NIST Technical Note 1744

Annex 47 Report 2:

Commissioning Tools for Existing and 

Low Energy Buildings

Christian Neumann 

Dirk Jacob

Henk Peitsman 

Harunori Yoshida

Hideki Yuzawa

Takeshi Watanabe

Katsuhiro Kamitani

Hiroshige Kikuchi 

Oliver Baumann

Daniel Choinière 

Natascha Milesi Ferretti

http://dx.doi.org/10.6028/NIST.TN.1744
NIST Technical Note 1744

Annex 47 Report 2:

Commissioning Tools for Existing and 

Low Energy Buildings

Christian Neumann 

Dirk Jacob

Fraunhofer Institute for Solar Energy Systems

Henk Peitsman 

Netherlands Organization for Applied Scientific Research

Harunori Yoshida

Kyoto University 

Hideki Yuzawa

Nikken Sekkei Research Institute

Takeshi Watanabe

NTT Facilities 

Katsuhiro Kamitani

Tonets Corporation 

Hiroshige Kikuchi 

Hitachi Plant Technologies

Oliver Baumann

Ebert and Baumann Consulting Engineers

Daniel Choinière 

Natural Resources Canada

Canada Centre for Mineral and Energy Technology 

Energy Technology Centre

Natascha Milesi Ferretti

National Institute of Standards and Technology

Engineering Laboratory

http://dx.doi.org/10.6028/NIST.TN.1744

September 2012

U.S. Department of Commerce 

Rebecca Blank, Acting Secretary

National Institute of Standards and Technology 

Patrick D. Gallagher, Under Secretary of Commerce for Standards and Technology and Director 
Certain commercial entities, equipment, or materials may be identified in this

document in order to describe an experimental procedure or concept adequately.

Such identification is not intended to imply recommendation or endorsement by the

National Institute of Standards and Technology, nor is it intended to imply that the

entities, materials, or equipment are necessarily the best available for the purpose. 

National Institute of Standards and Technology Technical Note 1744

Natl. Inst. Stand. Technol. Tech. Note 1744, 247 pages (May 2012) 

http://dx.doi.org/10.6028/NIST.TN.1744

CODEN: NTNOEF
I

CITATION

Neumann, C. et al. Commissioning Tools for Existing and Low Energy Buildings, a Report of 

Cost-Effective Commissioning of Existing and Low Energy Buildings. NIST Technical Note 

1744. May 2012. 

This publication was simultaneously published by Natural Resources Canada as Neumann, C. et 

al. Commissioning Tools for Existing and Low Energy Buildings, a Report of Cost-Effective 

Commissioning of Existing and Low Energy Buildings, May 2012.

Copies of this report may be obtained from the Annex 47 web site at: http://www.ieaannex47.org , the NIST website at: http://www.nist.gov, or from the IEA/ECBCS Bookshop at: 

www.ecbcs.org.
II

Preface

International Energy Agency

The International Energy Agency (IEA) was established in 1974 within the framework of the 

Organization for Economic Co-operation and Development (OECD) to implement an 

international energy program. A basic aim of the IEA is to foster co-operation among the twentyeight IEA participating countries and to increase energy security through energy conservation, 

development of alternative energy sources and energy research, development and demonstration 

(RD&D).

Energy Conservation in Buildings and Community Systems

The IEA coordinates research and development in a number of areas related to energy. The 

mission of one of those areas, the Energy Conservation for Building and Community Systems 

Program (ECBCS) is to develop and facilitate the integration of technologies and processes for 

energy efficiency and conservation into healthy, low emission, and sustainable buildings and 

communities, through innovation and research.

The research and development strategies of the ECBCS Program are derived from research 

drivers, national programs within IEA countries, and the IEA Future Building Forum Think Tank 

Workshop, held in March 2007. The R&D strategies represent a collective input of the Executive 

Committee members to exploit technological opportunities to save energy in the buildings sector, 

and to remove technical obstacles to market penetration of new energy conservation 

technologies. The R&D strategies apply to residential, commercial, office buildings and 

community systems, and will impact the building industry in three focus areas of R&D activities: 

• Dissemination; 

• Decision-making; and

• Building products and systems.

Participating countries in ECBCS: Australia, Austria, Belgium, Canada, P.R. China, Czech 

Republic, Denmark, Finland, France, Germany, Greece, Italy, Japan, Republic of Korea, the 

Netherlands, New Zealand, Norway, Poland, Portugal, Spain, Sweden, Switzerland, Turkey, 

United Kingdom and the United States of America.

The Executive Committee

Overall control of the program is maintained by an Executive Committee, which not only 

monitors existing projects but also identifies new areas where collaborative effort may be 

beneficial. To date the following projects have been initiated by the executive committee on 
III

Energy Conservation in Buildings and Community Systems (completed projects are identified by 

(*)):

Annex 1: Load Energy Determination of Buildings (*)

Annex 2: Ekistics and Advanced Community Energy Systems (*)

Annex 3: Energy Conservation in Residential Buildings (*)

Annex 4: Glasgow Commercial Building Monitoring (*)

Annex 5: Air Infiltration and Ventilation Centre 

Annex 6: Energy Systems and Design of Communities (*)

Annex 7: Local Government Energy Planning (*)

Annex 8: Inhabitants Behavior with Regard to Ventilation (*)

Annex 9: Minimum Ventilation Rates (*)

Annex 10: Building HVAC System Simulation (*)

Annex 11: Energy Auditing (*)

Annex 12: Windows and Fenestration (*)

Annex 13: Energy Management in Hospitals (*)

Annex 14: Condensation and Energy (*)

Annex 15: Energy Efficiency in Schools (*)

Annex 16: BEMS 1- User Interfaces and System Integration (*)

Annex 17: BEMS 2- Evaluation and Emulation Techniques (*)

Annex 18: Demand Controlled Ventilation Systems (*)

Annex 19: Low Slope Roof Systems (*)

Annex 20: Air Flow Patterns within Buildings (*)

Annex 21: Thermal Modelling (*)

Annex 22: Energy Efficient Communities (*)

Annex 23: Multi Zone Air Flow Modelling (COMIS) (*)

Annex 24: Heat, Air and Moisture Transfer in Envelopes (*)

Annex 25: Real-time HVAC Simulation (*)

Annex 26: Energy Efficient Ventilation of Large Enclosures (*)

Annex 27: Evaluation and Demonstration of Domestic Ventilation Systems (*)

Annex 28: Low Energy Cooling Systems (*)
IV

Annex 29: Daylight in Buildings (*)

Annex 30: Bringing Simulation to Application (*)

Annex 31: Energy-Related Environmental Impact of Buildings (*)

Annex 32: Integral Building Envelope Performance Assessment (*)

Annex 33: Advanced Local Energy Planning (*)

Annex 34: Computer-Aided Evaluation of HVAC System Performance (*)

Annex 35: Design of Energy Efficient Hybrid Ventilation (HYBVENT) (*)

Annex 36: Retrofitting of Educational Buildings (*)

Annex 37: Low Exergy Systems for Heating and Cooling of Buildings (LowEx) (*)

Annex 38: Solar Sustainable Housing (*)

Annex 39: High Performance Insulation Systems (*)

Annex 40: Building Commissioning to Improve Energy Performance (*)

Annex 41: Whole Building Heat, Air and Moisture Response (MOIST-ENG) (*)

Annex 42: The Simulation of Building-Integrated Fuel Cell and Other Cogeneration Systems 

(FC+COGEN-SIM) (*)

Annex 43: Testing and Validation of Building Energy Simulation Tools (*)

Annex 44: Integrating Environmentally Responsive Elements in Buildings

Annex 45: Energy Efficient Electric Lighting for Buildings (*)

Annex 46: Holistic Assessment Tool-kit on Energy Efficient Retrofit Measures for 

Government Buildings (EnERGo)

Annex 47: Cost-Effective Commissioning for Existing and Low Energy Buildings

Annex 48: Heat Pumping and Reversible Air Conditioning

Annex 49: Low Exergy Systems for High Performance Buildings and Communities

Annex 50: Prefabricated Systems for Low Energy Renovation of Residential Buildings

Annex 51: Energy Efficient Communities

Annex 52: Towards Net Zero Energy Solar Buildings

Annex 53: Total Energy Use in Buildings: Analysis & Evaluation Methods

Annex 54: Analysis of Micro-Generation & Related Energy Technologies in Buildings

Annex 55: Reliability of Energy Efficient Building Retrofitting - Probability Assessment of 

Performance & Cost (RAP-RETRO)
V

Annex 56: Energy and Greenhouse Gas Optimised Building Renovation

Working Group - Energy Efficiency in Educational Buildings (*)

Working Group - Indicators of Energy Efficiency in Cold Climate Buildings (*)

Working Group - Annex 36 Extension: The Energy Concept Adviser (*)

Working Group - Energy Efficient Communities

Annex 47

The objectives of Annex 47 were to enable the effective commissioning of existing and future 

buildings in order to improve their operating performance and to advance the state-of-the-art of 

building commissioning by:

Extending previously developed methods and tools to address advanced systems and low 

energy buildings, utilizing design data and the buildings’ own systems in commissioning;

Automating the commissioning process to the extent practicable;

Developing methodologies and tools to improve operation of buildings in use, including 

identifying the best energy saving opportunities in HVAC system renovations; and

Quantifying and improving the costs and benefits of commissioning, including the 

persistence of benefits and the role of automated tools in improving persistence and 

reducing costs without sacrificing other important commissioning considerations.

To accomplish these objectives Annex 47 has conducted research and development in the 

framework of the following three areas:

Initial Commissioning of Advanced and Low Energy Building Systems 

This area addressed what can be done for (the design of) future buildings to enable cost-effective 

commissioning. The focus was set on the concept, design, construction, acceptance, and early 

operation phase of buildings. 

Commissioning and Optimization of Existing Buildings

This area addressed needs for existing buildings and systems to conduct cost-effective 

commissioning. The focus here was set on existing buildings where the commissioning process 

must be performed with incomplete or out-of-date documentation.

Commissioning Cost-Benefits and Persistence 

This area addressed how the cost-benefit situation can be represented. Key answers were 

provided by developing international consensus methods for evaluating commissioning costbenefit and persistence. The methods were implemented in a cost-benefit and persistence 

database using field data.
VI

Annex 47 was an international joint effort conducted by 50 organizations in 11 countries:

Belgium • KaHo St-Lieven

• Ghent University 

• Passive House Platform

• Université de Liège 

• Katholieke Universiteit Leuven

Canada • Natural Resources Canada (CETC-Varennes)

• Public Works and Governmental Services Canada

• Palais de Congres de Montreal

• Hydro Quebec

Czech Republic • Czech Technical University

Finland • VTT Technical Research Centre of Finland

• Helsinki University of Technology

Germany • Ebert-Baumann Consulting Engineers

• Institute of Building Services and Energy Design 

• Fraunhofer Institute for Solar Energy Systems 

Hong Kong/China • Hong Kong Polytechnic University

Hungary • University of Pécs

Japan • Kyoto University

• Kyushu University

• Chubu University 

• Okayama University of Science 

• NTT Facilities 

• Osaka Gas Co.

• Kansai Electric Power Co.

• Kyushu Electric Power Co.

• SANKO Air Conditioning Co 

• Daikin Air-conditioning and Environmental Lab

• Tokyo Electric Power Co

• Tokyo Gas Co.

• Takenaka Corp.

• Chubu Electric Power Co.

• Tokyo Gas Co., Ltd.

• Tonets Corp

• Nikken Sekkei Ltd

• Hitachi Plant Technologies

• Mori Building Co.

• Takasago Thermal Engineering Co., Ltd. 

• Institute for Building Environment and Energy Conservation

Netherlands • TNO Environment and Geosciences

• University of Delft 

Norway • Norwegian University of Science and Technology

• SINTEF

USA • National Institute of Standards and Technology

• Texas A&M University

• Portland Energy Conservation Inc.

• Carnegie Mellon University

• Johnson Controls

• Siemens

• Lawrence Berkeley National Laboratory
VII

FOREWORD

This report summarizes part of the work of IEA-ECBCS Annex 47 Cost-Effective 

Commissioning of Existing and Low Energy Buildings. It is based on the research findings 

from the participating countries. The publication is an official Annex 47 report.

Report 1, ‘Commissioning Overview’ can be considered as an introduction to the commissioning 

process. 

Report 2, ‘Commissioning Tools for Existing and Low Energy Buildings’ provides general 

information on the use of tools to enhance the commissioning of low energy and existing 

buildings, summarizes the specifications for tools developed in the Annex and presents building 

case studies. 

Report 3, ‘Commissioning Cost Benefit and Persistence’ presents a collection of data that would 

be of use in promoting commissioning of new and existing buildings and defines methods for 

determining costs, benefits, and persistence of commissioning, The report also highlights 

national differences in the definition of commissioning.

Report 4, ‘Flowcharts and Data Models for Initial Commissioning of Advanced and Low Energy 

Building Systems’ provides a state of the art description of the use of flow charts and data 

models in the practice and research of initial commissioning of advanced and low energy 

building systems.

In many countries, commissioning is still an emerging activity and in all countries, advances are 

needed for greater formalization and standardization. We hope that this report will be useful to 

promote best practices, to advance its development and to serve as the basis of further research in 

this growing field.

Natascha Milesi Ferretti and Daniel Choinière

Annex 47 Co-Operating Agents 
VIII

ACKNOWLEDGEMENT

The material presented in this publication has been collected and developed within an Annex of 

the IEA implementing agreement on Energy Conservation in Buildings and Community 

Systems, Annex 47, “Cost-Effective Commissioning of Existing and Low Energy Buildings”.

This report, together with three companion Annex reports, is the result of an international joint 

effort conducted in ten countries. All those who have contributed to the project are gratefully 

acknowledged. 

On behalf of all participants, the members of the Executive Committee of IEA Energy 

Conservation in Building and Community Systems Implementing Agreement as well as the 

funding bodies are also gratefully acknowledged.

A list of participating countries, institutes, and people can be found at the end of this report.

REPORT EDITORS:

Christian Neumann (Fraunhofer ISE, Germany)

Harunori Yoshida (Kyoto University, Japan)

Daniel Choinière (Natural Resources Canada)

Natascha Milesi Ferretti (NIST, USA)
IX

TABLE OF CONTENTS

1 EXECUTIVE SUMMARY................................................................................................................................0

2 COMMISSIONING AND ANALYSIS APPROACHES FOR EXISTING AND LOW ENERGY 

BUILDINGS.................................................................................................................................................................4

2.1 ANALYSIS APPROACHES ..................................................................................................................................4

2.1.1 Top-Down Approach..............................................................................................................................6

2.1.2 Bottom-Up Approach.............................................................................................................................7

2.1.3 Performance Metrics .............................................................................................................................7

2.2 SPECIAL ASPECTS OF EXISTING BUILDINGS .....................................................................................................8

2.3 INITIAL CX OF ADVANCED & LOW ENERGY BUILDING SYSTEMS ..................................................................10

2.3.1 General Characteristic of Advanced & Low Energy Systems..............................................................10

2.4 REFERENCES CHAPTER 2................................................................................................................................12

3 MONITORING GUIDELINES AND DEPLOYMENT OF SENSORS FOR EXISTING BUILDINGS.13

3.1 INTRODUCTION ..............................................................................................................................................13

3.2 NEED FOR MONITORING GUIDELINES AND PRESENT STATUS.........................................................................14

3.2.1 SHASE Energy Performance Measurement Manual for Building Equipment & System–Japan .........15

3.2.2 “A Specifications Guide for Performance Monitoring Systems”, University of California Lawrence 

Berkeley National Laboratory – USA ................................................................................................................15

3.2.3 “EnOB Monitoring Guidelines” – Germany .......................................................................................15

3.2.4 IPMVP .................................................................................................................................................15

3.2.5 CEN Standards - Europe .....................................................................................................................16

3.2.6 Recommendations................................................................................................................................18

3.3 REQUIREMENTS AND PROBLEMS FOR SENSOR DEPLOYMENT IN EXISTING BUILDINGS ..................................18

3.3.1 Problems Installing Sensors in Existing Buildings..............................................................................18

3.3.2 Results of Survey on Sensor Deployment in Existing Buildings...........................................................19

3.3.3 Requirements for Installing Sensing Systems in Existing Buildings....................................................21

3.4 REFERENCES CHAPTER 3................................................................................................................................23

4 CX TOOLS FOR EXISTING BUILDINGS AND LOW ENERGY BUILDINGS ....................................25

4.1 NEEDS FOR CX TOOLS....................................................................................................................................25

4.1.1 Needs from the European Point of View..............................................................................................25

4.1.2 Needs from the North American Point of View...................................................................................26

4.1.3 Needs from the Japanese Point of View...............................................................................................28

4.1.4 Needs for Existing Building (EB) Cx Tools .........................................................................................29

4.1.5 Needs for Low Energy Building (LEB) Cx Tools.................................................................................32

4.2 VISUALIZATION OF DATA AND RESULTS........................................................................................................33

4.2.1 Classification of Visualization Techniques..........................................................................................33

4.2.3 Example of Performance Evaluation Process Using Visual Graphs...................................................50

4.3 AUTOMATED TOOLS ......................................................................................................................................59

4.3.1 General Features of Existing Building and Low-Energy Building Commissioning ............................59

4.3.2 Features of EB and LEB Cx Tools Developed in Annex47..................................................................64

4.4 CASE STUDIES FOR VARIOUS APPROACHES ...................................................................................................66

4.4.1 German ModBen Project – Top-Down Approach ...............................................................................66

4.4.2 Netherlands – Bottom-Up Approach....................................................................................................83

4.4.3 NEDO Promotion Program (Top-down and Bottom-up), Japan.........................................................99

4.4.4 SHASE Energy Performance Measurement Manual .........................................................................109

4.4.5 DABOTM a BEMS-Assisted Commissioning Tool, Canada (Bottom-Up Approach Tool)..................124

4.4.6 Optimization of Automation Functions with Dynamic Simulation as the Energy Management System 

(OASE) 143

REFERENCES CHAPTER 4 .......................................................................................................................................148

5 CONCLUSIONS ............................................................................................................................................151
X

6 ANNEX 47 PARTICIPANTS........................................................................................................................154

APPENDICES..........................................................................................................................................................157

6.1 APPENDIX 1: SURVEY ON SENSORS FOR ENERGY MANAGEMENT IN EXISTING BUILDING ........................157

6.2 APPENDIX-2 EXAMPLE OF ERROR IN SENSING SYSTEM..............................................................................164

6.3 APPENDIX-3 SENSORS USED BY BEMS....................................................................................................165

6.4 APPENDIX-4 FEATURES OF EB AND LEB CX TOOLS DEVELOPED WITHIN ANNEX 47...............................166

6.5 APPENDIX-5 INFORMATION USED IN EB AND LEB CX TOOLS DEVELOPED WITHIN ANNEX 47................227
0

1 EXECUTIVE SUMMARY

Building Commissioning (Cx) is a quality assurance process for the design, construction, and 

operation of buildings. Although recognized as a valuable means to ensure that buildings reach 

their operating potential, the building commissioning process is not widely adopted 

internationally. Researchers from Annex 47, an international research project, identified that a 

major barrier to market penetration is the lack of commissioning methods and tools to ensure that 

advanced components and systems reach their technical potential and operate energy-efficiently. 

Results of research to develop guidelines and tools to help overcome that barrier for both 

existing and future buildings are presented in this report. The results address commissioning and 

analysis approaches, guidelines for monitoring and the use of sensors, commissioning tools for 

existing and low energy buildings, and a collection of international case studies. 

Commissioning and Analysis Approaches

Although there are international differences in project delivery processes, a general description 

for building commissioning is useful. Commissioning is generally carried out using either a topdown approach (first evaluating whole system performance and then moving to lower levels), or 

bottom-up approach (beginning with single components and widening analysis to system level, 

as needed) and includes the application of relevant performance metrics to evaluate decision 

points. The needs for commissioning existing buildings are quite different than those for the 

initial commissioning of advanced and low energy buildings.

The freedom to implement best-practice measures is most easily introduced in the design-phase. 

However, when commissioning existing buildings, design and operational data is often 

unavailable, inaccessible, erroneous or outdated. Most building have not been commissioned 

before and therefore, for existing buildings, commissioning often relies on data that is already 

available or that can be acquired at low cost. Automating portions of the commissioning process 

is one approach to improving cost-effectiveness. 

In commissioning advanced and low energy buildings, procedures need to explicitly address 

energy consumption at the system or whole building level and the peak demand that are not 

typically addressed in current, conventional approaches. Furthermore, new building test 

procedures for innovative systems are not available in libraries or guides. Advanced and lowenergy systems generally require a higher level of control and also greater control accuracy than 

conventional systems. Innovative buildings are, almost by definition, ‘one-of-a-kind’ and require 

functional test procedures that are customized to the design of that unique building.

To improve the building delivery process, research and development must address the need for: 

1) Methods to document design intent that extends to integration of systems at the whole 

building level, and can be clearly understood by the design team, commissioning agents 

and operators;

2) Design review guidelines for low-energy buildings to help catch problems early;
1

3) Functional test methods that adequately address innovative system operation and 

integration issues, and; 

4) Functional test methods that compare expected energy performance to actual energy 

performance during commissioning and diagnose causes of differences.

Commissioning yields the best results if begun early on in the project and integrated into every 

phase—throughout design, construction, performance verification and acceptance—until stable 

and optimal operation of the building systems is achieved.

Monitoring Guidelines 

Systematic approaches for the evaluation of data, beyond simple benchmarking, are often 

missing. Furthermore, the unknown benefits versus the costs of installing additional 

measurement equipment present a major constraint. It is recommended that a project-specific list 

of necessary measurements and/or sensors be developed by:

• Identifying systems to be assessed;

• Defining specific checks and performance metrics that are appropriate for these 

systems;

• Deriving the measurements needed to prepare/calculate the desired performance 

metrics; and

• Defining and using a unified point naming convention, particularly when 

monitoring massive systems.

In existing buildings, the three most significant problems related to installing sensors were 

identified as cost, physical constraints from existing facilities, and accuracy. Field experience 

was distilled to provide proven approaches that help to overcome these problems, including the 

use of wireless technology and non-invasive sensors.

Commissioning Tools

The diversity of Annex participants provided insight to the needs for commissioning tools from 

the European, North American, and Asian points of view.

In Europe, energy audits and performance contracts are well established, but third-party 

commissioning is a new concept, unknown to many consultants and building owners. Although 

the expertise needed for commissioning is available, when performed, commissioning is 

generally limited to a handover activity. Standardized descriptions/guidelines and tools that can 

support a systematic commissioning process are absent. 

In North America, barriers to assuring maximum building performance exist at each phase of the 

building life-cycle: design, construction and turnover, and ongoing operations and occupancy. 

Cheaper, more accurate and more robust tools to automate the commissioning process for new 

and existing buildings are needed to improve building performance while reducing labor and 

other costs. These tools will also help the market better respond to the outsized demand for 

skilled designers, commissioning providers and operators. 
2

In Japan, the meaning and scope of commissioning varies from engineer to engineer. Pioneering 

research work has implemented many kinds of commissioning tools, for example, the use of 

computer simulation to estimate performance of heating, ventilating and air-conditioning 

(HVAC) systems or sub-systems, and tools to visualize results. Still, adoption is slow. The most 

significant barriers are the difficulties interfacing with system data, lack of experience and 

training tools, and lack of robust commissioning tools that can address system interactions.

Across the globe, there is a universal need to measure, monitor, and process huge amounts of 

data in order to operate the building and systems optimally. Advanced visualization techniques 

can be used to display the information that is hidden in performance data and/or the recorded 

operation data of buildings and systems, and, thus, is valuable for commissioning of buildings 

and systems. Visualization is also relevant for transporting data and information from design 

analyses, such as simulation and modelling, and to make this information available in subsequent 

project phases. Visualization techniques can be used to show:

• Change of variables over time;

• Relations between two or more variables;

• Statistical information / distribution of values;

• Spatial information/distribution;

• Partitioning/percentage share of properties;

• Comparison of scalars (elements of different size); and

• Process / information flow.

In Annex 47, a total of eighteen automated and semi-automated commissioning tools were 

developed. Eight categories were selected to describe the features of existing building and low 

energy building commissioning tools: objectives, functions, data-management, implementation, 

operability, analytical engine, end users, and benefits. Detailed features of major tools are listed 

in Appendix 4. The focus of many of the commissioning tools is fault detection and diagnosis, 

followed by optimization, design and data handling. The primary target of most tools is system 

level commissioning, followed by whole building and component level at the same rate, then 

control level. Most tools have been developed for existing buildings rather than low energy 

buildings, because automated tools for low energy building (LEB) Cx are still in their infancy. 

Case studies were selected from the real building implementation of these tools to present three 

analysis approaches.

1) Top-down approach - The German ModBen project deals with the systematic performance 

evaluation of existing non-residential buildings, an ongoing commissioning process. A 

four-step procedure was developed for ModBen and is illustrated in Figure 1-1.
3

Figure 1-1 The four-step ongoing commissioning procedure on a time scale

2) Bottom-up approach - In the Netherlands, two bottom-up examples were presented. One 

involved an investigation of tenant complaints and leveraged functional performance 

testing tools. The second example is a blind analysis of an air-handling unit (AHU) based 

on a functional description of the AHU as a basis for deriving expert rules, data collected 

from a building automation system, and no additional sensors. 

3) Combined approach - A Japanese promotion program for the introduction of efficient 

energy systems in housing/buildings assists with the introduction of a building energy 

management system. In this project, grant-aided businesses specify their demands for 

achieving energy-conservation. The implementation report includes information on 

quantitative target values and conditions for their attainment, how to analyze measured 

data, solve problems under an energy management system consisting of experts, such as a 

designer and others, in addition to the building’s owner, and the procedure for reporting 

results. This process is considered similar to the commissioning process.

The tools and case studies discussed illustrate the potential for improved building performance 

through the use of software tools in the commissioning process. The tools provide features for 

operational fault detection and and/or for optimization that help to identify and realize potential 

savings.

FDD and 

Optimization Monitoring

Collect System Information and 

Installation of Data Acquisition 

Equipment

Benchmarking
4

2 Commissioning and Analysis Approaches for Existing and Low 

Energy Buildings

2.1 Analysis Approaches

A building and its services can be defined as a hierarchical structure as shown in Figure 2-1. At 

the highest level, Level 1, a building is seen in its entirety. The lowest level, Level 6, looks at

components in a subsystem (e.g., a fan.) The second level is divided into a building’s services 

systems (on left) and the building itself (on right). The systems part consists primarily of 

physical objects, such as building structure and envelope.

Figure 2-1 Building divided into its service systems, subsystems and components (Figure source: 

[1])

Building as an Entirety

Building with its services system

Other Building

Services Systems

HVAC System HVAC 

Plant

Lighting

System

Air Handling

System

Hydronic

Heating and

Cooling

System

Air

Distribution

System

Interior, Envelope Structure

Furniture, etc.

Building Services

HVAC System, Other Building Services Systems

Building

Interior, Envelope, Structure

1

2

3

4

5

6

Water

Distribution

Systems

Air

Treatment

System

Fans Air terminals Pumps Pipes

External 

walls

Roof Floors on 

ground 

Walls

Windows

Other

Vertical

Transport

Receptacles

Ducts

Chillers

Boilers

Heat

rejection 

equipment

Heating coils

Cooling coils

Humidifiers

Dehumidifiers

Filters

Service Hot 

Water

Solar 

shading
5

The overall systems may be viewed from different levels:

• The whole system;

• A subsystem, involving several components; or

• Single components that are considered critical.

As a result, any perspective analysis can occur from a top-down or bottom-up approach. The 

source book of IEA ECBCS Annex 25 [2] gives these explanations concerning the different 

approaches.

“The two problems can be presented in the following manner: When undesired operation is 

observed on the building level, what is the cause of the problem on the level of subsystems or 

components? When a fault is observed on the component level, what is the seriousness of that 

fault in terms of building performance on the level of the building as a whole? In Figure 2-2 the 

problems are represented by arrows. The tail of each arrow indicates an observation of a fault 

or undesired operation, while the head indicates the result of the reasoning. 

“Instead the fault is detected in the building or in the component level; it can as well be detected 

in the subsystem level. In the last case one must be able to deduce the impact of the fault in the 

subsystem level in a higher level (building level, for example), and on the other hand, be able to

find the cause of the fault in a lower level (component level, for example).

Figure 2-2 Schema of top-down and bottom-up approaches

(Figure source: [2])

“Problems can be named according to the principles used for their resolution. In the top down -

approach the starting point is some performance property of a building which describes the 

function of the entire system, such as energy consumption, proceeding towards smaller details. 

In similar fashion, the bottom up - approach begins with some detail, the significance of which 

for the whole is not necessarily known, and ending up with the consequence of the fault from the 
6

standpoint of the whole. When starting from the subsystem level, both approaches must be 

utilized.”

2.1.1 Top-Down Approach

The top-down approach consists in “going down” from system level to component level, through 

some subsystems. The whole system performance is first verified and the inquiry is then 

extended in turn to lower levels, following observed malfunctions or questions to be answered. 

The first task is not to verify if a certain component is performing “well” or “poorly”, but rather 

to check if the overall performance of the system and the interaction between components and 

subsystems is acceptable or within the “normal” range.

The Annex 25 source book [2] also states that:

“What becomes of a problem after the error has been observed, however, is locating it in the 

building’s many subsystems and constituent components. How does localization of the error 

proceed, and to what degree should it be possible to explain the reason for its occurrence? It 

should be possible to arrange the reasoning so that it would proceed according to either the 

order of probabilities or some other corresponding order of importance. For example, if it is a 

question of how to reduce energy wasting, the first thing that should be considered for inspection 

is the most probable sub-process which might have been damaged or, alternatively, the partial 

system which consumes the most energy. If the fault is found there, work should continue 

according to some preliminary determined system within the subsystem to its components or 

something similar.

“Localizing the fault in the hierarchical tree would thus proceed to the level possible using 

existing process data, which has either been obtained from measurements or requested from the 

user. If there is no control of reasoning of this type, implementation of the reasoning would be 

too difficult. This leads in practice to a situation in which no resolution of the problem is 

obtained.”

This describes the limitation of the top-down approach: it is well suited to detect (major) faults, 

but, by definition, not every subsystem and component is monitored. Fault diagnosis could be 

difficult or even impossible if necessary information is missing. For fault diagnosis, one has to 

know how a certain fault shows up on high level signals such as the total energy consumption. 

This could be an easy task for faults such as deficient operating schedules that are not in 

synchronization with occupancy (demand). However, for more complex faults at subsystem or 

component levels (e.g., coil fouling, loss of refrigerant or wrongly adjusted operation modes),

fault diagnosis could be difficult or impossible and the prognosis is worse when there are 

multiple faults. Furthermore, the occurrence of a single fault in a component might not evident at 

high level signals such as energy consumption, but may nevertheless have a significant influence 

on energy, comfort or lifetime of the component.
7

The top-down approach is a common solution that combines permanent measurements for the 

overall performance with temporary measurements at the subsystem or component level for a 

more detailed analysis of systems that are identified as deficient.

2.1.2 Bottom-Up Approach

The bottom-up approach starts with the analysis of a single component and steps up,

progressively, to the whole building level. It might be most appropriate for initial 

commissioning. It allows a safer identification of local faults, but it might also result in wasted 

effort because of lack of global vision of the problems.

It is impractical to monitor the operation of all building components. It is therefore essential that,

prior to implementation of the bottom-up approach, priorities be established for the investigation 

of representative samples of those components and faults for which a failure is either probable or 

would have the greatest effect on the property or set of properties of the building. 

2.1.3 Performance Metrics

When assessing building performance it is important that the data are presented in a pre-defined 

manner that renders it informative. This is achieved using standardized performance metrics.

Deru and Torcellini [3] give the following definitions/characteristics for performance metrics:

“A metric is a standard definition of any measurable quantity, and a performance metric is a 

standard definition of a measurable quantity that indicates some aspect of performance. Many 

other terms are used with a similar meaning, such as performance indicator, performance index, 

and benchmarking.

“Performance metrics need certain characteristics to be valuable and practical. A performance 

metric should:

• Be measurable (or able to be determined from other measurements);

• Have a clear definition, including boundaries of the measurements;

• Indicate progress toward a performance goal; [and]

• Answer specific questions about the performance.”

This definition is applied to energy performance of buildings and performance metrics for 

different levels according to Figure 2-3. Deru and Torcellini defined three levels of performance 

metrics: Tier 1Metrics, Tier 2 Metrics, and Indicators.

Tier 1 metrics provide a high level building overview e.g., from monthly/annual data such as 

utility bills. Tier 2 metrics provide a breakdown of the performance of subsystems and/or 

components and typically requires hourly or sub hourly data. Indicators are above Tier 1 

metrics, and they aggregate complex information to show planning level trends toward goals.
8

Figure 2-3 Application of performance metrics in building performance 

evaluation[3]

Even though Deru and Torcellini focused on energy performance metrics (based on 

measurements of energy flows) the definition of performance metrics and tiers can be applied to 

any general analysis that incorporates any kind of measurable data (see also: Gillespie, et al.[4]). 

In general, the definition of performance metrics follows the identification of systems to be 

monitored and questions to be answered concerning their performance

2.2 Special Aspects of Existing Buildings

For new construction, ongoing commissioning may be introduced in the design phase, giving the 

building owner and design engineer freedom in their decisions. With existing buildings that were 

never the subject of any kind of commissioning, this freedom is missing. The design and 

structure of the building and the HVAC system are fixed. Introducing ongoing commissioning in 

existing buildings poses two significant challenges: 1) lack of design data and 2) lack of 

sufficient budget to collect robust data.

For existing buildings, information about the construction and systems may be unavailable, decentralized, inaccessible, or contain erroneous or invalid documentation. Metering data is 

normally reduced to the minimum necessary for the energy billing.

Although it is feasible to prepare detailed documentation, create building modeling and/or install 

numerous sensors to measure data for the analysis, the cost is deemed too high for this to be 

standard procedure in existing buildings. The budget is a significant factor when considering 

measurement equipment as well as the effort required for acquisition of information during the 

audit.
9

As a result, tools for existing buildings often rely on data which is either already available or that

can be acquired at low cost. Consequently, the choice of approach used in analysis of an existing 

building (top-down or bottom up) often depends on the availability of measured data. The 

analyst may combine the following complementary approaches:

1. Use existing sensors only

This approach tries to make “the best” of the situation. An example of a top-downapproach would be benchmarking based on utility bills (monthly/annually). 

2. For a bottom-up analysis

This approach is only reasonable if a building automation system (BAS) is installed that 

can provide a lot of data from the system. As the data from the BAS might not be 

sufficient to analyze every component – the outcome of this approach might be arbitrary 

or incomplete.

3. Use and/or install a consciously chosen set of sensors.

This approach tries to use or install only necessary sensors to answer specific questions 

while adhering to budget requirements.

In practice, a mixture of these approaches will likely be applied as listed in Table 2-1. 

Table 2-1 Combination of top-down/bottom up approaches and use of existing/new sensors

top-down bottom-up

Use existing sensors 

only

Benchmarking based on 

utility bills/meter readings 

(monthly / annual)

Analysis based an data provided 

by existing BAS

Use and/or install a 

chosen set of sensors

Detailed monitoring of 

building’s energy balance.

Detailed monitoring and fault 

detection and diagnosis (FDD) of 

building systems.

While a top-down approach (that is more than simple benchmarking) often starts with an energy 

balance and likely requires new sensors, a bottom-up approach often works with existing sensors 

to avoid high costs for measurement equipment.

Furthermore, a top-down approach is better suited to continuous, long-term evaluation of overall 

building performance, while a bottom-up approach should only be used to track problems of 

single components with a temporary measurement.

In general, both approaches are necessary to achieve optimum performance because the topdown approach might not detect or diagnose problems at the component level, while the bottomup approach cannot make an assessment of the overall energy consumption.

The deployment of sensors in buildings is discussed in more detail in Chapter 3.
10

2.3 Initial Cx of Advanced & Low Energy Building Systems 

2.3.1 General Characteristic of Advanced & Low Energy Systems 

As buildings and systems become more and more integrated and advanced to strive for better 

performance and higher energy efficiency, more attention must be given to the dynamic 

operation and system interactions. Modern buildings use advanced technology to reduce the 

energy demand. Systems are designed and sized close to their optimal operation point to provide 

the highest efficiency. Furthermore, systems are integrated to avoid contradictory or even 

conflicting operation modes, as well as to leverage synergies. Examples include advanced room 

climate concepts with integrated controls for lighting, daylighting, heating and cooling; energy 

recovery in air and water systems; co- and tri-generation. 

Another trend in advanced buildings is to use the building façade and thermal building mass to 

provide better comfort in occupied zones. Strategies include passive as well as active elements, 

such as natural ventilation; night time ventilation for pre-cooling a building; use of solar gains 

for heating; etc. Radiant heating and cooling systems have the potential to provide a high level of 

comfort with a low energy demand but require particular attention when it comes to the proper 

operation and control. The same is true for low energy systems in general, since these systems 

are operated at temperatures close to the demand to provide better efficiency on the generation 

side and avoid energy losses on the distribution and terminal side.

Generally, advanced and low-energy systems require a higher level of control and also greater 

control accuracy than conventional systems. Not only do they operate with a higher dynamic, but 

also within a relatively narrow bandwidth where they perform most efficiently. Due to intensive 

interaction between systems, required operations and any interference with it must be based on 

sound knowledge of the consequences of any action. 

Commissioning for advanced and low-energy buildings must therefore be a planned, systematic 

quality control process with the objective to ensure that all the building’s energy related systems 

are installed and calibrated, and perform interactively according to the design intent and 

operational needs as defined in the owner’s project requirements (OPR), and specified in the 

basis of design (BOD), and construction documents (CD).

The scope of the commissioning process follows, in detail, the entire project delivery process. At 

the beginning, the OPRs give a general outline of the project expectation, usually on a building 

level. Typical requirements include function and functionality, energy efficiency, sustainability 

goals, and, not least, cost. With the BOD, the design team develops solutions on a system level, 

such as recommendations and design criteria for HVAC systems, the building envelope, and the 

basic concept for controls and building automation. The level of detail then reaches the 

component level during design development and the preparation of specifications. The 

commissioning process follows this gradual development with reviews of the associated design 

documentation.
11

Then, as construction commences, checks and verification begin at the component level, 

continue through the system level, and finally are undertaken at the general building level once 

again. This strategy ensures efficiency in execution, completeness of the review and verification 

process, and that associated prerequisites are met for each testing phase. Figure 2-4 illustrates the 

general commissioning process and the focus and level of detail for each step.

Figure 2-4 An overview of the general commissioning process

It is critical that owners, design professionals and contractors be involved in this quality control 

process, as well as users, occupants, and operations and maintenance staff. Since not all project 

team members are available throughout the entire project duration, it is the Commissioning 

Agent’s (CxA) function and main responsibility to facilitate and establish communication, 

understanding, and information flow between them. Commissioning yields the best results if 

begun early on in the project and integrated in the delivery process throughout design, 

construction, performance verification and acceptance until a stable operation of the building 

systems is achieved. 

Commissioning during the design phase is intended to achieve the following specific objectives:

• Ensure that the design and operational intent are clearly documented;

• Ensure that recommendations for improvements are communicated to the design team 

during design to aid the development of commissioning and avoid later contract 

modifications; and

• Ensure that commissioning for the construction phase is adequately reflected in the 

construction documents.

Commissioning during project construction is intended to achieve these specific objectives:

• Ensure that applicable equipment and systems are installed properly and receive adequate 

operational tests by installing contractors;

Building Level

Operation Diagnostics

Owner Project Requirements

System Level

Functional Performance Tests

Design Concept & Basis of Design

Component Level

Design Development & Specifications

Installation Checks & Verification

Time Line
12

• Verify and document proper performance of equipment and systems through normal and 

other likely operational modes necessary to meet design intent;

• Ensure that O&M documentation provided to Owner is complete; and

• Ensure that the Owner’s operating personnel are adequately trained.

2.4 References Chapter 2

[1] Nilsson, P -E. (Ed), 2003. Achieving the Desired Indoor Climate – Energy Efficiency 

Aspects of System Design, Studentlitteratur, the Commtech Group, Lund, Sweden.

[2] J. Hyvarinen, Karki S (eds.) « Final Report Vol 1: Building Optimization and Fault 

Diagnosis Source Book » Finland, Technical Research Centre of Finland, Building 

Technology, 1996, 324 pp.

[3] M. Deru and P. Torcellini, Performance Metrics Research Project – Final Report”, 

Technical Report, National Renewable Energy Laboratory, USA, NREL/TP-550-38700, 

October 2005.

[4] K. Gillespie, et.al., “A Specifications Guide for Performance Monitoring Systems”, CEC 

PIER-DOE project, USA, 2007.
13

3 Monitoring Guidelines and Deployment of Sensors for Existing 

Buildings

This chapter gives a general overview of problems connected to the monitoring and deployment 

of sensors in existing buildings and some typical problems related to their installation and 

accuracy. 

3.1 Introduction

The following statement is taken from Kenneth Gillespie et al. [5]. 

“Those who evaluate the performance of buildings and their energy using systems have long 

known that it takes the attention of a knowledgeable and dedicated team to obtain the quality of 

data necessary to determine how well a building is actually performing as well as identify means 

for improving it. This team may include a measurement analyst, instrumentation vendors, an 

installation contractor and the owner’s staff. The problem is that buildings are not designed for 

measuring their performance. This is particularly true of flow. It is also believed that obtaining 

such data is a luxury; that it is not needed for system control or day to day operations.” 

This is not only true for existing buildings in the U.S. but likely describes the situation in many 

countries around the world.

It is important to understand that the problem described above comprises more than just missing 

sensors and measurements. Systematic approaches for the evaluation of data that go beyond 

simple benchmarking are often missing. Therefore, it is often unclear to the building owner or 

operation staff which measurements are necessary. Furthermore, the unknown benefits and costs 

of installing additional measurement equipment present a major constraint.

Ideally, the steps to develop a list of necessary measurements or sensors can be described as 

follows:

1. Identify system to be assessed.

This could be the whole building, a subsystem (e.g., air handling unit), or a single component 

(e.g., heat pump). At the same time, the system limits (in the sense of control volume) have to 

be defined.

2. Define specific questions related to these systems.

E.g., “What is the system’s COP?”, “Is the building’s specific daily energy consumption in an 

acceptable range?”, or “Does the operation mode of the air handling unit fit the actual limit 

conditions?”

3. Define performance metrics that can validate the performance of the system. 

This could be anything from simple calculations to complex evaluation routines, (e.g., from 

the definition of a generator’s COP to the automated outlier detection for a building’s heat 

consumption).

4. Derive necessary measurements to prepare/calculate performance metrics.
14

5. Adopt a unified point naming convention. This is especially helpful for massive systems.

Thus, the need for measurements depends on the system being considered and the questions to be 

answered, and, in turn, this choice of systems and questions is often highly dependent on the 

available budget. For example: A simple benchmarking for a building’s total yearly energy 

demand requires fewer measurements and less budget than comprehensive performance 

monitoring of a complex air handling unit. Temporary measurements can cost less than 

permanent installations while answering the same questions.

Compared to new construction, existing buildings pose additional constraints. For new 

construction, monitoring may already be considered during the design phase and thus be 

integrated in planning HVAC systems and building energy management system (BEMS). In 

existing buildings this is not the case and monitoring often must build on the given system which 

is generally not geared for performance monitoring. Depending on the available measurement 

sensors and data handling equipment, the requirements and cost for additional installations can 

vary widely. Furthermore, in existing buildings, problems typically arise from the HVAC 

system’s structure that can make additional installations difficult and even impossible.

Hence, for existing buildings, the commissioning provider and building owner and/or operations 

staff must agree on the level of detail for the analysis (i.e., choice of systems and questions) that 

correspond to a given budget. From there, a sensor list can be developed. Some iterations might 

be necessary to adjust the monitoring system to the budget. 

This task is specific to the building and/or building owner. However, as buildings and HVAC 

systems consist largely of similar systems, subsystems and components, the question may arise 

whether it is worthwhile to provide general guidelines for performance monitoring of existing 

buildings, at least for the most typical systems.

3.2 Need for Monitoring Guidelines and Present Status

A clear experience for many investigators shows that many buildings have only main energy 

meters if at all. Typical questions to ask when assessing building performance are: How much 

energy is used and for what? What are the operating conditions (weather, schedules, set points 

for temperatures and flow rates)?

A lot of individual approaches for the deployment of sensors have been developed in practice 

and in the scientific community (also see Chapter 2). However, more general and systematic 

compilations of necessary measurement points and evaluation routines for the assessment of the 

performance of typical systems (i.e., monitoring guidelines) are rare.

Ideally, general monitoring guidelines should include the following for a building’s typical 

systems, subsystems and components or HVAC system respectively:

• List of necessary measurements;

• Definition of the kind and quality of data acquisition;

• Definition of type and quality of data storage and data handling;
15

• Definition of necessary time resolution and duration (permanent or temporary) of 

measurements;

• Definition of performance metrics and typical benchmark values;

• Definition of a unified point naming convention; and

• Examples of applications of the guidelines.

This list should be defined further for various levels of detail concerning the analysis to account 

for varying budgets. 

Today there are only few examples for general monitoring guidelines that cover points 

mentioned. Examples of such guidelines are provided in 3.2.1-3.2.5.

3.2.1 SHASE Energy Performance Measurement Manual for Building Equipment 

& System–Japan 

SHASE (The Society of Heating, Air-Conditioning and Sanitary Engineers of Japan) published

the “Energy Performance Measurement Manual for Building Equipment & Systems.”[6] This 

manual (written in Japanese) organizes views on performance measurement methods for airconditioning facilities and plumbing sanitary systems, and defines standards of recommended 

measurement points for evaluation, deployment of sensors, code name of measurement points, 

evaluation index, and tips to evaluate the performance for each sub-system.

3.2.2 “A Specifications Guide for Performance Monitoring Systems”, University 

of California Lawrence Berkeley National Laboratory – USA

The Specifications Guide for Performance Monitoring Systems [7] defines three different levels 

of performance monitoring (basic, intermediate and advanced) with increasing levels of detail of 

the analysis but nonetheless comprising the whole building or HVAC system. The performance 

metrics, the measurement system requirements, the data acquisition and archiving and the data 

visualization and reporting are defined for each level. The type and accuracy of sensors are 

provided as well as a unified point-naming convention.

