Guidelines for the Evaluation of Building Performance · Building EQ - Guidelines for the...

Guidelines for the Evaluation of

Building Performance

Editors: Christian Neumann

Dirk Jacob Fraunhofer Institute for Solar Energy Systems

Freiburg, Germany

Building EQ - Guidelines for the Evaluation of Building Performance

Fraunhofer ISE 29.02.08 I

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/SI2 .448300

This report was prepared as a deliverable of Workpackage 3 of Building EQ.

February 2008

For more information visit us at:

www.buildingeq.eu

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 29.02.08 II

CONTENT

Abstract ............................................................................................................... 1

1. Introduction ................................................................................................ 3

2. Guidelines – The general outline .............................................................. 5

2.1. Ongoing Commissioning..............................................................................5

2.2. The Building EQ approach (4-step approach) ............................................7

2.3. The Minimal Data Set ....................................................................................9

3. Guidelines – The Details of the steps..................................................... 12

3.1. Step 1: Benchmarking (Operational Rating) ...............................................13

3.1.1 General Description ......................................................................13 3.1.2 Flow Chart ....................................................................................13 3.1.3 Stock Data ....................................................................................15 3.1.4 Measurements ..............................................................................16 3.1.5 Performance Metrics & Evaluation Techniques............................16 3.1.6 Outcomes / aims of this step.........................................................17

3.2. Step 2: Certification (Asset rating) ..............................................................18

3.2.1 General Description ......................................................................18 3.2.2 Flow chart .....................................................................................18 3.2.3 Stock Data ....................................................................................20 3.2.4 Measurements ..............................................................................20 3.2.5 Performance Metrics & Evaluation Techniques............................20 3.2.6 Further actions..............................................................................21 3.2.7 Outcomes / aims of this step.........................................................21

3.3. Step 3: Optimisation – Overview ...............................................................22

3.3.1 General description.......................................................................22 3.3.2 Outcomes / aims of the step .........................................................24

3.4. Step 3a: Standard analysis (measurement based)...................................25

3.4.1 General description.......................................................................25 3.4.2 Flow chart .....................................................................................25 3.4.3 Stock Data ....................................................................................27 3.4.4 Measurements ..............................................................................27 3.4.5 Performance Metrics & Evaluation Techniques............................27


Fraunhofer ISE 29.02.08 III

3.4.6 Further actions..............................................................................35 3.5. Step 3b: Standard analysis (model based) ...............................................36

3.5.1 General description.......................................................................36 3.5.2 Flow chart .....................................................................................36 3.5.3 Stock data.....................................................................................38 3.5.4 Measurements ..............................................................................38 3.5.5 Performance Metrics & Evaluation Techniques............................38

3.6. Step 4: Regular Inspection .........................................................................39

3.6.1 General Description ......................................................................39 3.6.2 Flow Chart ....................................................................................39 3.6.3 Stock Data ....................................................................................41 3.6.4 Measurements ..............................................................................41 3.6.5 Performance Metrics & Evaluation Techniques............................41 3.6.6 Outcomes / aims of this step.........................................................42

4. Measurement and verification / Definition of Baselines ....................... 43

4.1. Baseline Regression models (IPMVP Option C).......................................44

5. Measurement equipment and data transfer ........................................... 50

5.1. Technical issues..........................................................................................50

5.2. Data transfer ................................................................................................51

5.3. Cost ..............................................................................................................53

6. National approaches ................................................................................ 54

6.1. Germany.......................................................................................................54

6.1.1 How the 4 step procedure is realized............................................54 6.1.2 Availability of stock data................................................................55 6.1.3 Availability of measured data........................................................56 6.1.4 Tools for step 3 (analysis and optimization)..................................60 6.1.5 What barriers were identified / are expected in the course of the 4

step procedure?............................................................................60 6.1.6 What are the possible links between the national implementation of

the EPBD and CC?.......................................................................61 6.2. Sweden.........................................................................................................61

6.2.1 How the 4 step procedure is realized............................................61


Fraunhofer ISE 29.02.08 IV

6.2.2 What barriers were identified / are expected in the course of the 4 step procedure?............................................................................66

6.2.3 Cost for additional measurement equipment ................................67 6.2.4 What are the possible links between the national implementation of

the EPBD and CC (including prescribed maintenance procedures on national level)?.........................................................................67

6.3. Italy ...............................................................................................................67

6.3.1 How the four step procedure is realized .......................................67 6.3.2 Availability of stock data................................................................68 6.3.3 Availability of measured data........................................................70 6.3.4 Tool for step 3 (analysis and optimization) ...................................73 6.3.5 What barriers were identified / are expected in the course of the 4

step procedure?............................................................................74 6.3.6 What are the possible links between the national implementation of

the EPBD and CC?.......................................................................75 6.4. Finland..........................................................................................................75

6.4.1 How the 4 step procedure is realized............................................75 6.4.2 Availability of stock data................................................................78 6.4.3 Availability of measured data........................................................80 6.4.4 Tools for step 3 .............................................................................83 6.4.5 What barriers were identified / are expected in the course of the 4

step procedure..............................................................................83 6.4.6 What are the possible links between the national implementation of

the EPBD and CC?.......................................................................84 7. Possibilities for further analysis ............................................................. 85

7.1.1 Stock Data ....................................................................................85 7.1.2 Measurements ..............................................................................87 7.1.3 Performance Metrics & Evaluation Techniques............................90 7.1.4 Outcomes / aims of further analysis............................................102

ANNEX 103 ANNEX 1 CHECKLIST “OPERATIONAL RATING” 104 ANNEX 2 EVALUATION OF QUESTIONNAIRE “CERTIFICATION DATA” 113


Fraunhofer ISE 29.02.08 1

Abstract

These Guidelines are based on the results of the report “The EPBD and Commis-sioning” /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.

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-

4

Steps1. Benchmarking (Operational Rating)

2. Certification (Asset Rating)

3. Optimisation

4. Regular Inspection

321

Time

Benchmark(Operational Rating)

Certification (Asset Rating) + Availability of hourly data

Operation without faultsand optimized &Savings calculated

continuous commissioning



cide upon a minimal data set that is able to reveal the characteristics of the per-formance 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 10-15 days. Besides technical difficulties and missing documentation, administrational problems (locating responsibilities, contractual and security issues) may play a ma-jor 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 se-rious data analysis.

The cost for acquiring the minimal data set that was experienced in the Building EQ project is in the order of 10.000 to 30.000 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 sav-ings are achieved.

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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 = 8.000 € (MIN) / 25.000€ (MAX)



1. Introduction

The building sector is responsible for more than 40 percent of the European en-ergy 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 non-residential 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 ener-getic/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 pre-scribes energy certificates for new and existing buildings might offer some oppor-tunities 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 build-ing performance.

Another quite new approach, that first was established in USA, is ongoing com-missioning. 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 en-ergy 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 practi-cal application and potential connections to the EPBD. Measurement based tech-niques are considered as well as model based techniques and functional perform-ance 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 en-ergy performance. However, asset ratings for existing buildings – which are inves-tigated 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 im-plementations in the Member States. That is, there will be no common data set for all Member States that can be exploited for performance analysis.



On the other hand, there exist a lot of valuable assessment techniques like: Benchmarking, Visualisation, model based techniques and Functional Perform-ance 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 over-view 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.



2. Guidelines – The general outline

This chapter describes the general structure of the process developed for evalua-tion 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.

2.1. 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 Commis-sioning 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 de-sign documents and available energy measurements on whole building level. Furthermore, the owners requirements are defined and the availability of in-house staff is checked.

