apply. Savings from single energy savings measures can be estimated by calculation or extrapolation from short term measurements (even if weather dependent) using good engineering knowledge. Option D can be used with some short term data for calibration of the model. As this option requires a big effort it is not recommended, unless a model is not available anyhow (e.g. from asset ratings) and measurements are available. At the same time the expected saving potential should be significant. A year of consumption data can be recorded first, to generate a baseline before introducing energy saving measures Baseline Regression models (IPMVP Option C) As regression models for monthly data (according to IPMVP Option C) are easy to apply and are able to deal with multiple energy saving measures they are of spe- Fraunhofer ISE

50 cial interest. In the framework of the building EQ routines for the generation of baselines has been developed. However, this option is intended for projects where savings are expected to be large enough to be discernible from the random or unexplained energy variations that are normally found at the level of the whole facility meter. The larger the saving, or the smaller the unexplained variations in the baseyear, the easier it will be to identify savings. Also the longer the period of savings analysis after ECM installation, the less significant is the impact of short term unexplained variations. Typically savings should be more than 10% of the baseyear energy use if they are to be separated from the noise in baseyear data. Option C usually involves regression techniques to identify the baseyear consumption as a function of several independent variables. IPMVP gives no concrete method or model to be used for this task. Usually regression models are represented in form of energy signatures, e.g. like the ones given in /5/ Figure 14 example of 4 parameter linear change point models for heating and cooling energy signatures Change-point linear models based on monthly or weekly meter readings are well known in the field of measurement and verification of energy saving measures. The change-point is typically defined for the ambient temperature. Figure 14 shows an example for a simple model where the ambient temperature is the only independent variable. Fraunhofer ISE tested different multiple-linear model on that basis. For one of the independent variables a change-point is defined (typically ambient temperature), which divides the model space in two parts with different Parameters. The general form of the model is as follows: b Y = b Where: Y 0< 0> + b + b cp< cp> ( X cp CP) ( X CP) cp + b + b i< i> X X i i for for X X cp cp CP > CP dependent variable (consumption for heat, cold, electricity) Fraunhofer ISE

51 X cp CP b 0 b m m variable for which a change-point is defined location of change point Parameters of the model number of independent variables Actually the model describes 2 separate linear models (one below and one above the change point). Besides the identification of the parameters of the model, the location of the change point must be determined what is done by an optimization routine. In /5/ a two level grid search algorithm is applied for that. Fraunhofer ISE successfully tested an algorithm which uses an standard optimization algorithm for this task (a combination of golden section search and successive parabolic interpolation). This model is well suited for weekly or monthly values. If based on daily values the difference of the indoor temperature from actual day to previous day is a valuable additional variable for the model. In addition to the shown terms, the model can be split for weekdays and weekends or occupied weeks or holiday weeks respectively. An example of a script in R ( which performs the regression and identification of the change point from monthly basis is shown below (further scripts are available from the project website ( ######################################################### # Example # # Linear Regression Analysis for Energy Signatures # # for the Demonstration Buildings in Building EQ # ######################################################### ######################################################### # file select section ######################################################### # choose file to analyse, # you can type it in here (remove comment mark #) # file <- "Monatswerte/Variation_spez/Variation_014.dat" file.target <- file.choose() ######################################################### # file read section ######################################################### # read in data file # first row must contain header (ASCII strings seperated by ",") #"Ta,Iglob,Top,Presence,Qel,Qh,Qk" # meaning: #Ta: outdoor Temp. Ta [ C] #Iglob: global irradiation [W/m²] #Top: Opterational room temperature [ C] #Presence: 0,1 [-] #Qel: total consumption of electricity [kwh/month] or [W/m²] #Qh: total consumption of heat [kwh/month] or [W/m²] # Qk: total consumption of cooling energy [kwh/month] or [W/m²] # data rows ASCII numbers seperated by:"," Fraunhofer ISE

52 # decimal point: "." #everything ca be adjusted type in: help("read.table") building <- read.table(file.target, header=true, sep=",", dec=".") ######################################################### # if the column names are not the way described above they # can be changed by the following command, # the names must be in the appropriate oder (must eventually be adjusted) names(building) <- c("ta","iglob","top","presence","qel","qh","qk") ######################################################### # analysis section ######################################################### # the formula to be used for linear regression (as string) # can be modified, see: help("lm") help("formula") # I(Ta-CP) means, that Variable Expression (Ta-CP) is used for regression # without I() it would mean, that -CP is excluded from the model model = "Qh ~ high / (I(Ta-CP)+Iglob+Presence+Qel)" # storing some information in unique target variables to store # them with the model further down. # the script will be eassier to change to other variable this # way, since this is the only place to change descriptions in # the model file. file names have to be changed further down. target.value <- building$qh target.name <- "Qh [W/m²]" target.x <- building$ta # definition of a function to be optimised # only needed because the optimal value for CP is needed linmod <- function(cp,formul) { # addin a new colums (variables) to data.frame building # with the information if the row belongs to below changepoint # "<<-" makes it available outside the function building$low <<- building$ta<cp # with the information if the row belongs to below changepoint building$high <<- building$ta>=cp # linear regression is done by lm() # the result is stored in mod # "<<-" makes it available outside the function mod <<- lm(as.formula(formul),data=building) # the summary of the linear regression model is stored in sum summa <<- summary(mod) # this is assigning the function linmod the value R² summa$adj.r.squared } # here the optimal (highes R²) change point value CP is searched for opt_mod <-optimize(linmod,c(5,20),tol=0.001,maximum=true, formul=model) CP <- opt_mod$maximum ######################################################### # output section screen ######################################################### # some statistical values # R²: coeffcient of determination Fraunhofer ISE

53 r_squ <- opt_mod$objective #standard derivation StAb <- sd(mod$residuals) #CV: coefficient of variation CV <- sd(mod$residuals)/mean(target.value) # print summary print(summa) # predict the Heating energy values with the linear regression model # store the values in the data.frame building under the column name pred # using the values stored in building for Ta, Iglob... building$pred <- predict(mod) # plot the results of the prediction plot(building$pred ~ building$ta, xlab="ta / [ C]", ylab = "Qheat/[W/m²]", xlim=c(-10,30),ylim=c(0,15),cex=1.5) # add the original (target) values to the plot points(as.formula(paste("target.value ~ building$ta")), col="red",cex=1.5) # add a vertical line at the Change Point to the plot abline(v=cp) # add a grid to the plot grid(col="lightgrey",lty="dotted") # add the R² value to the plot text(20,0.90*par()$yaxp[2]+0.10*par()$yaxp[1],paste("r²: ",format(r_squ,digits=4))) # add the sd value to the plot text(20,0.85*par()$yaxp[2]+0.15*par()$yaxp[1],paste("sd: ",format(stab,digits=4))) The output of the script is the following: Call: lm(formula = as.formula(formul), data = building) Residuals: e e e e e e e e e e e e-03 Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) 4.230e e hightrue e e highfalse:i(ta - CP) e e *** hightrue:i(ta - CP) 1.895e e highfalse:iglob e e * hightrue:iglob e e highfalse:presence 1.065e e * hightrue:presence 2.514e e Fraunhofer ISE

54 highfalse:qel e e * hightrue:qel e e Signif. codes: 0 '***' '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: on 2 degrees of freedom Multiple R-Squared: 1,Adjusted R-squared: F-statistic: 9216 on 9 and 2 DF, p-value: In dependency of the kind of building or utilization respectively it might be worthwhile or necessary to use other models. One standard example is schools where there is a significant difference between the facility s energy use during the school year and summer break. Separate regression models may need to be developed for different usage periods here. The model will deliver the consumption of the building before the introduction of any energy saving measure. The savings after the introduction of energy saving measures an then be calculated as difference between the model output and the actual measured consumption. Further information on the IPMVP and the numerical algorithms are available from the member area of the project website ( Fraunhofer ISE

55 5. Measurement equipment and data transfer 5.1. Technical issues In the framework of Building EQ a minimal data set (see chapter 2.3) is to be acquired at least on an hourly basis. This chapter tries to highlight some of the practical issues connected to the data acquisition. In order to produce hourly data, the recording of raw data should occur on 5-10 minutes intervals at least for state variables. Actual hourly values of state variables of e.g. supply temperatures are not suited for further evaluation as their frequency might be much higher than 1 hour. Even hourly average values of state variables are not suited because - e.g. for supply temperatures - the averaging should only be done for operation periods if they are to be correlated with the energy t5ransported in the corresponding circuit. Typically (considering the minimal data set), the sensors for energy and water consumption, for outdoor air temperature and for particular system temperatures are installed in the buildings. The sensors for the additional weather and indoor climate data are typically only installed if the building is equipped with an air conditioning system. However, even if the sensors are installed the availability in the sense of transferability to other analysis systems in most cases is not given. Generally, the availability of measured data is better for buildings with a BAS that comprises a management level. The BAS can principally provide a lot of data. Unfortunately most BAS are not equipped with data transfer features such as a data base interface, an OPC-server or simply the capability to export ASCII files with measured values. Furthermore energy data is typically not available from the BAS. Nevertheless, if a BAS is installed it should be checked if it can be enhanced in order to provide the minimal data set at reasonable cost. In some cases (if the enhancement of the BAS is complex) it might be less costly to install an extra data logger in connection existing and/or additional sensors. If no BAS is available the installation of a data logger is the only choice (if the building owner doesn t decide to upgrade his building with a BAS anyway). Another point which has to be observed for high time resolution of data logging (hourly or sub hourly) is the amount of traffic the fieldbus has to handle. If it comes to a significant amount of data (>> 100 data points) an adjustment of the fieldbus communication might be necessary. Furthermore, in most cases heat and water metres must be equipped with a pulse output or fieldbus interface. In the case of a fieldbus interface they must be further equipped with a grid connected power supply because batteries runs quickly out of power in the case of frequent readings. The effort for acquisition of additional data that exceeds the minimal data set depends heavily on the presence of a BAS. If a BAS is enhanced to deliver the minimal data set, additional data from the BAS (e.g. control signals, schedules, etc) is available almost at no additional cost. On the contrary, if no BAS is available or if the additional data are not available from the BAS, the measurement of these addi- Fraunhofer ISE