3.2.3 “EnOB Monitoring Guidelines” – Germany

The EnOB Monitoring Guidelines (written in German) specify the requirements for performance 

monitoring in the German EnOB program [8], which is a loan program for the design and 

construction of low energy buildings. It defines measurement system requirements and some 

overall performance metrics. It gives only one level of detail, that being on the data acquisition 

system but provides no information on accuracy.

3.2.4 IPMVP 

The International Performance Measurement and Verification Protocol (IPMVP) [9] describes 

concepts and options for determining energy and water savings in buildings. Development of the 

protocol is sponsored by the U.S. Department of Energy (DOE) and an international coalition of 
16

facility owners/operators, financiers, contractors and Energy Services Companies (ESCOs). It 

gives four options for calculating 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 energy conservation measure (ECM) was applied; separate from the energy use 

of the rest of the facility. Measurements may be either short-term or continuous. Partial 

measurement means that some but not all parameter(s) may be stipulated.

B: Retrofit isolation

Savings are determined by field measurement of the energy use of the systems to which 

the ECM was applied; separate from the energy use of the rest of the facility. Short-term 

or continuous measurements are taken throughout the post-retrofit period.

C: Whole Building

Savings are determined by measuring energy use at the whole facility level. Short-term or 

continuous measurements are taken throughout the post-retrofit period.

D: Calibrated simulation

Savings are determined through simulation of the energy use of components or the whole 

facility. Simulation routines must be demonstrated to adequately model actual energy 

performance measured in the facility. This option usually requires considerable skill in 

calibrated simulation.

Depending on the availability of historical consumption data and the kind of energy saving 

measure that is to be implemented, various approaches may be chosen. Option A and B deal with 

single measures that can be separated by (partial) measurements and thereby determine the 

savings that can be gauged. Option C and D refer to the whole building level, so multiple 

measures can be evaluated using these options. However, there is no further specification of 

measurement or performance metrics.

3.2.5 CEN Standards - Europe

Some European standards encourage use of measurements for their implementation. These 

standards are developed for performance estimation of equipment. The standards can be 

categorized as follows:

Requirements for Inspection

Standards EN 15239:2007 [10], EN 15240:2007 [11] and EN 15378:2007 [12] provide 

instruction for inspection of ventilation, air-conditioning, and boiler and heating systems, 

respectively. These standards support essential requirements of EU Directive 2002/91/EC on the 

energy performance of buildings (EPBD) [13]. In EN 15240:2007 it is noted that energy 

consumption or running time meters may have been installed on air-conditioning systems. 

Regular notation of the meter readings can help assess operation of the air conditioning system. 

Standard EN15378 lists which measurements are required or optional depending on plant 

capacity. All three standards list what should be checked during inspection, however, 
17

measurement equipment and measurement accuracy have not been defined. Therefore, a 

guideline on possible measurement equipment for inspection purposes could be useful.

Advice following inspection

After completion of plant or equipment inspection, recommendations for possible improvement 

must be included in the inspection report. Some of these recommendations imply use of new 

measurements. For example, the standard for the inspection of air-conditioning systems, EN 

15240:2007 [10], states that if there is no energy metering in place, a recommendation would be 

to install appropriate energy consumption metering to the largest energy consuming airconditioning plant, and subsequently to record the consumption on a regular basis. In addition, 

the standard for inspection of boilers and heating systems [11] lists possible improvements that 

include measurement of energy use. Although an inspection report includes recommendations 

regarding measurement, there is no advice on sensor type, measurement accuracy and 

integration.

Testing

Standards EN 308:1997 [14], EN 12599:2000 [15], EN 13829:2000 [16], and EN 13187:1998 

[17] relate to the testing of components. For example, standard EN 12599:2000 [15] is for testing 

ventilation and air-conditioning systems. The standard explains measurement accuracy and 

different methods together with equipment location for measuring air flow .Standard EN 

13829:2000 [16] gives instruction on how to measure the amount of air infiltration using 

pressure differential. In this standard measurement, accuracy and equipment location are also 

given. To measure thermal irregularities in building envelopes, a sensor for infrared radiation 

must be used, as mentioned in Standard EN 13187:1998 [17]. In this standard, measurement 

accuracy for theinfrared method is given. As these four standards show, standards for testing and 

measurement require detailed instruction and requirements for accuracy of measurement. 

Therefore a new guideline for sensors and measurements must include sensor description, so that 

a proper sensor can be chosen for a given type of measurement.

Requirements for Measurement

In the standard for performance requirements for ventilation and room-conditioning systems, 

EN13799 [18], it is mentioned that the design criteria constitutes the basis for measurements that 

will be carried out during the hand-over process. In addition, this standard gives general 

requirements for control and monitoring, and states that a measuring concept shall be identified 

at an early stage of the project and necessary measuring devices installed. Even though this 

standard gives general requirements for measurement definition in the design phase, there is no 

specification on measurement equipment and accuracy. Therefore a guideline for sensors can be 

a useful reference to this standard on performance requirements, to support sensor deployment in 

the design phase.
18

Common Metrics

All nine standards mentioned can be used for performance verification, inspection, or 

measurement. Regardless of the standard purposes, there are some common metrics to be 

measured. For example, in standards EN 308:1997 [14], EN 15239:2007 [10], and EN 

12599:2000 [15] air flow rate should be measured. In some standards there are parameters that 

should be measured indirectly by measuring other parameters, like equipment efficiency or 

Specific Fan Power (SFP, a parameter that quantifies the energy-efficiency of fan air movement 

systems) for example. Since there are common metrics that should be measured directly or 

indirectly in different standards, a guideline on sensor deployment as a reference to the above 

standards can encourage use of a particular sensor for a given application.

3.2.6 Recommendations

From the experience of the Annex 47 team it seems worthwhile to further develop such 

systematic approaches and publicize them for general use. For certification systems such as

Leadership in Energy and Environmental Design (LEED) (USA), Building Research 

Establishment Environmental Assessment Method (BREEAM) (UK), Deutsche Gesellschaft für 

Nachhaltiges Bauen (DGNB) (Germany) or CASBEE (Japan), it would be interesting to 

integrate systematic monitoring guidelines.

3.3 Requirements and Problems for Sensor Deployment in Existing 

Buildings 

3.3.1 Problems Installing Sensors in Existing Buildings

Sensing systems are essential for measuring the performance of constructed facilities. 

Nevertheless, awareness of performance evaluation is low, and construction cost constraints 

often limit the number of permanently installed sensors. This has led to there being too few 

sensors in existing buildings.

To grasp the performance of such existing buildings requires the installation of additional 

sensors. The installation of sensing systems in existing buildings involves the following 

problems:

• Limited budgeting for the installation of a sensor system;

• Insufficient space and other constraints in existing facilities; and

• The accuracy limitations of added sensors. Kao and Pierce [19] found that sensor errors 

can increase the annual energy requireents attributable to an air handling system by as 

much as 30 % to 50 %.
19

3.3.2 Results of Survey on Sensor Deployment in Existing Buildings

To understand the current situation, we conducted a survey on sensors installed in existing 

buildings to investigate the four points listed.

1) Is the number of sensors installed adequate for energy management?

2) Can additional sensors be installed notwithstanding restrictions such as installation space? 

3) Are the sensors sufficiently accurate for energy management?

4) Does installation of additional sensors for energy management require any action by the 

building owners, such as budgeting measures? 

The number of responding design offices, general contractors, construction companies, 

consulting companies and others surveyed are:

Asia: Japan 60 

North America: United States 6, Canada 1 

Europe: Germany 1, The Netherlands 1, Belgium 1, Norway 3

The response breakdown is shown in Table 3-1, while the results summary is presented in detail 

in Appendix 1.

 Table 3-1 Response rates

Asia (Japan) North America Europe

Design offices 15 % 0 % 0 %

General contractors 23 % 17 % 0 %

Construction 

companies 38 % 0 % 20 %

Consultants 5 % 33 % 60 %

Others 20 % 50 % 20 %

1) Sensor installation situation

The following categorization of Building Energy Management Levels (A to F) was proposed by 

the SHASE BEMS Technical Committee and it was used in our investigation of the current state 

of energy level management.

A. The total amount of energy can be computed for the entire building. (type of energy; 

monthly units) 

B. The total amount of energy can be computed for the entire building. (type of energy; 

daily and hourly units) 

C. Energy can be measured or computed by type of use. (e.g., lighting, air conditioning, 

satellites, elevators, etc.) 
20

D. Energy can be measured or computed by system. (e,g., office system, conference room 

system, executive office system, computer room system, parking area system, etc.) 

E. Energy can be measured or computed by floor. 

F. Energy can be measured or computed for specific machines or subsystems. (e.g., cooler, 

heating system, etc.) 

Results show that it is currently possible to compute the total amount of energy for the entire 

building for almost all buildings (level A). Nevertheless, less than 50 % of buildings have an 

energy management level B, and levels C through F account for less than 30 %. Looking at the 

proportions for levels C to F by region, we see that Japan has a higher proportion of high energy 

management level buildings, (15 % to 30 %) than Europe (5 % to 15 %) or North America (10 %

or less).

Furthermore, the survey’s respondents thought measurement by use (level C) or daily or hourly 

measurement for the entire building (level B) should be considered, wherever possible. Most 

respondents in favor of level C considered investigation of energy conservation to be impossible 

without measurement by use. Level B respondents considered short-period (hourly) minimum 

sensor measurements to be optimum for obtaining energy consumption trends and profiles, 

taking cost-benefit into consideration.

Investigation of problems with the sensing system also produced responses of ‘not enough 

sensors’ from nearly all respondents, leading to the conclusion that few buildings have a 

sufficient number of sensors installed. 

2) Sensor installation space

Existing buildings in which there is sufficient space for installation of sensors is 52 % for North 

America, 39 % for Europe, and 16 % for Japan. The problem of space for sensor installation is 

particularly prevalent in Japan, because machine rooms are designed to be extremely compact.

3) Sensor accuracy

Very few responses regarding sensor accuracy were obtained. We believe that either the 

respondents had low concern for sensor accuracy or had insufficient knowledge of sensor 

accuracy to respond.

4) Sensor types and budgeting for additional sensor installation 

The results of investigating budgets for the installation of additional sensors for the energy 

management levels of items revealed that most budgets approved attainment of level A but full 

approval was 50 % or less for level B, and full budgeting for levels C through F was 30 % or 

less. 

The proportion of full approval for levels C to F is higher in Japan (18 % to 30 %) than in North 

America (20 % or less) or Europe (20 % or less). The survey found building owners in Japan 

were more concerned with energy management than those in Europe and North America.
21

3.3.3 Requirements for Installing Sensing Systems in Existing Buildings

From the survey results, we conclude that although existing buildings have inadequate numbers 

of sensors, budgets and space are problems for the installation of additional sensors. Problems 

for additional sensor installation and countermeasures are listed in Table 3-2.

Table 3-2 Problems and countermeasures for installing sensors in existing buildings

Problem

Countermeasures

Sensors Transmission System Data Processing

Cost - Fewer sensors

- Simple and quick Installation

- Develop simple calibration 

tools

- Wireless or power 

line transmission

- Use of automated 

software tools to check 

for wiring errors

- Use of sensor data 

built into devices

Constraints in

Existing Facilities 

- Select ultrasonic flow meters 

(no need to cut ducts)

- Select pressure differential 

flow meters (work with shortrun ducting)

- Wireless transmission - Open processing via 

gateway (BACnet, etc.) 

(communication 

protocol)

Accuracy - Select integrating 

calorimeters (calibrated with 

difference in readings of two 

temperature sensors)

- Correction of 

measured data with 

relative values

1) Cost

To conduct a detailed performance evaluation, the number of sensors is increased to match 

the desired degree of detail. That, however, is accompanied by an increase in cost. The 

additional cost is not only for hardware; high labor costs for installation are included. The 

following are ways to reduce the total cost of the sensing system.

• Develop tools for checking wiring for errors 

• Develop simple calibration tools 

• Use data from sensors built into machinery 

• Use wireless communication or power lines to transmit data (reduce wiring cost)

• Design for future additional installations

o Centralize installation at location of power meters

o Install ducting with straight-run lengths for installation of flow meters

• Reduce number of sensors (Use minimum number of sensors for the purpose; 

standardize sensor placement.)

• Simple and quick sensor installation
22

2) Constraints in existing facilities

When installing sensors in existing buildings, certain constraints must be overcome. 

Examples of new inventions to overcome these constraints are:

• Sensors

o Flow meters such as ultrasonic flow meter that can be installed without cutting 

ductwork 

o Flow meters that can be installed within a small space (a short straight-run 

length) such as a special sensor that computes flow rate from the relationship

between flow speed and pressure drop 

• Transmission system

o Dispersion of sensor locations to reduce wiring cost by using wireless 

communication, power line wiring communication, etc. 

• Communication protocol

o Data communication with existing BAS, machines, etc. by implementing open 

protocol (BACnet, LON, etc.)

• Accuracy

Under cost, space and other constraints, it is necessary to ensure that accuracy is adapted to 

purpose. The way in which overall accuracy of the sensing system is attained is illustrated in 

Figure 3-1.

Sensing Transferring Analog 

Conversion

A to D 

Conversion

Digital Data 

Treatment

Sensing Error/ 

Output Error

Cable 

Resistance Amplifying Error Linearity Error Digit Trouble

V fluctuation - V fluctuation Sampling Error Rounding Error

Noise Noise - Holding Error -

Temperature 

Drift - Temperature Drift - -

Figure 3-1 Effect of hidden errors on overall accuracy in the monitoring system
23

Errors in the sensing system stem from analog errors in the sensing elements and wiring, and 

conversion errors when converting the digital signals to analog format. In addition, the 

measured values from two temperature sensors in a flow meter are used in computing the 

amount of heat, multiplying individual errors. Thus, when performing calibration to correct 

errors, it is important to calibrate the sensing system as a whole. (When the combinations of 

sensor elements, wiring, AD converters, etc. are changed, the overall characteristics also 

change, so re-calibration of the new combinations becomes necessary.) A detail example of 

error in sensing system is presented in Appendix 2.

Compatibility with existing sensors is also important. Values relative to existing sensors and 

sensor validity and correction, etc. should be checked. When measuring temperature 

difference with sensors installed at the intake and outlet of a refrigeration unit for example, it 

is important to check sensor accuracy with the refrigeration unit turned off using the 

difference in temperature readings of the two temperature sensors to correct the output value.

3.4 References Chapter 3

[5] Kenneth, Gillespie, et. al., “A Specifications Guide for Performance Monitoring 

Systems”, CEC PIER-DOE project, USA, 2007.

[6] Society of Heating, Air-Conditioning and Sanitary Engineers of Japan) published the 

“Energy Performance Measurement Manual for Building Equipment & Systems.” 

http://www.shasej.org/English/

[7] University of California, Lawrence Berkeley National Laboratory "A Specifications 

Guide for Performance Monitoring Systems" Specifications Guide Version 1 3/23/2007

[8] EnOB Monitoring Guidelines, http://www.enob.info/en/research-areas/enob-keyresearch-areas/monitoring-and-analysis-accompanying-research-for-model-projects/

(website updated November 2010).

[9] IPMVP Technical Committee. 2010. International Performance Measurement 

&Verification Protocol Volume 1: Concepts and Options for Determining Energy and 

Water Savings. U.S. Dept. of Energy: 86. 

[10] EN15239, Ventilation for buildings, Energy performance of buildings, Guidelines for 

inspection of ventilation systems, Bruxelles, 2007.

[11] EN15240, Ventilation for buildings, Energy performance of buildings, Guidelines for 

inspection of air-conditioning systems, Bruxelles, 2007.

[12] EN15378, Heating system in buildings, Inspection of boilers and heating systems, 

Bruxelles, 2007.

[13] The Energy Performance of Buildings, Directive 2002/91/EC of the European 

Parliament and of the Council, European Union, 2002.

[14] EN308, Heat exchangers, Test procedures for establishing performance of air to air and 

flue gases heat recovery devices, Bruxelles, 1997.
24

[15] EN12599, Ventilation for buildings, Test procedures and measuring methods for 

handling over installed ventilation and air-conditioning systems, Bruxelles, 2000.

[16] EN13829, Thermal performance of buildings, Determination of air permeability of 

buildings, Fan pressurization, Bruxelles, 2000.

[17] EN13187, Qualitative detection of thermal irregularities in building envelope, Bruxelles, 

1998.

[18] EN13799, Ventilation for non-residential buildings, Performance requirements for 

ventilation and room-conditioning systems, Bruxelles, 2007.

[19] J.Y. Kao, E.T. Pierce, Sensor errors: their effects on building energy consumption 

ASHRAE J. (1983), pp. 42–45 Dec.
25

4 Cx Tools for Existing Buildings and Low Energy Buildings

4.1 Needs for Cx Tools

4.1.1 Needs from the European Point of View

The term “commissioning” is less prevalent in Europe – though many services offered do relate 

to commissioning. Furthermore, the situation in most countries is very different for existing 

buildings than for new construction.

For existing buildings, most companies and consultants offer third-party services ranging from 

energy concepts to energy audit and performance contracting. However, no general systematic 

approach or standardization exists yet. These services are usually individual approaches based on 

a company’s expertise and knowledge; so consequently, no tools are distributed or used (except 

common spreadsheet programs).

For new buildings the situation is different as commissioning is typically not offered as a thirdparty service in most countries. Furthermore, in most cases commissioning is only performed 

during the hand-over as initial commissioning. This is usually performed by the builder or 

planner. In many cases this initial commissioning is only a rough check of completeness of 

installation and principal functionality. A commissioning process as described in IEA ECBCS 

Annex 40 [20] for new constructions is not at all usual. Tools for a systematic and more 

standardized approach are also absent.

Discussion of energy efficiency in buildings in Europe grew significantly after the European 

Performance of Building Directive (EPBD) came into force [21]. (The Council of the European 

Union passed directive 2002/91/EC in December 2002.)

The purpose of the directive is to make energy use in buildings more effective and by that 

contribute to reducing greenhouse gases. This can significantly reduce dependence on energy 

and provide improved security.

The directive determines requirements for: 

• A calculation methodology for building energy performance;

• Minimum requirements regarding energy performance of new buildings and buildings 

comprehensively refurbished;

• Energy performance certification;

• Regular inspections or advisories regarding heat boilers; and

• Regular inspections of HVAC systems.

In light of this list, the EPBD agrees in principle with the concept and supports commissioning. 

Unfortunately, current member states have not implemented this for potential use.
26

While commissioning extends over the entire life of a building, certification according to the 

EPBD will likely only be performed once. The EPBD does not prescribe an ongoing evaluation 

of building performance. Accordingly, certification can only be one part of the ongoing

commissioning process. 

In the case of asset ratings (theoretical calculation of the energy demand), certification can 

deliver the actual building status and a theoretical target value for energy performance. Thus, it 

could be integrated in phase I of the development of the ongoing commissioning process. 

However, asset ratings for existing buildings are currently only prescribed in Denmark and 

Austria. Most Member States will have operational ratings (based on utility bills) for existing 

buildings, which as currently defined, are not suited for any detailed analysis.

The only systems explicitly dealt with in the EPBD are the inspection of air-conditioning 

systems and boilers. The last is implemented nationally, either as inspections or as information, 

depending on each country’s need. Again, the kind of inspection and the intervals in most 

Member States are not suited to the ongoing commissioning process.

For more information, see (www.buildingsplatform.org or www.buildingeq.eu)

Generally speaking, commissioning in Europe is currently a service offered by a few experts 

only; many are researchers in that field.

However, a lot of the expertise needed for commissioning is available. But neither consultants 

nor customers are familiar with the concept of commissioning. Standardized 

descriptions/guidelines and tools that can support a systematic commissioning process are 

absent.

4.1.2 Needs from the North American Point of View

Cheaper, more accurate and more robust tools to automate the commissioning process for new 

and existing buildings will improve building performance while reducing labor and other costs. 

These tools will also help the market better respond to the need for skilled designers, 

commissioning providers and operators. 

In North America, barriers to assuring maximum building performance exist at each phase of the 

building life-cycle:

• Design: Owners and designers often ignore the benefits of design stage commissioning 

activities, including documentation of design intent and design review to verify 

constructability, testability, operability and maintainability. There is a lack of tools for 

design phase commissioning. 

• Construction and turnover: Owners often perceive commissioning during construction as 

an unnecessary cost. There are insufficient tools to streamline the labor-intensive 

processes of testing and integrating multiple building systems to reduce costs. There is no 

accepted procedure for short term testing to compare energy performance against 

requirements and expectations developed during the design phase. 
27

• Ongoing operations and occupancy: Few maintenance staffs have the time, tools, or 

training to perform the ongoing assessments of building performance required to assure 

the persistence of energy savings obtained during construction phase commissioning or 

retro-commissioning. Simple or automated diagnostic tools are necessary to make 

ongoing assessment easier and more cost-effective and reduce or eliminate the need for 

repeated retro-commissioning.

Four areas of tool development needs were identified by a commissioning and diagnostics 

research team funded by the U.S. Department of Energy [22].

1. Design Intent Documentation Tool

The documentation of design intent is a key element of commissioning, and one often 

overlooked. It is an effective means of communicating important information to operating staff, 

and others, who might not otherwise receive it. Design intent documentation also provides a 

useful communication tool among members of the design team and between the team and the 

building owner. 

There is a need to provide tools to streamline and reduce the cost of documenting design intent. 

Lawrence Berkeley National Laboratory created Design Intent Tool (DIT), which provides a 

structured approach to recording design decisions that impact a facility's performance in areas 

such as energy efficiency. However, this tool is currently configured for use with hi-tech 

buildings such as data centres. The Design Intent Tool needs to be reconfigured for common 

types of commercial buildings and its usefulness tested in a range of situations, and promoted in 

the industry for widespread use. 

2. Automated Functional Testing Tools

Presently there are almost no commercial automated functional testing tools available (the 

exception is a VAV box tool produced by Siemens). Instead, functional testing is a process that 

is time-consuming and costly, relying on commissioning providers or skilled technicians who 

manually test each system and how it interacts with other systems. 

LBNL is developing automated and semi-automated versions of a functional testing tool for air 

handling units that focus on detecting and diagnosing faults in mechanical equipment. Limited 

testing has been performed; enough to demonstrate proof-of-concept, but there is a need for more 

comprehensive field testing. There is also a need to broaden the scope of these tools to include 

testing of controls, other systems and subsystems, including systems in low energy buildings, 

and operation at the whole-building level. 

3. Existing Building Commissioning Cost-Benefit Analysis Tools

Existing building commissioning providers are often faced with a challenging problem. They can 

identify performance issues, and the necessary corrective measures, but have difficulty 

quantifying savings without spending a considerable amount of time on calculations or models. 
28

Without these savings figures, they find it difficult to convince the building owner or utility 

sponsor that the implementation cost is worth the benefit. 

Analysis tools are needed to help commissioning providers quickly calculate the energy savings 

from measures for common and low-energy building systems. Several benefits would result from 

this streamlined, standardized method of energy savings analysis. At the project level, 

commissioning providers will be able to provide the information to building owners with data to 

make implementation decisions, resulting in a higher implementation rate and increased energy 

savings. The tools will also allow providers to spend less time performing calculations and more 

time assisting the owner with implementation.

4. Automated Fault Detection and Diagnosis (FDD) Tools

Few commercially available automated FDD tools exist in North America; tools that are 

commercially available are not yet widely implemented. Prototype tools exist and field testing is 

underway for specific systems. For example, embedded FDD for AHUs and VAVs is being 

tested with several controls manufacturers. Demand for these tools is increasing as users see the 

benefits of continuous monitoring. Current R&D is underway to develop/advance FDD tools, 

and in the next few years, it will be necessary to extend diagnostic capabilities to additional 

components that impact operational performance, as well as to the broader, whole-building 

energy view.

More diagnostic products are needed with tools geared to user needs, specifically:

• Simplify current tools for ease of installation and use by building operators or 

consultants;

• Define/develop ‘dashboards’ that link to benchmarks;

• Identify malfunctioning components; and

• Track persistence of savings over time.

4.1.3 Needs from the Japanese Point of View

The term “commissioning” is becoming popular in Japan. Although defined in the SHASE 

guidelines, the meaning and scope of commissioning varies from engineer to engineer. Recently, 

energy saving services for existing buildings along with pioneering research work has both been 

done in the name of commissioning. For these projects many kinds of commissioning tools are 

used. In most cases computer simulation to estimate performance of HVAC systems or subsystems and visualization tools are used as a commissioning tool. To date, a variety of FDD tools 

have been developed in research but their use in real projects is rare for these reasons:

• No commercial tools are available;

• Interface to supply measured data to tools is not provided or inefficient. Users must 

develop the interface as an extra task;

• Using tools requires too much experience or training; and
29

• Integrated commissioning tools which can test a variety of HVAC components or subsystems all at once are not available. This means that tool usage is cumbersome;

The first trial of a functional performance test (FPT) in a real building was planned as a research 

activity of the SHASE commissioning technical committee (TC) in July 2009 in Japan. This 

provided an opportunity for a trial use of several commissioning tools to test their performance 

and determine needs for improvement. Building Systems Commissioning Association is 

planning a web site to distribute tool resources to volunteer users in collaboration with the 

SHASE technical committee. 

Japanese energy conservation regulations were revised in 2008 and became effective April 2009. 

The new law requires building owners to report the total energy consumption by summing up all 

consumptions used in every building owned or rented by each organization. Prior to the revision 

the report only required reports from building owners whose building energy consumption 

exceeded a fixed level on the basis of each building. To comply with this revision the installation 

of measuring systems for energy consumption becomes an inevitable issue, especially in rented 

buildings and, if a measuring system can only measure the total consumption of a building, a 

software tool is desirable to estimate subdivided energy consumption per tenant from the total 

consumption. For example, for an air-conditioning system which serves many tenants and only 

the total energy consumption is measured, commercial software is available to estimate tenant’s 

energy consumption based on measurements of operating time. Software tools can sum up all 

energy consumption of a large number of rented areas and provide the summarized information 

to building users to encourage reducing energy use are going to increase sales as such services 

rise. This kind of management tool is a very useful tool to rationalize commissioning work and, 

as a result, helps to expand commissioning. 

Energy Service Companies (ESCO) projects are the most common practice for energy 

conservation in Japan. In ESCO, estimating the baseline of energy use is a key, however, few 

reliable estimation tools of the nature of a commissioning tool have been developed. There is a 

need for tool development for application in existing building commissioning as well.

4.1.4 Needs for Existing Building (EB) Cx Tools

Existing building complexity is growing, so new faults occur regularly. In the near future, energy 

savings will be obtained mainly through optimal control and early fault detection of building 

HVAC systems as mentioned in Hyvarinen and Karki [23]. Optimization and FDD tools have 

been seen as promising tools for ongoing commissioning of existing building because of their 

potential to aid in the persistence of commissioning performance improvements. Many examples 

of both optimization and FDD tools are found in the literature on existing buildings. Before the 

needs for existing building Cx tools are discussed, a brief overview of some Cx tools will be 

given.

Since the existing building energy management control strategies are mostly heuristic, there is a 

need to systematically examine and improve them, as mentioned in Haung et al. [24]. For 
30

example, Lu et al. [25] used a modified genetic optimization algorithm to find the optimal set 

points of the controllable variables in an HVAC system with cooling coils. In order to obtain 

effective energy management for an existing HVAC system, an evolutionary programming 

algorithm was coupled with the simulation tool to provide the optimal combination of the chilled 

water and supply-air temperatures in Fong et al.[26]. A model based supervisory control strategy, 

which use a hybrid optimization technique, for online control and operation of building central 

cooling water systems is presented in Ma et al.[27]. Common for the above tools is that some 

optimization methods were implemented to find optimal control strategy for HVAC systems, 

while the methods have not been integrated to BEMS and they have not been widely 

implemented.

Most FDD tools are based on combinations of predicted building performance and a knowledgebased system. Principles and application of six FDD tools are compared briefly here. A modelbased feed-forward control schema for fault detection is described in Salsbury and Diamond

[28]. An example of monitoring-based commissioning by use of information monitoring and 

diagnostics system (IMDS) was reported in Haves et al. [29] and Piette et al.[30]. FDD tools can 

use the statistical classifier, as reported in the following methods: principal component analysis 

(PCA) method for sensors as presented by Wang and Xiao[31], the combination of model-based 

FDD (MBFDD) method with the support vector machine (SVM) method as presented by Liang 

and Du[32], and the transient analysis of residual patterns as presented by Cho et al.[33]. Air 

handling unit performance assessment rules (APAR) is a fault detection tool based on expert 

rules [34]. Even though all the above tools are robust in solving problems, they are still not 

widely implemented.

Katipamula [35 and 36] gives a summary of the situation of FDD in buildings. He finds that most 

of the studies completed at the time of the publication (2004) were concerned with individual 

components or systems. In his article he only presents works which deal with compact airconditioning equipment, heat pumps, compression chillers and air handling units. 

Most of the studies deploy simplified physical models (white box) or black box models in the 

form of regression models, neural networks or ARX models. Detailed white box models are 

rarely used. 

It is mainly classification methods that are used for diagnosis, giving the character of the 

residuals (difference between the output signals of the model and the real system). The 

procedures are used to detect faults with characteristics that are known from experiments or 

simulations. Although most authors (irrespective of the procedures selected) report that the 

procedure they used was able to detect the faults sought, Katipamula objects that most surveys 

were only carried out in the laboratory and not in the field. 

Moreover, Katipamula arrives at several conclusions:

• Very few products for FDD exist on the market and these are usually specialised (for 

individual components) and not automated.
31

• The (automatic) generation of fault threshold values for detecting faults has not yet been 

studied in depth, but represents an important aspect in automation.

• The calibration of models requires training data on correct operation. To apply the model 

on a broad basis it is necessary to develop the model either during the manufacture of the 

components in the factory, or automated during operation (online). There is still a need for 

research, particularly on the second approach. Deployment in existing buildings is seen as 

problematic because measuring data for correct operation is required for the calibration of 

the model. In existing buildings it is much more likely that equipment and components 

were not being run correctly before an energy audit. 

• Most of the studies mentioned work with procedures for fault diagnosis, whereby 

significant faults arising singly can be detected. Detection of several faults arising at the 

same time is thereby not possible. There is also a need for research in this area. 

• In general, the amount of measured data available in buildings and its quality is low. The 

development of cost-effective and reliable sensors is therefore an important step to the 

spread of FDD in buildings. The gradual introduction of open communication standards in 

building technology is seen as a major opportunity.

• There is not enough information on the cost-benefits relationship of FDD in buildings. 

In Katipamula’s view there is a great need for research in the field of FDD in buildings, 

particularly with regard to practical use and automation. 

Based on the existing building needs and current research on FDD and building optimization, 

needs for Cx tools can be classified as follows:

• Faults in operation

Faults in operation can appear due to different reasons, for example faulty construction, 

malfunctioning equipment, incorrectly configured control systems, and inappropriate operating 

procedures [37]. In addition, due to abnormal physical changes, ageing, or inadequate 

maintenance of HVAC components, HVAC components easily suffer from complete failure 

(hard fault) or partial failure (soft fault) [38]. Since buildings are exposed to different conditions 

and are used by human, many different faults can always appear.

• Continuous building change

The trend with existing buildings is to have them refurbished, renovated and extended; 

consequently they are acquiring new functions. When a building gets a new function, it is 

necessary to update that function in a Cx tool . Although a new function may require a new Cx 

tool for both optimization and FDD, in most of the cases the existing Cx tools can treat the 

function because of its similarity with the existing. Consequently, development of tools that 

enables common use is needed to adapt with continuous building change. 

Change in the existing building use
32

An existing building can change purpose completely or partially due to different factors (change 

in building use, change in building tenants, extension, etc.). To ensure the performance of the 

building, commissioning should be performed at each change. This required a financial and labor

investment, specifically when the process is done manually. This issue can be minimized by 

implementing Cx tools which rationalize the work by performing optimization or FDD work at 

each occasion of changes or even better continuously. In such case, automation of Cx tools is an 

valuable feature. 

• Automation of the Cx tools

Technology is available for greater application of Cx tools, (wireless communication, data 

management, web-based communication, etc...) and for their automation. R&D of automatic 

commissioning is increasingly active. When complete automation is achieved, easy 

implementation and application of Cx tool in day to day operation will be the reality.

• New technologies

Development of new technologies in HVAC systems, building construction and communication 

necessitate the development of new Cx tools. Currently, web-based databases allow for wider 

use of energy monitoring. For example, development in data management and web-based 

technologies provide an opportunity for new Cx tools, while existing Cx tools should be adapted 

accordingly.

4.1.5 Needs for Low Energy Building (LEB) Cx Tools 

Low Energy Building (LEB) are charachterized as those buildings equipped with new HVAC or 

energy systems such as high COP chillers, inverter controlled pumps and fans, photovoltaic cells, 

co-generation equipments, etc. as well as environmentally enhanced functions that use natural 

ventilation, high air tightness and thermal insulation, solar heating, day-lighting, ground thermal 

energy storage, etc. Commissioning work of these non-conventional systems and building 

functions is difficult when compared to conventional systems and buildings. Work will improve 

significantly when tools are available to:

1) Estimate system and building performance under given operational conditions, including 

weather conditions, in order to test that performance meets design requirement;

2) Carry out fdd of sophisticated systems and building functions; and

3) Optimize hvac and energy systems by the use of simulation. 

The reason tools are useful in commissioning is not only due to the sophistication of systems and 

buildings but also due to the practical limitations of optimization using the system itself. An 

example of this is an air-conditioning system with seasonal energy storage. Cool energy is stored 

in the ground in winter and discharged from the ground in summer. The efficiency of the system 

is determined by the flow rate and the temperature set point of the circulated cold water. The 

optimal values can be obtained by carrying out experiments many times with different values but 

one experiment requires one year. This means that getting optimal values is impossible in a 
33

practical sense; simulating system performance is an effective approach to solve it. Although 

many ongoing Cx tools for existing buildings also fit LEBs, the utilization of low energy systems 

and environmental energy sources require additional tools, especially for optimization)

In this report, Cx tools are classified into two categories; visualization tools and automatic tools. 

Visualization tools display the performance of an HVAC system in order to detect faulty 

operation or energy loss. In this case the ability of a tool depends on how adequately selected 

data and a graph type visualize the symptoms of various faults clearly and effectively. Automatic 

tools are more sophisticated and can detect faults automatically using computer software. Many 

Cx tools were developed in previous ECBCS activities such as Annex 25, Annex 34 and Annex

40, however, most are currently only available for conventional HVAC systems and do not 

address relevant advanced systems and system combinations in LEBs.

4.2 Visualization of Data and Results 

Building automation systems (BAS) and building energy management systems (BEMS) have 

been used in buildings for several decades and are especially important for modern facilities with 

advanced and low energy building systems. BAS measure, process, and monitor huge amounts of 

data to operate the building and systems more or less properly. Often, the data is only used to 

signal failures or break-downs of systems or components. Further use of the data to analyze and 

diagnose the building operation is limited to the lack of analysis methods and tools.

Advanced visualization techniques can be used to display the information that is otherwise 

hidden in performance data and/or the recorded operation data of buildings and systems, and, 

thus, are valuable for commissioning of buildings and systems. Different diagrams and plot types 

display operation patterns, which quickly help to categorize and analyze large amounts of data, 

their correlation, frequency, as well as other useful statistical information. These operation 

patterns allow us to evaluate the quality of operation and to identify optimization potential by 

comparing them with optimal and/or anticipated operation patterns. Furthermore, the 

optimization potential can be estimated by additional visual or numerical analysis of the 

deviation between optimal and existing (real) operation patterns. 

Visualization is also relevant for transporting data and information from design analyses, such as 

simulation and modelling, and making this information available in subsequent project phases. 

4.2.1 Classification of Visualization Techniques 

Depending on the system to be evaluated, the questions to be answered and the time resolution of 

the measured data or analysis, different visualizations can be used separately or in combination.

Principally, visualization techniques can be used to show:

• Change of variables over time;

• Relations between two or more variables;

• Statistical information / distribution of values;
34

• Spatial information/distribution;

• Partitioning/percentage share of properties;

• Comparison of scalars (elements of different size); and

• Process / information flow.

Each of these techniques will be explained in Section 4.2.1.1 thru Section 4.2.1.7.

Before being able to visualize data, significant pre-processing is often necessary. The effort and 

complexity associated with this task can far exceed that of displaying the data. Here is an 

example presenting typical pre-processing stepsthat might be necessary when starting with raw 

data from a BAS or BEMS.

1) Plausibility Check (e.g., remove gross outliers)

Change the time resolution of data (e.g., condense 10-minute values to hourly or daily 

values)

2) Filtering

“Filtering” denotes the creation of a subset of data that satisfies a certain condition (e.g., 

subset of the measurements of energy consumption below a certain outdoor air 

temperature or at a certain operation mode). Thus, the behavior of variables under certain 

boundary conditions can be studied.

3) Grouping

Data can be grouped according to certain conditions (e.g., heating energy can be grouped 

for workdays and weekends). Different operation modes can thus be compared.

Filtering and grouping are particularly important to extracting information from measured data, 

e.g., to show average supply temperature of a heating circuit working independently of the pump 

control signal. 

Additional pre-processing is frequently performed for performance analysis.

4) Data Conversion or Creation

Converting measured raw data into a value with a different or specific unit is often useful for 

analysis. For example, converting the unit of “electrical energy consumption in kWh” into 

“energy consumption per unit area in kWh/m2”, or “fossil fuel consumed in whole building 

in kJ”, or “emission of global warming gases per year in kg-CO2/year” is often performed. 

Creating a new value, such as the coefficient of performance (COP) of a chiller or the 

WTF/ATF value of an air/water distributing system, using the relevant data, is also needed 

for performance analysis of a component or a system.

5) Calculating Residual Values

Using a new variable by taking the difference between two measured values often enables 

effective analysis rather than using raw data. For example, differences between inlet and 

outlet temperatures can show the performance of a chiller or an air-handling coil. The 

difference between a measured value and the control set point of the value is effectively used 

for FDD because the magnitude of the difference can easily show a faulty operation.
35

Selecting the variables to be used in a visualization tool is a critical issue. If an inappropriate set 

of variables are selected, useful information will not be extracted to detect faulty operation or 

evaluate performance. 
36

Additional aspects to consider in design of a visualization tool include:

• Tool Aim

In some tools, measured variables are shown without clear objectives and the tools have 

no effective use. The purpose of a visualization tool must be as clear as possible. 

• Tool Users

The type of visualization technique used depends on the end user. Current visualization 

tools are designed to be used by maintenance staff. However HVAC system designers, 

construction engineers, building owners, tenants, etc. should be involved in actions to 

increase energy performance. For example, showing energy costs visually to managers is 

important because they are responsible for budget planning. Appropriate visualization 

tools for all stakeholders are crucial. 

• Software to Build Tools 

Commercial spreadsheet software is used often in data handling and graph drawing. 

Although ease of use is a great advantage of this kind of software, limited capability in 

graph design and data handling is a problem. Use of a sophisticated data base system like 

SQL and specialized graph drawing software should be considered in tool development. 

This issue is discussed extensively in final report 4 of Annex 47.
37

4.2.1.1 Change of variables over time 

Conventional time series plot 

Time series plots are probably the most typical way to display data. The plots in

Figure 4-1 show the change of one or more variable over time. As shown in the example, 

plots can also be stacked.

Figure 4-1 Example of a conventional time series plot

These plots are used to evaluate trends, profiles or step-like changes of the variable in 

time. A significant amount of pre-processing might be necessary if the time resolution of 

the raw data differs from the time resolution for the desired display.

However, time series plots do not easily reveal the interrelation between two or more 

sensors in the displayed data.

Carpet plot

Carpet plots are used to display (long) time series of a single variable in the form of a 

color map, which often reveals a pattern (like weekly operations). Measurement values 

are portrayed in different colors. For days with a similar course of measurement values 

the color pattern is respectively similar. Such patterns can be visually identified quickly. 

This kind of plots helps to identify occupancy and operation schedules. Examples are 

shown in Figures 4-2 and 4-3.
38

Figure 4-2 Ideal carpet plot example (e.g., a fan running Monday to 

Friday from 8.00 to 18.00.)

Figure 4-2 Carpet plot for real weather and consumption data.

Figure 4-2 depicts and ideal carpet plot where the course of each day runs along the y-axis from 

the “bottom” (y=0:00) to the “top” (y=24:00) and days are plotted next to each other accordingly 

on the x-axis. The time resolution is 1 h. Figure 4-3 plot depicts carpet plots of real data. From 

top to bottom, the plots display water consumption, electrical use, heating, and outdoor air 

temperatures. Naturally, the patterns are more blurred than in the ideal example. 

0

4

8

12

16

20

24

Time 



Day / Date 

S S M T W T F S S M T W T F S S M T

Off

On

Color scale
39

Layer graph 

This type of graph, shown in Figure 4-4, represents a time-series progression from 

changes in the thickness of each layer, with the objective of showing transitions in the 

total amount and breakdown of each item. Time is plotted on the horizontal axis, 

measurements on the vertical axis.

Figure 4-3 Example of layer graph

4.2.1.2 Relations between two or more variables

Correlation plots can be used for the following purposes:

1. To show correlation between controlled and controllable parameters (e.g., to show 

correlation between outdoor temperature and desired or achieved supply air temperature, 

CO2 level and air amount, etc.);

2. For energy auditing of one or many buildings (e.g., to show correlation between the 

yearly consumption and the floor area);

3. To present a scatter diagram (e.g., the manufacturer’s catalogue values can be compared 

against measured data enhanced by regression lines and presented as the correlation plot).

Scatter plots 

This type of graph plots values for items that are thought to be mutually related as 

distributions along the axes. 

Scatter plots show the dependency of two or more variables. Additional information can 

be gained if the values are grouped (e.g., grouping by day or by a certain operation 

Trend of electricity consumption 

( kitchen / medical center - 600Bed / winter)

electricity consumption (kWh)

Time (Hour)

HVAC Fan lighting refrigerator

serving wagon kitchen instruments
40

mode). Potentially, several scatter plots can be combined to scatter plot matrices to show 

the interdependency of more than two variables.

With the bottom-up approach, this is often used for clarifying details about the subjects 

being compared, such as the manufacturer’s catalog values. A typical usage in a topdown approach would be to generate so called energy signatures (energy demand plotted 

against outdoor air temperature)

With the addition of regression lines, it can also be used in the analysis of trends or the 

development of simulation models. Figure 4-5 shows plots that were used to help 

identify weather dependent control strategies. The time resolution for these plots is 1 day. 

Each dot represents a daily mean value.The plots are also called a signature or in the case 

of energy plots, an energy signature.

Figure 4-4 Scatter plot of energy and water consumption versus outdoor air 

temperature, grouped by workday and weekends. [DCW=domestic cold 

water , P.EL= electrical power, and P.DH=power for domestic heating ]

[oC]
41

Figure 4-5 On the left, a scatter plot of COP distribution by change of outdoor air temperature, and on the right a

scatter plot of ATF distribution of heat load change. [COP is Coefficient of Performance of a refrigerator and ATF 

is Air Transfer Factor.]

Figure 4-6 shows two plots used to help detect failures or performance degradation.

Figure 4-6 On the left, the scatter plot of control error by set value of room temperature. On the right, the right, a 

scatter plot of the regression analysis results for energy consumption of a supply air fan.

The plot on the left side of Figure 4-7 is used to helps identify failures or performance 

degradation which appear as outliers. For the plot on the right side of Figure 4-7, the thinner 

curves indicate confidence range of a fault. Faulty conditions can be statistically detected by the 

existence of operation points plotted outside the confidence range curves. 

COP of the Heat Source

COP

Outside Air Temperature(C)

ATF

ATF of Supply Air Fan

Heat Load(MJ)

VAV Zone

Measure Temp. – Set Point Temp.

Room Temperature Set point (C) Flow Rate (kg/s) Rotation Rate (kg/s)

Fault

Fault

Regression line

confidence interval

(model approximation)

confidence interval

(model prediction)

Sample ( Normal ) Sample ( Fault )

electrical consumption (kWh)
42

4.2.1.3 Statistical information/distribution of values

Histograms

A histogram is one of statistical graphs with frequency on the horizontal axis and series 

on the vertical axis, which visually represents the frequency distribution. An example is 

shown in Figure 4-8.

Figure 4-7 Example of a histogram

Descending-order graph 

This type of graph, shown in Figure 4-9, plots data for an item in order of size.

This is often used for confirming trends in periodic measurements for both bottom-up and 

Top-down approaches. With a single graph, it’s possible to comprehend time-oriented 

trends or perform annual comparisons. Figure 4-9 

Figure 4-8 Example of a descending order graph

Cooling Load(kW)

0

1000

2000

3000

4000

5000

1 amount of time(hour) 874

Load(kW)

Heating Load(kW)

0

1000

2000

3000

4000

5000

1 amount of time(hour) 874

Load(kW)

Bins

Histogram with Normal Distribution Overlay Curve

Counts 40 60 80 100 120 140 160

100

80

60

40

20

0
43

Portfolio

This type of graph shows growth rate on the vertical axis and share on the horizontal axis.

See Figure 4-10. The circles represent result values. This enables one to clarify the 

growth level, importance, and location of the point.

Figure 4-9 Example of a portfolio graph

Box-plot 

This graph type presents statistical data and confirms trends. 

Figure 4-10. Box plots on daily basis

Boxplots display differences between populations. The spacings between the different parts of 

the box displays the degree of dispersion (spread) and skewness in the data, and identify outliers. 

They are non-parametric. Figure 4-11 displays heat and electricity consumption on different 

weekdays. The boxplots shows the difference in consumption between workdays and weekends 

and the daily distribution. 

IC Memory

processor

On-Boat Memory

High

High

Low

share of market Low

market growth rate

On-board Memory

DRAM

Market Share

Market Growth Rate
44

4.2.1.4 Spatial information/distribution

Maps

Metrics on a map as shown in Figure 4-12 help to understand spatial distribution of 

statistical values. 

Figure 4-11 Example of a map-based data representation

Pictures (e.g., infrared photographs)

Pictures such as that seen in Figure 4-13 can show heat distribution image by analyzing 

infrared rays radiated from an object.