• Phase 2: Implementation and Verification Phase can be further split into six steps:

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.

o Develop performance baselines Document existing energy performance, system conditions and all known comfort problems. Development of a metering plan.

o Conduct system measurements and develop CC measures Identify current operation schedules, set points and problems, de-velop solutions to existing problems, Develop improved operation and control schedules and set points, identify potential cost-effective energy retrofit measures.

o Implement CC measures Implement solutions for existing operational and comfort problems, implement and refine improved operation and control schedules.

o Document comfort improvements and energy savings Document all achieved improvements



o Keep commissioning continuous Maintain achieved improvements and provide measured annual en-ergy 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 pa-rametric studies in order to identify major saving potentials.



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 out-comes 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 Overview over 4-step-procedure

Step No.

name description

1 Benchmarking (Operational Rating)

Gather basic consumption and stock data to perform an operational rating. Derive a first clas-sification / baseline of the building performance

2 Certification (Asset rating)

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) + Optimisa-tion

Calculate and document energy savings

4 Regular Inspection Introduce an ongoing monitoring to maintain an efficient operation.



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 condi-tions 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.

Figure 3 Scheme of the 4-step procedure on a time scale

It is important to notice that the continuous commissioning approach can be intro-duced 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

4

Steps1. Benchmarking (Operational Rating)

2. Certification (Asset Rating)

3. Optimisation

4. Regular Inspection

321

Time

Benchmark(Operational Rating)

Certification (Asset Rating) + Availability of hourly data

Operation without faultsand optimized &Savings calculated

continuous commissioning



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. Fur-thermore a minimal data set of measured data was developed that is described in the next chapter and that applies to the whole procedure.

2.3. 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 build-ings 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.



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 condi-tions

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 Flow / return Temperatures of main water circuits

°C h main heat/cold distribution. ”Main” in this context refers to the distribution in the building and not to a primary distribu-tion such as a district heating system.

supply air temperature of main AHUs

°C h only if supply air is thermody-namically treated

supply air relative humidity of main AHUs

% h only if supply air is humidified / dehumidified

*h= hourly

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 un-derstand how the load is met.



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.



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 con-sumption and stock data and first classifi-cation / baseline of the building perform-ance

Asset rating according to national implementa-tion of the EPBD, if applicable.

Analysis of the building performance, identification and implementation of energy saving measures and optimization of per-formance

Maintain optimized performance by ongo-ing (minimal) monitor-ing

Stock Data minimal building de-scription

Depending on national implementation, if ap-plicable (otherwise see step 3)

3a: Only basic data (step 1)

3b: Data of building and HVAC system for simplified model

Additional stock data ac-cording to individual needs

No additional stock data needed

Measured Data

Utility bills / own me-ter 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 con-sumption / signatures

Depending on national implementation

3a: standard analysis (measurement based)

3b: standard analysis (model based)

individual approaches

Energy consumption as major metric

Further Ac-tions

(only if required: Installation of data acquisition)

(only if required: Installation of data acquisition)

Installation of data acquisi-tion (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 opera-tion

Energy saving measure-ments introduced

Documentation of energy savings

Persistence of energy efficient performance



3.1. Step 1: Benchmarking (Operational Rating)

3.1.1 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.

3.1.2 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 po-tentials.

• 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 con-sumption 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 poten-tials.


Figure 4 Flowchart for Step 1: Benchmarking (Operational Rating)

Contractor

Contractor + building owner / in-house staff

building owner / in-house staff

Involved parties:

Annual historical consumption

data available?

Main sensorscorrectly installed?

Monthly historical consumption data

available for at least 9 months?

Hourly historical consumption dataavailable for at least

2 months?

no

yes

Record subhourlydata (e.g. 10

minute values)

Perform OR with weather correction

( annual baseline)

Actual consumption lower than reference

building?

IntroduceStep 2

Create Signatures from monthly

consumption and weather data ( monthly

baseline)

Is shape of Signature „normal“?

Introduce Step 3a


( annual baseline)


building?

Create Signatures from monthly

consumption and weather data ( monthly

baseline)

Is shape of Signature „normal“?


( annual baseline)


building?

no

yes yes

no

yes

no

IntroduceStep 2

IntroduceStep 4

yes

nono

IntroduceStep 2

IntroduceStep 4

yes

yes

IntroduceStep 4

no

Introduce Step 3a

(After 12 months)

1.4.1 1.4.2 1.4.3 1.4.4 1.4.5 1.4.6

1.4.7

1.4.8

1. Start

3. Actions

2. Analysis

4. Next Steps

Install measurement equipment for

minimal data set



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 remarks

General data e.g. location and year of construction

Area / reference Values Reference values for calculation of specific consumption, e.g. useful floor area, gross vol-ume, etc.

Energy consumption Annual consumption and utilization of every energy carrier delivered to the building

Water consumption Annual consumption and utilization of water delivered to the building

Main utilization main utilization of the building or major building zones respectively

Tariffs (optional) 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.



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 read-ings 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

3.1.5 Performance Metrics & Evaluation Techniques

The performance indicators for step one are specific values for the energy con-sumption that might be displayed as specific consumption values or as characteris-tic 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/m²or

kWh/m³per year or month

Calculate the specific consumption values from the measured consumption and e.g. the gross conditioned area. This can be compared to sta-tistically 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 devel-oped as an energy signature (regression model, see chapter 4)

Also cost data can be utilised for equivalent performance metrics.



3.1.6 Outcomes / aims of this step

First classification of building performance

if monthly metre readings and weather data are available: baseline (regres-sion model)

Rough insight in possible saving potentials



3.2. Step 2: Certification (Asset rating)


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.

3.2.2 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 build-ing. 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 as-set 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 uni-versal 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.


Figure 5 Flowchart for Step 2: Asset rating

from 1.4.4(monthly data and baseline available)

from 1.4.2(annual data and baseline available)

From -> 1.4.6(monthly / hourly data and monthly baseline available)

yes


minimal data set


minute values)

no

Introduce Step 3a

2.4.1

1. Start

2. Analysis

3. Actions

4. Next Steps

Perform AR according to

national implemen-

tation of EPBD.

All parameters and boundary conditions

are available?

Check Target Value from AR.

Actual consumption lower than target

value?

yes

no

IntroduceStep 4

2.4.3

Introduce Step 3b

Acquire stock data

If st

art

= 1

.4.6

2.4.2

Contractor



Involved parties:



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 com-mon is very few (less than 10%, see Annex 2).

A more general approach is given in step 3b.

3.2.4 Measurements

If necessary (see flowchart) the measurement equipment for the minimal data set according to 2.3 is installed.


The performance metrics that are used according to the standard certification process for asset ratings in the Member States. Probably the most common per-formance metric will be specific end-energy or primary energy demand with re-spect 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 charac-teristics [kWh/m²a]

Heat transmission value of the building envelope [W/m²K]

End-Energy for the subsystems: heat-ing, domestic hot water, lighting, ventilation, cool-ing [kWh/m²a]* and sepa-rated for the different en-ergy sources (gas, oil, electricity)

Asset rating

Total primary energy for heating, ventilation and DHW (not included: cool-ing, air conditioning in summer) [kWh/m³a]

Classification from A (very good) to G (very bad) for the winter heating consumption (re-gionally)

Heating system perform-ance: ηg as ratio between Building Heat Requirement and Total primary energy con-sumption

Operational rating (Sweden has only OR)

Measured annual energy less the tentants' (or us-ers') energy [kWh/m²a]

Energy: Measured total annual en-ergy use for heating of the building and electricity for operational purposes con-trolled 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 reno-vated buildings + small residential buildings)

Total end-use energy, in-cluding heating and cool-ing 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 residen-tial buildings only.



3.2.6 Further actions

As shown in the flow chart the installation of measurement equipment might be necessary.


Asset rating provides theoretical target value for consumption

Deeper insight in system

Identification of major energy consumers



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.

3.3.1 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 op-eration, 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 diag-nosis must be performed (see /1/)

Typical problems addressed by FDD and Optimization in existing buildings accord-ing 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 pro-gramming 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 lim-ited.

• Lacking hydraulic balancing Pipe and duct systems are often not hydraulically balanced, especially after re-constructions or changes in use. Generally this results in increased energy consumption and/or decreased comfort.