56 tional data by means of a data logger might produce significant extra cost. However, these costs are small compared to those of implementing a new BAS Data transfer For the Building EQ project all data has to be transferred to a centralized data server. Remote data access is an important issue if it comes to remote services, i.e. if analysis must or should be done remotely. In order to guarantee an efficient and save data transfer the preferred way of remote data access is realized via an internet-connection by using an VPN-tunnel. The security is assured by a firewall. The internet connection itself can be realized via a DSL-Modem / DSL-Router or via an in-house network. The figures below shows the connections that were realized in the Building EQ project. Demonstration Building Fraunhofer ISE Data Source (e.g. BAS, ennovatis smartbox, Separate monitoring computer, Datalogger) Firewall Data- Source (e.g. PIX 501) DSL - Modem or DSL - Router (e.g. Speedport 200 from Telekom) DSL flatrate Internet Firewall Data Server Demonstration Building Fraunhofer ISE Data Source (e.g. BAS, ennovatis smartbox, Separate monitoring computer, Datalogger) Firewall Data- Source (e.g. PIX 501) LAN in-house network Firewall in-house network DSL flatrate Internet Firewall Data Server Figure 15 Examples of realization of remote data access via internet using DSL Modem /Router (above) or an in-house network (below) The Data Source can be any kind of computer or data logger that is able to provide data via a database interface (e.g. SQL), an OPC-Server or just as ASCII files (e.g. one file per day). Fraunhofer ISE

57 If there are more then one user that has to have remote data access the Firewall Data Source must have a fixed IP-address. If a DSL-modem is used, it should be obtained directly from the DSL provider in order to guarantee compatibility. A DSL-modem normally does not need any further configuration. It is highly recommended to assure that the DSL-tariff is a flatrate as otherwise the cost of data transfer might be quite high. If the Data Source is directly connected to the inhouse network this connection must also be secured by a firewall (note: this is not shown in the scheme above) IN the case that the in-house network is used, the data access is realized by a VLAN between the Firewall Data Source and the Firewall in-house network. The Firewall Data Source receives a fix IP-address via NAT (not PAT). This facilitates the remote access also (if necessary and allowed) by third parties (e.g. maintenance crew). If it is for - any reason (e.g. technical, security, policy) not possible to implement one of the above described kinds of connection, it is also possible to provide the data via an ftp- or web-server (maybe without direct connection to the BAS or data source respectively). In most cases this implies that the data is not available online but only e.g. in daily intervals. Fraunhofer ISE

58 5.3. Cost The cost for acquiring the minimal data set that was experienced in the Building EQ project is in the order of to EUR per building. However, the cost depends very much on the actual state of the system (BAS available?, metres available, etc.) and not so much on the building size. General rules can not be given. A look at the yearly energy cost helps to classify the investment cost. In the case of the demonstration buildings in the Building EQ project the yearly energy cost are between EUR and EUR. That is, the investment cost for measurement equipment for the minimal data set is in average about 10% of the yearly energy cost. Even if the savings produced by the continuous commissioning approach would only be 5-10%, the static amortisation would be only between 0,5 and 3 years (neglecting the cost for service at this time). Figure 16 gives estimated static pay back times for the investment cost of the data acquisition for the demonstration buildings in Germany and Finland for which cost data were available. Note that the payback time is not yet based on real investment cost but on an approximated range. In the course of the Building EQ project the cost benefit of continuous commissioning will be further investigated. yearly energy cost / [ /a] heat/gas electricity static payback MAX static payback MIN Germany_bui1 Germany_bui2 Germany_bui3 Germany_bui4 Finland_bui1 Finland_bui2 Finland_bui3 Finland_bui static payback for monitoring / [a] Figure 16 Estimated static payback of monitoring in demonstration buildings based on real yearly energy cost. Assumption: energy savings through Continuous Commissioning=10% Cost for data acquisition = (MIN) / (MAX) Fraunhofer ISE

59 6. National approaches 6.1. Germany Chapter will give a short overview over the realization of the 4-step procedure in the German demonstration buildings. The subsequent chapters will focus on special issues like availability of data and used tools How the 4 step procedure is realized Step 1: Benchmarking (Operational Rating) The benchmarking is normally based on consumption data of the last 3 years according to the utility bills. In some cases additional monthly readings done by the operation stuff is available. The net floor area or gross building volume that is needed in order to calculate the specific energy or water consumption are usually not readily available and must be determined separately. Weather data must be available (typically degree days) for weather correction. Furthermore, weather correction is only to be applied to the weather dependent parts of the energy consumption. Accordingly DHW consumption is to be subtracted. If monthly metre readings are available a baseline in form of an energy signature can be developed. For many locations the necessary weather data (outdoor air temperature, insolation) is freely available from the Deutsche Wetter Dienst ( as daily values. Monthly values can be derived from them. In the past the amount of reference values for non-residential buildings in Germany was quite low. Several efforts were made to improve the situation in the future. Actual Sources for benchmarking values for non-residential buildings are: VDI3807 /6/, ages GmbH /7/, ARGE Benchmark /8/, database of IEMB /9/. For these data sources the data of several thousand buildings has been gathered. However, as non-residential buildings tend to be quite individual concerning the kind of utilization the benchmarking might still be insufficient to asses the energy consumption of a building in a proper way. Step 2: Certification The certification (asset rating) according to the German standard DIN V requires a lot of stock data of building and HVAC system to be gathered. However, one has to distinguish between the calculation to be performed for the official certificate and a customized calculation. For the calculation for the certificate a lot of standard values (e.g. for the utilization profiles) are prescribed by the DIN standard. Accordingly these values don t have to be collected and the effort of gathering the data is reduced On the other hand, the customized calculation which can give a much more realistic picture of the building performance - asks for detailed description of the real building. Fraunhofer ISE

60 Especially for older buildings with incomplete documentation the acquisition of stock data for the customized calculation is a time consuming task which is comparable to the data acquisition for a thorough energy audit. Usually it takes several days and includes at least one on-site inspection. Even though the German implementation of the EPBD (DIN 18599) is quite detailed some systems can not be modelled (e.g. Control of air handling units (VAV systems), central air handling units with recirculation, bore hole heat exchangers or night ventilation). Thus, for some buildings even with the customized approach a matching with the real building behaviour can not be expected. The installation of the data acquisition for the minimal data set which has to be done at the end of step 2 is discussed more in detail, in chapter Step 3: Optimisation Step 3 will start with the customization of the calculation for the certificate. Furthermore the minimal data set of measured data will be evaluated according to the standard analysis defined in chapter 3.3. From this a more detailed (but still standardized) picture of the building will be available. Any additional measured data (compared to the minimal data set) will also be object to visualization and (if applicable) statistic analysis. If any of these analysis routines reveals an unexplained deviation from the expected behaviour of the building more detailed analysis will be performed. Principally, it will be decided if either functional performance testing or system simulation is more suited for the examination of the building. The choice will strongly depend on whether the fault can be localized or not. In the case that faults or optimization potentials can be identified, their cost benefit relation will be estimated and discussed with the building owner and/or operator. If energy conservation measures are implemented, their real cost benefit relation will be calculated by dividing their cost by the savings determined by comparing the actual energy demand with the baseline developed in step 2 (or a refined version). Chapter gives a more detailed description of the used tools. Step 4: Regular Inspection In step 4 the statistics developed for the standard analysis for monitoring of energy consumption will be utilized. In the ideal case step 4 is just an outlier detection based on hourly data which fires an alarm if the systems shows abnormal behaviour Availability of stock data The availability of stock data depends very much on the age of the building and on the level of documentation that was prepared during construction. In general the older the building the less (and less actual) the available documentation is. In most cases documentation for the geometry is available (at least a bigger part) while the construction of building parts may not. Most difficult to get is an actual documentation of the HVAC systems, especially if the building is complex and was object to several refurbishments. Fraunhofer ISE

61 In any case an physical inspection of the plant is necessary in order to assure that the documents are actual and all plants are considered. As an average it takes about 3 days to gather and inspect all data. Table 10 gives an overview over the availability of stock data in the German demonstration buildings and in addition some other buildings that are examined by the German partners in other projects. Table 10 Availability of stock data in the German buildings stock data ThyssenKrupp MWME Düsseldorf Verfügungsgebäude Uni Stuttgart Kreiskrankenhaus Hagenow Building 1 (mwz) Building 2 (EADS) Building 3 (LEH) Building 4 (enngp) Building 5 (DVZB) Year of construction complexity of HVAC plant* m m m m h l h l h floor plans, views, sections** + o + o o o floor areas o o + + o o Construction of building elements (walls, floors, roof, windows) Kind of utilization and schedules schematic drawings of HVAC system operation strategies and schedules of HVAC system product data of HVAC equipment effort for acquisition of stock data in days o(-) - o o o o + o + + o o + + o o o + o + o o 0 o + o o o o + o *m = medium, l = low, h = high **+ = complete and actual information, o = partial information only or not actual (on-site inspection was necessary), - = no information available (has to be stipulated) Availability of measured data In the framework of Building EQ a minimal data set (see chapter 3.2.4) is to be acquired at least on an hourly basis. Typically, the sensors for energy and water consumption, outdoor temperature and for system temperatures are installed in the buildings. The sensors for the additional weather and indoor climate data are typically only installed if the building is equipped with an air conditioning system. Fraunhofer ISE