Figure 4-12 Example of an infrared photograph

population

Population according to 

administrative divisions

×ten thousand

°C

40.0

37.8

35.5

33.3

31.0

28.8

26.5

24.3

22.0

Population Levels according to 

Japanese Administrative Divisions
45

Three-dimentional (3-D) images/ visualization

3D images can show environmental characteristics schematically. The images in 

Figure 4-14 are examples of the results of CFD analysis representing wind direction, 

strength of airflow, and temperature distribution by colors and arrows (vectors).

Figure 4-13 Examples of 3-D images

Diffuse matter

Air Velocity
46

Contour plot

Contour maps elegantly show data attributes and distribution. The example in the upper 

part of the figure shows changes in air velocity distribution for a particular cross-section. 

The red dots indicate the position of the temperature sensors used for gathering the 

temperature measurements on which the CFD analysis was based. The example in the 

middle part of the figure shows a dimensionless view of the amount of time that air from 

the inlet reaches a certain location (referred to as “age of air”). The example in the bottom 

of the figure is a contour representation of the temperature distribution inside a rackmount unit of a data center.

Figure 4-14 Examples of a contour plot

temperature sensor

air-conditioning 

equipment

rack-mount unit

Unit : C

Age of air

Velocity

Age of air
47

4.2.1.5 Partitioning/percentage share of properties

Pie chart

Pie charts show proportions of each value of the total in a circle format. An example is 

shown in Figure 4-16.

Figure 4-15 Example of a pie chart

elevator

plumbing sanitary

ventilation

outlet

lighting

hot-water supply

Fan

Pump

Heat source

(accessories)

Heat source

Heat source

Fan & Pump hot-water supply

Lighting & 

Outlet

others

Power
48

100 % stacked bar chart 

This chart type is also used to show the proportion of each value. Two examples are 

shown in Figure 4-17. Proportions are often plotted along the horizontal axis, with the 

items being compared along the vertical axis.

Figure 4-16 Example of a stacked bar chart

Stacked bars (evaluation. normalized)

Stack bars enable comparisons of structural elements, characteristics, or transitions of the 

items being compared, because the quantities of each part of the items are piled on top of 

each other. The items to be compared are often plotted along the horizontal axis, with 

measurements along the vertical axis. An example is shown in Figure 4-18.

Figure 4-17 Example of a normalized stacked bar chart

Energy supply ratio of Japan

Coal Oil

hydraulic power 15.7hydraulic power 5.5

nuclear 

power

1 次 P

冷却水 P 冷却水 P

1 次 P

Primary energy consumption 

[MJ/m2/year]

Primary energy consumption 

[MJ/m2/year]

Primary pump

Cooling water 

pump

Primary pump

Cooling water 

pump

Heat source for cooling Heat source for heating

Operation case Operation case

Electric power primary energy cooling tower Electric power primary energy cooling water pump Gas primary energy refrigerator

Electric power primary energy refrigerator

Electric power primary energy secondary pump

Electric power primary energy primary pump

Electric power primary energy primary pump

Electric power primary energy refrigerator

Electric power primary energy secondary pump

Gas primary energy refrigerator
49

Clustered bar chart

Clustered bar chart enables comparisons between items and of overall quantities, with a 

number of data points associated to each item. An example is shown in Figure 4-19.

Figure 4-18 Example of a clustered bar chart

4.2.1.6 Compare scalars (elements of different size)

Bar chart

Bar charts compare magnitudes of simple values, and trends over time such as by month.

The items to be compared are often plotted along the horizontal axis, with measurements 

along the vertical axis. An example is shown in Figure 4-20.

Figure 4-19 Example of a bar chart

Radar chart 

A radar chart is used to display multivariate data. Three or more variables are 

represented on axes starting from the same point in the chart. This type of graph is 

designed to compare the magnitude or quantity of a number of items in a comprehensive 

manner. An example is shown in Figure 4-21.

S-ATF Removed heat quantity

Power consumption Supply air volume

0

20

40

60

80

100

120

140

160

180

S-ATF

Power consumption Supply air volume

Removed heat quantity Case 2-[1] RA=26°C

Case 1-[3]

SA=18°C

Case 2-[2]

SA=28°C

Case 1-[1]

SA=14°C

BEMS 

result

Case 1-[2]

SA=16°C

Case 3-[1]

Tw=7°C

Case 3-[2]

TW=10°C

LCEM 

reference

Electric Power(MWh

/month)

Apr May Jun Jul Aug Sep Oct Nor Dec Jan Feb Mar

Room temperature(°C)

(GJ

/year) Electric Power (MWh/month) Electric Power (MWh/month)

Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar
50

Figure 4-20 Example of a radar chart used to compare the performance of the two co-generation systems (CGS) visulally 

using the targeted values. 

4.2.2

4.2.3 Example of Performance Evaluation Process Using Visual Graphs

Data Management

It is advisable to organize the applications of commissioning tools and evaluation indices 

according to the actual commissioning procedure and the parts to be studied, such as in Figure 4-

22, and to clarify evaluation references and other criteria beforehand. An example of energy 

flows and measurement design in the commissioning procedure is shown in Figure 4-23.

Figure 4-21 Example of commissioning procedure

0. 0

0. 2

0. 4

0. 6

0. 8

1. 0

総合効率CGS1

総合効率CGS2

発電効率CGS1

発電効率CGS2

稼働率CGS1

稼働率CGS2

電力依存率

熱依存率

total efficiency (CGS-1)

total efficiency (CGS-2)

generating efficiency CGS-1

generating efficiency CGS-2

operation rates CGS-1

operation rates CGS-2

dependence rate (heat)

dependence rate (electricity)

Building

Floor

Room

Zone

Year Month Day Hour

The study of the 

monthly energy 

consumption of the 

whole building

The study of the 

annual energy 

consumption of the 

whole building

The study of the 

daily energy 

consumption of the 

whole building

The study of the 

hourly energy 

consumption of the 

whole building

What is the tendency to 

Energy consumption 

according

to season and hour?

The study of the 

monthly energy 

consumption of 

the floor /room

The study of the 

setpoint

temperature

The study of the 

mixing loss

The study of the 

running time

The study of the 

lighting and outlet

The study of the 

outdoor air flow

The study of the 

annual energy 

consumption of 

the floor /room

Which part is energy used? What is a cause with energy consumptions?
51

Figure 4-22 Example of energy flow measurement design
52

26,000m2

42,686GJ/yaer

~5,000m2

~5,000 5,000~m210,000m2

10,000~25,000m2

25,000~50,000m2

50,000m2~

~5,000m2

~5,000 5,000~m210,000m2

10,000~25,000m2

25,000~50,000m2

50,000m2~

architectural area(m2)

primary energy consumption(MJ/year)

Example of Performance Evaluation Processes

Tables 4-1 and 4-2 present examples of performance evaluation processes implementing visualization techniques for an entire building, 

and for building components, respectively.

Table 4-1 Example of an evaluation process for an entire building

Graph Summary Indices Evaluation 

Method

• Compare the 

primary energy 

consumption with 

those of other 

buildings of the 

same type.

• Scatter diagram 

with total floor area 

plotted along the 

horizontal axis and 

equivalent primary 

energy 

consumption along 

vertical axis.

• Remember that the 

characteristics of 

the buildings used 

for comparison are 

not clear.

• Amount of 

incoming 

electrical 

energy 

• Amount of gas 

consumed

• Amount of 

mains water 

used

• Amount of 

chilled water 

received (with 

DHC) 

• Amount of 

steam received 

(with DHC) 

• For the same 

applications, the 

plots will be close 

to the regression 

line. 

• If the amount of 

energy consumed 

is greater than 

that of other 

buildings, 

compare details 

and detect 

problem areas. 

≈
53

• Determine time 

bands in which 

consumption is 

highest, from the 

trends in amounts 

of electrical power 

(or gas or oil) 

consumed in the 

entire building. 

• Estimate energy 

saving and load 

equalization items 

from the 

consumptions for 

each application. 

• Compare load 

fluctuations by 

plotting data such 

as load ratios in a 

clustered bar chart. 

• Amounts of 

incoming 

electrical 

energy, gas, 

and oil

• Amounts of 

electrical 

energy, gas, 

and oil used in 

applications

• Detect whether 

there is a peak. 

• Perform load 

equalization, with 

the aim of 

reducing installed 

capacity and 

energy costs. 

• Compare 

daytime and 

nighttime 

consumptions for 

hourly rates, or 

workday/weekday 

consumptions for 

daily rates. 

• Understanding 

the load 

increase/decrease 

characteristics 

will facilitate the 

progress of 

energy-saving 

investigations. 

electrical consumption (kWh)

availability factor(-)

Electrical consumption / Availability factor

Hour

electrical outlet

Heating Source

Lighting

Electric Demand

Pump / Fan

Availability factor
54

• Assess by a 

number of items 

relating to energy 

consumption, such 

as quantity used, 

timing, and specific 

time efficiency. 

• Confirm all of the 

results of 

comparing the 

energy 

consumption state 

of electrical power, 

gas, etc., with 

reference values.

• Amount of 

electrical 

energy 

• Amount of gas 

• Oil 

• Amount of 

heat generated 

by heat source 

• Electrical 

power 

consumption 

• Assess by a 

certain period of 

time, such as in 

one-week units.

• Set target values 

before 

implementing 

energy-saving 

measures and 

assess 

increases/decreas

es, then set 

further target 

values in 

accordance with 

PDCA for the 

energy-saving 

measures that set 

target values. 

Energy source Energy consumption(cumulate)

Energy consumption

Amount 

of time Efficiency

visual

Actual

visual

Actual

visual

Actual

visual

Actual

visual

Actual

visual

Actual

visual

Actual

visual

Actual

visual

Actual

visual

Actual

visual

Actual

visual

Actual

visual

Actual

visual

Actual

visual

Actual

visual

Actual

visual

Actual

visual

Actual

visual

Actual

visual

Actual

HVAC

Lighting

others

Oil

Gas

Electrical power

Electrical generation

Total
55

• Select items for 

the focus of energy-

saving efforts, from 

the proportions of 

the amounts of 

electrical power 

(gas, oil) consumed 

by the entire 

building. 

• Determine 

changes in the 

overall 

consumptions and 

changes in the 

proportions of 

energy 

consumptions for 

the entire building 

caused by 

reductions, by 

comparing data 

before and after the 

implementation of 

energy-saving 

measures.

• Select items to be 

measured.

• Amounts of 

incoming 

electrical 

energy, gas, 

and oil for the 

entire building 

• Amounts of 

electrical 

energy, gas, 

and oil for each 

application.

• Make rough 

estimates of the 

effects of energysaving measures 

from changes in 

the amounts of 

energy consumed 

and energy saving 

ratios. 

• Compare 

proportions by 

application before 

and after the 

implementation of 

energy-saving 

measures, and 

confirm any 

effects on other 

applications due 

to those 

measures. 

• Investigate 

measures aimed 

at items which 

always have the 

largest 

proportions, to 

maximize effects 

of the energy-

saving measures. 

Before After

Total Total

electrical outlet

Heating Source

Lighting

energy saving

Pump / Fan

others

electrical outlet

Heating Source

Lighting

Pump / Fan

others
56

Table 4-2 Example of an evaluation process for building components

Graph Summary Indices Evaluation Method

Study energy 

consumption trend 

management for a 

single building 

and a number of 

buildings, heat 

source and airconditioning trend 

management for 

facilities 

(confirmation of 

actual 

performance in 

operation and 

operating state), 

and evaluate 

energy savings. 

• Amount of 

incoming 

electrical 

energy 

• Amount of 

electrical 

energy per 

application

• 

Cooling/heating 

water 

temperature of 

facilities

• 

Cooling/heating 

water flow rate 

of facilities

• Indoor 

environment 

• Operating 

time of 

facilities

• Confirm 

compliance with 

different types of 

control, by 

consistently 

determining 

operating state of 

the various 

facilities, in addition 

to determining 

energy usage state. 

• Evaluate by 

comparisons with 

past data, catalog 

values, other 

property, and with 

design values, and 

improve 

maintenance and 

operating methods.

Graph:

period:

File:

Interval: update advanced settings 

Analysis

note

Electrical consumption of heat source machines

2007/8/15 2007/8/16 30min

Time series graph

▲

▲

Title Refrigerator COP・Capacity-Cooling WaterIinlet Temperature

Refrigerator-1 COP

Date / / /

Refrigerator-1 Capacity

Cooling WaterIinlet Temperature(C)

Capacity (MJ)

Time of Day

o
57

• Calculate and 

compare 

contribution ratios 

for items that can 

be assumed to 

have causal 

relationships: 

chiller COP, 

chilled water outlet 

temperature, and 

coolant water inlet 

temperature. 

• Assess if causal 

relationship exists 

for each item, by 

displaying data on 

a radar chart. 

• These results 

can be used when 

analyzing various 

causes, by varying 

objective variables 

and explanatory 

variables. 

• Amount of 

heat generated 

and power 

consumption of 

each chiller

• Chilled water 

outlet 

temperature, 

chilled water 

inlet 

temperature, 

chilled water 

flow rate, 

coolant water 

inlet 

temperature, 

chiller 

evaporator 

temperature, 

chiller 

condenser 

pressure 

• Ensure that it is 

possible to create 

graphical displays 

selected from a 

large number of 

target items, so that 

even managers with 

little specialist 

knowledge can 

extract items with 

large contribution 

ratios.

• In determining 

specific operating 

conditions for 

efficiency 

improvement, 

create a scatter 

diagram of items 

with large 

contribution ratios 

and chiller COPs, 

for further study.

• Optimal tuning of 

heat source 

equipment of 

thermal storage 

tank system

• Tuning of heat 

source equipment 

to suit actual 

loading

• The points are 

the start/stop 

points of the 

follower operation 

equipment. 

• Amount of 

heat generated 

by each 

equipment item 

of 

• Amount of 

heat remaining 

in thermal 

storage tank 

Check that no 

residual storage 

occurs. The system 

operates optimally 

if the combined 

ambient 

temperatures are at 

design values. If 

there is residual 

storage, follow the 

procedure below to 

modify the control 

parameters. 

Centrifugal Refrigerator-2 COP

Chilled Water Outlet Temperature

Cooling Water inlet 

Temperature

Load Factor

Chilled Water Inlet 

Temperature Chilled Water Flow Rate

Cooling Water Flow Rate

Condenser pressure

Evaporator Temperature

Absorption Cooling and Heating Machine

Centrifugal Refrigerator-1

Centrifugal Refrigerator-2

Refrigerator COP

Chilled Water Outlet Temp

Cooling Water inlet Temper

Load Factor

Energy consumption (Elec

Outside Air Temperature

Outside Air Relative Humid

Chilled Water Inlet Tempera

Chilled Water flow rate

Cooling Water flow rate

Condenser pressure

Evaporator Temperature

Condenser Temperature

Chilled Water Outlet Tempe

temperature difference Temperature difference

Temperature difference

Refrigeration Capacity

Centrifugal Refrigerator-3

Centrifugal Refrigerator-4

Centrifugal Refrigerator-5

Refrigeration Capacity GJ/h

Hour

remaining amount % Remaining

Amount

heat storage

Refrigerator
58

• Check that the 

system that controls 

increases or 

decreases in the 

number of chillers in 

operation depending 

on the cooling load is 

operating correctly. 

• Ameliorate any drop 

in efficiency due to 

low-load operation of 

the chillers. 

• Cooling load 

• Quantity of heat 

generated by 

chiller

• Chiller state 

(on/off) 

• Since changes in load 

ratios when a number of 

chillers are operating 

will vary with different 

control methods, make 

sure that control 

specifications are 

reflected in the displays 

of target values. 

• When modifying the 

operating order by 

rotating the units, 

change the stacking 

order in the 

corresponding graph.

• Sensor measurement 

compliance evaluation 

• When using BEMS to 

analyze and evaluate 

data, ensure data 

measurements are 

compliant.

All measurements 

from sensors and 

meters

• Compare all values

with their design values.

• Review all data to 

verify there are no 

major errors. 

• Evaluate/examine the 

various sensors' 

connections at different 

locations, before 

evaluating equipment 

COPs. 

Refrigeration Capacity kW

Load Factor

(%)

Load Factor

(%)

Hour

Value

Inlet

Outlet

Secondary Pumps

ON-OFF of 

associated 

equipment
59

4.3 Automated Tools

4.3.1 General Features of Existing Building and Low-Energy Building

Commissioning

Eight categories for discussion are used when describing features of existing building (EB) and 

low-energy building (LEB) commissioning tools: objectives, functions, data-management, 

implementation, operability, analytical engine, end users, and benefits. Each of these categories 

are discussed here.

Objectives

The objectives of EB and LEB commissioning tools are diverse. However, the main objectives 

are: estimating design performance, detecting and diagnosing faults (FDD), optimizing HVAC 

operation without sacrificing room environment, and auditing energy consumption based on 

statistical or simulated reference values. Tool targets range from whole buildings to components, 

in addition to control systems. Some sophisticated tools will have multiple objectives, for 

example, ranging from fault detection to optimal operation. Some tools will have a single 

objective such as analyzing the performance of a natural ventilation system. Conventional tools 

could be easily applied to EB with minor modifications; however tools for LEB must incorporate 

new features. 

Section 4.3.2 presents 18 new tools developed in Annex 47. Table 4-3 shows the objectives of 

each tool. The number of commissioning tools aimed at LEB is low with only one tool existing

for LEB commissioning in design review. 

Functions

Each commissioning tool has a variety of functions to achieve its objectives. Verification testing 

using results from simulation or statistical analysis is the basic function of a tool when 

performing FDD, building optimization and HVAC systems and design review. As seen in 

Section 4.2 on HVAC systems performance, verifying test results, fault existence, etc. by visual 

and articulated graphs, charts and tables is another important feature of tools. Although most 

tools can produce commissioning reports with little labor cost through computer use, this type of 

auxiliary function is highly valued in real applications because of the importance of budgets.

The functions of a sophisticated tool are often complex. Verbally explaining the functions and 

structure of such tools with any accuracy is difficult. In this report an IDEFØ model is used to 

systematically explain a Cx task’s complicated structure. IDEFØ is a method designed to model 

the decisions, actions, and activities of an organization or system. IDEFØ was derived from a 

well-established graphical language, the Structured Analysis and Design Technique (SADT). 

Precise explanation of IDEFØ is found in Report 4 of Annex 47.

Figure 4-24 shows an IDEFØ model for the tool used to optimize an HVAC system with ground 

thermal storage system. The left-hand figure shows that the tool estimates the optimal operation 
60

values (output) from the point of view of energy conservation. Optimal values are obtained 

using:

• Weather conditions and room air conditions (input);

• Various set points, operational conditions and system specifications (control); and

• System models and software programs (mechanism). 

In general, IDEFØ expresses the parameters including mechanisms and controls, at the top and 

bottom of each box, respectively. The controls represent the application’s related constraints 

(cost, time, regulations, etc.). Mechanisms are resources (tools, methods, etc.) that enable the 

application to work. 
61

Figure 4-23 Example of IDEFØ
62

Data Management

A variety of data types are used in a 

commissioning tool. They are classified into five 

types: design data indicating thermal 

performance of building walls and HVAC 

component performance; data of system state 

such as chilled water temperatures and flow 

rates; environmental data such as weather and 

room air humidity; data representing the 

operation schedule; and statistical data such as 

the baseline of monthly energy consumption. 

Most tools are designed to obtain all data except 

design data electronically from BEMS or via the 

Internet, although in some cases data may be 

obtained through off-line data files. (A list of abbreviations for data processing models is listed 

in the call-out box.) The amount of design data increases with a building’s size; as a result, they 

are stored in a computer aided design (CAD) system database with a data structure known as 

Building Information Modeling (BIM.) New data structure systems defined by Standard for the 

Exchange of Product Model Data (STEP), Industry Foundation Classes (IFC), aecExtensible 

Markup Language (XML), and Automating Equipment Information Exchange (AEX) are 

becoming the standards. By using this kind of data structure more effective data exchange or data 

share is achieved compared to a data structure like Common Separated Values (CSV). Usage of 

an appropriate database like Structured Query Language (SQL) is essential but all the tools 

developed within Annex 47 do not necessarily have this feature yet. Simpler data structure like 

Hierarchical Data Format (HDF5), which is not as sophisticated as IFC, are used especially for 

processing measured data. 

In existing building (EB) cases it is difficult to obtain design data because the database is 

typically inaccessible. Some tools require training data to run. In case of an EB, training data are 

normally available but for a new low energy building (LEB) they are not. Therefore some tools 

may not be used with LEB. 

Data transfer protocol is another important issue in data management. The protocol used in 

BACnet and LONTalk, which are open networks, enables effective data exchange. But in many 

EB the protocol cannot be used due to communication protocols which are proprietary to a 

specific manufacturer or are not open. 

BIM: Building Information Modeling

STEP: Standard for the Exchange of 

Product Model Data

IFC: Industry Foundation Classes

XML: Extensible Markup Language

AEX: Automating Equipment Information 

Exchange

CSV: Comma Separated Values

SQL: Structured Query Language

HDF: Hierarchical Data Format
63

Implementation

Hardware and software issues affect the implementation of tools on computers. Since computer 

calculation capabilities have increased while the cost of computers and related hardware devices 

have decreased, hardware issues pose less of a challenge. In contrast, issues related to software 

management, particularly program language, remain significant. Tools have been and are still 

being developed using various languages such as FORTRAN, C, JAVA, MATLAB, etc. As a 

result, developing a new tool that is compatible with existing tools that use various languages is

challenging. This is why certain tools are operated semi-automated or are otherwise not fully 

automated. 

Operability

A tool’s operability depends on its simplicity and ease of use. Its features rely heavily on 

performance of the visual interface featured in the tool. Many types of graphic sub-tools are 

available and suitable visual environments are easily found and implemented. Some tools utilize 

the visual environment provided for in software functions such as MATLAB/Simlink. 

Operability of the tools developed in Annex 47 varies; however many unique and convenient 

visual interface tools were implemented as seen in Section 4.4. 

Analytical Engine

A variety of analytical engines are available whose mechanisms are based mainly on simulation, 

statistical analysis, linear and non-linear programming, expert rules, and fuzzy logic. Many 

analytical engines were developed and tested in real commissioning projects during Annex 25, 

Annex 34 and Annex 40. In addition, many analytical engines are also available as 

commercialized or open source software. For example, common CFD software and airconditioning load calculation software like EnergyPlus are used as an analytical engine in some 

tools. For LEB commissioning, however, development of new types of analytical engines which 

can estimate a building’s energy performance using CFD software or heat flow simulation of the 

building envelope is needed. Some examples are shown in Section 4.4.

End Users

The principal end users of commissioning tools are commissioning providers; however, building 

owners, building managers, facility managers, operators of HVAC systems are also potential 

users. Generally speaking, each user requires different functions. For example, building owners 

and managers are interested in tools that help reduce energy consumption, while commissioning 

providers and HVAC system operators want tools that help find and diagnose faults for system 

optimization. If building system designers use some tools at the planning or designing phase and 

store the information prepared for the tool in a data base, quality, productivity and cost of 

commissioning work will improve because seamless information flow is established throughout 

all construction phases. This is what Annex 47 emphasizes for future commissioning processes. 
64

Benefits

Commissioning tools provide many benefits; however, the major benefit is energy conservation, 

especially in EB. Other benefits are improved room environment and a resulting reduction in 

resident complaints, increased hardware lifespan by FDD, ensured persistent energy conservation 

performance, reduced system operational costs, conservation of historical operating records, 

better building operator training, reduction in required commissioning reporting in Leadership in 

Energy and Environmental Design (LEED) and the Energy Performance in Buildings Directive 

(EPBD) certification, and ensured optimal operation. Finally, tools reduce commissioning cost 

and improve the quality of the work by lowering commissioning effort and human error

In the case of LEB commissioning, work is often more sophisticated than with that of 

conventional buildings.

4.3.2 Features of EB and LEB Cx Tools Developed in Annex47

Automated and semi-automated Cx tools were developed in Annex 47. Detailed features of 

major tools are explained according to the structure in Section 4.3.1 and listed in Appendix 4. 

Table 4-3 summarizes the tools, their Cx purpose, targeted system, component, and targeted 

building type. FDD is the main purpose of this selection of Cx tools, followed by optimization, 

design and data handling. As for the Cx target, the system level is the primary target, followed by 

whole building and component level at the same rate, then control level. Most tools are 

developed for existing building (EB) rather than low energy building (LEB), as automated Cx 

tools for LEB Cx are currently in development. 

To understand the function of a Cx tool, as well as to inform tool developers of a tool’s 

requirements, it is important to list the type of data necessary and how it is processed. To this 

end, IDEFØ diagrams in Appendix 4 are useful. 

The data used for each tool are further categorized based on two features: one feature is based on 

elements from which data are taken or related; namely, building, room environmental 

requirement or criteria, weather, energy, HVAC system, HVAC component, control system and 

maintenance. The other feature is based on the stages when data are taken; namely design stage, 

operational stage and measured stage. According to this categorization, data used for each tool 

are tabulated as shown in Appendix 5.
65

Table 4-3 Tool purpose, target and building type

No Tool name Design FDD Optimization

Data 

Handling

Whole 

Building System Component Control EB LEB

01

DABO (Diagnostic Agent for 

Building Optimisation), version 

2007

       

02 CITE-AHU   

03 Proto-type FDD tool "i-BIG" for 

HVAC systems    

04 Performance Analysis Tool for 

Heating System    

05

A tool to be used for energy 

analysis and fault detection on 

the whole building level

  

06

Optimization Tool for AirConditioning System Operation 

Considering Thermal Load 

Prediction Errors

  

07 An on-going commissioning tool 

for VRV package systems   

08

A tool to monitor continuously 

oprational data of each HVAC 

equipment 

  

09 Initial Cx tool for HVAC system 

in large enclosure   

10

On-going type FDD tool with 

pattern recognition for HVAC 

systems 

  

11

A Tool for optimizing the 

operation

of heating cooling plants

     

12

A tool for optimizing the HVAC

system with ground thermal 

storage 

   

13 A Tool to estimate energy

baselines using Simulation   

14

Tools for Life Cycle Energy

Management (LCEM) for 

Buildings

      

15

Energy analysis module (on 

Energy Plus) for VRF airconditioning system 

 

16

CFD coupled simulation tool for 

annexation system of cool/heat 

tube and natural ventilation 

  

17 Proto-type FDD tool for 

laboratory exhaust system   

18

Energy Performance 

Commissioning of Existing 

Buildings and Building Portfolios 

  

Purpose Cx Target Building 

Type
66

4.4 Case Studies for Various Approaches

4.4.1 German ModBen Project – Top-Down Approach 

General Approach – a four-step procedure

The German ModBen project deals with the systematic performance evaluation of existing nonresidential buildings. This can be regarded as an ongoing commissioning process. ModBen 

evaluates the developed methods and tools in six demonstration buildings.

Introducing an ongoing commissioning process to a building is a multi-level process. In ModBen 

the process is a top-down-approach that starts at building level and descends to selected single 

components, if necessary. The building’s available information (stock data and measurement) 

will increase from step to step, allowing more detailed analysis of building performance.

A four-step procedure was developed for ModBen. Figure 4-25 illustrates the procedure on a 

time scale. 

Figure 4-24 The 4-step procedure on a time scale

It is important to note that the ongoing commissioning approach may be introduced after Step 1 

if a classification of the building is available at that stage.

Each step is described in detail with the definition of necessary stock data, measured data, kind 

of analysis, further actions, and outcomes (Table 4-4). Furthermore, tools were developed for 

every step (see Table 4-5).

Benchmarking

Collect System Information and 

Installation of Data Acquisition 

Equipment

FDD and 

Optimization

Monitoring
67

Table -4-4 Overview of 4-step procedure

Table -4-5 Overview of tools

Step ModBen-Tools

1 Benchmarking Check list 1

(Excel based checklist for data gathering)

2 Collect system information 

and installation of data 

acquisition equipment

Check list 2

(Excel based checklist for data gathering)

and EnEV+ (German implementation of EPBD)

3a FDD

(Measurement based)

Predefined Visualization 

(manual)

Rule-based system (under development)

(automated)

3b Optimization 

(Model based)

DIN V 18599 

(ennovatis EnEV+)

Simplified simulation-model (under development)

(IDA-ICE)

4 Monitoring Automated outlier detection

(regression model + clustering of day types)

Persistence of energy 

efficient performance

 Faultless / optimized 

operation

 documentation of energy 

savings and cost

 Certificate

 Theoretical target value

 Sub-hourly data available

First classification + baseline 

(yearly/monthly)

Outcomes

Implementation of energy None

saving measures

Installation of data acquisition 

(sub-hourly data)

Further Actions None

Automated outlier 

detection of energy 

consumption

Model based Analysis

 Manual / automated FDD

(measurement/rule based)

 Manual Optimization

(model based)

Detailed Energy Balance 

(static model, monthly)

Primary Energy demand

Overall “ COP”

Analysis Benchmarks / signatures

Minimal dataset Minimal data set

Additional measurements only 

according to individual needs

Utility bills / own meter None

readings (yearly / monthly)

Measured Data

Additional stock data only None

according to individual needs

Stock Data minimal building description According to DIN 18599

Maintain optimized 

performance by ongoing 

(minimal) monitoring

Analysis of the building 

performance, identification 

and implementation of energy 

saving measures and 

optimization of performance

Certification according to 

national implementation of 

the EPBD. 

Gather basic consumption 

and stock data and first 

classification / baseline of the 

building performance

Description

Name Simple Benchmark Certification FDD + Optimization Regular Inspection

Step 1 Step 2 Step 3 Step 4

Persistence of energy 

efficient performance

 Faultless / optimized 

operation

 documentation of energy 

savings and cost

 Certificate

 Theoretical target value

 Sub-hourly data available

First classification + baseline 

(yearly/monthly)

Outcomes

Implementation of energy None

saving measures

Installation of data acquisition 

(sub-hourly data)

Further Actions None

Automated outlier 

detection of energy 

consumption

Model based Analysis

 Manual / automated FDD

(measurement/rule based)

 Manual Optimization

(model based)

Detailed Energy Balance 

(static model, monthly)

Primary Energy demand

Overall “ COP”

Analysis Benchmarks / signatures

Minimal dataset Minimal data set

Additional measurements only 

according to individual needs

Utility bills / own meter None

readings (yearly / monthly)

Measured Data

Additional stock data only None

according to individual needs

Stock Data minimal building description According to DIN 18599

Maintain optimized 

performance by ongoing 

(minimal) monitoring

Analysis of the building 

performance, identification 

and implementation of energy 

saving measures and 

optimization of performance

Certification according to 

national implementation of 

the EPBD. 

Gather basic consumption 

and stock data and first 

classification / baseline of the 

building performance

Description

Name Simple Benchmark Certification FDD + Optimization Regular Inspection

Step 1 Step 2 Step 3 Step 4

Model-based Analysis

 Manula/automated FDD 

(measurement/rule-based)

 Manual Optimisation 

(model-based)
68

Deployment of sensors

In the ModBen framework a quantified set of measured data must be delivered from every 

demonstration building. This set was established as it was believed to be the minimum amount of 

measured data necessary to facilitate a rough overall assessment of the system’s performance.

The minimum data set is listed in Table 4-6.

Table 4-6 Minimum data set of measurements

Item Measured value Remarks

Consumption Total fuel consumption e.g., gas, oil, biomass

Total consumption of district heat

Total consumption of district cold

Total consumption of electricity

Total water consumption

Weather Outdoor air temperature From own weather station or weather data 

provider

Outdoor relative humidity From own weather station or weather data 

provider

Global irradiation From own weather station or weather data 

provider

Indoor Conditions Indoor temperature Choose one or more reference zone for that 

measurement

Indoor relative humidity Choose one or more reference zone for that 

measurement

System Flow/return temperatures of main 

water circuits

Main heat/cold distribution. 

Main refers to distribution within the building 

and not to a primary distribution such as a 

district heating system. 

Control signals of pumps and 

fans

Main drives 

Supply and return air temperature 

of main AHUs

Only if supply air is thermodynamically 

treated

Supply air relative humidity of 

main AHUs

Only if supply air is humidified/dehumidified

In the ModBen framework this minimum data set is to be achieved using a time resolution of 5

min to 10 min. Typically, sensors for energy and water consumption and for system temperatures 

are installed in the building. Sensors for weather data and indoor climate are typically only 

installed if the building is equipped with an air conditioning system.

However, even when sensors are installed, the transfer function to other analysis systems is 

generally not provided. Availability of measured data is enhanced in buildings with a BAS that 

comprises a management level. In theory, the BAS can provide a lot of data. Unfortunately, the 

BAS in most existing buildings is not equipped with data transfer features such as a data base 
69

interface, an Open Process Control (OPC) server or the ability to export ASCII files with 

measured values. As well, energy data is typically not available from the BAS. The technology 

might be available but, for cost reasons, is often not installed.

Nevertheless, if a BAS is installed it should be checked to see whether it can be modified to 

provide the minimal data set at a reasonable cost. If the enhancement of the BAS is complex, it 

might be more cost efficient to install an extra data logger with additional sensors. If no BAS is 

available, the installation of a data logger is the only option unless the building owner decides to 

upgrade the building with a BAS.

Another aspect to observe in high time data logging (hourly or sub hourly) is the amount of 

traffic the fieldbus must handle. In the case of significant amounts (>> 100 data points) 

modifications to the fieldbus communication are necessary.

In most cases, heat and water meters must be equipped with a pulse output or fieldbus interface. 

In the case of a fieldbus interface, they must also be equipped with a grid-connected power 

supply because of frequent readings.

In the ModBen project, the cost of acquiring the minimal data set is 15 000 EUR to 60 000 EUR 

per building, depending on the system’s actual condition. However, in most cases the cost 

averages a reasonable 5 % to 20 % of annual energy costs.

The effort to acquire additional data that exceeds the minimal data set relies on the presence of a 

BAS. If a BAS is enhanced to deliver the minimal data set, additional data from the BAS (e.g., 

control signals, schedules, etc.) is available at little cost but if a data logger is used, acquiring 

additional data results in significantly greater cost.

Table 4-7 gives an overview of the cost of the data acquisition system in the ModBen project 

demonstration buildings. From our experience, we can conclude that:

• If the main part of the measurement equipment and a BAS are installed, the BAS should 

be enhanced for monitoring, especially if many datapoints (>>100) are to be recorded. 

• If only a few measurement points are required and/or no BAS is installed, a separate data 

logger is the better solution.
70

Table 4-7 Overview of data acquisition cost and estimated static amortization (red = estimated; blue =

calculated ; ??= unknown)

DVZ Barnim Großpösna M+W Zander 

Zentrale

EADS 88 LEH Kraft 

Foods

BAS and 

measurement 

equipment 

already available 

- Yes No Yes Partially Yes Partially

Kind of data 

acquisition

- BAS Data Logger BAS Data 

Logger

BAS Data 

Logger

Number of data 

points

300 20 500 80 480 450

Annual energy 

cost

EUR 63 000 4 900 617 000 150 000 360 000 ??

Cost of data 

acquisition and 

gathering system

EUR 30 000 3 000 21 000 30 000 15 000 60 000

Cost of data 

acquisition and 

gathering system

% of 

annual 

energy 

cost

48 % 61 % 3 % 20 % 4 % ??

Expected minimal 

energy savings

% 5 % 5 % 5 % 5 % 5 % 5 %

EUR/year 3 150 245 30 850 7 500 18 000 ??

Static pay back years 9.5 12.2 0.7 4.0 0.8 ??

Performance metrics

ModBen applies and evaluates different pre-defined performance metrics according to the topdown approach described. The next sections give a short general description while the next 

chapter provides examples from demonstration buildings.

• Statistic Benchmarking (annual/monthly data in Step 1)

Several sources exist for reference values for energy consumption of non-residential 

buildings in Germany. The values are statistically derived from building stock. Typically, 

they are listed according to the building’s main use (office, school, shop, etc.). 

Unfortunately, most of these benchmarks miss detailed specifications, such as year of 

construction, building size or HVAC system type, which reduces the usefulness of these 

benchmarks. 

• Building specific target value 

According to the EPBD, certification delivers a building’s specific target value for energy 

demand. German implementation of the EPBD (DIN V 18599) is complex and includes 
71

most HVAC systems. The building is further divided into multiple zones according to 

use.

Although certification works with standard use profiles, the calculation model can be 

adjusted to the building’s actual use (operation and occupancy schedules, setpoints) – to a 

certain extent. This “calibration” requires considerable effort and results are not directly 

comparable to measured energy consumption 

• Pre-defined visualization of minimal data set for manual fault detection and diagnosis 

Standard visualizations for the minimal data set were defined in order to extract 

information from the data. Visualization includes carpet plots for checking time patterns 

of operation and occupancy and scatter plots for identifying control strategies and time 

series as reference and for detailed examinations.

These plots can be manually inspected in order to find faults and deficiencies when 

analyzing building performance for the first time. 

• Rule based fault detection and diagnosis

The variables of the minimal data set will show different correlations for faultless 

operation and operation with faults. Theoretically, it is possible to calculate the 

correlations for each case to be able to identify faults from real measured data.

ModBen is still developing a rule based system which is able to detect these typical faults 

from measured data but this function will not be discussed further in this report.

• Simplified simulation models for optimization 

Numerical Optimization requires simulation models that run fast and so enable many 

variations. ModBen develops simplified models to reduce the numerical effort required 

for simulation. These models can be calibrated with real measured data and used for 

optimizing the control strategy of real buildings. This feature remains under development.

• Automated outlier detection for ongoing monitoring and maintenance of fault free/optimized 

operation

If acceptable or even optimal operation of an HVAC system is achieved, it will still 

require maintenance and monitoring. Building operation or performance must be 

monitored weekly or daily. As operational staffs do not have time for this, ModBen has 

developed a method to automate the process. 

The building’s current performance is identified first using a regression model, which is 

then combined with a clustering process that identifies day types and consumption 

patterns.
72

The calibrated model is then used for a daily automated check of energy and water 

consumption.

Building case study

Examples show how measured values of the minimal data set are used to analyze or monitor the 

building. Not all analysis routines or performance metrics are presented, just the visualization 

(manual fault detection) and automated outlier detection.The example used for data visualization 

is the ThyssenKrupp Kreuzgebäude in Essen shown in Figure 4-26.

Figure 4-25 ThyssenKrupp Kreuzgebäude in Essen, Germany

The Kreuzgebäude is a typical office building built in the 1980s with an insulation standard 

(mean U-value= 1.3 W/m2K, percent of façade’s glazing = 30 %). The building’s energy supply 

is provided by district heat and electricity. 

The building is heated with warm water radiators; most offices are ventilated by openable 

windows. Roughly 25 % of the spaces are equipped with a central ventilation system with heat 

recovery and a heating coil for preheating of air.

Approximately 30 % of the spaces are equipped with split units for cooling in summer. 

The building’s annual energy cost is about 150 000 EUR.
73

Data acquisition

With Kreuzgebäude no BAS was available, so the minimal dataset is acquired using a data 

logger as depicted in Figure 4-27. Necessary sensors were installed with most communication 

done via the modbus protocol. To acquire 85 data points costs roughly 25,000 EUR.

Figure 4-26 Data acquisition system installed in Kreuzgebäude. Blue represents the data 

logger. Data is transferred to Fraunhofer ISE via internet (VPN tunnel).

Skizze

Aufbau der Meßtechnik im Kreuzgebäude

Anschluß der Smartbox 

ohne Erweiterugsoptionen

Stromzähler

Controlling Server

Freiburg

Temp.

rel. Feuchte

Pix Box VPN Tunnel

Außentemperatur

Außenluft Feuchte

Impuls

8x 4-20mA

Vor-Rücklauf

Impuls

Globalstrahlung

Signalverstärker

AD-Wandler

ZÄHLER

Wasserzähler

Hausanschluss

Fernwärme

UP

Heizkreise

Dachaufbauten

Heizungskeller

Weitere 

Kellerräume

MBus

MBus

Legende

Stromzähler

Hausanschluss

Innentemperatur

Sender Temp 

Empfänger

ModBus

2. OG

4. OG

Temp.

AD-Wandler Zu/Abluft

PT100

ModBus

Modbus

Strom

Außensplitgeräte

StromLüftungszentrale

ModBus

AD-Wandler

PT1000

0-10V

6x 4-20mA

Stromzähler
74

Visualization

Manual fault detection using pre-defined data visualization is done in a top-down manner, from 

overall energy consumption to system/subsystem levels.

The plot in Figure 4-28 illustrate some findings in Kreuzgebäude.

Energy consumption

Figure 4-27 Time series plot of energy and water load (hourly values)

Figure 4-28 shows the time series plot for energy and water consumption together with outdoor 

air temperature. Although weekly patterns and the dependency of heating consumption on 

outdoor air temperature is plotted, the following figures show that additional information can be 

extracted. Time series plots are used only as reference plots.
75

Figure 4-28 Carpet plot of energy and water load (hourly values) from bottom to top: outdoor air temperature, 

district heat, electricity split units*, electricity ventilation*, total electricity, total water consumption, DHW consumption.

(*not part of minimal data set), arrows show missing data

Figure 4-29 shows carpet plots of energy and water consumption. Daily and weekly patterns are 

more obvious than in Figure 4-28. The building’s occupancy – from water and electricity 

consumption – is Monday to Friday, 07:00 to 19:00. The electricity plot shows energy 

consumption Saturday mornings as well, which seems unnecessary as water consumption (and

occupancy) is zero at that time. The district heat plot reveals that the night and weekend setback 

is deficient, at least at low outdoor air temperatures.
76

Figure 4-29 Energy and water load signatures (daily means) by weekdays and weekends. 

Figure 4-30 is a scatter plot (signatures) of energy and water consumption in relationship to 

outdoor air temperature. The missing set back of heating during weekends is obvious in the 

lowest plot. This contradicts electricity and water results that show low or no occupancy on 

weekends.

The electricity plot shows that cooling is no major consumer as the electricity load increases only 

slightly with higher outdoor air temperatures. 

[oC]
77

Heating circuits

Figure 4-30 Carpet plot of heating circuit sensors (arrows show missing data)

Figure 4-31 shows a carpet plot for one of the building’s four heating circuits. Arrows overlaid 

on the carpet plot for the supply temperature and temperature difference show the same 

deficiency in night and weekend setback that was already recognized for the heating load in 

Figure 4-29.

Also, the pump is in constant operation, representing another potential for optimization. The 

pump could be turned off during unoccupied periods and changed to variable flow. 

The plot also shows that the heating is turned off (in May) regardless of the outdoor air 

temperature.
78

Air Handling Units

Figure 4-31 Carpet plot of air handling unit sensors arrows show missing data

Figure 4-32 shows a carpet plot for one of the air handling units. The operation schedule is 

Monday to Friday, 06:00 – 21:00 and Saturday 06:00 – 14:00. (Electricity – the top plot - is used 

here as the AHU operational signal) 
79

Saturday operation is unnecessary and will be turned off.

The heating coil’s primary pump is in constant operation during the entire heating season and is 

turned off manually in May. Like the pumps for the heating circuits the pump could be turned off 

during unoccupied periods and changed to variable flow.

The temperature difference between the heating coil’s supply and return temperature (dT SUPRET HC) shows odd fluctuations in summer – even though the primary pump is turned off. This 

occurs because the secondary pump (see Figure 4-33) starts up as soon as the outdoor air 

temperature falls below 20 °C to avoid freezing. This causes unnecessary heat loss and heats up 

the air supply in summer, which causes extra cooling load. This operation has to be changed. It 

will be tested to turn off the primary pumps completely as the secondary pumps are obviously 

capable of supplying the heating coil. This fault impacts building optimisation. Figure 4-34

shows that the temperature difference on the water side of the heating coil is very low (2 oC to 3

oC). This means that the primary pump is set to over capacity.

Figure 4-32 Layout of air handling units

OAT

EXH to outside

EXH from inside

SUP AT

+

Secondary pump

Primary pump

T

T
80

Figure 4-33 Scatter plot air handling unit sensors. Color scale represents 

the control signal of the heating coil pump.

Results and Conclusions

In the best-case scenario, manual fault detection by sensor visualization in the minimal data set 

revealed energy savings of up to 15 % of annual energy consumption.

Using a typical minimal data and frequent faults (basic faults make it easier to detect) the 

following were identified:

• Insufficient or no setback of heating and cooling during nights, weekends or 

unoccupied periods;

• Missing adjustment to operating schedules between sub-systems in the HVAC 

system;

• Heating/Cooling curve incorrectly set;

• Mass flow rates too high/over capacity;

• Wrong size of generators; and

• Faults in measuring equipment; 

Automated fault detection

[oC] [oC]
81

Once an optimal or acceptable performance is reached, it must be monitored to maintain it.

As this monitoring would be tedious for operational staff a method was developed to 

automatically detect abnormal energy consumption values on a daily basis by using the minimal 

data set.

The method is based on a regression model combined with a clustering process for identification 

of day types, i.e., days with varying consumption profiles in all delivered energy and water. In 

Germany this will typically be heat (e.g., gas or district heat) and electricity.

The regression model is trained using data that contains at least three months of measurements. 

The training identifies basic patterns in energy and water consumption. After training, the model 

can be used for daily fault detection (validation phase, see Figure 4-35).

Figure 4-34 Automated fault detection method with training and validation phases

With two test buildings it was shown that the method can identify the consumption profile 

reasonably well and detect real and manually inserted outliers. The method will be further tested 

on other buildings.

Figure 4-36 shows a comparison of the measured values of heat load and the prediction of the 

model for one of the demonstration buildings.

Remove outliers in data

Training

Measurements 

from building

Regression model 

representing building’s 

consumption profile

Clustering process to 

extract information on 

day-types

Assignment of day-type

Validation

Measurements 

new day

Comparison between 

measured and predicted 

values 

Regression model 

predicting daily energy 

consumption
82

Figure 4-35 Comparison of daily heating demand at demonstration building with estimated values of regression 

model 

Details of the black box model are as follows:

• Daily heating demand is modeled by applying a black box model which 

can also be developed to monitor electricity or water demand

• Black box model contains clustering process to automatically identify 

building’s day-types (days with different consumption patterns)

• A multiple linear regression model estimates model parameters

• The model’s estimated values are compared to the measured values at 

the building site and outliers can be identified on a day-to-day basis

• The model is not dependent on the building layout and can be applied 

without extensive prior knowledge of the construction characteristics

• Measured data from the building site is the only key component

0 5 10 15 20

28.02 04.03 09.03 14.03 19.03 24.03 29.03 03.04 08.04 13.04 18.04 23.04 28.04 03.05 08.05

 

 

 

 y g gy p

Days in 

 

 

Daily Energy Demand of Pheat in W/m2

 

 

 

Working days

Weekends or Holidays

Detected Unnecessarily High Energy Consumption

Measured Values at Building

Estimated Values Black Box Model

 

 

! !