• Setpoints / resets Settings (e.g. temperatures, flow rates) are often adjusted over time based on personal preferences, to compensate for inadequate system operation. In addi-tion, 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 per-formance.

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 differ-ence 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 gen-eral 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 met-rics Evaluation techniques

Pre-defined visualization (with guidance for interpretation)

rule based analysis

Calculate building specific benchmark based on model of building + HVAC system (monthly values)

Strength Easy to apply

Can be applied in any building without adjustment.

Building specific reference value can be provided

Strong link to EPBD

“Deeper” insight in building and sys-tems

Weakness Delivers no theoretical reference value

Energy conservation opportunities might be hard to identify without de-tailed system knowledge

Weak link to EPBD

Requires more effort as model has to be created according to specifics of the building.

“Calibration” of model might be difficult



3.3.2 Outcomes / aims of the step

• Identification of energy conservation opportunities

• Energy saving measures introduced

• Faultless / optimized operation

• Documentation of energy savings



3.4. Step 3a: Standard analysis (measurement based)


Step 3a tries to transform the measured data according to chapter 2.3 in informa-tion about the building performance. Furthermore faults and possible saving poten-tials 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.

3.4.2 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 3.4.5.

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 ana-lyst can also decide to do additional analysis or measurements according to chap-ter 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 ad-justed according to chapter 4.


Figure 6 Flowchart for Step 3a: Standard analysis (measurement based)

from 1.4.1No data available

From -> 1.4.7, 2.4.1(sub hourly data

available)


minimal data set


minute values)

IntroduceStep 4

3a.4.3

1. Start

2. Analysis

3. Actions

4. Next Steps

Standardized visualization of

data

Is “unusual”behavior detected by statistics, rule or

by expert?

Check for possible ECM

Could any ECM be identified?

yes

IntroduceStep 4

3a.4.1

(After at least 2 months of data are available)

yes

no no

Implement ECM

Calculate / measure savings

Adjust Baseline

IntroduceStep 4

3a.4.2

Further analysis (additional

measurements, simulation, etc.)

To be decided by the analyst

Contractor



Involved parties:



3.4.3 Stock Data

Only stock data similar to step 1 is needed (basic information about the building and HVAC system).

3.4.4 Measurements

The minimal data set according to 2.3.


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 over-view 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 pat-terns).

• 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 con-dition (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.



Filtering is also extremely important considering that there are no flow me-asurements 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 or-der to eliminated dynamic effects.

The following examples should illustrate the issues discussed above

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 6th.

Elec

trici

ty/ [

kW]

heat

/ [kW

]



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 Janu-ary 2005. 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 2007. 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 differ-ence between the operation on workdays and weekends. Furthermore the weather-dependent part of the load can be principally identified.

Wärmeenergieverbrauch VFG mit Witterungsbereinigung über Gradtage

0

100.000

200.000

300.000

400.000

500.000

600.000

700.000

800.000

900.000

kWh/

a

Wärme 742.757 838.302 771.819 734.776 488.415 558.341 502.6872001 2002 2003 2004 2005 2006 2007

36,7% 27,7% 34,9%



Figure 10 Carpet plot on hourly basis Electricity consumption. The carpet plot shows clear weekly “patterns” that indicate the dif-ference between night and day operation as well as between weekdays and weekends.

Figure 11 Boxplots on daily basis Heat and Electricity consumption on different weeksdays. The boxplots shows the differ-ence 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 con-sumption 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 prin-cipal rules can be established, e.g.:

Hea

t/

[W/m

²]

Weekday

Elec

tric

ity/

[W

/m²]

Weekday

Electricity on different weekdaysHeat on different weekdays



• The change point (the outdoor temperature at which the heat consumption becomes weather independent) should be located in the range between 10-20 °C.

• The weather independent load (above the change point) should correspond to the domestic hot water consumption (if there are no other heat consum-ing processes). For typical office buildings this should be near zero.

• The slope of the weather dependent part of the signature should corre-spond 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 auto-mated way by rules. The Building EQ project will develop sets of such rules for the different operation patterns in a later stage.



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 consump-tion

• 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 pe-riods of time in which the corresponding circuit was in operation. As indica-tor for operation the difference between supply and return temperature can be calculated. (the minimal data set does not - due to cost reasons - con-tain 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.



Table 9 Pre-defined visualization for minimal data set (see 2.3)

Type of chart Values for display Remarks

Time resolution: Months / Weeks

Time series Consumption and outdoor air tem-perature / moisture

as reference (can also be done with yearly data)

System temperatures and outdoor air temperature

as reference

Scatterplots Consumption vs. outdoor air tempera-ture (“signatures”)

For cold: additionally vs. absolute outdoor air humidity / enthalpy

Identification of weather dependent and independent part of consumption and influence of utilization (scatter)

Time resolution: Days

Time series plot Consumption and outdoor air tem-perature / humidity

as reference


as reference

Scatterplots Consumption vs. outdoor air tempera-ture (“signatures”)

For cold: additionally vs. absolute outdoor air humidity / enthalpy

Grouping: type of day

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)

Supply temperatures (water side) vs. outdoor air temperature


Identification of control of supply temperatures and potentially different operation modes.

Supply air temperature vs. outdoor air temperature

In case of AC system: Supply air humidity vs. outdoor air temperature



indoor temperature vs. outdoor air temperature

In case of AC system: additionally indoor humidity vs. outdoor air humid-ity


Classification of indoor climate

Boxplots Consumption per Weekday Identification of day types: (i.e. days with significantly different loads (nor-mally: workdays <-> weekends)



(continued)


Time resolution: Hours

Time series plot Consumption and outdoor air tem-perature / humidity

as reference


as reference

Scatterplots Supply temperatures (water side) vs. outdoor air temperature

Filter: Difference of supply- and return tem-perature must exceed a certain limit (e.g. 2K)





Filter: Difference between supply air and indoor air temperature (or humidity respectively) must exceed a certain limit.



indoor temperature vs. outdoor air temperature

In case of AC system: additionally indoor humidity vs. outdoor air humid-ity


Classification of indoor climate, identi-fication of unusual states

Boxplots Consumption per hour of the day Identification of typical consumption profiles for different types of days.



(continued)


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.

Identification of operation patterns (daily, weekly, seasonal)



Filter: Difference between supply air and indoor air temperature (or humidity respectively) must exceed a certain limit.

Identification of operation patterns (daily, weekly, seasonal)

indoor temperature / humidity Identification of operation patterns (daily, weekly, seasonal)

outdoor air temperature as reference

solar radiation as reference

Rules for automated detection of unusual operation patterns will be further devel-oped in Workpackage 5 of the Building EQ project.

3.4.6 Further actions

If not already available the measurement equipment for recording of the minimal data set has to be installed.



3.5. Step 3b: Standard analysis (model based)


For step 3b a model of the building and HVAC plant is used for the detailed analy-sis of saving potentials. The model will be used to calculate monthly energy con-sumption 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 rat-ing 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 in-terconnections between the components) are not described correctly or com-pletely.

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 (“check-list2”) for data acquisition was developed that is available from the project-website (www.buildingeq.eu).

The model based approach and the checklist will be further developed in WP 5 (development of tools).

3.5.2 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 possible-from 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 ad-justed according to chapter 4.

http://www.buildingeq.eu/


Figure 12 Flowchart for Step 3b: Standard analysis (model based)

from 2.4.2At least annual

data available and measurement

equipment installed

IntroduceStep 4

3b.4.2

1. Start

2. Analysis

3. Actions

4. Next Steps

no no

Implement ECM

Calculate / measure savings

Adjust Baseline

IntroduceStep 4

3b.4.1

Create model with data from AR

(step 2)

Calibrate model with at least 4 months of data

Calibration successful?

yesPerform Parametric

study / Optimization

Could any ECM be identified?