62 However, even if the sensors are installed the availability in the sense of transferability to other analysis systems in most cases is not given. Generally, the availability of measured data is better for buildings with a BAS that comprises a management level. The BAS can principally provide a lot of data. Unfortunately most BAS are not equipped with data transfer features such as a data base interface, an OPC-server or simply the capability to export ASCII files with measured values. Furthermore energy data is typically not available from the BAS. Nevertheless, if a BAS is installed it should be checked if it can be enhanced in order to provide the minimal data set at reasonable cost. In some cases (if the enhancement of the BAS is complex) it might be less costly to install an extra data logger with additional sensors. If no BAS is available the installation of a data logger is the only choice (if the building owner doesn t decide to upgrade his building with a BAS anyway). Another point which has to be observed for high time resolution of data logging (hourly or sub hourly) is the amount of traffic the fieldbus has to handle. If it comes to a significant amount of data (>> 100 data points) an adjustment of the fieldbus communication might be necessary. Furthermore, in most cases heat and water metres must be equipped with a pulse output or fieldbus interface. In the case of a fieldbus interface they must be further equipped with a grid connected power supply because batteries runs quickly out of power in the case of frequent readings. The cost for acquiring the minimal data set is in the order of to EUR (net cost) per building. However, the cost depends very much on the actual state of the system. The effort for acquisition of additional data that exceeds the minimal data set according to chapter depends heavily on the presence of a BAS. If a BAS is enhanced to deliver the minimal data set, additional data from the BAS (e.g. control signals, schedules, etc) is available almost at no additional cost. On the contrary, If a data logger is used these additional data produce significant extra cost. Fraunhofer ISE

63 Table 11 summarizes the situation in the German demonstration buildings and in some additional buildings that are examined by the German partners in other projects Fraunhofer ISE

64 Table 11 Availability of measured data for the minimal data set in the German buildings stock data ThyssenKrupp MWME Düsseldorf Verfügungsgebäude Uni Stuttgart Kreiskrankenhaus Hagenow Building 1 (mwz) Building 2 (EADS) Building 3 (LEH) Building 4 (enngp) Building 5 (DVZB) Minimal data set consumption total consumption of fuels na na na na + na total consumption of district heat total consumption of district cold o o + na na o + na na na na + na na o na na na total consumption of electricity o o o total consumption of water o o o + + o weather data outdoor air temperature o o outdoor rel. humidity global irradiation - - o indoor climate indoor temperature indoor relative humidity - - o system temperatures Flow / return Temperatures of main water circuits supply air temperature of main AHUs supply air relative humidity of main AHUs o o o o na + o o na + data acquisition BAS na o + na o - o na + data logger - na na - na na na + na + = available, o = available but had to be renewed/enhanced, - = not available, had to be installed, na = not applicable Fraunhofer ISE

65 6.1.4 Tools for step 3 (analysis and optimization) The following tools will be used in step 3: standard analysis A standard analysis based on predefined visualization and statistical analysis of the minimal data set is to be used to study the system behaviour. The statistical approaches will also be used to provide a baseline for the original operation. ennovatis EnEV+ / VEC The tool of the partner ennovatis is a software implementation of the German DIN standard This standard is the German implementation of the EPBD. It allows to calculate the official certificate (EnEV+) as well as doing customized calculations (VEC) with real metrological data, real utilization profiles and system data. The customized calculation will be used to assess the real performance of the building. Functional performance test If a certain subsystem can be identified as deficient, a functional performance might be applied. This can even be done by an external expert. Simulation with IDA-ICE As the tool of ennovatis provides an IFC export of the geometry and properties of the building envelope, even the application of dynamic simulation is of interest as the effort for the creation of a model can be drastically reduced by this step. The tool IDA-ICE is an equation based simulation tool which provides IFC import and a very flexible integration of new models of any complexity What barriers were identified / are expected in the course of the 4 step procedure? The main barriers that were already identified are as follows: Lack of documentation of buildings. Poor availability of consistent measured data with high quality (calibration of sensors is not usual). Operation staff is offended by the monitoring as it is perceived as a monitoring of their work Utilities are offended by the monitoring as it may result in changes of contracts General scepticism of the building owners concerning the cost-benefit of continuous commissioning Lack of data on cost-benefit of monitoring systems that is needed to convince building owners Lack of low price and high quality (wireless) sensors BAS are not designed for analysis tasks Fraunhofer ISE

66 BAS needs external programming to extract energy relevant data Barriers that are expected in the further course of the project are connected to the identification and implementation of energy conservation measures What are the possible links between the national implementation of the EPBD and CC? Asset ratings could be a good starting point for CC as they deliver a lot of information about the building. Unfortunately, asset ratings for existing buildings will not be performed in many cases because the building owner has the free choice between asset and operational ratings. As operational ratings are much cheaper it is more than probable that most owners will chose them. However, if a major refurbishment is planned asset ratings can be the first step of a systematic approach Sweden How the 4 step procedure is realized Step 1: Benchmarking (Operational Rating) The energy use for the benchmarks are normally based on utility bills, but most large building owners also make their own manual monthly meter readings. One special Swedish problem with the benchmarks is the floor area. Floor area inside external walls The first problem is that the floor area which must to be used in the official energy certification process, the net floor area A temp (defined as the floor area inside the external walls) is not available for any Swedish building owner. This is because it is not defined in the Swedish Standard SS Area and volume of buildings Terminology and measurements and consequently it has never been used before. Owners of premises buildings typically use the premises area LOA, or a smaller subdivision of this LOA:V the area used for the activity in the building. Some owners of public buildings apply the used area - BRA. BRA is A temp less: o o o o o internal wall areas between tenants; internal walls between tenants and communication areas (stair wells, etc.) internal walls between tenants and service areas (mechanical rooms, etc.) columns and shafts next to an external wall regardless of their thickness internal walls, columns, shafts etc, not next to an external wall, but thicker than 300 mm For the 123 office buildings audited in the first year of the STIL 2 study A temp was in average 3.5% larger than BRA. Consequently the difference between BRA and A temp is not large in average, but it can be substantial for Fraunhofer ISE

67 individual buildings. Almost all commercial building owners use LOA and it is not a simple procedure to convert this floor area to A temp. LOA is BRA less other areas (communication and service). So in practice all Swedish premises buildings and multi-family buildings must be re-measured. From the start this results in a negative attitude from most building owners towards how the energy certification process is implemented. However, A temp must be used in the energy certification process and it will be available when the energy certification is by law finished by 1 January 2009 at the latest. Total consumption of electricity The Swedish energy certification is based on operational rating. In the official interpretation of operational rating the energy use of the occupants, tenats or users of the building is not included. Typically this energy is only electricity. The reason is mainly because in almost all buildings with tenants, regardless if they are commercial or residential, the tenant has his/her own electricity subscription. Of privacy reasons this energy data is not available for the building owner and the tenant cannot be forced to present it. Nor can the electric grid companies deliver the data to the building owner without a written permit from each tenant. Another reason is that the energy certification applies for the normal use of the building and not including the tenants energy is one way to handle this dilemma (not necessarily a good way). Almost all owners of public buildings, e.g. municipalities and local authorities, have some kind of internal tenant agreements with departments inside its own organisation. Typically, the building owner is responsible for all energy and is transferring the costs to the internal tenants, sometimes without telling them anything about the measured energy use behind the costs. In many public buildings the total consumption of electricity is available. Consequently, the total consumption of electricity is often really hard to get hold of in most Swedish premise and multi-family buildings. Building utilisation Regarding utilisation of buildings Statistics Sweden divided premises buildings into twelve categories already in the first energy (oil) statistics in the early 1970s. However, this division is more based on tradition, and on national economics, than on the energy behaviour of the buildings. In some categories the spread of the annual energy use between individual buildings is very large, e.g. one category includes all type of schools, from preschools to universities. This means that it includes anything from a small preschool, the size of a one-family house, to a huge university laboratory. The published Swedish reference values are based on reliable statistics, for the defined categories, when it comes to heating, whereas the electricity controlled by the building owner is based on a few case studies. However, a multi-year project, STIL 2, is auditing the total energy consumption in about 1000 premises buildings during the years 2005 to Published results for the first year are for office and administration buildings and for the second year for schools {pre-schools to voluntary schools (gymnasieskola), corresponding to upper secondary school}. During 2007 care build- Fraunhofer ISE

68 ings, including health care, are audited and in 2008 it will probably be sports and assembly buildings. Consequently, the published preliminary reference values for the twelve categories are uncertain and they will not be more reliable until the first batch of energy certifications are finished. Energy benchmarks The benchmarks used are measured annual energy use per floor area. Heating is normalized to a standard year for the location via heating degree days for each month. The measured heating degree days are not freely available but must be purchased (rather expensively) from the Swedish Meteorological and Hydrological Institute. No weather normalization procedures exist for cooling. Energy signatures are known in Sweden, but not used for weather normalization since they not are included in any of the building energy management software that are commercially available. Since Swedish electric generation traditionally is hydropower Sweden has no tradition of thinking in, or using, primary energy. This has not changed despite that since nearly thirty years nuclear power is responsible for about half of the electricity generation. Consequently, all energy carriers are traditionally added with a weight factor of one. This is implicitly assumed in the energy certification process. The ongoing national implementation of the Energy Services Directive will during 2008 result in energy weight factors that are intended to be used on the national level, both in average and on the margin. This work might result in future use of weighted energy on the building level. Step 2: Certification As the Swedish energy certification is based on operational rating, excluding the users energy, the needed stock data is limited and regarding energy more or less only utility energy data is needed. Energy consumption As stated above it is typically hard to measure the total consumption of electricity in premises buildings in Sweden. The national energy certification process only requires the energy controlled by the building owner. About 60 % of the floor area of Swedish premises buildings have district heating and it represents more than 70 % of the heating energy of the premises buildings. District heating is easy to measure. Utility data are typically either monthly or quarterly. Natural gas is so far unusual in Sweden and only used in the South and West parts of the country where it is imported from the Danish North Sea. It covers only about 6 % of the heating energy for premises buildings. One reason for the general negative attitude in Sweden towards the planned Russian-German gas pipeline under the Baltic sea is a fear for a new gaspipeline to Sweden. This is seen as a threat by the bio-fuel market and as a possible future source for not wanted Swedish carbon dioxide emissions. Oil boilers in all types of buildings are fazed out in a rather fast pace and will not be of any significance in a few years time. In 2002 oil stood for nearly 12 % of the heating energy of premises buildings but it had decreased to only 5 % in Cogeneration in buildings is virtually non-existing in Sweden. Fraunhofer ISE