!

!
83

4.4.2 Netherlands – Bottom-Up Approach

General approach 

TNO Built Environment and Geosciences used a three-step approach for performance analysis of 

buildings and HVAC systems. This procedure follows a general bottom-up approach, 

concentrating on the technical and functional condition monitoring, concerning operation and 

maintenance of HVAC systems.

The three-step procedure

The three main steps are:

1. Simple benchmark on comfort;

2. Analysis and optimisation; and

3. Regular inspection and check of action plan.

Starting points of this approach:

 Take available (measurements) from BAS and try to use it for recommissioning; and

 Special comfort problems need to be solved (additional sensors may be needed).

The commissioning projects executed until now in 90 buildings were based for 95 % on recommissioning projects. The main reason was to solve problems of the tenants in the buildings, 

which have resulted already in numerous comfort complaints.

The client approach for a performance test is illustrated in Figure 4-36.
84

Figure 4-36 Principle of evaluating a building’s HVAC systems

The building and the building user with its specific business processes determine the inside 

climate/comfort and energy demand. Whether the energy demand of the building can be met, 

depends on the HVAC concept and on the installation’s operational management. For our 

investigation it was assumed that the original design was sufficient for the desired comfort in the 

building, so there is no preceding check of the design and capacity of the installation’s 

components.

Some random checks of the installation and separate equipment, as shown in the lowest two tiers 

in the pyramid of Figure 4-36, were executed. Experience shows that when the quality of the 

lowest two tiers is insufficient, the upper tiers/levels are also of insufficient quality.

A TNO-study of more then 90 buildings of different types showed comfort complaints and high 

energy consumption to be the most common faults.

The problems are:

 The average energy use of buildings is 25 % higher than expected;

 70 % of HVAC systems do not work as expected; and

 90 % of complaints are attributed to poor HVAC system performance.

The causes are:

 15 % design faults; and

 85 % (i) completion (handover) of HVAC system and (ii) operation and maintenance.
85

We will focus on technical and functional monitoring aspects concerning operation and 

maintenance of HVAC systems. 

The details of the steps

Table 4-8 presents a comfort-oriented approach.

Table -4-8 Overview of the 3-step procedure

Step 1 Step 2 Step 3

Name Benchmark on comfort FDD + optimization Regular inspection and check 

of action plan

Description Compile a basic survey list of 

complaints and stock data and first 

classification / baseline of the building 

performance

Analysis of the building 

performance, identification 

and optimisation of 

performance

Check that targets identified in 

step 2 are achieved

Stock Data Minimal building description Additional stock data 

according to individual needs

None

Measured 

Data

Make a first analysis of BAS dataset. 

Manual checks concerning sensors and 

visual inspections.

Minimal dataset + additional 

measurements according to 

individual needs.

None

Analyse Benchmark based on survey of 

complaints

Standard analysis

System specific analysis

Step 2 targets as major metric

Further 

actions

None Installation of data acquisition 

(hourly data)

None

Outcomes First classification of the type of 

complaints

Faultless / optimized 

operation

Documentation of 

improvements actions/ 

proposals

An improved or adjusted action 

plan

Tools “Checklist 1” - “Analysis framework”
86

The Quick scan

The following section describes every step in detail. The activities executed for each building is 

based on a so called quick scan [39] which describes all the necessary activities, including a 

survey list to be filled out. The four main activities executed for each building are:

Preparation and Start-up meeting (Step 1)

A start-up meeting with the building owner will be held to discuss basic assumptions and 

exchange of information. We explain clearly that we concentrate on the technical and functional 

condition monitoring concerning operation and maintenance of HVAC systems.

Inspection of buildings and installations (Step 2)

Global inspection of buildings and installations will focus on:

1) Evaluation of design of HVAC system

An inventory will be made of the HVAC system. The 

focal point of this inventory:

• Does the original installation concept fit with the 

current building user and its operational management?

• Is zoning necessary? If so, is this realised in the 

installation?

• Is the outdoor air temperature sensor placed correctly, 

in a representative location?

• What comfort level can we expect with this installation concept?

• Can we expect with this building design some limitations in the expected (realised) 

comfort?

2) Inventory of the realised comfort conditions

Manual checks will be executed concerning:

• Room air temperatures;

• Room radiation temperatures; and

• Air velocity.

The question to be answered is: can we expect these measured values when the 

installation is working optimally?
87

3) Design use versus actual use

Design building use:

• Which internal heating load was taken into account?

• Type of organisation / operational management?

• Which type of room functions were defined and assigned?

Actual building use:

• Does the current function of the rooms / zones correspond the original design?

• Is it possible to restructure the building interior without changes to the installation?

• If sensors are installed, are they placed in a representative place and position?

4) Performance of the HVAC system

The performance of the HVAC system will be checked on three levels:

• Production of heating and cooling (boilers, chillers) 

• Distribution of heat and cold in the building (water and air)

• Emission of heat and cold in the zones and rooms.

The type of checks executed on the three levels:

• Is test, adjust and balancing (TAB) executed in conformance with the design?

• Check of temperatures (water and air)

• Reliability / calibration of temperature sensors in the zones

• Performance and adjustment of controllers

Note:We use as much as possible, including data point information collected by the BAS/ BEMS

and available monitoring (TAB) reports.

Analysis and reporting (Step 2)

The following analysis and reporting items will be executed:

1. Analysis of the inspections

2. Short reports of analysis and conclusions

3. Causes of comfort complaints

4. Suggestions for recommissioning of building and HVAC system/ installations

Final meeting and discussion (Step 3)

A final meeting per building is planned to explain and clarify the results and the actions to be 

taken. 
88

Building Case Study 1

Benchmark on comfort

A simple benchmark on comfort is presented in Figure 4-38. The key performance indicator, 

complaints per person per year is calculated as follows: in Building 1 are 1500 persons and 1080 

complaints were received ovet the year. Therefore, 1080 complaints/1500 persons = 0.72 

complaints per people per year. From 32 buildings, an average key performance indicator is 

derived of 16 % comfort complaints per person per year. After resolution, a target value of 8 % is 

realistic. 

Figure 4-37 A simple benchmark on comfort based on survey lists of complaints

• Are comfort improvements realistic?

Our task is successful if the complaint list of the building user is strongly reduced.

An example is given in Figure 4-38.

KPI Comfort complains per (year x person)

0%

10%

20%

30%

40%

50%

60%

70%

80%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

Number of buildings

Key Performace Indicator (KPI) comfort complains

Realistic target value: 8%

Average: 16%
89

Figure 4-38 Reduction of user complaints after improvements executed by TNO.

In this example the complaints are from 12 building of a insurance company. The benchmark on 

comfort showed comfort-related complaints were at a level of 20 % in those 12 buildings. After 

improvements, the measured reduction was between 5 % and 10 %.

Building Case Study 2

Deployment of sensors

A building owner in the Netherlands gave permission to investigate the real performance of one 

of the AHU’s in his building. It was executed free of charge for the building owner in a so-called 

SME project. One objective was to convince installers and service companies of the benefits of 

FDD of HVAC systems. 

The building selected was a hospital in the town of Almere (Figure 4-39). The AHU was selected 

by the building owner. 

Figure 4-39 Plan of the hospital in the town of Almere
90

The starting point of the performance test is a blind test, based on:

• Functional description (6 pages) of HVAC system and selected AHU;

• Principal layout of the AHU (Figure 4-42);

• No additional sensors installed;

• Use of available sensors in the BAS;

• Data collection prepared by building owner; and

• Excel files forwarded by e-mail to TNO.
91

Figure 4-40 The layout of the AHU.
92

Table 4-9 presents an overview of the points measured in the AHU.

Table 4-9 Data set of measurements

Flevo Hospital, city of Almere, The Netherlands

Date : 8 March 2007

Subject : AHU measuring points

Data collection : every 5 minutes for benefit of FDD test

Item Code in 

drawing

Measured value Unit Remarks

Weather Outdoor air temperature °C own weather station

Outdoor relative humidity % own weather station

Indoor 

conditions

01-MT-02 Indoor air temperature °C Use return air temperature building part A 

01-TE-04 Indoor air relative humidity % Use return air rel. humidity building part A

01-MT-03 Indoor air temperature °C Use return air temp. building part D+F 

01-TE-05 Indoor air relative humidity % Use return air RH building part D+F

Generation Chillers Chilled water temperature Chiller 1 °C Central cold water production

Chilled water temperature Chiller 1 °C Central cold water production

Heating Supply water temperature of main central 

heating system

°C Central hot water production by gas 

boilers

AHU Set-point absolute humidity, supply air. g/kg

Set-point absolute humidity, return air. g/kg

01-CP-01 On-Off circulation pump twin-coil 1/0

01-CP-02 On-Off circulation pump heat exchanger 1/0

01-AV-01 Operation return fan 100 % or 50 % 1/0

01-TV-01 Operation supply fan 100 % or 50 % 1/0

01-TZA-01 Frost protection thermostat 1/0

01-MT-01 Relative Humidity supply air %

RH

Lowest measured outside air temperature of 

all outside sensors

°C

Temperature set-point supply air °C

01-TE-03 Temperature supply air °C

01-TE-02 Return water temperature of heat exchanger °C

01-TE-01 Supply water temperature twin-coil °C

01-RA-03 Control device (valve) cooling coil % Control signal

01-RA-04 Control device (valve) humidifier % Control signal

01-RA-02 Control device (valve) heat exchanger % Control signal

01-RA-01 Control device (valve) twin-coil % Control signal

01-PT-01 Pressure drop return fan Pa

01-PT-02 Pressure drop supply fan Pa
93

The operation matrix of the AHU is presented in Table 4-10.

Table 4-10 The operation matrix of the AHU

AHU building part A, D and F Operation and Control AHU

AHU components 1 2 3 4 5 6 7 8

Fresh air damper Fc Fo Fo Fo Fc

Circulation pump twin-coil Aut Aut Aut Aut Aut On

Control device (valve) twin-coil 0 Co Co Co 0

Circulation pump heat exchanger Aut Aut Aut Aut On On On

Control device (valve) heat exchanger 0 Co Co Co 100 Co Co

Control device (valve) cooling coil 0 Co Co Co 0

Supply ventilator LOW (50 %) Off Off On Off Off

Supply ventilator HIGH (100 %) Off On Off On Off

Steam control device (valve) 0 Co Co Co 0

Return ventilator LOW (50 %) Off Off On Off Off

Return ventilator HIGH (100 %) Off On Off On Off

Exhaust air damper Fc Fo Fo Fo Fc

Control

Supply air control Of Co Co Co Off

Supply air humidity control Of Co Co Co Off

Return water temp. control heat exchanger Of Co Co

Explanation operation management Operation situation Exceptional operation situat.

Off

On

Fc

Fo

Co

Aut.

Aut.

L

H

Switch Off

Switch On

Fully closed by controller

Fully open by controller

Regulate/ control by DDC

Off / On through DDC

Closed / Open through DDC

Low

High

1

2

3

4

OFF

IN Daily operation

IN Night operation

5

6

7

8

Fire

Frost protection

T-Outside < frost limit

Start up AHU
94

The AHU set-points and operation schedule are presented in Table 4-11.

Table 4-11 AHU set points and operation schedule

Performance metrics

• Definition of expert rules

The analysis approach is based on fault detection and fault diagnosis. Using information from the

AHU’s principal layout (Figure 4-40) and Table 4-9 to Table 4-11, expert rules are derived. 

Table 4-12 presents an example of the algorithm of six knowledge rules.

Set up points Set-points

Desired supply air temperature when return air temperature < 22 °C 20 °C

Desired supply air temperature when return air temperature > 26 °C 16 °C

Desired return temperature heat exchanger during start up situation 30 °C

Frost protection when outside air temperature is lower then: 5 °C

Frost protection twin-coil 8 °C

Enabling cooling when outside air temperature above: 15 °C

Xp control twin-coil 10

Xp control heat exchanger 10

Xp control cooling coil 10

Tn temperature control 3 min

Enabling steam humidifier after AHU start after: 10 min

Enabling steam humidifier when outside air temperature below: 15 °C

Desired return air humidity 6.2 gr/kg dry air

Xp control steam humidifier 10

Xn control steam humidifier 3 min

Maximum control signal steam valve 50 %

Maximum humidity content 9.0 g/kg dry air

Minimum pressure drop for testing belt break (after 2 minutes) 20 Pa

Clock settings

Operation period AHU in LOW speed (50 %) Continue

Operation period AHU in HIGH speed (100 %) Monday till Friday from 08:00 till 22:00
95

Table 4-12 Algorithm knowledge rules

Fault Knowledge rule Description

1 IF mode=1: |Tsa - Tsa,s| ≥ εt

With:

Tsa = Supply air temperature 

Tsa,s = Supply air temperature set point

εt = threshold parameter accounting for errors 

in temperature measurement

Mode= 1, Operation maximum fan speed

(100 % )

Supply air temperature of AHU doesn’t reach set 

point (outside the dead band) by maximum fan 

speed.

Potential causes:

Controller not well tuned

Capacity of heat exchanger is undersized

Hot water supply temperature is too low

Not enough cooling capacity in cooling coil

Cold water temperature is too high

Chiller is off

2 IF Mode = 0: AHU = 1 There is no fit with operational schedule

Potential causes:

HVAC system is overridden manually

Summer and winter months not accounted for

Bank holidays not accounted for

Recommendations:

Check if HVAC system is overridden manually

Check software settings related to Winter- and 

Summer

Check holiday settings in the software 

3 IF mode = 1: Tsa - Tsa,max ≥ εt

With:

Tsa,max = 20.0 C, T supply air max. 

AHU supply air temperature exceeds maximum

of 20.0 °C by maximum fan speed

4 IF mode = 1: Tra - Tra,s,max ≥ εt

With:

Tra = exhaust air temperature 

Tra,s,max = 21.0 C, T exhaust air max.

 

Exhaust air temperature exceeds its maximum 

value of 21.0 °C by maximum fan speed

5 IF Mode (0,1): pump twin-coil=0,

pump heat exchanger = 1

The heat recovery pump (Twin-coil)must not be 

OFF while heat exchanger pump is ON

6 IF Mode (0,1): number of ON-OFF 

transitions per hour ≥ Smax

The system is unstable if the number of ON-OFF 

transitions per hour ≥ Smax
96

Results

BAS system monitoring period: Nov. 12, 2006 to Feb. 2, 2007.

Useful measurements : 19.025

Day regime measurements : 7921

Night regime measurements : 11104

Time interval : 5 min (1,585 h, 66 d).

Software platform: spreadsheet

Table 4-13 summarizes the expert rule analysis results. For example, when evaluating the 7921 

day regime measurements for case No. 1, the supply temperature was found to not reach the 

setpoint 177 times, or approximately 2.2 % of the operating period. Similarly, when evaluating 

the 11104 night regime measurements for case No. 1, the supply temperature was found to not 

reach the setpoint 6376 times, or approximately 57.4 % of the operating period. 

Table 4-13 Expert rules analysis results

No Test condition Test condition occurs Error %

Day regime

(Fans 100 %)

Error %

Night regime

(Fans 50 %)

1 Supply air doesn’t reach 

set point.

Fans 100 % (Daily operation):

εt = 1.0 C, 177 times the supply air exceeds its set point

Fans 50 % (Night operation):

εt = 1.0 C, 6376 times the air supply exceeds its set point

2.2 % 57.4 %

2 AHU test fan speed.

Fans run 100 % instead 

of 50 %.

734 times (147 h!) 9.3 %

3 T- supply air is 

maximum 20 C.

Fans 100 %: T-supply > 20.5 C: 63 times

Fans 50 %: T-supply > 20.5 C: 2211 times 0.8 % 19.9 %

4 T- return air is 

maximum 21.0 C

Fans 100 %: T- return air > 21.5 C: 2,749 times

Fans 50 %: T- return air > 21.5 C: 6,878 times 34.7 % 62.0 %

5 Absolute humidity 

return air doesn’t reach 

set point.

Fans 100 %: Set point +/- 2.0 gram: 1889 times

Fans 50 %: Set point +/- 2.0 gram: 1766 times 23.9 % 15.9 %

6 Valve twin-coil not 

fully open when heat 

exchanger ON.

This situation reach: 106 times

1.3 %

7 Switch heat exchanger 

pump on and off 

frequently.

Number of times that pump switched on/off < 5 min: 1,322

16.7 %

8 Switch both chillers on 

and off frequently.

Re-adjust chiller controllers to avoid frequent switching on-off in 

very short time.

9 Control signal of the 

three-way valve of the 

cooling coil.

The three-way valve cannot be closed fully. Based on monitoring 

data, the minimum valve position is 20 %.

The valve cannot be checked for the presence of a threshold value 

or something else, because of other temperature sensors.
97

Conclusions based on information from Table 4-13 is unstable control behavior. The table 

shows that during night regime (50 % air flow) the control of the supply air is unstable. Figure 4-

42 illustrates this for a selected week of the supply air set-point and the measured air supply. 

The figure shows unstable behavior of control during night regime (50 % air flow).

Figure 4-41 Set-point and temperature of supply air.

The second row shows that for 147 hours fans were working in day regime (100 % air flow) 

instead of night regime (50 % air flow). An example of this fault is presented in Figure 4-43, 

which shows the BAS system does not account for holiday periods.

T-supply air Set point / Measured supply air

16,0

17,0

18,0

19,0

20,0

21,0

22,0

13-11-2006 14-11-2006 15-11-2006 16-11-2006 17-11-2006 18-11-2006 19-11-2006

Time

Degree Centigrade

Setp. T Inbl

T Inblaas

Set-point T-supply

T-Supply air
98

Figure 4-42 The supply and return fan pressure in day and night regime.

Conclusions are based on two examples of the bottom up approach.

The first example is based on troubleshooting without using detailed information of the BAS 

system for analysis. The starting point of this approach is the tenant complaints concerning 

building comfort. The objective is to reduce the amount of complaints to an acceptable level, 

which is why functional test are always done in the building and its HVAC system. Additional 

simple data logging sets are used as a basis for specific data collections for a short period, 

enabling us to solve the problems.

The second example is a blind analysis of an AHU based on:

• Functional description of the AHU as a basis for deriving expert rules;

• Data collected from a BAS system; and

• No additional sensors being used.

It is possible to blindly investigate AHU performance using information collected by the BAS 

system. The following information on the HVAC system is required:

• Design and recent modifications;

• Testing, adjusting and balancing reports; and

• Functional description.

Supply fan and Return fan pressure

0,0

20,0

40,0

60,0

80,0

100,0

120,0

140,0

160,0

180,0

200,0

220,0

240,0

260,0

280,0

300,0

320,0

340,0

360,0

380,0

400,0

24-12-2006 25-12-2006 26-12-2006 27-12-2006 28-12-2006 29-12-2006 30-12-2006 31-12-2006 01-01-2007 02-01-2007

Time

Pascal

dP Toev.

dP Ret. Fan
99

The case study is summarized as follows:

• A total of 16 simple expert rules are made from the functional description;

• Faults are detected and solved based on this outcome. The most important detected fault was 

the sometimes frequent ON/ OFF change of the two chillers. This was why one of the chillers 

burnt out.

• The starting point for introducing ongoing commissioning is that the basic performance of 

the HVAC system is OK. If not, Re-commissioning must be executed before starting ongoing

commissioning;

• Even if the HVAC systems works under normal circumstances as expected, use of diagnostic 

tools may reveal imperfections;

• Ongoing commissioning is necessary to monitor the performance of the HVAC system 

during its whole life cycle;

• Diagnostic instruments support the ongoing commissioning process. They (i) save on 

maintenance costs, (ii) increase the life span of components, and (iii) save energy.

• There is a perceptible shift in the type of HVAC maintenance contracts. The ordinary 

maintenance contract based on effort obligation/commitment is changing to maintenance 

contracts based on performance contracts. In this case, diagnostic instruments will be helpful 

in clarifying delivered performance;

Our experience up to now is that after hand over of the HVAC system to the owner/user, they 

are unwilling to invest in ongoing commissioning.

4.4.3 NEDO Promotion Program (Top-down and Bottom-up), Japan 

New Energy and Industrial Technology Development Organization (NEDO), an independent 

administrative body, is the largest core agency in Japan that promotes R&D and the proliferation 

of Japanese industrial technology and energy/environmental technology. It offers proliferation 

programs for the development and introduction of new energy/energy-conservation technology, 

as well as R&D projects on industrial technology, etc. One of NEDO’s grant-aided projects is the 

Promotion Program for the Introduction of Efficient Energy Systems in Housing/Buildings (to 

assist with introduction of BEMS). This project offers subsidies to businesses that install BEMS 

in their buildings and meet the program’s criteria, aiming to promote energy conservation under 

an appropriate control.

Criteria for Subsidy Eligibility:

• The possibility exists for introduction of BEMS to existing, newly-built, 

extended or rebuilt buildings;

• Energy consumption can potentially be reduced by BEMS;

• Energy performance can be measured by equipment segment such as heat 

sources (refrigerating machines, heat pumps, cooling towers), pumps, 

lightings/outlets, and others;
100

• A well equipped energy management system exists to collect measurement 

data and store it; and

• To be able to accomplish the grant-aided project, and to report for 3 

consecutive years after introduction of BEMS.

The project started in 2002; by 2008 more than 200 businesses had enrolled. The distribution of 

grant-aided businesses by building size is shown in Figure 4-43 on the left. Large-sized buildings 

with over 10,000 m2 make up a greater portion. The breakdown of grant-aided businesses by 

building usage is shown in Figure 4-43 on the right. Various businesses have enrolled. Among 

them those using their buildings as offices and stores represent the majority. Figure 4-44 shows 

the energy-conservation attainment levels of the grant-aided businesses by building usage. All 

businesses attained higher levels of energy-conservation than the targets set out in the planning 

stage.

Figure 4-43 (Left): Grant-aided Businesses by Building Size< m2 (Right): Grant-aided Businesses by Building Usage

Figure 4-44 Level of energy conservation attained by Building Usage

These are the actual results in fiscal year (FY) 2007 of a total of 212 grant-aided businesses 

enrolled from FY 2003 to FY 2006: 

34

64

30

31

15

35 9

<10,000m2 ≧10,000m2 and <20,000m2

≧20,000m2 and <30,000m2 ≧30,000m2 and <40,000m2

≧40,000m2 and <50,000m2 ≧50,000m2 and <100,000m2

≧100,000m2 

80

18 32 9 20

5

26

7 12 11

Office Store

Hospital School

Hotel Assembly Hall

Complex Research institution

Computerized processing center Miscellaneous

258

213

168

150

207

155

198

346

131

203

219

0

50

100

150

200

250

300

350

400

Office

Store

Hospital

School

Hotel

Assembly

Hall

Complex

Research

institution

Computerized

processing

center

Miscellaneous

Overall

Average Attainment Level (%)
101

Total amount of energy-conservation: 2,423 TJ/year

Rate of energy-conservation: 10.1 %

Cost-benefit performance: 91.1 MJ/year/1,000 yen 

The reasons all grant-aided businesses attained their energy-conservation targets and derived 

benefits are:

1. Adequate target values are set

The grant-aided business reports their energy consumption to NEDO before the 

introduction of BEMS. Predicted values of energy consumption, as well as grounds for 

the prediction when introducing BEMS, are examined at the time of inclusion. The 

appropriateness of the targets is evaluated by a third party.

2. Attainment levels must be measured and reported. 

This program requires participating businesses to measure energy consumption by 

element; e.g., heat sources, lighting/outlets etc., and report levels of goal attainment based 

on actual energy consuming performance. If target values are not attained, they must 

report the reasons to NEDO. This motivates the businesses to make every effort to attain 

their goals. 

3. Energy management system of grant-aided businesses is established.

For grant-aided businesses to attain their target values of energy-conservation, it is 

fundamental that their energy management systems be well established. NEDO decides 

whether or not to include a business by checking their energy management system. The 

business organizes their energy management system with participation of experts such as 

designers, instrumentation manufacturers, members of ESCO, etc., regularly checks the 

status of goal attainment; and, in the case of non-attainment, analyzes the causes and 

makes necessary improvements. 

Figure 4-45 shows the relationship between frequency of utilizing BEMS data and energyconservation rates in grant-aided businesses. Many businesses use BEMS data once a month. 

Also, it was confirmed that regularly considering improvement measures in performance 

confirmation meetings tends to result in higher rates of energy-conservation.
102

Figure 4-45 Confirmed results on frequency of utilizing BEMS data

The “Commissioning Process” is used to document and verify target fulfillment. Clearly 

detailed targets are established in writing within the OPR, including quantitative values and 

conditions for their attainment, and a third-party CA supporting the fulfillment.

Although the Promotion Program for the Introduction of Efficient Energy Systems in 

Housing/Buildings (to assist with BEMS Introduction) is not planned prior to assumption of such 

commissioning, in this project, grant-aided businesses specify their demands for achieving 

energy-conservation in a document in the application form. The implementation report includes 

information on quantitative target values and conditions for their attainment, how to analyze 

measured data, solve problems under an energy management system consisting of experts, such 

as a designer and others, in addition to the building’s owner, and the procedure for reporting goal 

attainment status to NEDO. This process is considered similar to the Commissioning Process. 31 133 30 12

8

2

0

30

60

90

120

150

Daily Weekly Monthly Once/3

months

Less

than

once/6

months

NCW

(Not

complied

with)

Number of Businesses

145

34

24

65

0

30

60

90

120

150

Daily Weekly Monthly Once/3

months

Once/6

months

Nunmer of Businesses

53

97

28

25

13

0

30

60

90

120

150

Monthly Once/3

months

Once/6

months

Yearly NCW

(Not

complied

with)

Number of Businesses

8.9

13.5

10.8

6.46.3

0

5

10

15

20

Daily Weekly Monthly Once/3

months

Once/6

months

Average Ratio of Energy Consumption

(%)

9.2

19.5

10.3

8.8

6.6

0

5

10

15

20

Daily Weekly Monthly Once/3

months

Once/6

months

Average Ratio of Energy Consumption

(%)

11.5

10.9

8.3

5.9

0

5

10

15

20

Monthly Once/3

months

Once/6

months

Yearly

Average Ratio of Energy Consumption

(%)

(a)Frequency of Data Checkup (b)Frequency of Data Analysis (c)Frequency of Performance Confirmation

Meeting

(d)Execution of Data Checkup (e)Execution of Data Analysis (f)Execution of Performance Confirmation

Although data analysis is made once a month, a performance confirmation meeting is held less than once per 

three months.

every
103

Deployment of sensors

Grant-aided businesses deploy sensors in their buildings to meet requirements for measurement 

items, as part of the project criteria and to determine the cost-benefit performance of the energyconservation strategy, as recommended by NEDO. Figure 4-46 shows the requisite and 

recommended measurement items.

4. Requisite Measurement Items

The building’s energy consumption from heat sources, pumps, lighting/outlets and other 

lines related to energy consumption volume must be measured and monthly integrated 

values reported. 

5. Recommended Measurement Items

Figure 4-46 Segmented image of sensor deployment in grant-aided project

It is recommended that measurement of each target for each energy-conservation strategy be 

verified. For example, when introducing a variable temperature control for cooling water in a 

water-cooled refrigerating machine, the effect of this strategy is verified by measuring the 

temperature of cooling water and the motive power of the refrigerating machine, the cooling 

tower fan and the cooling water pump.

Performance Metric

The performance metric is divided into two classifications: the mandatory one required for 

reports to NEDO and the one each business independently defines to promote energyconservation.

1. Mandatory metric required for NEDO reports

A business is required to annually report that their planned value for the rate of energyconservation in the whole building is attained for the three years following the project’s 

implementation. Therefore, the rate of energy-conservation in a whole building is mandatory as a 

performance metric.

Heat 

sources Pumps Lightings/

outlets Others

Whole 

building

By 

consuming 

point

By system

By 

equipment

By space

Energy

BEMS

Heat 

sources Pumps Lightings/

outlets Others

Whole 

building

By 

consuming 

point

By system

By 

equipment

By space

Energy

BEMS

Area of definitely required measurements

Area of recommended measurements
104

A planned value is found using Formula (1); and an actual performance value using Formula (2). 

If events alter the planned value, e.g., changes to operational conditions, etc., after the planning 

stage, modifications must be made to redefine targets and specifications.

Rate of Energy-conservation (Planned Value) [ %] = (E ref – E pre)÷ E ref ×100: Formula (1)

Rate of Energy-conservation (Actual Value) [ %] = (E ref – E) ÷ E ref ×100: Formula (2)

Note:

E ref: Standard annual energy consumption of a whole building (MJ/Year)

For new construction:

To be calculated in accordance with the Coefficient of Energy Consumption

(CEC). The value should be set by predicting any effects on the energyconservation approach exempt from the grant-aided project.

For existing buildings:

To be calculated from a mean value of actual performance in the last three 

years. 

E pre: Planned annual energy consumption of a whole building (MJ/Year)

A value taking into account the effects of energy-conservation strategies included in the grantaided project.

E: Actual performance value of annual energy consumption in a whole building (MJ/Year)

CEC is an index related to the equipments’ established energy consumption efficiency based on 

Japan’s "Regulation on rationalization of the use of energy." It is used as a criterion of energy 

conservation performance in building in Japan, and it shows energy conservation in the smallest 

value. The criteria value is set for air conditioning facilities, ventilation equipment, lighting 

equipment, equipment for supplying hot water, and the elevator equipment depending on the 

building’s usage. There is an obligation in Japan to report to the government when building an 

architectural area in excess of 2 000 m2

, or when doing large-scale repair work. A rule for the 

relative calculation of the index value is provided.

CEC = Energy consumed by object equipment ÷ Reference value of energy consumed by 

object equipment

2. Metric independently established by each business for promoting energy-conservation

The research results on metrics that each business independently establishes to promote their 

energy-conservation project are shown in Figure 4-47. Many businesses include a building’s 

overall power consumption volume, overall cold energy consumption volume and overall heat 

consumption volume, as well as monitoring information of indoor conditions such as 

temperature and humidity. It is assumed they use a top-down approach. Meanwhile, some 

businesses adopt metrics to evaluate equipment and systems such as usage-based temperature 
105

differences and unit COP of heat source equipment. Some businesses may adopt a bottom-up 

approach.

Figure 4-47: Adoption rate of performance metric by grant-aided businesses

Building case study 1: Businesses using top-down approach

Research on businesses adopting a top-down approach shows that they check monthly integrated 

values for energy consumption in a whole building or by consuming point and the comparison 

ratio of annual energy consumption volume by consuming point to the previous fiscal year, etc. 

(see Figure 4-48 and Figure 4-49). For improvement steps, many businesses identify the month 

and the consuming points that show increased energy consumption in an energy-conservation 

confirmation meeting, then discuss possible causes for the increase with those involved, and 

make on-site confirmation(See Figure 4-51).

For example, overall energy consumption volume in the building is higher than the planned 

value. The energy consumption volume in the lighting/outlet lines is higher compared to the 

previous year. This is identified through deliberation in an energy-conservation confirmation 

meeting and confirmed by checking if lights are not being turned off or the lighting-up status(See 

Figure 4-49).

The attainment level of the energy-conservation project tends to depend on the level of expertise 

involved in performance confirmation meetings. 97.8 64.1 62.8 39 17

9

88.3

77.1

16.6

11.2

1.8

8.1

0

20

40

60

80

100

01 02 03 04 05 06 07 08 09 10 11 12

Ratio of making analysis (%)

01:Overall power consumption

02:Overall cooling load

03:Overall heating load

04:Usage-based temperature differences

05:Unit COP of heat source equipment

06:System COP of heat source equipment

07:Transfer efficiency of pumps

08:Transfer efficiency of fans

09:Outdoor air cooling efficacy

10:Indoor temperature

11:Indoor humidity

12:Correlation between indoor temperature and

 indoor humidity

Use for bottom-up approach
106

Figure 4-48 Comparison of annual energy consumption by consuming point between a target year and 

previous year

Figure 4-49 Biographic energy data comparison enables confirmation 

of attainment of conservation goals

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

200520062007200520062007200520062007200520062007200520062007200520062007200520062007200520062007200520062007200520062007200520062007200520062007

4 5 6 7 8 9 10 11 12 1 2 3

Month

Primary Energy Consumption[MJ]

1 Power unit– heat sources 2 Power unit - pumps 3 Power unit - others

4 Lightings/outlets 5 power generating capacity 6 Heat sources

7 Misc

5% 17%

5%

26%

49%

17%

49%

26%

0%

3%

0%

0% 3%

0%

Power unit– heat

sources

Power unit - pumps

Power unit - others

Lightings/outlets

power generating

capacity

Heat sources

Misc

2006

2007
107

Figure 4-50 Steps for improving performance by businesses using top-down 

approach

Building case study 2: Businesses using bottom-up approach

Figure 4-51 shows a grant-aided business adopting a variable flow control of cold-water pumps. 

It is a plotted display of the measurement values in a year: the horizontal axis shows calorimetric 

values for water conveyance rates of cold-water pumps and the vertical axis shows the motive 

power of pumps. The higher a value is plotted, the lower its efficiency. Trendlines for low-level 

pumps are plotted in a higher position than hig-level pumps, showing they have lower efficiency. 

This suggests that values of pressure set for low-level pumps are relatively high and leave room 

for readjustment. This way, the grant-aided businesses adopting bottom-up approaches prepare 

performance metrics and graphs for each method of energy-conservation (see Figure 4-52) and 

make improvements using expert opinions from performance confirmation meetings. Operators 

involved in performance confirmation meetings can learn how to assess conditions, and rational 

reinforcement of the management system can be anticipated. In addition, those who adopt 

bottom-up approaches use top-down approaches concurrently, and have a greater tendency to 

show high level goal attainment than those not adopting bottom-up approaches.

BEMS

Energy

Heat 

sources Pumps Lightings/

outlets Others

Whole 

building

By 

consuming 

point

By system

By 

equipment

By space

BEMS

Energy

Heat 

sources Pumps Lightings/

outlets Others

Whole 

building

By 

consuming 

point

By system

By 

equipment

By space
108

Figure 4-51 Improving Performance by adopting a bottom-up 

approach

Examples of the bottom up approach using a graph of BEMS data include:

1) Comparing actual energy consumption in a whole building with the planned value, and 

then to judge the priority for conducting improvements. 

2) Determining energy consumption for heat source and pump with compared values from 

the previous fiscal year, or others.

3) Checking the efficiency of refrigerating machines and the operating condition of variable 

flow pumps by making a graph of BEMS data, and to judge presence or absence of 

problems. 

4) Determining a course of action through deliberation in an energy-conservation 

confirmation meeting.

Figure 4-52 Improving performance of businesses using bottom-up 

approach

BEMS

Energy

Heat 

sources Pumps Lightings/

outlets Others

Whole 

building

By 

consuming 

point

By system

By 

equipment

By space

BEMS

Energy

Heat 

sources Pumps Lightings/

outlets Others

Whole 

building

By 

consuming 

point

By system

By 

equipment

By space
109

4.4.4 SHASE Energy Performance Measurement Manual 

In Japan, while awareness of the need for energy-conservation is rising, there are few buildings 

in which performance of the architectural equipment is understood. Even though the 

performance is grasped, the methods for understanding differ with buildings; the absence of 

uniform standards is seen as problematic. In an aim at solving this problem, SHASE prepared the 

“Energy Performance Measurement Manual for Building Equipment & Systems.” This manual 

organizes views on performance grasping methods for air-conditioning facilities and plumbing 

sanitary systems, and also shows practically available measurement methods and how to process 

data obtained with these methods, by equipment & system. In addition, it covers specifications 

regarded as common elements of measurement and how to use measuring equipment available 

for the measurement. Moreover, this manual presents a point of view for grading of measurement 

in accordance with purposes for obtaining data, points to consider in electrical measurement 

inevitable to the grasping of energy consumption, and some examples of measurement/analysis 

for several real buildings. 

Utilization of this manual makes it possible to make analyses by specifying measurement points 

and evaluation indexes for performance grasping at levels of system and equipment. It is being 

used as a bottom-up approach. The table of contents of this manual is shown in Table 4-14. 

How to Use the Manual 

When designing an actual measurement plan, one first envisages performance items desired to 

grasp/evaluate in reference to the analysis examples illustrated in Chapters 3 and 4, and then 

chooses numerical evaluation items to indicate the performance. Secondly, one chooses system 

diagrams and standard measurement points and configures these on actual equipment and 

systems. Meanwhile, there are different groups of evaluation items such as component, subsystem, and whole system, etc. according to the range of performance grasping. Since expenses 

and labor hours differ widely according to fineness of measurement contents, measuring period 

and measuring equipment used, three levels of measurement from Level 3 (minute measurement) 

through Level 1(standard measurement) are set according to specifications of measurement. 

Consideration is given so that one can conduct well-balanced and complete measurement by 

taking the difference of levels into account when designing a measurement plan. In addition, for 

instructions on selecting and installing measuring equipment, refer to the items for individual 

equipment in Chapter 6.

Table 4-14 Table of Contents of SHASE Energy Performance Measurement Manual for Equipment & Systems

1 Objective and general configuration

2 Basic view to evaluate energy performance

3 Energy performance grasping for air-conditioning facilities

3.1 Heat source systems

3.2 Water conveyance systems (after secondary pumps)
110

3.3 Air-conditioning facilities

3.4 Ventilation systems

4 Energy performance grasping for plumbing sanitary systems

4.1 Outline of targeted sanitary systems

4.2 Water supply systems

4.3 Hot-water supply systems

4.4 Recycling systems of used water/rainwater 

4.5 Drainage systems 

5 Regarding the construction of electrical systems suitable to energy performance 

grasping

5.1 Objective of this chapter 

5.2 Clarification of intent of energy performance grasping and its reflection to the 

construction of electrical systems 

5.3 Problems when constructing electrical systems in energy performance grasping 

5.4 Points to consider when constructing electrical systems corresponding to energy 

performance grasping 

5.5 Examples of the construction of electrical systems according to the intent of 

energy performance grasping 

5.6 Reference cases of the construction of electrical systems that energyconservation performance grasping is conducted for

6 Methods and equipment for measurement

6.1 Objective of this chapter 

6.2 Measuring equipment

6.3 Types of measuring equipment

6.3 Guidelines for installation of measuring equipment 

6.5 Measuring systems

6.6 Types of measuring systems

6.7 Handling of measurement data

6.8 How to confirm the validity of measurement data

6.9 Deployment in future

7 Conclusion

Attachment

1 Performance Management/Energy Management Sheet

2 Description on the naming codes of TSC21

3 Energy Performance Measurement Manual for Equipment & System (final text) & 

CD recoding sample data
111

Measuring the usage of electricity by electrical system may enable efficient grasping of energy 

performance. For this, giving consideration at the design stage of electrical systems is effective. 

Several examples of related recommended system design are presented in Chapter 5. 

To add to the convenience of this manual’s usage on site or for batch grasping an entire picture 

in brief at the design stage, the “Performance Management/Energy Management Sheet,” is found 

at the end of the manual as Attachment 1. It includes measurement items, levels, outline of 

evaluation items, numerical processing, graphic representations, evaluation methods and feedback methods for measuring bracketed results.

For the management and handling of individual measurement points and data, a uniform rule for 

identifying data is recommended. This manual suggests the use of \ naming codes that reflect the 

type of target equipment and data contents, etc. as specific naming conventions. Attachment 2 

provides naming rules and a list of codes.

A CD recording raw data of measurements in actual buildings on which evaluation results shown 

in this manual are based is provided as Attachment 3. It demonstrates an analysis for each item 

evaluated and the plotting process.

Sensor Deployment

The manual provides uniform standards for the planning of sensor deployment, as follows:

• Unification of target range for performance evaluation

For optimum understanding of architectural equipment systems, it is best to evaluate the validity 

of the target systems’ performance by referring to performance in buildings similar to that of the 

target building. If the range of target systems for evaluation differs between buildings, however, 

it will be impossible to make equal comparisons. For instance, with the heat source system 

shown in Figure 4-53, secondary pumps may be included in the range of heat source system. In 

this manual, the target range for each system is unified, and clearly defined through mapping. 

Figure 4-53 Unified image of target range for performance evaluation

9 AN47-B-M5-JP-NSRI-1

Standardization point 1

Standardization of the energy Standardization of the energy 

performance performance measurement measurement technique. technique.

“ The target range unified for each system“

The range of Heat source 

system 

Cooling Tower

Pcd

Chiller

Pc1

heat source systems 

(9 types)

2nd pump system

AHU system

Ventilation system
112

• Unified location for installing and naming measurement points 

Even though the same evaluation indexes are used, numerical values will have different 

significance if the measurement point concept used for calculations differs. Also, as names of 

measurement points vary between buildings, the time required for a third party to grasp 

measurement data is lengthy. This manual sets out unified standards for naming and locating 

measurement points for architectural equipment. TSC21 naming code is used to name points 

(See Figure 4-54).

Figure 4-54 Overview of measurement point names

When installing measurement points, it is crucial to prepare a plan so that performance may be 

grasped in the early stages of design and construction, for purposes of consistency, accuracy, and 

to further reduce initial costs. Minimum measurement points must be specified along with clearly 

defined objectives for conducting performance evaluation. Figure 4-55 shows the evaluation 

indexes and measurement points required to conduct the performance evaluation of a heat source 

system, including a water-cooling electric chiller. When the chiller’s unit COP is chosen as an 

evaluation index with which to evaluate the energy performance of a heat source system, it is 

fundamental to measure the temperature at the cold water inlet and outlet, the flow rate of cold 

water and the chiller’s electric power consumption. The temperature at the cooling water inlet 

should also be measured for comparison with the performance curve provided by the 

manufacturer. 

20 AN47-B-M5-JP-NSRI-1

F1

E1

E2

E3

E4

T3

No.1

No.1

“E1”

“E2”

“E3”

“E4”

“T1”

“T2”

“T3”

“F1”

T2

T1

Let’s try to use TSC / naming code 

→ WCR[1]_PPE

→ WCR[1]_CT_FAN_PPE

→ WCR[1]_Pcd_PPE

→ WCR[1]_Pc[1]_PPE

→ WCR[1]_TWc_in

→ WCR[1]_TWc_out

→ WCR[1]_TWcd_in

→ WCR[1]_GWc

Standardization of the energy Standardization of the energy 

performance performance measurement measurement technique. technique.

For example measurement in WCR[1] system
113

Figure 4-55 Correlation between evaluation indexes and 

measurement points

• Designing reasonable measurement plans 

The desired level of measurement accuracy is based on the measurement equipment/system, 

recording technique (media), recording interval and period, etc. Table 4-15 shows the 

recommended accuracy levels by item. The example provided in Table 4-16 shows levels at 

typical measurement points of a heat storage air-conditioning system. Table 4-17 shows standard 

measuring instruments. When assessing performance in a heat storage air-conditioning system, 

focus should be placed on energy performance and the status of nighttime transition of load. 

Particularly accurate measurements are essential for thermal quantity (flow rate/temperature), 

electric power consumption and start-stop information. Use of Measurement Level 1 is ruled out 

because of its inability to accurately measure integrated thermal quantity. For systems whose 

calculations/evaluations are made using integrated thermal quantity data such as heat source 

equipment COP, thermal storage efficiency and thermal storage tank utilization factors, etc., the 

measuring plan or specifications should stipulate the use of Levels 2 or 3 measurement. 

Many types of automatic recoding instruments have recently become available at low prices; 

temporary use of Level 2 should be done at the acceptance inspection. Even if a central 

monitoring system is well established, close attention should be paid since recording on magnetic 

media may leak from the original specification.

Although calorimeter usage is common when measuring thermal quantity, as flow rate and 

temperature data are required for any performance evaluation, it may become necessary to set up 

electromagnetic flow-meters when DDC is not used as a control system, or when measurement 

signals from electromagnetic meters for existing control systems cannot be removed. Calculating 

thermal quantity (both instantaneous and integrated values) and processing primary data are done 

more efficiently with regard to cost and data confirmation using functions on a measuring PC or 

central monitoring system. 

In some cases, 1-minute measurement/recording intervals may be necessary for an operational 

acceptance inspection at the component level. When conducting performance evaluations, the 

21 AN47-B-M5-JP-NSRI-1

○:Must Need for calculation △:Best Need for evaluation

COP (WCR)

WTF (Pc1)

WTF (Pcd)

Capacity (WCR)

Capacity (CT)

System COP 

Load rate (Heat Source) 

WCR_TWc_out

WCR_TWc_inWCR_GWcWCR_PPEWCR_Pc1_PPEWCR_Pc1_PPEWCR_Pcd_PPE

WCR_CT_PPE

WCR_TWcd_out

WCR_TWcd_in

WCR_GWc

Measurement 

point

Evaluation 

indexes

Measurement points for calculate for 

evaluation indexes

COP (WCR)

WCR_TWc_outWCR_TWc_in

WCR_GWcWCR_PPE

WCR_TWcd_in
114

large amount of data may pose an obstacle to data operation/analysis. Therefore, it is desirable to 

make the recording intervals variable.

For basic performance evaluation the recording interval should be set at 15 minutes. If set at less 

than 15 minutes at Levels 2 or 3, a mean value should be recorded automatically every 15 

minutes. If this is impossible, automatic recordings should be made in the maximum assignable 

recoding interval; later the mean value for 15 minutes should be calculated at the analysis stage. 

In addition, if thermal quantities are calculated in the analysis stage, the recording interval for 

temperature and flow rate should be pre-set to the same length as the thermal quantity 

measurement interval. .