Further analysis needed (additional

measurements, simulation)

To be decided by the analyst

yes

Contractor



Involved parties:



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 3.5.1 additional information about certain com-ponents 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.

3.5.4 Measurements

Minimal data set according to 2.3.


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 dis-cussed 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 dis-cussed 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 ex-change of old components). Even if these measures are not primarily addressed by Building EQ, they can be examined and discussed in step 3b.



3.6. Step 4: Regular Inspection


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.

3.6.2 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 3.6.5. Principally the different starting points are:

• Annual data

• Monthly data

• Hourly data

• Hourly data + model


Figure 13 Flowchart for Step 3b: Standard analysis (model based)

Contractor



Involved parties:

from 1.4.3, 2.4.3

annual consumption data

available

1. Start

2. Analysis

4. Next Steps

yes

Back to Step 1

Check consumption with

annual baseline

Significant deviation in consumption?

no

from 1.4.5, 2.4.3

Monthly consumption data

available

from 1.4.8, 3a4.1, 3a.4.2, 3a.4.3

Hourly data available

Check consumption with monthly baseline

(“signature”)

Any outliers detected?

no

yes

Back to Step 1

Check consumption on

daily / hourly basis by means of

an identified “patterns”


no

yes

Back to Step 1

from 3b.4.1, 3b.4.2Hourly data and model

available

Check consumption on

daily / hourly basis by means of

an identified “patterns” and in

comparison to model


no

yes

Back to Step 1



3.6.3 Stock Data

No additional stock data is needed for step 4.

3.6.4 Measurements

No additional measurements are needed for step 4


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 pre-vious years after a weather correction was performed. The weather correc-tion 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 identi-fied 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 pa-rameters 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.




• Regular inspection of performance (detection of unusual behaviour or changes in operation).

• Persistence of energy efficient performance



4. Measurement and verification / Definition of Baselines

An important thing concerning the justification of a continuous commissioning ap-proach is the measurement and verification of savings. By measuring or calculat-ing and documenting the achieved saving the cost-benefit of the ongoing perform-ance evaluation can be determined.

In order to measure or calculate energy savings, a baseline for the pre-retrofit pe-riod must be determined. This can then be compared to post-retrofit energy con-sumption 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 build-ings /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 con-tinuous. 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 sys-tems 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 post-retrofit period.

• D: Calibrated simulation Savings are determined through simulation of the energy use of compo-nents 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.



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 cho-sen.

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 pos-sible) energy saving measures. Consequently, the following recommendations are given:

• If at least annual historical consumption data is available, option C is rec-ommended. 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 If an energy saving measure is identified that is weather independ-ent (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.

o 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.

o 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 meas-urements are available. At the same time the expected saving po-tential should be significant.

o A year of consumption data can be recorded first, to generate a baseline before introducing energy saving measures.

4.1. 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-



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 sav-ing, 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 instal-lation, the less significant is the impact of short term unexplained variations. Typi-cally 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 consump-tion as a function of several independent variables. IPMVP gives no concrete method or model to be used for this task. Usually regression models are repre-sented 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 signa-tures

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 inde-pendent 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:

( )( )⎩

⎨⎧

>+−+≤+−+

=>>>

<<<

CPXforXbCPXbbCPXforXbCPXbb

Ycpiicpcp

cpiicpcp

0

0

Where:

Y dependent variable (consumption for heat, cold, electricity)



Xcp variable for which a change-point is defined

CP location of change point

b0 – bm Parameters of the model

m 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 optimiza-tion 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 respec-tively.

An example of a script in R (http://www.r-project.org) which performs the regres-sion and identification of the change point from monthly basis is shown below (fur-ther scripts are available from the project website (www.buildingeq.eu):

######################################################### # 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:","

http://www.r-project.org/



# 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



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: 1 2 3 4 5 6 7 -3.872e-03 3.502e-02 -3.482e-02 -1.674e-02 -9.673e-18 7.186e-17 -2.734e-18 8 9 10 11 12 -2.355e-17 -4.263e-17 3.265e-02 -1.611e-02 3.872e-03 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.230e+00 1.003e+00 4.219 0.051848 . highTRUE -4.180e+00 1.046e+00 -3.998 0.057253 . highFALSE:I(Ta - CP) -8.098e-01 1.583e-02 -51.138 0.000382 *** highTRUE:I(Ta - CP) 1.895e-03 3.459e-02 0.055 0.961287 highFALSE:Iglob -2.795e-02 3.804e-03 -7.348 0.018022 * highTRUE:Iglob -3.733e-05 3.505e-04 -0.107 0.924899 highFALSE:Presence 1.065e+00 1.947e-01 5.468 0.031858 * highTRUE:Presence 2.514e-02 2.429e-01 0.103 0.927010



highFALSE:Qel -6.520e-01 1.212e-01 -5.380 0.032854 * highTRUE:Qel -1.095e-02 1.002e-01 -0.109 0.922946 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.04513 on 2 degrees of freedom Multiple R-Squared: 1,Adjusted R-squared: 0.9999 F-statistic: 9216 on 9 and 2 DF, p-value: 0.0001085

In dependency of the kind of building or utilization respectively it might be worth-while 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 (www.buildingeq.eu).



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 ac-quired at least on an hourly basis. This chapter tries to highlight some of the practi-cal 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 con-sumption, 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 transfer-ability to other analysis systems in most cases is not given. Generally, the avail-ability of measured data is better for buildings with a BAS that comprises a man-agement 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 or-der to provide the minimal data set at reasonable cost. In some cases (if the en-hancement 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 de-pends heavily on the presence of a BAS. If a BAS is enhanced to deliver the mini-mal 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-



tional data by means of a data logger might produce significant extra cost. How-ever, these costs are small compared to those of implementing a new BAS.

5.2. 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 re-mote 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.

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).

Demonstration Building

(e.g. PIX 501)

Data Source(e.g. BAS,

ennovatis smartbox,Separate monitoring

computer, Datalogger)

DSL - Modem or

DSL - Router

(e.g. Speedport 200 from Telekom)

Fraunhofer ISE

Internet

Firewall Data ServerDSL flatrateFirewall

Data-Source

Demonstration Building

(e.g. PIX 501)

Fraunhofer ISE

Internet

Firewall Data ServerDSL flatratein-house

network

LANFirewallData-

Source

Firewallin-house network

Data Source(e.g. BAS,

ennovatis smartbox,Separate monitoring

computer, Datalogger)



• 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 pro-vider 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 other-wise the cost of data transfer might be quite high.

• If the Data Source is directly connected to the inhouse network this connec-tion 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.



5.3. Cost

The cost for acquiring the minimal data set that was experienced in the Building EQ project is in the order of 10.000 to 30.000 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 en-ergy cost are between 100.000 EUR and 400.000 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 (ne-glecting 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 invest-ment cost but on an approximated range. In the course of the Building EQ project the cost benefit of continuous commissioning will be further investigated.

0

50 000

100 000

150 000

200 000

250 000

300 000

350 000

400 000

Ger

man

y_bu

i1

Ger

man

y_bu

i2

Ger

man

y_bu

i3

Ger

man

y_bu

i4

Finl

and_

bui1

Finl

and_

bui2

Finl

and_

bui3

Finl

and_

bui4

year

ly e

nerg

y co

st /

[€/a

]

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

stat

ic p

ayba

ck fo

r mon

itorin

g / [

a]

heat/gaselectricitystatic payback MAXstatic payback MIN

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 = 8.000 € (MIN) / 25.000€ (MAX)



6. National approaches

6.1. Germany

Chapter 6.1.1 will give a short overview over the realization of the 4-step proce-dure in the German demonstration buildings.

The subsequent chapters will focus on special issues like availability of data and used tools.

6.1.1 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 ac-cording 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. Fur-thermore, 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 (www.DWD.de) 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. Ac-tual 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 18599 re-quires 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” cer-tificate 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 stan-dard. Accordingly these values don’t have to be collected and the effort of gather-ing the data is reduced

On the other hand, the customized calculation – which can give a much more real-istic picture of the building performance - asks for detailed description of the real building.