69 Regarding Swedish premises buildings the main energy carriers are district heating and electricity from the grid. BAS Computerised BAS are nowadays very common in most premises buildings of some size. This makes the theoretical possibilities to use the BAS for measuring large, but the practical possibilities are probably much smaller. This is due to the fact that BAS typically not are capable to store and transfer data in a simple way to data bases and other uses. The Swedish Energy Agency had a technical procurement competition in 2005 and the winner of this was Larmia Control, a small Swedish company with a very user friendly system that easily can transfer large quantities of short time data to databases. Another result was that a revised competition specification of BAS was used in practical procurement and the BAS manufacturers had suddenly a whole new pressure from the building owners for BAS that are really useful from the building owners point of view. Two of the selected Swedish demonstration buildings have a BAS from the winning manufacturer. Measurements The energy meters are typically only read manually by the operation staff but increasingly getting more incorporated in the BAS. The electric net and the district heating utilities typically have automated meter reading. From 1 July 2009 all electric utility meters must be read at least monthly. This has resulted in a large upgrading of all electric meters in Sweden and from the summer of 2009 automated hourly readings will be in place for all customers. However, the electric net utilities still do not have any central strategy how the building owner can get access to this data in electronic form. Inside Building EQ it is a prerequisite that mainly the BAS can be used for the hourly measurements. In some of the Swedish demonstration projects additional metering will be necessary. The return water temperature in hydronic heating and cooling circuits is seldom measured. Indoor environment Because of the bad Swedish experiences from the energy saving measures carried out in buildings during the 1970s and 1980s indoor environment is very much in focus in the Swedish energy certification process. It is very important that the proposed energy efficiency measures do not degrade the indoor environment. The energy certificate must show that the Obligatory Ventilation Control has been carried out. Also must be shown if voluntary measurements of radon gas has been carried out. It is very important to realize the huge emphasis that is put on the indoor environment in connection with the energy certification in Sweden. Step 3: Optimisation Software for calculation of the energy use of buildings has just very recently became of any interest for Swedish building owners and their consultants. This is because the new Swedish building code requires that the measured energy use (excluding occupants energy use) of new buildings must fulfil the code requirements for the second year after completion. Since the building owner is responsible for that the Code requirements are fulfilled he suddenly requires reliable energy cal- Fraunhofer ISE

70 culations of his consulting engineer. The new requirements came into force on 1 July The only possible building energy modelling involved in the Swedish energy certification process is the calculation of the energy savings due to different proposed cost-effective energy efficiency measures. For some types of measures this requires a baseline energy model representing the buildings present state. The tools used for this varies between the consulting companies involved. Typically tools that need limited input data, and are easy to use, are involved. Examples are the softwares BV² or VIP+. Some companies have their own simplified software models. Only in very special cases the Swedish programme IDA-ICE (Indoor Climate and Energy) is used mainly because of the extended and detailed input data. The American programme Energy Plus is only used in research projects. The stock data for step 3 is typically not easy available but much of it will be required for calculations of cost-effective energy efficiency measures which is a major part of the energy certification process. In Sweden the general knowledge of FDD is very limited and no commercial software is available. Similarly Functional Performance Tests are more or less unknown. The baseline for the energy use of the buildings inside Building EQ will probably be calculated with help of the simulation software BV². In general, this is a one-zone model programme but with rather advanced possibilities to model the building services systems. For one or two of the demonstration buildings IDA-ICE may be used but the commercial version programme still have rather limited possibilities to model the building services systems so the advantage over BV² is not given. The commercially available IDA-ICE do not have the possibilities for adding your own models except e.g. control modules etc. However, CIT Energy Management has access to IDA Builder which compiles the source code with your own models. The measured minimal data set will be analysed as described in chapter 3.3 via the standard analyses. The FDD and possible Functional Performance Tests will be based on tests in practice and the results will depend very much on if the faults can be localised. Because of the cold climate in Sweden there is a focus on the proper function of the air-to-air heat recovery equipment in the air handling units. In premises buildings this equipment is typically rotary heat exchangers or run-around coil loops. Consequently, this is a major point in the FDD but to make any meaningful measurements of the function, winter conditions (below 0 C) are needed and also well located sensors. The last is because of temperature stratification in the air streams and is a problem with most BAS. In addition the sensors are typically uncalibrated. Step 4: Regular Inspection The Swedish building owners involved in the Building EQ project all have elaborate quality systems. This means that the possibility to integrate future regular inspections in the quality systems will be investigated. This will make the use of the regular inspections much more likely. Probably there is also a possibility to integrate automated regular inspections in the Larmia Control BAS where outlying measured data may trigger an alarm. Fraunhofer ISE

71 6.2.2 What barriers were identified / are expected in the course of the 4 step procedure? According to law all Swedish premises buildings (public and commercial), as well as multi-family buildings, must be energy certified by 1 january In public buildings and in all buildings with (residential and commercial) tenants the energy certificate must be displayed in a prominent place. After 1 January 2009 very much of the data for step 1 and parts of step 2 will be available. Inside this project the availability of data is more uncertain since it is carried out before that date. The application of the results from Building EQ will not be applied until after the big energy certification wave in Sweden. This means that almost all future use of the developed methodologies will be applied on premises buildings that are to be re-certified. The reason for a re-certification before the ten years have expired is probably that some energy efficiency measures have been carried out and the building owner wants to display a better energy rating of his building. The implementation of the methodologies from Building EQ will be easier in an already energy certified building. What are the main difficulties in acquiring the stock data? As mentioned, at present the floor area that has to be used in the energy certification is not available in the start of the energy certification process. In general, the availability of the stock data is depending on the age of the building and also on the building owner. One advantage in Sweden is that the Obligatory Ventilation Control (OVK) has been in place for nearly fifteen years and almost all building owners have OVK data with air flow rates. These flow rates are supposed to be the ones need for the present use of each room in the building. In the beginning of OVK the air flow rates were the ones required in the rooms when the building was erected bit this was changed after some years. However, only the main air flow rates through the air handling units are measured at the OVK and only a sample is taken of the measured air flow rates in the rooms. The OVK must be carried out every second or third year for buildings with balanced mechanical ventilations systems. What are the main difficulties in acquiring the measured data? The energy uses of the occupants/tenants are generally not available. To automatically read each tenants electricity meter in buildings with many tenants may be complicated and expensive. In some buildings it might be possible to install one meter on the wiring supplying the whole building, possible with the help of the local net utility. Because of the selected demonstration buildings the theoretical possibilities to get measured data from the BAS are good but the implementation phase is still to come. Are there any administrational issues (tenant/landlord problem)? The availability of tenants energy is always a tenant/landlord problem. To get the data from the local net utility, if possible at all, requires written permits from each tenant. The FDD and Functional Performance Tests may not under any circumstances disturb the tenants. Fraunhofer ISE

72 6.2.3 Cost for additional measurement equipment For the minimal measured data set : Not known yet. For additional measurements in Step 3: Not known yet What are the possible links between the national implementation of the EPBD and CC (including prescribed maintenance procedures on national level)? 6.3. Italy Unfortunately the link in Sweden is rather weak because of the Swedish energy certification model is based on operational rating excluding the occupants energy use. The needed stock data is rather limited and the measurements planned inside Building EQ are all outside the normal energy certification process. Only the required calculations of the energy savings needed for proposing cost-effective energy efficiency measures may have any link to continues commissioning How the four step procedure is realized Step 1: Benchmarking (Operational Rating) Usually the benchmarking of a non-residential building is made by gathering the yearly consumption of energy. If available, additional data concerning monthly consumption are used. If there is a heat service contractor for the building the useful area or volume are determined for contract purpose, otherwise the geometric reference values have to be determined through the analysis of the building projects. Once determined those values the specific energy or water consumption can be calculated. Climatic data (temperature, relative humidity, solar radiation) that are available are essentially monthly average provided form the UNI /10/ standard, which are available for all the Italian provinces (101 locations). Other data useful for the Italian climate are the IGDG /11/ data, they are in the TMY format and are provided for 68 locations. If monthly read of the energy consumption are available those could be used with the climatic data for elaborate an energy signature of the building. Up to now there are not comprehensive studies of the non-residential buildings energy behaviour, so there are not reference values. An experience on this field consist on the Italian participation to the Green Building Council /12/, but reference values are elaborated for LCA purpose and referred to a limited number of buildings. In the Italian implementation of the EPBD there are only target values for new building constructions. The standard heating energy consumption for the building stock has been assumed to be 120 kwh/(m2*a) in Lombardia region. Therefore a reference value for the simple benchmark must be elaborated at least through a first analysis of the building. Step 2: Certification The part of the Italian legislation that refers to the energy certification consist in the Legislative Decrees 192/05 and 311/06. In Italy only the Asset Rating through a standardized calculation procedure is going to be considered, which aim to deter- Fraunhofer ISE