Table 4-15 Measurement levels 

Measuring equipment Recording method Recording interval Measurement 

interval

Level 3

(detail,

Expensive)

Electronic detector,

Data logger,

Central Monitoring 

System

Automatic recording by 

electromagnetic media

Instantaneous value –

15 min

Integrated value –

1 h

Instantaneous 

value – less than 

15 min

Integrated value 

–

1 h

Level 2

(moderate)

Temporary electronic 

detector, 

Data logger

Automatic recording by 

log sheets or

Electromagnetic media

Ditto Ditto

Level 1

(simple,

Inexpensive)

Measuring instruments 

fixed on site

Manual recording by 

visual inspection 1 h 1 h
115

Table 4-16 Target Measurement Points and Measurement Level

Measurement items

Flow 

rate

Integrate

d quantity 

of water

Pressure 

of 

cold/hot 

water

Water 

temperature

Current 

value

Integral

power 

consumptio

n

Electric 

power

Startstop 

state

Heat pump 3,2 2,1 3,2 2,1 3,2 3,2

Chiller 3,2 2,1 3,2 2,1 3,2 3,2

Cooling tower 2,1 2,1 2,1 2,1 3,2 3,2

Condensing 

Water pump

2,1 2,1 2,1 2,1 3,2 3,2

Primary pump 3,2 2,1 3,2 2,1 3,2 3,2

Secondary pump 3,2 2,1 3,2 2,1 3,2

Thermal storage 2,1 3,2 2,1

Power receiver/

Transformer

3,2 3,2

Table 4-17 Standard Measuring Instruments

Measurement items Level 3, 2 Level 1

Flow rate Ultrasonic flowmeter, 

Electromagnetic flowmeter

Instant flowmeter 

(area or pitot-tube type)

Integrated water quantity Integrating flowmeter with pulse transmitter Water meter

Water pressure Pressure transmitter Bourdon tube pressure gauge

Water temperature Platinum resistance temperature detector,

Thermocouple

Glass stick thermometer

Round-shape thermometer

Current value Alternating current transducer Alternating current meter

Integral power Consumption Voltmeter with transmitting equipment Voltmeter

Electric power Electric power transducer Electric power meter

Start-stop state No-voltage a junction, Constant voltage 

Generator

Visual inspection
116

Performance Metrics

• Unification of Evaluation Index for Performance Evaluation

Although various evaluation indexes are used to evaluate the performance of architectural 

equipment, the fact is that different definitional equations are used for similarly named 

evaluation indexes. This manual specifies major evaluation indexes and their respective 

architectural equipment, clarifies their definitional equations, and categorizes them for use as 

uniform standards. Figure 4-56 shows the COP in an electric water-cooled chiller, and Figure 

4-57 shows the COP of a heat source system. As understanding the operating condition is a 

prerequisite to doing an energy performance evaluation, the manual defines the respective 

evaluation indexes (Figure 4-58). 

Figure 4-56 Chiller Unit’s COP

Figure 4-57 Evaluation Image of Heat Source System COP

11 AN47-B-M5-JP-NSRI-1

Energy Performance of

Equipment COP of Chiller

COP = Qr ÷ Er

T1

T2 F1

Qr is calculated by

T1,T2:cold water temp 

F1:cold water flow rate

E1

Er is calculated by

E1:electric energy

T3

Reference data

T3:condensing water temp

Standardization of the energy Standardization of the energy 

performance performance measurement measurement technique. technique.

12 AN47-B-M5-JP-NSRI-1

System COP of Heat 

source system

SCOP = Qs ÷ Es

Energy Performance of

System Type 1

T1

T2 F1

Qs is calculated by

T1,T2:cold water temp 

F1:cold water flow rate

T2 F1

T1

Es is calculated by

E1~E4:electric energy

E1

E1

E2 E2

E3 E3

E4

E4

T3

Reference data

T3:condensing water temp

T3

Standardization of the energy Standardization of the energy 

performance performance measurement measurement technique. technique.

Sum up data from each 

chiller
117

Figure 4-58 Configuration of Evaluation Indexes

• Criteria for Performance Evaluation

As a prerequisite to an evaluation based on performance metrics, measurement data must be 

validated. Although information from a sensor is generally regarded as correct, attention is 

required as, sensors can be defective. The followings points should be considered:

a) Basic Check List

Validate absolute values by defining an absolute range.

Check characteristic values by cross-checking characteristic formula of equipment. 

b) Validity Check List

To check if equipment is in a valid status ( e.g., to neglect temperature and flow rate while the 

equipment is not in operation, etc.)

c) Cross-checking

To check errors, etc. in data correlation. (e.g., during cooling operation of a heat pump, water 

temperature at the outlet is 5 °C lower than that at the inlet, etc.)

d) Trend Checking

To check the status of temporal variations (trend). Express multiple related data in the same

graph to make efficient and effective checking possible.

These checks should be done in the early stages of measuring works. When invalid data are 

found: check power source, recalibrate measuring equipment and check measuring equipment 

wiring immediately. Although the calibration of permanent measuring equipment is done at the 

construction stage, prior display confirmation is recommended to avoid future problems. 

[Example 1]

10 AN47-B-M5-JP-NSRI-1

Evaluation Indexes

Standardization point 2

“ Evaluation indexes related to the 

energy performance “

Energy Performance 

of Equipment

Energy Performance 

of System

Standardization of the energy Standardization of the energy 

performance performance measurement measurement technique. technique.

Energy Performance

Operating Condition

Energy Performance

Operating Condition

influence

influence

Select Indexes in accordance 

with the purpose
118

Although the recording accuracy of temperature is within ±0.5 °C, the temperature difference has 

a maximum error range of ±1.0 °C. If the temperature range difference of cold/hot water is 

∆t=5 °C in the design, the difference of temperature/thermal quantity can reach ±20 %. Even if 

the recording accuracy of temperatures of outward and returning water is satisfactory, necessary 

corrections should be made as required during data analysis, since any variance can have a 

profound impact on the difference in temperature/thermal quantity. 

[Example 2]

An electromagnetic flow-meter combines detection and computing features; which are precalibrated by the manufacturer. There are multiple flow-meters of the same size; altering the 

detection or computing features after delivery can compromise accuracy. As well as historical 

management, special attention is required since defects may not be found out unless data is 

carefully checked during actual operations. 

Setting Data Recording Intervals 

If the recording interval for data measurement (primary data) is less than 15 minutes, the mean 

value for 15 minutes should still be calculated. Experience shows that a plot based on 15-minute 

intervals is best for evaluating daily trends. Shorter intervals for analysis purposes waste time 

when producing graphs etc. 

Calculating Integrated Thermal Quantity

When measuring temperature and instantaneous flow rate without a calorimeter, integrated 

thermal quantities should be calculated using a personal computer. If the data recording interval 

is less than 15 minutes, an instantaneous thermal quantity should be calculated based on the 

primary data, then integrated, before performing the averaging described in Article 2 of the 

SHASE manual). 

Graphing

It is important to improve the visualization of measurement data by graphing. The manual offers 

information on graphs and how best to interpret them for grasping the performance of respective 

equipment and systems.

Building case study 3: Usage with an existing building – Shinkawa Project

This example, in which the sensor is installed into an existing building, confirms the relevance of 

the SHASE manual. 

• Outline of Shinkawa building

The Shinkawa office building has a total area of 5 940 m2

. Figure 4-59 outlines the building and 

its equipment. The HVAC system was recommissioned to an air-source heat pump and ice 

storage system, and planned using the SHASE manual. The HVAC system is shown in Figure 

4-60.
119

Figure 4-59 Outline of Shinkawa building

Figure 4-60 HVAC System diagram measurement points

AN47-B-M5-JP-TONETS-1 5

Shinkawa Shinkawa Building Outline Building Outline

Description of HVAC System

1)Air-Sorce Brine Heat Pump(60kW×2)

2)Ice Thermal Storage Tank

64m3×2,IPF=8%,also used heating water 

thermal storage tank in winter.

3)AHU/CAV on each floor and FCU for 

perimeter zone.

4)VWV sysytem W/2-way controlvalves.

5)BEMS:DDC control used LAN of fiber and 

centralized supervisory control by PC.

Size:Total floor area 5,940m2

Year of construction:1988 Winter

Type of building:Office

(This building has lent all floors.)

Location:Japan,Tokyo Met.

AN47-B-M5-JP-TONETS-1 6

HVAC System diagram System diagram 

Air-Source Brine Heat Pump

Ice Thermal 

Storage Tank

Change over 

valve for 

cooling/heating

Primary pumps

Secondary pumps

Air Handling Unit

Fan Coil Unit

AN47-B-M5-JP-TONETS-1 7

Kind of measurement data Kind of measurement data

■The number of measurement

The number of measurement points is relatively large

compared to this size building!

These measurement points were selected to evaluate

the energy performance of this building and the HVAC 

system.
120

Figure 4-61 Number of measurement points

• Type of measurement data

The measurement data shown in Figure 4-61 reveals a number of measurement points that is 

relatively large when compared to the building size. These measurement points were selected to 

evaluate energy performance and HVAC system according to SHASE manual standards.

All of these measurement points are measured by BEMS. The data is used to evaluate everyday 

operational management and performance.

• Sensor deployment

Sensor deployment and measurement points are shown in Figure 4-62. A comparison between 

the building’s measurement points (red areas) and those of the SHASE manual (green areas) is 

illustrated in Figure 4-63. Most measurement points correspond to points in the SHASE manual. 

In this building, sensor deployment can result in the most detailed performance check (Level 3) 

specified in the SHASE manual. It is too expensive to add the sensor which later ran short to 

evaluate the fault cause and attain operational rationalization. Even if it is used with an 

inexpensive temporary measuring device, the problem with sensor accuracy occurs. The 

measurement plan should be done at the construction stage. It is important to deploy sensors for 

evaluation and verification that may be needed in future operation phases. This is an important 

aspect of Commissioning.

The division of levels discussed in the SHASE manual is:

Level 1: To grasp overall building characteristics.

Level 2: To grasp system characteristics.

Level 3: To grasp equipment characteristics
121

Figure 4-62 Sensor deployment and measurement points

Relationship between evaluated performance and measurement points

Figure 4-63 shows the relationship between evaluation and measurement points. 

This measurement point is sufficient to evaluate the energy performance of the HVAC system. 

The practical methods for data measurement and evaluation practiced as part of the three stages 

in the bottom-up-approach are explained in Figure 4-63. Step 1 is to check operational status to 

see if any unnecessary operation is being performed. Its settings are checked against the 

operation plan, or checked to see if preset values are valid. In Step 2, effective performance of 

equipment is checked to see whether it conforms to the measurement data for each component. 

Step 3 is an energy evaluation to ascertain the energy efficiency and load shifting ratio of the ice 

thermal storage system. It is checked for HVAC system performance and whole building energy 

consumption.

In this evaluation procedure, a component is evaluated first, then a subsystem, and, finally a 

whole system. 

AN47-B-M5-JP-TONETS-1 9

Sensor Deployment Sensor Deployment/measurement /measurement

T H

T H

T H

T T

T T

F

F

T

T

T

T

F

kW

kW

kW kW

S S

kW kW

S

S

S S

T

T

T

T

T

T

T

T

F

P

P

%

%

The measurement item in which The measurement item in which 

detailed evaluation is possible detailed evaluation is possible 

is deployed. is deployed.

SHASE Manual

Shinkawa point
122

Figure 4-63 Relationship between evaluated performance and measurement points

Evaluation of energy performance that uses measurement data

a.Energy performance of Air source heat pump

Evaluation of COP for air source heat pump is shown in Figure 4-64. The horizontal axis shows 

the outside temperature; the vertical axis shows COP. It can evaluate if the operating 

performance is correct by comparing driven COP with the reference value according to outside 

temperature.

b.Energy performance of secondary pump

Improved control of the number of secondary pumps is shown in Figure 4-65. The horizontal 

axis shows the water flow rate and the vertical axis shows Pump power consumption. The top 

figure shows that the data indicates energy is being wasted because the number of operating 

pumps is too high. The bottom figure shows improved operating conditions.

AN47-B-M5-JP-TONETS-1 10 Check compatibility of the present state and the setup schedule Check compatibility of the present state and the setting value Calculate of COP_compornent Check thermal storage condition Calculate of present thermal storage capacity and check of setting value Check about operating condition Evaluate of operating efficiency of HP Check about Operating Inverter control Number Control Calculate of thermal load Evaluate of thermal load vs OA condition Amount of consumption of Energy Verification of COP vs OA condition

Evaluate of thermal storage system efficiency

How to use measurement data How to use measurement data
123

Figure 4-64 Evaluation screen of COP of air source heat pump

Figure 4-65 Improved control of the number of secondary pumps

Conclusions

The Shinkawa project uses an evaluation technique from the bottom-up approach described in 

the manual. Advance troubleshooting consists of determining the performance to be evaluated 

prior to installing the sensors. By performing this evaluation periodically, optimal operation is 

attained and maintained. This confirms the effectiveness of the SHASE manual.

AN47-B-M5-JP-TONETS-1 14

Performance verification Performance verification

inlet air temperature [°C]

HP COP_compornent

brine outlet temperature

inlet air temperature drops

The specification value at design.

The performance checked by shop inspection.

Scatter chart of COP vs OA temp. efficiency will improve

We can be checked about detailed operating performance

at different conditions.

AN47-B-M5-JP-TONETS-1 15

Optimization Optimization:FPT

Water flow rate [L/min]

Secondary pumps ; Operating Number Control & Inverter 

Water flow rate [L/min]

Pump power consumption [kW]

2nd pump operation

1st pump operation

Before

The operating number was 

controlled by the output of 

inverter. This control is not good.

Solution

We optimized control parameter 

of operating number .

1st pump operation

2nd pump operation After

The optimized operational status 

has been checked again.

The amount of used electricity of 

secondary pump was reduced 10%.

This is useless operation.

Good control!
124

4.4.5 DABOTM a BEMS-Assisted Commissioning Tool, Canada (Bottom-Up 

Approach Tool) 

Since 1998, the CANMET Energy Building in Varennes (Canada) has been used to test and 

demonstrate various tools developed in the context of the CANMET Intelligent Building 

Operating Technologies R&D plan [40], of which DABO is the central component. In this 

context and for the last eight years an ongoing commissioning process has been conducted. 

DABO, a software package that uses a hybrid technology composed of conventional and 

artificial intelligence techniques to ensure optimum operation of building systems has been used 

actively in the project delivery system for the continuous monitoring of all HVAC equipment 

and meters (e.g., terminal unit, air handling unit, plant equipment and energy meters), the 

analysis of the incoming information, the detection and diagnosis of major HVAC component 

faults, non optimum set points and sequences of operation and the monitoring of implemented 

measures .

The benefits obtained through the use of the DABOTM software are listed here:

Energy savings Improved performance of mechanical systems

Reduced greenhouse gas emissions Rapid and automatic fault detection

Improved occupant comfort Energy performance monitoring

Capabilities for troubleshooting of malfunctions Reduced operating costs

General approach:

DABO includes two parts: the commissioning module and a report generator.

The commissioning module, designed to assist and perform some functions described in the 

ongoing commissioning process section is a module of DABO, which serves as the interface 

between the end-user (e.g., building operator, commissioning agent, and energy manager) and 

the control system (BEMS) [41]. As shown in Figure 4-67, the tool continuously monitors the 

building control data and stores it in a structured database to be used on-line or upon request. 

Data resulting from standardized test procedures invoked manually or automatically are also 

stored in the database. The database functions as a server for reasoning algorithms that perform 

intelligent analyses of the monitored data, performs additional automated tests of components 

and systems, identifies faults and diagnoses them, and evaluates potential improvements in 

energy efficiency. The tool produces reports adapted to the different partners involved in the 

ongoing commissioning process (building operators, service technicians, energy managers, 

commissioning agents, HVAC&R engineers).
125

Deployment of sensors:

DABO uses various data values coming from the building energy management system (BEMS). 

This normally includes all control inputs, outputs, setpoints, schedules, alarms and timers. The 

performance of DABO depends on the quality and variety of the data supplied to its artificial 

intelligence engine. 

Performance Metric:

Standardized test procedures are performed at three levels using a bottom-up approach. At the 

first level, an hourly component analysis of individual HVAC devices and equipment is 

performed automatically using a combination of control loop indices and expert rules to verify 

their proper operation.

The second level of testing consists of an integrated system analysis to verify the operation and 

energy performance of the overall HVAC system over a longer period of time (e.g., hours, days, 

weeks or months). At this level a set of component performance indices and expert rules is also 

used in the analysis.

The third level performs basic energy performance and operation control quality reports that 

provide the information required to evaluate potential energy measures on specific devices. 

Specific applications of the fault detection and diagnostic (FDD) methods implemented in 

DABO are described further in Section C of the IEA Annex 34 final report [42].

Figure 4-66 Ongoing commissioning tool for HVAC systems embedded in DABO
126

DABOTM produces four types of report to assist the user in assessing the performance of the 

building, its systems and devices. These reports primarily display the data in a graphical format 

and have been designed to allow the most efficient review and use of the information [43]. The 

reports are summarized in the following list and then presented in further detail.

• Fault Detection and Diagnosis Reports

• Points Reporter

• Commissioning Reports (COMM Reports)

• Fault Management Reports

Fault Detection and Diagnosis Reports (FDD Reports)

These reports display data generated by the various fault detection functions (these are defined as 

«services» in the software). Data are shown in a color-coded table. Figure 4-67 shows the 

DABOTM FDD Report (no. 1) with the list of symptoms detected for a single cell (no. 2), and for 

a single symptom with the related individual graph (no. 3).

The FDD report displays the number of detected symptoms of a failure by date and hour-by-hour 

for all of a building’s air handling, air terminal devices and control zones. It shows also the 

operation mode and provides access to detailed reports (available by clicking on the + button). 
127

Figure 4-67 Example of Fault Detection and Diagnosis Reports
128
129

Point Reporter (Graph and Data) 

These reports display data from building points selected by the user. Data can be shown as a line 

chart or a table. These reports can be requested from the FDD report, from the commissioning 

report, or from the "Tools" menu. 

The Points Reporter displays raw data and the results of simple computations in graphic or table 

format for the relevant data collected or computed during the 4 hours before and 4 hours after a 

symptom is detected. The data displayed are sorted by date and time. Using the “Save Data” 

option, raw data can be extracted to a separate file for use in other applications (for example: 

spreadsheet software). Figure 4-68 illustrates a point reporter report.

Figure 4-68. A Point Reporter Report

Commissioning Reports (COMM Reports)

These reports display data generated by the various commissioning functions or «services» or 

data from the building. Data are also shown in a color-coded table. These reports can be 

customized by the user for additional statistical analysis. 

Commissioning Reports display performance values of devices on an hourly basis or 

summarized for an entire day, week, or month. Users can adapt the report by filtering devices by 

system device, system name, floor, orientation, family, and type. This allows users to observe 

time-based device performance variations. 
130

This type of report is used, for example, to manually detect non-optimum set points, to identify 

devices with insufficient capacity, to identify systems with non-optimum sequences, to report 

equipment performance levels, or to report comfort performance. COMM Reports are available 

for all DABOTM devices. 

Figure 4-69 displays performance indices for the supply temperature (PI_T) and supply 

temperature set point (PI_TSP) as well as the average (AVG_CZ_PI_C) and maximum 

(MAX_CZ_PI_C) zone connected cooling loads for two air-handling units. This report is used to 

detect if a temperature reset control strategy, which adjusts the supply air temperature set point 

as a function of the actual cooling load, performs well. 

Note that the performance index for the supply temperature (PI_T) of the two air-handling units 

varies over the course of the day, indicating that a temperature reset strategy is being used. 

For the M2 Laboratories unit, PI_T displays the same variation and has approximately the same 

magnitude as the average zone cooling load (AVG_CZ_PI_C). This is the expected behavior 

when the reset strategy is functioning correctly. 

On M3, the strategy is probably not aggressive enough since while the cooling load is quite low 

(≈7 %), the supply air temperature index is near the mid range (≈38 %). This indicates that the 

supply air temperature may be too low for the given load and could generate a reheat load.

Figure 4-69. Typical Commissioning Report for 3 Air Handling Units

Figure 4-70 shows a daily VAV box performance report. Performance indices identified by the 

letter ‘a’ consist of the VAV box basic monitored control points (supply air flow ≈ 43 l/s, supply 

airflow set point ≈ 45 l/s, ATD supply air temperature ≈ 18 °C and heating coil valve command 

≈ 0 %). Those identified by the letter ‘b’ are performance indices for the air terminal device 

airflow ≈ 30 %, airflow set point ≈ - 1 %, cooling load ≈ 16 %, damper ≈ 43.8 % and heating 

load = 0 % , while those identified by the letter ‘c’ display information from the zone and AHU 

(AHU supply air temperature ≈ 20 °C, supply fan command ‘1= on’ and zone air temperature = 

23 °C). 

Data are shown in grey to indicate that the AHU that supplies air to the zone is not operating 

during these hours. The data indicate that the VAV-reheat box is supplying 30 % of its design 

airflow, the damper position is approximately 43 % open, the reheat coil valve is closed, the 

cooling load is close to 17 %, and the heating load is at 0 %. The data also indicate the ATD 
131

supply air temperature is slightly higher than the AHU supply air temperature. This could be due 

to a small heating valve leak, or could indicate the need to recalibrate one or both sensors. It 

could also be due to normal heat gain as the air travels through the air distribution system.

Figure 4-70: Air Terminal Devices – typical Commissioning Report

Figure 4-71 shows the heating network performance report for one day. The performance indices 

are the average connected heating load (AVG_PI_H ≈ 22 %), maximum heating load 

(MAX_PI_H = 100 %), supply water pressure N_SIP ≈ 157 pa, supply water pressure set point 

N_SISP ≈ 140 pa, supply water temperature N_SIT ≈ 76 °C, supply water temperature set point 

N_SITSP ≈ 53 °C, and the temperature control valve N_V = 100 % bypass. 

In this case, the heating hydronic network operates at 6 % of its full capacity (PI_DEV ≈ 6 %), 

temperature and pressure indices are over their design normal range of operation (PI_T and PI_P 

are over 100 %), and the temperature and pressure are significantly higher than their respective 

set points, making temperature and pressure set point indices very positive (PI_TSP and PI_PSP 

= 12 and 48 %). 

Recall that the set point indices are normally approximately equal to zero when the temperature 

or pressure is maintained at its set point value. Since the valve command is 100 % in bypass and 

there is a heating load on the circuit, this report indicates that the valve and actuator should be 

inspected to ensure there is no leakage and/or problem with the actuator linkage.
132

Figure 4-71. NetNode Commissioning Report 

Fault Management Report (FMR) 

This type of report displays, by device type, confirmation of a fault repair. 

The Fault Management Report (FMR) is used by the building operator/manager to manage 

information concerning confirmed faults in the building’s operation detected by the DABO FDD 

module and subsequent operational corrective actions needed or done. In this report the user can 

review actual and historical data for a specific device, for all devices of the same type, or for all 

devices of a building. 

Function of the information required: the FMR report can be displayed and printed in a summary 

or detailed format. 

The following information is reviewed in FMR reports: 

Device type, System name, Fault number, Fault description, Fault start time, Confirmation date, 

Fault priority, Primary benefit, Secondary benefit, Impact on energy savings, Cost, Responsible 

operator, Operator's comments, Confirmation of repaired fault and Repair technician's comments

Figure 4-72 shows for all air handling units in a building a summary display of all faults detected 

that need to be repaired. Figure 4-73 is an example of a detailed ‘repaired faults’ report. This 

report is useful to produce a status report of actions done during a specific period. 
133

Figure 4-72 Typical Fault Management Report for Air Handling Unit in DABOTM
134

Figure 4-73 Typical repaired failures report for an air handling unit in DABOTM

Building case study: Ongoing Commissioning Project

The demonstration building is the CANMET Energy Building in Varennes, Québec, Canada. 

Built in 1992, the single-floor, 3600 m2 building includes office space for 90 people, two 

laboratories, two industrial pilot plants, conference rooms and a cafeteria.

The building, designed to be energy efficient, incorporates low energy technologies such as a 

passive solar preheating device, ice bank storage, photovoltaic cells, as well as a central gas 

heating plant and a central electric chilled water plant. Each area of the building is served by a 

specific air system designed for its occupation. The HVAC systems are centrally controlled by a 

BEMS system. (Table 4-18)
135
136

Table 4-18 CTEC-Varennes HVAC Systems

HVAC systems Capacity Location

Heating

Fire tube boilers (2) 470 kW each Building

1 primary and 5 secondary hydronic circuits, 7 pumps, constant volume

Cooling

1 air cooled chiller 406 kW Building

2 ice bank tanks 1145 kW-h Building

1 hydronic circuit, 2 pumps, constant volume

Air Handling system

M1 (CAV, HEA) 2, 735 L/s Pilot plant1

M2 (VAV, 100 % fresh air, HEA,CO) 5,815 L/s Laboratories

M3 (VAV, HEA, CO) 5,500 L/s Office phase 1

M4 (CAV, HEA) 1,265 L/s Storage phase 1

M5 (CAV ) 160 L/s Mechanical room

M6 (CAV) 1,030 L/s Boiler room

M30 (VAV, HEA, CO) 1,660 L/s Office phase 2

M31 (CAV, HEA) 5,200 L/s Pilot plant 2

M32(CAV) 2,000 L/s Mechanical room 2
137

The ongoing commissioning process started in 1999 and continued until 2009. It aimed at 

resolving operating problems, improving comfort, optimizing energy use and recommending 

retrofits where necessary. Delivery of the ongoing commissioning project system included a 

series of tasks performed in four steps: planning, investigation, implementation and hand off (see 

Table 4-19). Tasks surveyed with DABO are shown in italics. As it is an ongoing commissioning 

process, the investigation and implementation have been gradually and continuously performed 

over the 2000 to 2006 period. Since 2006, DABO is still used on a regular basis to ensure the 

persistence of savings and detect new deficiencies.

Results for the investigation and implementation period 2000 to 2006 were presented in ‘Four 

Years of Ongoing Commissioning in CTEC-Varennes Building with a BEMS Assisted Cx 

Tool’[44] (Choinière 2004) and are summarized in the following section. 

Results for the hand off and persistence period include new deficiencies that occurred during 

normal operation, as well as deficiencies that were not detected during commissioning of the 

various optimization projects (2006 to 2009). 
138

Table 4-19 Ongoing Commissioning Project Delivery System at CETC-V

PLANNING

• Choose team

• Define project objectives, scope and deliverables

• Review building documentation and energy bills

• Develop commissioning plan

• Initiate cooperation with building operation team

INVESTIGATION (continuous over 6 years, 2000-06)

• Assessment

o Site, design and occupant needs assessment

• Installation of DABO

• Develop and carry out diagnostic tests and system monitoring

• Analyze monitoring results

• Develop list of deficiencies and improvements

o Include capital improvement opportunities

o Include training recommendations

• Select the most cost effective opportunities

IMPLEMENTATION (continuous over 6 years, 2000-06)

• Implement improvements identified in investigation phase

• Retest and re-monitor to confirm the results

• Adjust, if necessary, improvements carried out during investigation phase

• Review energy consumption reduction estimates

• Building Operator training and occupant information

HAND OFF- PERSISTENCE (continuous since 2006)

• Prepare and present final report

o As-Built Re-Commissioning work

o New sequence of operations manual

o Testing and balancing (TAB) report (air, water)

o Energy baseline

o Check-up of energy bills (3 months)

o Proposal for EE measures with longer payback

• Implement an ongoing commissioning process and energy management 

plan

o Ensure that the use of DABO is well understood by operators 

so as to maintain re-commissioning benefits
139

Results for Investigation and Implementation Step (1999 to 2006)

• Implement a continuous energy management plan 1998 (in house staff)

• Reset operation schedules (AHU, hydronic circuits) (1999)

• Optimize controls and sequence of operation

o Function of actual needs

o Peak load management (chiller, humidification) (2003)

o Avoid simultaneous heating and cooling

• Reset set points (AHU, hydronic circuits) (2000 to 2005)

o Minimum fresh air

o Supply pressure and temperature

o Night set back

• Fixed minor deficiencies

o Sensor calibration

o Low heating capacity in some rooms

o Replacement of leaking valves

• Investment in measures with short payback

o Addition of DDC controls (chiller, boiler 2001)

o Link AHU M2 to solar wall (March 2003)

o VSD on 3 fans (March 2002)

• Energy efficiency project (2005 to 2006) $250,000

o Conversion of pneumatic room controls to DDC 

o Off peak electric boiler (200kW)

o AHU M2 100 % fresh air convert to a recirculation system

o Connection on AHU M4 fresh air to solar wall

o VSD on 6 pumps and fan motors 

• Energy reduction

o Figure 4-74 shows the impact on the energy consumption of the ongoing

commissioning project implemented at the CETC-V since 1998. During this 

period 1999 to 2006, measures implemented have resulted in a 35 % reduction in 

electricity usage and 45 % reduction in natural gas consumption. 
140

Results for the Hand-off and Persistence Phases (2006 to 2008)

Table 4-20 presents results, in terms of the number of faults detected but not yet repaired, 

summarized and classified as a function of the type of fault, the level of priority to repair the 

fault and the impact on the energy used. Table 4-21 presents results, in terms of number of faults 

that were detected and repaired, summarized and classified as a function of the type of fault, the 

level of priority to repair the fault and the impact on the energy used.

Types of faults include:

• Sensor fault: This includes complete failure and incorrect reading of sensors 

• Defective output: This includes stuck, incorrect feedback position, and defective leakage 

of valve, damper, fan, humidifier, coil, controller, fan and pump motor, etc.

• Control program fault: logic (set point and output, tuning, signal instability), 

inappropriate sequence of operation, incorrect minimum position, non optimal control set 

point and schedule, control point set in manual

• Installation problem: inappropriate control device installation or connection to control 

system, undersized or oversized components, 

Priority to be repaired includes:

• None: These faults have little impact on energy consumption or occupant comfort and are 

difficult to repair without significant modification to the system design.

• Low: These faults usually have no impact on comfort and energy. They only affect the 

information provided to a user in the diagnosis and analysis of control system equipment.

• Moderate: These faults have an impact on energy consumption or the behavior of all 

system operations. The can also have an influence on the expected life duration of 

equipment problems.

• High: These faults usually have considerable impact on occupant comfort or a high 

impact on energy consumption

Impact on the energy consumed: This criteria indicates if the fault causes an increase in energy 

use. 
141

Table 4-20 Number of faults detected but not repaired during 2006 to 2009

Number of faults detected but not repaired during 2006 to 2009

Type of fault Priority Energy

None Low Moderate High

Sensor 8 2 1

Defective output 1 1

Control logic 2 9 2 2

Installation 5 2 1 2

Total 7 20 5 6

During 2006 to 2009, a total of 126 faults occurred in the building energy system; 94 have been 

repaired while 32 are still in effect. Those repaired had no impact on energy consumption or 

comfort; no major changes to system design were required. In terms of categories, 35 relate to 

sensors, 37 to output, 39 to logic fault while 15 stem from problems related to installation or 

design. A total of 54 faults have or had an impact on energy consumption. A total of 116 faults 

were automatically detected by the various DABOTM Fault detection and Diagnosis services 

while 10 were detected manually or analyzed using DABOTM Commissioning Reports.

Table 4-21. Number of Faults Detected and Repaired during 2006 to 2009

Number of faults detected and repaired during 2006 to 2009

Type of fault Priority Energy

None Low Moderate High

Sensor 20 4 1 12

Defective output 31 3 2 20

Control logic 20 3 3 12

Installation 3 2 1 1 5

Total 3 73 11 7 49

Energy reduction

Figure 4-74 shows the impact on energy consumption of the ongoing commissioning project 

implemented at the CETC-V since 1998.

During this period, measures implemented resulted in a 43 % reduction in electricity usage and 

an 81 % reduction in natural gas consumption. In 2008 to 2009, cost savings were $91,861 CDN, 

representing 49 % of the building’s energy bills. Cumulative savings since 1998 are $592,281

CDN while the whole-building energy use dropped from 2248 MJ/m2 to 1016 MJ/m2. 
142

Figure 4-74 . Ongoing Commissioning Impact on CETC-V Building Energy Consumption

Conclusions 

An ongoing commissioning process ensures that buildings reach and operate at their optimized 

energy cost and performance levels, while ensuring comfortable conditions for occupants. The 

CANMET Energy project generated a 43 % reduction in the use of electricity and a 81 %

reduction in natural gas consumption during 1998 to 2008. 

DABO, a BEMS-assisted commissioning tool, monitored the enormous amounts of data 

produced and provided an extensive analysis of incoming data. 

The use of a BEMS-assisted commissioning tool has helped to circumvent commissioning 

barriers by automating parts of the process, which has reduced the costs for commissioning. 

Developing a detailed, systematic and automated approach has improved the quality assurance 

process and overall performance of the building. Furthermore, automating this essentially 

manual process has allowed its application on an ongoing basis, generating benefits over the 

entire life of the CETC-Varennes building.

The optimization process and the ‘DABOTM’ tool are being demonstrated in more than 10 

projects. Demonstration projects include some of the first Canadian LEED buildings and the 

participation of major Canadian facility management firms and commissioning providers.

DABOTM is a tool in constant evolution. Current research efforts are concerned with the 

development of an energy predictor, new fault detection and diagnosis modules for heating and 

cooling networks, and new analyses of BEMS data to enhance the commissioning process. 

Reduction in Energy consumption (%)

-90%

-80%

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

référence2

1998-991999-002000-012001-022002-032003-042004-052005-062006-072007-082008-09

Electricity-energy Natural Gas- Energy

10 %

0 %

-10 %

-20 %

-30 %

-40 %

-50 %

-60 %

-70 %

-80 %

-90 %
143

4.4.6 Optimization of Automation Functions with Dynamic Simulation as the 

Energy Management System (OASE)

Office buildings and commercial buildings, particularly those subject to stringent requirements 

regarding comfort and usage, comprise highly complex systems. They are designed using an 

integrated approach and are marketed as "innovative" or "intelligent". It is crucial that these 

buildings be fine-tuned and optimized after their initial startup. Investigations show that, during 

operation, many buildings do not meet their target energy and thermal comfort performance; this 

could be due to the fact that the time allocated for commissioning is often too short. 

Additionally, ongoing optimization is rare. More specifically, energy-efficiency-based 

adjustments to accommodate changes in building systems’ equipment or building occupancy are 

often not made and the influence of parallel system operation is often ignored. 

The right course has to be set out during design; the optimization of building systems cannot be 

left until after operation has begun. The design goal is sustainability throughout the whole 

lifecycle of buildings and their systems. Requirements for energy efficiency and occupant 

comfort must be considered and implementable at planning, system design and energy 

management stages. Building optimization must be made possible during operation.

What methods and tools are needed? Answers were found in the research project "OASE" 

(Optimization of Automation Functions with Dynamic Simulation as the Energy Management 

System), funded by the German Federal Ministry of Economics and Technology (BMWi). With 

the methodology of Operation Diagnostics developed in this R&D project, recorded building 

automation data is used to identify weak points in terms of energy performance, as well as any 

operational shortcomings, and to derive optimization potential from them. The OASE 

methodology has since been tested in a variety of demonstration buildings. It supplements 

current research activities with the goal of advancing the introduction of holistic building energy 

concepts in practice. This includes the development of "cost-effective energy management for 

residential and office buildings" (KENWO – “Kostengünstiges Energiemanagement für Wohnund Bürogebäude“) and development of an "Energy Navigator," which enables partially 

automated enhanced building operations by means of interfaces with the building automation 

system.

OASE – Methods and Procedures

It is worthwhile to evaluate office buildings and commercial buildings over several years, 

examining and assessing their energy consumption and occupant comfort based on measurement. 

With this strategy, laborious analysis methods and costly measurement equipment can be 

avoided. The Operation Diagnostics process developed in the research project OASE provides a 

standardized methodology and various tools that use and analyze the available energy-related 

data from the building automation systems. This makes it possible to identify shortcomings and

malfunctions in building operation and to derive optimization measures. The results are 

improved building performance and effective building management.
144

Measurement Data Visualization and Operation Diagnostics

In OASE, Operation Diagnostics, the visualization of measurement data aids in the detection of 

symptoms of malfunction and identifies optimization opportunities, even in complex systems. 

Measured data points from building automation or energy management systems are graphically 

displayed. In so-called “carpet plots”, color-coded measurement values are shown in bars (one 

bar for each day), which are arranged sequentially over the measurement period (see Figure 

4-78). Operating states that recur in daily and weekly cycles become identifiable as patterns. In 

scatter plots, the same data is plotted so as to display the interdependencies between various 

control parameters. Thus, structures and patterns, or deviations, can be identified quickly, and 

conclusions made regarding the operation of the building’s mechanical systems. With Operation 

Diagnostics, the systems’ functionality and performance can be examined and assessed. The goal 

is to optimize control parameters and detect flawed, ineffective or inefficient system and building 

operation.

Operation Prognostics

Operation Prognostics is a methodology to incorporate dynamic building operation into design. 

Operating functions and control sequences are developed in the early design phases, optimized 

for the building and the technology used and documented in the form of operation patterns, the 

same graphical means used for the Operation Diagnostics. During the design phase, the 

operational concept is repeatedly adjusted and refined. Simulation programs can be used for 

optimization. Operation Prognostics thus allows better use and implementation of the design 

solution during operation. The goal is to save as much energy as possible during operation, 

minimizing the building's lifecycle costs and environmental impact.

Holistic Building Operation

System optimization should not be left until after operation has begun; it must be taken into 

consideration during design. This applies to the sizing, as well as the interaction of separate 

systems and system components. Thus, in the course of Operation Prognostics, methods and 

tools should be developed that facilitate the integration of operational strategies into planning 

and operation. This is to establish the link between integrated building design and holistic facility 

management.

Early Applications of Operation Diagnostics

Operation Diagnostics systematically analyzes key energy-related data from the already installed 

building control system in its current state and enables it to be used for optimization of 

operations involving heating, cooling, ventilating, air-conditioning and lighting. It focuses on 

measures that require no investment and that can be implemented with existing system 

technology. In order to verify its benefits to architects, owners, mechanical system designers and 
145

building operators, as well as to test its applicability to various system concepts and building 

types, the methodology was put through field tests in several demonstration buildings.

Building case study 1: The Munich Re East Building

This office building was built in 1965 as a reinforced concrete structure with an aluminum 

curtain wall facade. The mechanical system was replaced in 1997. The building is partially airconditioned and ventilated. Centralized cooling is provided by means of a chilled water system 

and free cooling; heating is provided by means of a district heating connection. In the OASE 

project, Munich Re's east building was subjected to operation diagnostics combined with a 

measurement program to record additional temperature values and flow rates in the heating and 

cooling loops. From the analysis of the operation data, a series of quick-win measures for the 

heating and cooling systems were derived, which enables low to no-cost optimization of 

operational management. For instance, it was shown that energy savings could be achieved by 

reducing pump run times and optimizing control of the cooling loops. The data analysis enabled 

comparison between the set operating times and the usage requirements, as well as verification 

of sensors' functionality. In addition, the project provided a valuable base test for the 

visualization and analysis procedures of operation diagnostics and for optimizing the 

methodologies, tools and procedures.

Figure 4-75 The Munich Re East building, Königinstrasse in 

Munich, Germany

Building case study 2: Laboratory building ‘Forschungszentrum Jülich’

This 3-story laboratory from the 1960s was completely refurbished by 2002; emphasis was 

placed on a new concept for ventilation and cooling. The operation diagnostics was limited to 

individual data point visualization via carpet plots and accompanying annotation. Nevertheless, it 

was possible to validate the refurbishment's forecast savings potential, and the operation 

diagnostics helped to reveal further optimization potential regarding dynamic building and 

system operation. Visualization and analysis of hourly temperature data over the course of one 

year provided the operator with important tips for optimizing the building’s supply air 
146

temperature settings. The tool also enabled clear identification and display of data transfer errors. 

In view of an upcoming general overhaul of the operational control system at 

Forschungszentrum Jülich, it would be practical to incorporate an operation diagnostics tool into 

the daily operational control of the building.

Figure 4-76 The laboratory building before (top) and 

after (bottom) refurbishment

Building case study 3: Gebhard Müller School in Biberach

This new building was built in summer 2004. It was designed as a vocational school building, a 

so-called 3-liter house. Comprehensive building operation simulations were conducted in the 

design phase. As part of the research program "Energy-Optimized Building" (EnOB), the 

concept was measured and optimized during operation from the beginning. This was supported 

by operation diagnostics, resulting in numerous adjustments to building automation including the 

optimization of operating times.

Ground water serves as the building's main heat source and only cold source; it is used with a 

heat pump system in heating mode, and directly through a heat exchanger in cooling mode. With 

the high electricity consumption caused by the provision of ground water, Operation Diagnostics 

made it possible to adapt total ground water requirements to suit actual usage profile, i.e., 

requirements were reduced and the efficiency of the two ground water heat pumps increased. 

Analysis of requirements and optimization also resulted in a realignment of operation sequences. 

The wood pellet boiler installed solely to cover peak loads was also used to supply heat to the 

ventilation system at a higher temperature during the day. This operational change increased 
147

utilization of the boiler's capacity. Operation could be optimally coordinated with the concrete 

core activation (radiant heating and cooling), thereby increasing heat pump efficiency.

 Figure 4-77. Gebhard Müller School in 

Biberach, Germany

Figure 4-78. Carpet plot of heat pump operation in the Gebhard-Müller School (excerpt)

Conclusion 

Tools developed in the OASE project offer new ways to visualize measurement data and thus 

facilitate a systematic, goal-driven analysis of building operation. Their practical benefits for 

architects, mechanical system designers, building operators and owners have been demonstrated 

through several actual-building applications. Currently, basic functions of the OASE tool can be 

found in a number of building automation systems. The detailed analysis options are also 

gradually being introduced into ongoing optimization processes.

It remains to be seen whether more widespread application of operational diagnostics will affect 

building design practice. Ultimately, for energy-saving building operation, the decisive course 

has to be set during design rather than after operation. In practice, however, obstacles are often 

met in the implementation of identified savings opportunities, which cannot be overcome with 

analysis tools alone.

Heat Pump Return Temperature

Heating Circuit 2 Supply Temperature
148

For instance, in buildings with facility management, staffs are often unfamiliar with systems, 

usage periods and comfort requirements. Furthermore, service contracts usually contain general 

statements on energy optimization, which are not linked to remuneration provisions. No 

motivation or incentive is provided for the identification and implementation of performance 

measures. The service provider benefits from energy savings only after a while – if at all.

Operation Diagnostics contribute significantly to energy-efficient building operation. It "refines" 

measurement values and data from building controls and automation, producing findings that 

enable systematic optimization measures. Deficiencies, malfunctions and system defects can be 

identified sooner rather than later, even if the building occupancy changes. As a result, motivated 

building occupants can be proactive, and the building operator can react before complaints arise 

without having to rely on an expert witnessing the system malfunction or defect at the time that it 

occurs.

References Chapter 4

[20] IEA. 2004. International Energy Agency. Annex 40: Commissioning of Buildings and 

HVAC Systems for Improved Energy Performance. http://www.commissioning-hvac.org.

[21] The Energy Performance of Buildings, Directive 2002/91/EC of the European Parliament 

and of the Council, European Union, 2002.

[22] M. Frank et al. “State-of-the-Art Review for Commissioning Low Energy Buildings: 

Existing Cost/Benefit and Persistence Methodologies and Data, State of Development of 

Automated Tools and Assessment of Needs for Commissioning ZEB” NIST 

Interagency/Internal Report (NISTIR) 7356. 2007.

[23] J. Hyvarinen, S. Karki (Eds.), Building Optimization and Fault Diagnosis Source Book, 

IEA Annex 25, VTT, Finland, 1996.

[24] W.Z. Haung, M. Zaheeruddin, S.H. Cho, Dynamic simulation of energy management 

control functions for HVAC systems in building, Energy Conversion and Management 47 

(2006) 926–943.

[25] L. Lu, W. Cai, L. Xie, S. Li, Y.C. Soh, HVAC system optimization – in building section, 

Energy and Buildings 37 (2005) 11-22.

[26] K.F. Fong, V.I. Hanby, T.T. Chow, HVAC system optimization for energy management 

by evolutionary programming, Energy and Buildings 38 (2006) 220-231.

[27] Z. Ma, S.W. Wang, X. Xu, F. Xiao, A supervisory control strategy for building cooling 

water systems for practical and real time applications, Energy Conversion and 

Management 49 (2008) 2324-2336.

[28] T.I. Salsbury, R.C. Diamond, Fault detection in HVAC systems using model-based 

feedforward control, Energy and Buildings 33 (2001) 403-415.

[29] P. Haves, R.J. Hitchcock, K.L. Gillespie, M. Brook, C. Shockman, J.J. Deringer, K.L. 

Kinney, Development of a Model Specification for Performance Monitoring Systems for 

Commercial Buildings, Proceedings of the 2006 ACEEE Summer Study on Energy 
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Efficiency in Buildings, Pacific Grove, California, Lawrence Berkeley National 

Laboratory, 2006.

[30] M.A. Piette, S.K. Kinney, P. Haves, Analysis of an information monitoring and 

diagnostic system to improve building operation, Energy and Buildings 33 (2001) 783-

791.

[31] S.W. Wang, F. Xiao, AHU sensor fault diagnosis using principal component analysis 

method, Energy and Buildings 36 (2004) 147–160.

[32] J. Liang, R. Du, Model-based Fault Detection and Diagnosis of HVAC systems using 

Support Vector Machine method, International Journal of Refrigeration (2007) 1-11.

[33] S.H. Cho, H.C. Yang, M. Zaheer-uddin, Byung-Cheon Ahn, Transient pattern analysis 

for fault detection and diagnosis of HVAC systems, Energy management and 

conservation 46 (2005) 3103–3116.

[34] J. Schein, S.T. Bushby, N.S. Castro, J.M. House, A rule-based fault detection method for 

air handling units, Energy and Buildings 38 (2006) 1485–1492.

[35] S. Katipamula and M. Brambley, 2005 Methods for Fault Detection, Diagnostics, and

Prognostics for Building Systems—A Review, Part I, HVAC&R Research Volume 11, 

Number 1.

[36] S. Katipamula and M. Brambley, 2005 Methods for Fault Detection, Diagnostics, and 

Prognostics for Building Systems—A Review, Part II, HVAC&R Research Volume 11, 

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[37] P. Haves , D. Claridge, M. Lui, Report assessing the limitations of EnergPlus and SEAP 

with options for overcoming those limitations, California Energy Commission Public 

Interest energy Research Program, HPCBS #E5P2.3T1, 2001.

[38] F. Xiao, S.W. Wang, Progress and methodology of lifecycle commissioning of HVAC 

systems to enhance building sustainability, Renewable and Sustainable Energy Reviews 

(In Press) (2008).

[39] “Sustainable control and maintenance in buildings”, Quick scan protocol, TNO, January 

2008.

[40] Jean Gilles. 2004. A climate change solution; “Intelligent Building Operating 

Technologies”. http://cetcvarennes.nrcan.gc.ca.