Especially for older buildings with incomplete documentation the acquisition of stock data for the customized calculation is a time consuming task which is compa-rable 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 de-tailed 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 6.1.3.

Step 3: Optimisation Step 3 will start with the customization of the calculation for the certificate. Fur-thermore 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 stan-dardized) picture of the building will be available.

Any additional measured data (compared to the minimal data set) will also be ob-ject to visualization and (if applicable) statistic analysis.

If any of these analysis routines reveals an unexplained deviation from the ex-pected behaviour of the building more detailed analysis will be performed. Princi-pally, 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 6.1.4 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” behav-iour.

6.1.2 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 documen-tation 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.



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 build-ings and in addition some other buildings that are examined by the German part-ners in other projects.

Table 10 Availability of stock data in the German buildings

stock data

Thys

senK

rupp

MW

ME

Düs

seld

orf

Verf

ügun

gsge

bäud

e U

ni S

tuttg

art

Kre

iskr

anke

nhau

s H

agen

ow

Bui

ldin

g 1

(mw

z)

Bui

ldin

g 2

(EA

DS)

Bui

ldin

g 3

(LEH

)

Bui

ldin

g 4

(enn

GP)

Bui

ldin

g 5

(DVZ

B)

Year of construction 1985 1961 1995 1994 1998 1976 1998 1990 2007

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 ele-ments (walls, floors, roof, windows)

o(-) - o o o o + o +

Kind of utilization and sched-ules

+ o o + + o + + +

schematic drawings of HVAC system

o o + o + o + + +

operation strategies and schedules of HVAC system

+ o 0 o + o + + +

product data of HVAC equip-ment

o o + 0 + o + o +

effort for acquisition of stock data in days

3 4 4 4 3 3 3 1 2

*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)

6.1.3 Availability of measured data

In the framework of Building EQ a minimal data set (see chapter 3.2.4) is to be ac-quired at least on an hourly basis. Typically, the sensors for energy and water con-sumption, outdoor temperature and for system temperatures are installed in the buildings. The sensors for the additional weather and indoor climate data are typi-cally 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 transfer-ability to other analysis systems in most cases is not given. Generally, the avail-ability of measured data is better for buildings with a BAS that comprises a man-agement 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 or-der to provide the minimal data set at reasonable cost. In some cases (if the en-hancement 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 log-ger 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 10.000 to 25.000 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 ac-cording to chapter 3.2.4 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. con-trol signals, schedules, etc) is available almost at no additional cost. On the con-trary, If a data logger is used these additional data produce significant extra cost.



Table 11 summarizes the situation in the German demonstration buildings and in some additional buildings that are examined by the German partners in other pro-jects



Table 11 Availability of measured data for the minimal data set in the German buildings

stock data

Thys

senK

rupp

MW

ME

Düs

seld

orf

Verf

ügun

gsge

bäud

e U

ni S

tuttg

art

Kre

iskr

anke

nhau

s H

agen

ow

Bui

ldin

g 1

(mw

z)

Bui

ldin

g 2

(EA

DS)

Bui

ldin

g 3

(LEH

)

Bui

ldin

g 4

(enn

GP)

Bui

ldin

g 5

(DVZ

B)

Minimal data set

consumption

total consumption of fuels na na + + + na na + na

total consumption of district heat

o o + na na o + na na

total consumption of district cold

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

o o + - + - + + +

supply air temperature of main AHUs

o o + - + - + na +

supply air relative humidity of main AHUs

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



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 behav-iour. 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 Ger-man DIN standard 18599. 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 utili-zation 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 perform-ance 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 proper-ties 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 re-duced 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.

6.1.5 What barriers were identified / are expected in the course of the 4 step proce-dure?

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 moni-toring of their work

• Utilities are offended by the monitoring as it may result in changes of con-tracts

• 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 con-vince building owners

• Lack of low price and high quality (wireless) sensors

• BAS are not designed for analysis tasks



• 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.

6.1.6 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 informa-tion 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.

6.2. Sweden


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 Atemp (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 02 10 53 –1999 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 Atemp less:

o internal wall areas between tenants;

o internal walls between tenants and communication areas (stair wells, etc.)

o internal walls between tenants and service areas (mechanical rooms, etc.)

o columns and shafts next to an external wall regardless of their thickness

o 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 Atemp was in average 3.5% larger than BRA. Consequently the difference be-tween BRA and Atemp is not large in average, but it can be substantial for



individual buildings. Almost all commercial building owners use LOA and it is not a simple pro-cedure to convert this floor area to Atemp. 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, Atemp 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 offi-cial interpretation of operational rating the energy use of the occupants, te-nats or users of the building is not included. Typically this energy is only electricity. The reason is mainly because in almost all buildings with ten-ants, 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 authori-ties, have some kind of “internal tenant agreements” with departments in-side 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 avail-able. 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 build-ings into twelve categories already in the first energy (oil) statistics in the early 1970s. However, this division is more based on tradition, and on na-tional economics, than on the energy behaviour of the buildings. In some categories the spread of the annual energy use between individual build-ings is very large, e.g. one category includes all type of schools, from pre-schools 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 2010. Published results for the first year are for office and administration buildings and for the second year for schools {pre-schools to voluntary schools (gymnasi-eskola), corresponding to upper secondary school}. During 2007 care build-



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 proce-dures 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 tradi-tionally 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 certifica-tion 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 typi-cally 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 gas-pipeline 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 de-creased to only 5 % in 2005. Cogeneration in buildings is virtually non-existing in Sweden.



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 trans-fer 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 manu-facturers 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 win-ning 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 custom-ers. 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 hy-dronic heating and cooling circuits is seldom measured.

• Indoor environment Because of the bad Swedish experiences from the energy saving meas-ures carried out in buildings during the 1970s and 1980s indoor environ-ment is very much in focus in the Swedish energy certification process. It is very important that the proposed energy efficiency measures do not de-grade 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 im-portant 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 be-came of any interest for Swedish building owners and their consultants. This is be-cause the new Swedish building code requires that the measured energy use (ex-cluding 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-



culations of his consulting engineer. The new requirements came into force on 1 July 2007.

The only possible building energy modelling involved in the Swedish energy certifi-cation process is the calculation of the energy savings due to different proposed cost-effective energy efficiency measures. For some types of measures this re-quires 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 mod-els. 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 re-quired for calculations of cost-effective energy efficiency measures which is a ma-jor part of the energy certification process.

In Sweden the general knowledge of FDD is very limited and no commercial soft-ware is available. Similarly Functional Performance Tests are more or less un-known.

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 ser-vices 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 build-ings 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 meas-urements 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 inspec-tions in the quality systems will be investigated. This will make the use of the regu-lar 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.




According to law all Swedish premises buildings (public and commercial), as well as multi-family buildings, must be energy certified by 1 january 2009. 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 imple-mentation of the methodologies from Building EQ will be easier in an already en-ergy 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 ventila-tions 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 possibili-ties 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 per-mits from each tenant. The FDD and Functional Performance Tests may not under any circumstances disturb the tenants.



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

6.2.4 What are the possible links between the national implementation of the EPBD and CC (including prescribed maintenance procedures on national level)?

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 re-quired calculations of the energy savings needed for proposing cost-effective en-ergy efficiency measures may have any link to continues commissioning.

6.3. Italy

6.3.1 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 10349 /10/ standard, which are available for all the Italian provinces (101 locations). Other data useful for the Ital-ian 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 build-ing.

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 build-ings. 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-



mine the primary energy consumption for the winter heating and the DHW produc-tion. Cooling energy needs are not fully implemented up to now, but work is ongo-ing 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 certifi-cation 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 certifica-tion 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 docu-mentation 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 instru-ment 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.


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



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 ty-pology, heat or cold power, etc. and others like elevators). Nevertheless a building audit and a visit in the HVAC equipment room is neces-sary in order to assure the correspondence of the installations with the schemes and drawings.

If the building is older than 10-15 years, generally the available data are very lim-ited 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 infor-mation about the wall layers (usually is even impossible to determinate the thick-ness 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.



Table 12 Availability of stock data in the Italian demonstration buildings

stock data

Polte

cnic

o di

Mila

no

build

ing

23

Polte

cnic

o di

Mila

no

build

ing

22

Polte

cnic

o di

Mila

no

build

ing

15

Year of construction 2007 1999 1961

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*** 10 10 15

*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.


For the purpose of the project measured data (minimal data set) have to be gath-ered 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 sys-tem”. 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 possi-bility 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,



it has to be assumed that historical or even actual data about the system function-ing 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 agree-ment 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 5.000-30.000€, 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.



Table 13 Availability of measured data for the minimal data set in the German buildings

stock data

Polte

cnic

o di

Mila

no

build

ing

23

Polte

cnic

o di

Mila

no

build

ing

22

Polte

cnic

o di

Mila

no

build

ing

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 cer-tified station that is installed on the higher building of the central campus.



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 “stan-dard” 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 dem-onstration 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 pur-poses. This is under development following the further definitive imple-mentation 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 optimiza-tion 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 fa-cilities.

• 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 dy-namic simulation software, implement all the information gathered about the building envelope, the HVAC system set-points and the people occu-pancy. 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.



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.


Within the application of the four step procedure at the three demonstration build-ings 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 neces-sary at least one visit in the building; this have to concretized obtaining all the necessary authorization from the different head of department, admin-istrative responsible, heat management contractor, etc.

• Lack of documentation of buildings: the three different buildings have dif-ferent age (1961, 1999, 2007); obtain a quality documentation of the en-velope 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 dif-ficult, 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 man-agement 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 spe-cific 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 crimi-nal assignation of the contract for public provision of services impose that each two years the public service contract has to be renewed through a



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 com-missioning interventions and a decisive limit for made the commissioning continuous.


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, elec-tricity 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 en-velope, a good level of information about the heating/DHW system and in the fu-ture 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 pro-vided by the law, therefore is not usual that the building is inserted in a mainte-nance procedure which can implement the CC easily.

6.4. Finland


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 con-sumption 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



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 da-tabase 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 data-bases 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 con-struction).

• Owners of large building stock (commercial buildings, state owned build-ings, etc) have their own energy monitoring systems with their own aver-age 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 build-ings 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 walk-through 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 elec-tricity for the building (this includes fans, pumps, electrical heating, outdoor light-ing, lifts, escalators, car heating, frost protection heating, fixed lighting). So basi-cally 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.



There are some problems connected to the “total energy consumption” in the en-ergy 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 doz-ens 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’ inter-vals 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.




For the building data in Finland mainly applies what has been said in 6.1.2 for Germany: the availability of stock data depends very much on the age of the build-ing 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 15-20 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 commer-cially 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 calcula-tions or investigations to find out which is the official and correct one.



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.



Table 14 Availability of stock data in the Finnish demo buildings

stock data

Sena

te H

Q

Aur

ora

2

Dep

artm

ent M

echa

nica

l En

gine

erin

g

Stat

e Tr

easu

ry

Year of construction / renovation 2002 2006 1966/2004 1984/2008

complexity of HVAC plant h h h h

floor plans, views, sections** + + + +

floor areas + + + +

Construction of building elements (walls, floors, roof, win-dows)

+ + + +

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


In Finland buildings usually have their own energy meters. In cities and municipali-ties 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 op-tion 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.



Energy monitoring is at high level in Finland and most building owners have a very good database of actual measured consumption figures. Usually energy monitor-ing 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 val-ues. 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 so-lar 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 ac-cording to chapter 2.3, the costs can be estimated in the range of 8.000-25.000€, 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 re-quired 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 measure-ment 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 esti-mated in the range of 10.000-45.000€, depending on the HVAC-system complex-ity.



Table 15 Availability of measured data for the minimal data-set in the Finish demo buildings

stock data

Sena

te H

Q

Aur

ora

2 D

epar

tmen

t M

echa

nica

lEn

gine

erin

g

Stat

e Tr

easu

ry

Minimal data set

consumption still under renovation

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

+= available, o= available but had to be renewed/ enhanced, - = not available, had to be installed, na = not applicable



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 ad-vanced 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 simula-tion on a whole building level. In Finland commonly used dynamic simula-tion 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, func-tional performance tests might be done.

6.4.5 What barriers were identified / are expected in the course of the 4 step proce-dure

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 de-tect any energy wasting faults by simple benchmark values because the faults and operational mistakes and false settings will most likely compen-sate 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) diffi-cult.

• Telecommunications security and reliability e.g fire wall settings could pro-duce problems. On the other hand open networks facilitates more inde-pendent communication and offer increased use of building measurement data.




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 us-ing 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 re-quirement.

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 en-ergy 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 em-phasis 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 con-nection to continuous commissioning. The certificate must include suggestions on energy efficiency improvements and these can only be found when the key pa-rameters in the building operation are checked. All energy using systems have to be audited briefly, checking operation schedules, temperature settings, heat re-covery performance, lighting controls, electrical heating settings, etc. These are all parameters that are included in the CC site visits.



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.

7.1.1 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, operat-ing 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)



(continued)


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 gen-erator 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 condi-tions

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 effi-ciency

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)



(continued)


water loops

scheme of the loop - simplified scheme of the loop with major com-ponents

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)

7.1.2 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 conserva-tion 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 equip-ment (sensors).

short term measurements Temporary measurements conducted over a period of some hours to sev-eral 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 acquisi-tion 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 con-ducted (spot, short time, long time) is to be decided by the analyst on basis of the analssis done according to chapter 3.



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 / sub-system

unit kind of meas-urement*

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, light-ing, shading etc. if applicable

Generators

fuel consumption kWh ltm / stm / spm h / sh e.g. gas for a gas boiler

electric energy con-sumption

kWh ltm / stm / spm h / sh Electric energy consumption of generator and associated equip-ment (e.g. for a compression chiller: chiller, chilled water pump, con-denser 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 tempera-tures

°C ltm / stm / spm h / sh for hot water, chilled water, con-denser water (if applicable)

water flow m3/h ltm / stm / spm h / sh for hot water, chilled water, con-denser water (if applicable)

control signals - ltm / stm / spm h / sh For generator and associated equipment



(continued)

Measured value / sub-system

unit kind of meas-urement*

time resolution**

Remarks

Air Handling Units (AHUs)

electric energy con-sumption

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 com-ponents such as heating or cool-ing coils or humidifiers may be conducted

air – humidity % ltm / stm / spm h / sh Humidity of supply, return, outside and mixed air if applicable

Additionally spot at the inlet and outlet of single components such as heating or cooling coils or hu-midifiers may be conducted

air – pressure Pa ltm / stm / spm h / sh Static pressure

coil – water tempera-tures

°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 tech-niques should be investigated, which allow to replace “expensive” measurements by simpler (and maybe less accurate) ones. Especially the substitution of long-term measurements by spot- or short-term measurements will be of interest. Ex-amples for this can be:



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 re-spectively. Determine flow from characteristic curve. Permanent recording of operation hours of the fan or pump respectively.


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: www.buildingeq.eu):

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 op-eration 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):



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



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 iden-tification of profiles. Usually these models are used for fault detection (iden-tification 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 val-ues, the state variables or the identified parameters can be “benchmarked”. Furthermore these models can be used for optimisation tasks on the sys-tem level i.e. together with other models. More complex models usually require additional measurements. Further-more 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).



Table 18 Step 1: performance metrics & evaluation techniques (optional)

subsystem /

Performance metric Evaluation technique description possible result

BIM

des

irabl

e

addi

tiona

l m

easu

rem

ents

ne

cess

ary

BA

S d

esira

ble

Whole building / building zone:

Specific energy consumption (fuels, heat, cold, electricity)

Visualisation XY-plots:

• Specific energy consumption vs. outdoor air temp.

carpet plots:

• Specific energy consumption

• Qualitative characteristics of energy signature

• Qualitative load pattern

• Manual detection of gross faults

Statistical Analysis identification of energy signatures via multiple linear regression (automati-cally),

• Quantitative characteristics of en-ergy signature

• Automated detection of gross er-rors

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 sequenc-ing of most efficient generators

• optimisation of energy cost by load shifting



(continued)

subsystem /


BIM

des

irabl

e

addi

tiona

l m

easu

rem

ents

ne

cess

ary

BA

S d

esira

ble

Whole building / building zone

consumption profiles

Statistical Analysis Energy signatures (daily / weekly data) • detection of abnormal energy con-sumption on a weekly / daily basis

( )

Statistical Analysis Statistical analysis of time series of hourly consumption data (“Daytyping”)

• detection of abnormal energy con-sumption on a daily/hourly basis

• quantitative identification of con-sumption profiles for use in other models (e.g. identification of elec-tricity or water consumption profiles for use in simulations)

( )



(continued)

subsystem /


BIM

des

irabl

e

addi

tiona

l m

easu

rem

ents

ne

cess

ary

BA

S d

esira

ble

generators

Correct / efficient per-formance (COP)

visualisation XY-plots:

• Power (input/output) vs. out-door 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, con-denser water)

• Temp. difference (hot, chilled, condenser water)

• Detection of poor efficiency

• Detection of poor partload perform-ance

• Detection of deficient control schemes (flow temperature)

• Detection of under-/ over-sizing

• Detection of “abnormal” pattern of operation



(continued)

subsystem /


BIM

des

irabl

e

addi

tiona

l m

easu

rem

ents

ne

cess

ary

BA

S d

esira

ble

generators

Correct / efficient per-formance (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 parame-ters

• Detection of faults in operation (ab-normal COP)

• Utilisation for global optimisation (system simulation)

Functional Performance Tests

Perform standardised test on compo-nent

• Fault detection / ensure of operation according to design intent



(continued)

subsystem /


BIM

des

irabl

e

addi

tiona

l m

easu

rem

ents

ne

cess

ary

BA

S d

esira

ble

AHUs

Control schemes


• 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: tempera-tures/humidity (outdoor, sup-ply, 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)



(continued)

subsystem /


BIM

des

irabl

e

addi

tiona

l m

easu

rem

ents

ne

cess

ary

BA

S d

esira

ble

AHUs

Control schemes

Simulation with model Simulation with detailed model or con-nected 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)





(continued)

subsystem /


BIM

des

irabl

e

addi

tiona

l m

easu

rem

ents

ne

cess

ary

BA

S d

esira

ble

Water loops

operation scheme/profiles


• Flow Temp. vs. outdoor air temp.

• Temp. difference vs. outdoor air temp.

• Flow Temp. vs. Control signal of pump / valve

Carpet plots:

• Control signal of pumps / valves

• Detect deficient control schemes

• Detect deficient operation patterns (schedules)

Functional Performance Tests (FPT)





(continued)

subsystem /


BIM

des

irabl

e

addi

tiona

l m

easu

rem

ents

ne

cess

ary

BA

S d

esira

ble

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 utilisa-tion profiles

General issues

set points

Visualisation carpet plots:

• setpoints

XY-plots:

• setpoints vs. outdoor air temp.

• Detect abnormal operation patterns

• Detect abnormal



(continued)

subsystem /


BIM

des

irabl

e

addi

tiona

l m

easu

rem

ents

ne

cess

ary

BA

S d

esira

ble

General issues

efficient staging /sequencing of genera-tors

Simulation with model Simulation of building and systems for determining the most efficient genera-tors for the actual operation, potentially combined with weather or load fore-casts

• Highly energy-efficient generation



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.



ANNEX



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 (http://www.buildingeq.eu).



Annex 2 Evaluation of questionnaire “certification data” In order to find a basis for a common approach on the European level and to better understand the differences and the scope of the national implementations of the EPBD, Fraunhofer ISE prepared a questionnaire that was completed by every par-ticipant.

The following table shows the results of the evaluation of the questionnaire. AT the actual state it must be stated that no common data set that can be derived for the analysis of building performance. Accordingly the consortium will have to define an "artificial" common data set.

Table 19 questionnaire for certification data with results. Green = data will be available after certifi-cation, red = not available, gray = not clear yet.


A Energy Certificate Data 1 type of national indicators B General Building Data 2 building location: city y y Y v3 building location: region y y Y 4 building location: climate zone (y) y Y 5 building: erection year y Y 6 utilisation y y Y 7 conditioned reference floor area y y Y 8 conditioned gross floor area n n ? 9 conditioned floor area y y probably Y 10 conditioned useful floor area y n Y 11 conditioned building volume y y N 12 value of further indicator of building size n n N 13 fraction of the conditioned floor area sup-

plied with a heating system y y N

14 fraction of the conditioned floor area sup-plied with a mechanical ventilation system y n Y

15 fraction of the conditioned floor area sup-plied by a cooling / air-conditioning system y n N

16 situation of attached neighbour buildings (or apartments) y y Y

17 further building type indicator 0 n N 18 Indicator for indoor environmental quality n n Y C Building/Zone Envelope Data complete building envelope: 19 total transmission heat transfer coefficient

of the building Y y N 0




20 transmission heat transfer according to thermal bridges Y y N 0

walls: 21 Total wall area Y y N 0 22 Average U-value of wall y y N 0 23 areas of different wall types y y N 0 24 U-values of different wall types y y N 0 windows: 25 Total window area y y N 0 26 average U-value of windows y y N 0 27 average g-value of windows y y N 0 28 areas of different window types y y N 0 29 U-values of different windows y y N 0 30 g-values of different windows y y N 0 31 type of shading systems y y N? 0 roofs and upper floor ceilings 32 total roof area y y N 0 33 average U-value of roof elements y y N 0 34 types of roofs y y Y 0 35 areas of different roof types y y N 0 36 U-values of different roof types y y N 0 basement areas: 37 total basement area y y N 0 38 average U-value of basement y y N 0 39 areas of different basement types y y N 0 40 U-values of different basement types y y N 0 D System Data heat generation for space heating and

hot water supply

41 types of heat generators y y Y 0 42 energy carrier of each heat generator y y Y 0 43 use of heat generators y y Y? 0 44 erection year/period of heat generators y y Y? 0 45 capacity of heat generators y y Y? 0 46 operating temperatures of heat generators y y N? 0 47 performance data y y N 0 48 district heating generation types y y N? 0 49 district heating fuels y y N? 0 0 heating system: control and heat dis-

tribution

50 heat transport to the rooms y y Y 0 51 heat emission into the rooms y y Y 0



51 insulation grade of distribution pipes of the space heating system outside the heated part of the building y y N 0


52 alignment of main supply pipes for heating distribution y y N 0

53 control of the heat emission y y Y 0 54 heat distribution set temperature y y Y 0 55 control of heat distribution temperature y y Y 0 hot water: heat distribution 56 type of heat distribution for hot water sup-

ply y y Y 0

57 insulation grade of distribution pipes of the hot water system y y N 0

58 alignment of horizontal main supply pipes for hot water distribution y y N 0

ventilation 59 types of mechanical ventilation systems y y Y 0 60 air change of the mechanical ventilation

systems y y Y 0

61 control of air change y y Y 0 Air Conditioning/Cooling (AC) 62 use of air conditioning / cooling systems y n Y 0 63 cold transport to the rooms y n Y 0 64 cold emission into the rooms y n Y 0 65 types of cold generation systems y n Y 0 66 types of heat sinks for cold generation

systems y n Y 0

67 energy carrier of the cold generation sys-tems y n Y 0

68 erection year/period of cold generation systems y n Y? 0

69 capacity of cold generators y n Y? 0 70 operating temperatures of cold generators y n Y? 0 71 performance data y n N 0 72 types of heat generators used by the air

conditioning/cooling system y n Y 0

73 use of heat generators used by the air conditioning/cooling system y n Y 0

74 energy carrier of the heat generators used by the air conditioning/cooling system y n Y 0