73 mine the primary energy consumption for the winter heating and the DHW production. Cooling energy needs are not fully implemented up to now, but work is ongoing on this issue. To comply the step 2 requests of data is necessary to gather a limited set of data for the building envelope and for the HVAC systems. The certification process is simplified by the use of some tabulated values (e.g., internal gains), which permit to reduce the data gathering effort. Generally at least a on-site visit is necessary in order to verify the correctness of the assumed values and to check the situation of the HVAC system. For small buildings usually the certification process needs 3-5 days, whereas for larger buildings like non-residential ones the time can be much more, in relation to the architecture complexity, the age of the building and the available documentation. The certification process permits to gather a minimal set of data and to investigate the building in a simplified way, even if limited to the heating and DHW production energy consumption. Beyond the energy consumption determination a table with some hints have to be compiled in order to address what major intervention may be made for improving the performance of the building. Therefore a real analysis of energy savings opportunities is normally out of the certification procedure, being only part of an energy auditing. Step3: Optimization Due to the limits of the Italian certification process, for step 3 Fault Detection and Diagnosis and Optimization will be necessary a detailed analysis of the building and his HVAC system, especially of the cooling system. The Italian legislation provides guidelines concerning the maintenance of the HVAC system /13/, in term of general inspections. According to the available documentation the technical data of the cooling system will be acquired. This analysis will be integrated with the analysis of the measured data gathered (minimum set of data + other available data) as described in the chapter 3.3. The analysis of the measured data (statistical, visual, etc.) will permit to identify deviations form expected behaviour of the building-hvac system. Once defined which optimization could be made an estimation of the costs and benefit will be calculated and the corrective intervention could be discussed with the building owner. If the intervention is carried out, the concrete benefits can be defined in term of energy savings; comparison with the baseline developed will be a useful instrument to show the results obtained to the building owner. Step 4: Regular Inspection Today regular inspection is limited to a programmed maintenance issue. One or twice a year a system inspection is performed to verify the system functionality and check the value of the nominal efficiency, but only for boilers. Such programmed maintenance is based on the already mentioned guidelines Availability of stock data The stock data availability ranges from cases where a comprehensive description of the building structure, envelope and HVAC systems is present to cases where the amount of data is definitely poor. Generally if the building has been constructed in the last ten years digital detailed information on the project are available and Fraunhofer ISE

74 therefore a lot of information could be extracted from them. In this case the level of detail can comprise the information about the wall layers and thermal bridges (brick, insulation, plaster board, etc.), give information about the data transmission system (internet connections, telephone system, etc.), give an idea of all the service system installed (mainly the HVAC system like pump typology, heat or cold power, etc. and others like elevators). Nevertheless a building audit and a visit in the HVAC equipment room is necessary in order to assure the correspondence of the installations with the schemes and drawings. If the building is older than years, generally the available data are very limited and only on paper format. For what concern the structure and the envelope is usually available only the general geometry of the building and there are not information about the wall layers (usually is even impossible to determinate the thickness of the wall). The HVAC system has often been renewed or updated (following the evolution in thermal comfort needs and the renovation of the building) and therefore usually a complete design of the actual plants is not available. The time that is necessary to gather data and to analyse them could vary from 3-5 days in case of complete data availability to a couple of weeks if they have to be consistently integrated by a survey on the building. Fraunhofer ISE

75 Table 12 Availability of stock data in the Italian demonstration buildings stock data Poltecnico di Milano building 23 Poltecnico di Milano building 22 Poltecnico di Milano building 15 Year of construction complexity of HVAC plant* m m m floor plans, views, sections** + o o floor areas + o o Construction of building elements (walls, floors, roof, windows) + o - Kind of utilization and schedules o o o schematic drawings of HVAC system + o o operation strategies and schedules of HVAC system + o o product data of HVAC equipment o o - effort for acquisition of stock data in days*** *m = medium, l = low, h = high **+ = complete and actual information, o = partial information only or not actual (on-site inspection was necessary), - = no information available (has to be stipulated) *** this amount of days is mainly due to the administrative difficulties connected to the university. The information are fragmented between the maintenance staff, the heat management contractor staff, the designers, the producers, etc. Another problem is connected to the authorizations for enter to the buildings and to the facilities areas: although the authorization of the pro-rector each department, secretaries, etc. permit the access only after internal authorization Availability of measured data For the purpose of the project measured data (minimal data set) have to be gathered on hourly or sub-hourly basis, using sensors and data acquisition devices. Those are usually installed in the building itself and the data converge usually to the HVAC control room, before being sent to the data storage system, if existent. In Italy the approach, to install measurement sensors, centralize the data collection and have technical supervisors working out data analysis, it is not often used. Usually the facilities of medium or higher dimension are equipped with a simple control systems manageable by a computer directly connected and placed in the control room of the HVAC system. This could be named control management system. Even new buildings are rarely equipped with a BAS or this one cover only some aspects connected to the safety and security systems. Therefore the possibility to find a building which is equipped with a system that has stored energy consumption data and can export them is limited. Therefore, except in rare cases, Fraunhofer ISE

76 it has to be assumed that historical or even actual data about the system functioning are not available. Also in the case of most of the existing control system, the installation of new data acquisition equipment (sensors, data logger, etc.) has to be considered, due to the difficulties to face in upgrading for commissioning purposes. The existing control systems are often of low quality and usually the necessary additional electronic components are not market available or there are practical problems (e.g., not enough space in the control board). It would be always better to reach the agreement with the building owner for the installation of a BAS system with the purpose to perform a commissioning procedure for the building. Stand alone local data collection system, that are not network connected (wired or wireless) have to be avoided for commissioning purpose. Costs related to the data acquisition devices can be estimated in the range of , depending on the HVAC system complexity and on the existence of a previous system of acquisition that can be updated. The next table summarize the situation of the availability of measured data for the demonstration buildings in Italy. Fraunhofer ISE

77 Table 13 Availability of measured data for the minimal data set in the German buildings stock data Poltecnico di Milano building 23 Poltecnico di Milano building 22 Poltecnico di Milano building 15 Minimal data set Consumption* total consumption of fuels New, - - na total consumption heat from district heating na na o total consumption of cold from district cooling na na na total consumption of electricity New, total consumption of water New, weather data** outdoor air temperature o o o outdoor rel. humidity global irradiation indoor climate indoor temperature - - o indoor relative humidity system temperatures Flow / return Temperatures of main water circuits o o o supply air temperature of main AHUs o o o supply air relative humidity of main AHUs o o o data acquisition BAS na na na data logger - - o + = available, o = available but had to be renewed/enhanced, - = not available, had to be installed, na = not applicable * The campus have collected the energy consumption data (costs are due to global contracts with the energy producer or distributors) in a centralized manner up to now, so specific building consumption is difficult to be evaluated. Concerning water the data is not stored, due mainly to the very low cost of water in Milan. ** Although the buildings have not a complete system for acquire the external climate data, the Italian team of BuildingEQ will have access to the weather data that are obtained from a meteorological certified station that is installed on the higher building of the central campus. Fraunhofer ISE

78 6.3.4 Tool for step 3 (analysis and optimization) A specific software for this step will be developed from the Building EQ partners, based on the EPBD and CEN standards. This will be used therefore for analysis and optimization purpose. Other tools that will be used in step 3: standard analysis The minimal data set, once collected, will first be analysed through standard analysis like statistical analysis of specific simulation in order to evaluate the thermal behaviour of the building. This evaluation is intended to be the baseline for further investigations. If this is not possible, e.g., for the Building 23 of Italian demonstration buildings (this is a new building), the baseline will be elaborated from the certification process. CENED software /14/ The regional government of Lombardia, where the project s Italian demonstration buildings have been selected, released, through its agency, a software tool for the building s energy certification. As far as possible, with the main limitation connected to the fact that the software is based on the asset rating procedure for EPBD application, this one will be used for the analysis of the building. The task of the optimization study can be done with such a software on a very limited set of parameters. DOCET/15/ software This software has been elaborated by ITC CNR (Construction Technology Institute of the National Centre of Research) for Energy certification purposes. This is under development following the further definitive implementation of the EPBD in Italy. So this software too follow the approach to analyse the building under an Asset Rating approach. His use for an analysis of the thermal behaviour of the building and for possible optimization is therefore limited as the CENED software. Energy Team ES3 software /16/ some of the installation of measurement device used in the demonstration projects are provided by the company Energy Team Italia. The devices are supported by the ES3 software, which permit to analyse the historical data through time related graphs and implement internal calculation for data acquisition post processing. Within the limit of the software the users can implement some calculation in order to obtain, through the analysis of the evolution of the data, the results of the intervention on the building facilities. TRNSYS software /17/ In cases where it could turn out necessary for the level of detail of the study a complete simulation of the building can be performed by a dynamic simulation software, implement all the information gathered about the building envelope, the HVAC system set-points and the people occupancy. If chosen, this approach will be time consuming and therefore has to be seen as validation of others approaches. Functional performance test The analysis of the HVAC system behaviour can highlight partial system faults. In these cases a functional performance test can be performed, through the intervention of an external expert if necessary. Fraunhofer ISE

79 This list is not intended to be exhaustive to the set of tools that can be used for perform the data analysis or to study the effects of the optimization of the system. Some other tools, both software or in the field tools, could eventually be used for an ad hoc analysis of certain part of the system What barriers were identified / are expected in the course of the 4 step procedure? Within the application of the four step procedure at the three demonstration buildings chosen among the campus building stock the main barriers that have been identified are: Administrative difficulties: due to the specificity of the university buildings it is necessary to involve a lot of different actors, which are depositary of the different information. Therefore the building management area, the heat management service contractor, the administrative staff, the IT service department, etc. have to be informed of the necessities connected to the following of the procedure and give their approval for each step of the procedure. Another point consist in the limitation on the access to the buildings: for evaluate the envelope and the HVAC systems it is necessary at least one visit in the building; this have to concretized obtaining all the necessary authorization from the different head of department, administrative responsible, heat management contractor, etc. Lack of documentation of buildings: the three different buildings have different age (1961, 1999, 2007); obtain a quality documentation of the envelope and the HVAC system depends on the age of the building and is connected to the design practise in the different decade of construction. Lack of availability of measured data: among the three building only the older one is connected to the central heating station and permit to have the data collected up to now. The first analysis of this data show that they are not always of good quality (need of calibration, inconsistency of the values, etc). The system management system in the other two buildings don t collect the data in a proper way and is not connected to the central heating station. Difficult of integration: the actual systems can be integrated with some difficult, physical space for install the measurement device and for connect the data logger have to identified, the control room of the system is not connected to internet, etc. Energy consumption of different source: the campus have a heat management contractor and obtain the specific consumption of the buildings is difficult for both the fact that is a sensible data of the contract and that is not necessary to manage each building differently and analyse the specific consumption. The electricity consumption also is paid with a singular contract, so the data of specific electricity consumption are not usually taken in account. Duration of the heat management contract: a specific law for avoid criminal assignation of the contract for public provision of services impose that each two years the public service contract has to be renewed through a Fraunhofer ISE