[41] Choinière D. 2001. Un agent de détection et diagnostic de fautes pour les bâtiments. 

Congrès de l’Association québécoise pour la maîtrise de l’énergie. Quebec City, Quebec, 

Canada.

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HVAC System Performance. Final Report. Editors Arthur Dexter and Jouko Pakanen. 

[43] D.Choinière, and M Corsi. 2003. A BEMS-assisted commissioning tool to improve the 

energy performance of HVAC systems. Proceedings of ICEBO 2003. Berkeley, CA.

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with a BEMS-Assisted Cx Tool. Proceedings of ICEBO 2004. Paris, France.
150
151

5 Conclusions

Commissioning – and especially ongoing commissioning – must cope with vast amounts of 

information and data. Information on the building’s characteristics and HVAC system, as well as 

measured data, e.g., of the energy consumption, state variables or control signals, must be 

managed. In more complex or low energy buildings with advanced technologies this task 

requires computer-aided processes or software tools because humans cannot manage the wealth 

of data or extract specific information.

The tools and case studies discussed illustrate the potential for improved building performance 

through the use of software tools in the commissioning process. The tools provide features for 

operational fault detection and diagnosis (FDD) and/or for optimization that help to identify and 

realize potential savings.

Most developed tools focus on the operation phase (existing buildings). Only a handful address 

the design phase (new constructions). This seems natural as commissioning has close ties to the 

operation phase and most of the built environment already exists. However, ideally, 

commissioning starts in the early design phase of construction. In this context, it should be noted 

that design and operation phases demand very different tool features:

Tools for design phase (new construction)

The design phase is characterized by considerable choice in solutions for building construction 

and HVAC systems. The design team must find a way to meet the owner’s project requirements 

(OPR). Typically this involves comparing various design alternatives that (ideally) are based on 

simulation. Consequently, a design phase Cx tool should support the assessment of different 

design alternatives. 

More important is documentation of the OPR, basic design, target values (e.g., energy demand),

and all decisions and knowledge gathered during the design phase. This information is essential 

to measurement and verification in the latter operation phase. The current absence of complete 

and/or up-to-date building documentation is one of the biggest barriers to the introduction of 

commissioning in existing buildings. Cx tools should therefore support information management 

over the building’s lifecycle.

Tools for operation phase (existing buildings)

In the operation phase, most commissioning is made up of FDD and optimization. Unlike in the 

design phase, the commissioning provider must deal with a given system without a chance to 

replace major equipment (as long as no significant refurbishment is planned). In fact, many 

existing buildings were not commissioned during the design phase and many may not even have 

been commissioned during hand over. As a result, the commissioning provider must often cope 

with a situation in which a lack of information and little measured data exist.
152

Consequently, the features and scope of the operation phase vary significantly, depending on the 

data and information needed to apply the tool.

For Example, the application of a simulation model for optimization requires detailed 

information about the building’s structure and HVAC design. If these are unavailable (and there 

is insufficient budget to gather them) it cannot be used for commissioning. However, measured 

data may be available that can be used for visualization, e.g to create energy signatures that 

reveal characteristics of the energy consumption. The kind of tool that might be applied for 

commissioning depends on an existing building’s history and documentation.

Chapter 4.3 gives an overview of developed tools.

Aside from the kind of tool, data visualization – either for manual FDD or presentation of 

analysis results - was identified as a vital feature by the Annex 47 team. Chapter 4.2 gives an 

overview. Automation is still uncommon in the commissioning process (e.g., for FDD). 

Visualization therefore provides experts with a valuable tool for revealing information hidden in 

data.

The international status of ongoing commissioning (ongoing Cx) and Cx tool requirements can 

be summarized as follows:

• There is no common understanding of the term “ongoing commissioning” among the 

different countries. 

• With the exception of the USA, ongoing Cx has yet to be established as a well defined 

third-party service. Nevertheless, every country offers services that form part of the 

ongoing Cx process. 

• All experts stress the importance of ongoing Cx for the persistence of energy efficient 

operation of buildings.

• All countries (including the US) state that there are few if any commercial and/or easy to 

use tools available that can be used for ongoing Cx. 

• Moreover, all countries state that automation of the tools and thereby a reduction of labor

cost connected to ongoing Cx is crucial for a wider application

• Cost-benefits of ongoing Cx-tool application have yet to be documented.

The Annex 47 team identified the following needs for further research and tool development to 

support ongoing commissioning in new and existing buildings:

• Automation and a more robust application of tools (e.g., for FDD or functional testing) 

remains an important issue to reduce costs. 

• Tools should be better integrated, i.e., not just provide one feature like FDD for an AHU. 

This will make the application of tools increasingly relevant to building owners or 

operations staff. The number of tools required to cover all ongoing Cx-required 

functionalities should be reduced.

• Tools must be made easy to use and their interfaces improved.
153

Finally, tools should be better integrated in the whole commissioning process. To do so, the 

process itself must be better defined and standardized. An example is the need for monitoring 

guidelines discussed in Chapter 3.2.
154

6 Annex 47 Participants

Name Country Affiliation

Alexis Versele Belgium KaHo St-Lieven

Hilde Breesch Belgium KaHo St-Lieven

Stephane Bertagnolio Belgium University of Liege

Daniel Choinière Canada Natural Resources Canada

Zhongxiian Gu China TNO Beijing

Yongning Zhang China Tsinghua University

Karel Kabele Czech Republic Czech Technical University

Pavla Dvorakova Czech Republic Czech Technical University

Michal Kabrhel Czech Republic Czech Technical University

Mika Violle Finland Helsinki University of Technology

Jorma Pietilainen Finland Technical Research Centre of Finland (VTT)

Hannu Keranen Finland Helsinki University of Technology

Lari Eskola Finland Helsinki University of Technology

Satu Paiho Finland Technical Research Centre of Finland (VTT)

Hossein Vaezi-Nejad France Dalkia

Oliver Baumann Germany Ebert & Baumann Consulting Engineers

Steffen Plesser Germany Institute of Building Services and Energy Design (IGS)

Christian Neumann Germany Fraunhofer Institute for Solar Energy Systems 

Dirk Jacob Germany Fraunhofer Institute for Solar Energy Systems 

Anatoli Hein Germany Institute of Building Services and Energy Design (IGS)

Michele Liziero Germany/Italy Politecnico di Milano, Guest Scientist ISE

Jochen Schaefer Germany Ebert & Baumann Consulting Engineers

Shengwei Wang HK/China Hong Kong Polytechnic University

Xinhua Xu HK/China Hong Kong Polytechnic University

Zhenjun Ma HK/China Hong Kong Polytechnic University

Zhou Cxiang HK/China Hong Kong Polytechnic University

Xiao Fu Linda HK/China Hong Kong Polytechnic University

Na Zhu Hong Kong Hong Kong Polytechnic University

Zoltan Magyar Hungary University of Pecs

Csaba Fodor Hungary University of Pecs
155

Name Country Affiliation

Harunori Yoshida Japan Kyoto University

Motoi Yamaha Japan Chubu University

Mingjie Zheng Japan Sanyo Air Conditioning

Yasunori Akashi Japan Kyushu University

Hiroo Sakai Japan Hitachi Plant Technologies

Katuhiro Kamitani Japan Tonets Corporation

Ryota Kuzuki Japan Tokyo Gas Co

Katsuhiko Shibata Japan Takasago Thermal Eng. Co

Fulin Wang Japan Kyoto University

Masato Miyata Japan Kyoto University (student)

Hirotake Shingu Japan Kyoto University (student)

Hiromasa Yamaguchi Japan Kansai Electric Power Co

Ryusi Yanagihara Japan Tokyo Electric Power Co

Hideki Yuzawa Japan Nikken Sekkei Research Institute

Takao Odajima Japan Takenaka Corp.

Hirobumi Ueda Japan Osaka Gas CO., Ltd

Masahiro Shinozaki Japan Kyushu Electric Power Co.

Katsuhiro Kamitani Japan Tonets Corporation

Mingjie zheng Japan SANKO AIR CONDITIONING CO.,LTD

Katsuhiko Shibata Japan Takasago Thermal Engineering Co..Ltd

Vojislav Novakovic Norway Norwegian University of Science and Technology 

(NTNU)

Natasa Djuric Norway NTNU

Marko Masic Norway NTNU

Vojislav Novakovic Norway NTNU

Henk Peitsman the Netherlands Netherlands Organization for Applied Scientific 

Research (TNO)

Luc Soethout the Netherlands TNO

Ipek Gursel the Netherlands University of Delft

Natascha Milesi

Ferretti

USA National Institute of Standards & Technology

David Claridge USA Texas A&M University

Hannah Friedman USA Portland Energy Conservation Inc.
156

Name Country Affiliation

Omer Akin USA Carnegie Mellon University

Ashish Singhal USA Johnson Controls

Tudy Haasl USA Portland Energy Conservation Inc

Phil Haves USA Lawrence Berkley National Laboratory
157

APPENDICES

6.1 APPENDIX 1: Survey on Sensors for Energy Management in 

Existing Building

Three problems exist: cost, space, and owner agreement to install sensors in an existing building. 

We assessed the current status of these problems using the “Questionnaire on sensors for energy

management in existing buildings”. The questionnaire has four parts;

1. Do existing buildings have sufficient sensors to carry out energy management?

2. Do existing buildings have sufficient space for the installation of additional sensors?

3. Are affordable sensors in the marketplace equipped with sufficient accuracy for 

purposes of energy management?

4. Do owners demonstrate an understanding of the need for additional sensors and allow 

for this in their budgets?

Number of respondents:

Asia: Japan 60

North America: USA 6, Canada 1

Europe: Germany 1, the Netherlands 1, Belgium 1, Norway 3 

Table 0-1 Response rates

Japan North America Europe

Design offices 15 % 0 % 0 %

General contractors 23 % 17 % 0 %

Construction companies 38 % 0 % 20 %

Consultants 5 % 33 % 60 %

Others 20 % 50 % 20 %

1. Do existing buildings have sufficient sensors for energy management?

1) Survey about actual and required energy management level of existing building

We investigated actual and required energy management levels of existing buildings, using

the “Building Energy Management Levels” discussed in SHASE.

【Building Energy Management Levels】

A. A total amount of consumed energy is measured for the whole building (for every energy 

type, monthly data).
158

B. A total amount of consumed energy is measured for the whole building (for every energy 

type, daily and hourly data).

C. Each energy amount categorized for HVAC, lighting, consents, elevators, etc., is 

measured.

D. Each energy amount categorized for office areas, conference areas, communication 

machine rooms, parking lots, etc.

E. Each energy amount for each floor.

F. Energy consumed by each equipment type, such as chillers, cooling towers, pumps, etc. 

or each sub-system, such as power plant, air-conditioning system, pump system, etc.

Figure 0-1 shows actual energy management levels. Figure 0-2 shows required energy 

management levels from respondents’ answers. 

In Japan, most buildings measure the total amount of energy consumed for the whole building, 

for every energy type, with monthly data (Level A). However, the percentage of Level B is less 

than 50 %, and that of Levels C-F less than 30 %.

Level A ratios for America and Europe are similar to those of Japan. American and European 

ratios of Levels B-F are less than those of Japan.

In contrast to actual energy management levels, respondents consider that each energy amount 

categorized should be measured: HVAC, lighting, consents, elevators, etc. (Level C), or total 

amount of daily and hourly energy consumed for the whole building (Level B).

Respondents in Japan answered that the required level is Level C; respondents in America 

answered Level B and respondents in Europe answered Levels B or C.

Many respondents who answered Level C consider that, to improve energy performance it is 

essential to analyze each energy amount categorized by use (like HVAC, lighting, consents, 

elevators, etc.). Many respondents who answered Level B consider that, from a cost benefit 

aspect, it would be best to measure only a few sensors but use an hourly high time resolution for 

greater understanding of consumption trends.
159

Figure 0-1 Actual energy management levels

Figure 0-2 Required energy management levels

2) Survey on dissatisfaction with sensors and monitoring systems in existing buildings

We investigated points of dissatisfaction related to sensors and monitoring systems in existing 

buildings. Figure 0-3 illustrates the survey results. Most respondents are dissatisfied with fewer 

sensors in existing building. And many are dissatisfied with sensors not being calibrated. Many 

respondents consider sensor calibration to be important. The ratios for America and Europe are 

similar to those of Japan.

0 20 40 60 80 100

F. Energy consumed for each

equipment

E. Energy amount for each floors. 

D. Energy amount for each type of

areas

C. Energy amount for each uses

B. Total amount of energy, daily and

hourly data

A. Total amount of energy, monthly

data

%

Japan

America

Europe

0 20 40 60 80 100

F. Energy consumed for each

equipment

E. Energy amount for each floors. 

D. Energy amount for each type of

areas

C. Energy amount for each uses

B. Total amount of energy, daily and

hourly data

A. Total amount of energy, monthly

data

%

Japan

America

Europe
160

Overall, fewer respondents in Japan are dissatisfied with inadequate sensor accuracy, poor 

accuracy in monitoring systems, or miss sensor selection when compared with. respondents in 

America and Europe (30 % and 70 % respectively).

Figure 0-3. Dissatisfaction with sensors and monitoring systems in existing buildings

2. Do existing buildings have sufficient space for the installation of additional sensors?

Figure 0-4 shows survey results on machine room space. The percentage of buildings with 

enough machine room space to install additional sensors is 16 % in Japan, 52 % in America, and 

39 % in Europe. Machine room space in America is large compared to that of Japan. Small 

machine room space poses a challenge for sensor installation in Japan.

0 20 40 60 80 100

Miss selection of sensors

Bad accuracy of a monitoring

system

Sensors have inadequate

accuracy

No calibration for sensors

Less numbers of sensors

%

Japan

America

Europe
161

Figure 0-4. Machine room space survey

3. Do affordable sensors in the marketplace have sufficient accuracy for purposes of energy 

management?

There were few valid responses concerning accuracy but there were several valid responses 

concerning sensor type. 

Figure 0-5 shows the sensor types most commonly used in existing buildings. They are: 

resistance temperature detectors (water: 82 %, air: 66 %); thermo couples (water: 12 %, air: 25

%); ultrasonic water flow rate (56 %); electromagnetic flow (39 %); thermal air flow rate (39 %); 

Karman vortex (21 %); and Pitot tubes (11 %).

Enough space

Possible to install 

additional sensors 

with no guaranty

Impossible to install

(a) Japan

42%

11%

31%

16%

Not enough but a certain space for installing additional 

sensors in rather rough accuracy of ±10%

Enough space

Possible to install 

additional sensors 

with no guaranty

Impossible to install

(a) Japan

42%

11%

31%

16%

Not enough but a certain space for installing additional 

sensors in rather rough accuracy of ±10%

52%

12%

13%

23%

(b) America

Enough space

Not enough but a certain space for installing additional 

sensors in rather rough accuracy of ±10%

Possible to install 

additional sensors 

with no guaranty

Impossible to install

52%

12%

13%

23%

(b) America

Enough space

Not enough but a certain space for installing additional 

sensors in rather rough accuracy of ±10%

Possible to install 

additional sensors 

with no guaranty

Impossible to install

39%

20%

34%

7%

(c) Europe

Enough space

Not enough but a certain space for installing additional 

sensors in rather rough accuracy of ±10%

Possible to install 

additional sensors 

with no guaranty

Impossible to install

39%

20%

34%

7%

(c) Europe

Enough space

Not enough but a certain space for installing additional 

sensors in rather rough accuracy of ±10%

Possible to install 

additional sensors 

with no guaranty

Impossible to install

±10 %

±10 %

±10 %
162

Figure 0-5 Survey on sensor type

4. Do owners of existing buildings understand the need of additional sensors and allow for 

this in their budgets?

Figure 0-6 shows the survey results of owner reactions to budgeting. Most respondents in Japan 

answered that many owners completely agree with the Level A budget but there are some cases 

where owners agree with Levels C-F budgets. In most cases, owners seem to disagree with 

budgets of high level energy management systems in Japan. 

Trends in America and Europe are similar to those in Japan but the ratio of American and 

European owners completely in agreement with Levels C-F are less.

82%

12%

6%

66%

25%

9%

(a) Type of temperature sensor

Water Air

JP JP

Resistance temperature detector

Thermo 

couple

Others

Resistance temperature detector

Others

Thermo 

couple

82%

12%

6%

66%

25%

9%

(a) Type of temperature sensor

Water Air

JP JP

Resistance temperature detector

Thermo 

couple

Others

Resistance temperature detector

Others

Thermo 

couple

Electromagnetic

39%

5%

25%

21%

39%

Ultrasonic 11% 

(b) Type of flow rate sensor

Water Air

JP JP

Others

Ultrasonic

56%

Others

4%

Thermal 

type 

Karman vortex 

Ptot tube

Electromagnetic

39%

5%

25%

21%

39%

Ultrasonic 11% 

(b) Type of flow rate sensor

Water Air

JP JP

Others

Ultrasonic

56%

Others

4%

Thermal 

type 

Karman vortex 

Ptot tube
163

Figure 0-6 Owner reaction to budgeting 

Japan

0% 20% 40% 60% 80% 100%

F. Energy consumed for each equipment

E. Energy amount for each floors. 

D. Energy amount for each type of areas

C. Energy amount for each uses

B. Total amount of energy, daily and hourly

data

A. Total amount of energy, monthly data

Completely agree

Partially agree

Not agree

America

0% 20% 40% 60% 80% 100%

F. Energy consumed for each equipment

E. Energy amount for each floors. 

D. Energy amount for each type of areas

C. Energy amount for each uses

B. Total amount of energy, daily and hourly

data

A. Total amount of energy, monthly data

Completely agree

Partially agree

Not agree

Europe

0% 20% 40% 60% 80% 100%

F. Energy consumed for each equipment

E. Energy amount for each floors. 

D. Energy amount for each type of areas

C. Energy amount for each uses

B. Total amount of energy, daily and hourly

data

A. Total amount of energy, monthly data

Completely agree

Partially agree

Not agree
164

6.2 APPENDIX-2 Example of error in sensing system 

Figure 0-7 shows a temperature sensing method that uses PT100 Ω (JIS C1604 class elements. 

The analog portion of the temperature sensing error is shown. 

Figure 0-7 Indoor temperature sensing with temperature elements

1) Sensors 

PT100 Ω (JIS C1604 class A) 

The resistance of this element changes from 100.00 Ω to 123.24 Ω over a temperature range of 0

°C to 6 °C. The error is +/-0.15 °C at 0 °C. At 60 °C, there is a temperature dependence factor of 

0.002*60=0.12, so the effective error at 60 °C is (0.15+0.12=) +/-0.27 °C. This is the value 

specified by JIS C1604 class A (2). 

2) Printed wiring boards

The resistance in the wiring on the copper foil printed wiring board that carries the signal from 

the PT100 Ω element to the external pin is added as a positive resistance, which is effectively the 

measurement error. The value of the error calculated from the size of the copper foil, etc., is 0.04

°C. Thus, the overall error within the temperature measurement range (0 °C to 60 °C) is +0.04

°C. 

3) Wiring 

A bridge circuit that is incorporated on the instrument side, as the measurement circuit diagram 

shows, cancels out wiring resistance by using three-wire wiring. However, three-wire wiring is 

not entirely the same, so wiring error also exists here, and the error differs with the installation 

location. 

In addition to the analog errors described above, sensing system error includes AD conversion 

error, which occurs in the conversion of the analog signal to a digital form. Furthermore, because 

the measurement results from two temperature sensors and the flow sensor measurement results 

are used to calculate the amount of heat, the errors in those measurements are included.

Temperature 

element 

Printed wiring board 

Wiring Wiring 

resistance Measurement 

circuit

1

2 3

Temperature 

element 

Printed wiring board 

Wiring Wiring 

resistance Measurement 

circuit

Temperature 

element 

Printed wiring board 

Wiring Wiring 

resistance Measurement 

circuit

1

2 3

(r)
165

6.3 APPENDIX-3 Sensors Used by BEMS

The specifications of sensors used by BEMS (measurement range, accuracy, and output) are 

summarized in ‘A Guide to Sensors for BEMS’ in ANNEX 16 (p. 10). One sensor that is 

currently often used in performance evaluation but is not described in that document is the 

ultrasonic flow meter. Typical specifications for that type of meter are listed below. 

Ultrasonic type

Accuracy: RD +-(1.0 to 3.0) %

Nominal diameter: (13 to 5000) mm

Flow range: (0 to 60, 000_ m3/h
166

6.4 APPENDIX-4 Features of EB and LEB Cx Tools Developed 

within Annex 47 

No. 01 SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME DABO (Diagnostic Agent for Building Operation) Version2007

COUNTRY / ORGANIZATION Canada Canmet Energy Technology Centre-Varennes

TARGETED BUILDING SYSTEM TYPE(S) Institutional/Commercial

CONTACT PERSON: Daniel Choinière

The objective of the tool is to assist the building operator and commissioning providers in 

the early detection and diagnosis of faults and optimization of suboptimal operating 

parameters and sequence of operation in HVAC systems. These actions are often 

included as part of an ongoing commissioning process.

Table 0-2 summarizes the actions that the tool performs for fault detection and diagnosis at the 

component level, system level and energy and comfort performance auditing level.

Table 0-2. Functions of DABOTM

Component Level – Fault Detection and Diagnosis

• Sensor: temperature, relative humidity, air flow, pressure, CO2, current

• incorrect reading, complete failure

• Damper, valve, actuator

• stuck, incorrect position feedback, incorrect minimum position, defective leakage

• Coil, humidifier, fan

• insufficient capacity

• Controller

• logic, tuning, signal, unstable 

(set point, output)

• Set points: temperature, pressure, ventilation, relative humidity, CO2

• Point in manual

System-Plant Level – Fault Detection and Diagnosis, Optimization

Helps building operators perform analysis for the following:

• Undersized and oversized components, systems and plants

• Non-optimum set points

• Temperature, pressure, humidity, ventilation, start-up time

• Inappropriate sequences of operation and schedules

• Poor energy management and peak load control

Objectives
167

Energy, Operation Quality Control and Comfort Performance Report

• Energy and comfort profile (hour, day and month)

• Room level, equipment level (terminal unit device, AHU, heating/cooling power plant)

• Building level (electric, gas and oil meter)

• Historical database for energy audit or retrofit 

• Monitoring of energy saving measures

Functions of the tool are described using IDEFØ. Figure A01-1 shows the IDEFØ diagram of the 

tool. The tool monitors measurement and control point data and analyzes it to detect symptoms 

of abnormal behavior in various HVAC components, such as un-calibrated or failed sensors, 

actuator or linkage failures, controller instabilities, non-optimal sequences of operation, etc. The 

tool can also diagnose the possible causes and provide explanations for abnormal behavior. It 

goes beyond the capabilities of conventional BEMS alarms by utilizing the same knowledge and 

reasoning that an expert building engineer would apply to detect problems. The tool analyzes all 

systems continuously (24 hours a day, 365 days per year).

The tool also produces reports adapted to the different professionals involved in the operation of 

buildings (building operators, service technicians, energy managers, commissioning agents, 

HVAC&R engineers).

Applied in an ongoing commissioning process, the functions of the tool are:

During the investigation phase :

• Help to detect where and when there is a problem

• Identification of potential problem

• Identification if installation meets occupant needs and if installation type 

corresponds to building utilization

• Is optimal sequence of operation implemented?

• Identification of basic optimal sequence and low payback energy saving 

measures to check

During the implementation phase :

• Use to check if problem persists

• Used to check if new control strategy performs as intended

• Use to see new energy efficiency measures

During the hands-off and persistence phase:

• Use DABO periodically to detect faults that may prevent operation of optimal 

control strategy

• Help perform tasks on the Persistence Check List to ensure performance

Functions
168

Figure 0-8. IDEFØ diagram of DABO

See Appendix 5 for details on required Input Data, Design data, and Operation data.

Measured data: Control point values (Sensor, command, setpoint, schedule, etc.) from the BEMS

Output: DABO produces four types of reports:

FDD report displays data generated by the various fault detection services. Data are shown in a 

colored table. 

Commissioning report (COMM) displays data generated by the various commissioning services 

or data from the building. Data are shown in a colored table. This report can be configured by the 

user for additional statistical analysis. 

Points report displays data from building points selected by the user. Data can be shown as a line 

chart or a table. This report can be requested from the FDD report, from the commissioning 

report, or from the "Tools" menu. 

Fault management report (FMR) displays confirmed and repaired faults by device type.

Data Management
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DABOTM resides on a Personal Computer and analyzes incoming data from the BEMS. It is 

compatible with most existing building control systems. 

DABOTM was designed using UML standards in ‘Together’ environment. The DABO platform 

was developed in JBuilder and Together environments (Borland Enterprise Studio 7 for Java) in 

the JAVA programming language. 

Excelsior v4.1 software was also used to create Windows (2000 and XP professional) 

executables from Java machine dependent packages.

The fault detection expert rules are coded with an expert system programming language called 

“OPS/JAVA”.

The connectivity to BEMS application is coded in C++.

DABOTM uses a relational database usually managed on an SQL 2000 database server or 

equivalent.

DABO’s real-time optimization service analyzes data at three levels:

At the first level, an automatic component analysis of individual HVAC devices and equipment 

is performed using current commissioning and FDD procedures to verify their proper operation. 

These tests are performed each hour. This level uses a combination of calculated performance 

indices and a Rule Based Expert System to perform the analysis; further explanations are 

provided in the following sections. The DABO reports for the Fault Detection and Diagnosis 

Component analysis are called FDD reports.

The second level of testing consists of an integrated semi-automatic system analysis to verify the 

operation and energy performance of the overall HVAC system over a longer period of time 

(e.g., hours, days, weeks or months). This level uses a combination of calculated performance 

indices, graphical statistical analysis and HVAC system optimization reference tables to help 

users of the tool to perform an ongoing commissioning analysis of the installation. Further 

explanations of the performance indices, statistical analysis and reference optimization table are 

provided in the following sections. The DABO reports for the fault detection and diagnosis 

(system and plants) analysis are called commissioning (COMM) reports.

On the third level basic energy, comfort and operation control quality auditing is performed. This 

step uses a combination of calculated performance indices, graphical statistical analysis and user 

defined report structures to audit the installation. It also helps to follow energy saving measures 

and to provide full building historical data that could be used in various detailed energy audits 

and in the development of HVAC and lighting system optimization concepts. The DABO reports 

Implementation

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for the energy, operation control quality and comfort performance analysis are also called 

commissioning (COMM) reports.

The tool is based on a combination of artificial intelligence algorithms (Rule Based Expert 

System), calculation of performances indices and graphical statistical analysis.

The tool is designed to be used by building operators, service technicians, energy managers, 

commissioning agents, and HVAC&R engineers as a function of their respective needs. (fault 

detection, system optimization, performance evaluation, ensure persistence of commissioning 

and energy savings measures benefits)

Benefits of DABO within the ongoing commissioning process are: 

General:

• 15 % to 30 % energy savings

• Improved comfort and reduction of complaints

• Facilitate maintenance

• Ensure Persistence in energy savings measures

• Extend HVAC equipment life

During Investigation and Implementation:

• Encourage widespread application by reducing its cost

• Improve the quality assurance process 

• Allow application during entire life of the building

During Hands-on and Persistence of Benefits

• Help to structure the continuous process 

• Optimize the periodic check list verification

• Improve the quality assurance process

• Reducing persistence cost

• Keep historical Data

The tool is actually tested in 10 recommissioning projects (demonstration) across Canada. It is 

applied for air- handling unit, air distribution system, heating and cooling network and energy 

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171

management strategies. Demonstration projects include office buildings, a warehouse, a 

convention center and a courthouse.

No. 02 SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME CITE-AHU

COUNTRY / ORGANIZATION USA/ National Institute of Standards and Technology

TARGETED BUILDING / SYSTEM TYPE(S) Air Handling Units

CONTACT PERSON: Natascha Milesi Ferretti

CITE-AHU is a software program designed to facilitate the analysis and maintenance of air 

handling units in office buildings. Operational faults for AHUs in office buildings can 

negatively impact energy consumption, maintenance costs, thermal comfort for the occupants 

and environmental protection. 

CITE-AHU aims to facilitate the monitoring of AHUs and provide the ability to detect faults 

related to improper operation, using the APAR algorithm (expert rules), to present results in a 

hierarchical way with a list of likely fault causes, and to assist in documenting results, providing 

electronic reporting templates.. The goal is to optimize the system performance in terms of 

occupant comfort and energy management. It was conceived exclusively for tertiary buildings

(i.e., offices, shops, hospitals) equipped with one (or several) AHUs. The fault detection method 

developed can be applied to several types of single-duct air-handling units. 

The tools main function is to detect faults in the air-handling unit. Non-standard features include 

heat recovery, humidifier, and economizer applications. Active testing, injecting test signals to 

evaluate system responses under various conditions, are carried out using BACnet. Passive 

monitoring, using BEMS data, can be performed regularly (daily, weekly, or monthly).

The types of faults that can be detected are:

Mechanical failures such as failed/drifting sensors, stuck/failed actuators, duct blockage, 

leaking valves/dampers, slipping/broken belts, etc.,

Control problems such as PID loop tuning, inappropriate setpoints, manual overrides, and 

incorrect sequences/programming,

Degraded performance including heat exchanger fouling,

Objectives

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172

Design faults such as undersized duct/piping, mismatched components, inappropriate 

zoning.

Data is obtained directly through the BEMS. Typically 1 min to 10 min data readings are 

needed. This data can be obtained using the BACnet Data Source, a NIST-developed program 

that facilitates data collection with and BACnet enabled devices. The time-series data is 

collected in CSV files with columns for the date and time stamp, along with the requited input 

data, presented below.

Input Data

• Supply, return, mixed, and outdoor air temperature 

• Outdoor air humidity and/or return air humidity and/or enthalpy (for enthalpy-based

economiser control

• Heating coil valve signal, cooling coil valve signal, damper control signal

Output Data

A graphical user interface presents weekly summary of data analysis results, providing users 

with the ability to drill down for greater detail and additional visualization tools

In addition to the graphical display, fault reports are generated and can be archived. In Active 

testing, control signals and setpoint changes are made directly to the controllers using the 

BACnet Data Source.

Training Data

No training data required, but expert knowledge of the system is needed to configure the tool 

(control sequences, fault thresholds) for the application.

CITE-AHU resides on a personal computer and analyzes the data from the BEMS using 

imported CSV files. In its current version, developed in Visual Basic, expert rules are 

incorporated into the software. 

The BACnet Data Source used to facilitate data collection is implemented in C and C++. It is 

implemented by user who creates test sequences or scenarios into the Active Test Module of 

Cite-AHU. The Active Test Module is designed to inject test signals directly to the controllers. 

Communication is established with the BACnet dll by sending request parameters in an array, 

and the command is then sent to the AHU.

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CITE-AHU software uses the control signals to detect the specific operating mode. For each 

operating mode of the air handling unit, expert rules have been developed in order to detect 

common operational faults. Each time that the conditions of an expert rule are satisfied, faulty 

operation is identified. In the event of awkward repetition of certain faults, the software allows 

the user to deactivate the faults for each operating mode. Each fault corresponds to one or several 

fault causes. The software posts the cause the most often detected and thus the most probable.

There are 8 operating modes:

Heating: the supply air is heated by the heating unit (this air is composed of X % of fresh 

air and (100-X) % of return air);

Free cooling: fresh air is mixed with return air and is neither heated nor cooled

mechanically;

Mechanical cooling with 100 % of outdoor air: the supply air composed of 100 % of

outdoor air and is cooled by the cooling coil unit;

Mechanical cooling with a minimum of outdoor air: the supply air composed of a

minimum amount of outdoor air and is cooled by the cooling unit;

Night cooling: this is for free cooling during unoccupied periods when temperatures permit

supply air;

Frost-protection: this operating mode of the air handling unit is switched on when outdoor

air is too cold in order to protect the installation;

Stop: the air handling unit is stopped; and

Unknown: this operating mode is detected when none of other operating modes are

activated and detected.

Each operating mode has its own rules of operation. When the operating data is evaluated, faults

are detected by the software. Furthermore, because in many cases, there are multiple causes that

can cause a specific fault, a list of possible fault causes are presented to the user.

If certain faults keep recurring, the software allows the user, for each operating mode, to 

deactivate the detection of faults which you no longer wish to observe (i.e., if a service call has 

been scheduled to address a specific problem

Steps followed by the CITE-AHU software:

Operability
174

Figure 0-9 Steps followed by CITE-AHU

The FDD tool (Visual Basic) is a rule-based expert system, using principles developed by NIST.

The primary users of the tool will be (1) commissioning agents performing tasks to verify correct 

installation and operation, and (2) building operators/ energy managers who want to monitor 

performance of multiple AHUs on an ongoing basis, performing seasonal pre-functional checks

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CITE-AHU aids building operators in decision-making by analyzing data collected from the 

building energy management system (BEMS) and evaluated using a set of expert rules governing 

the proper operation of the mechanical equipment. Thus, CITE-AHU software has many 

advantages in particular:

• It can analyze large batch files (which is processed by means of computer);

• It analyzes and preprocesses information collected from the BEMS;

• It provides fault detection of the installation by applying expert rule analysis;

• It presents results simplified in the forms of graphs and tables; and

• It provides a diagnosis by writing a list of possible causes of each fault detected.

• It also determines investigation priorities thanks to a system of hierarchical organization 

of the faults.

DESCRIPTION OF TWO AHU CONFIGURATIONS

The first AHU layout, shown in Figure 0-10, is a constant air volume system with the following

components:

• A constant flow air supply fan supplying the terminal units via a single duct air 

distributionnetwork to provide conditioned air to the various building zones;

• An exhaust fan to extract air from the occupied zones;

• Fresh air, mixed and exhaust dampers to regulate the flow of air supplied to the 

building;

• A heating coil unit to heat the supply air;

• A cooling coil unit to cool the supply air;

• A filtration unit to trap particles in the air; and

• A humidifier to regulate the relative humidity of the supply air.

Benefits

Target Systems to be Tested
176

Figure 0-10 Layout for AHU 1

The second AHU layout, shown in the Figure 0-11, is a variable air volume system with the 

following

components:

• A variable flow air supply fan supplying the terminal units (VAV unit: Variable Air 

Volume)via a single duct air supply network to distributes the air to the various parts of 

the building;

• An extractor fan to extracts air from these areas;

• Fresh air, return and mixed and exhaust registers to enable part of the air extracted from

the building to be used in order to heat up the delivery air;

• A heating coil unit to heat the delivery air; and

• A filtration unit to traps particles in the air.
177

Figure 1-11 Layout for AHU 2

Case Studies (Completed)

Korean Institute for Energy Research, Taejon, Korea, 3 AHUs

Centre Scientifique et Testing Du Batiment (CSTB), Paris, France, 2 AHUs

NIST whole building emulation w/ real controllers, Gaithersburg, MD, USA, 2 AHUs

Case Studies (Planned)

NIST Campus, Gaithersburg, MD, USA, 6 AHUs

No. 3 SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME Proto-type FDD tool "i-BIG" for HVAC systems

COUNTRY / ORGANIZATION The Netherlands / TNO Built Environment and Geosciences 

TARGETED BUILDING / SYSTEM TYPE(S) Any type of buildings / Air handling Units

CONTACT PERSON: Henk Peitsman, Lucienne Krosse

The tool is for fault detection and diagnosis (FDD) of HVAC systems, regarding the operation of 

the HVAC system and also to review the operation conditions of HVAC components to enable 

component and system level optimization, which will be termed continuous optimization. The 

tool is installed in a PC, (i) connected to the data monitoring network of the BEMS or (ii) by 

Objectives
178

remote control. The use of the current tool supports keeping air-handling units (AHU’s) 

maintaining normal status constantly. The tool helps detect faulty situations. 

The function of the tool is to detect faulty places through the whole AHU system. Most 

component level faults and some system level faults can be detected using the tool.

The tool can maintain optimal performance of the AHU by ongoing commissioning (ongoing 

monitoring).

Examples of faults: (i) faults in system control, (ii) faults in schedules of operation, and (iii) 

design errors (e.g., undersized or oversized equipment), and (iv) inappropriate room temperature 

settings by occupants. 

Data from existing BEMS must be used whenever possible. Data of 10-minute intervals or less 

are necessary to detect fault or poor performance operation. If no data from BEMS are available, 

assumptions must be made concerning (i) operation of HVAC, (ii) threshold values. The 

measured time-series data is stored in CSV files, which have a defined time label in each row. 

The time label indicates year, month, day, day of the week, hour, minute and second.

Input Data

• Weather data;

• Room temperature and humidity;

• Inlet/outlet water of heating and cooling coils;

• Supply air temperature;

• Schedule of operation;

• All type of set points; and

• Mass flow rates, if available.

i-BIG resides on a personal computer and analyzes the data from the BEMS. In its current 

version, expert rules are incorporated in the software. The i-BIG tool is developed in Visual 

Basic.

A user interface is developed based on continuously monitoring input data of the HVAC system. 

It shows graphical output of sensor and control signal as a function of time, and associated fault 

Functions

Data Management

Implementation

Operability
179

conditions and diagnoses. System faults or troubles, including possible causes or inappropriate

operation, are presented. Trend curves are presented on the screen. Operation vizualisation 

module presents (i) analog input sensor signals and (ii) control signals.

The FDD tool (Visual Basic) is a rule-based expert system, using principles developed by NIST.

The primary end users of the tool will be (i) service companies responsible for HVAC system 

maintenance. They will use the component level diagnostic information to assist them with 

troubleshooting and to prioritize maintenance activities; (ii) building operators/energy managers 

who want to gauge the performance status of the HVAC systems; (iii) Re- commissioning 

providers can also use the tool to prevent faulty operation and ensure good performance of the 

HVAC systems.

Benefits of i-BIG use in ongoing commissioning process:

• Improved comfort and reduction in complaints;

• Energy savings: (10 to 35) %;

• Facilitate maintenance and service companies;

• Extend HVAC equipment lifecycle;

• Optimize periodic check list verification; and

• Keep historical data.

The tool is actual tested in two projects to demonstrate its benefits for AHU system FDD. Data 

is automatically collected in our laboratory from an AHU in a swimming pool (remote). A 

second project is executed at a hospital AHU.

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Benefits

Target Systems to be Tested
180

No. 4 SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME Performance Analysis Tool for Heating System

COUNTRY / ORGANIZATION Norway/ Norwegian University of Science and Technology

TARGETED BUILDING / SYSTEM TYPE(S) Heating system

CONTACT PERSON: Natasa Djuric

The tool is aimed at heating substations in the district heating supply. It is for use at the system 

level to estimate performance. The operators usually assumes causes of faults in the case of a 

problem. With this tool the answer can be found systematically. The tool is configured to 

estimate abnormal indoor temperature and gauge if there are some biases in the set parameters. 

In addition, it can estimate changing energy consumption due to bias. The tool can assist in 

determining faults in the heating system.

The tool consists of two parts: the calculation part and the fault diagnosis part, shown as 2D 

plots. Tool functions are described using IDEFØ in Figure 0-12. The calculation part consists of 

two modules: the heat balance model, which is shown as Activity C1 and C3 in Figure 0-121, 

and the optimization module, which is shown as Activities C2 in Figure 0-12. The fault diagnosis 

part is shown as Activity C4.

Figure 0-12 outlines the information required for application of the tool using the IDEFØ

diagram. The input data on the left are used as hourly data. Index 1 beside input data in Figure 1 

implies that this data set is used for model calibration, while Index 2 implies data used for fault 

detection. The input data are obtained by additional measurements (like outdoor temperature and 

tap water energy consumption) and logging in BEMS. The output data are used for 2D plots.

The calculation part of the tool is developed in MATLAB and consists of two modules: heat 

balance model and the model calibration/optimization module. The tool is configured using 

BEMS data and on-site measurements. In addition, approximate values of the building envelope 

characteristics and radiator properties are required.

Objectives

Functions

Data Management

Implementation
181

Figure 0-12 IDEFØ tool diagram

The tool output is of practical use to building operators. Figure 1 shows the calculation part that 

is research work, while rules for fault diagnosis, the tool output, are of practical use to building 

operators. Tool outputs are given as 2D plots both for correlation and time-series. This way, fault 

diagnosis rules are generated by detecting biases in performances.

The tool is based on the simple heat balance model. The model is calibrated by comparing the 

measured data to model output data for each time step. The system model is developed in 

MATLAB. The calibration is performed using a sequential quadratic programming algorithm.

The tool is designed to be used by building operators.

NODE: TITLE: Performance Analysis Tool for Heating System NO.: C0

Date, Time

Measured: 

Outdoor Air Temp. 1,

Energy Cons. of Tap 

Water 1 C1

Calculate Indoor Air Temp., 

Return Water Temp., and 

Total Herating Energy 

Consumption

Setpoint for Supply 

Water Temperature

Heat Balance

Measured:

Outdoor Air Temp. 2 

Energy Cons. of Tap 

Water 2

C2

Calculate UA for walls 

and kA for radiators Measured: 

Indoor Air Tem. 1,

Return Water Temp. 1, 

Total Heating Energy 

Consumption 1

Indoor Air 

Temp.

MATLAB Optimization toolbox 

in MATLAB

C3

Calculate Indoor Air Temp., 

Return Water Temp., and 

Total Herating Energy 

Consumption

Measured:

Indoor Air Tem. 2, 

Supply Water Temp. 

Total Heating Energy 

Consumption 2

MATLAB

Minimal percent difference between 

measured and calculated values

kA

UA

C4

FDD

MATLAB

Report 

on Faults

Consumption

Return Water

 Temp.

Consumption

Indoor Air

 Temp.

Operability

Analytical Engine

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182

It is difficult to get a model that performs similarly to a real system. By using an optimization 

algorithm a simple model can be easily calibrated against real data. When the model is 

established, it is simple to utilize it for fault detection by comparing the model outputs to actual 

performances.

The tool was tested on the Material Technology Building on the campus of the Norwegian 

University of Science and Technology in Trondheim. Data collection and logging was easily 

supported by the building operators. Afterwards, results and fault causes were easily understood 

when presented in 2D plot formats.

No. 5 SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME [not yet defined]

COUNTRY / ORGANIZATION Germany / Fraunhofer Institute for Solar Energy Systems

TARGETED BUILDING / SYSTEM TYPE(S) Whole Building (non-residential)

CONTACT PERSON: Christian Neumann

The tool can be used and supports a top-down analysis approach for the evaluation of building 

performance. To date, tool features are: predefined visualization based on a minimal data set and 

automated outlier detection for energy and water consumption (see next paragraph). The tool is 

used for manual fault detection and diagnosis based on visualization, followed by continuous 

monitoring using the outlier detection feature.

Originally, the idea was to couple the tool wherever possible with the EPBD but as a result of the 

actual state of the EPBD, linkage between commissioning and EPBD is difficult.

The analysis features offered in the tool are: 

• Predefined visualization (time series, scatterplots, carpetplots) based on a minimal data 

set (see below). 

• Visualization is based on a minimum data set with a unified point naming convention. 

That is, as soon as the data point names are defined the visualization is performed 

automatically, including data filtering and grouping.

Benefits

Target Systems to be Tested

Objectives

Functions
183

• Outlier detection on energy and water consumption. This feature is also based on the 

minimal data set. It identifies the energy signature based on daily energy consumption 

values, taking into account different day types. After training it can be used for outlier 

detection.

• A feature still under development is model-based analysis (simplified dynamic) that 

calculates reference values for daily energy consumption.

The minimal data set is recorded every (5 to 10) min on an hourly basis 

The data (measured data and meta data) is persistently stored in an HDF 5 file. The data is 

structured within the file according to project, subproject, subsystem, sensorgroup, sensor. Charts 

and analysis results are also stored in the data. This allows different user interfaces (like 

webfrontend, GUI, console) without implementing functionality every time.

The minimal data set recorded comprises: total delivered energy consumption, total water 

consumption, weather data, temperature and humidity of reference rooms, system temperatures 

of main loops (air and water), major AHU exhaust and supply air moisture.

The software is developed with python (www.python.org), an open-source scripting language. 

To implement functionality, several existing libraries (open source) will be used (like matplotlib 

for generating charts, scipy for numerical operations, etc.)

Statistic environment R (www.r-project.org) will be used for statistics analysis.

For our projects, results are presented as a web-based service (see: www.modben.org and 

www.buildingeq.eu). Data and visualizations are accessible there. A GUI is under 

development(May 09).

A robust linear regression approach combined with clustering and standard optimization routines 

are used for statistics analysis (identification of energy signatures, detection of outliers). The 

model based approach developed now is based on CEN standards 

As the manual FDD based on visualization requires system knowledge, the end users to date are 

trained operation staff. We hope to automate at least part of the analysis.

Data Management

Implementation

Operability

Analytical Engine

End Users
184

Visualization:

Easy to apply, applicable to nearly every building, automated data processing and information 

generation

Automated oulier detection:

Quick detection of abnormal energy/water consumption. 

Whole building/Overall building performance. 9 demonstration buildings in Germany and eight 

additional buildings throughout Europe.

No. 6 SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME Optimization Tool for Air-Conditioning System Operation 

Considering Thermal Load Prediction Errors

COUNTRY / ORGANIZATION Japan / Kyushu University

TARGETED BUILDING / SYSTEM TYPE(S)

CONTACT PERSON: Yasunori Akashi, Daisuke Sumiyoshi

This tool is intended for on- going commissioning and be applied mainly to commercial use 

central air-conditioning systems. 

This tool optimizes energy consumption and the indoor environment by changing set values of 

the HVAC&R system.

Input Data

• Weather data (outdoor temperature and humidity, general weather conditions)

• Room temperature and humidity

• Inlet/outlet water temperature of chiller and coil

• Supply air temperature

Benefit

Target Systems to be Tested

Objectives

Functions

Data Management
185

• Schedule of operation

• These data can be obtained from BEMS.

Output Data

Optimized set-points concerning the following:

• Room temperature and/or humidity;

• Inlet or outlet water temperature of chiller and coil; and

• Supply air temperature, etc.

The analysis is carried out in the following manner.

1. One day after weather data are predicted from current weather data. 

2. Prediction load data are made.

3. The air-conditioning system simulation is carried out using prediction load data.

4. Optimize set-points by repeating step 3. A genetic algorithm is used for the optimization 

method.

Not Defined

FORTRAN

This tool is included in BEMS and can be operated automatically. The Building operator 

manages its operation.

This tool can optimize the operational method considering thermal load prediction errors.