75 insulation grade of distribution pipes and/or ducts of the cooling/air conditioning systems y n N 0

76 set temperature of the cold distribution systems y n Y? 0

Pump efficiency



77 information on pump efficiency y y N 0 Lighting 78 types of lighting systems y n Y 0 79 electric power of the lighting systems y n N? 0 Germany Italy Sweden Finland

80 average illuminance of the zone / building y n N 0 81 control of the lighting system y n Y? 0 E Calculated Energy Demand (Asset

Rating)

Energy demand for heating and hot

water supply

83 gross heating energy demand of the build-ing y y N 0

84 net heating energy demand of the building y y N 0 85 heat losses of the space heating caused

by non-ideal heat emission control y n N 0

86 distribution losses of the heating system y y N 0 87 storage losses of the heating system y y N 0 88 hot water energy demand of the building y y N 0 89 hot water distribution losses y y N 0 90 hot water storage losses y y N 0 91 net heat output of the different heat gen-

erators for space heating and hot water supply

y y N 0

92 net heat output of different heat genera-tors for space heating y y N 0

93 energy input to different heat generators (for space heating and hot water supply) y y N 0

94 energy input to the different heat genera-tors for space heating y y N 0

95 auxiliary electric energy of the heating

system y y N 0

96 auxiliary electric energy of the ventilation system y y N 0

97 auxiliary electric energy of the hot water system y y N 0

Cooling /AirConditioning 98 cold energy demand of the building y n N 0 99 cold energy distribution losses y n N 0 100 cold storage losses y n N 0 101 cold output of cold generation systems y n N 0 102 energy input to cold generation systems y n N 0 103 net heat output of heat generators used by

the air conditioning / cooling system y n N 0



104 energy input of different heat generators used by the air conditioning / cooling sys-tem

y n N 0

105 electric auxiliary energy demand of the air conditioning / cooling system y n N 0


Ligthing and other electric energy de-

mand

106 electric energy demand for lighting y n N 0 107 information on other demand n n N 0 108 total other electric energy demand of the

building n n N 0

Cogeneration 109 total heat generation by CHP y n N 0 110 total electric and mechanic energy gen-

eration by CHP y n N 0

111 type of energy carrier used by cogenera-tion engines y n N 0

112 energy input to cogeneration engines y n N 0 F Basic Parameters of Operational Rating 113 method of operational rating y n Y 0 114 first month and year of measurement

period y n Y 0

115 last month and year of measurement period y n Y 0

116 information on climate or weather correc-tion y n Y 0

117 heating climate index of the measured energy consumption y n Y 0

118 value of the heating climate index (meas-ured energy consumption) y n Y 0

119 other climate index of the measured en-ergy consumption n n 0 0

120 value of the other climate index (meas-ured energy consumption) n n 0 0

G Summary of Energy Consumption and

Energy Generation (for both Operational and/or Asset Rating)

Energy Consumption (measured and/or

calculated values)

Electric Energy consumption 121 uses that are included in the measured

electric energy y y Y 0



122 Indicator for comparability of measured to calculated values N n N 0

Consumption of other energy carriers Germany Italy Sweden Finland

123 uses that are included in the measured consumption values of the different energy carriers (non-eletricity)

y y Y 0

124 Indicator for comparability of measured to calculated values y n 0 0

Energy generation in the Building 125 types of energy generation in the building y Y Y 0 126 Indicator for comparability of measured to

calculated values 0 n 0 0

H Primary Energy, CO2 Emissions and

benchmarks (for both operational or asset rating, respectively)

127 definition of primary energy y y N 0 128 definition of CO2 (y) n Y 0 129 definition of other weighted energy type n y Y 0 130 primary energy demand of the building y y N 0 131 CO2 emissions of the building (y) n Y 0 132 weighted energy demand of the building

y

in develop-ment (e.g. Biomass =

0.5)

Y 0

133 benchmark available y n Y 0



Resources

/1/ “The EPBD and Continuous Commissioing”, Jagemar et.al., CIT Energy Manage-ment AB, Sweden, 2007 This report was prepared in the framework of the IEE project Building EQ “Tools and methods for linking EPDB and continuous commissioning”, www.buildingeq.eu.

/2/ „A Specifications Guide for Performance Monitoring Systems“, Haves et.al., Law-rence Berkley National Laboratories, USA, 2006 http://cbs.lbl.gov/performance-monitoring/specifications

/3/ „Continuous Commissioning Guidebook“, Claridge et.al., Energy Systems Labora-tory, Texas A&M University, Federal Energy Management Program, USA, 2002 http://eber.ed.ornl.gov/commercialproducts/contcx.htm

/4/ „International Performance Measurement and Verification Protocol“, USA, 2001 http://www.evo-world.org/index.php?option=com_content&task=view&id=40&Itemid=63

/5/ “Inverse Modeling Toolkit: Numerical Algorithms”, Kissock, Haberl, Claridge, ASHRAE Transactions, Volume 109, Part 2, 2003

/6/ “VDI 3807 - Characteristic values of energy consumption in buildings – heating and electricity”, Verein Deutscher Ingenieure, 1998

/7/ „Verbrauchskennwerte 2005“, ages GmbH Münster, 2007

/8/ „Benchmarks für die Effizienz von Nichtwohngebäuden“, ARGE Benchmark, 2007

/9/ „Datenbank zu Energieverbrauchkennwerten von Gebäuden des Bundes, der Länder und Kommunen“, Institut für Erhaltung und Modernisierung von Bauwerken e.V. (IEMB): im Auftrag des Bundesministeriums für Verkehr, Bau und Stadtent-wicklung, August 2006

/10/ UNI 10349:1994 Heating and cooling of buildings: climatic data

/11/ “Italian Climatic data collection "Gianni De Giorgio" (IGDG)” http://www.eere.energy.gov/buildings/energyplus/weatherdata_sources.html#IGDG

/12/ “Review of GBTool and Analysis of GBC 2002 Case-Study Projects”, november 2002, http://www.iisbe.org/iisbe/gbc2k5/gbc2k5-start.htm ”Sostenibilità ambientale degli edifici: criteri di valutazione”, Lelloni, Meroni http://www.edilio.it/news/edilionews.asp?tab=Notizie&cod=6067

/13/ “Linee guida per la definizione di protocolli tecnici di manutenzione predittiva sugli impianti di climatizzazione”, (GU n. 256 del 3-11-2006 - Suppl. Ordinario n. 207).

/14/ www.cened.it

/15/ www.docet.itc.cnr.it/

/16/ www.energyteam.it/ � soluzioni � telecontrollo � modulo sinottico

/17/ http://sel.me.wisc.edu/trnsys/

/18/ “Operation & Maintenance Best Practice – A guide to achieve Operational Effi-ciency”, PNNL for FEMP, USA 2004 http://www1.eere.energy.gov/femp/operations_maintenance/om_bpguide.html

http://cbs.lbl.gov/performance-monitoring/specifications

http://eber.ed.ornl.gov/commercialproducts/contcx.htm

http://www.evo-world.org/index.php?option=com_content&task=view&id=40&Itemid=63

http://www.evo-world.org/index.php?option=com_content&task=view&id=40&Itemid=63

http://www.iisbe.org/iisbe/gbc2k5/gbc2k5-start.htm

http://www.edilio.it/news/edilionews.asp?tab=Notizie&cod=6067

http://www1.eere.energy.gov/femp/operations_maintenance/om_bpguide.html

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