80 public call for tender. In a CC optic this seems to be a limit for the public building because the duration of HVAC system maintenance and heat provision contract is lower than the expected payback period for a commissioning interventions and a decisive limit for made the commissioning continuous What are the possible links between the national implementation of the EPBD and CC? Italian implementation of the EPBD is only based on the Asset Rating approach. Unfortunately the implementation is partial and is referred mainly to the heating and DHW consumption. Summer air-conditioning, ventilation, solar shading, electricity consumption for lighting is not or partially considered up to now, they will be implemented in a near future. Therefore a lot of information about the building envelope, a good level of information about the heating/dhw system and in the future about the cooling system too are available through the certification process. This is a good basis for the CC procedure. Up to now there is not a legislative obligation for a global maintenance program of the HVAC system, except for what concern the boiler. Each six month or yearly a verification must be made in order to guarantee a minimum level of performance of the generator, thus a CC procedure can assure the continuous respect of this value. For what concern the HVAC system only some guidelines have been provided by the law, therefore is not usual that the building is inserted in a maintenance procedure which can implement the CC easily Finland How the 4 step procedure is realized Step 1: Benchmarking (Operational Rating) In Finland the benchmarking is normally the yearly energy consumption divided by gross building volume or gross area (kwh/m³ or kwh/m²). Heating energy consumption is weather corrected (normalised) using degree-days. Degree-day values are published monthly by the Finnish meteorological institute. For heating energy benchmarking usually the per building volume value is used. For electricity the per gross area value is becoming more popular. For water it is usually the yearly consumption per gross building volume (dm³/m³) and in residential buildings sometimes also per tenant per day -values are used (dm³/person, day). There are several sources for reference values: Motiva maintains a database of consumption data from all buildings where an energy audit has been made. The average consumption data before the energy audit is categorized by building type and for some types also Fraunhofer ISE

81 by construction year. This is probably the most known and most widely used source of reference values. The Association of Finnish Local and Regional Authorities maintains a database of energy and water consumption measured in municipality owned buildings. They publish average consumption data per building type. Many utilities, district heating suppliers and water suppliers have databases of their own and they give feedback to their clients in energy and water bills, comparing the measured consumption to the average value of that specific building type (sometimes also categorized per year of construction). Owners of large building stock (commercial buildings, state owned buildings, etc) have their own energy monitoring systems with their own average specific reference energy consumption figures - and there are also monitoring software suppliers with average reference values. Step 2: Certification The building energy performance certificate in Finland has several variations: certification for new buildings is an asset rating calculated for small buildings using a standardized calculation method calculated for large buildings using any suitable calculation method certification for existing buildings is an operational rating, given either by the landlord in a very simple format an energy auditor in connection with a thorough energy audit an authorized certification consultant based on a simplified walkthrough audit The energy certificate has a rating for the total energy use per gross building area kwh/m²,a categorized into classes A-G with reference values for ten different building types. The total energy consumption consists of heating energy, cooling energy and electricity for the building (this includes fans, pumps, electrical heating, outdoor lighting, lifts, escalators, car heating, frost protection heating, fixed lighting). So basically the electricity consumption is total consumption minus socket load. For existing buildings the actual measured energy consumption for three years is needed, from this data the average consumption is calculated. The yearly heating energy consumption is weather corrected by using degree days. The average for three years is weather corrected to Jyväskylä, located in central Finland. This eliminates the effect of the location of the building. Fraunhofer ISE

82 There are some problems connected to the total energy consumption in the energy certificate: cooling energy is very seldom measured (usually only when connected to a district cooling network) and the electricity consumption of chillers is not usually metered either - the rules for estimating the cooling energy use are not very accurate and there will be plenty of speculation connected to this issue the tenants electricity metering usually includes all electricity use (lighting and socket loads) and in many buildings there may be several (even dozens of) electricity supplies for the tenants and if the building owner has an energy certificate made, the consumption data from the tenants is not necessarily available - the estimation of the fixed lighting energy use on building level will be based most often on rough estimates rather than measured data. The gross building area has been the target for some criticism: buildings with large unheated garages included in the gross area will have the best energy class. Step 3: Optimisation In Finland the step 3 is going trough in basically the same way than in Germany, but a dynamic energy simulation will be widely utilized as part of the fault detection and optimization. Remote access audits and system monitoring are used to detect energy wasting malfunctions and faults in settings and operation. This procedure utilizes BAS trend logs to detect faulty equipment and faulty settings. Hourly power demand data - where available - could be utilized more efficiently in the fault detection and optimisation. Step 4: Regular inspection Building system audits and inspections repeated at 6 months or 12 months intervals are becoming more and more popular among building owners. The site visit serves two purposes: the most obvious energy efficiency issues are checked and also the level of maintenance is evaluated. There may be several maintenance contracts related to energy using building service systems and the building owner wants to be sure that all the obligations in the contract have been fulfilled at an adequate level. Fraunhofer ISE

83 6.4.2 Availability of stock data For the building data in Finland mainly applies what has been said in for Germany: the availability of stock data depends very much on the age of the building and on the level of documentation that was prepared during construction or during a major renovation. The older the building the less there is documentation available. For a building which is years old, usually some of the design documents are not available. Most problematic are cases where the building has had several owners and the documents have been lost in the transaction processes. Building service system drawings (HVAC and electricity) are very difficult to keep up to date without proper document management, especially if there are frequent refurbishments when new tenants move in. Since 2000 an operation and maintenance manual has been mandatory for all new buildings and renovated buildings. There are several database versions commercially available but also paper version manuals have been prepared. The majority of the manuals have the main data of the building in a very compact format. A problem in energy benchmarking is related to the gross building volume and area. Sometimes there are several different figures and it requires some calculations or investigations to find out which is the official and correct one. Fraunhofer ISE

84 Table 14 gives an overview of the availability of stock data in the demo buildings in Finland. All of these buildings have been recently built or renovated and the design documents and operation manuals have been properly prepared and saved. Fraunhofer ISE

85 Table 14 Availability of stock data in the Finnish demo buildings stock data Senate HQ Aurora 2 Department Mechanical Engineering State Treasury Year of construction / renovation / /2008 complexity of HVAC plant h h h h floor plans, views, sections** floor areas Construction of building elements (walls, floors, roof, windows) Kind of utilization and schedules o o o o schematic drawings of HVAC system operation strategies and schedules of HVAC system product data of HVAC equipment o o o still under renovation effort for acquisition of stock data in days <1 <1 <1 1 m= medium, l= low, h= high += complete and actual information, o= partial information only or not actual (on-site inspection was necessary), -= no information is available Availability of measured data In Finland buildings usually have their own energy meters. In cities and municipalities most buildings are connected to a district heating system and the consumption is measured for each building. The situation in water supply is the same, in urban areas most buildings have their own water meter. In electricity there is more variation: there may be an energy meter for the total consumption in a building and the tenants have their own sub meters and they pay for their electricity consumption according to the sub meter readings. The other option is that each tenant has his own electricity contract and his own meter and pays for the electricity consumed. So the electricity use of the occupants is generally not available. Energy and water meters in most buildings are read monthly. Mostly the meter readings are taken manually by the maintenance staff and the readings are fed into a monitoring system. Fraunhofer ISE

86 Energy monitoring is at high level in Finland and most building owners have a very good database of actual measured consumption figures. Usually energy monitoring is done on monthly level and the measured values are compared to calculated targets or to the consumption of the same month in the previous year. The most problematic cases are buildings with oil-fired boilers where the energy consumption can not easily be monitored very accurately. Manual meter reading and data input has the risk of faulty readings and input values. Usually the monitoring software does a rough data check-up and informs the user if the reading is out of range compared to the previous meter reading. Many utilities and service providers have remote monitoring services and monthly data - usually also hourly data - is available. Consumption data reading and energy monitoring may also be connected to a building automation system. In these cases the energy and water meters have a pulse output. The minimal data-set defined in the Building EQ project can usually be obtained from any Finnish building with a building automation system. Relative humidity is not usually measured as there is no humidification in the air handling units. The solar radiation values are usually not measured locally, but can be acquired from the Meteorological Institute. All of the Finnish demo buildings have these values measured in the BAS. The trend-logs have to be set building by building to store this data at desired intervals and to store it for a required time period. To provide all data acquisition devices, which are required for minimal data set according to chapter 2.3, the costs can be estimated in the range of , depending on the HVAC-system complexity. In Finland most large buildings and often also smaller buildings are equipped with a BAS. Normally BAS covers most of the minimal data set measurements. If a BAS is installed in the building, the cost-effective way to add some possibly required extra data acquisition devices is usually to connect these to the BAS. In that case the overall costs might be lower than the estimated level. Requirements for additional measurements and devices come up when FDD and optimization process are active. The number of the data acquisition devices and costs depend on the FDD and optimization needs. This can cause large variation in the required devices and costs. One of Finland s demo buildings (Senate HQ) was realized with a very large and particular energy measurement system. It can be assumed that the most of FDD and optimization needs for additional measurement can be solved by this kind of system. To approach the same type of energy measurement level that is used in the Senate HQ the additional costs can be estimated in the range of , depending on the HVAC-system complexity. Fraunhofer ISE

87 Table 15 Availability of measured data for the minimal data-set in the Finish demo buildings stock data Senate HQ Aurora 2 Department Mechanical Engineering State Treasury Minimal data set consumption still under total consumption of fuels na na na na total consumption of district heat total consumption of district cold + total consumption of electricity total consumption of water weather data outdoor air temperature outdoor rel. humidity na na na na global irradiation indoor climate indoor temperature indoor relative humidity na na na na system temperatures Flow / return Temperatures of main water circuits supply air temperature of main AHUs supply air relative humidity of main AHUs na na na na data acquisition BAS data logger na na na na renovation += available, o= available but had to be renewed/ enhanced, - = not available, had to be installed, na = not applicable Fraunhofer ISE

88 6.4.4 Tools for step 3 The following tools will be used in step 3: Visualisation techniques and statistical analysis A standard analysis according to chapter 3.4 will be performed, when minimal data set measurement are available Simulation by utilizing building information models (optimisation) Building information models (BIM) may exist, if the architect is using advanced 3D modelling tools in the design. In most cases the spatial BIM has to be created for separately to allow efficient use of dynamic energy simulation on a whole building level. In Finland commonly used dynamic simulation software are IDA ICE and RIUSKA, which both are able to utilize BIM in neutral and open data format IFC (industry foundation classes). RIUSKA is also capable for spatial whole building simulation and used in Finnish BuildingEQ demonstration buildings. Additional stock data will be gathered to support data from BIM model. Functional Performance Tests (FPT) When standard analysis has observed deviation in building system, functional performance tests might be done What barriers were identified / are expected in the course of the 4 step procedure The main barriers that have been expected are: If there are several buildings with different functions or a very large building complex connected to one main energy meter, it may be very difficult to detect any energy wasting faults by simple benchmark values because the faults and operational mistakes and false settings will most likely compensate each other If the energy monitoring does not work properly and the consumption data has major errors or data is missing, no reliable benchmarking is possible. If the building automation system does not operate properly and the data in the system is not reliable, the fault detection is not possible. Poor documentation and poorly made graphics in the user interface of the BAS make fault detection (and also the every-day use of the system) difficult. Telecommunications security and reliability e.g fire wall settings could produce problems. On the other hand open networks facilitates more independent communication and offer increased use of building measurement data. Fraunhofer ISE

89 6.4.6 What are the possible links between the national implementation of the EPBD and CC? The energy certificates based on asset rating for new buildings could be a starting point for continuous commissioning - when the rating is properly calculated by using a reliable calculation method. The asset rating certificate is valid for four years and has to be replaced by the operational rating when the building has been in use for three years. If the asset rating calculation takes into account the actual use of the building and the performance of the building service system, the energy certificate can be used as the target consumption in the energy monitoring. However, in order to make use of the energy calculation, more documentation on the initial data is needed than what the energy certificate shows. Monthly target values are the minimum requirement. There is a danger of having cheapie versions of energy certificates on the market in the future as there is no quality control for the asset rating calculations. An energy certificate is mandatory when applying for a building permit for a new building or when there is a major renovation. The preparation of the certificate is just one more task for the designer group and - in the worst case scenario - needs to be done in a great hurry without any data on the future tenants and the actual use of the building. A poorly made calculation will not serve the purpose of a consumption target. When the energy issue is important for the building owner and has the proper emphasis during the design process, the properly calculated asset rating is a good basis for monitoring and continuous commissioning. The energy certificate based on operational rating and prepared in connection with an energy audit or based on a site visit and a walk-through audit has a close connection to continuous commissioning. The certificate must include suggestions on energy efficiency improvements and these can only be found when the key parameters in the building operation are checked. All energy using systems have to be audited briefly, checking operation schedules, temperature settings, heat recovery performance, lighting controls, electrical heating settings, etc. These are all parameters that are included in the CC site visits. Fraunhofer ISE

90 7. Possibilities for further analysis Besides the more or less standardized analysis described in chapter 3 there are a many more opportunities for further and more detailed analysis which might be necessary if it comes to specific problems. This chapter tries to give an overview over these additional possibilities Stock Data For the analysis of special subsystems further stock data might be necessary to be gathered. Table 16 lists the stock data of major subsystems. Most of the data in the following table will be available in the case that the asset rating according to step 2 delivers a building information model (BIM). If this is not the case the analyst has to decide in dependency of the available data and associated cost which additional data is to be gathered. Table 16 stock data for optional further analysis grouped by subsystem data / subsystem unit remarks building zones geometric data - gross floor area/ volume/envelope construction of building elements - construction of walls, floor, roof, windows utilisation - including: kind of utilisation, occupancy schedule, operating schedule (heating, cooling, ventilation if appropriate), internal loads ventilation system - kind of ventilation (e.g. natural, exhaust, supply and return, with/without heat recovery) heat/cold emission systems - e.g. radiators, surface heating/cooling, supply air lighting system - Kind of lighting system shading system - Kind of shading system (outside/inside, static/variable,etc.) setpoints - setpoints for heating, cooling, humidity and lighting if appropriate (and information about control schemes) (continued on next page) Fraunhofer ISE

91 (continued) data / subsystem unit remarks generators kind of generator - e.g. boiler, chiller, cooling tower, etc. capacity kw nominal capacity of generator use of generator - which end uses (e.g. space heating, DHW, supply of an absorption chiller) does the generator serve? performance data - performance at full load and part load operating temperatures C flow and return temperatures of associated loops at design conditions (e.g. hot water flow and return temperature for a boiler) flows m3/h or kg/h flows of the associated loops at design conditions control scheme - control scheme of the generator (including dependencies to other generators) Air Handling Units (AHUs) scheme of the AHU - line diagram showing the principle construction of the AHU uses - Which uses or zones respectively does the AHU serve Fan - kind of fan, nominal power, control (variable or constant speed) nominal airflows m3/h nominal airflows (outside, return, supply) nominal pressure difference Pa nominal pressure difference for the whole unit heat recovery - if present: kind of heat recovery, nominal efficiency coils - inlet / outlet temperature and flow rate at design conditions humidification - kind of humidifier, capacity dehumidification - kind of dehumidifier, capacity control sequence - set points for heating, cooling, humidification, dehumidification, control sequence, operating schedule (continued on next page) Fraunhofer ISE

92 (continued) data / subsystem unit remarks water loops scheme of the loop - simplified scheme of the loop with major components uses - which systems does the water loop serve (e.g. radiator heating, floor heating, cooling coil, etc.) Pump - kind of pump, nominal power, control (variable or constant speed) operating temperatures C flow and return temperatures loops at design conditions flow m3/h or kg/h nominal flow at design conditions nominal pressure difference Pa nominal pressure difference control scheme - control scheme for loop (set points, set backs, operating schedule) Measurements Additionally to the measurements of the minimal data set according to 2.3, it might be necessary to install further measurements to identify or locate energy conservation measures or to be able to monitor the system or to determine the real energy savings after the retrofit. Three different kind of measurements can be distinguished /18/: spot measurement: Only one single measurement for systems or components with only one operation mode (e.g. measurement of power demand of a constant speed fan). These measurements are normally conducted with portable equipment (sensors). short term measurements Temporary measurements conducted over a period of some hours to several weeks to identify the performance of time varying systems (e.g. profile of a load). These measurements are normally conducted with portable equipment (sensors and data logger). long term Measurements Permanent measurements that are recorded by a stationary data acquisition device which is able to transfer the data to a central data server. Which additional measurements are to be conducted and how they should be conducted (spot, short time, long time) is to be decided by the analyst on basis of the analssis done according to chapter 3. Fraunhofer ISE

93 Principally long term measurements are required to determine consumption and boundary conditions (such as weather). Spot and short term measurements can be applied to identify or further investigate faulty or non-optimal operation. Table 17 list possible additional measurements. Table 17 Optional additional measurements for further analysis Measured value / subsystem unit kind of measurement* time resolution** Remarks building zones zone air temperature C ltm / stm h / sh zone air rel. humidity % ltm / stm h / sh supply air temperature C ltm / stm h / sh electricity consumption kwh ltm / stm h / sh electricity consumption for lighting kwh ltm / stm h / sh delivered heat kwh ltm / stm h / sh delivered cold kwh ltm / stm h / sh illumination level lux ltm / stm h / sh control signals - ltm / stm h / sh for emission (e.g. operation of heating system),ventilation, lighting, shading etc. if applicable Generators fuel consumption kwh ltm / stm / spm h / sh e.g. gas for a gas boiler electric energy consumption kwh ltm / stm / spm h / sh Electric energy consumption of generator and associated equipment (e.g. for a compression chiller: chiller, chilled water pump, condenser water pump) generated electricity kwh ltm / stm / spm h / sh Generated electricity (e.g. from a CHHP) Generated heat / cold kwh ltm / stm / spm h / sh Generated thermal energy (e.g. heat from a boiler) flow / return temperatures C ltm / stm / spm h / sh for hot water, chilled water, condenser water (if applicable) water flow m3/h ltm / stm / spm h / sh for hot water, chilled water, condenser water (if applicable) control signals - ltm / stm / spm h / sh For generator and associated equipment Fraunhofer ISE

94 (continued) Measured value / subsystem unit kind of measurement* time resolution** Remarks Air Handling Units (AHUs) electric energy consumption kwh ltm / stm / spm h / sh Electric energy consumption of AHU (e.g. fans, dampers, pumps) air temperatures C ltm / stm / spm h / sh Temperature of supply, return, outside and mixed air if applicable Additionally spot measurements at the inlet and outlet of single components such as heating or cooling coils or humidifiers may be conducted air humidity % ltm / stm / spm h / sh Humidity of supply, return, outside and mixed air if applicable air pressure Pa ltm / stm / spm h / sh Static pressure Additionally spot at the inlet and outlet of single components such as heating or cooling coils or humidifiers may be conducted coil water temperatures C ltm / stm / spm h / sh Flow and return temperatures of coils coil water flow m3/h ltm / stm / spm h / sh Flow rate of coils control signals - ltm / stm h / sh valves, dampers, actual setpoints water consumption l/h ltm / stm h / sh for humidifiers water loops water temperatures C ltm / stm h / sh Flow and return temperature of loop water flows m3/h ltm / stm h / sh Flow rate in loop pressure difference Pa ltm / stm h / sh Head loss of single components control signals - ltm / stm h / sh Control signals of pumps, valves **h= hourly, sh=sub hourly *spm= spot measurement, stm=short term measurement, ltm= long term measurement In order to reduce the cost for measurements, alternative measurements techniques should be investigated, which allow to replace expensive measurements by simpler (and maybe less accurate) ones. Especially the substitution of longterm measurements by spot- or short-term measurements will be of interest. Examples for this can be: Fraunhofer ISE

95 electrical energy consumption For any component with constant load (instead of electrical metre): spot metering of power + permanent recording of operating hours (which is especially simple if a BAS is present) air or water flowrate: For circuits with constant flow (instead of flow metre): spot measurements of pressure difference and power of fan or pump respectively. Determine flow from characteristic curve. Permanent recording of operation hours of the fan or pump respectively Performance Metrics & Evaluation Techniques A big variety of possible evaluation techniques is existing. The following list just gives an short overview (for further details please refer to the report The EPBD and Continuous Commissioning which is available from the project website: Visualisation techniques By means of a special ( intelligent ) way to visualise the measured data (e.g. XY-plots or carpet plots) the performance or characteristics of the operation of a system or component can be examined. These techniques rely only on measured data and need no mathematical model. The visualised data can be inspected manually e.g. by the operation staff or automatically by some kind of pattern recognition algorithm. They are well suited for fault detection and diagnosis. Context dependent visualization People are able to identify information from patterns most easily. This can be utilized to visualize data in different contexts. Such contexts are Comparison of yearly, monthly, daily or hourly data from different units (e.g. apartments) of one building energy stoplights or Comparison of yearly, monthly, daily or hourly data from different units (e.g. rooms) of one building Some examples are given in the figures below (reference: ennovatis): Fraunhofer ISE

96 Figure 17: Comparison of energy consumption of apartments in a apartment building (basis daily, monthly and yearly averages) Note differences of up to a factor of 10 due to different user behaviour occur. Figure 18: Energy consumption in a office building. Note at least a factor of 4 due to different user behaviour Fraunhofer ISE

97 Statistical analysis / Black box models Black Box models are purely measurement based models without physical parameters. This could be regression models for energy signatures or identification of profiles. Usually these models are used for fault detection (identification of abnormal energy use). They are not well suited for diagnosis and optimisation. Simulation with simple or detailed models Models can be created for the whole building and/or single components like generators. Complexity of models can range from single parameters to a sophisticated collection of sub-models. For FDD and Optimization the models must be calibrated in the sense that their parameters are adjusted in such a way that the model resembles the behaviour of the real system. After calibration either the consumption values, the state variables or the identified parameters can be benchmarked. Furthermore these models can be used for optimisation tasks on the system level i.e. together with other models. More complex models usually require additional measurements. Furthermore detailed stock data is needed to set up such a model (BIM). Functional Performance Tests (FPT) FPTs are standardised test on the system level or component level that are used to ensure that the system is functioning according to the design intent and efficiently. In contrast to the above mentioned evaluation technique, these test are active, i.e. the system is not only monitored passively but is actively forced into specific operation points. Also the analysis of the data that is recorded during the test is standardised. Descriptions of many FPTs for different systems are available e.g. on the internet. FPTs are well suited for FDD and optimisation. The following table which gives an overview over possible performance metrics and evaluation techniques that can be used for further analysis (in addition to the 4-step approach described in 3). Fraunhofer ISE

98 Table 18 Step 1: performance metrics & evaluation techniques (optional) subsystem / Performance metric Evaluation technique description possible result BIM desirable additional measurements necessary BAS desirable Whole building / building zone: Specific energy consumption (fuels, heat, cold, electricity) Visualisation XY-plots: Specific energy consumption vs. outdoor air temp. Qualitative characteristics of energy signature Qualitative load pattern carpet plots: Manual detection of gross faults Specific energy consumption Statistical Analysis identification of energy signatures via multiple linear regression (automatically), Quantitative characteristics of energy signature Automated detection of gross errors Simulation with models (only for buildings / zones with uniform utilisation) Automated Calibration of model with fixed model structure (identification of Parameter, e.g. building / zone internal mass). Simulation (parameter variation) for optimisation (potentially in connection with other models) optimisation of heating/cooling schedule and setpoints provide load profile for other models (e.g. of generators) optimisation of staging or sequencing of most efficient generators optimisation of energy cost by load shifting Fraunhofer ISE

99 (continued) subsystem / Performance metric Evaluation technique description possible result BIM desirable additional measurements necessary BAS desirable Whole building / building zone consumption profiles Statistical Analysis Energy signatures (daily / weekly data) detection of abnormal energy consumption on a weekly / daily basis ( ) Statistical Analysis Statistical analysis of time series of hourly consumption data ( Daytyping ) detection of abnormal energy consumption on a daily/hourly basis quantitative identification of consumption profiles for use in other models (e.g. identification of electricity or water consumption profiles for use in simulations) ( ) Fraunhofer ISE

100 (continued) subsystem / Performance metric Evaluation technique description possible result BIM desirable additional measurements necessary BAS desirable generators Correct / efficient performance (COP) visualisation XY-plots: Power (input/output) vs. outdoor air temp. COP vs. ambient temp. COP vs. Power (input/output) Normalised power (output) vs. normalised power (input) (part load performance) Flow Temp. vs. outdoor air temp. or vs. power (output) Temp. difference (hot, chilled, condenser water) vs. outdoor air temp. carpet plots: Power (output/input) COP Flow temp. (hot, chilled, condenser water) Temp. difference (hot, chilled, condenser water) Detection of poor efficiency Detection of poor partload performance Detection of deficient control schemes (flow temperature) Detection of under-/ over-sizing Detection of abnormal pattern of operation Fraunhofer ISE

101 (continued) subsystem / Performance metric Evaluation technique description possible result BIM desirable additional measurements necessary BAS desirable generators Correct / efficient performance (COP) simulation with models Automated calibration of models with fixed model structure (identification of Parameter). Simulation (parameter variation) for optimisation (potentially in connection with other models) Fault detection on model parameters Detection of faults in operation (abnormal COP) Utilisation for global optimisation (system simulation) Functional Performance Tests Perform standardised test on component Fault detection / ensure of operation according to design intent Fraunhofer ISE

102 (continued) subsystem / Performance metric Evaluation technique description possible result BIM desirable additional measurements necessary BAS desirable AHUs Control schemes Visualisation XY-plots: Supply air temp vs. outdoor air temp. Electricity consumption vs. outdoor air temp. Coils-temp-difference/flow temp. / energy consumption vs outdoor air scatter plot matrices: Including: temperatures/humidity (outdoor, supply, return), control signals (damper, valves, pumps) carpet plots: Controls signals (dampers, valves, pumps) Electric energy consumption. Thermal energy consumption / temp. difference Detect deficient control schemes (air side) Detect deficient coil control scheme / efficiency (water side) Detect deficient operation patterns (schedules) Fraunhofer ISE

103 (continued) subsystem / Performance metric Evaluation technique description possible result BIM desirable additional measurements necessary BAS desirable AHUs Control schemes Simulation with model Simulation with detailed model or connected component models respectively Detection of faults in operation (e.g. abnormal supply air temp., energy consumption) Utilisation for optimisation of control schemes Utilisation for global optimisation (system simulation) Functional Performance Tests (FPT) Perform standardised test on component Fault detection / ensure of operation according to design intent Fraunhofer ISE

104 (continued) subsystem / Performance metric Evaluation technique description possible result BIM desirable additional measurements necessary BAS desirable Water loops operation scheme/profiles Visualisation XY-plots: Flow Temp. vs. outdoor air temp. Detect deficient control schemes Detect deficient operation patterns (schedules) Temp. difference vs. outdoor air temp. Flow Temp. vs. Control signal of pump / valve Functional Performance Tests (FPT) Carpet plots: Control signal of pumps / valves Perform standardised test on component Fault detection / ensure of operation according to design intent Fraunhofer ISE

105 (continued) subsystem / Performance metric Evaluation technique description possible result BIM desirable additional measurements necessary BAS desirable General issues Operation schedules - Visualisation carpet plots: Control signals Detect abnormal operation patterns statistical analysis Statistical analysis of time series of hourly data ( Daytyping, e.g. control signals or energy consumption) detection of abnormal operation on a daily/hourly basis quantitative identification of utilisation profiles General issues set points Visualisation carpet plots: Detect abnormal operation patterns setpoints XY-plots: setpoints vs. outdoor air temp. Detect abnormal Fraunhofer ISE

106 (continued) subsystem / Performance metric Evaluation technique description possible result BIM desirable additional measurements necessary BAS desirable General issues efficient staging /sequencing of generators Simulation with model Simulation of building and systems for determining the most efficient generators for the actual operation, potentially combined with weather or load forecasts Highly energy-efficient generation Fraunhofer ISE

107 7.1.4 Outcomes / aims of further analysis Identification or location of saving potentials that were not captured by the 4- step approach based on certification and the minimal data set. Fraunhofer ISE

108 ANNEX Fraunhofer ISE

109 Annex 1 Checklist Benchmarking For step 1 Benchmarking a checklist in Excel format was developed that is used to gather the data for the demonstration buildings. This checklist is also available from the project website ( Fraunhofer ISE

110 Fraunhofer ISE Building EQ - Guidelines for the Evaluation of Building Performance

111 Fraunhofer ISE Building EQ - Guidelines for the Evaluation of Building Performance

112 Fraunhofer ISE Building EQ - Guidelines for the Evaluation of Building Performance

113 Fraunhofer ISE Building EQ - Guidelines for the Evaluation of Building Performance

114 Fraunhofer ISE Building EQ - Guidelines for the Evaluation of Building Performance

115 Fraunhofer ISE Building EQ - Guidelines for the Evaluation of Building Performance

116 Fraunhofer ISE Building EQ - Guidelines for the Evaluation of Building Performance

117 Fraunhofer ISE Building EQ - Guidelines for the Evaluation of Building Performance