Implementation

Operability

Analytical Engine

End Users

Benefit

Target Systems to be Tested
186

The HVAC&R experimental analysis building in Kyushu University campus will be used to 

verify the tool prototype. We are planning to use one more office building for a case 

study(TBD).

IDEF representations of the tool are shown in Figures 0-13, 0-14, 0-15, and 0-16.

Figure 0-13 IDEFØ tool diagram (Root)

The operating policy 

is decided. 

The best set values

are calculated with 

the tool. 

Operation actually

Optimization policy (evaluation function)

Optimized set values

Building owner

Cx provider

Operator

PC

Confirmation and review of operation condition

Operation data

Operator(Only the watch)

A

B

C

Used At: Author: Y. Akashi (Kyushu Univ.) and D. Sumiyoshi(Building Research Institute) Date : February 20th, 2009 Context:

Node : Title : Optimization Tool for Air-Conditioning System Operation Considering Thermal Load Prediction Errors Number : Root
187

Figure 0-14 IDEFØ tool diagram (B)

Figure 0-15 IDEFØ tool diagram (B2)

Grasp of prediction 

error

Optimized 

calculation

Forecast of 

weather data

Forecast of 

internal heat 

generation

Forecast result 

of weather data Forecast result 

of internal heat generation

ARIMA model HASP/ACLD/8501

Operation data

HASP/ACLD/8501

Genetic algorithm

B1

B2

B4

Used At: Author: Y. Akashi (Kyushu Univ.) and D. Sumiyoshi(Building Research Institute) Date : February 20th, 2009 Context:

Node : Title : Optimization Tool for Air-Conditioning System Operation Considering Thermal Load Prediction Errors Number : B

Weather data 

(temperature 

and humidity)

B3

The weather 

forecast

Past forecast 

result

Correction data 

of forecast value

Optimized set 

values

Comparison between

simulation result and 

estimation

Decision of 

forecast value

Heat load 

simulation

Calculation of heat 

load in the room

Heat load in the room

(Simulation result)

Heat load in the room

(Estimation)

HASP/ACLD/8501

Forecast 

procedure

B21

B22

B24

Used At: Author: Y. Akashi (Kyushu Univ.) and D. Sumiyoshi(Building Research Institute) Date : February 20th, 2009 Context:

Node : Title : Optimization Tool for Air-Conditioning System Operation Considering Thermal Load Prediction Errors Number : B2

Room temperature 

and humidity

B23

Weather data 

(temperature and 

humidity)

A day of the week

Correction of assumption value

Forecast result 

of internal heat 

generation

Quantity of flowing 

water at coil, 

Inlet and outlet 

temperature of 

water at coil

Assumption value 

of internal heat 

generation

Not 

coincide

Coincide
188

Figure 0-16 IDEFØ diagram of the tool (B4)

No. 7 SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME An ongoing commissioning tool for VRV package systems

COUNTRY / ORGANIZATION Japan / Chubu University

TARGETED BUILDING / SYSTEM TYPE(S) VRV Package Systems

CONTACT PERSON: Motoi Yamaha

The tool's objective is to evaluate performance of VRV packaged HVAC systems, whose 

capacity is almost impossible to measure when operating. The tool predicts energy consumption 

of the VRV systems from refrigeration cycle simulations without measuring their cooling 

capacity directly. By comparing predicted energy consumption with one that is actually 

measured malfunction or inadequate operation may be detected. Improved operations and 

management could result. 

The functions of this tool are:

Evaluating

combination of set 

values

Selection and 

intersection

Making of 

combination of set 

values (formation 

of population)

Air-conditioning 

system simulation

Combination 

of set values

Operation result

Genetic Algorithm

B41

B42

B44

Used At: Author: Y. Akashi (Kyushu Univ.) and D. Sumiyoshi(Building Research Institute) Date : February 20th, 2009 Context:

Node : Title : Optimization Tool for Air-Conditioning System Operation Considering Thermal Load Prediction Errors Number : B4

B43

Optimized 

set values

Forecast 

data

Choices of set 

values

Evaluation 

result

Correction data 

of forecast value

Building data

Air-conditioning system data

HASP/ACLD/8501

Heat source model 

Coil model

Pump model

Fan model etc.

evaluation function

The next generation

Genetic Algorithm

Objectives

Functions
189

• energy consumption calculation for given building and weather data;

• detection of inadequate operation; and

• determination of base line from calculation in certain period.

Functionality is shown in the IDEFØ flow chart in Figure 0-17.

Figure 0-17 IDEFØ Flow of the tool

Continuous comparison between estimated and measured energy consumption can detect: 

• Design errors (e.g., undersized or oversized equipment);

• Installation problems (e.g., excessive length of refrigerant pipes);

• Faults in system control; and

• Inappropriate room temperature settings where occupants control VRV systems 

individually.

Input data are energy consumption of VRV systems, outdoor temperature, humidity and building 

and system configurations.

The analytical engine produces the comparison of estimation and measurements. Discrepancies 

reveal system faults or poor operation. Tools can be used for ongoing system analysis.

It is impossible to conduct load calculation under exact conditions used by the building. The tool 

should be calibrated for the specific building. This is under development. 

The tool was developed for university buildings. Since building management systems were not 

applied to such buildings, the tool is currently being used in off-line calculations. The tool 

combines computer program and package software; it could be implemented in any existing 

buildings whose data can be presented in a common file format.

Comparison of 

predictions and 

measurements Calculation

of base line

Building 

specifications

Specifications

of systems

Weather

data

Energy

Consumption

Refrigerant

cycle model Statistics

package

Weather data

Predicted

consumption

Energy base 

line

Data Management

Implementation
190

Tool programs consist of:

• heat load calculation programs: independent from the tool itself and that use software 

such as EnergyPlus or TRNSYS.

• energy consumption predictions: a dedicated program that uses a simulated refrigerant 

cycle.

• data analysis: comparison and statistical analysis are made using Excel, R, etc.

Cooling and heating load are calculated from load calculation programs. The estimated energy 

consumption of VRV systems is obtained from the model that simulates the refrigeration cycle of 

VRV systems. The comparison will be presented. 

The tool is not fully developed for building operators or energy manager unaccustomed to using 

computer programming. Therefore end users are currently limited to researchers.

Although quantitative benefit was not obtained using the tool, it can evaluate performance of 

VRV systems, which is difficult measure.

The tool is used exclusively with VRV packaged HVAC systems.

Operability

Analytical Engine

End Users

Benefits

Target Systems to be Tested
191

No. 8 SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME OH Saver

COUNTRY / ORGANIZATION Japan / Hitachi Plant Technologies, LTD.

TARGETED BUILDING / SYSTEM TYPE(S) non-residential (office)building / overall performance

CONTACT PERSON: Hiroo Sakai

The tool is aimed at monitoring the operating condition for HVAC system operation under the 

most adequate set-points to ensure minimal energy consumption. The tool is also used to 

optimize operation but for commissioning the whole system, sub-systems and equipments.

The main functions of the tool are to:

• monitor operating condition and current set-points;

• calculate optimum operating condition and set-points to minimize total energy 

consumption;

• calculate the difference between optimum operation and normal manual operation; and

• edit data to analyze performance trends for purposes of commissioning.

Data management operations include:

• monitoring data every 5 minutes;

• filing data on temperature and flow rate for chilled water, condenser water, air 

supply/return;

• making a list of set-points from optimization calculation; and 

• editing the operating data for commissioning.

The tool is available for all office building HVAC system operation except non-central HVAC 

systems, such as a multi-packaged system. It requires data monitoring and directing devices and 

a data transferring system like Ethernet, BACnet and LONTALK.

Objectives

Functions

Data Management

Implementation
192

The tool is developed for application in the automated operation of HVAC system. It requires an 

adjustable period of one season to adjust equipment objects (mathematical models to explain the 

operating characters) as they are on site. An IDEF representation of the tool is shown in Figure 

0-18.

The analysis is driven by the following evaluation function:

, where:

f(x): Evaluation function;

x: Optimization variable;

kE: Primary energy conversion coefficient for electricity[kJ/kWh];

kG: Primary energy conversion coefficient for gas [kJ/ m3];

Ei(x): Consumed power [kW]; and 

Gi(x): Gas consumption volume [m3/h] .

Non-residential building owners

In initial use in the Tokyo area, about 21 % of the energy used for HVAC systems was saved 

throughout the entire year and 25 % saved in summer.

Non-residential buildings

f (x) = k ∑ E (x)+k ∑G (x) E i G i

Operability

Analytical Engine

End Users

Benefits

Target Systems to be Tested
193

Figure 0-18 IDEFØ Flow of the OH Saver tool

No. SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME Initial Cx Tool for HVAC System in Large Enclosure

COUNTRY / ORGANIZATION Japan/SANKO Air Conditioning CO.,LTD.

TARGETED BUILDING / SYSTEM TYPE(S) Any Type

CONTACT PERSON: Mingjie Zheng

The tool conducts initial commissioning at the design phase of large enclosures such as

gymnasiums. The tool can be used to predict indoor climate and to detect possible design faults.

Figure 0-19 shows the IDEFØ diagram and tool function. Figure 0-20 is a detailed diagram of 

Activity B0 in Figure 1(1).

The tool’s main functions are:

To obtain room air temperature and flow direction distribution by CFD. (Activity A0 in Figure 

0-19)

Calculate energy 

consumption of 

components

Judge whether the 

energy consumption 

is minumum

Change set points; 

inlet water temp., 

supply air temp., and 

so on 

System 

Simulation

Optimal Operational Method

Conversion 

facter

Used At: Author: H. Kikuchi (Hitachi Plant Technologies, Ltd., JPN) Date : February 18th, 2009 Context:

Node : Title : A tool to monitor continuously operational data of each HVAC equipment Number : Root

Calculate primary 

energy consumption 

of whole system

Room air temp. & humid.

Outdoor air temp. & humid.

Outdoor air volume

Supply air volume

Inlet and outlet water temp.

Inlet and outlet chilled 

water temp.

Water flow rate

Chilled water flow rate

Energy 

consumption of 

conponents

Primary energy 

consumption of 

conponents

Optimal 

operational 

method

set points; inlet water 

temp., supply air temp

Objectives

Functions
194

• To calculate supply air temperature by PID simulation. (Activity B0 in Figure 1(1))

• To calculate pick-up time. (Activity C0 in Figure 1(1))

• With this tool, analysis can be performed to optimize design and operation parameters. 

The 10-minutes interval database is necessary to test and verify the simulation model. The input 

data include indoor air temperature gradient, supply air temperature and volume, building skin 

material characteristics, outside surface temperature of building skin, and control parameters of 

supply air temperature.

The output data include indoor air temperature gradient, the warm-up time at the peak-load day.

Training data is the same as input data. In the training process, the output data will be compared 

with measured values.

This tool comprises a CFD software available at stores, a PID analysis tool developed by the 

author, and a data transfer text file.

A convenient interface comes installed with the CFD software. Because the PID analysis tool is 

easy to use, there is no need to develop another interface.

CFD, PID analysis tool.

Commissioning authority, system designer.

This tool is very useful in detecting design errors in HVAC systems used in large enclosures.

Two case studies will be conducted in gymnasiums in Japan.

Data Management

Implementation

Operability

Analytical Engine

End Users

Benefit

Target Systems to be Tested
195

Figure 0-19 IDEFØ tool diagram (Root)

Node: Title: Initial Cx tool for HVAC System in Large Enclosure Number: Root

Used At: Author: Mingjie Zheng (Sanko Air Conditioning CO.,LTD.) Date: March 6th, 2009 Context

Room shape, boundary condition of each 

outer surface, mesh shape and division rate

CFD calculation

Shape and position of each air supply 

aperture, flow in/out air velocity, 

turbulence energy and loss rate

Weather conditions (outdoor air 

temperature, solar radiation, etc), internal 

heat generation and generating position

Turbulence model, finite difference scheme, 

convergence judgment criteria of the coupled 

linear equation and pressure correction

Air temperature of room sensor or 

RA temperature Calculate supply air 

temperature by PID 

simulation

Supply air temperature

A0 B0

Calculate pick-up time 

by comparing the set 

point of room air 

temperature with air 

temperature of sensor 

or RA temperature

Set point of 

room air 

temperature

Output: room air 

temperature and flow 

direction distribution

Output:

pick-up time

Room air temperature or set 

point of RA temperature

PID control 

parameter

Time step of room air 

temperature control 

Dynamic characteristic 

of valve

Time delay and 

dead band of 

room air sensor

C0
196

Figure 0-20 IDEFØ tool diagram (B0)

Node: Title: Initial Cx tool for HVAC System in Large Enclosure Number: B0

Used At: Author: Mingjie Zheng (Sanko Air Conditioning CO.,LTD.) Date: March 6th, 2009 Context

PID

B1

RA temperature

Calculate supply air 

temperature

Opening rate of 

AHU valve

B2

Output: supply air temperature

Room air temperature or set 

point of RA temperature

PID control 

parameter

Time step of room air 

temperature control 

Dynamic characteristic 

of valve

Time delay and 

dead band of 

room air sensor

Air temperature of room 

sensor or RA temperature
197

No.10 SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME Ongoing Type FDD Tool with Pattern Recognition for VAV 

systems

COUNTRY / ORGANIZATION Japan/SANKO Air Conditioning CO.,LTD.

TARGETED BUILDING / SYSTEM TYPE(S) Any Type

CONTACT PERSON: Mingjie Zheng, Song Pan

The aim of this tool is to detect and diagnose existing faults in HVAC systems at the operational 

phase as an aspect of ongoing commissioning.

Figure 0-21 shows the IDEFØ diagram and tool functions. Figure 0-22 and Figure 0-23 are 

detailed diagrams of Activity B0 and C0 in Figure 1(1) respectively.

The tool’s main functions are:

• To Perform Fourier analysis for measurement. (Activity A0 in Figure 1(1))

• To calculate detection and diagnosis parameter with measurement and their Fourier 

transformation by statistical method. (Activity B0 in Figure 1(1))

• To select optimum detection and diagnosis vector by detection rate increment method. 

(Activity C0 in Figure 1(1))

• To judge system state using optimum detection and diagnosis figure or Mahananobis’s 

generalized distance.(Activity D0 and E0 in Figure 1(1))

With this tool, parameters expressing the performance and characteristics of VAV systems can 

be calculated with measured data and their Fourier transformation value. Based on these 

parameters, the system state can be judged as being normal to faulty.

A 10 min intervals database is necessary to calibrate the calculating model. The data consist of 

air temperature and supply air volume of each VAV unit on VAV HVCA system, in normal state 

and various faulty operations.

Create data base in normal state: After modulating the VAV HVAC system well and ensuring it 

is in operating normally, the air temperature and supply air volume of each VAV unit are 

measured at 10 min intervals to form the normal state database. 

Objectives

Functions

Data Management
198

Create data base in various faulty states: Run dynamic simulation program to calculate air 

temperature and supply air volume of each VAV unit in a normal state. After ensuring simulated 

results coincide with the normal database gathered above, run the program again in various 

faulty states at 10 min intervals to form the faulty data base.

Wherever possible, use data from existing BEMS for normal state and various faulty state 

operations.

The optimal parameter vector calculated using the air temperature and air volume data of the 

VAV unit to be measured online is then evaluated using Mahananobis’ generalized distance for 

normal or faulty operation.

This tool is developed with FORTRAN90.

A user interface will be developed to continuously monitor input data of the HVAC system.

A simulation tool was developed by authors using FORTRAN90.

Commissioning authority, system operator.

This tool is useful in detecting existing faults in HVAC systems and diagnosing cause.

The tool has been used in case studies conducted on both VAV systems.

Implementation

Operability

Analytical Engine

End Users

Benefit

Target Systems to be Tested
199

Figure 0-21 IDEFØ tool diagram (Root)

Used At: Author: Mingjie Zheng and Song Pan (Sanko Air Conditioning CO.,LTD.) Date: March 6th, 2009 Context

Node: Title: On-going Type FDD Tool with Pattern Recognition for VAV system Number: Root

Calculate detection 

and diagnosis 

parameter

Fourier analysis

Supply air temperature and 

volume of AHU

1 and 2 dimension Fourier 

transformation 

Select the optimum 

detection and 

diagnosis vector by 

detection rate 

increment method

Detection 

and diagnosis 

parameter

Judge the system 

state by the 

optimum detection 

and diagnosis figure 

or Mahananobis’s 

generalized distance

Optimum 

detection and 

diagnosis vector

Air volume of VAV unit, air 

temperature of each room

A0

B0

C0 D0

Inlet cooling water 

temperature of AHU

Outlet cooling water 

temperature of AHU

Cooling water 

flow rate of AHU

Return air temperature of AHU and 

set point of room air temperature
200

Figure 0-22 IDEFØ tool diagram (B0)

Make the measurement to be 

detection and diagnosis 

parameter

Calculate detection and 

diagnosis parameter with 1 

dimension Fourier 

transformation of each room 

air temperature by statistic 

method

Calculate detection and 

diagnosis parameter with 1 

dimension Fourier 

transformation of supply air 

volume for each VAV unit by 

statistic method

Calculate detection and 

diagnosis parameter with 2 

dimension Fourier 

transformation of each room 

air temperature by statistic 

method

Calculate detection and 

diagnosis parameter with 1 

dimension Fourier 

transformation of supply air 

volume for each VAV unit by 

statistic method

Outlet cooling water 

temperature of AHU

Cooling water 

volume of AHU

Return air temperature of 

AHU and set point of 

room air temperature

Supply air 

temperature of AHU

Parameter

P1~P5

1 dimension Fourier 

transformation of each 

room air temperature

Parameter

P6~P13

1 dimension Fourier 

transformation of supply air 

volume for each VAV unit

Parameter

P14~P21

2 dimension Fourier 

transformation of each 

room air temperature

Parameter

P22~P26

Parameter

P27~P31

2 dimension Fourier 

transformation of supply air 

volume for each VAV unit

B1 B2 B3 B4 B5

Intlet cooling water 

temperature of AHU

Node: Title: On-going Type FDD Tool with Pattern Recognition for VAV system Number: B0

Used At: Author: Mingjie Zheng and Song Pan (Sanko Air Conditioning CO.,LTD.) Date: March 6th, 2009 Context
201

Figure 0-23 IDEFØ tool diagram (C0)

Node: Title: On-going Type FDD Tool with Pattern Recognition for VAV system Number: C0

Used At: Author: Mingjie Zheng and Song Pan (Sanko Air Conditioning CO.,LTD.) Date: March 6th, 2009 Context

Detection and 

diagnosis parameter

Calculate the detection 

rate of each parameter 

and select the 

parameter with the 

highest detection rate Output: The 

optimum detection 

and diagnosis vector

Increase parameter, 

calculate the detection 

rate and select the 

vector at each 

dimension with the 

highest detection rate

1 dimension Vector 

with the highest 

detection rate Convergence judge by 

detection rate 

increment

Convergence 

judgment criteria of 

detection rate

Vector at each dimension with the highest detection rate

No convergence

C1 C2 C3

Vector at each 

dimension with the 

highest detection rate

 Convergence
202

No. 11 SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME A Tool for Optimizing Heating/Cooling Plant Operation

COUNTRY / ORGANIZATION Japan / Kyoto University

TARGETED BUILDING / SYSTEM TYPE(S) Any type

CONTACT PERSON: Harunori Yoshida

This tool checks current operation of a heating/cooling plant visually and optimizes operation 

automatically. The tool consists of two modules: a visualization module and a performance 

simulation module. The visualization module can describe current operation in carpet graphics 

and detect improper operation. The performance simulation module consists of equipment and 

equipment control models. This module aims at finding an optimized operation method through 

simulating the performance of the plant running at different levels.

The tool consists of two modules that offer two different functions:

• Operation visualization module. It uses carpet graphics to describe the current situation, 

thereby easily detecting improper operation. 

• Performance simulation module. This module consists of the equipment models 

configured in a heating/cooling plant and the models of equipment control. With it, the 

performance of a proposal for optimizing a heating/cooling plant operation can be tested.

The following data are used:

• Input data for operation visualization module: average water temperature of thermal 

storage tank, time to determine thermal storage mode (storage or discharge), power 

consumption of heat source machines

• Input data for Performance simulation module: weather data, water flow rate, water 

temperature set point, heating/cooling load, control set points

• Output of operation visualization module: operation visualization carpet graphics

• Output of performance simulation module: plant energy consumption and operation 

optimization proposal

Figure 0-24 shows inputs and output of the operation visualization module.

Objectives

Functions

Data Management
203

Figure 0-24 IDEFØ diagram of the Operation visualization module (Root)

Figures 0-25, 0-26, and 0-27 show the inputs, output and internal data flow of the Performance 

simulation module.

Figur

e 0-25 IDEFØ diagram of performance simulation module (Root)

Used At: Author: F. Wang and H. Yoshida (Kyoto Univ., JPN) Date: Mar. 6, 2009 Context:

Node: Title: A tool for optimizing the operation of heating cooling plants Number: Root

Used At: Author: F. Wang and H. Yoshida (Kyoto Univ., JPN) Date: Mar. 6, 2009 Context:

Node: Title: A tool for optimizing the operation of heating cooling plants Number: Root

Average water temperature of thermal storage tank

Time

Power consumption of heat source machines

Operation 

visualization 

module

A

Carpet graph of 

operation state

Threshold for judging 

operation state

Software MATLAB

Date

Time

Cooling/heating load

On/off state 

determination

A

Operation 

priority order

Plant performance 

simulation

B

Optimal operation 

determination

C

Load threshold of 

start/stop heat 

source machine

On/off state of

heat source 

machine

Plant energy 

consumption

Optimal operation 

method

Software 

MATLAB/Stateflow Software MATLAB

Set point of 

chilled/hot water 

temperature

Chilled/hot water flow rate

Outdoor air temperature/humidity
204

Used At: Author: F. Wang and H. Yoshida (Kyoto Univ., JPN) Date: Mar. 6, 2009 Context:

Node: Title: A tool for optimizing the operation of heating cooling plants Number: B

Figure 0-26 IDEFØ diagram of performance simulation module (B)

Software 

MATLAB/SIMULINK

Chiller/heat pump model

Date

Time

Cooling/heating load

Thermal storage 

tank simulation

B1

Operation 

priority order

Chiller / heat 

pump simulation

B2

Cooling tower / 

pump simulation

B3

Load threshold of 

start/stop heat 

source machine

On/off state of 

heat source 

machine

Cooling water 

temperature

Power consumption

Software 

MATLAB/Stateflow

Software 

MATLAB/SIMULINK

Cooling tower/pump model

Set point of 

chilled/hot water 

temperature

Chilled/hot water flow rate

Cooling water 

temperature

Power consumption

Outdoor air temperature/humidity
205

Used At: Author: F. Wang and H. Yoshida (Kyoto Univ., JPN) Date: Mar. 6, 2009 Context:

Node: Title: A tool for optimizing the operation of heating cooling plants Number: C

Figure 0-27 IDEFØ diagram of performance simulation module (C)

The software is developed in MATLAB language and MATLAB Simulink and Stateflow 

toolbox is used to develop the tool. The tool can be implemented in a MATLAB environment or 

as a stand-alone.

To make the tool easy to use, a graphic user interface is developed using the MATLAB Simulink

and Stateflow function.

The MATLAB curve fitting function is used to develop equipment models and their performance 

curves. Operation mode (running combined equipment) is determined automatically and visually 

using Stateflow function. All equipment is connected using the Simulink function to develop a 

whole plant model and simulate its energy performance. 

Implementation

Operability

Analytical Engine

Software 

MATLAB/SIMULINK

Cooling tower/pump model

Date

Time

Cooling/heating load

Operation 

priority order 

optimization

C1

Different 

operation 

priority order

Cooling water 

temperature 

optimization

C2

Thermal storage 

optimization

C3

Load threshold

of start/stop 

heat source 

hi

Optimal water flow rate

Software 

MATLAB/Stateflow

Software 

MATLAB/SIMULINK

Thermal storage tank 

model pump model 

Different cooling 

tower water outlet 

temperature

Chilled/hot water flow rate

Plant power consumption

Optimal operation order

Power consumption of 

chiller/cooling tower/pump Optimal cooling water temperature

Different water 

flow rate for 

thermal storage

Power consumption of 

chiller/pump
206

End users can be:

• Heating/cooling plant designer;

• Heating/cooling plant operator; and/or

• Heating/cooling plant commissioning authority.

Visualizes current operation using carpet graphics for easy detection of improper operation.

Automates and visualizes operation mode determination to save manpower and time.

Finds optimal operation method quickly, which is impossible through experimentation

The tool is tested in two district heating cooling plants in Osaka Japan. Proper plant operation is 

confirmed visually. Performances of recommended improvements are checked. Experiments are 

also conducted to verify recommendations.

No. 12 SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME Tool for Optimizing HVAC System

with Ground Thermal Storage

COUNTRY / ORGANIZATION Japan / Kyoto University

TARGETED BUILDING / SYSTEM TYPE(S) HVAC system with Ground Thermal Storage

CONTACT PERSON: Harunori Yoshida

The tool can simulate the performance of HVAC systems with ground thermal storage systems. 

Water cooled by cooling towers is circulated through pipes pre-installed in foundation piles to 

cool the ground in winter; in summer the cool water is fed to AHU cooling coils to pre-cool 

supply air. Although the system utilizes natural energy, if operated improperly the system may 

consume more energy than a common HVAC system with chillers, due to the large amount of 

energy used for water circulation. The tool can determine the system’s operational method.

Figure 0-28. IDEFØ diagram of the tool’s functions. Figures 0-29, 0-30, 0-31, and 0-32 are 

expanded diagrams of Activity A in Figure 0-28, Activity A2 in Figure 0-29 Activity A3 in 

Figure 0-30 and Activity B in Figure 0-28 respectively. 

End Users

Benefits

Target Systems to be Tested

Objectives

Functions
207

The tool’s main functions are:

• To calculate energy consumption of the HVAC system based on weather data, room air 

temperature, operation schedule and given operational methods. (Activity A in Figure 0-

28)

• To calculate outlet water temperature from the pipes buried in the ground based on water 

temperature and water flow rate. (Activity B in Figure 0-28)

• To determine optimal operation values among the operation methods given. (Activity C in 

Figure 0-28)

Details of the functions and calculations are described in the expanded IDEFO diagrams in 

Figures 0-28 to 0-32.

Figure 0-28 shows the required information for the tool and its internal data flow. The input data 

are weather data, room air temperature, operation schedule, and operation methods. The output 

data are the optimal operation values. The measured time-series data are stored as a CSV file, 

which has a defined time label in the first 7 rows. These rows indicate year, month, day, day of 

the week, hour, minute and second, Rows 1 to Row 7.

Figure 0-28 IDEFØ tool diagram(Root)

Data Management
208

Figure 0-29 IDEFØ tool diagram (A)

Figure 0-30 IDEFØ tool diagram (A2)
209

Figure 0-31 IDEFØ tool diagram (A3)

Figure 0-32 IDEFØ tool diagram (B)
210

This tool runs on the MATLAB/Simulink platform and runs FEM thermal analysis software 

ANSYS in the background. . 

We developed a graphical user interface using Simulink. The HVAC component model is 

expressed as a block. By double-clicking the block, a pop-up dialog box appears, enabling model 

parameters to be easily modified.

MATLAB, Simulink, ANSYS (FEM thermal analysis software)

Cx providers, Design engineers, Building operators

The long heat transfer time underground makes it difficult to attempt various operational 

methods experimentally. Simulation therefore becomes a powerful system tool. 

The tool was used at an office building in Takamatsu, Japan. The building exchanges heat 

underground using foundation piles. In winter, circulated water chilled by cooling towers flows 

through tubes in the foundation piles to cool down in ground. In summer, circulated water cooled 

by the ground flows through cooling coils to cool down supply air.

Implementation

Operability

Analytical Engine

End Users

Benefits

Target Systems to be Tested
211

No. 13 SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME A Simulation Tool to Estimate Baseline Energy

COUNTRY / ORGANIZATION Japan / Kyoto University

TARGETED BUILDING / SYSTEM TYPE(S) Any type

CONTACT PERSON: Harunori Yoshida

The tool can estimate energy baselines for ongoing commissioning or implementation of an 

energy saving retrofit, and make adjustments using operation conditions following the retrofit. 

The tool consists of two modules: a heating/cooling load calculation module and an energy 

consumption estimation module, which consists of sub-models of the building’s energy 

consuming equipment. Hourly energy consumption is calculated using estimated heating/cooling 

loads.

The measured time-series data is stored in CSV files, which are assigned defined time labels in 

the first 7 rows. Rows 1 to 7 indicate year, month, day, day of the week, hour, minute and 

second, respectively.

Tool input is weather data, room air temperature and humidity, fresh intake air volume, airconditioning system operation schedules, lighting systems and occupancy. Output is energy 

baseline adjusted by conditions following commissioning or an energy savings retrofit.

Objectives

Functions

Data Management
212

Figure 0-33 IDEFØ tool diagram (Root)

This tool is developed on the MATLAB/Simulink platform.

We developed a graphical user interface using Simulink. By double-clicking the block, a pop-up 

dialog box appears, enabling model parameters to be easily modified.

Simulation based air-conditioning load calculation and building system component models by 

MATLAB/ Simulink

Cx providers, Energy Service Companies, Building operators

Implementation

Operability

Analytical Engine

End Users
213

The tool can calculate a building’s energy baseline using various conditions such as weather, 

occupancy and equipment operations.

The case study was conducted at a mid-scale office building in Osaka Japan. The tool was used 

in the building; the model was tested using measured operational data.

No. 14 SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME Life-Cycle Energy Management (LCEM)

COUNTRY / ORGANIZATION Japan / Government Building,

Ministry of Land, Infrastructure and Transport (MLIT)

TARGETED BUILDING / SYSTEM TYPE(S) Any 

CONTACT PERSON: TBD

LCEM : Life-Cycle Energy Management is being developed to assess and evaluate HVAC 

energy performance throughout a building’s lifecycle (e.g., planning, design, construction, 

operation, maintenance, refurbishment and demolition). The LCEM project was conducted for 3 

years beginning in April 2004, under the leadership of the Japanese Ministry of Land, 

Infrastructure and Transport. As owner and property manager, the ministry’s Government 

Buildings Department made initiatives for the development of LCEM .

The tool aims to provide a platform for shared measurement among all the various stakeholders 

concerned with each stage of the building’s life cycle. It also aims to be operable in spreadsheets 

(e.g., MS Excel) and act as a visual aid to support communication.

The functions of this tool are :

• Basic static analysis by simulation – assessing energy performance for overall system, 

subsystems and/or individual equipment units and components being operated with 

different characteristics;

• Assessment to confirm performance at 1. equipment fabrication stage and 2. test 

operations/adjustment stage using design sate reference requirements;

• Assess performance by comparing design values and test operation status values (actual 

performance) during acceptance procedures and test operation, etc.; and

Benefits

Target Systems to be Tested

Objectives

Functions
214

• Predict performance at off-peak times (not just peak load performance) under various 

outdoor conditions such as mid-summer, mid-winter and partial load seasons (rainy 

season and early winter).

Input data:

Outdoor air condition/heat load in each room (hourly, all year) Users select load patterns from 

existing heat load estimation tools. Simplified calculation tool in spreadsheet is also being 

developed

Characteristic curve of each HVAC component (load-efficiency curve)

Output:

Status and energy consumption of each component

Suggestions for equipment size and division of capacity in terms of energy performance

LCEM is being developed by Microsoft Excel (TM) with visual aid objects to support stakeholder 

communication.

Figure 0-34 Life-cycle performance assessment and commissioning for HVAC systems

Required 

performance

Initial performance

at point of transfer Performance 

during operations

Improvement Improvement

Efficiency 

drop caused 

by unexpected 

usage

New Construction Refurbishment

Efficiency drop 

caused by change 

of operating 

conditions

Efficiency drop 

caused by performance 

degradation of 

equipments

Planning / Design Construction Operations Planning / Design Construction

Performance 

during operations

Performance 

during operations

・Commissioning

・Tuning

Required 

performance

Initial performance

at point of transfer Performance 

during operations

Improvement Improvement

Efficiency 

drop caused 

by unexpected 

usage

New Construction Refurbishment

Efficiency drop 

caused by change 

of operating 

conditions

Efficiency drop 

caused by performance 

degradation of 

equipments

Planning / Design Construction Operations Planning / Design Construction

Performance 

during operations

Performance 

during operations

Required 

performance

Initial performance

at point of transfer Performance 

during operations

Improvement Improvement

Efficiency 

drop caused 

by unexpected 

usage

New Construction Refurbishment

Efficiency drop 

caused by change 

of operating 

conditions

Efficiency drop 

caused by performance 

degradation of 

equipments

Planning / Design Construction Operations Planning / Design Construction

Performance 

during operations

Performance 

during operations

・Commissioning

・Tuning

Data Management

Implementation
215

1. Each equipment unit is defined in a spreadsheet cell. A block of cells includes characteristics 

and specifications for one equipment unit. The block of cells is named an “object composed 

cells” or just object. 

2. Users select, cut and paste an object from the list in order, from left to right, to make an 

HVAC model.

3. Users input control parameters (e.g., cooling water temperature).

4. Cross references occur almost every time with any change to data. This prompts iteration 

calculation in the worksheet.

Optimization of HVAC operation is simulated by convergent calculation using ad-in solvers in 

spreadsheet

The target end users are :

• Designers;

• Builders;

• Building maintenance managers; and

• Government officials responsible for public building planning, operations and 

maintenance.

冷却水ポンプ PD-E-1-00 ガス焚吸収式冷凍機 RA-H-1-01 冷温水一次ポンプ PC-E-1-00

エラー状態 0 エラー状態 0 エラー状態 0

運転 1 運転状態 0:停止 1:運転 1 運転状態 0:停止 1:運転 1

水量 リットル/min 2500 運転モード 0:停止 1:冷房 2:暖房 1 運転モード 0:停止 1:冷房 2:暖房 1

冷却水往温度°C 24.55 運転順位 2 運転順位 2

冷却水還温度°C 27.95 定格冷却量 kW 527 水量 リットル/min 1113

冷凍機負荷率clr(-) 0.73 冷水水量 リットル/min 1113 冷温水往温度°C 7.00

冷温水出口水温 °C 7.00 冷温水還温度°C 11.93

冷温水入口水温 °C 11.93 定格水量 リットル/min 1450

冷却水水量 リットル/min 2500 最低水量 リットル/min 725

冷却水入口水温 °C 24.55 熱源機定格能力 kW 527

冷却水出口水温 °C 27.95

冷水出口水温設定値 °C 7.0

温水出口水温設定値 °C 60.0

運転順位(冷却) 2

運転順位(加熱) 1

運転順位 2

冷却水出口水温 °C 27.95

冷/暖房能力  kW(暖房は負) 383

冷温水水量比 % 74

冷却水水量比 % 100

冷凍機負荷率 % 73

ガス消費率 % 63

ガス消費量 Nm3/h 24.38

消費電力 kW 3.20

成績係数 - 1.23

冷凍機属性

定格冷房能力 kW 527

定格暖房能力 kW 425

定格冷温水量 リットル/min 1512

定格冷却水量 リットル/min 2500

定格消費電力 kW 3.20

定格ガス消費量 Nm3/h 38.80

ガス消費補正係数 a - 1.00

ガス消費補正係数 b - 0.00

冷凍機制御

Gas Absorption

Chiller Objects

冷温水一次ポンプ PC-E-1-00 熱源機No1

エラー状態 0 運転判定

運転状態 0:停止 1:運転 1 1

運転モード 0:停止 1:冷房 2:暖房 1 運転モード

運転順位 1 1

水量 リットル/min 1113 運転順位

冷温水往温度°C 7.00 1

冷温水還温度°C 11.93 循環水量

定格水量 リットル/min 1450 1113

最低水量 リットル/min 725 出口水温

熱源機定格能力 kW 527 7.0

入口水温

ポンプ制御 11.9

水量制御方式 0:定速 1:定圧 2:最小吐出圧 2 定格水量

1450

水量初期値 リットル/s Vwin 33.20 最低水量

圧力初期値 kPa Pin 220 725

必要な揚程 Pd 69 定格能力

必要な回転数 Nd 28 527

実際の回転数 N 28 エラー状態

実際の揚程 P 69 0

消費電力 kW Pe 1.9

ポンプ属性

設計流量 リットル/min Vpl 1450

設計揚程kPa Ppl 176

実揚程 kPa 0

配管圧力 P=A*Q^n+B : A 0.2

(kPa) :B 0

ポンプ効率 e 0.67

最低水量 リットル/min Vmin 725

定格周波数 Hz Npl 50

上限周波数 Hz Nmax 60

下限周波数 Hz Nmin 20

ポンプP-Q特性 : a -1.41E-01

 P=a*Q^2+b*Q+c: b 3.99E+00

 (リットル/s - kPa) : c 243.35

動力補正係数 aec 1

動力補正係数 bec 0

Primary Pump

Object

冷却塔 CT-H-1-00 冷却水ポンプ PD-E-1-00

エラー状態 0 エラー状態 0

運転状態 0:停止 1:運転 1 運転 1

冷却水量 l/min 2500 水量 リットル/min 2500

冷却水出口温度°C 24.55 冷却水往温度°C 24.55

冷却水入口温度°C 27.95 冷却水還温度°C 27.95

外気湿球温度°C 20.00 冷凍機負荷率clr(-) 0.73

定速,定圧時水量比 % 100.00

0:定速 1:定圧 2:変流量 0

必要な揚程 kPa Pd 265.0

必要な回転数 Hz Nd 50

実際の回転数 Hz N 50

実際の揚程 kPa P 265

消費電力kW Pe 14.74

変流量時の流量比 Vdr 89.14

定格水量  リットル/min Vpl 2500

定格揚程 kPa Ppl 265

実揚程 kPa 0

配管特性 :n 2

P=AQ^n+B :A 0.153

(Pa) :B 0

ポンプ効率 e 0.75

定格回転数Hz Npl 50

上限周波数Hz Nmax 60

下限周波数Hz Nmin 30

ポンプP-Q特性 : a -0.02

 P=aQ^2+bQ+c: b 0

 (リットル/s - kPa) : c 300

動力補正係数 aec 1

動力補正係数 bec 0

冷却水ポンプ属性

冷却水ポンプ制御

Cooling Water 

Pump object

外気 B-O-1-00 冷却塔 CT-H-1-00

湿球温度°C 20.0 エラー状態 0

運転状態 0:停止 1:運転 1

冷却水量 l/min 2500

冷却水出口温度°C 24.55

冷却水入口温度°C 27.95

外気湿球温度°C 20.00

冷却水設定温度 °C 15

冷却水温度制御 1

冷却水量比 % 100

計算状態 Good

Tdsim 24.55

送風機消費電力kW 4.60

冷却水出口温度°C 24.55

冷却塔余裕係数 % 100

送風機定格消費電力kW 4.60

定格冷却水量 l/min 2500

消費電力補正係数 a 1.00

消費電力補正係数 b 0.00

冷却塔制御

冷却塔属性

Cooling Tower

Object

Water Flow rate

Return Water Temp.

(Boundary)

Outside air WB

(Boundary)

Operability

Analytical Engine

End Users

Figure 0-35 Example of system Configuration
216

The benefits of this tool are that it :

• Performs management taking into account operational status in off-peak period;

• Quantitatively supports plan, design, and operation;

• Monitors continuous performance assurance using consistent indicators throughout 

lifecycle, and performs PDCA cycle;

• Detects and analyzes potential missed defects; and

• Securely performs operator training.

HVAC system including advanced HVAC components such as double/triple effect absorption 

heater-chiller in cogeneration system, cooling control with outdoor air, and thermal energy 

storage.

No. 15 SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME EnergyPlus+VRF

COUNTRY / ORGANIZATION Japan / DAIKIN AIR-CONDITIONING AND 

ENVIRONMENTAL LAB

TARGETED BUILDING / SYSTEM TYPE(S) VRF package systems for office buildings

CONTACT PERSON: Sumio Shiochi

This tool specifically analyzes the energy consumption in the building’s variable refrigerant flow 

(VRF) air-conditioning system. The VRF system features multi indoor unit connection. 

EnergyPlus simulates the energy consumption, cooling (heating) capacity, and thermal condition 

of each zone of a centralized A/C system.

This module has the heat-pump VRF (cooling/heating) and heat recovery VRF function. In the 

heat-pump VRF function, cooling/heating power consumption is simulated for each VRF 

outdoor unit, depending on the operating mode. In the heat recovery VRF function, power 

consumption is calculated by weight condition factors (cooling or heating).

In this module, energy consumption comparisons between VRF A/C systems and centralized 

A/C systems are available.

Benefits

Target Systems to be tested

Objectives

Functions
217

The performance curve is used to calculate VRF power consumption. The performance curve is 

defined by the second order equation. These are the capacity characteristic, power input 

characteristic, partial load characteristic, and combination ratio characteristic (indoor-unit to 

outdoor-unit), etc. The calculation result is expressed as output according to EnergyPlus.

This module is developed on the EnergyPlus platform. The VRF module is customized to the 

existing EnergyPlusUnitary module.

The VRF module’s operability depends on the EnergyPlus IDF editor. All required VRF 

module input parameters are set in the IDF editor.

The calculation engine is the EnergyPlus solver. The VRF module is built into the EnergyPlus 

Solver.

The end user is a professional engineer licensed to evaluate HVAC system energy consumption. 

The simulation is used to optimize the building’s air-conditioning system.

By selecting high performance and energy saving air-conditioning systems, building owners in 

the US may receive a tax incentive, thereby reducing construction costs.

There is no current plan.

Data Management

Implementation

Operability

Analytical Engine

End Users

Benefit

Target Systems to be Tested
218

Figure 0-36 IDEF representation of Energy Plus _VRF

Calculate capacity of 

indoor unit and energy 

consumption

Calculate capacity of 

condensing unit and 

energy consumption

Indoor unit 

model Condensing unit model

Pipe length &

Vertical interval

Used At: Author: Sumio Shiochi Date : February 18th, 2009 Context:

Node : Title : EnergyPlus + VRF Number : Root

Correct capacity of 

indoor unit in 

consideration with pipe 

arrangement 

Outdoor Temp.

Energy 

consumption of 

condensing unit

Suction Temp.

Zone Load

Capacity of

Condensing unit

Correction

coefficient

Pipe mode

Correction 

equation

Capacity of indoor unit

Energy consumption of 

indoor unit

Indoor air temp. and humid.

Specification of 

indoor unit

Correction 

equation

Specification of 

condensing unit

Correction

equations
219

No.16 SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME CFD Coupled Simulation Tool for Natural Ventilation System 

with Earth Tube

COUNTRY / ORGANIZATION Japan/SANKO Air Conditioning CO.,LTD.

TARGETED BUILDING / SYSTEM TYPE(S) Natural Ventilation System with earth Tube

CONTACT PERSON: Mingjie Zheng, Song Pan

In the case of large spaces with exceptional vertical air temperature distribution, the simple 

method for natural ventilation systems cannot correctly represent the physical phenomenon, 

resulting in possible design fault at the planning phase. A CFD coupled simulation tool uses an 

earth tube to assess a natural ventilation system’s air volume.

Figure 0-37 shows the IDEFØ diagram and tool functions. Figure 0-38 is a detailed diagram of 

Activity B0 from Figure 0-37.

The tool’s main functions are:

• To use the earth tube to calculate the outlet air temperature. (Activity A0 in Figure 0-37)

• To calculate each aperture’s air volume and flow direction. (Activity B0 in Figure 0-37)

• To calculate the room’s vertical air temperature. (Activity C0 in Figure 0-37)

• To assess if the air volume at each aperture and the vertical air temperature become 

convergent. (Activity D0 and E0 in Figure 0-37)

Using this tool, we can predict the air volume of a natural ventilation system with an earth tube 

at the design phase. In the acceptance and operation phases, the model can be modified, to 

accurately predict the annual natural ventilation air volume and determine a system operation 

strategy.

Input data: 

• Weather data (wind velocity, wind direction, outdoor air temperature, solar radiation 

etc.);

• Earth tube parameters (size, length, depth, inlet air temperature, soil temperature, etc.);

• Parameters of natural ventilation apertures (number, height, area, etc.); and

Objectives

Functions

Data Management
220

• CFD parameters (room shape, boundary conditions, energy and loss coefficient of 

turbulent flow at supply air outlet, solar radiation, internal heat gain, etc.)

Output data: 

• Air volume and flow direction of each aperture; and

• Vertical air temperature distribution.

This tool uses CFD software available commercially, a natural ventilation analysis tool and an 

earth-air exchanger calculator developed by the authors.

CFD software is equipped with a convenient user interface. User interfaces for the natural 

ventilation analysis tool is in development.

CFD, natural ventilation analysis tool 

Commissioning authority, System designer

This tool is practical for assessing designed air volumes of natural ventilation systems with an 

earth tube.

The tool was tested in a case study at an elementary school gymnasium in Toyama Province, 

Japan.

Implementation

Operability

Analytical Engine

End Users

Benefit

Target Systems to be Tested
221

Figure 0-37 IDEFØ tool diagram (Root)

Used At:

Title: CFD Coupled Simulation Toll for Natural Ventilation System with Earth Tube

Context

Node: Number: Root

Author: Mingjie Zheng and Song Pan (Sanko Air Conditioning CO.,LTD.) Date: March 6th, 2009 

Earth-air heat exchange 

calculation

Width, height , length and 

buried depth of earth tube

Temperature of soil, 

heat transfer rate of 

soil and tube

Outdoor air

Outlet air temperature of earth tube

Number/height/a

rea of apertures

Vertical air 

temperature

Air volume and flow direction of 

each aperture

Wind velocity 

and direction

Surface pressure 

coefficient and opening 

rate of each aperture

Natural ventilation 

calculation with the 

consideration of 

vertical air temperature

CFD calculation

Supply air volume of 

floor apertures

Room shape, size and 

position of each aperture

Internal heat generation 

and generating position

Boundary condition of 

each outside surface

A0

B0

C0

Convergence 

judgment of supply air 

volume of floor 

apertures

Supply air volume 

of floor apertures

Convergence

No convergence

Convergence judgment criteria (ε1) of 

supply air volume of floor apertures 

Convergence judgment 

of air volume of each 

aperture and vertical air 

temperature 

Output:

Air volume and flow 

direction of each aperture, 

vertical air temperature

Convergence 

judgment 

criteria (ε2) 

of vertical air 

temperature

D0

E0

Mesh shape and 

division rate

Turbulence 

energy and loss 

rate

Turbulence model and 

finite different scheme

Convergence judgment 

criteria of the coupled 

linear equation and 

pressure correction

Convergence

No convergence

Convergence 

judgment criteria 

(ε3) of air 

volume for each 

aperture
222

Figure 0-38 IDEFØ tool diagram (B0)

Node: Title: CFD Coupled Simulation Toll for Natural Ventilation System with Earth Tube Number: B0

Used At: Author: Mingjie Zheng and Song Pan (Sanko Air Conditioning CO.,LTD.) Date: March 6th, 2009 Context

Friction resistance 

calculation of earth 

tube

Supply air volume 

of floor apertures Friction resistance of 

earth tube

Natural ventilation calculation with the 

consideration of vertical air temperature 

distribution

Air velocity 

calculation of each 

aperture

Wind velocity

Air velocity of 

each aperture

Outlet air temperature of 

earth tube

Surface pressure 

coefficient and opening 

rate of each aperture

Wind 

direction

Supply air volume of 

floor apertures

Air volume and flow 

direction of each aperture

B1

B2

B3

Width, height and length 

of earth tube

Number/height/area 

of apertures
223

No.17 SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME FDD Proto-type tool for laboratory exhaust system

COUNTRY / ORGANIZATION Japan

TARGETED BUILDING / SYSTEM TYPE(S) Laboratory, factory

CONTACT PERSON: Takao Odajima

This tool’s primary aim is the optimization of HVAC energy consumption in a laboratory, where 

many spot exhaust equipment units are installed. Spot exhaust equipment units are mandatory to 

maintain chemical and biological safety for laboratory employees. Optimization is achieved by 

enhancing exhaust flow rate. The tool’s objective is to optimize supply and exhaust flow rates to 

save energy.

Figure 0-39 shows the IDEFØ tool diagram. Figure 0-40 is an expanded diagram of Activity A 

The main function of the tool is:

• To grasp exhaust equipments’ operation status, such as fume hoods. 

• To grasp sash opening height of fume hoods (Activity A in Figure 0-39).

• To calculate exhaust flow rates of each fume hood (Activity B in Figure 0-39).

• To calculate exhaust flow rate of whole exhaust system.

• To calculate air supply flow rate for each room and whole supply system. (Activity C in 

Figure 0-39)

• To calculate energy consumption. (Activity D in Figure 0-39)

The details of Activity B are described in Figure 0-40, which are the expanded IDEFØ

diagrams.

Input data:

• Outdoor air condition/room temperature,

• Status of fume hoods (ON-OFF), opening height of fume hood, 

• Flow rate of fresh air intake

Objectives

Functions

Data Management
224

Output data:

• Characteristics of fume hood operation

• Visual information of outstanding operation

• Action list 

The tool is developed without market software. 

Figure 0-39 Root level IDEF representation

Implementation
225

Figure 0-40 Root level IDEF representation (2)

Graphical interface is developed.

The tool is developed without market software . 

Building operator, facility manager, laboratory occupants (researchers)

The benefits to users are that:

• It may help facility managers detect an exhaust system fault, or the inappropriate 

behavior of occupants.

• It may call laboratory occupants’ attention to take proper actions to minimize energy.

Interface

Analytical Engine

End Users

Benefit
226

• It may optimize total energy consumption while maintaining chemical/biological safety.

Tool is under development, and testing is planned at a pharmaceutical laboratory, upon 

completion in early 2011. 

Target Systems to be Tested
227

6.5 APPENDIX-5 Information Used in EB and LEB Cx tools 

Developed within Annex 47

This section provides the matrix of information used in developing each of the tools listed. 

Although the printed version of this document has limitations on viewing the entire window of 

embedded files, the full matrix can be obtained from the individual tool developers.
228

I) II) III)

Design data

(ex. area, conponent performance, etc.)

Operation data

(ex. set point, operation time, etc.)

Measured data

(ex. temperature, humidity, etc.)

a)

Building data

(ex. area size, U values, solar

shading, etc.)

b)

Room environmental requirement

or criteria

 (ex. room temperature set point,

air quality, etc.)

Return air humidity set point reasonable value

Maximum reasonable value for return air humidity in the summer

Minimum reasonable value for return air humidity in the summer

Reasonable value for maximum return air temperature summer

Reasonable value for ma

Zone temperature performance indice, First level alarm,

Zone temperature performance indiceSecond level alarm ,

Zone Temperature performance indiceFirst level alarm,

Zone pressure performance indiceSecond level alarm ,

Zone pressure performance indiceN

ControlZone/CO2Sensor/Status

ControlZone/HumiditySensor/SetPoint

ControlZone/HumiditySensor/Status

ControlZone/PressureSensor/SetPoint

ControlZone/PressureSensor/Status

ControlZone/TemperatureSensor/SetPoint

ControlZone/TemperatureSensor/Status

c)

Weather data

(ex. temperature, solar radiation,

etc.)

Design summer outdoor air temperature

Design winter outdoor air temperature

Outdoor CO2 maximum

Outdoor CO2 minimum

Outside air temperature reference for heating degree calculation

Outside air temperature reference for cooling degree calculation

Threshold for zone air temperature setpoint

OutdoorCondition/CO2Sensor/Status

OutdoorCondition/Enthalpy/Status

OutdoorCondition/HumiditySensor/Status

OutdoorCondition/TemperatureSensor/Status

d)

Energy data

(ex. energy prices, statistics of

energy usage, etc.)

 

 

 

SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME DABO (for AHU)

COUNTRY / ORGANIZATION Canada

TARGETED BUILDING / SYSTEM TYPE(S) Commercial and Institutional

CONTACT PERSON: Daniel Choinière

 REQUIRED INFORMATION

A

A

C

C

C

C

C

C

C

C

C 

D

E

E

E

E

E

E

E

F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

H

H

H

H

H

A

A

C

C

C

C

C

C

C

C

C 

D

E

E

E

E

E

E

E

F

F

F

F

F

F

F

F

F

F

F

F

F

F

F

H

H

H

H

H
229

 

 

e)

HVAC system data

 (ex. system type, configuration of

system, etc.)

System Type

System configuration (location of sensors)

Outside air available for warm duct(dual duct)

Differential design supply air temperature,

cooling

Differential design supply air temperature,

heating

Outside air available for warm duct

Temperature rise across return fan

Temperature rise across supply fan

Recuperation coil infuence on AHU cooling

f) HVAC component data

 (ex. COP, water flow rate, etc.)

 

 

 

 

 

Pr essur e dr op acr oss f i l t er - maxi mum

Pr essur e dr op acr oss f i l t er - mi ni mum

Suppl y / out door ai r humi di t y maxi mum val ue

Suppl y / out door ai r humi di t y mi ni mum val ue

Out door ai r t emper at ur e maxi mum val ue

Out door ai r t emper at ur e mi ni mum val ue

Suppl y/r et ur n CO2 maxi mum

Suppl y/r et ur n CO2 mi ni mum

Ret ur n ai r humi di t y maxi mum val ue

Ret ur n ai r humi di t y mi ni mum val ue

Ret ur n ai r t emper at ur e maxi mum val ue

Ret ur n ai r t emper at ur e mi ni mum val ue

Recuper at i on coi l i nf uence on AHU heat i ng

Ret ur n f an maxi mum cur r ent

Ret ur n f an mi ni mum cur r ent

Ret ur n f an maxi mum f l ow

Ret ur n f an mi ni mum f l ow

Suppl y ai r pr essur e set poi nt maxi mum

Suppl y ai r pr essur e set poi nt mi ni mum

Suppl y ai r t emper at ur e mi ni mum val ue

Suppl y ai r t emper at ur e maxi mum val ue

Suppl y f an maxi mum ci r r ent

Suppl y f an mi ni mum cur r ent

Suppl y f an maxi mum f l ow

Suppl y f an mi ni mum f l ow

Maxi mum l i mi t val ue of sour ce ai r t emper at ur e

Mi ni mum l i mi t val ue of sour ce ai r t emper at ur e

Thr eshol d f or mi xed ai r damper cont r ol and st at us

Thr eshol d f or r et ur n / suppl y / mi xed ai r t emper at ur e wi t h

cool i ng pump ON

Thr eshol d f or r et ur n / suppl y / mi xed ai r t emper at ur e wi t h

heat i ng pump ON

Thr eshol d f or Ret ur n / suppl y / mi xed ai r t emper at ur e wi t h

no heat i ng, no cool i ng or di agnosi s mode

Thr eshol d f or coi l cont r ol and st at us

Thr eshol d, CO2 set poi nt O i d i i h d

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Pr essur e dr op acr oss f i l t er - maxi mum

Thr eshol d f or mi xed ai r damper cont r ol and st at us

Thr eshol d f or r et ur n / suppl y / mi xed ai r

t emper at ur e wi t h cool i ng pump ON

Thr eshol d f or r et ur n / suppl y / mi xed ai r

t emper at ur e wi t h heat i ng pump ON

Thr eshol d f or Ret ur n / suppl y / mi xed ai r

t emper at ur e wi t h no heat i ng, no cool i ng or

di agnosi s mode

Thr eshol d f or coi l cont r ol and st at us

Thr eshol d, CO2 set poi nt

Thr eshol d out si de ai r f l ow

Thr eshol d f or out si de ai r humi di t y

Thr eshol d f or out si de ai r t emper at ur e

Thr eshol d f or Ret ur n / suppl y ai r humi di t y

Thr eshol d f or r easonabl e val ue f or r et ur n ai r

t emper at ur e

Thr eshol d suppl y / r et ur n ai r f l ow

Thr eshol d on ent hal py

 

 

 

 

 

Pr essur e dr op acr oss f i l t er - maxi mum

Pr essur e dr op acr oss f i l t er - mi ni mum

Suppl y / out door ai r humi di t y maxi mum val ue

Suppl y / out door ai r humi di t y mi ni mum val ue

Out door ai r t emper at ur e maxi mum val ue

Out door ai r t emper at ur e mi ni mum val ue

Suppl y/r et ur n CO2 maxi mum

Suppl y/r et ur n CO2 mi ni mum

Ret ur n ai r humi di t y maxi mum val ue

Ret ur n ai r humi di t y mi ni mum val ue

Ret ur n ai r t emper at ur e maxi mum val ue

Ret ur n ai r t emper at ur e mi ni mum val ue

Recuper at i on coi l i nf uence on AHU heat i ng

Ret ur n f an maxi mum cur r ent

Ret ur n f an mi ni mum cur r ent

Ret ur n f an maxi mum f l ow

Ret ur n f an mi ni mum f l ow

Suppl y ai r pr essur e set poi nt maxi mum

Suppl y ai r pr essur e set poi nt mi ni mum

Suppl y ai r t emper at ur e mi ni mum val ue

Suppl y ai r t emper at ur e maxi mum val ue

Suppl y f an maxi mum ci r r ent

Suppl y f an mi ni mum cur r ent

Suppl y f an maxi mum f l ow

Suppl y f an mi ni mum f l ow

Maxi mum l i mi t val ue of sour ce ai r t emper at ur e

Mi ni mum l i mi t val ue of sour ce ai r t emper at ur e

Thr eshol d f or mi xed ai r damper cont r ol and st at us

Thr eshol d f or r et ur n / suppl y / mi xed ai r t emper at ur e wi t h

cool i ng pump ON

Thr eshol d f or r et ur n / suppl y / mi xed ai r t emper at ur e wi t h

heat i ng pump ON

Thr eshol d f or Ret ur n / suppl y / mi xed ai r t emper at ur e wi t h

no heat i ng, no cool i ng or di agnosi s mode

Thr eshol d f or coi l cont r ol and st at us

Thr eshol d, CO2 set poi nt O i d i i h d

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Pr essur e dr op acr oss f i l t er - maxi mum

Thr eshol d f or mi xed ai r damper cont r ol and st at us

Thr eshol d f or r et ur n / suppl y / mi xed ai r

t emper at ur e wi t h cool i ng pump ON

Thr eshol d f or r et ur n / suppl y / mi xed ai r

t emper at ur e wi t h heat i ng pump ON

Thr eshol d f or Ret ur n / suppl y / mi xed ai r

t emper at ur e wi t h no heat i ng, no cool i ng or

di agnosi s mode

Thr eshol d f or coi l cont r ol and st at us

Thr eshol d, CO2 set poi nt

Thr eshol d out si de ai r f l ow

Thr eshol d f or out si de ai r humi di t y

Thr eshol d f or out si de ai r t emper at ur e

Thr eshol d f or Ret ur n / suppl y ai r humi di t y

Thr eshol d f or r easonabl e val ue f or r et ur n ai r

t emper at ur e

Thr eshol d suppl y / r et ur n ai r f l ow

Thr eshol d on ent hal py
230

g)

Control system data

(ex. set point, PID parameters,

etc.)

 

 

 

 

 

 

 

 

, p Out si de ai r t emper at ur e swi t ch over mode,

f r ee cool i ng t o mechani cal cool i ng maxi mum

Out si de ai r t emper at ur e swi t ch over mode,

f r ee cool i ng t o mechani cal cool i ng mi ni mum

Maxi mum out si de ai r t emper at ur e f or heat i ng swi t ch

over mode

Out si de ai r t emper at ur e swi t ch over mode,

heat i ng t o f r ee cool i ng maxi mum

Out si de ai r t emper at ur e swi t ch over mode,

heat i ng t o f r ee cool i ng mi ni mum

Out si de ai r t emper at ur e swi t ch over mode,

heat i ng t o f r ee cool i ng t hr eshol d

Li mi t out si de ai r t emper at ur e t o sat i sf y a zone ai r

t emper at ur e wi t hout cool i ng devi ce.

Fr esh ai r damper at mi ni mum posi t i on

Thr eshol d f or r et ur n ai r humi di t y cont r ol and

status

Maxi mum t hr eshol d val ue i n mi xed ai r t emper at ur e

t hr eshol d cal cul at i on

Mi ni mum t hr eshol d val ue i n mi xed ai r t emper at ur e

t hr eshol d cal cul at i on

Maxi mum ( SOAT-RAT) i n mi xed ai r t emper at ur e

t hr eshol d cal cul at i on

Thr eshol d, pr eheat ai r t emper at ur e set poi nt

Thr eshol d, per cent age of f r esh ai r

Thr eshol d f or r et ur n ai r humi di t y set poi nt

Thr eshol d Suppl y ai r pr essur e set poi nt

Thr eshol d f or suppl y ai r t emper at ur e set poi nt

Thr eshol d, suppl y ai r t emper at ur e set poi nt

Thr eshol d suppl y/r et ur n f an vol ume command/st at us

Wi nt er ni ght f r eeze pr ot ect i on shut of f , out si de ai r

t emper at ur e

Mi xed ai r damper at mi ni mum posi t i on

Maxi mum r easonabl e mi xed ai r t emper at ur e i n Of f mode.

Mi ni mum r easonabl e mi xed ai r t emper at ur e i n Of f mode.

Per cent age, mi ni mum ai r f l ow

Suppl y ai r t emper at ur e r easonabl e maxi mum val ue f or f r ee

cool i ng and cool i ng mode

Suppl y ai r t emper at ur e r easonabl e mi ni mum val ue f or f r ee

cool i ng and cool i ng mode

Suppl y ai r t emper at ur e maxi mum val ue f or heat i ng mode

Suppl y ai r t emper at ur e mi ni mum val ue f or heat i ng mode

Suppl y/r et ur n di f f er ent i al ai r f l ow

Mi xed ai r t emper at ur e maxi mum val ue

Mi xed ai r t emper at ur e mi ni mum val ue

Mi ni mum f r esh ai r f l ow

 

 

 

 

 

 

 

 

, p Out si de ai r t emper at ur e swi t ch over mode,

f r ee cool i ng t o mechani cal cool i ng maxi mum

Out si de ai r t emper at ur e swi t ch over mode,

f r ee cool i ng t o mechani cal cool i ng mi ni mum

Maxi mum out si de ai r t emper at ur e f or heat i ng swi t ch

over mode

Out si de ai r t emper at ur e swi t ch over mode,

heat i ng t o f r ee cool i ng maxi mum

Out si de ai r t emper at ur e swi t ch over mode,

heat i ng t o f r ee cool i ng mi ni mum

Out si de ai r t emper at ur e swi t ch over mode,

heat i ng t o f r ee cool i ng t hr eshol d

Li mi t out si de ai r t emper at ur e t o sat i sf y a zone ai r

t emper at ur e wi t hout cool i ng devi ce.

Fr esh ai r damper at mi ni mum posi t i on

Thr eshol d f or r et ur n ai r humi di t y cont r ol and

status

Maxi mum t hr eshol d val ue i n mi xed ai r t emper at ur e

t hr eshol d cal cul at i on

Mi ni mum t hr eshol d val ue i n mi xed ai r t emper at ur e

t hr eshol d cal cul at i on

Maxi mum ( SOAT-RAT) i n mi xed ai r t emper at ur e

t hr eshol d cal cul at i on

Thr eshol d, pr eheat ai r t emper at ur e set poi nt

Thr eshol d, per cent age of f r esh ai r

Thr eshol d f or r et ur n ai r humi di t y set poi nt

Thr eshol d Suppl y ai r pr essur e set poi nt

Thr eshol d f or suppl y ai r t emper at ur e set poi nt

Thr eshol d, suppl y ai r t emper at ur e set poi nt

Thr eshol d suppl y/r et ur n f an vol ume command/st at us

Wi nt er ni ght f r eeze pr ot ect i on shut of f , out si de ai r

t emper at ur e

Mi xed ai r damper at mi ni mum posi t i on

Maxi mum r easonabl e mi xed ai r t emper at ur e i n Of f mode.

Mi ni mum r easonabl e mi xed ai r t emper at ur e i n Of f mode.

Per cent age, mi ni mum ai r f l ow

Suppl y ai r t emper at ur e r easonabl e maxi mum val ue f or f r ee

cool i ng and cool i ng mode

Suppl y ai r t emper at ur e r easonabl e mi ni mum val ue f or f r ee

cool i ng and cool i ng mode

Suppl y ai r t emper at ur e maxi mum val ue f or heat i ng mode

Suppl y ai r t emper at ur e mi ni mum val ue f or heat i ng mode

Suppl y/r et ur n di f f er ent i al ai r f l ow

Mi xed ai r t emper at ur e maxi mum val ue

Mi xed ai r t emper at ur e mi ni mum val ue

Mi ni mum f r esh ai r f l ow
231
232

I) II) III)

Design data

(ex. area, conponent performance, etc.)

Operation data

(ex. set point, operation time, etc.)

Measured data

(ex. temperature, humidity, etc.)

a)

Building data

(ex. area size, U values, solar

shading, etc.)

b)

Room environmental requirement

or criteria

 (ex. room temperature set point,

air quality, etc.)

Room air temperature setpoint, room air relative

humidity setpoint.

Room air temperature, room air relative

humidity.

c)

Weather data

(ex. temperature, solar radiation,

etc.)

Outdoor air temperature and humidity

d)

Energy data

(ex. energy prices, statistics of

energy usage, etc.)

e)

HVAC system data

 (ex. system type, configuration of

system, etc.)

Schematic diagram of AHU system weekly operation schedule ON-OFF signal AHU's

f) HVAC component data

 (ex. COP, water flow rate, etc.)

Specification of constant airflow measure rings,

maximum airflow rate of AHU's.

Flow rate supply- and return fan, temperature in

duct after outlet solar collector, outlet temperature solar collector, supply air temperature,

supply air temperature setpoint, temperature

after heat-exchanger, inlet temperature solar

collector

g)

Control system data

(ex. set point, PID parameters,

etc.)

Control strategy of AHU, max. frequency value

of frequency controllers fan's. Setpoints, Control signals (0-100%),

h)

Maintenance data

(ex. maintenance level, mumble of

maintainer, etc.)

SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME

COUNTRY / ORGANIZATION

TARGETED BUILDING / SYSTEM TYPE(S)

A FDD tool "i-BIG" for AHU's. 

The Netherlands / TNO Built Environment and Geosciences

In general AHU's.

Luc Soethout, Henk Peitsman

 REQUIRED INFORMATION

CONTACT PERSON:
233

I) II) III)

Design data

(ex. area, conponent performance, etc.)

Operation data

(ex. set point, operation time, etc.)

Measured data

(ex. temperature, humidity, etc.)

a)

Building data

(ex. area size, U values, solar

shading, etc.)

Area size: 13700 m2

Avreage U vaule for zones/rooms

A general building plan

Building zones

b)

Room environmental requirement

or criteria

 (ex. room temperature set point,

air quality, etc.)

Indoor air temperature Indoor air temperature

c)

Weather data

(ex. temperature, solar radiation,

etc.)

Outdoor temperature Outdoor temperature

d)

Energy data

(ex. energy prices, statistics of

energy usage, etc.)

Hourly energy consumption of the system

 - The hot water energy consumption

- The energy consumption of branches that

do not have measurements in the control

system

e)

HVAC system data

 (ex. system type, configuration of

system, etc.)

Schematic diagram of system

f) HVAC component data

 (ex. COP, water flow rate, etc.)

Specification of components: The size/capacity

of radiators in each room, The capacity of the

heat exchangers in the substation, The valves

characteristics in the substation

g)

Control system data

(ex. set point, PID parameters,

etc.)

Design supply temperature, Design

temperature difference

 - Heating supply temperature curve

(specification of the important points)

- Operational schedule

 - Heating supply temerature

- Hot water supply temperature

h)

Maintenance data

(ex. maintenance level, mumble of

maintainer, etc.)

1. Maintenance service hired in building

2. Control system maintenance: technical

support from the control system producer

SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME

COUNTRY / ORGANIZATION

TARGETED BUILDING / SYSTEM TYPE(S)

Performance Analysis Tool for Heating System

Norwegina University of Science and Technology

Hydronic Heating System

Natasa Djuric

 REQUIRED INFORMATION

CONTACT PERSON:
234

I) II) III)

Design data

(ex. area, conponent performance, etc.)

Operation data

(ex. set point, operation time, etc.)

Measured data

(ex. temperature, humidity, etc.)

a)

Building data

(ex. area size, U values, solar 

shading, etc.)

net floor area, utilization of zones, 

information about internal ains (size, 

distribution)

b)

Room environmental requirement or 

criteria 

 (ex. room temperature set point, air 

quality, etc.)

requirements for indoor climate depending 

on the kind of system

temperature, moisture of selected reference 

zones

c)

Weather data

(ex. temperature, solar radiation, 

etc.)

temperature , moisture, solar radiation 

(global horizontal)

d)

Energy data 

(ex. energy prices, statistics of 

energy usage, etc.)

if available: historic consumption data and/or 

planned energy demand

total delivered energy (fuel, district heat/cold, 

electricity) AND WATER 

CONSUMPTION

e)

HVAC system data

 (ex. system type, configuration of 

system, etc.)

kind of system, schemes, zonal distribution operation schedules

Supply and return temperatures of major 

distribution (water and air), in case of air: 

moisture of exhaust and supply if treated.

f) HVAC component data

 (ex. COP, water flow rate, etc.) if available: flow rates, capacities operation schedules

g) Control system data

(ex. set point, PID parameters, etc.)

if available: control strategy of AHUs, 

pumps, different operation modes control signals of drives (pumps, fans)

h)

Maintenance data

(ex. maintenance level, mumble of 

maintainer, etc.)

Christian Neumann

 REQUIRED INFORMATION

CONTACT PERSON:

 

TOOL NAME

COUNTRY / ORGANIZATION

TARGETED BUILDING / SYSTEM TYPE(S)

A tool to be used for energy analysis and fault detection on the whole building level

Germany / Fraunhofer ISE

non-residential (office) buildings / overall performance
235

I) II) III)

Design data

(ex. area, conponent performance, etc.)

Operation data

(ex. set point, operation time, etc.)

Measured data

(ex. temperature, humidity, etc.)

a)

Building data

(ex. area size, U values, solar

shading, etc.)

General information for calculating building

thermal loads such as floor plans, sections,

windows size, thermal properties of

walls/windows, etc.

Occupancy time

b)

Room environmental requirement

or criteria

 (ex. room temperature set point,

air quality, etc.)

Room temperature set point Indoor air temperature and humidity

c)

Weather data

(ex. temperature, solar radiation,

etc.)

Outdoor air temperature and humidity,

solar radiation

d)

Energy data

(ex. energy prices, statistics of

energy usage, etc.)

System and components energy

consumption

e)

HVAC system data

 (ex. system type, configuration of

system, etc.)

Schematic diagram of system Operation time System energy consumption and amount of

heat dealt with the system

f) HVAC component data

 (ex. COP, water flow rate, etc.) Specification of components Operation time

Components energy consumption and

amount of heat dealt with the components,

physical state quantities such as water

temperature, flow rate, pressure, etc.

g)

Control system data

(ex. set point, PID parameters,

etc.)

System control logics and strategies

including control set points, PID

parameters, etc.

h)

Maintenance data

(ex. maintenance level, mumble of

maintainer, etc.)

SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME

COUNTRY / ORGANIZATION

TARGETED BUILDING / SYSTEM TYPE(S)

Optimization tool for air-conditioning system operation considering thermal load prediction errors

Japan

Exsisted office buildings and centered HVAC systems

Yasunori Akashi, Daisuke Sumiyoshi

 REQUIRED INFORMATION

CONTACT PERSON:
236

I) II) III)

Design data

(ex. area, conponent performance, etc.)

Operation data

(ex. set point, operation time, etc.)

Measured data

(ex. temperature, humidity, etc.)

a)

Building data

(ex. area size, U values, solar

shading, etc.)

area, U values, solar shading....

data required for heat load calculations

b)

Room environmental requirement

or criteria

 (ex. room temperature set point,

air quality, etc.)

room temperature and room humidity

c)

Weather data

(ex. temperature, solar radiation,

etc.)

Standard weather data for heat load

calculation temperature, humidity, insolation

d)

Energy data

(ex. energy prices, statistics of

energy usage, etc.)

power consumption

e)

HVAC system data

 (ex. system type, configuration of

system, etc.)

f) HVAC component data

 (ex. COP, water flow rate, etc.)

Nominal capacity, nominal power

consumption, number of compressors, type

of refrigerant, nominal COP

power consumption

g)

Control system data

(ex. set point, PID parameters,

etc.)

h)

Maintenance data

(ex. maintenance level, mumble of

maintainer, etc.)

CONTACT PERSON:

Only adaptable for VRV package systems

SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME

COUNTRY / ORGANIZATION

TARGETED BUILDING / SYSTEM TYPE(S)

An on-going commissioning tool for VRV package systems

Japan / Chubu University

All types / VRV package systems

Motoi Yamaha

 REQUIRED INFORMATION
237

I) II) III)

Design data

(ex. area, conponent performance, etc.)

Operation data

(ex. set point, operation time, etc.)

Measured data

(ex. temperature, humidity, etc.)

a)

Building data

(ex. area size, U values, solar

shading, etc.)

b)

Room environmental requirement

or criteria

 (ex. room temperature set point,

air quality, etc.)

Set points for room temperature, humidity

control

Room Tempeature

Relative Humidity

c)

Weather data

(ex. temperature, solar radiation,

etc.)

Dry bulb temperature

Wet bulb temperature

Relative Humidity

d)

Energy data

(ex. energy prices, statistics of

energy usage, etc.)

e)

HVAC system data

 (ex. system type, configuration of

system, etc.)

Configuration of the system

Supply/ returen temperature of chilled

water and condenser water

Flow rate of chilled water, condenser

water

Temperature of supply air

Supply/ returen temperature of chilled

water and condenser water

Flow rate of chilled water, condenser

water

Temperature of supply air

f) HVAC component data

 (ex. COP, water flow rate, etc.)

Design data of each component

Allowable operational condition

g)

Control system data

(ex. set point, PID parameters,

etc.)

Design set points for each component

h)

Maintenance data

(ex. maintenance level, mumble of

maintainer, etc.)

SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME

COUNTRY / ORGANIZATION

TARGETED BUILDING / SYSTEM TYPE(S)

A tool to monitor continuously oprational data
 of each HVAC equipment 

Hitachi Plant / Japan

Office buildings

Hiroo Sakai

 REQUIRED INFORMATION

CONTACT PERSON:
238

I) II) III)

Design data

(ex. area, conponent performance, etc.)

Operation data

(ex. set point, operation time, etc.)

Measured data

(ex. temperature, humidity, etc.)

a)

Building data

(ex. area size, U values, solar

shading, etc.)

Space size, Window size, Over-all heat transfer

coefficient, material characteristics of building

skin

Warming up time, Size and shape of separated

mesh for CFD Outside surface temperatures of building skin

b)

Room environmental requirement

or criteria

 (ex. room temperature set point,

air quality, etc.)

Room temperature set point, Pick-up time Model of turbulent flow calculate, Finite

difference scheme Indoor air temperature gradient

c)

Weather data

(ex. temperature, solar radiation,

etc.)

 Outdoor air temperature, Solar radiation, Wind

speed

Outdoor air temperature, Solar radiation, Wind

speed

d)

Energy data

(ex. energy prices, statistics of

energy usage, etc.)

Size and position of lighting, furniture,officemachine and occupancyEnergy and loss coefficient of turbulent flow in

supply air outlet

Heat gain and schedule from lighting, officemachine and occupancy

e)

HVAC system data

 (ex. system type, configuration of

system, etc.)

Schematic diagram of system Operation time Operation time

f) HVAC component data

 (ex. COP, water flow rate, etc.)

Size and shape of supply air outlet and return

air inlet

Supply air volume,Temperature/humidity of

supply air Air temperature in duct and pipe

g)

Control system data

(ex. set point, PID parameters,

etc.)

Room temperature set point, PID parameters of

controller for the supply air temperature, Time

constant and sensitivity of thermostat

Integral sampling step, Control step of AHU

water valve opening, Time delay, Dead band of

room air temperature sensor

Supply air volume,Temperature/humidity of

supply air, AHU water valve opening

h)

Maintenance data

(ex. maintenance level, mumble of

maintainer, etc.)

Mingjie Zheng

 REQUIRED INFORMATION

CONTACT PERSON:

SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME

COUNTRY / ORGANIZATION

TARGETED BUILDING / SYSTEM TYPE(S)

Initial Cx Tool for HVAC System in Large Enclosure 

Japan /SANKO Air Conditioning CO.,LTD

Indoor climate analysis for the large enclosures
239

I) II) III)

Design data

(ex. area, conponent performance, etc.)

Operation data

(ex. set point, operation time, etc.)

Measured data

(ex. temperature, humidity, etc.)

a)

Building data

(ex. area size, U values, solar

shading, etc.)

b)

Room environmental requirement

or criteria

 (ex. room temperature set point,

air quality, etc.)

Set point of room air temperature Air temperature of each room

c)

Weather data

(ex. temperature, solar radiation,

etc.)

d)

Energy data

(ex. energy prices, statistics of

energy usage, etc.)

e)

HVAC system data

 (ex. system type, configuration of

system, etc.)

Sampling intervals of system's data, Threshold

of statistical data of measured data and their

Fourier Transform values

Operation state of HVAC system, Supply air

temperature and volume of AHU, Air volume of

VAV unit,

f) HVAC component data

 (ex. COP, water flow rate, etc.) Number of VAV units Sampling intervals of unit's data Inlet/outlet cooling water temperature of AHU,

Cooling water flow rate of AHU

g)

Control system data

(ex. set point, PID parameters,

etc.)

h)

Maintenance data

(ex. maintenance level, mumble of

maintainer, etc.)

Mingjie Zheng and Song Pan

 REQUIRED INFORMATION

CONTACT PERSON:

SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME

COUNTRY / ORGANIZATION

TARGETED BUILDING / SYSTEM TYPE(S)

On-going Type FDD Tool with Pattern Recognition for VAV Systems

Japan /SANKO Air Conditioning CO.,LTD

non-residential (office) buildings / performance
240

I) II) III)

Design data

(ex. area, conponent performance, etc.)

Operation data

(ex. set point, operation time, etc.)

Measured data

(ex. temperature, humidity, etc.)

a)

Building data

(ex. area size, U values, solar

shading, etc.)

Hourly heating/cooling load Hourly heating/cooling load

b)

Room environmental requirement

or criteria

 (ex. room temperature set point,

air quality, etc.)

c)

Weather data

(ex. temperature, solar radiation,

etc.)

Hourly temperature, relative humidity Hourly temperature, relative humidity

d)

Energy data

(ex. energy prices, statistics of

energy usage, etc.)

Energy prices

Hourly energy cost of each heating/cooling

source equipments (for the purpose of refining

equipment models)

e)

HVAC system data

 (ex. system type, configuration of

system, etc.)

f) HVAC component data

 (ex. COP, water flow rate, etc.)

Specifications of heating/cooling source

equipments, delivery pumps, cooling towers,

thermal storage tanks, heat exchangers used for

thermal storage systems

Set points of chilled/hot water temperature,

cooling water temperature

g)

Control system data

(ex. set point, PID parameters,

etc.)

Control logic for start/stop heating/cooling

source equipments, cooling tower fans

h)

Maintenance data

(ex. maintenance level, mumble of

maintainer, etc.)

SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME

COUNTRY / ORGANIZATION

TARGETED BUILDING / SYSTEM TYPE(S)

A Tool for optimizing the operation of heating cooling plants

Japan / Kyoto University

Any type

Harunori Yoshida

 REQUIRED INFORMATION

CONTACT PERSON:
241

I) II) III)

Design data

(ex. area, conponent performance, etc.)

Operation data

(ex. set point, operation time, etc.)

Measured data

(ex. temperature, humidity, etc.)

a)

Building data

(ex. area size, U values, solar

shading, etc.)

Site/Area size

Foundation pile/Number

Foundation pile/Diameter

Foundation pile/Depth

Water pipe/Shape

Water pipe/length

Water pipe/Inside diameter

Water pipe/Outside diameter

Soil/Heat thermal conductivity

Soil/Specific Heat

Soil/Density

Concrete/Heat the

b)

Room environmental requirement

or criteria (ex. room temperature

set point, air quality, etc.)

Return air condition/Temperature

Return air condition/Humidity

c)

Weather data

(ex. temperature, solar radiation,

etc.)

Outdoor condition/Temperature

Outdoor condition/Humidity

d)

Energy data

(ex. energy prices, statistics of

energy usage, etc.)

DHC plant/System COP

e) HVAC system data (ex. system

type, configuration of system, etc.)

HVAC system/System Type

HVAC system/Configuration

HVAC system/Mode (Storage or discharge)

HVAC system/Switch

Ground/Charged heat amount

Ground/Discharged heat amount

Ground/Soil temperatures

f) HVAC component data

 (ex. COP, water flow rate, etc.)

Pump for storage/Specification data

Pump for discharge/Specification data

Cooling Tower/Specification data

Air heat exchanger/Specification data

Cooiling coil/Specification data

Pump for storage/Energy consumption

Pump for discharge/Energy consumption

Cooling Tower/fan/Energy consumption

g)

Control system data

(ex. set point, PID parameters,

etc.)

Cooling coil/Water valve/Control strategy

 (the flow rate is controlled so that the outlet

water temperature of the coil attains the

temperature set point )

Pump for storage/water flow rate/Set point

Cooiling Tower/Fan/Inverter value/Set point

Cooiling Coil/Outlet water temperature/Set point

h)

Maintenance data

(ex. maintenance level, mumble of

maintainer, etc.)

SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME

COUNTRY / ORGANIZATION

TARGETED BUILDING / SYSTEM TYPE(S)

A Tool for Optimizing the HVAC System 
with Ground Thermal Storage 

Japan / Kyoto University

HVAC System with Underground Thermal Storage

Harunori Yoshida

 REQUIRED INFORMATION

CONTACT PERSON:
242

I) II) III)

Design data

(ex. area, conponent performance, etc.)

Operation data

(ex. set point, operation time, etc.)

Measured data

(ex. temperature, humidity, etc.)

a)

Building data

(ex. area size, U values, solar

shading, etc.)

Area Size (6169 m2), Air-conditioned area

(3942 m2), Wall configuration

b)

Room environmental requirement

or criteria

 (ex. room temperature set point,

air quality, etc.)

Number of Occupants (260 persons),

Calorific value of lighting and the office

machines

Room air temperature and humidity

c)

Weather data

(ex. temperature, solar radiation,

etc.)

Outdoor air temperature and humidity,

Grobal solar radiation

d)

Energy data

(ex. energy prices, statistics of

energy usage, etc.)

Energy consumption of whole building

e)

HVAC system data

 (ex. system type, configuration of

system, etc.)

Operating time Fresh air intake volume (2.46 m3

/hm2)

f) HVAC component data

 (ex. COP, water flow rate, etc.)

Inlet and outlet water temperature of

chiller, water flow rate of chiller

g)

Control system data

(ex. set point, PID parameters,

etc.)

h)

Maintenance data

(ex. maintenance level, mumble of

maintainer, etc.)

SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME

COUNTRY / ORGANIZATION

TARGETED BUILDING / SYSTEM TYPE(S)

A Tool to Estimate Energy Baselines

Japan / Kyoto University

Any type

Harunori Yoshida

 REQUIRED INFORMATION

CONTACT PERSON:
243

0

I) II) III)

Design data

(ex. area, conponent performance, etc.)

Operation data

(ex. set point, operation time, etc.)

Measured data

(ex. temperature, humidity, etc.)

a)

Building data

(ex. area size, U values, solar

shading, etc.)

floor area

location

items for cooling/heating load estimation

annual operation schedule

b)

Room environmental requirement

or criteria

 (ex. room temperature set point,

air quality, etc.)

hourly indoor cooling/heating load

set point of

 indoor temparature (DB,WB)

 on/off time

indoor temparature (DB,WB)

c)

Weather data

(ex. temperature, solar radiation,

etc.)

standard annual weather data outside air temparature(DB, WB)

supply water temparature

d)

Energy data

(ex. energy prices, statistics of

energy usage, etc.)

gas calolific value

volum of wasete heat from CGS

gas calolific value

amount of use of wasete heat from CGS

actual gas consumption

actual power consumption

actual water consumption

e)

HVAC system data

 (ex. system type, configuration of

system, etc.)

system type, capacity, ducts specification

system COP at partial load

HVAC on-off time

operation schedule

f) HVAC component data

 (ex. COP, water flow rate, etc.)

specifications of components

 chiller/heater, pump, AHU, fan,

 cooling tower, etc.

 COP at partial load

COP at actual load

g)

Control system data

(ex. set point, PID parameters,

etc.)

Set point of

 chilled water temparature for chillers

 cooling water for cooling towers

 required water head for pumps

Set point of min and/or max

 chilled water temparature for chillers

 cooling water for cooling towers

 required water head for pumps

actual data of inlet and/or outlet of

 chilled/hot water temparature

 cooling water temparature

h)

Maintenance data

(ex. maintenance level, mumble of

maintainer, etc.)

frequency of maintenance by components

COP recovery rate

SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME

COUNTRY / ORGANIZATION

TARGETED BUILDING / SYSTEM TYPE(S)

Tools for Life Cycle Energy Management (LCEM) for Buildings

Japan / Ministry of Land, Infrastructure and Transport (MLIT)

Public buildings

Public Building Association of Japan

 REQUIRED INFORMATION

CONTACT PERSON:
244

I) II) III)

Design data

(ex. area, conponent performance, etc.)

Operation data

(ex. set point, operation time, etc.)

Measured data

(ex. temperature, humidity, etc.)

a)

Building data

(ex. area size, U values, solar

shading, etc.)

Location ,Area size

Structure of building (Thickness,Material

properties,Window-size etc)

Zoning

b)

Room environmental requirement

or criteria

 (ex. room temperature set point,

air quality, etc.)

Set point

Ventilation

Infiltration

Internal load driving schedule(People,

lighting, Electric equipment, etc)

c)

Weather data

(ex. temperature, solar radiation,

etc.)

Typical Meteorological Year 2 (TMY) weather

format.

d)

Energy data

(ex. energy prices, statistics of

energy usage, etc.)

e)

HVAC system data

 (ex. system type, configuration of

system, etc.)

System type

Configuration of system Operation data

f) HVAC component data

 (ex. COP, water flow rate, etc.)

Performance curve(Total capacity,Power

input,PLR,Combination ratio)

g)

Control system data

(ex. set point, PID parameters,

etc.)

h)

Maintenance data

(ex. maintenance level, mumble of

maintainer, etc.)

SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME

COUNTRY / ORGANIZATION

TARGETED BUILDING / SYSTEM TYPE(S)

Energy analysis module (on Energy Plus) for the variable refrigerant flow(VRF) air-conditioning system 

Japan / Daikin Air-conditioning and Environmental, Laboratory, Ltd

HVAC System for the variable refrigerant flow(VRF) air-conditioning system 

Shingo Itoh

 REQUIRED INFORMATION

CONTACT PERSON:
245

I) II) III)

Design data

(ex. area, conponent performance, etc.)

Operation data

(ex. set point, operation time, etc.)

Measured data

(ex. temperature, humidity, etc.)

a)

Building data

(ex. area size, U values, solar

shading, etc.)

Width,height,length and buried depth of earth

tube

 mesh shape and division rate for CFD, Energy

and loss coefficient of turbulent flow

Outside surface temperatures of building skin,

Temperature of soil

b)

Room environmental requirement

or criteria

 (ex. room temperature set point,

air quality, etc.)

Model of turbulent flow calculate, Finite

difference scheme, Temperature of soil, Heat

transfer rate of soil and tube

Indoor air temperature gradient

c)

Weather data

(ex. temperature, solar radiation,

etc.)

 Outdoor air temperature, Solar radiation, Wind

speed, Wind direction

Outdoor air temperature, Solar radiation, Wind

speed, wind direction

d)

Energy data

(ex. energy prices, statistics of

energy usage, etc.)

Size and position of lighting, furniture,officemachine and occupancyHeat gain and schedule from lighting, officemachine and occupancy

e)

HVAC system data

 (ex. system type, configuration of

system, etc.)

Surface pressure coefficient and operating rate

of each aperture, Number/height/area of

apertures

Supply air temperature and volume of each

aperture

f) HVAC component data

 (ex. COP, water flow rate, etc.)

g)

Control system data

(ex. set point, PID parameters,

etc.)

Convergence judgement criteria of the coupled

linear equation and pressure correction

h)

Maintenance data

(ex. maintenance level, mumble of

maintainer, etc.)

SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME

COUNTRY / ORGANIZATION

TARGETED BUILDING / SYSTEM TYPE(S)

Japan /SANKO Air Conditioning CO.,LTD

CFD Coupled Simulation Tool for Natural Ventilation System with Earth Tube

annexation system of cool/heat tube and natural ventilation

Mingjie Zheng, Song Pan

 REQUIRED INFORMATION

CONTACT PERSON:
246

I) II) III)

Design data

(ex. area, conponent performance, etc.)

Operation data

(ex. set point, operation time, etc.)

Measured data

(ex. temperature, humidity, etc.)

a)

Building data

(ex. area size, U values, solar

shading, etc.)

Occupancy time

b)

Room environmental requirement

or criteria

 (ex. room temperature set point,

air quality, etc.)

Room temperature set point, Relative humidity

set point, Room air pressure difference set

point,

Room temperature ,Relative humidity , Room air

pressure difference

c)

Weather data

(ex. temperature, solar radiation,

etc.)

Hourly temperature, relative humidity

d)

Energy data

(ex. energy prices, statistics of

energy usage, etc.)

Energy prices Hourly energy consumption of each

heating/cooling source equipments

e)

HVAC system data

 (ex. system type, configuration of

system, etc.)

Schematic diagram of system, System type, Ahu

zones Operation time Supply and exhaust air volume,

f) HVAC component data

 (ex. COP, water flow rate, etc.) Specifications of HVAC equipments Exhaust VAV status, Operation status of each

fume hood operation, Sash opening height

g)

Control system data

(ex. set point, PID parameters,

etc.)

Control strategy of supply and exhaust VAV

control, supply and exhaust fan control and set

point control

PID parameters

h)

Maintenance data

(ex. maintenance level, mumble of

maintainer, etc.)

Assumed demand factor based on past

operational data Demand factor of fume hood operation

Takao Odajima

 REQUIRED INFORMATION

CONTACT PERSON:

SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME

COUNTRY / ORGANIZATION

TARGETED BUILDING / SYSTEM TYPE(S)

Proto-type FDD tool for laboratory exhaust system

Japan / TAKENAKA CORPORATION

Laboratory,factory
247

I) II) III)

Design data

(ex. area, conponent performance, etc.)

Operation data

(ex. set point, operation time, etc.)

Measured data

(ex. temperature, humidity, etc.)

a)

Building data

(ex. area size, U values, solar

shading, etc.)

Building type (purpose of use, classification

based on the main use)

Information on main and sub usages (e.g.

share of area)

Minimum is the usable net floor area, possibly

heated+total building volume

b)

Room environmental requirement

or criteria

 (ex. room temperature set point,

air quality, etc.)

Share of spaces with "abnormal" condition

requirements

c)

Weather data

(ex. temperature, solar radiation,

etc.)

 Mean outdoor temperature and monthly/yearly

heating (cooling) degree days, possibly other

weather data as well

d)

Energy data

(ex. energy prices, statistics of

energy usage, etc.)

Energy forms/carriers, Prices for heating

and electrical energy+water

Yearly or (preferably) monthly consumption

figures (preferably meeter readings) of heating

and electrical energy +water

e)

HVAC system data

 (ex. system type, configuration of

system, etc.)

Possibly operation times or classification

(8/12/16/24 hours/day)

System type for statistical analyses (classification

like natural/mechanical exhaust air/mechanical

intake+exhaust air/air conditining/heat recovery

f) HVAC component data

 (ex. COP, water flow rate, etc.)

g)

Control system data

(ex. set point, PID parameters,

etc.)

Control principles, manual or BAS or other

automation/remote control (e.g. BEMS)

h)

Maintenance data

(ex. maintenance level, mumble of

maintainer, etc.)

M&O-manual (form: paper/eelctronic)

i) User data

Amount of users/year or month (if available) like

pupils/students in schools etc. to be used in

benchmarking and performance metrics

SUBTASK B: Commissioning and Optimization of Existing Buildings

TOOL NAME

COUNTRY / ORGANIZATION

TARGETED BUILDING / SYSTEM TYPE(S)

Energy Performance Commissioning of Existing Buildings and Building Portfolios

Finland/VTT

All kind of existing buildings

Jorma Pietilainen

 REQUIRED INFORMATION

CONTACT PERSON: