NIST Technical Note 1727

Annex 47 Report 3:

Commissioning Cost-Benefit and 

Persistence of Savings

Hannah Friedman

Marti Frank

Kristin Heinemeier

Kim Crossman

Eliot Crowe

David Claridge 

Cory Toole

Daniel Choinière 

Natascha Milesi Ferretti

http://dx.doi.org/10.6028/NIST.TN.1727
NIST Technical Note 1727

Annex 47 Report 3:

Commissioning Cost-Benefit and 

Persistence of Savings

Hannah Friedman

Marti Frank

Kristin Heinemeier

Kim Crossman

Eliot Crowe

Portland Energy Conservation Inc.

David Claridge 

Cory Toole

Texas A&M University

Daniel Choinière 

Natural Resources Canada

CanmetENERGY

Energy Technology Centre

Natascha Milesi Ferretti

National Institute of Standards and Technology

Engineering Laboratory

http://dx.doi.org/10.6028/NIST.TN.1727

December 2011

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 1727

Natl. Inst. Stand. Technol. Tech. Note 1727, 252 pages (December 2011) 

http://dx.doi.org/10.6028/NIST.TN.1727

CODEN: NTNOEF
CITATION

Friedman, H. et al. “Annex 47 Report 3: Commissioning Cost-Benefit and Persistence of 

Savings”, National Institute of Standards and Technology, Technical Note 1727. December 

2011.

This publication was simultaneously published by Natural Resources Canada as Friedman, H. et 

al. “Annex 47 Report 3: Commissioning Cost-Benefit and Persistence of Savings”, a Report of 

Cost-Effective Commissioning of Existing and Low Energy Buildings. December 2011.

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.
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 [1] 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 

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 Behaviour 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 (*)

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)

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 cost-
benefit and persistence. The methods were implemented in a cost-benefit and persistence 

database using field data.

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
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 
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 are 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:

Hannah Friedman (Portland Energy Conservation, Inc)

David Claridge (Texas A&M University)

Daniel Choinière (Natural Resources Canada)

Natascha Milesi Ferretti (NIST, USA)
TABLE OF CONTENTS

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

BACKGROUND...............................................................................................................................................................1

COMMISSIONING COST-BENEFIT.......................................................................................................................................1

COMMISSIONING PERSISTENCE.........................................................................................................................................2

CONCLUSIONS ...............................................................................................................................................................2

1. INTRODUCTION.................................................................................................................................................. 5

2. COMMISSIONING COST-BENEFIT........................................................................................................................6

2.1 LITERATURE REVIEW OF COMMISSIONING COST-BENEFIT METHODOLOGIES AND DATA .........................................................6

2.2 REFERENCES..........................................................................................................................................................32

3. COMMISSIONING PERSISTENCE ....................................................................................................................... 36

3.1 METHODOLOGIES FOR DETERMINING PERSISTENCE OF COMMISSIONING BENEFITS .............................................................36

3.2 REVIEW OF LITERATURE ON THE PERSISTENCE OF COMMISSIONING BENEFITS IN NEW AND EXISTING BUILDINGS......................41

3.2.1 Introduction..............................................................................................................................................41

3.2.2 Persistence of Commissioning Measures in Existing Buildings.................................................................42

3.2.3. Persistence of Commissioning Measures in New Buildings......................................................................54

3.2.4. Strategies for Improving Persistence in New and Existing Buildings........................................................57

3.2.5. Related Reports........................................................................................................................................59

3.2.6. Methodologies for Determining Persistence of Commissioning Measures and Energy Benefits of 

Commissioning ...................................................................................................................................................60

3.2.7. Summary and Conclusions .......................................................................................................................61

3.3 INFLUENCE OF SAVINGS NORMALIZATION METHOD ON PERSISTENCE ...............................................................................62

3.3.1 Introduction..............................................................................................................................................62

3.3.2 Savings normalization methods evaluated ...............................................................................................63

3.4 RECENT PERSISTENCE STUDIES .........................................................................................................................83

3.5 REFERENCES........................................................................................................................................................101

4. TOOLS TO ENHANCE PERSISTENCE ................................................................................................................. 103

4.1 AN AUTOMATED BUILDING COMMISSIONING ANALYSIS TOOL (ABCAT) ........................................................................103

4.1.1 Introduction............................................................................................................................................103

4.2 THE DIAGNOSTIC AGENT FOR BUILDING OPERATION (DABOTM): A BEMS ASSISTED ON-GOING COMMISSIONING TOOL ........117

4.2.1 Introduction............................................................................................................................................117

4.3 REFERENCES........................................................................................................................................................128

5. INTERNATIONAL COMMISSIONING COST-BENEFIT AND PERSISTENCE DATABASE ......................................... 130

5.1 INTRODUCTION....................................................................................................................................................130

5.1.1 Cost-Benefit Methodology......................................................................................................................131

5.1.2 This Chapter ...........................................................................................................................................131

5.2 DATA COLLECTION ...............................................................................................................................................132

5.2.1 Data Collection Goals and Outcomes.....................................................................................................132

5.2.2 Accuracy and Completeness of Data......................................................................................................133

5.3 COST-BENEFIT ANALYSIS SUMMARY ........................................................................................................................133

5.3.2 Existing Building Commissioning (EBCx).................................................................................................138

5.4 DISCUSSION.......................................................................................................................................................154

5.5 RECOMMENDATIONS...........................................................................................................................................157

5.6 REFERENCES........................................................................................................................................................158
APPENDIX A: COMMISSIONING COST-BENEFIT DATABASE ................................................................................ 161

I: COMMISSIONING COST-BENEFIT PROTOCOLS REPORT....................................................................................................162

IEA ANNEX 47:.........................................................................................................................................................162

COST-EFFECTIVE COMMISSIONING FOR ..........................................................................................................................162

EXISTING AND LOW ENERGY BUILDINGS .........................................................................................................................162

A report documenting the cost-benefit methodology and protocols...............................................................162

agreed to by the Annex participants................................................................................................................162

Submitted to:....................................................................................................................................................162

Phil Haves, LBNL ...............................................................................................................................................162

Submitted by: ...................................................................................................................................................162

PECI ..................................................................................................................................................................162

May 23, 2007 ...................................................................................................................................................162

1.0 INTRODUCTION............................................................................................................................................ 163

1.1 BACKGROUND .....................................................................................................................................................163

1.2 THIS REPORT .......................................................................................................................................................164

2.0 COMMISSIONING PROJECT .......................................................................................................................... 164

2.1 CONFIDENTIALITY .................................................................................................................................................164

2.2 PROJECT INFORMATION.........................................................................................................................................164

2.3 TECHNICAL INFORMATION......................................................................................................................................164

2.4 COST DATA .........................................................................................................................................................165

3.0 ENERGY........................................................................................................................................................ 165

3.1 SOURCE, UNIT AND COST .......................................................................................................................................165

3.2 RESOURCE SAVINGS ..............................................................................................................................................165

3.3 RESOURCE USE AFTER COMMISSIONING ....................................................................................................................166

4.0. NON-ENERGY BENEFITS.............................................................................................................................. 166

4.1 NEW BUILDINGS...................................................................................................................................................166

4.2 EXISTING BUILDINGS .............................................................................................................................................166

5.0. ISSUES AND MEASURES.............................................................................................................................. 166

5.1 ISSUE DATA .........................................................................................................................................................166

5.2 MEASURE DATA ..........................................................................................................................................167

5.3 RESOURCE SAVINGS ..............................................................................................................................................167

5.4 NON-ENERGY BENEFITS .........................................................................................................................................167

5.5 REFERENCES........................................................................................................................................................167

II. DATA COLLECTION FORM (CX) .................................................................................................................................168

III: DATA COLLECTION FORM (EBCX) ............................................................................................................................189

IV: TABULATED SURVEY RESULTS (CX)...........................................................................................................................209

V: TABULATED SURVEY RESULTS (EBCX)........................................................................................................................214

APPENDIX B: THE STATE OF BUILDING SYSTEMS COMMISSIONING ................................................................... 224

APPENDIX C: RELEVANT PAPERS AND PRESENTATIONS ..................................................................................... 232

APPENDIX D: ANNEX 47 PARTICIPANTS ............................................................................................................. 235
Page 1 of 239

EXECUTIVE SUMMARY 

Background

Commissioning (Cx) of new and existing buildings has been shown to reduce energy usage and can also 

produce non-energy related benefits such as improved occupant comfort. When compared with other 

initiatives such as installation of high efficiency equipment or installing photovoltaic systems, 

commissioning is highly cost-effective, resulting in short investment payback periods.

Despite the proven benefits, commissioning is not “business as usual,” and this is generally attributed to 

the following factors:

1. The costs and benefits of commissioning are not clearly understood by the decision-makers in the 

commercial buildings industry.

2. This (Point 1) is further complicated due to the lack of a single definition of what the 

commissioning process includes.

3. Commissioning focuses on the operation of systems and their interactions, and there is a 

perception that operational improvements may not persist over time.

4. While commissioning can be explained as a logical sequence of steps, the details are complex 

and the outcomes aren’t tangible in the same way that, for example, high efficiency lighting is. 

That means it takes time for the buildings industry to become familiar and comfortable with the 

process.

This report is the result of an international research project that collected data to help overcome the 

barriers listed in items 1, 2, and 3. 

The core purpose of this report was to collect data that would be of use in promoting commissioning of 

new and existing buildings. A secondary purpose was to define methods for determining costs, benefits, 

and persistence of commissioning along with understanding national differences in the definition of 

commissioning.

Research was grouped under two broad headings: Commissioning Cost-Benefit, and Commissioning 

Persistence.

Commissioning Cost-Benefit

Literature Review of Commissioning Cost-Benefit Methodologies

Twelve studies were summarized, focused on studies where the cost-benefit methodologies were known. 

The majority were research studies of multiple buildings, and the studies ranged from research reports, 

databases, and marketing literature. These studies are summarized in three main aspects:

1. Author, year, format, audience, caveats and considerations,

2. Quantitative data – number of buildings, costs, benefits, payback, and

3. Methodologies used to determine quantitative data – subdivided into simple, moderate and 

complex.

Creation of an International Cost-Benefit Database

Financial and technical data was collected and analyzed from ten new building commissioning projects 

and 44 existing building commissioning projects, from seven countries.

This subtask commenced with efforts to determine what key data was to be collected (divided into 

“required” and “optional” data), and to develop the data collection forms. These data to be collected 

included:

1. Description of what was included in the commissioning process,

2. Commissioning cost,

3. Energy and cost savings from commissioning,
Page 2 of 239

4. What problems were found, and the solutions, and

5. Non-energy benefits.

Collected data was collated in spreadsheets for analysis and generation of charts of the key findings.

Commissioning Persistence

Literature Review on the Persistence of Commissioning Benefits

This review summarized the findings from five studies encompassing 37 commissioning projects from 

across the USA. Persistence of savings was expressed as a percentage of the original claimed savings, 

after a specified time has elapsed after the project (e.g., 75 % after five years). In addition to evaluating 

project savings, the studies covered persistence at the level of specific measures, including the reasons 

for measures not persisting.

Impact of Savings Normalization Method on Commissioning Persistence

This study reviewed two weather normalization methods that are used in calculating energy savings from 

commissioning, and compared their impact on commissioning persistence claims. The two methods 

evaluated were:

International Performance Measurement and Verification Protocol (IPMVP)

A baseline regression model (or calibrated simulation model) is created based on the precommissioning energy use and recorded temperature/humidity. In the post-commissioning period, 

weather data is collected, and the regression model is used to predict what the energy use would 

have been if commissioning hadn’t occurred. The actual energy use is subtracted from the 

modeled prediction, and this constitutes the energy saved.

Normalized Annual Consumption (NAC)

Similar basic principle to the IPMVP; a regression model (or calibrated simulation model) is 

created using baseline data. This model is applied to a standardized ‘average’ weather year 

based on the site location in order to calculate baseline annual energy use. In the postcommissioning period the regression model is recreated using post-energy and post-weather 

data, and this regression model is applied to the same ‘average’ weather year. The difference 

between the two modeled average years constitutes the savings.

Examples of Tools for Enhancing Persistence of Commissioning Benefits

There are a number of data collection and analysis tools that may be used for monitoring the persistence 

of commissioning improvements. This study described two such tools:

Automated Building Commissioning Analysis Tool (ABCAT)

This tool collects and compares whole-building energy use to modeled ‘optimal’ energy use, and 

identifies anomalies that point towards operational problems.

Diagnostic Agent for Building Operation (DABOTM)

This tool is an add-on module that uses the Building Automation System (BAS) to monitor 

performance at the sub-system level. It is designed as an aid for Ongoing Commissioning.

The basic architecture and functionality of each of these systems is described, along with case study 

examples of projects where they identified system problems and contributed to their solutions.

Conclusions

Commissioning Cost-Benefit 

The data collected through this research project begins to characterize the various types of 

commissioning processes that are occurring in Annex member countries internationally. While data was 

often difficult to obtain, we expanded our knowledge in two key areas: 

• The scope of the Cx process employed for new and existing buildings, and
Page 3 of 239

• Characterization of issues discovered through the Cx process including system type, likely origin 

of issue (design, construction/installation, Operations and Maintenance (O&M), or capital 

improvement), issue type, and measures implemented.

While the project results begin to develop a qualitative picture for how commissioning is evolving 

internationally, quantitative results were less apparent. For example, data on commissioning costs and 

energy savings were highly variable. Falling short of the data collection goals set by Annex member 

country representatives, it was not possible to make strong conclusions about the cost-effectiveness of 

Cx internationally. However, progress was made towards understanding and categorizing the state of the 

commissioning industry for new and existing buildings in Annex member countries. While all countries 

have Cx research occurring, the majority of countries are in an early adopter phase of industry 

development. Only a few countries can be categorized as having a developing commissioning industry in 

which services are becoming more commonly obtained by owners.

New Construction Cx costs ranged from 0.06 US$/ft2 to 2.57 US$/ft2

, (0.64US$/m2 to 27.63 US$/m2) 

suggesting the Cx process varied significantly and/or the way costs were attributed varies. Savings 

values were either not reported or considered unreliable as reference values, and so payback values 

were not calculated for New Construction Cx.

For Existing Building Cx, calculating simple payback results in a small data set (19 samples) but this can 

serve to illustrate a typical range of values (see following chart).

EBCx project costs vs. annual savings estimates

n = 19 projects

$-

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

$140,000

$- $50,000 $100,000 $150,000 $200,000

EBCx project cost (US$)

EBCx project annual savings (US$)

1-year

payback

2-year

payback

4-year

payback
Page 4 of 239

Project simple payback values ranged from 0.9 years to 45.7 years, with a median value of 3.7 years. 

Nine out of the 19 projects had a payback of greater than four years, and six had payback of between two 

and four years.

Higher payback is the result of either relatively high EBCx costs or relatively low resultant cost savings. 

For cases where payback was greater than four years in this study, the savings were relatively low (based 

on savings $/m2 ($/ft2

), as opposed the costs being relatively high.

Persistence of Commissioning Benefits

Current information on persistence of commissioning energy savings in existing buildings may be 

summarized as:

• Savings persistence at the time of the study (3 years to 20 years after commissioning) ranged from 

about 50 % to 100 % in all but a handful of buildings.

• Average savings persistence at the time of the study was about 75 % of the original savings.

• The most dramatic savings degradation was caused by undetected mechanical or control component 

failures. 

• Follow-up when needed has demonstrated persistence of commissioning savings for 7 years to 20 

years in a small number of buildings. ABCAT and DABOTM have demonstrated the benefits of having 

tools to support persistence.

• Determination of savings using a “normalized annual consumption” as the basis for savings 

determination produced less variation in savings and persistence than found when the actual weather 

during the baseline and post-commissioning periods was used. It also suggested that use of 

calibrated simulation for baseline determination may provide more stable results.
Page 5 of 239

1. INTRODUCTION 

Building Commissioning1 is a quality assurance process for the design, construction and operation of 

buildings. The formalized concept of commissioning originated in the UK in the 1960’s and developed in 

Hong Kong and Canada in the 1980’s. The process has been refined in individual countries and 

researchers are working with the building industry to merge the commissioning process with existing best 

practices to develop a more complete and streamlined approach to ensure that buildings operate as 

intended. Internationally, partnerships and collaborations to promote the commissioning process have 

accelerated awareness and adoption [Appendix B]. Although it is recognized as a valuable means to 

ensure that buildings reach their operating potential, the process is not widely adopted.

The principle barrier to market penetration is the perception of high cost of commissioning. Documenting 

the costs and the benefits, and disseminating that information is widely seen as critical to increasing the 

uptake of commissioning. The increased use of innovative, interacting, systems in low or zero energy 

buildings both increases the importance of commissioning and requires the development of 

commissioning methods and procedures for these systems. Furthermore, it is crucial that the benefits 

gained through commissioning persist over time so that building owners maximize their benefits.

The development of standardized methodologies for quantifying costs and benefits of commissioning and 

the evaluation of persistence of savings are seen as means to further increase market adoption of. This 

report presents the lessons learned in key studies, gathers and develops new information, and distills the 

information into a format that can useful in the development of a plan for future work. 

This report summarizes the work completed between 2005 and 2009 through the International Energy 

Agency Annex 47 Subtask C: Cost-Benefit and Persistence of Savings, and is organized as follows:

Chapter 2: Commissioning Cost-Benefit

This chapter includes a literature review of existing cost-benefit methodologies, including 

recommendations for improvements.

Chapter 3: Commissioning Persistence

This chapter discusses methodologies for measuring persistence of commissioning benefits, summarizes 

example projects from a literature review, and reports results of persistence studies performed by Annex 

participants.

Chapter 4: Tools to Enhance Persistence

This chapter describes two tools that have been developed for the purpose of tracking persistence of 

building performance – ABCAT and DABO.

Chapter 5: International Commissioning Cost-Benefit and Persistence Report

This chapter summarizes the results of the Annex 47 cost-benefit data collection efforts with discussion of 

findings and recommendations for future work.

Chapter 6: Conclusions on Commissioning Cost-Benefit and Persistence

 1 Commissioning- Clarifying Owner's Project Requirements (OPR) from viewpoints of environment, energy and facility 

usage, and auditing and verifying different judgments, actions and documentations in the Commissioning Process (CxP) 

in order to realize a performance of building system requested in the OPR through the life of the building.

Initial Commissioning, Re-commissioning, Existing Building Commissioning, and Ongoing Commissioning are 

defined in the Glossary of Terms produced by ECBCS Annex 40.
Page 6 of 239

2. COMMISSIONING COST-BENEFIT

2.1 Literature Review of Commissioning Cost-Benefit Methodologies 

and Data

2.1.1 Introduction

This chapter summarizes findings from a review of 12 commissioning cost-benefit studies. The chapter 

focuses specifically on the methodologies used to determine the costs and benefits of commissioning

(See Appendix A for report on methodology). In order to maintain this focus, only studies that make their 

methodologies explicit have been included. The majority of methodologies that were analyzed are 

research studies of multiple buildings, and only a few are case studies of just one or two buildings. A 

more exhaustive list of studies that include cost and benefit data, but not an extensive methodological 

discussion, can be found in the bibliography.

These 12 studies represent a variety of formats and intentions, which were each created to meet the 

funder’s goal. Among them are research reports, databases of cost-benefit information and a glossy, 

marketing-style brochure. Most of the research reports were undertaken to produce data to support utility 

and research programs and to help owners and commissioning providers gather the financial justification 

needed to implement New Building Commissioning (Cx) or Existing Building Commissioning (EBCx). 

There is a significant difference in methodological framework between studies implemented as “one-time” 

or “snapshot” analyses, and those set up to continually collect and incorporate new data. It is probably 

true that any methodology can be implemented on a continuous basis if its funding is also continuous. 

However, data collection methodologies that facilitate data entry by allowing respondents to easily enter 

their own data and use an automated or semi-automated analysis tool are better positioned for ongoing 

analysis. 

Table 2.1 summarizes the key cost and benefits as derived from the 12 studies. This indicates the 

minimum and maximum average values stated in the studies, rather than the complete range of values 

for all individual projects (which would have a larger range of values).
Page 7 of 239

Table 2.1 Summary of average costs and benefits from 12 studiesi

New construction 

US$/m2

(US$/ft2)

Existing buildings 

US$/m2

(US$/ft2)

Cx / EBCx cost $2.05 to $10.76

($0.19 to $1.00)

$0.86 to $4.31

($0.08 to $0.40)

Energy benefits $0.54 to $6.89

($0.05 to $0.64)

$1.18 to $2.80

($0.11 to $0.26)

Non-energy benefits $1.40 to $22.60

($0.13 to $2.10)

$1.18 to $1.94

($0.11 to $0.18)

Simple payback 4.8 years to 6.5 years 0.7 years to 3.2 years

i

Costs and benefits are presented as ranges to demonstrate the variances in the studies examined. Median or 

average values are not presented because underlying methodologies differ widely and such figures would not reflect 

actual costs and benefits experienced by building owners.

Table 2.2 describes the studies, including their format, expected use and audience, and any caveats and 

considerations that might affect how their conclusions are interpreted. Four of the studies were originally 

conducted as research projects, funded by government agencies and a non-profit corporation.2 Ten

reported their findings in published conference papers3

of the two that were never published as 

conference papers, one is a glossy brochure produced for marketing purposes and the other is an article 

written for subscribers to an energy research and information service.4

Table 2.2 also provides a side-by-side comparison of the studies’ data and findings. They represent a 

wide range of methodological approaches and resulting data on the costs and benefits of commissioning. 

Their data ranges from case studies of one to six buildings to more extensive analyses of 16 to 21 

buildings to two meta-analyses of data collected and analyzed by others, one of 44 buildings and the 

other of 175 buildings. Among building projects studied there is wide range in building size and type and 

in findings. 

 2 Funders were: U.S. Department of Energy, U.S. Environmental Protection Agency, Lawrence Berkeley 

National Laboratory, Northwest Energy Efficiency Alliance. 3 Conferences were: American Council for an Energy Efficient Economy Summer Study, National 

Conference on Building Commissioning.

4 Energy information service is ESource.
Page 8 of 239

Table 2.2. Description of cost-benefit studies (continued on next two pages)

Study/author Format Use/audience Caveats and considerations

Stum, 

ECM Cx 

(1994)

Conference paper Research  Only energy conservation measure (ECM) commissioning is studied – not whole 

building commissioning.

 Utility program costs are included as a cost of commissioning.

 This early study does not address non-energy benefits (NEBs).

Piette, 

Energy Edge Cx

(1995)

Technical report This was one of the first studies 

to show savings concretely, and 

audience is program planners, 

technology developers. 

 The study is focused on commissioning of ECMs in new construction, although 

additional unrelated deficiencies were reported.

 Some of the data collection and analysis were associated with a broader 

evaluation project. 

Haasl, 

5 Building Study 

(1996)

Conference Paper Funded by the U.S. 

Environmental Protection 

Agency, Global Change Division, 

and the U.S. Department of 

Energy to help formulate energy 

conservation policy and 

programs. 

 The study was an “Operations and Maintenance (O&M) investigative case 

study.”

 No detail provided on the standard energy calculations or modeling scope.

 NEB analysis was still in progress.

 Stated objective was to “demonstrate that energy saving opportunities exist... 

and can be realized through improvements in O&M.”

PECI/DOE, 

Deficiency 

Database (1996)

Research report DOE-funded to document 

deficiencies found through Cx 

and EBCx. 

 While technically not a cost-benefit methodology itself, this method offers insight 

into the value of incorporating a detailed deficiency database into any costbenefit methodology.

 Savings data only available for 35 deficiencies. 

 A deficiency database may be a lower cost version of a cost-benefit 

methodology. Typically Cx reports have some detail on measures at a findings 

level through an issues log or punchlists. Cx reports do not always have 

comparable detail regarding quantification of energy savings or non-energy 

savings and a cost accounting procedure. A deficiency database leads to an 

understanding of where the most common problems lie. 

Gregerson, EBCx

(1997)

Report for 

members of 

ESource

Audience was ESource members 

(utilities, ESCOs, Cx providers, 

researchers), to quantify a new 

field of efficiency opportunity

 Few reports cited measure costs and savings. Savings may be estimated, or as 

with the Texas LoanSTAR program (75 % of square footage in the study sample) 

per-building costs were estimated.

 The first major summary report on EBCx.

PECI, Brochure 

(1997)

Glossy brochure Audience was owners and Cx 

providers, for marketing.

 Summary metrics by sector, Cx and EBCx mixed.

 Original data not available.

Altweis

(2001)

Conference paper 

documenting 

methodology and 

detailed 

assumptions

Paper’s audience was Cx 

providers, to encourage them to 

collect and report such data. 

Audience for data is owners and 

prospective customers.

 Very small sample size, suitable for case studies or research projects.

 A wide range in savings reported, due to highly varying assumptions (scenarios).

 Savings calculation methodology will vary from Cx Agent to Cx Agent, no 

standard calculation provided (although the methodology is conceptually well 

defined).

 No discussion of costs.
Page 9 of 239

Study/author Format Use/audience Caveats and considerations

Mills, 

Meta-analysis

(2004)

Excel database,

Research report

Statistical analysis for U.S. Dept. 

of Energy 

 Largest Cx cost-benefit study to date. Focused on obtaining large number of 

projects to get a high-level view of Cx metrics. 

 Relied on availability, quality, and comparability of different primary data 

sources.

 Majority of building information comes from a few sources, especially for EBCx.

 Merits of Cx should be assessed based on the cost-effectiveness of the 

proposed measures, not necessarily only on what was implemented.

 May inappropriately attribute or not attribute costs to the Cx process since cost 

accounting conventions are not always followed.

 May underestimate benefits because energy savings from all measures are not 

captured in Cx reports, NEBs are not usually expressed in monetary terms, and 

financial benefits in terms of increased net operating income (NOI) are rarely 

determined. Furthermore, in a few projects studied, measured savings 

exceeded predicted savings.

 Time consuming to gather project information from secondary sources and 

interpret it, as opposed to having the cost-benefit data entered by the people 

involved with the project.

SBW, Northwest Cx 

& EBCx 

(2004)

Research report Utility program evaluation  Cost calculations include many costs associated with Cx, so figures may be 

higher than other studies. 

 NEBs calculations based on opinion of team members (willingness to pay and/or 

perceived value).

California 

Commissioning 

Collaborative’s 

Cxdatabase.com

(2004)

SQL database, 

exportable to Excel

One-page 

“datasheet” on 

each project

Conference paper 

describing 

database

Researchers – data that supports 

Cx research and utility incentive 

programs

Owners – defining the value of 

Cx to their business through data 

and case studies

Providers – third party source 

from which to give owners 

information. Help raise the bar 

for Cx documentation of results

 Data was stored as-entered by respondents – no analysis performed unless 

brought in by outside researchers 

 Datasheet was a one-page summary form automatically populated by data 

entered by respondents. 

 Little population as of February, 2006 – database is no longer online, and is not 

actively updated.

 While the original vision for this data included creation of case studies, none 

were created.
Page 10 of 239

Study/Author Format Use/Audience Caveats and considerations

Moore et al.

California EBCx 

programs lessons 

learned

(2008)

Presentation paper 

for ACEEE 

Summer Study 

(pdf)

Energy efficiency program 

implementers

 Report-out on 4 California EBCx Programs, covering 60 million square feet of 

commercial buildings (approximately 200 buildings)

 Describes program process experiences as well as presenting quantitative data

 Reports on:

o kWh savings recommended after investigation, compared with 

savings from measures selected by owner for investigation

o Simple payback for uncovered measures and measures selected 

by owner for implementation

o Breakdown of savings by measure type

o Simple payback by measure type

o Savings by building type

o Relationship between building EUI and EBCx savings

 All programs were managed by PECI under the same basic program design, so 

scope of work and approach to energy savings calculations are consistent 

across all programs. Commissioning provider costs and driven by the utility 

program design/budget, and so are consistent across the dataset.

 Much of the data is based on outcomes of EBCx investigations; at the time the 

paper was created, a high proportion of the projects had not completed 

implementation 

PECI & Summit 

Building 

Engineering

2007 California 

EBCx Market 

Characterization

(2008)

Report available 

from California 

Commissioning 

Collaborative 

www.cacx.org

Program implementers, providers  Estimates of potential costs and savings for EBCx in California, along with an 

assessment of how many EBCx providers would be required to fulfill that 

potential

 Projects used for the study were all under California utility programs, and so 

savings calculation approaches are quite consistent across the whole data set

 Forecasts based on a very large number of ongoing projects under utility 

programs; only a small number of these projects were complete at the time of

the report.
Page 11 of 239

2.1.2. Data Collection Strategies

By far the most common data collection method is the Researcher-Driven Model, in which a researcher 

was tasked with collecting and analyzing data. In more than 90 % of the studies a researcher was wholly 

or partially responsible for collecting documentation and data produced by others. In the handful that 

differed from this model, data collection was usually done “in house,” because the researcher also served 

as the commissioning provider on the projects that were studied. In two cases, however, data collection 

was accomplished through use of a database allowing providers and owners to submit data independent 

of the researcher. In two cases, data came from databases created for utility existing building 

commissioning programs, where project data was entered by program administrators as a part of the 

normal program process. A comparison of the data collection methodologies used in 13 different studies 

reviewed here is presented in Table 2.3. The discussion of the three cost methodology types (simple, 

moderate, and complex) shown in Table 2.3 is presented in Sec. 2.1.3.

The Researcher-Driven Model

In 10 of the 13 studies, the data collection strategy was driven by a researcher who collected 

commissioning project information and produced a cost-benefit report. In eight of those ten, the 

researchers relied heavily on project documentation, primarily the commissioning provider’s Final Report. 

Other documentation consulted included construction documents (for new buildings), issue logs and 

change orders. In more than half of the studies, other types of information were used to supplement 

written documentation. They include telephone surveys with key team members (two studies) and onsite 

inspection and monitoring (three studies).

When telephone surveys or interviews were employed, they were often used to gather data on nonenergy benefits (NEBs). This is logical, given that NEBs are hardest to measure using commissioning or 

building documentation because they depend most on the experiences of the people who manage and

occupy the building. In fact, there are two studies in which researchers were only interested in NEBs and 

in which the only source of data were telephone surveys and detailed interviews – no project 

documentation was collected (Haasl 1996; Bicknell 2004).

Among the ten studies that employed the researcher-driven model, there is much variation in the amount 

of data studied and level of detail collected, the logistics of obtaining documentation, and in the 

supplemental types of data and the data collection strategies. 

• Quantity of data varies from case studies of a single project to mid-range studies of five, six, 21 and 

44 projects to two large studies of 175 projects each.

• Level of detail ranges from whole-project level metrics to metrics for individual issues.

• Logistics of obtaining documentation includes submission by a utility that collected all the 

documentation and turned it over to the researcher, submission by owners and providers directly to 

the researcher, and the gathering of documentation by the researchers from commissioning providers 

and other researchers.

• Supplemental data includes telephone surveys and onsite inspections.

In the researcher-driven model, data collection almost always takes place after the commissioning 

projects are complete and documentation finished. As a result, the effort required and the data quality 

depends almost entirely on the diligence of the parties responsible for producing the documentation 

(usually the commissioning provider, general contractor or testing and balancing agent). Time is also an 

issue. The closer the study is to project close-out, the more likely it is that project documentation will be 

available and in good condition, and that the important parties will be able to answer any questions.

The Provider-Driven Model

In two studies, the researcher and commissioning provider were one and the same. As a result, their 

studies were able to utilize very detailed data collected throughout the commissioning project. However, 
Page 12 of 239

only a few projects were included, leading to these studies’ designation as case studies rather than 

statistically significant research studies (Haasl 1996; Altweis 2001).

The Research Database Model

In two studies, researchers created interactive databases to collect commissioning project data. In one, 

the database was created through a collaborative effort in which multiple researchers and commissioning 

providers helped define required and minimum inputs. The database itself was created as an online 

application, meaning it was accessible on the Internet. Thus once it was released, commissioning 

providers could use it to enter information about their projects in real-time5

. In the other, a database of 

categories deficiencies was developed (PECI/DOE 1996). 

A significant advantage to the database model of data collection is the ability of the researcher’s needs to 

influence the commissioning provider’s data retention efforts. Because providers know up-front what data 

the researcher wants, it can be supplied immediately while the documents are still available and the 

project is fresh in the provider’s mind. On the negative side, a database alone is incapable of performing 

analysis, and this model requires funding for several things: design and programming of the database, a 

researcher to analyze the data or work with programmers to build analysis functionality into the database, 

marketing of the database to the provider audience, ongoing database maintenance and support, and 

perhaps even funding to compensate providers for entering data.

The Utility Program Database Model

In two cases, data came from databases created for utility EBCx programs, where project data was 

entered by program administrators as a part of the normal program process. Data entry came from two 

sources: 1) Data entered directly into database by program administrators (including EBCx provider 

costs) and 2) Data uploaded from each project’s Master list of Findings, which lists descriptions, savings, 

and costs for each individual measure. EBCx providers enter data into the Master list of Findings 

spreadsheet, and program administrators upload this spreadsheet to the program database.

Each project’s Master List of Findings, undergoes program review (along with all supporting 

documentation, collected data and energy savings calculations).

The main advantages of this data are:

• Cost and savings information is available for each individual measure.

• All data underwent a rigorous quality control process that maximized consistency across the dataset

• Data is available for a high quantity of projects within a single U.S. state.

The main disadvantages of this data are:

• There is no non-energy benefits data included in the utility program databases.

• Data available at the time the two reference reports were created was predominantly based on 

unfinished projects, so it does not include much data on installed measures (only measures selected

for implementation by building owners). Data on installed measures for the California projects should 

be available by the end of 2009.

• EBCx provider costs are driven by utility program designs/budgets. This is not necessarily a 

disadvantage, but it does artificially influence the overall project costs.

Explanation of Estimated Effort 

Table 2.4 includes a column for “Estimated effort need to obtain and enter data.” The amount of time and 

difficulty required to both collect and submit project data is estimated as either low, medium, high, or 

variable. These rankings are not independently defined. Rather, they reflect the authors’ estimate of the 

relative effort required to gather data according to the study’s methodology, as compared to the other 

studies in this report. Thus a study with a “high” effort ranking was judged to employ a more time- and 

effort-intensive collection methodology than those deemed “low” or “medium.”

 5 This database is no longer online, and is not being maintained.
Page 13 of 239

Table 2.3. Cost and benefit data (continued on next 2 pages)6

Study/ Year # of 

buildings 

Total and 

median 

bldg. size

Costs (US$) Energy benefits Non-energy benefits 

(NEBs)

Cost effectiveness

Cx EBCx Cx EBCx Cx EBCx Cx EBCx

Stum, 

ECM Cx 

(1994)

6 218 722 ft2

20 320 m2

$3 060 

overall

$0.041/ 

ECM

$0.08/ft2

$0.86/m2

Simple

37 412 kWh/y

5.3 % of orig. 

ECM

Unrealized:

7.9 %

Moderate

N/A Recovered 

savings:

$0.033/kWh

Recovered + 

unrealized 

savings: 

$0.02/kWh

Piette, 

Energy Edge 

Cx (1995)

16 849 800 ft2

78 949 m2

27 000 ft2

 

(median)

2 508 m2

(median)

$0.19/ft2

$2.05/m2

Simple

N/A 9.48 

kWh/ft2∙y

102.04 

kWh/m2

$0.64/ft2

∙y

$6.89/m2

y

Complex

N/A not 

quantified

N/A Simple 

payback: 

average 

13.7 y; 

median: 

6.5 y

N/A

Haasl, 

5 Building 

Study 

(1996)

5 837 000 ft2

77 760 m2

1 313 197 ft2

(median)

122 000 m2

(median)

N/A $0.11/ft2

$1.18 m2

Simple

N/A $0.11/ft2

$1.18 m2

Simple

N/A Not 

quantified

N/A Simple 

payback: 10 

months 

PECI/DOE, 

Deficiency 

Database 

(1996)

16 Cx

28 EBCx 

(44 total)

4 million ft2

367 896 m2

67 000 ft2

 

6 224 m2

(median)

N/A N/A 83 % of all 

deficiencies 

related to 

energy 

92 % of 

operational 

deficiencies 

impact 

energy.

Avg savings/

deficiency = 

$892/y

Moderate

51 % of all 

deficiencies 

related to 

reliability and 

maintenance 

25 % of 

deficiencies 

related to 

comfort 

N/A N/A

 6 Dollar amounts have not been normalized to a common year. Methodological complexity listed in bold. 
Page 14 of 239

Study/ Year # of 

buildings 

Total and 

median 

bldg. size

Costs (US$) Energy benefits Non-energy benefits 

(NEBs)

Cost effectiveness

Cx EBCx Cx EBCx Cx EBCx Cx EBCx

Gregerson,

EBCx (1997)

44 9 million ft2

836 127 m2

N/A Approx 

$20 000

$0.19/ft2

$2.05/m2

Simple

N/A Avg $98 000

Med: $41 000

19.2 % avg 

savings

$0.49/ft2

$5.27/m2

Moderate

N/A Not 

assessed

N/A Simple 

payback: 

0.9 years7

PECI,

Brochure 

(1997)

75 

Cx and 

EBCx not 

separated 

Not available Median:

$0.15/ft2

$1.61/m2

No standardized metric

Moderate

Improved ( % of buildings):

Thermal comfort: 42 %

System function: 44 %

Indoor air quality: 23 %

O&M: 42 %

Simple

Not assessed

Altweis

(2001)

1 14 350 ft2

1 333 m2

Not 

reported

N/A Up to 

$0.13/ft2

∙y

$1.40/m2

N/A $0.17 to 

$2.10/ft2

∙y

$1.83 to

$22.60/m2y

Moderate

N/A Not 

reported

N/A

Heinemeier, 

Schools Cx

(2004)

1 N/A: 

methodology 

but no 

results.

N/A N/A N/A N/A N/A N/A N/A N/A

 7 Energy intensive buildings and even most of the efficient buildings had paybacks of less than two years. 
Page 15 of 239

Study/ Year # of 

buildings 

Total and 

median 

bldg. size

Costs (US$) Energy benefits Non-energy benefits 

(NEBs)

Cost effectiveness

Cx EBCx Cx EBCx Cx EBCx Cx EBCx

Mills, 

Metaanalysis

(2004)

175 

projects 

EBCx: 106

Cx: 69

30 400 000 ft2

2 824 252 m2

total

69 500 ft2

6 457 m2

(median Cx)

151 000 ft2

14 028 m2

(median 

EBCx)

$74 267

$1.00/ft2

$10.76/m2

[0.6 % 

construction cost]

Moderate

$33 696

$0.27/ft2

$2.90 m2

Moderate

$2533/y

$0.05/ft2∙y

$0.54/m2

∙y

Moderate

$44 629/y

$0.26/ft2∙y

$2.80m2

∙y

Moderate

$51 000/y

$1.24/ft2∙y

$13.35m2

∙y

Moderate

$17 000/y

$0.18/ft2∙y

$1.94 m2

∙y

Moderate

Simple 

payback: 

4.8 y8

Simple 

payback: 

0.7 y9 

SBW, 

Northwest 

Cx & EBCx 

(2004)

21 2.2 million ft2

204 386 m2

$71 79110

$0.85/ft2

$9.15/m2

Complex

$22 053

$0.31/ft2

$3.34/m2

Complex

$9 856/y

$0.09/ft2∙y

$.97/m2

∙y

Complex

$13 678/ year

$0.14/ft2∙y

$1.51m2

∙y

Complex

$13 609 

(one-time)

$0.13/ft2

$1.40/m2

∙

Complex

$10 534 

(one-time)

$0.11/ft2

$1.18/m2

Complex

Direct payback: 7.5 y

Total simple 

payback: 

6.1 y

Direct payback: 4.0 y

Total simple 

payback: 

3.2 y

Moore et al.

(2008)

21 Not stated, 

but 21 

projects total 

approx. 9 

million kWh 

annual 

savings 

forecast

Not 

reported

Average 6.9

%

Complex

Not reported 84 % of 

savings with 

<2-year 

payback 

(owner’s 

implementation costs 

only)

 8 Standard energy prices corrected for inflation. Data normalized to $2003. 9 Standard energy prices corrected for inflation Data normalized to $2003. 10 Costs for this study include only Cx provider fees – although payback information includes additional costs, for example, costs to other parties.
Page 16 of 239

Study/ Year # of 

buildings 

Total and 

median 

bldg. size

Costs Energy benefits Non-energy benefits 

(NEBs)

Cost effectiveness

Cx EBCx Cx EBCx Cx EBCx Cx EBCx

PECI & Summit 

Building 

Engineering

(2008)

Market characterization 

study forecasts 70 million 

ft2 annual potential for 

EBCx in California (6.5 

million). m2

$0.39/ft2

$4.20/m2

Complex

0.96 kWh/ft2∙y

10.3 kWh/m2∙y

Complex

Not reported Mean 3-year 

payback 

forecast

California 

Commissioning 

Collaborative’s 

Cxdatabase.com

(2004)

Two surveys were completed and five were in progress. Not assessed at this time due to lack of data and funding.
Page 17 of 239

Table 2.4 Comparison of data collection methodology (continued on next page)

Study/Author Level of detail Data sources Collection 

process

Timing of data 

collection

Data storage Estimated effort 

needed to obtain 

and enter data 

Stum, 

ECM Cx 

(1994)

Only looked at ECMs 

meriting greater 

resources (i.e., VFDs, 

economizers)

Inspection reports, 

onsite inspection of 

ECMs in small 

comm. and retail 

Reports provided by 

utility, onsite work 

done by authors.

Concurrent with and 

immediately 

following Cx 

activities

N/A Medium

Onsite inspections but 

only of a few measures

Piette, 

Energy Edge Cx

(1995)

Very detailed data 

collection on building 

characteristics to 

develop models, 

Commissioning 

report, on-site data 

collection.

Extensive evaluation 

project.

Within one or two 

years after 

construction.

Unix-based 

database.

High

Data for simulation.

Haasl, 

5 Building Study 

(1996)

Data required for 

standard calculations 

and simulations.

Provider collected. Data collected 

through EBCx 

process, including 

two weeks of 

monitored data on 

key systems.

Collected during 

EBCx process.

Not described. Medium

Building and system 

characteristic data 

needed for modeling 

and calculation, and 

monitored data.

PECI/DOE, 

Deficiency 

Database (1996)

Findings level detail. Half of data directly 

entered by Cx 

provider. Half by 

researcher (paid).

Review of final 

commissioning 

reports and issues 

logs.

Retrospective. Database (Excel) Variable

Depends on availability 

and organization of 

necessary info in Cx 

documentation.

Gregerson, EBCx

(1997)

No detail other than 

metrics on a projectlevel. No measurelevel detail.

Four Cx providers. 

70 % from TAMU 

and 25 % from 

PECI

Building 

characteristics, EUI, 

and cost and energy 

savings figures 

requested from Cx 

provider by 

researcher.

Retrospective from 

final Cx reports.

Not specified. Low

Very minimal data 

collected (although 

retroactive so it may be 

difficult to obtain.) 

PECI, Brochure 

(1997)

High level metrics Cx provider Phone interviews Retrospective Not specified Low

Minimal data collected 

(although retroactive so 

it may be difficult to 

obtain) 
Page 18 of 239

Study/Author Level of detail Data sources Collection 

process

Timing of data 

collection

Data storage Estimated effort 

needed to obtain 

and enter data 

Altweis

(2001)

Case study level data. 

Extensive detailed 

information required 

from project. Not 

difficult for the 

commissioning agent 

to obtain.

Commissioning 

agent collected 

information and 

conducted benefits 

analysis.

Commissioning 

provider review of 

notes and project 

documents.

Throughout the 

project.

Not specified. Low, for commissioning 

provider. 

High for anyone else.

Heinemeier, 

Schools Cx (2004)

Report showed a 

great deal of detail, 

but the intent is to 

define metrics that are 

easily gathered, from 

review of construction 

documents.

Complete 

construction 

documents.

Researcher obtained 

a copy of and 

reviewed all 

construction 

documents.

After the project was 

complete.

Not specified. Medium

Somewhat timeconsuming to review all 

documents.

Mills, 

Meta-analysis

(2004)

Based on the 

documentation 

available. Where little 

available, at minimum, 

project-level info was 

entered.

A few Cx providers 

and researchers 

entered many 

projects (paid). 

Smaller number of 

projects from 

unpaid Cx 

providers.

Review of past 

studies and final Cx 

reports/issues logs.

Projects completed 

between 1993 and 

2004

Retrospective from 

final Cx reports and 

previous studies.

Excel spreadsheet Variable

Depends on availability 

and organization of 

necessary info in Cx 

documentation.

SBW, Northwest 

Cx & EBCx 

(2004)

Identified all 

issues/findings, 

selected only 

significant and 

resolved issues

Extensive project 

documentation 

and surveys 

(both unpaid).

Project materials 

submitted by owner 

and telephone 

surveys with team.

While projects 

underway and within 

1 year after closeout (early 2003)

Database (no 

specific software 

identified)

High

Moore et al.

(2008)

High level utility 

program data 

reported.

Utility program 

databases

Paper created by 

program 

administrator with 

direct access to all 

programs’ data

Data compiled 

Summer 2008

Excel spreadsheet 

downloaded from 

program databases

Medium

Data is readily available

(but need to filter data 

for non-standard 

projects)

PECI & Summit 

Building 

Engineering

(2008)

High level forecasts of 

market potential

Data collected from 

various California 

utility program 

implementers

Data requests to all 

implementers. Data 

then adjusted for 

easier crosscomparison

Early 2008 Spreadsheet High
Page 19 of 239

Study/Author Level of detail Data sources Collection 

process

Timing of data 

collection

Data storage Estimated effort 

needed to obtain 

and enter data 

California 

Commissioning 

Collaborative’s 

Cxdatabase.com

(2004)

Three findings 

required, can 

accommodate 

unlimited number 

Cx provider or 

owner. (unpaid, but 

funding for entering 

data desired, 

requirement was 

written into scope

of some projects)

Respondent gathers 

data and enters into 

online forms.

Intended to be 

completed during 

project or 

immediately after 

completion. Can be 

completed at any 

time, if data is 

available.

Custom-built online 

database – project 

took several months

at a cost of approx. 

$20,000.

(www.Cxdatabase.o

rg)

Variable

Depends on whether 

respondent was aware 

of data requirements 

during the project and 

the quality of 

documentation. 
Page 20 of 239

2.1.3 Methodologies for Determining Costs, Energy Benefits and Non-Energy 

Benefits

Commissioning Costs

There is no widely used methodology for determining commissioning costs. To assist in the evaluation 

process, this report distinguishes three levels of complexity in cost methodologies: simple, moderate and 

complex. 

Table 2.5 summarizes their differences in terms of which costs are included and if the costs are validated.

Table 2.5 Comparison of cost methodologies

Cx provider’s fee Resolution costs 

(EBCx)

Costs to other 

parties

Validation 

method

Simple X X

Moderate X X X

Complex X X X X

Of the 12 studies examined, all include the commissioning provider’s fee as a cost of commissioning. 

Some include additional costs, for example, costs to other parties, although each study defines these 

costs differently. Only one study, with a complex methodology, makes an attempt to validate cost data by 

checking the respondent's data for consistency and to make sure cost figures fell within what researchers 

defined as a "reasonable range." (SBW Consulting 2004)

In general, the average cost of commissioning per square foot increases as the study’s cost methodology 

increases in complexity. As Table 2.6 shows, in existing buildings the cost of commissioning steps 

upward as the methodology becomes more complex. In the case of new buildings, the cost of 

commissioning has a generally higher trend; however, complex methodologies returned an average cost 

per square foot (m2

) slightly lower than moderate methodologies. Although not conclusive, it seems likely 

that the reported cost increases because complex methodologies account for costs incurred by several 

parties, whereas simple methodologies usually only account for the commissioning provider’s fee.

Table 2.6 Average cost of commissioning by methodological complexity

Existing buildings New buildings

Methodology # of buildings Average cost

(US$) # of buildings Average 

cost(US$)

Complex 8

$0.31/ft2

$3.34/m2 13

$0.85/ft2

$9.15/m2

Moderate 106

$0.27/ft2

$2.91/m2 69

$1.00/ft2

$10.76/m2

Simple 50

$0.18/ft2

$1.90/m2 16

$0.19/ft2

$2.05/m2
Page 21 of 239

A more detailed discussion of cost methodology types, with examples, follows.

Simple. A simple methodology uses only one or two cost categories to arrive at the overall cost of 

commissioning. Usually these cost figures are relatively easy to obtain. Examples include the 

commissioning provider’s fee and the cost to resolve an issue. In simple methodologies, these cost 

figures are self-reported and the study makes little or no attempt to validate the data.

An example of a simple methodology can be found in two early studies: Piette (1995) and Stum (1994). 

Piette calculated the cost of commissioning by taking a percentage of the overall energy efficiency 

measure cost. Stum defined the cost of commissioning as the self-reported commissioning provider’s fee 

plus the administrative costs of the utility commissioning program that funded the projects. 

Moderate. A moderately complex methodology uses more than two cost categories to arrive at the 

overall cost of commissioning. For example, cost categories could include incremental costs to all parties, 

travel costs, and negative impacts like increased change orders. Moderately complex methodologies 

include a broader array of costs in the cost of commissioning than a simple methodology, although the 

study stops short of applying a validation process to the data. The methodology may include differences 

in cost accounting between Cx and EBCx.

An example of a moderate methodology can be found in Mills et al. (2004) and in Cxdatabase.com 

(2004). Mills et al.’s cost definition includes several figures: the provider fee, the coordination costs 

incurred by other parties and on EBCx projects, the resolution costs. Cxdatabase.com’s cost definition 

includes the provider fee, incremental costs incurred by other parties, the cost of O&M staff participation 

(if specified by the owner) and on EBCx projects, the resolution costs. Neither Mills et al. nor 

Cxdatabase.com makes any attempt to verify the cost figures reported in project documentation or by 

respondents.

Complex. Like a moderate methodology, a complex methodology differentiates several categories of 

commissioning costs. However, studies employing complex methodologies do attempt to validate cost 

figures. 

An example of a complex methodology is found in SBW Consulting (2004). Cost includes the provider 

fee, incremental fee increases for other parties, travel expenses and resolution costs. Researchers used 

a telephone survey to ask key commissioning team members 1) if they increased their bid for the project 

to account for commissioning activities and 2) if there were any significant non-labor costs associated 

with commissioning. Respondents were then asked to attach a dollar amount to each. If the respondent 

was unable to provide a dollar figure, researchers asked them to estimate the additional labor hours and 

provide a labor rate, from which researchers calculated the incremental cost to that respondent. As a 

quality assurance measure, researchers also evaluated whether the data supplied by respondents “were 

consistent and fell within reasonable ranges” (SBW Consulting 2004).

Energy Savings 

There are a variety of methods for determining energy savings from commissioning. This study evaluates 

energy benefit methodologies, like cost methodologies, according to their level of complexity.
Page 22 of 239

Table 2.7 Comparison of energy savings methodologies

Issues ID 

and/or 

baseline 

comparison 11

Energy 

calculations

Energy 

modeling

Normalization of 

energy data

Attention to 

measure 

interactions

Validation 

method

Simple X

Moderate X / / / /

Complex X X X X X X

Key: X = always present; / = sometimes, but not always present

Of the 12 studies examined, all used either an issues identification or baseline comparison method to 

determine energy savings. Moderate methodologies employed some form of energy calculations, 

modeling, or data normalization. Only complex methodologies were attentive to measure interactions and 

data validation. 

The following is a more detailed discussion of energy benefit methodology types, with examples.

Simple. In existing buildings, simple methodologies compare before and after energy consumption 

without normalization of data. They may also obtain information directly from the building owner or 

manager regarding energy savings or comparisons of performance.

Examples of simple energy benefit methodologies are found in Cxdatabase.com (2004) and Heinemeier 

(2004). Cxdatabase.com asked survey respondents to provide energy savings numbers for each reported 

finding. Respondents were asked to also provide the calculations they used to arrive at the figures, but 

this information was not required. No standardized process for calculating energy savings was created. In 

Heinemeier’s methodology, energy use per square foot of commissioned buildings was compared to 

those that were not commissioned. Building pairs were of similar size and type, and monthly utility bills 

were used to gather energy use data. Commissioned building energy use was also compared against 

standardized benchmarks.

Moderate. Moderately complex methodologies use project documentation to identify significant 

commissioning findings/issues that have been resolved, and then use engineering calculations or 

parametric modeling to determine the energy benefit. A validation process using measured data may be, 

but is not necessarily, applied. Moderately complex methodologies may also apply normalization 

techniques to before and after energy consumption. 

An example of a moderately complex energy benefit methodology is found in the SBW Consulting (2004). 

Researchers used a three-step process, shown in Fig. 2.1, to identify issues that resulted in a “stream” of 

energy and/or non-energy benefits. First they used project documentation to identify all issues. Then they 

determined which issues were “significant” relative to their affect on total building area or occupants, 

resolution cost and/or long term impact. (SBW Consulting 2004) Of significant issues, they determined 

through documentation and/or telephone surveys which issues had been or would likely be resolved. 

Energy and non-energy benefits were only calculated for issues deemed significant and resolved.

 11 Two methods for determining energy (and non-energy) savings are issue identification and baseline comparison. In

issue identification energy savings are determined first at the issue level and then added to arrive at the total savings 

for the project. The baseline comparison method looks only at whole-building energy benefits. The researcher 

establishes the building’s “baseline” energy use and then compares it to energy use after commissioning. This 

method can be a more straightforward process in existing building projects, where there is a “before” snapshot. In 

new construction it is more difficult because the “baseline” is hypothetical and must be simulated. 
Page 23 of 239

Figure 2.1 Sample issue identification methodology (SBW Consulting, 2004)

Complex. Studies utilizing complex methodologies employ detailed engineering calculations or models to 

estimate energy savings. Examples range from detailed building simulations that require extensive 

information about building characteristics to very detailed engineering calculations based on measured 

data. Complex methodologies for new construction commissioning benefits address nuances such as the 

range of assumptions that go into the hypothetical baseline (i.e., what is assumed to have occurred 

without commissioning). More complex methods also address the interaction between commissioning 

measures, and the interaction with related activities like energy retrofits. Results can be reported per 

measure, or for a whole building (which is not simply the sum of individual measures). 

An example of a complex energy benefit methodology is found in Altweis (2001). This study used 

engineering calculations to estimate energy use both with and without identified findings/issues. 

Researchers developed both a “most likely” and a “least cost solution” scenario, depending on 

assumptions about what would have occurred in the absence of commissioning.

Non-Energy Benefits (NEBs)

In the assessment of non-energy benefits there is a great diversity of methodologies in use. Here again, 

in Table 2.8, non-energy benefit methodologies are classified according to their complexity.

Table 2.8 Comparison of non-energy benefit methodologies

Monetary value 

NOT assigned

Monetary value 

assigned

Monetary value 

validated

Simple X

Moderate X

Complex X X
Page 24 of 239

Of the 12 studies examined, 5 do not assign a monetary value to NEBs. Those that do use methodologies 

ranging from simple processes that do not employ standard calculations or checks on respondents’ 

information to a highly complex system in which NEB dollar values are calculated several different ways 

and the most conservative number selected. Here too, only the most complex methodologies attempt to 

validate the data.

Contrary to the direct relationship between methodological complexity and commissioning costs, with 

NEBs there appears to be an inverse relationship: the more complex the methodology the lower the 

monetary benefit reported. This holds true for both new and existing building commissioning. 

It is apparent that methodology significantly impacts reported non-energy benefits. The study employing 

avoided cost calculations (a moderate methodology) returned higher savings than the study that 

determined the owner’s perceived value of the benefit (a complex methodology). This is due to a 

fundamental difference between these two methodologies. Although both methods are hypothetical, 

avoided cost calculations estimate the full cost that would have been incurred, had the benefit not been 

received. Whereas the owner’s perceived value is the amount the owner is willing to pay for the benefit –

often less than the avoided cost. Further study in this area is clearly needed to determine how the nonenergy benefit valuation method relates to the goals of the cost-benefit methodology. 

Table 2.9 NEBs of commissioning by methodological complexity i

New Buildings Existing Buildings

Methodology

Number of 

Buildings

Average NEB Savings

(US$)

Number of 

Buildings Average NEB 

Savings (US$)

Complex 13

$0.13/ft2

$1.40/m2

8

$0.11/ft2

$1.18/m2

Moderate 23

$0.17/ft2 - $6.96/ft2

$1.83/m2 - $74.92/m2

10

$0.10/ft2 - $0.45/ft2

$1.08/m2 -

$4.84/m2

Simple no data no data

i Moderate data is presented as a range because a validation method was not employed. 

The following is a more detailed discussion of non-energy benefit methodology types, with examples.

Simple. An example of a simple methodology for assessing non-energy benefits is found in 

Cxdatabase.com (2004). Here, respondents are asked to identify which benefits they received and have 

the option, but not the requirement, to supply a dollar value for the benefit. No standardized calculations 

are employed, and there is no process for evaluating the dollar values supplied by respondents.

Moderate. An example of a moderately complex methodology for calculating NEBs is found in Mills et al. 

(2004). Here, the researcher arrived at the NEB dollar value by adding the first-cost dollar value of nonenergy savings and the ongoing labor cost savings, estimated as labor hours saved. Other NEBs were 

accounted for using a Yes/No checklist with an estimated dollar value supplied optionally. 

Complex. An example of a complex methodology for assessing NEBs is found in SBW Consulting 

(2004), see Fig. 2.2. Researchers developed three different ways to assign a dollar value to a “stream” of 
Page 25 of 239

benefits flowing from a specific finding/issue, and then used the most conservative (lowest dollar value) 

estimate. All three calculations were based on the responses of key commissioning team members given 

in telephone surveys. (See Table 2.10 for additional details).

Figure 2.2 Sample complex NEB methodology (SBW Consulting, 2004)

1. Willingness to pay (WTP)

Survey question: 

“If all the non-energy benefits (and negative effects) that we talked about were taken away, what do 

you think would be the maximum amount you would be willing to pay to get back those benefits, on 

an annual or monthly basis?”

2. Sum of individual computed benefits

Respondents asked to compare the commissioning provider’s fee to the specific commissioning 

benefit. All benefits are then summed for a total NEB value. An example survey question:

“Would you say that compared to your annualized commissioning costs, the contractor call-backs 

are...about 10 % more valuable, about 1 to 1.5 times more valuable, twice as valuable, more than 

twice as valuable?” Or, “Don’t know/refused.”

3. Overall net value

Respondents were asked to identify significant impacts from a given list (e.g., reducing operational 

deficiencies). Their responses were weighted, to give the opinions of providers and facility staff more 

importance than contractors and designers. The gross dollar value of each impact was then multiplied 

by the importance factor.
Page 26 of 239

Table 2.10 Overview of cost-benefit evaluation methodologies (continued on next 3 pages)

Study/Author Costs Energy Benefits Non-energy Benefits (NEBs) Persistence Assumptions

Stum, 

ECM Cx 

(1994)

Simple Cost of Cx 

provider services 

+ program 

administration 

costs

Moderate Engineering calculations 

and computer simulations 

from original ECM savings 

predictions

N/A N/A

Piette, 

Energy Edge 

Cx (1995)

Simple Percent of overall 

energy efficiency 

measure cost, 

costs by energyefficiency 

measure are 

used when 

available.

Complex Used deficiency 

identification and postconstruction utility data to 

tune as-built simulation 

models. Deficiencies were 

“removed” from the 

models to determine 

savings. Deficiencies 

categorized as Directly, 

Indirectly, and Un-related 

to the ECM, and Static or 

Dynamic. 

Simple Categorized as control, 

indoor environmental 

quality (IEQ), equipment 

life, O&M. No value 

assigned.

Simple (but 

the 

modeling 

itself was 

not simple)

“Low” and “High” 

lifetime scenarios 

defined in the 

modeling.

Haasl, 

5 Building 

Study (1996)

N/A N/A Simple Categorize deficiencies 

into deficiency type 

(maintenance, 

documentation, training, 

operations, installation, 

design); HVAC 

subsystem; and affected 

component. Additional 

categorization for controls 

related findings.

Simple All deficiencies were 

categorized, including nonenergy related 

deficiencies. In total (Cx 

and EBCx), 51 % of all 

deficiencies related to 

reliability and maintenance 

(25 % significantly 

related). In total (Cx and 

EBCx), 25 % of all 

deficiencies related to 

comfort (13 % significantly 

related).

N/A
Page 27 of 239

Study/Author Costs Energy Benefits Non-energy Benefits (NEBs) Persistence Assumptions

PECI/DOE, 

Deficiency 

Database 

(1996)

Moderate Categorized as 

Assessment or 

Implementation. Included 

consultant, 

contractor, and 

building staff 

time, as well 

as parts and 

lease costs for 

monitoring 

equipment. 

Did not include 

“research” 

related costs.

Moderate “Potential” energy 

savings reported. A 

combination of 

standard engineering 

calculations and DOE2 

building simulations, 

with short-term 

diagnostic monitored 

data used to inform the 

calculations or model.

Moderate Only includes extended 

equipment life, which is 

the most easily quantified 

effect. Categorized as 

extended equipment life 

through reduced hours of 

operation and through 

reduced short cycling. 

Calculated based on 

assumptions of reduced 

hours, reduced lifetime 

through short cycling, and 

nominal life.

N/A N/A

Gregerson,

EBCx (1997)

Simple Costs reported 

or estimated 

by each Cx 

provider. 

Costs include 

Cx fee, 

monitoring 

costs, and the 

cost of 

implementing 

measures 

except for inhouse facility 

staff time 

during normal 

working hours.

Moderate Project documentation 

may have utilized 

engineering 

calculations, models, or 

pre- and postconsumption 

measurement to 

quantify savings. 

Report notes that rigor 

with which energy 

savings were 

calculated varies 

significantly.

N/A N/A

PECI, Brochure 

(1997)

Simple Cost range 

and median 

cost.

Simple Savings range by 

building type. 

Conducted phone 

interviews.

Moderate Identified NEB 

quantitatively for many 

different categories

N/A
Page 28 of 239

Study/Author Costs Energy Benefits Non-energy Benefits (NEBs) Persistence Assumptions

Altweis

(2001)

N/A Complex Used engineering 

calculations to estimate 

energy used with and 

without identified 

deficiencies. Provided Most 

Likely and Least Cost 

Solution scenarios, 

depending on assumptions 

for what would have 

occurred absent 

commissioning.

Moderate Used simple calculations 

and extensive assumptions 

to estimate impacts to 

factors such as lost 

productivity, lost sales due 

to late building completion 

and equipment 

replacement. Provided 

Most Likely and Least Cost 

Solution scenarios, 

depending on assumptions 

for what would have 

occurred absent 

commissioning.

Simple Lifetime assumed 

by measure and 

benefit (most are 

first-year impacts or 

flat over assumed 

lifetime).

Heinemeier, 

Schools Cx

(2004)

N/A. Simple Comparison of monthly 

utility bills (electricity and 

gas (kBtu) per square foot), 

between commissioned and 

uncommissioned buildings 

(well matched pair or large 

sample size 

recommended), also 

comparing commissioned 

buildings with benchmarks 

(e.g., CBECS).

Moderate Comparison of well-defined 

metrics collected during 

construction and operation 

phases, between 

commissioned and 

uncommissioned buildings 

(well matched pair or large 

sample size 

recommended). 

Simple Many benefits are 

first year. 

Persistence not 

addressed. 
Page 29 of 239

Study/Author Costs Energy Benefits Non-energy Benefits (NEBs) Persistence Assumptions

Mills, 

Meta-analysis

(2004)

Moderate Cx and EBCx: includes 

Cx provider fee, Cx 

coordination costs of 

other parties

Cx: Does not include 

resolution cost for 

“quality assurance” 

findings or cost to fix 

design flaws

EBCx: Includes 

resolution cost 

Moderate Project documentation 

may have utilized 

engineering 

calculations, models, or 

pre- and postconsumption 

measurement to 

quantify savings. 58 % 

of EBCx and 28 % of Cx 

projects verified 

measures to be 

implemented.

Moderate First cost non-energy 

savings ($), and ongoing 

labor cost savings (type, 

labor hours saved), and 

includes a list of other 

NEBs (Y/N, $)

Simple 

(number 

taken from 

other 

studies, 

which are 

more 

complex)

Persistence data 

collected where 

available from other 

studies (LBNL, 

TAMU)). Used their 

methodology (see 

Persistence chapter 

of this report).

For the majority of 

buildings, 

persistence or 

measure life was 

not addressed. 12

SBW, Northwest 

Cx & EBCx

(2004)

Complex Includes incremental 

fee increases, travel 

expenses, and 

resolution costs to each 

party, as reported by 

respondents.

Moderate Used project 

documentation to 

identified significant and 

resolved issues, then 

used standard 

engineering calculations 

or parametric modeling 

to get savings.

Complex Dollar value estimated 

three different ways 

based on telephone 

survey data with most 

conservative figure used.

N/A

California 

Commissioning 

Collaborative’s 

Cxdatabase.com

(2004)

Moderate Includes minor capital 

improvements as a cost 

of EBCx. 13

Includes incremental 

costs to other parties

Simple Respondent provides 

info for energy-savings 

calculations for each 

finding, not required to 

perform calculation. 

Persistence and 

avoided cost info 

optional. No 

standardized 

calculations for energy 

savings.

Simple Respondents asked to 

identify which benefits 

they received, and given 

the option of entering a 

dollar value for the 

estimated avoided cost. 

No standardized 

calculations for avoided 

costs.

Not 

assessed

 12 The fast payback times for Cx measures are most likely shorter than the period of erosion of savings. 13 Allows owner to specify whether O&M staff participation is a cost or a benefit. Does not include resolution costs for “quality assurance” findings as reported by 

respondents.
Page 30 of 239

Study/Author Costs Energy Benefits Non-energy Benefits (NEBs) Persistence Assumptions

Moore et al.

(2008)

n/a Only provides payback 

based on owner’s costs.

Complex Complex spreadsheet 

calculations, qualitychecked by utility 

program administrators

n/a n/a

PECI & Summit 

Building 

Engineering

(2008)

Complex EBCx provider costs 

and implementation 

costs included.

Complex Based on utility 

programs’ data, which 

uses detailed energy 

calculations and a 

rigorous review

n/a n/a
Page 31 of 239

2.1.4. Recommendations and Decision Points

Table 2.10 displays the 12 diverse studies reviewed in this report. The studies represent a large range of 

data collection, costs, energy benefit and non-energy benefit methodologies. Although this makes 

generalizations difficult, their collective efforts point to several recommendations moving forward, and 

several decision points to which any new study must attend.

Recommendations

1. Building commissioning data should be greatly diversified. In the majority of these 

studies, building information comes from only a few sources, like a handful of commissioning 

providers or a large university research department. It is thus unclear how well the findings of 

these studies will apply to the worldwide commissioning industry. Moving forward, an attempt 

should be made to gather building data from a much broader base. To date, it has been difficult 

to collect data from diverse projects because owners do not tend to ask for (or pay for) this kind of 

data on their own projects, and commissioning providers therefore do not gather it. Collecting 

commissioning data in a consistent way requires artificial injection of a research project or 

program to help standardize the way data is gathered and reported by market actors.

2. A complex cost-benefit methodology may require continuous data collection throughout 

the commissioning project, extensive interviews, or both, to acquire a sufficiently detailed 

reporting of costs. The data required for complex, and sometimes even moderate, cost 

methodologies will be difficult to obtain with a retroactive data collection methodology relying 

solely on documentation. It is nearly impossible to determine from documentation costs that are 

not explicitly defined during the project. For example, a study may want to include in its cost 

calculation the cost to the contractor of coordinating with the commissioning provider. If this cost 

is not defined either during or immediately after the commissioning process it will not be included 

in documentation (although it may be obtained through a timely interview). As a result, retroactive 

studies relying mostly on project documentation are often forced to “take what they can get,” a 

methodology which does not lead itself to a consistent definition of commissioning costs. A study 

employing a complex cost methodology should facilitate accurate data entry by using a collection 

methodology that allows easy entry of data and thus helps avoid the need for retrospective 

information-gathering based on project documentation. 

3. Data validation is an important aspect of any cost-benefit methodology. In cases where 

data is not verified, the accuracy of the cost-benefit results may be at risk. 

Decision Points in Creating a Cx/EBCx Cost-Benefit Methodology

Creating an appropriate and feasible commissioning cost-benefit methodology that achieves the goals of 

the project requires careful planning around some key decision points. Ultimately, these decisions lead to 

a methodology that can have a range of levels of effort to collect and analyze data, as is shown in this 

chapter. The following key decision points emerged during this analysis of cost-benefit methodologies.

General

• What is the goal of data collection and who is the expected audience?

• What data and formats are appropriate to the study goals and audience? 

• What resources do researchers have available to them? This includes both financial resources as 

well as current and potential data sources. 

• How important is verification of data? (Possible levels: reasonableness check, oversight of energy 

and non-energy benefits calculations, and verification for persistence.)
Page 32 of 239

Data Collection

• Will the study be a one-time event that looks retrospectively at past projects, or will data collection 

and analysis occur continuously with current and future projects?

Costs

• Should the cost to resolve problems identified by the commissioning provider be counted as a cost of 

commissioning? If these resolutions are major design changes, should they be counted as a cost of 

commissioning?

• Should the commissioning-related costs of designers, contractors, and operating staff be counted as 

costs of commissioning?

• Are tasks performed by a commissioning provider that is out of the scope of commissioning counted 

as a cost of commissioning? For example, designers are generally tasked with developing the design 

intent documents. If the designer does not complete these documents, but the commissioning 

provider must have a complete set to functionally test the systems against, then often the 

commissioning provider will complete the task.

• Are costs treated differently for new construction commissioning and existing building 

commissioning?

Energy Benefits

• Will the methodology be whole building or measure-based?

• Will the methodology require monitored data or rely on calculations, and will calculations be validated 

by monitored data?

• How will measured data be collected (e.g., utility bills, dataloggers, or trends from the building 

automation system)?

• How will it be tracked that identified measures are implemented?

• Will standardized calculations be used, or guidelines for calculations or modeling?

• What standardized documentation must be collected to support modeling or calculation?

• How will persistence of savings be estimated or verified?

Non-Energy Benefits

• Will an attempt be made to quantify the financial consequences of non-energy benefits?

• If not, how will non-energy benefits be reported and verified?

• If so, will the financial non-energy benefit be self-reported, or will a verification methodology be 

employed?

2.2 References

Altwies, Joy and Ian B. D. McIntosh. 2001. “Quantifying the Cost Benefits of Commissioning” in 

Proceedings of the 9th National Conference on Building Commissioning. PECI.

Friedman, Hannah, Tudi Haasl, and Ken Gillespie. 2004. “Creating California’s Online Commissioning 

Case Study Database: Case Studies Go High Tech” ” in Proceedings of the ACEEE 2004 Summer Study 

on Energy Efficiency in Buildings, 3-64 – 3-75, Washington, DC: ACEEE. 

Gregerson, Joan. 1997. Commissioning Existing Buildings. E Source TU-97-3. Boulder, CO: E Source, 

Inc.
Page 33 of 239

Haasl, Tudi, K. Stum, and M. Arney. 1996. “Better Buildings through Improved O&M – A Five Building 

Case Study.” in Proceedings of the 5th National Conference on Building Commissioning. 

Heinemeier, Kristin, Michael Martin, Balaji Santhanakrishnan, Anita Ledbetter, Jim Shoop, Wes Harvey, 

Joseph Martin, and Frank Thomas. 2004. “Commissioning of New Schools: A State Funded Study of 

Costs and Benefits” in Proceedings of the ACEEE 2004 Summer Study on Energy Efficiency in Buildings, 

3-88 -3-99, Washington, DC: ACEEE.

Mills, Evan, Hannah Friedman, Tehesia Powell, Norman Bourassa, David Claridge, Tudi Haasl and Mary 

Ann Piette. 2004. “The Cost-Effectiveness of Commercial-Building Commissioning.” LBNL – 56637, 

December, 2004.

Moore, Emily, 2008. “California Retrocommissioning Programs: Lessons Learned”, Proceedings of the 

14th biennial ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, California.

Piette, Mary Ann, B. Nordman, and S. Greenberg. 1995. “Commissioning of Energy-Efficiency Measures: 

Costs and Benefits for 16 Buildings.” LBNL – 36448, April.

Piette, Mary Ann and Bruce Nordman. 1995. “Energy and Cost Savings from Commissioning Efficiency 

Measures in 16 Buildings” in Proceedings of the 4th National Conference on Building Commissioning. 

Portland Energy Conservation, Inc. (PECI). 1997. “What Can Commissioning Do for Your Building?”

PECI. 1996. “Building Systems Deficiency Database.” Report to the U.S. Environmental Protection 

Agency/U.S. Department of Energy under Assistance Agreement CX822837-01-0, December, 1996.

PECI and Summit Building Engineering, “2007 California RCx Market Characterization”, Report prepared 

for the California Commissioning Collaborative.

SBW Consulting. 2004. “Cost-Benefit Analysis for the Commissioning in Public Buildings Project,” 

submitted to the Northwest Energy Efficiency Alliance, posted May, 2004.

Tso, Bing, Marc Schuldt, and Jun Quan. 2004. “The Costs and Benefits of Achieving Silver LEED for Two 

Seattle Municipal Buildings” in Proceedings of the ACEEE 2004 Summer Study on Energy Efficiency in 

Buildings, 3-338 -3-350, Washington, DC: ACEEE.

Tso, Bing, Michael Baker, and Andrzej Pekalski. 2002. “The Costs and Benefits of Commissioning 

Oregon Public Buildings” in: Proceedings of the ACEEE 2002 Summer Study on Energy Efficiency in 

Buildings, 3.373-3.384, Washington, DC: ACEEE. An earlier version is also included, from the 

Proceedings of the 8th National Conference on Building Commissioning, 2000. PECI. (Based on SBW 

Consulting. “Cost-Benefit Analysis for the Commissioning in Public Buildings Project”)

Stum, Karl, Tudi Haasl and Dan Krebs. 1994. “Costs and Savings of ECM Commissioning and Inspection 

– Case Studies from One Utility” in Proceedings of the 2nd National Conference on Building 

Commissioning, PECI.

Additional Cost-Benefit Studies

Bicknell, Charles and Liza Skumatz. 2004. “Non-Energy Benefits (NEBs) in the Commercial Sector: 

Results from Hundreds of Buildings” in Proceedings of the ACEEE 2004 Summer Study on Energy 

Efficiency in Buildings, 4-10 – 4-22, Washington, DC: ACEEE. 

Bushell, Christopher, Jack Wolpert, Mandeep Singh, and William Burns. 2002. “Retro Commissioning at 

Chicago Public Schools” in Proceedings of the 10th National Conference on Building Commissioning, 

PECI.
Page 34 of 239

Chao, Mark and David B. Goldstein. 1998. “Commissioning, Energy Efficiency, and the Asset Value of 

Commercial Properties: Perspectives from Appraisers and Financial Stakes for Owners” in Proceedings 

of the 6th National Conference on Building Commissioning, PECI.

Claridge, David E., Charles H. Culp, Song Deng, W.D. Turner, J.S. Haberl, and Mingsheng Liu. 2000. 

“Continuous Commissioning of a University Campus” in Proceedings of the 8th National Conference on 

Building Commissioning, PECI.

Cohan, David and Phil Willems. 2001. “Construction Costs and Commissioning” in Proceedings of the 9th 

National Conference on Building Commissioning, PECI.

Coleman, James D. 1998. “Three Building Tune-Up Case Studies” in Proceedings of the 6th National 

Conference on Building Commissioning, PECI.

Cox, Robert and April Williams. 2000. “Quantifying Costs & Benefits of Commissioning” 

in Proceedings of the 8th National Conference on Building Commissioning, PECI.

Deall, Jerry H., Jack S. Wolpert, Mandeep Singh, and James Kelley. 2002. “Savings Due to 

Commissioning at the King Center, (Performing Arts Building)” in Proceedings of the 10th National 

Conference on Building Commissioning, PECI.

Dorgan, Chad, Robert Cox, and Charles Dorgan. 2002. “The Value of the Commissioning Process: Costs 

and Benefits.” The Austin Papers: Best of the 2002 International Green Building Conference, Compiled by 

Environmental Building News, pp. 25-30.

Khan, Aleisha. 2002. “Retrocommissioning of Two Long-Term Care Facilities in California. 

Institute for Market Transformation 2002, available online at www.imt.org/papers.

Kumar, Satish and William J. Fisk. 2002. “IEQ and the Impact on Building Occupants” in the 

ASHRAE Journal.

Kumar, Satish and William J. Fisk. 2002. “The Role of Emerging Energy-Efficient Technology in 

Promoting Workplace Productivity and Health: Final Report” Publication source unknown. February 13, 

2002.

Martinez, Mark S. 1999. “Energy Efficient Retrofit Commissioning – Tricks of the Trade to 

Avoid Claims and Unhappy Customers” in Proceedings of the 7th National Conference on Building 

Commissioning, PECI.

McHugh, Jonathon, Lisa Heschong, Nehemiah Stone, Abby Vogen, Daryl Mills, and Cosimina Panetti. 

2002. “Non-Energy Benefits as a Market Transformation Driver” in Proceedings of the ACEEE 2002 

Summer Study on Energy Efficiency in Buildings, 6.209-6.220, Washington, DC: ACEEE. 

Milton, Donald K. 2002. “IEQ and the Impact on Employee Sick Leave” in the ASHRAE Journal, 2002.

Nelson, Norman L. 1999. “Avoiding Litigation through Proper Commissioning: My 

Experience as an Expert Witness” in Proceedings of the 7th National Conference on Building 

Commissioning, PECI.

Nicholls, Andrew, Sean McDonald, and John D. Ryan. 2002. “Estimating the Benefits from Building 

Technologies: Issues, Challenges, and Lessons Learned from Blue Ribbon Panel Reviews” in 

Proceedings of the ACEEE 2002 Summer Study on Energy Efficiency in Buildings, 10.221-10.236, 

Washington, DC: ACEEE.
Page 35 of 239

Parker, Gretchen and Mark Chao. 1999. “Management and Documentation of Building Energy 

Performance by Real Estate Investment Trusts” in Proceedings of the 7th National Conference on 

Building Commissioning, PECI.

Parker, Gretchen, Mark Chao, and Ken Gillespie. 2000. “Energy-related Practices and Investment Criteria 

of Corporate Decision-Makers” in Proceedings of the ACEEE 2000 Summer Study on Energy Efficiency in 

Buildings. Washington, DC: ACEEE. 

Parks, Jim, Mazin Kellow, Debby Dodds, and Greg Cunningham. 2001. “Getting the Most Out of Your 

Building: SMUD’s Commissioning Program” in Proceedings of the ACEEE 2000 Summer Study on 

Energy Efficiency in Buildings, 4.247 - 4.256, Washington, DC: ACEEE; also published as “SMUD’s 

Retrocommissioning Program: Improving With Time” in Proceedings of the 9th National Conference on 

Building Commissioning, PECI.

Tseng, Paul. 1998. “Building Commissioning: Benefits and Costs.” Heating/Piping/Air Conditioning, April, 

1998, pp. 51-59.

Veltri, Anthony. 2002. “Development of an Integrated Commissioning Strategy and Cost Model” in 

Proceedings of the 10th National Conference on Building Commissioning, PECI.

Wei, Guanghua, Mingsheng Liu, Martha J. Hewett, and Mark W. Hancock. 2001. “Continuous 

Commissioning of a Hospital Complex” in Proceedings of the 9th National Conference on Building 

Commissioning, PECI.

Wilkinson, Ronald J. 2000. “Establishing Commissioning Fees” ASHRAE Journal. February 2000, pp. 41-

51.
Page 36 of 239

3. COMMISSIONING PERSISTENCE

3.1 Methodologies for Determining Persistence of Commissioning 

Benefits

3.1.1 Commissioning Benefits

The benefits of Cx and EBCx are normally verified as part of the project, whether they are energy-related 

benefits or non-energy benefits (NEBs). How those benefits last over time is a subject of much study and 

discussion, in two principle areas:

• How will certain Cx/EBCx measure types persist over time (typically measured as percentage 

degradation over certain time periods in years),

• The robustness of methods used to ascertain measure persistence.

The one-time, or inherently persistent benefits normally reduce construction costs directly or indirectly. 

Table 3.1 lists a number of reported benefits of commissioning (Mills et al. 2005, Friedman et al. 2002, Liu 

et al., 2002) that appear to generally fall in the category of inherently persistent benefits. They have been 

grouped as design benefits, construction benefits, early occupancy benefits, and “other”, primarily based 

on when they occur in the design/construction process. The benefit from design improvements inherently 

occurs once, but this benefit persists until the building is renovated or equipment fails and is replaced. 

Many more design benefits than those listed may result from commissioning. The benefits that speed up 

or make the construction process flow more smoothly will clearly provide a one-time benefit. The benefits 

that make early occupancy a more seamless process will generally be one-time benefits, though the 

items related to safety and liability may be viewed as on-going benefits. 

The role of commissioning in qualifying a building for a Leadership in Energy and Environmental Design 

(LEED) rating or participation in a utility program may provide long-term benefits, but are treated as 

inherently persisting. A thorough EBCx process can be a significant enabling factor for a thorough 

building retrofit. 

Whether viewed as one-time benefits, or as longer term, the commissioning benefits shown in Table 3.1 

will not be considered among those that may degrade over time. Hence they will be assumed to be 

inherently persistent in the context of the persistence methodology presented here.

The benefits listed in Table 3.2 have also been reported as commissioning benefits (Mills et al. 2005, 

Friedman et al. 2002, Liu et al., 2002), but these are items related to the operation of the building that are 

thought to be more likely to change over time, particularly if they are the result of the implementation of 

practices that are not widely understood by the community of building operators. Hence these benefits 

are treated as commissioning benefits that may not persist.
Page 37 of 239

Table 3.1 Inherently persistent benefits of commissioning

Design Benefits

• Equipment right-sizing 

• Improved equipment layout

Construction Benefits

• Improved project schedule 

• Clarified delineation of responsibilities among team members 

• Fewer change orders 

• Less disagreement among contractors 

• Reduced contractor call-backs 

• More vigilant contractor behavior (knowing that Cx will follow their 

work)

• Reduced testing and balancing (TAB) costs

Early Occupancy Benefits

• Smoother process and turnover 

• Less disruption to occupancy and operations during turnover

• Fewer warranty claims

• Improved safety 

• Reduced liability

Other

• Compliance with LEED or other sustainability rating system

• Qualification for rebate, financing or other services

• Qualification for participation in utility program 

• An enabling factor for comprehensive system overhaul

Table 3.2 Commissioning benefits that may not persist

Easier to Quantify

Reduced energy consumption 

Ensured or improve indoor environment /occupant comfort 

Ensured adequate indoor air quality

Improved water utilization 

More Difficult to Quantify

Repaired or accelerate repair of a problem 

Avoided premature equipment failure 

Reduced operations and maintenance costs 

Increased occupant productivity

Improved documentation 

Increased in-house staff skills, knowledge, awareness

Improved operational efficacy 

Provided sustainable engineering solutions to operational problems

Ensured proper system performance (energy and non-energy 

systems)
Page 38 of 239

3.1.2 Measures of Benefit Persistence

The first four items shown in Table 3.2 are commissioning benefits that may be quantified if suitable 

baselines for comparison are available. The International Performance Measurement and Verification 

Protocol (IPMVP 2001) is widely used to determine savings in energy and water resulting from either 

retrofits or operational changes. It provides procedures that may also be applied to new buildings if the 

impact of commissioning measures implemented can be accurately assessed in a simulation. Comfort 

has been widely studied, and measures of comfort such as dry bulb temperature and relative humidity 

can be measured and logged. Likewise, CO2 and other measures of indoor air quality may be measured. 

It is assumed that new buildings will provide comfort and quality indoor air, so it will be difficult to 

document commissioning benefits to comfort or indoor air quality in new buildings. However, when 

commissioning is carried out in an existing building, these changes can be documented with appropriate 

measurements before and after commissioning. These measurements are most likely to be made if a 

serious comfort and/or air quality problem provides a significant part of the motivation for commissioning 

the building.

The remaining items listed in Table 3.2 are much more difficult to document, beyond the documentation 

of specific commissioning measures that have been implemented and verification that these measures 

are still in place months or years later. Hence the only further treatment of these benefits within the 

proposed methodology will be through documentation of specific measures related to these benefits. 

Given this context, and based on a review of the existing literature on persistence of commissioning 

benefits (Frank et al. 2005), the proposed methodology for determining persistence of commissioning 

benefits will specifically treat the persistence of the energy, water, comfort, and indoor air quality benefits 

of commissioning in a quantitative manner. It will treat all other benefits through examination of the 

persistence of specific commissioning measures that have been implemented.

3.1.3 Persistence of Energy Benefits from Commissioning

The energy benefits of commissioning will initially be determined using an appropriate methodology from 

the International Performance Measurement and Verification Protocol (IPMVP 2001). This protocol 

provides a general approach that compares measured energy use or demand before and after 

implementation of an energy savings program using the equation:

Energy Savings = Base year Energy Use – Post-Retrofit Energy Use ± Adjustments

The "Adjustments" term brings energy use in the two time periods to the same set of conditions by 

adjusting for differences in weather, occupancy, plant throughput, and equipment operations. These 

adjustments are made routinely for weather changes, or as needed for occupancy changes, scheduling 

changes, etc. 

Four basic options are presented for determining energy savings within the IPMVP. These options are 

briefly described in Table 3.3. Within the context of this methodology, the only option that is considered 

appropriate for determining the energy savings from commissioning of a new building is Option D, 

Calibrated Simulation. This permits the calibration of a simulation to the measured consumption of the 

building following commissioning, followed by simulation of the changes made during commissioning. For 

existing buildings that are commissioned, energy savings from comprehensive commissioning projects 

may be evaluated using either Options C or D. If the savings from the commissioning process are too 

small to evaluate in one of these ways or only one or two measures are expected to result in energy 

savings, then Option B may be appropriate. Option A will rarely be appropriate. The detailed procedures 

in the protocol are to be used.

Following determination of energy savings in multiple years using the selected procedure, savings from 

each year in which savings are determined will be further normalized to a common weather year to 

eliminate bias in the persistence determination from weather differences in the different years. Other 

adjustments may also be made when warranted by known conditions. 
Page 39 of 239

Table 3.3 The four IPMVP energy savings options

Source: IPMVP 2001
Page 40 of 239

3.1.4 Persistence of Water Savings Benefits from Commissioning

The IPMVP methodologies for determining water savings are the same as those used to determine 

energy savings. In these cases, it becomes important to consider precipitation if the building water 

consumption includes water uses for exterior landscaping. 

3.1.5 Persistence of Commissioning Measures

In some cases, appropriate metering is not installed or baseline information needed to determine energy 

savings is not available. In other cases, the measures of interest may not impact energy consumption, 

but may impact other benefits of commissioning as discussed in the section on “Measures of Benefit 

Persistence.” In these cases, persistence shall be determined by comparing a list of documented 

commissioning measures that were implemented during the commissioning process with the measures 

that are subsequently documented as being in place or operational during the time when persistence is 

being checked.

When used to evaluate measures that impact energy consumption, the most comprehensive systematic 

listing of measures that may be considered is probably that of Mills et al. (2005). They used a matrix that 

included the specific commissioning measures in the four categories listed in Table 3.4. These measures 

were then considered as being applied to deficiencies in the areas or systems shown in Table 3.5. 

Table 3.4 Specific commissioning measures

Design, Installation, Retrofit, Replacement

• Design change

• Installation modifications

• Retrofit/equipment replacement

• Other

Operations and Control

• Implement advanced reset

• Start/stop (environmentally determined)

• Scheduling (occupancy determined)

• Modify setpoint(s)

• Equipment staging

• Modify sequence of operations

• Loop tuning

• Behavior modification/manual changes to operations

• Other

Maintenance

• Calibration

• Mechanical fix

• Heat transfer maintenance

• Filtration maintenance

• Other

Miscellaneous

• Deficiency unmatched to specific measure
Page 41 of 239

Table 3.5 Areas or systems in which measures correct deficiencies

HVAC (combined heating and cooling)

Cooling plant

Heating plant

Air handling and distribution

Terminal units

Lighting

Envelope

Plug loads

Facility-wide (e.g. EMCS or utility related)

Other

Deficiency unmatched to specific measure

3.2 Review of Literature on the Persistence of Commissioning 

Benefits in New and Existing Buildings

3.2.1 Introduction 

In recent years the topic of persistence of benefits has gained more interest both for existing building and 

new building commissioning. Several studies have been performed and published examining both 

aspects of this topic. This review will summarize the key results of these studies. The categories 

presented are persistence of commissioning measures in existing buildings, persistence of 

commissioning measures in new buildings, strategies for improving persistence in new and existing 

buildings, and related reports. This topic is relatively new, and the only relevant projects identified in the 

literature to date involve a total of 37 buildings:

• 10 Existing Buildings Commissioned at Texas A&M University – Turner et al. (2001) and Claridge et 

al. (2002, 2004)

• 8 Existing Buildings Commissioned in Sacramento, California – Bourassa et al. (2004)

• 8 Existing Buildings Commissioned in Oregon – Peterson (2005)

• 1 Existing Building Commissioned in Colorado – Selch and Bradford (2005)

• 10 New Buildings Commissioned – Friedman et al. (2002, 2003a, 2003b)

Since the total literature identified consists of published papers and reports from only five projects directly 

related to persistence, the summaries presented for each project are considerably more detailed than is 

customary in a literature review.
Page 42 of 239

3.2.2 Persistence of Commissioning Measures in Existing Buildings

Texas A&M: Ten Buildings at Texas A&M University

A study was performed in 2000 to evaluate the persistence of savings in ten buildings on a university 

campus three years after the buildings participated in existing building commissioning (Turner et al. 

2001). The objectives of the study were to determine quantitatively how much savings degradation 

occurred and the major causes of any observed degradation. The investigation did not focus on the 

detailed measures implemented in each building but rather on the degree to which the measures 

implemented in the EBCx process had been maintained, as indicated by examination of energy use data, 

the EBCx reports, and the control settings in place on the main energy management control system.

The study was conducted in five major parts. First, buildings were selected to be studied. Second, 

savings calculations were performed based on energy usage data from the different periods needed. 

Third, field examination and commissioning follow-up was conducted on two buildings in which major 

savings degradation occurred. Fourth, operational and controls changes that could have contributed to 

changes in building performance after commissioning were identified. And fifth, calibrated simulations of 

some of the buildings were performed to verify the effects of the identified changes on energy 

consumption.

A preliminary group of 20 buildings which had been commissioned in 1996 or 1997 was initially selected. 

An office review of information on the EBCx measures implemented and available information on 

operating parameters before and after existing building commissioning was then conducted. Based on 

this review, the ten buildings with the most complete information concerning the EBCx process and 

energy consumption data were selected. None of the buildings in this group received capital retrofits 

during the period of 1996 to 2000. Five buildings were commissioned in 1996 and the other five were 

finished in 1997. In each of these buildings, commissioning measures were identified by the EBCx

provider and then implemented by the provider, after receiving the concurrence of the building owner’s 

representative. Since all ten buildings were located on a university campus, they primarily consisted of 

classrooms, laboratories, and offices, with one volleyball arena. 

The energy usage data for these buildings had been monitored and was obtained beginning with the 

period shortly before EBCx and ending in 2000 when the study was performed. For comparison 

purposes, all of the energy data was normalized to a single year of weather data. Because the weather 

data for the year 1995 most closely approximated average weather conditions for the years studied, it 

was chosen as the baseline year. Energy use before and after the EBCx process were compared. In this 

study savings from the EBCx process were determined by using Option C of the International 

Performance Measurement and Verification Protocol, which determines savings using measured energy 

use at the whole facility level. This required that baseline models of the consumption be formulated for 

each major source of energy use in each building. Chilled water and hot water energy consumption were 

measured for each year, and three-parameter or four-parameter change-point models of cooling and 

heating consumption were determined as functions of ambient temperature using a modeling program. 

The process of calculating the yearly savings required the development of five separate chilled water 

models and five hot water models for each building, one for each year, including the baseline model. The 

consumption and savings for each year were then normalized to 1995 weather by using the models for 

each year's data with the 1995 temperature data to determine the savings for each year. Electricity 

savings were determined without normalization since the buildings did not have chillers, and electricity 

consumption is not appreciably affected by ambient temperature. 

Follow-up was performed on two buildings with significant savings degradation. This was done primarily 

through a field investigation of the buildings to determine what changes had occurred that would produce 

the changes. Equipment performance and Energy Management Control System (EMCS) control settings 

were examined to evaluate possible causes for degradation.
Page 43 of 239

Information was then gathered on controls and operational changes that had occurred in the buildings 

during the period studied. This was done by examining the EBCx reports and interviewing the engineers 

and maintenance personnel who had responsibility for each building. These interviews provided 

identifiable reasons for many of the changes in savings seen in the buildings.

In order to quantify the effect of each operational or control change identified, it was decided that the 

energy usage of the buildings would be modeled using a computer simulation program. The rough 

simulations would then be calibrated until they provided accurate representations of the actual energy 

use. These simulations would then demonstrate how much of an effect each control or operational 

change had on the building energy use. 

Texas A&M: Results

All ten buildings showed significantly reduced chilled water and hot water energy consumption since 

EBCx, although the savings generally decreased somewhat with time. Eight buildings had larger HW 

savings in 1998 than in 1997 as a consequence of hot water loop optimization conducted in 1997 and 

final EBCx actions. Overall the electricity consumption remained fairly constant, with three buildings 

showing small increases in consumption (negative savings). The average electricity savings for the ten

buildings from 1997 to 2000 were 10.8 %. Figure 3.1 and Figure 3.2 show the chilled water and hot water 

savings trends for the years following the building EBCx.

Figure 3.1 Chilled water savings persistence after EBCx.

Overall, chilled water savings for the three years following EBCx averaged 39.3 % of the precommissioning baseline. Eight of the buildings showed good persistence of savings for chilled water (less 

than 15 % change during the three to four years after EBCx), while the other two displayed significant 

degradation. The Blocker building had 19 % degradation, and the G. R. White Coliseum had a dramatic 

savings degradation of 38 %. 

Hot water consumption was reduced significantly in the years following EBCx, but the savings fluctuated 

widely from year to year. Savings increased from 1997 to 1998 in most buildings due to optimization in 

the hot water loop in 1997 and some ongoing EBCx work. The ten buildings averaged hot water savings 

of 65.0 % after EBCx.
Page 44 of 239

Figure 3.2 Hot water savings persistence after EBCx.

Based on the historic campus energy costs of $4.42/GJ ($4.67/MMBtu) for chilled water, $4.50/GJ 

($4.75/MMBtu) for hot water, and $0.02788/kWh for electricity, the cumulative savings from EBCx in 

these ten buildings were $4 439 000 for the period of 1997 to 2000. Only three buildings had year 2000 

savings greater than 1998 savings, and the increase in two of these was approximately 2 % of baseline 

consumption, which is well within the range of normal year-to-year variation. The savings of the other 

buildings decreased.

Follow-up investigations of the two buildings with significant savings degradations revealed serious 

equipment malfunction and controls failure. In the Kleberg building, two chilled water control valves were 

found to be leaking badly, and combined with a failed electronic to pneumatic switch and high water 

pressure, caused low discharge temperatures and continuous reheat operation. In addition, failed 

sensors caused the outside air dampers to remain fully open, and leaking damper actuators in a number 

of VAV boxes resulted in simultaneous heating and cooling. The G. R. White Coliseum was found to 

have a controls malfunction resulting in simultaneous heating and cooling, with two of the thirteen air 

handling units operating in heating mode, while the rest operated in cooling mode. These equipment and 

controls problems in these two buildings were the primary causes of the savings degradation observed. 

Because these problems did not result in comfort problems in the buildings, they may have gone 

undetected had the energy consumption not been monitored and compared with previous data.

The energy management control system settings were evaluated for the buildings to determine why the 

changes in savings occurred. Three major control settings were examined: cold deck or cooling 

discharge temperatures, hot deck temperatures, and static pressure settings. The cold deck or cooling 

coil discharge temperatures were reset during EBCx to save chilled water consumption. It was found that 

for eight of the ten buildings in 2000, the temperatures had been lowered and were requiring more 

cooling. This led to chilled water savings degradation, particularly in the Blocker building. Five of the ten 

buildings had dual duct systems, and of these five, three of the hot deck temperature set points were at 

different values in 2000 than they had been upon completion of EBCx. This resulted in more hot water 

consumption. Static pressure set points affected chilled water, hot water, and electricity consumption. Of 

the nine buildings with variable air volume systems, only one (Koldus) still had the same static pressure 

set point in 2000 that it had been set to during EBCx. The other buildings were requiring more static 
Page 45 of 239

pressure, and therefore using more energy. It is worth noting that the Koldus building showed no serious 

savings degradation of any kind in this study. 

Data were gathered from engineers and maintenance personnel to attempt to verify the controls changes 

and explain them. It was found that the G. R. White Coliseum, which saw significant savings degradation 

in chilled water and hot water savings, had experienced malfunctions in air handling unit controls that 

caused simultaneous heating and cooling to occur throughout the year. Almost all of the savings 

degradation for this building could safely be attributed to these problems. It was also found that the 

Kleberg building had experienced some significant equipment problems that could explain some of the 

degradation in savings that occurred. No other building was reported to have experienced equipment 

problems of the same caliber as these two cases, but controls changes in the other buildings were 

verified through investigation. With the assembly of this type of information, simulated calibrations could 

be made for the buildings. Lack of data and other problems such as the one mentioned for the G. R. 

White Coliseum. G.R. White allowed only five of the ten buildings to be simulated. Three simulations 

were performed for each of these buildings, one for the pre-commissioning period, one for the year after 

EBCx, and one for the year 2000. Factors considered in the simulations included control settings 

changes, operator overrides on the controls, and physical changes in the system such as broken or 

repaired valves, sensors, etc. Detailed simulations of the control changes in Eller O&M, Harrington Tower, 

VMC Addition and Wehner showed that the RMS difference between the changes observed between the 

post-commissioning periods and year 2000 was only 1.1 %, suggesting that the changes in savings for 

these buildings were almost entirely due to the control changes identified. 

Overall, equipment malfunction and changes made in cold deck and hot deck temperature settings 

following EBCx were the major reasons for changes in chilled water and hot water energy consumption 

and savings after EBCx. 

Table 3.6 is a summary of the money saved in the year 1998 as compared with the money saved in the 

year 2000 for each of the ten buildings examined.
Page 46 of 239

Table 3.6 Cost savings calculations for the year 1998 and the Year 2000.

No. Buildings Type Baseline 

Energy 

Use 

(GJ/y)

(MWh/y)

Year 1998 Savings Year 

2000 

Savings

Energy 

Use 

(GJ/y)

(MWh/y)

Savings Cost Savings

GJy

MWh/y

Each 

US$/y

Total 

US$/y

Total 

US$/y

1 Blocker

CHW 24 218 20 605 3 613 $ 15 993

$ 76 003 $ 56 738

HW 9 216 1 768 7 448 $ 33 533

Elec. 4 832 3 883 950 $ 26 477

2 Eller O&M

CHW 32 311 19 687 12 623 $ 55 875

$ 120 339 $ 89 934

HW 8 001 1 218 6 783 $ 30 539

Elec. 4 891 3 675 1 217 $ 33 925

3 G.R. White 

Coliseum

CHW 19 911 8 979 10 932 $ 48 386

$ 154 973 $ 71 809

HW 22 319 580 21 740 $ 97 875

Elec. 1 480 1 168 312 $ 8 712

4 Harrington 

Tower

CHW 14 959 8 883 6 076 $ 26 895

$ 64 498 $ 48 816

HW 7 276 964 6 311 $ 28 413

Elec. 1 666 1 336 330 $ 9 189

5 Kleberg 

Building

CHW 62 534 36 894 25 640 $ 113 491

$ 313 958 $ 247 415

HW 43 059 1 281 41 777 $ 188 086

Elec. 5 511 5 067 444 $ 12 380

6 Koldus 

Building

CHW 23 173 13 703 9 470 $ 41 916

$ 57 076 $ 61 540

HW 2 218 421 1 798 $ 8 093

Elec. 2 850 2 597 253 $ 7 067

7 Richardson 

Petroleum

CHW 30 096 16 497 13 599 $ 60 191

$ 120 745 $ 120 666

HW 19 230 5 895 13 335 $ 60 035

Elec. 1 933 1 914 19 $ 519

No. Buildings Type Baseline 

Energy 

Use (GJ/y)

(MWh/y)

Year 1998 Savings

Year 

2000 

Savings Energy 

Use (GJ/y)

(MWh/y)

Savings Cost 

Savings Savings

GJy

MWh/y

Each 

US$/y

GJy

MWh/y

Total 

US$/y

8 VMC 

Addition

CHW 43 143 25 406 17 738 $ 78 513

$ 87 059 $ 92 942

HW 3 766 2 153 1 613 $ 7 260

Elec. 4 186 4 140 46 $ 1 286

9 Wehner 

CBA

CHW 20 249 14 073 6 177 $ 27 339

$ 47 834 $ 68 145

HW 14 130 10 250 3 880 $ 17 469

Elec. 2 555 2 446 109 $ 3 026

10

Zachry 

Engr. 

Center

CHW 43 071 18 334 24 738 $ 109 496

$ 150 400 $ 127 620

HW 8 098 3 408 4 690 $ 21 114

Elec. 7 502 6 793 710 $ 19 789

Type Totals

Year 

1998 Year 2000

Chilled Water 313 666 183 062 130 605 $ 578 096

$1 192 884 $ 985 626

Hot Water 137 314 27 940 109 374 $ 492 417

Electricity 37 407 33 018 4 389 $ 122 371

* The baseline energy use data for two buildings were created based on the average savings of the other buildings 

because they did not have enough data.

**To obtain MMBtu/yr, multiply the number of GJ/yr by 0.9478.
Page 47 of 239

Texas A&M: Conclusions

Table 3.7 summarizes the savings history of this group of ten buildings. The savings in 1998 following 

initial retro commissioning corresponded to average energy cost savings of 39 % for the ten buildings. 

Savings decreased to 32.3 % over the next two years – still a highly significant level of savings. 

Table 3.7 Summary of savings history in ten retro-commissioned buildings at Texas A&M

Baseline use

(US$/y)

1998 Cx savings

(US$/y)

Persistence of 

savings in 2000

(US$/y)

10 Buildings $3 049 487 $1 192 000

(39.1 %)

$984 516

(32.3 %)

8 Buildings $2 195 307 $723 376

(32.9 %)

$666 108

(30.3 %)

2 Buildings $854 180 $468 624

(55 %)

$314 408

(37 %)

Investigation showed that two of the buildings, G. R. White Coliseum and Kleberg, accounted for 3/4 of 

the total savings degradation, and both had experienced major equipment and controls malfunctions 

which were the primary causes of their degradation. Following correction of these problems, savings 

were restored to earlier levels. In the remaining eight buildings, savings changes were rather small, 

declining from 32.9 % to 30.3 % in aggregate.

All but one of eight buildings had experienced at least some changes in EMCS control settings. To verify 

the impact of the EMCS changes on energy consumption, the calibrated simulation process was 

performed on the four buildings with the most complete data sets. Simulation was conducted for a precommissioning period, a post-commissioning period soon after EBCx and for the year 2000 for each 

building. It was found that the changes in consumption observed following EBCx in these buildings were 

consistent with those due to the identified controls changes, with an RMS difference of only 1.1 %. 

Control changes accounted for the savings increase observed in the Wehner Building as well as the 

decreases observed in the other three buildings. This suggests that the changes in savings these four 

were almost entirely due to the control changes. 

Based on the results of this study of ten buildings, it was concluded that:

• Basic existing building commissioning measures are quite stable,

• Savings should be monitored to determine the need for follow-up, and

• Steps should be taken to inform operators of the impact of planned/implemented control changes.

SMUD: Eight Buildings in SMUD Program in Sacramento

In 2003, a study was performed by the Lawrence Berkley National Laboratory (LBNL) on eight buildings 

that had undergone EBCx through the Sacramento Municipal Utility District (SMUD) EBCx program 

(Bourassa et al. 2004). The objective of the study was to determine the extent to which EBCx measures 

were implemented, and the magnitude and persistence of energy savings achieved. Another objective 

was to see if the two primary goals of the SMUD EBCx program had been met: reduced overall annual 

building energy consumption, and improved energy efficiency awareness and focus in the customer. The 

eight buildings selected for the study consisted of six office buildings, one laboratory, and one hospital. 

Four of the buildings participated in EBCx in 1999, and the other four in 2000. In this program, the EBCx

provider worked with the building operators to develop the recommended measures. The measures 

selected for adoption were subsequently implemented by the building staff and/or contractors over a 

period of up to two years.
Page 48 of 239

SMUD: Energy Analysis

The energy savings obtained in the years following EBCx were determined and compared. In order to be 

able to compare energy savings in the different buildings over the years examined, baseline energy 

consumption was established for each building based on pre-EBCx energy use. Electricity use data were 

collected from monthly utility bills for each building. Four buildings also had metered data recorded at 15 

minute intervals. Gaps in utility bills were filled from site records or regression analysis. 

The energy consumption data were normalized to a common weather year and to a common billing cycle 

of 30.5 d. The savings were calculated using spreadsheets, based on the normalized data, which 

allowed for a simpler and more robust statistical comparison. Another set of savings was also calculated, 

based on the EBCx report predictions. Adjustments were made for a capital retrofit in one of the 

buildings. The cost of EBCx was also estimated for each of the buildings, based on three categories: 

SMUD’s EBCx costs, the site’s EBCx costs, and the EBCx measure implementation costs. Based on the 

estimated costs and savings, simple payback periods for EBCx at each of the sites were calculated and 

compared.

The electrical savings observed for each building over the years following EBCx are shown in Fig. 3.3

Figure 3.3 Electrical savings following EBCx for each of the buildings.

The aggregate savings for the sites are shown in Fig. 3.4. The buildings are grouped together according 

to the number of years of data available after EBCx. Note that the “two-year” line includes data for all 

buildings for the first two years following EBCx, that the “three-year” line includes data for all buildings 

having three years of data following EBCx, and the “four-year” line includes data for all buildings having 

four or more years of data following EBCx. Comparison with the data in Fig. 3.3 suggests that the peak in

year 3 may be largely due to the one building whose savings peaked in year 3.
Page 49 of 239

Figure 3.4 Plot of aggregate post-EBCx electricity savings.

These plots demonstrate the observed trend in energy savings for the commissioned buildings. During 

the first two years the savings generally increased. This was expected because of the length of time 

needed for the EBCx measures to be implemented. In the third year the savings began to level off, and 

the fourth year generally showed a declination in the electricity savings. A comparison with the predicted 

savings estimated in the EBCx reports revealed that on average these reports underestimated the 

savings by 27.5 %.

The average electricity savings for all the sites over all the years was 7.3 % per year. Natural gas usage 

could only be obtained for four of the buildings. The savings for natural gas were considerably lower, but 

since Sacramento is dominated by cooling needs, the lower natural gas savings only reduced the 

average total energy savings in these four buildings to 6.1 % per year.

The payback periods for the EBCx projects all proved to be attractive, with the longest period being 2.3 

years. Table 3.8 lists the estimated costs, annual savings, and payback period for each site, as well as a 

price per square foot of the building. 
Page 50 of 239

Table 3.8 Costs, energy savings, and payback periods for the eight sites studied.

Building EBCx 

study 

costs 

(agent 

cost 25k, 

balance 

incurred 

by site)

Estimated 

measure 

implmentation

costs

Predicted

average 

annual 

savings 

($)

Post

EBCx 

average 

annual 

savings 

($)

Predicted 

simple 

pay-back

Post

EBCx 

simple 

payback

EBCx 

study 

costs 

($/m2)

EBCx 

study & 

implement. 

costs 

($/m2)

Office 1 $28 000 $1 710 $24 500 $13 000 1,2 2,3 2,05 2,15

Office 2 $26 500 $20 500 $21 900 $27 900 2,1 1,7 0,75 1,29

Lab 1 $26 000 $12 370 $64 800 $40 100 0,6 1,0 3,01 4,41

Hospital $28 300 $11 180 $35 200 $30 900 1,1 1,3 1,18 1,61

Office 3 $25 400 $150 $6 400 $22 400 4,0 1,1 0,65 0,65

Office 4 $26 817 $8 380 $8 400 $22 600 4,3 1,6 0,86 1,18

Office 5 $26 817 $4 350 $9 100 $15 800 3,4 2,0 0,86 0,97

Office 6 $26 700 $3 000 $11 200 $48 600 2,7 0,6 0,97 1,08

All sites 

total

$214 533 $61 650 $181 600 $221 200 1,5 1,2 0,97 1,29

*To obtain costs in $/ft2, multiply the number of $/m2 by 0.0929.

SMUD: Measure Persistence Analysis

A series of interviews and site visits were used to determine the persistence in the EBCx measures 

recommended. The eight EBCx reports recommended a total of 81 corrective measures, of which 48 

were implemented. Of these 48, it was found that 81 % had persisted, in that they were still in effect at 

the time of the study. It was discovered that four of the measures had been abandoned completely, all of 

which were air distribution component recommendations. Five of the measures had undergone evolution 

by the building engineers because the original measures had not resolved the problems.

Surveys were given at the sites to determine attitudes regarding the EBCx process, as well as its 

benefits. All of the sites reported that EBCx was a worthy process. Four of the sites listed training as the 

primary non-energy benefit from EBCx. The most cited downside to EBCx was the time intensive nature 

of the process. All of the sites came out of the EBCx process with ideas on how to retain the 

commissioning benefits over time, the most common solutions being preventative maintenance plans. All 

of the sites would undertake EBCx again, but only two had potential internal funding.

SMUD: Conclusions

Some important EBCx process factors that this study identified were: 

• The commissioning authority is most effective when he is both an expert and a teacher.

• Building engineers prefer to evolve the settings on a recommendation that doesn’t work, rather than 

revert to the previous condition. 

• EBCx appears to raise energy efficiency awareness. 

• EBCx funds are constrained within building management budgets. 

The energy analysis results showed: 
Page 51 of 239

• Analyses should not emphasize first-year savings because savings typically take two to three years to 

fully manifest. 

• Energy savings persist to four years or more, although some degradation begins in the third year. 

• The EBCx energy savings predictions were reasonably accurate. 

• Building managers lack tools for tracking energy performance. 

• EBCx cost pay back was shorter than the apparent savings persistence.

• EBCx focused mostly on electricity savings and some natural gas tradeoffs in the savings occurred.

The study suggested several recommendations for the SMUD EBCx program: 

• Develop measure implementation tracking agreements, possibly with inspections. 

• Explore methods to conduct a three year post-EBCx energy consumption analysis using the billing 

history. 

• Develop simple Performance Tracking Tools for the building operators. 

• Develop an extension to the program whereby participants are eligible for new incentives in year 4 to 

evaluate and update the EBCx as necessary.

On the whole, the SMUD EBCx program’s two broad goals were met at the eight sites. Aggregate postEBCx savings were strong, peaking at approximately 4 420 MWh and the program helped educate site 

staff about energy efficiency and the role operations and maintenance plays. 

Oregon: Oregon Case Study

A study performed in Oregon in 2004 examined eight Intel buildings that had been retro-commissioned in 

1999 and 2000 (Peterson 2005). The buildings were located on the Intel Jones Farm and Hawthorn 

Farms campuses. Kaplan Engineering and PECI performed the EBCx for these buildings through funding 

from Portland General Electric (PGE). At the time EBCx occurred, it was estimated that electricity 

savings of nearly 3.5 million kWh annually would result from the low cost energy efficiency measures 

(EEMs) proposed. The purpose of this study was to examine the energy usage of the buildings to 

determine what percentage of the original savings was still being achieved four years later. At the same 

time, it was desired to determine how many of the EEMs proposed were still being utilized.

Three of the buildings studied were located on the Hawthorn Farms Campus (HF), and were designated 

HF1, 2, and 3. The buildings combined for a total of 59 457 m2 (640 000 ft2

), and were served by a 

central chiller and boiler plant. HF1 had Direct Digital Controls (DDC) interfaced with pneumatic 

actuators, and the other two buildings were upgraded to DDC control in 2000. The remaining five 

buildings studied were located on the Jones Farm Campus (JF). They combined for a total of 130 063 m2

(1 400 000 ft2

), with over 40 major air handling systems served by two central chiller plants and two hot 

water boiler plants. Most of the spaces on both campuses were served by variable air volume (VAV) 

systems.

Three reports generated at the time of EBCx were examined to determine what measures had been 

implemented. The current status of these measures was determined through random sampling, with 

functional testing or trending being used as appropriate. For HF1, the terminal reheat units were serviced 

at the time of EBCx to ensure proper damper motion. At the time of this study, random sampling 

discovered no noticeable damper movement from full cooling to full heating in 60 % of the units. The 

savings for this measure did not persist, probably due to the aging pneumatic system. For HF 1, 2, and 3, 

EBCx had modified outside air intake controls to allow for the economizing cycle to function. At the time 

of the study, random sampling revealed this measure to still be functioning. For the HF chillers, EBCx

had lowered the condenser water set point from 23.9 o

C (75 oF) to 21.1 oC (70 oF), while raising the 

chilled water set point from 5.6 o

C (42 oF) to 7.2 oC (45 oF). This measure was also found to be in 

operation at the time of this study. 

For the JF buildings, air handling units and terminal boxes were scheduled at the time of EBCx to reflect 

occupancy patterns, scheduling unoccupied hours as 6 PM to 6 AM on weekdays and all day on 
Page 52 of 239

weekends. At the time of this study, JF3 was evaluated, and the control was found to be working fairly 

well, with only a couple of override issues. Additional savings opportunities for the JF buildings were also 

identified in this study, including air flow and scheduling opportunities and control overrides that needed 

adjustment. For the HF chillers, the leaving condenser water set point was lowered as part of the EBCx 

project. 

Overall at the Hawthorn Farms campus the EEMs were found to have been maintained, with the 

exception of the terminal unit reheat optimization in HF1. Of the original projected savings in the three 

buildings at Hawthorn Farms, 89 % of the electric savings and 0 % of the natural gas savings were still 

being achieved at the time of this study. In the five buildings at Jones Farm, the results were more mixed 

and less quantifiable. The recommended scheduling changes were still programmed at a high level, but it 

appeared that numerous control overrides at a zone or box level had been made. Some overrides may 

have been due to changes in space use (such as conversion to a lab), but in many instances conference 

and training rooms were maintaining occupied modes around the clock. The trending done on some of 

the variable speed air handlers showed little difference between day and nighttime airflow suggesting that 

terminal box scheduling was not having an impact on overall airflow.

Oregon: Summary

Of the eight EBCx projects studied in Oregon in 1999 and 2000 quantitative findings were reported for 

three buildings and qualitative findings for the other five. For the three buildings on the Hawthorn Farms 

campus, totaling 60 000 m2 in floor area, 89 % of the original electric savings were achieved in 2004 and 

0 % of the natural gas savings were achieved in 2004. For the five buildings on the Jones Farm campus 

with 130 000 m2 of floor area, the results were mixed and less quantifiable. It was found that scheduling 

changes were still programmed at a high level, but numerous control overrides at a zone or box level had 

been made.

Colorado: Office Building in Colorado

A study completed in 2005 evaluated the persistence of EBCx savings in a large office building in 

Colorado (Selch 2005). Of the studies of this kind done to date, this study appears to have chosen the 

largest window of time over which to look at persistence. The office building was commissioned in 1995, 

which resulted in verified savings of 14 % in electrical demand, 25 % in electrical use, and 74 % in gas 

use. In 2003, the building was again commissioned, at which time the status of the energy conservation 

measures implemented in the initial commissioning effort was evaluated.

The computation of savings was done in two ways. The overall energy use of the building for each year 

was obtained from utility bills. These data were then normalized to account for factors such as weather 

differences, changing occupancy patterns in the building, and added construction in the building. In this 

way the yearly energy use could be accurately compared to the baseline, pre-commissioned energy use. 

The other savings calculation method was an individual measure evaluation. Specific measures that 

impacted individual HVAC system components were examined. To perform the calculations, Options B & 

C of the International Performance Measurement and Verification Protocol (IPMVP 2001) were employed, 

Option B being used for individual measure evaluation, and Option C for whole building usage 

comparison.

Table 3.9 summarizes the results of the individual measures evaluation. Using the same baseline, the 

savings achieved from the 1996 commissioning effort (20 %) are compared with savings achieved in 

2003 (17 %). It was supposed that the persistence of savings was represented by the difference in 

achieved savings between the two commissioning efforts. Therefore, , the 2003 savings was calculated 

as a percentage of the original 1996 savings (i.e., (1,330,000/1,600,000)*100) to represent the 

persistence of savings (83 %). 

As noted in Table 3.9, it was calculated that 83 % of the electrical use savings had persisted, while 86 %

of the electrical demand savings had persisted. The results of the whole building energy use comparison 

appear in Figure 3.5 and Figure 3.6. The left chart in each figure represents the raw values, while the 

right chart displays adjusted, normalized values.
Page 53 of 239

Table 3.9 Electric savings persistence summary

Figure 3.5 Annual electrical demand, raw and adjusted.

Figure 3.6 Annual electrical use, raw and adjusted.

The annual demand and consumption values that were adjusted to account for changing conditions 

indicated that the savings achieved from EBCx had largely persisted. This was concluded with greater 

confidence due to the corroboration of the independent measure analysis.

Baseline use 1996 savings 2003 savings

Electricity (80,000,000 kWh) 20 %

(1,600,000 kWh)

83 % persistence

(17 % savings)

(1,330,000 kWh)

Demand (1564 kW) 14 %

(219 kW)

86 % persistence

12 % savings

(188 kW)

Gas - 74 % Complete persistence
Page 54 of 239

The study reported that a large majority of the energy savings measures implemented in the original 

commissioning effort had persisted, as had their resultant energy savings. This was in spite of changing 

conditions in the building, including a complete change in operation staff. It was concluded that ECMs of 

this nature can persist for at least eight years even with limited support from operators and staff. 

However, it was noted that continued, on-going support to the building staff as part of the original 

commissioning effort probably would have resulted in complete persistence of the savings achieved.

3.2.3. Persistence of Commissioning Measures in New Buildings

PECI PIER Study 

In the summer of 2002, a study was completed that had begun in the fall of 2001 under a California 

Energy Commission Public Interest Energy Research (PIER) project (Friedman et al. 2003). The purpose 

of the study was to examine ten buildings that were commissioned at building start-up in order to address 

the persistence of benefits from the commissioning process. This study drew qualitative conclusions 

about the persistence of new building commissioning, focusing on three issues: how well the benefits of 

commissioning persisted, the reasons for declining performance, and the methods that can be used to 

improve the persistence of benefits achieved through commissioning. A quantitative assessment of 

persistence by measure (“this measure has an expected persistence of X years”) was outside the scope 

of this project, since a large number of buildings would have been required to determine the figures for 

each measure.

To evaluate the persistence of commissioning benefits on new buildings, the buildings first had to be 

selected. To qualify for the study, the facility needed to have been commissioned as a new building or 

major retrofit between two and eight years prior to the study. Due to the difficulty in finding such buildings 

with adequate commissioning documentation in California, five buildings were selected in the Pacific 

Northwest, and five more in California. It was not feasible to limit the study to buildings that followed the 

full commissioning process, from pre-design through final acceptance and post-occupancy, as described 

in ASHRAE Guideline 1 (ASHRAE 1996). The most completely commissioned and documented buildings 

were sought, but these typically did not include design-phase commissioning.

For each building, three to eight items were identified that were documented to have been fixed during 

commissioning. The changes and repairs made during commissioning generally fell into three categories: 

hardware, control system, and documentation improvements. Due to the focus on energy savings 

measures in the study, the hardware and control system changes with the greatest energy implications 

were of highest interest, as well as measures dealing with comfort and reliability. The amount of 

documentation available for each measure was also a driving force in measure selection. It was 

necessary to only evaluate those measures that had actually been implemented and documented. 

Routine maintenance issues or measures deemed static once corrected (such as equipment 

disconnected from the power supply) were not looked at. With the limited amount of time and funding for 

the study, it was necessary to focus on measures whose current status could easily be compared to the 

as-commissioned status and which would affect energy consumption. Because of the bias in selecting 

these measures, and the underestimation of savings persistence due to the limited number of measures 

considered, the results of the study were presented qualitatively.

For purposes of the study, it was decided that if the measure resulted in better performance than the precommissioning condition, then the measure was said to have persisted, even if it had been adapted to 

meet real operating conditions of the building. In some cases the persistence of a measure was 

somewhat subjective.

The people with the most knowledge about the control system at each site were interviewed. Some sites 

were identified for site visits, and for the others a second interview was conducted to discuss the current 

status of the commissioning measures. Six of the buildings were visited, during which the persistence of 

the selected commissioning measures was investigated, and the work environment and resources 

available to the operations staff were evaluated. 
Page 55 of 239

PECI PIER - Results

It was found that the process of finding qualified buildings for the study in California was difficult. 

Qualified buildings were located more easily in Oregon, most likely because of the longer history of new 

building commissioning in the Pacific Northwest. California had numerous existing buildings involved in 

EBCx projects, but new buildings having undergone commissioning at least two years earlier were 

sparse. For many of the commissioned buildings considered for the study, commissioning reports had 

not been written, so the information that could have been used by operations personnel to more efficiently 

operate the building essentially was lost. Often times in lieu of a report, the commissioning activities 

would simply be placed on a “punch list” for maintenance personnel to work on, who, when they had 

completed them usually did not document the changes. In other buildings the reports had been written, 

but were not readily available to the operations staff, having been filed away in storage and not easily 

accessible. In many cases where documentation did exist, it was not clear when or if the commissioning 

measures had been implemented, as they were noted as “recommendations” or “pending.” These issues 

led to the conclusion that the term “commissioning” had been applied to a variety of different activities, 

including troubleshooting items and checklists, indicating a lack of consistency in the way the term was 

being applied.

Table 3.10 summarizes the commissioning measures studied and their level of persistence. A light gray 

square indicates that the measure persisted, while a black square indicates that the measure did not 

persist. A square split in half horizontally indicates that more than one measure was investigated in the 

category.
Page 56 of 239

Table 3.10 Persistence of equipment and controls fixed during commissioning.

Key to shading:

Black cell: Measure did not persist

Grey cell: Measure persisted

Split cells indicate multiple measures

Across the ten buildings studied, patterns about the types of commissioning fixes that persisted emerged. 

For the 56 commissioning fixes selected, well over half of the measures persisted. It was not surprising 

that hardware fixes, such as moving a sensor or adding a valve, persisted. Furthermore, when control 

algorithm changes were reprogrammed, these fixes often persisted, especially when comfort was not 

compromised. Many design phase fixes may have persisted in a similar way, but these were not able to 

be studied since only one building was commissioned in the design phase. 

The types of measures that tended not to persist were the control strategies that could easily be changed, 

such as occupancy schedules, reset schedules, and chiller staging. Four out of six occupancy schedules 

did not persist. Chiller control strategies did not persist in three out of four cases, most likely due to the 

complex nature of control in chilled water systems. The study of sensor issues was limited to major 

sensor problems that were corrected during commissioning, such as sensor failure or excessively faulty 

readings. With this selection bias applied, two out of five sensor repairs did not persist.

Among the commissioning measures implemented, a few cases involved technologies that were new or 

different from normal practice. Due to lack of documentation, these measures were not included in this 

study, but it was observed during the investigation that these measures generally did not persist. This 

was attributed to a lack of operator training for the technologies.

PECI PIER - Discussion

The study suggested three possible reasons for lack of persistence among some measures. The first 

was limited operator support and high operator turnover rates. Operators often did not receive the Commissioning report on site Commissioning report used Control sequences available Chiller control Cooling tower control Boiler control Hydronic control Economizer control algorithm Discharge air temperature reset Simultaneous heating and cooling VFD modulation Dessicant cooling Duct static pressure Space temperature control Terminal units Piping and fitting problems Valve modification Wiring and instrumentation Sensor placement or addition Sensor error or failure Scheduling Skylight louver operation Occupancy sensor

 - yes

Office Building 1 (1996) no - yes

Office Building 2 (1996) no - no

yes yes no

Office Buidling 4 (1994) no - 

no - yes

Medical Facility 1 (1998) yes yes yes

yes yes yes

 - yes

Lab and Office 3 (2000) no - no

California Pacific Northwest

BUILDING 

(year commissioned)

CENTRAL 

PLANT

AIR HANDLING AND 

DOCUMENTS DISTRIBUTION

no

Lab and Office 2 (1997)

Medical Facility 2 (1998)

Office Building 5 (1997)

no

OTHER

PREFUNCTIONAL 

TEST

Lab and Office 1 (1995)

Office Building 3 (1994)
Page 57 of 239

training necessary or they did not have sufficient time or guidance for assessing energy use, and the 

training given new operators who came in after the commissioning was usually inadequate. The second 

reason involved poor information transfer from the commissioning process. For nearly every case 

studied, the commissioning report was either difficult to locate, or was not even located on site, which 

reduced the ability of building operators to review commissioning measures implemented. The third 

reason for lack of persistence was a lack of systems to help track performance. Operators spent most of 

their time responding to complaints and troubleshooting problems, leaving little time to focus on 

assessing system efficiency. Aside from this, lack of information and knowledge impeded the efficiency 

assessment by building operators.

The persistence of commissioning benefits was found to be highly dependent on the working environment 

for building engineers and maintenance staff. A working environment that was supportive of persistence 

included adequate operator training, dedicated operations staff with the time to study and optimize 

building operation, and an administrative focus on building performance and energy costs. Trained 

operators were found to be knowledgeable about how the systems should run and, with adequate time 

and motivation to study the system operation, these operators evaluated and improved building 

performance. In five buildings, operators participated in the commissioning process and came away with 

a good understanding of their systems. In addition, good system documentation in the form of a system 

manual served as a troubleshooting resource for operators at two buildings. It was noted that 

administrative staff can help enable a supportive working environment by placing high priority on energy 

efficient systems and operator training. Only a few of the buildings studied seemed to operate in this 

environment, and the measures investigated at these facilities had the highest rate of persistence. 

Some of the measures simply persisted by default – no maintenance being required to keep them 

operational. If comfort issues were not a factor, or the measure involved programming buried deep within 

code, the measures tended to persist.

The study recommended four methods for improving persistence. First, operators should be provided 

with training and support. Especially with high operator turnover, adequate training is needed for benefits 

to persist, and a working environment with energy efficiency as a high priority is also beneficial. Second, 

a complete systems manual should be provided at the end of the commissioning process. This will serve 

as a reference for building operators, and will allow the systems knowledge gained from the 

commissioning process to be available over the long term. Third, building performance should be 

tracked. New building commissioning efforts should help to implement mechanisms for performance 

tracking, including what information to track, how often to check it, and the magnitude of deviations to 

address. Fourth, commissioning should begin in the design phase to prevent nagging design problems. 

Changes made on paper before construction has begun tend to be more cost effective and have higher 

levels of persistence.

The study concluded with a recommendation that more in-depth, quantitative studies be performed to 

investigate the life of commissioning measures and carry out cost-benefit analyses for new building 

commissioning. It was further recommended that a manual of guidelines for improving persistence be 

developed to give guidance and direction to building operators with regard to energy efficiency.

3.2.4. Strategies for Improving Persistence in New and Existing Buildings 

As a follow-up to the study of persistence of commissioning benefits for new buildings performed in 

California and Oregon, and the study of persistence of EBCx benefits done at Texas A&M (both 

described previously), a report was issued in July 2003 addressed to building owners, managers, and 

operators suggesting methods for improving the persistence of commissioning benefits for both new and 

existing buildings (Mills et al. 2004). The report began by summarizing the key conclusions of both 

studies, namely that many commissioning benefits tend to persist fairly well, but that significant 

opportunities still exist for improving overall savings persistence. The report then proposed that an 

emphasis on certain key elements of energy analysis and efficiency would pave the way for long-term 

success in building operation and energy use. In particular, seven recommendations were discussed at 

length: design review, building documentation, operator training, building benchmarking, energy use 
Page 58 of 239

tracking, trend data analysis, and EBCx. A summary of the discussion of each of these topics is 

presented here.

Design Review

As much as one-third of major commissioning problems can be traced back to the design phase of the 

project, and these problems often plague building operators throughout the life of the building. Allowing 

professional engineers to review the design while it still in the design phase, is a cost-effective way to 

prevent future problems. Correcting design problems on paper is easier and less costly than attempting 

to correct them once the building is completed. Some of the issues to be considered in reviewing a 

design are test port location, equipment accessibility, load calculations and minimum flow settings, control 

system sequences and point lists, and standard design details. The process of design review should 

begin as soon as possible to allow opportunity for correction.

Building Documentation

Good system documentation is not a common practice currently in the construction environment. While it 

may seem like a costly and time-consuming effort, this documentation is the best way to ensure that the 

knowledge base obtained during design, construction, and commissioning of the building is preserved, 

and will aid in maintaining commissioning benefits. The three most vital items to document are the final 

design intent, the sequences of operation, and the system diagrams. Other important documents include 

the operator’s log, commissioning summary report, general description of facility and systems, as-built 

documents, detailed description of each system, location of all control sensors and test ports, and 

capabilities and conventions of the DDC system. The best time for this documentation to occur is during 

the construction phase of the building. For existing buildings, a good time is during a retrofit or EBCx. 

The documentation should be compiled into a systems manual that is readily accessible.

Operator Training

Effective operator training will allow the benefits of building commissioning to persist, and will aid in 

preventing problems. Training opportunities exist for building operators during the commissioning 

process, through manufacturers and vendors, in operator certification programs, and using building 

documentation. It is also essential that new operators be trained sufficiently so that the knowledge 

gained by the previous operator is not lost. Some suggested training topics include: descriptions of 

equipment, equipment start-up and shut-down procedures, operation and adjustment of controls, review 

of system documentation, common troubleshooting problems, maintenance requirements and schedules, 

health and safety issues, special tools and spare parts inventory, and emergency procedures. 

Building Benchmarking

Benchmarking refers to measuring the energy use of a building relative to other buildings, and provides a 

way to track energy use over time and compare it with the competition. This will allow building owners 

and operators to prioritize initiatives and improve energy efficiency. Several tools exist to aid in the 

benchmarking process. Two of these are the ENERGY STAR® Portfolio Manager, which uses a number 

of factors to make meaningful comparisons with other buildings under different conditions, and the CalArch Building Energy Reference Tool, which is a quick and simple tool for comparing energy use per 

square foot.

Energy Use Tracking

Tracking utility bills or metered data is an effective method for recognizing energy use problems that may 

not result in comfort problems, and therefore might not be noticed any other way. It is essential for 

continued energy efficiency and persistence in commissioning benefits. The energy use curves should 

be compared for different years to look for patterns, anomalies, and peaks and valleys. An Energy 

Information System (EIS) is a useful tool for automating utility tracking. It saves time, provides immediate 

feedback, can gather additional data, and can allow access over the Internet.
Page 59 of 239

Trend Data Analysis

DDC systems allow points to be trended over time. Knowing how to interpret these trended data is 

essential for identifying and correcting problems in building energy consumption and performance. The 

data should be examined regularly to determine if the system and its individual components are 

functioning as desired. Automated diagnostic tools exist to aid in this process, having automated 

capabilities in the following categories: data acquisition, archiving and pre-processing, detection, and 

diagnosis. Two tools available are ENFORMA and PACRAT. PACRAT can be used as an ongoing 

diagnostic tool.

Re-commissioning14

The process of re-commissioning, especially when it draws on building documentation and previous 

commissioning activities, is very effective in maintaining commissioning benefits. The time to consider recommissioning largely depends on the effectiveness of operations and maintenance strategies and 

overall building performance. Commissioning can be performed by an outside commissioning provider 

when an outsider’s view is considered helpful, or it may be done in house. In-house commissioning 

increases the knowledge level of those participating with regards to building operation. Continuous 

Commissioning® is an ongoing commissioning process developed by the Energy Systems Laboratory at 

Texas A&M University that has the same general goal as EBCx, but focuses strongly on the persistence 

of commissioning benefits.

The report concluded by reiterating the need to pursue the topics addressed during and after the 

commissioning process to maintain the benefits of commissioning over the long-term.

3.2.5. Related Reports

A report was compiled in 2004 that evaluated the cost effectiveness of commissioning in new and existing 

buildings (Mills et al. 2004). The largest study of its kind to date, it examined the results of commissioning 

for 224 buildings across 21 states. Among the existing buildings commissioned, a median payback 

period for commissioning was reported to be 0.7 years. For new buildings, this value was found to be 4.8 

years. Both of these figures excluded non-energy benefits, which would increase the savings 

experienced.

While persistence of savings was not the primary focus of the study, it was examined briefly since it plays 

a role in determining overall savings. Figure 3.7 shows the persistence of savings results for 20 of the 

buildings in the study, with a four year period following commissioning in each building. The savings are 

indexed by a comparison of the year’s consumption to the pre-commissioning baseline consumption. The 

savings are compared by category: electricity, fuel, chilled water, and steam/hot water. 

 14 Re-commissioning is a term sometimes applied when EBCx is carried out on a building that was commissioned as 

part of the construction process. Some organizations use the term EBCx irrespective of whether or not the building 

was previously commissioned.
Page 60 of 239

Figure 3.7 Emergence and persistence of energy savings (weather normalized).

An important factor noted in the report was the fact that in many cases of commissioning, the 

recommended measures were implemented gradually; indicating that the first year after commissioning 

was not the best year for calculating savings. On the other hand, it was also observed that after time 

some of the savings began to degrade due to changing building conditions, operations, or aging. As seen 

in the figure, the maximum value for savings was reached and subsequently savings began to degrade. 

This effect was smallest for electricity, but much more noticeable for chilled and hot water and steam.

With regard to persistence of commissioning benefits, the report concluded that tracking energy 

consumption for evidence of significant consumption increases is the most important means of 

determining the need for follow-up commissioning, and that while controls changes by building operators

account for a portion of savings degradation, hidden component failures are perhaps the greatest culprit 

in persistence problems.

3.2.6. Methodologies for Determining Persistence of Commissioning 

Measures and Energy Benefits of Commissioning

The EBCx studies that provided a quantitative evaluation of the persistence of energy benefits of 

commissioning, used variations on two different approaches to evaluate the persistence of energy 

benefits.

The study of ten Texas buildings (Turner et al. 2001) used a variation on Option C of the IPMVP that 

normalized for weather differences between years by selecting a “normal” year of weather data in the 

sequence available that most closely met long-term norms. A suitable three-parameter or four-parameter 

regression model of the baseline year was created along with models of the performance of the building 

in each year evaluated.

Then the annual consumption for each year was determined by running the appropriate model with the 

appropriate year of weather data. The study of eight SMUD buildings (Bourassa 2004) used the same 

methodology, except that they used a long-term average weather year instead of selecting one of the 

available years of weather data. The Colorado study used a different approach, evaluating savings 

persistence with IPMVP Option C with baseline adjustments and IPMVP “Option B” was used to 

determine savings for specific measures in operation. The Oregon study did not specify how savings 

were evaluated.
Page 61 of 239

The study of eight buildings in Oregon (Peterson 2005) and the Colorado building (Selch and Bradford 

2005) used a different approach. These studies examined each of the measures that had been 

implemented and determined whether the measures were still in place and functioning. Peterson found 

that in three of the buildings, she could quantify the savings associated with measures that had been 

disabled after four years. It was found that numerous measures implemented in the other five buildings 

were still in place, but there were also numerous overrides and changes that had occurred as well. It was 

not possible to quantify the degree of persistence in these buildings. Selch and Bradford (2005) found 

that they were able to quantify the savings associated with measures that had been disabled.

The study of ten new buildings that had been commissioned in Oregon and Washington (Friedman et al. 

2003b) used a methodology that quantified the number of measures that were still in place, but it did not 

seem appropriate to try to quantify the energy savings associated with these measures. The four EBCx

studies all discussed the measures found to be still operating and those that had been changed. The 

Texas study used calibrated simulation to evaluate measures that had been changed. The other studies 

were not explicit in the methods used to evaluate the impact of measure changes.

3.2.7. Summary and Conclusions

The results of studies from five projects related to commissioning, either in new or existing buildings, 

described represent the extent of research that has been performed with regard to the persistence of 

commissioning benefits over time. 

The savings in the buildings that were retro-commissioned generally showed some degradation with time. 

In retro-commissioned buildings, savings generally decreased with time, but there is wide variation from 

building to building. For the buildings where savings persistence was quantified:

• Savings persistence at the time of the study (3 years to 8 years after commissioning) ranged from 

about 50 % to 100 % in all but one or two buildings.

• Average savings at the time of the study were about 75 % of the original savings.

• The most dramatic savings degradation was caused by undetected mechanical or control component 

failures. 

For the new buildings, well over half of the 56 commissioning fixes persisted. Hardware fixes, such as 

moving a sensor or adding a valve, and control algorithm changes that were reprogrammed generally 

persisted. Control strategies that could easily be changed, such as occupancy schedules, reset 

schedules, and chiller staging tended not to persist. It was also found that the extent to which persistence 

occurs is also related to operator training.

As is evident, the number of buildings studied in all of the papers described here represents a very small 

portion of commercial buildings that have undergone commissioning or EBCx. More research is needed 

to: 

• Develop a uniform methodology for determining commissioning persistence.

• Determine the persistence of savings from a broader sample of buildings.

• Develop simple tools for tracking performance of commissioning measures.

• Develop practical methods for owners and operators to better maintain commissioning savings.
Page 62 of 239

3.3 Influence of Savings Normalization Method on Persistence

3.3.1 Introduction

There are several different existing building commissioning processes including existing building 

commissioning (EBCx) and Continuous Commissioning® (CC®

). The purpose of commissioning as 

detailed by ASHRAE is to ensure proper operation of a building according to the design intent 

(ASHRAE,1996). Existing building commissioning often involves making retrofits and optimizing building 

operation to achieve greater energy efficiency and energy savings.

The means used to determine these savings vary in both complexity and ease of performance. The 

International Performance Measurement and Verification Protocol (IPMVP) defines acceptable 

approaches for determining energy savings in buildings that have undergone energy conservation 

measures (ECMs) such as those carried out during commissioning (IPMVP 2002). Under the IPMVP 

there are four separate savings determination methods, Options A-D. Option A and Option B are not 

appropriate for determining whole building commissioning savings. 

Option C of the IPMVP uses whole building data to develop consumption models such as regression 

models while Option D uses calibrated simulations to determine building energy consumption. 

Regression models are a quick way to relate heating and cooling consumption to the outside dry bulb 

temperature for a specific time period using that period’s consumption data. Calibrated simulations 

require a series of inputs to a simulation tool that are adjusted, or calibrated, until the simulated 

consumption closely matches the heating and cooling data as a function of the outside dry bulb 

temperature. Calibrated simulations are valuable because they can identify and verify potential causes 

for changes in consumption from year to year but are much more time consuming than regression 

models. 

Once an energy consumption model (regression model or calibrated simulation) that determines 

consumption as a function of outside dry bulb temperature has been obtained for baseline and postcommissioning periods, commissioning savings can be determined by two different weather normalization 

approaches:

• The standard International Performance Measurement and Verification Protocol (IPMVP) weather 

normalization approach (IPMVP 2002)

• The Normalized Annual Consumption (NAC) weather normalization approach (Fels 1986).

The standard IPMVP weather normalization approach calculates actual savings as the difference 

between the post-commissioning energy consumption determined by the pre-commissioning baseline 

model when using the post-commissioning weather data and the measured energy consumption during 

the post-commissioning period. In contrast, energy consumption models can be weather-normalized to 

“normal” or average weather conditions to mitigate the effects of varying weather from year to year. The 

common term for the annual consumption under the “normal” weather year is Normalized Annual 

Consumption or NAC (Fels 1986). Under the NAC weather normalization approach, each of the energy 

consumption models uses the “normal” weather year and savings are determined by the difference in the 

baseline and post-commissioning consumption. 

Since the reported savings from commissioning are essential in telling the success of a commissioned 

building, it is important to know in some terms how the savings and persistence of savings results of one 

savings determination procedure compare to the other. In particular, it is useful to know whether use of 

the NAC weather normalization approach provides less variability in the persistence of commissioning 

savings than use of the standard IPMVP weather normalization approach when using a set of different 

weather years. Likewise, it is valuable to identify whether use of Option C of the IPMVP with regression 

modeling provides less variability in the persistence of commissioning savings than use of Option D of the 

IPMVP when using a set of different weather years. Persistence of savings refers to the degree to which 

post-commissioning savings are maintained from year-to-year. Specifically, the persistence of savings is 
Page 63 of 239

the absence of change in savings between the first post-commissioning period and any later subsequent 

post-commissioning period.

Using the weather normalization approaches and IPMVP savings determination methods, about 30 

existing buildings have been analyzed in previous studies to quantify savings persistence and identify 

reasons for changes in savings from year to year after commissioning has been performed. Similarly, 

over 100 buildings that have undergone major retrofits have been analyzed to quantify savings 

persistence. 

Additional commissioned and retrofitted buildings have been previously analyzed where savings results 

are given without persistence results. Further research on additional buildings is important because a 

larger set of buildings documenting savings persistence can help identify ways to make commissioning 

savings persist longer and encourage more to take advantage of the benefits of this energy saving 

process.

The objectives of this chapter are to determine:

• Whether use of the NAC weather normalization approach provides less variability in 

commissioning savings and the persistence of commissioning savings than use of the 

standard IPMVP weather normalization approach, and to quantify any difference observed.

• Whether use of Option C of the IPMVP with regression modeling provides less variability in 

commissioning savings and the persistence of commissioning savings than use of Option D 

of the IPMVP, and to quantify any difference observed.

3.3.2 Savings normalization methods evaluated

Pre- and post-commissioning data were collected from the Civil Engineering/Texas Transportation 

Institute (CE/TTI) Building on the Texas A&M University campus. CE/TTI is used to compare the savings 

variability of the NAC and the standard IPMVP weather normalization approaches, and to compare the 

savings variability of Option C with regression modeling and Option D of the IPMVP. Only chilled and hot 

water results are used. Electricity results are not analyzed because electricity consumption is assumed to 

be mostly constant, independent of outside air temperature for the buildings on the Texas A&M University 

chilled and hot water campus loops. 

Hourly chilled water and hot water data are used to calculate daily average consumption. For days in 

which 19 or more of the hours of data exist, the average from those hours is used as the daily average. 

This number is recommended by the Energy Systems Laboratory (ESL) data analysis group based on 

their experience. For days in which 18 or fewer hours of data exist, the daily average is determined by 

linear interpolation from daily averages of prior and subsequent days. Energy balance plots are used to 

screen the daily consumption data (Shao 2005). Note that this data screening method requires electricity, 

chilled water, and hot water data. Electricity data is used to screen for poor chilled water and hot water 

data but is not analyzed for reasons previously mentioned. Data identified as potentially erroneous are 

discarded. If one or more of the three data streams has poor data quality for a particular day then the 

other streams of data are discarded since the energy balance data screening method can only indicate 

good data quality for all or none of the three streams.

Review of the pre-commissioning data for CE/TTI shows very little accuracy or availability. In order to 

create an accurate pre-commissioning baseline, the commissioning report (Chen et al. 2004) is used to 

document key building and HVAC system parameters essential for creating a simulation for the precommissioning period by changing inputs of the calibrated simulation from the first post-commissioning 

period. 

IPMVP Savings Methods

Once consumption data has been screened, Option C with regression models and Option D of the IPMVP 

can be used to determine savings. These savings methods use regression models and calibrated 

simulations to obtain a relationship for energy consumption as a function of the outside air dry bulb 
Page 64 of 239

temperature. Three-parameter change-point (3P CP or PRISM) and four-parameter change-point (4P 

CP) regression models are used to model cooling and heating consumption. Using AirModel (Liu 1995), 

calibrated simulations are created that model chilled and hot water consumption.

Data for CE/TTI are separated into pre- and post-commissioning periods. Where possible, consumption 

data periods are divided into full calendar years. In some cases, however, periods are modified to be 

either shorter or longer than one calendar year where large periods of data are missing or of poor quality, 

or when the commissioning process takes place in the middle of a year. Katipamula et al. (1995) 

concluded that regression modeling of large commercial buildings can be accurate and reliable with at 

least three to six months of daily data. This data length requirement is met for all models and care is 

given to ensure that data spans a broad temperature range when less than a year of data is available. 

This process maximizes the amount of data that can be used for the study. As a result, each building has 

different pre- and post-commissioning period lengths. Consumption from each model is annualized by 

using a full weather year’s temperature data, making comparison between periods of different lengths 

possible. A description of the weather data used in conjunction with the weather normalization 

approaches utilized follows the discussion of Options C and D.

Regression models are created for each of the post-commissioning periods using CE/TTI data. 

Calibrated simulations are also performed for each of CE/TTI’s pre- and post-commissioning periods 

using AirModel. Since there are insufficient pre-commissioning data available to adequately model 

building chilled and hot water consumption, the first post-commissioning period consumption is simulated 

according to building and system characteristics stated in the commissioning report. After calibrating this 

simulation, the inputs are adjusted to reflect the pre-commissioning operation of the HVAC systems as 

documented in the commissioning report. In this manner, baseline consumption models are obtained for 

chilled and hot water. A regression model is also created from the calibrated simulation baseline output 

for both chilled and hot water to determine savings with Option C of the IPMVP. 

Option C with Regression Models

The regression models used under Option C of the IPMVP to determine consumption in this study use 

four-parameter change point models. These models find energy consumption expressions as a function 

of daily average temperature. Chilled and hot water typically employ a three- or four-parameter change 

point model in commercial buildings.

Equation 1 expresses the functional form of four parameter models.

E = a + b1 * ( TOA – TCP )

- + b2 * ( TOA – TCP )+ (1)

where a is the energy use at the change point temperature, Tcp, and b is the slope. TOA is the ambient 

temperature. The notation ( )+/-

indicates that the quantities within the parenthesis should be positive or 

negative as the sign indicates; otherwise they are set to zero.

Option D

Option D of the IPMVP uses calibrated simulations to determine energy savings. When using AirModel to 

simulate building energy consumption, the user must specify two files, the input file and the weather 

source file. The input file includes specific quantities for the building and system parameters and 

characteristics such as conditioned floor area, room temperature, cold deck temperature, total and 

outside air flow settings, and night-time setback schedules. For this study, the weather file includes daily 

averaged values for dry bulb temperature and dew point temperature, although AirModel has the option of 

entering hourly values. 

After running the AirModel simulation, the simulated output must be calibrated to the measured 

consumption data. The basis of the simulation inputs for CE/TTI comes mainly from commissioning 

reports, building blue prints, and trips to the building for assessment. A brief description of the calibration 
Page 65 of 239

process is given here. A more detailed procedure of the simulation calibration process is given by 

Claridge et al. (2003) and Wei et al. (1998).

The term “calibration signature” (Claridge et al. 2003) is defined as follows:

×100% − = MaxiumumMeasuredEnergy

residual CalibrationSignature (2)

where

residual = SimulatedConsumption − MeasuredConsumption (3)

The maximum measured energy is the maximum heating or cooling energy use recorded over the 

temperature range of the particular data file being used. The calibration signature is a normalized plot of 

the difference between measured energy use and simulated energy use over a specified temperature 

range. For each temperature, a measured energy use value and a simulated energy value exist. The 

difference in these values for each point is divided by the maximum measured energy use and multiplied 

by 100 %. These values are then plotted versus temperature. 

The calibration signature is now compared to published characteristic signatures of the given HVAC 

system type in the given climate. A characteristic signature is identical to a calibration signature, except 

that instead of comparing simulated and measured values, it compares two simulations. One simulation 

is taken to be the baseline or “measured” value. Then, by varying parameters one by one, signatures can 

be plotted and compared. Characteristic signatures (Claridge et al. 2003) are defined as:

= ×100%

MaximumEnergyConsumption

ChangeInEnergyConsumption CharacteristicSignature

(4)

As mentioned, the baseline model is treated as the “measured” case, and maximum energy consumption 

comes from this model. 

Characteristic signatures can be generated for each HVAC system type. The majority of the CE/TTI 

building’s HVAC systems are single-duct variable-air-volume (SDVAV), thus making it most practical to 

refer to SDVAV characteristic signatures when calibrating simulations. The parameters of major 

importance for which characteristic signatures should be generated include minimum air flow rate (VAV 

systems), floor area, preheat temperature, internal gains, outside air flow rate, room temperature, 

envelope U-value, and economizer. 

Two indices used for evaluating the accuracy of a simulation are the “Root Mean Square Error” and the 

“Mean Bias Error.” The Root Mean Square Error (RMSE) is defined as:

2

1

2

− =

∑=

n

residual

RMSE

n

i

i

(5)

where n is the number of total data points. The RMSE is a good measure of the overall magnitude of the 

errors, but does not give any reflection of bias, since no indication is made as to whether the errors are 

positive or negative. A good simulation minimizes the RMSE and can achieve 10 % to 20 % CV-RMSE 

(IPMVP 2002). It is generally difficult to reduce this to smaller than 5 % to10 % CV-RMSE (Claridge et al. 

2003). The Mean Bias Error (MBE) is defined as:

n

residual

MBE

n

i

∑ i

= = 1 (6)
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where n is the number of data points. The MBE is an overall measure of how biased the data is, since 

positive and negative errors cancel each other out. The MBE should be minimized in calibrating a 

simulation and should be less than ±20 % of the mean consumption (IPMVP 2002).

Calibration signatures combined with characteristic signatures are used to quickly calibrate a simulation. 

The calibration signatures for heating and cooling generated for the simulation are compared with the 

characteristic signatures from the corresponding system and climate type, to see which change of 

parameter or parameters most closely resembles the calibration signature. Normally one parameter is 

changed at a time in the correct direction and according to the magnitude needed. For example, if the 

calibration signature is in the range of 20 % for low temperatures, and a similar characteristic signature 

shows the same trend, but is in the range of only 5 %, the parameter adjustment would need to be 

significantly greater than what was done to get the characteristic signature in order to increase the 

magnitude. The adjustment is of course limited by reasonable values.. Once the parameter has been 

decided on, it is changed and the simulation is run again. The RMSE is calculated again, and calibration 

signatures are again generated and compared with the characteristic signatures. This process is 

repeated until the RMSE is minimized, and the calibration signature is flat and settled around zero. At 

this point, the simulation can be considered to be calibrated to the measured data. In most cases, 

however, it is difficult to obtain a completely flat calibration signature for both cooling and heating 

consumption. A well calibrated simulation has a CV-RMSE of 10 % to 20 % (Claridge et al. 2003) and the 

MBE should be less than 20 % of the mean consumption (IPMVP 2002).

Weather Normalization Approaches

The energy consumption models are now used to determine savings with two weather normalization 

approaches—NAC and standard IPMVP. In order to obtain a measure of variability in commissioning 

savings between the two weather normalization approaches, a set of different weather years is obtained 

to drive the energy consumption models. A set of different savings results is then obtained with both the 

NAC and standard IPMVP weather normalization approaches. 

The weather year’s data representing College Station, TX and used to create a set of savings results for 

the NAC and standard IPMVP weather normalization approaches were obtained from a few sources, 

depending on the year. For years 1973 to 2005, hourly weather data from the Easterwood Airport 

weather station was retrieved from the National Climatic Data Center (NCDC) weather database. For the 

years 1997 through June of 2004, NCDC data is used in conjunction with weather data from the Energy 

Systems Laboratory (ESL) database to fill in any missing data points.

As with the energy consumption data, hourly dry bulb and dew point temperature data are used to 

calculate daily average values. For days in which 19 or more of the hours of data exist, the average from 

those hours is used as the daily average. For days in which 18 or fewer hours of data exist, the daily 

average is determined by linear interpolation from daily averages of prior and succeeding days. 

Additionally, daily average weather data from all available weather years are averaged to form a longterm average weather year. Each day’s data of this long-term average weather year represents the 

average of all weather years’ data for that day. For example, the daily average data for January 21 of the 

long-term average weather year is the average of all January 21 data from the existing weather years’ 

data.

NAC Weather Normalization Approach

The NAC weather normalization approach determines savings as the difference between pre- and postcommissioning model consumption during a “normal” weather year. By using the same weather across 

all pre- and post-commissioning models, the variation in the consumption due to different weather 

patterns from year to year is minimized. Generally, long-term average weather data is used as the 

“normal” weather year when using the NAC weather normalization approach. This study, however, uses 

each of 29 different College Station, Texas weather years obtained from NCDC as the “normal” weather 

year. Each of these 29 weather years is used with every one of CE/TTI’s pre- and post-commissioning 

energy consumption models (both regression models and calibrated simulations) to obtain 29 sets of 
Page 67 of 239

normalized annual consumption. The savings are then determined in each of the post-commissioning 

periods for each weather year used.

Standard IPMVP Weather Normalization Approach

The standard IPMVP weather normalization approach employed to determine actual savings from 

commissioning activities calculates the difference between post-commissioning energy consumption 

determined by the baseline model with the post-commissioning period weather and the measured energy 

consumption taken from the post-commissioning period. In order to annualize the measured energy 

consumption, the model created from consumption data is used to determine the annual measured 

energy consumption by using the full weather year’s ambient temperature data to drive the model. A 

more typical procedure in cases where there is missing data in a post-commissioning time period is to use 

the post-commissioning model to generate any missing data to add to the actual measured data. This 

approach is not used in this study, however, due to the necessity of using post-commissioning time 

periods of less or greater than one year in several instances. 

Since the standard IPMVP weather normalization approach uses the measured post-commissioning 

energy consumption to determine savings, there is just one set of savings for each post-commissioning 

period. In order to form a larger sample size of savings results from the standard IPMVP weather 

normalization approach to compare to the NAC weather normalization approach, a method is employed 

that randomly selects a College Station weather year from the NCDC weather years retrieved as the 1st

post-commissioning year, another as the 2nd post-commissioning year, yet another as the 3rd postcommissioning year, and so forth. As an example of this methodology, assume that a random run of 

weather years selected to find savings for the six CE/TTI post-commissioning periods are 1984, 1976, 

1999, 1998, 1993, and 1974. The 1st post-commissioning period savings under the standard IPMVP 

weather normalization approach would be determined by subtracting the “measured” 1st postcommissioning period consumption determined by the period’s model normalized to 1984 weather from 

the consumption of the baseline model normalized to 1984 weather. The 2nd post-commissioning period 

savings under the standard IPMVP weather normalization approach would be determined by subtracting 

the “measured” 2nd post-commissioning period consumption determined by the period’s model normalized 

to 1976 weather from the consumption of the baseline model normalized to 1976 weather. The 1999, 

1998, 1993, and 1974 weather years would similarly be used to determine the savings of the 3rd, 4th, 5th, 

and 6th post-commissioning periods, respectively. Other sets of random runs of weather years are used 

to obtain a set of savings results to determine whether the NAC weather normalization approach provides 

less variability in the persistence of commissioning savings than the standard IPMVP weather 

normalization approach. The 29 specific sets of random runs are given in Table 3.11. It should be 

reemphasized that the baseline regression model for CE/TTI used here is created from the synthetic 

“data” of the baseline calibrated simulation output. 
Page 68 of 239

Table 3.11 Sequence of College Station weather years for 29 different random runs used with 

both Option D and Option C with regression models in conjunction with the standard IPMVP 

weather normalization approach.

Run 1997 1998 1/1999-

4/2000 2001 1/2002-11/2002

9/2003-

6/2004

1 1993 1993 1980 1985 1984 2001

2 1984 1976 1999 1998 1993 1974

3 1977 1998 2001 1984 1991 1999

4 1979 2005 1978 2005 1978 1991

5 1992 1981 2003 2005 1998 1998

6 1980 1996 1975 1979 1985 2004

7 1976 2000 1992 1981 1990 2000

8 1990 1973 1999 2004 2002 Avg Yr

9 Avg Yr Avg Yr 1996 2002 1985 Avg Yr

10 1990 1983 1997 1999 1978 1990

11 2000 1997 1994 1977 1983 1999

12 2004 1993 1996 1973 1984 2001

13 1991 1985 1990 2005 1994 2003

14 1991 2000 2000 1984 1985 2001

15 1973 1998 1990 1999 1997 1997

16 1993 1975 2003 2002 1975 2004

17 1974 1978 2001 2000 1985 Avg Yr

18 1975 1981 1973 1997 2004 1984

19 1999 Avg Yr 1996 1999 2002 2000

20 2004 1973 2004 1998 2002 1990

21 Avg Yr 1985 1979 1985 1996 1984

22 1992 1985 2002 2001 1989 1992

23 1994 1989 1989 1976 1991 1981

24 1981 Avg Yr 1999 2001 1978 2001

25 2004 1990 1977 1999 2000 1979

26 1979 1996 1980 1998 1977 1978

27 2004 1993 1979 2003 1978 2000

28 1994 2000 1997 1981 1999 2003

29 1999 1983 1973 1996 1975 1996

CE/TTI Regression Models and Calibrated Simulations

The CE/TTI Building has been commissioned twice. The first commissioning took place between August 

1996 and September 1996. The second commissioning took place between December 2002 and August 

2003.

Post-commissioning regression models and calibrated simulations are created for time periods where 

consumption data is available. Post-commissioning time periods are divided into full calendar years when 

possible. A period of missing hot water data, however, as well as the nine-month second commissioning 

period make this difficult to follow and consumption data period lengths are altered to lengths both shorter 

and longer than 12 months. The following is a list of the post-commissioning time periods for which 

regression models and calibrated simulations are created:

1. 1997

2. 1998

3. January 1, 1999 toApril 24, 2000

4. April 24, 2001to December 31, 2001

5. January 1, 2002 to November 30, 2002

6. September 24, 2003 to June 22, 2004
Page 69 of 239

Table 3.12 summarizes the goodness-of-fit measures for each time period’s calibrated simulation. The 

1996 pre-commissioning simulation has no goodness-of-fit measures because it has no measured 

consumption data to be compared to.

Table 3.12 Goodness-of-fit measures for CE/TTI AirModel calibrated simulations.

RMSE 1996 

Pre-Cx

1997 1998 1/1999-

4/2000

4/2001-

12/2001

1/2002-

11/2002

9/2003-

6/2004

CHW

(MMBtu/day) n/a 4.4687 4.6170 4.2340 4.6359 4.4325 3.9846

CHW (GJ/day) n/a 4.7147 4.8712 4.4671 4.8912 4.6766 4.2040

HW (MMBtu/day) n/a 2.0499 2.3970 2.3941 2.0091 2.7280 4.0636

HW (GJ/day) n/a 2.1628 2.5290 2.5260 2.0097 2.8782 4.2873

MBE 1996 

Pre-Cx

1997 1998 1/1999-

4/2000

4/2001-

12/2001

1/2002-

11/2002

9/2003-

6/2004

CHW

(MMBtu/day) n/a -0.0381 -0.1636 0.4876 -0.0088 -0.6075 -0.1620

CHW (GJ/day) n/a -0.0402 -0.0173 0.5145 0.0093 -0.6410 -0.1702

HW (MMBtu/day) n/a -0.2631 -0.4496 -0.0782 0.5751 0.2108 0.9088

HW (GJ/day) n/a -0.2776 -0.4743 -0.0825 0.6068 0.2224 0.9588

CV-MBE 1996 

Pre-Cx

1997 1998 1/1999-

4/2000

4/2001-

12/2001

1/2002-

11/2002

9/2003-

6/2004

CHW n/a -0.09 % -0.35 % 1.23 % -0.02 % -1.32 % -0.49 %

HW n/a -2.96 % -6.13 % -1.05 % 8.45 % 2.12 % 9.52 %

CV-RMSE 1996 

Pre-Cx

1997 1998 1/1999-

4/2000

4/2001-

12/2001

1/2002-

11/2002

9/2003-

6/2004

CHW n/a 10.04 % 9.78 % 10.67 % 9.65 % 9.66 % 12.10 %

HW n/a 23.03 % 32.66 % 32.02 % 29.51 % 27.42 % 42.56 %

All of the baseline and post-commissioning chilled and hot water models are four parameter change point 

models (4P CP). Regression model goodness-of-fit measures are found in Table 3.13.

Table 3.13 Goodness-of-fit measures for CE/TTI regression models.

CHW 

1996 

PreCx1997 1998 1/1999-

4/2000 2001 1/2002-11/2002

9/2003-

6/2004

MBE (MMBtu/day) n/a -0.0002 -0.0002 0.0000 0.0938 0.0001 -0.0692

MBE (GJ/day) n/a -0.0002 -0.0002 0.0000 0.0889 0.0000 -0.0656

RMSE (MMBtu/day) n/a 4.9755 5.2263 5.0009 4.7574 4.7104 4.1297

RMSE (GJ/day) n/a 4.7158 4.9536 4.7399 4.5091 4.4646 3.9141

CV-RMSE n/a 11.17 % 11.07 % 12.61 % 9.90 % 10.27 % 12.54 %

HW 

1996 

PreCx1997 1998 1/1999-

4/2000 2001 1/2002-11/2002

9/2003-

6/2004

MBE (MMBtu/day) n/a 0.0005 -0.0003 0.0000 0.0063 0.0000 -0.2100

MBE (GJ/day) n/a 0.0005 -0.0003 0.0000 0.0060 0.0000 -0.1990

RMSE (MMBtu/day) n/a 1.7554 2.4857 2.0631 1.5637 2.2109 3.4344

RMSE (GJ/day) n/a 1.6638 2.3560 1.9554 1.4821 2.0955 3.2552

CV-RMSE n/a 19.72 % 33.87 % 27.60 % 22.97 % 22.22 % 35.97 %
Page 70 of 239

It is interesting to note that the goodness-of-fit measures results of the calibrated simulations and 

regression models in Table 3.12 and Table 3.13 show that the calibrated simulations generally have a 

smaller RMSE and CV-RMSE than the regression models for chilled water. The results for hot water, 

however, show the opposite occurs—regression models generally have lower RMSE and CV-RMSE 

values than calibrated simulations. The significance of this result, however, is difficult to ascertain 

because AirModel links the chilled water and hot water consumption together while chilled water and hot 

water regression models are created independent of each other.

Results

The chilled water and hot water consumption, savings, percent savings, and change in percent savings 

are listed in Table 3.14. These values are determined with Option C of the IPMVP using the regression 

models and the NAC weather normalization approach. The long-term average College Station weather 

year is used as the “normal” weather year.

Table 3.14 CE/TTI chilled water and hot water consumption, savings, percent savings, and change 

in percent savings using the NAC weather normalization approach and Option C with regression 

models.

Year/Period 1996 

Pre-Cx

1997 1998 1/1999-

4/2000 2001 1/2002-11/2002

9/2003-

6/2004

Values in MMBtu/yr

CHW Use 17356 16491 15849 14890 14767 15822 13256

CHW Savings Baseline 864 1507 2466 2589 1534 4100

CHW % Savings Baseline 5.0 % 8.7 % 14.2 % 14.9 % 8.8 % 23.6 %

CHW Change in % Savings n/a n/a 3.7 % 5.5 % 0.7 % -6.1 % 14.8 %

HW Use 3625 2804 2770 2553 3127 3828 2923

HW Savings Baseline 821 856 1072 498 -203 702

HW % Savings Baseline 22.7 % 23.6 % 29.6 % 13.7 % -5.6 % 19.4 %

HW Change in % Savings n/a n/a 0.9 % 6.0 % -15.9 % -19.3 % 24.9 %

Values in GJ/yr

CHW Use 16450 15630 15022 14113 13996 14996 12564

CHW Savings Baseline 819 1428 2337 2453 1454 3886

CHW % Savings Baseline 5.0 % 8.7 % 14.2 % 14.9 % 8.8 % 23.6 %

CHW Change in % Savings n/a n/a 3.7 % 5.5 % 0.7 % -6.1 % 14.8 %

HW Use 3436 2658 2625 2420 2964 3628 2770

HW Savings Baseline 778 811 1016 472 -192 665

Table 3.14 shows favorable savings results for chilled water and hot water. The chilled water and hot 

water each experience savings increases from the first to second and from the second to third postcommissioning periods. Overall, aggregate site savings decline sharply between the 2001 and 1/02-

11/02 periods, dropping from 14.6 % to 7.1 %. Hot water savings show an especially sharp drop between 

these two periods, dropping from 13.7 % to -5.6 %. The second commissioning of the building, 

performed after the 1/02-11/02 period, appears to be worthwhile, as the hot water and aggregate site 

savings increase to 19.4 % and 15.6 %, respectively, based on the 1996 pre-commissioning baseline. 

While only the first year of post-commissioning data is available for the second building commissioning, 

the aggregate site savings return to a level similar to the peak achieved before the 2nd commissioning 

occurred. Over six post-commissioning periods, CE/TTI averages 12.5 % chilled water savings, 17.2 %

hot water savings, 7.9 % electricity savings, and 11.4 % aggregate site savings.
Page 71 of 239

SAVINGS AND VARIABILITY OF SAVINGS FROM DIFFERENT SAVINGS METHODOLOGIES 

RESULTS

Option D MBE Adjustment

Each of the calibrated simulations has an associated mean bias error (MBE) that if left unadjusted may 

significantly affect the post-commissioning savings depending on the magnitude of the MBE and its sign 

(positive or negative). The regression models created have essentially no MBE and consequently are not 

adjusted. In order to avoid biased savings and persistence results, adjustments are made to each of the 

annual consumption values determined by the calibrated simulations to offset the MBE of the calibrated 

simulation. Table 3.15 shows the MBE of the calibrated simulations from the pre-commissioning period 

and each of the post-commissioning periods. The annual adjustment given to each period’s consumption 

determined with the calibrated simulations is also shown. The annual adjustment represents the opposite 

(positive or negative) of the MBE expressed as a daily value multiplied by 365 (days/yr). 

Table 3.15 Calibrated simulation MBE and corresponding annual consumption adjustment for 

chilled and hot water.

Year/Period 1996 

Pre-Cx

1997 1998 1/1999-

4/2000 2001 1/2002-11/2002

9/2003-

6/2004

Values in MMBtu

CHW MBE (MMBtu/day) -0.0381 -0.0381 -0.1636 0.4876 -0.0088 -0.6075 -0.1620

Annual CHW Adjustment 

(MMBtu/yr) 13.90 13.90 59.70 -177.96 3.23 221.75 59.14

HW MBE (MMBtu/day) -0.2631 -0.2631 -0.4496 -0.0782 0.5751 0.2108 0.9088

Annual HW Adjustment 

(MMBtu/yr) 96.031 96.031 164.1 28.53543 -209.925 -76.9577 -331.702

Values in GJ

CHW MBE (GJ/day) -0.0361 -0.0361 -0.1551 0.4622 -0.0083 -0.5758 -0.1535

Annual CHW Adjustment (GJ/yr) 13.17 13.17 56.58 -168.67 3.06 210.18 56.05

HW MBE (GJ/day) -0.2494 -0.2494 -0.4261 -0.0742 0.5451 0.1998 0.8614

Annual HW Adjustment (GJ/yr) 91.019 91.019 155.5 27.04626 -198.970 -72.9415 -314.392

NAC Versus Standard IPMVP Weather Normalization Approach

The percent savings results from the NAC weather normalization approach generally show good 

agreement with the percent savings results from the standard IPMVP weather normalization approach. 

Chilled water consumption and savings results are shown for both the NAC and standard IPMVP weather 

normalization approaches in Table 3.16 with Option C using regression models and Table 3.17 with 

Option D (MBE adjusted). The NAC weather normalization approach in these tables uses the long-term 

average College Station weather year. The differences in percent savings between the two weather 

normalization approaches shown in the tables for each post-commissioning period vary but there are only 

two post-commissioning periods where the difference is greater than 1 % and one post-commissioning 

period where the difference is greater than 2 %. However, some of these differences are relatively large 

compared to the percent savings of these post-commissioning periods. For example, the average 1997 

percent savings between the two weather normalization approaches using Option D (MBE adjusted) are 

4.34 % (see Table 3.16). The difference in savings, 0.58 %, represents 13.4 % of the average savings. 

In other words, while percent savings differences shown in Table 3.16 and Table 3.17 may seem small, a 

small difference may be significant if the overall savings is not that large. The chilled water percent 

savings differences between the NAC and standard IPMVP weather normalization approaches using 

Option D (MBE adjusted) average 0.78 % over all post-commissioning periods while the differences using 

Option C with regression models average 0.64 %.
Page 72 of 239

Table 3.16 Chilled water consumption and savings results using Option D (MBE adjusted) of 

IPMVP to compare NAC and standard IPMVP weather normalization approaches. The long-term 

average weather year is used for the NAC weather normalization approach.

Year/Period 1996 

Pre-Cx

1997 1998 1/1999-

4/2000 2001 1/2002-11/2002

9/2003-

6/2004

NAC Weather Normalization Approach

Normalized to 

Long-Term 

Avg Weather

CHW Use (MMBtu/yr) 16800 16024 15409 14745 14469 15639 13069

CHW Use (GJ/yr) 15923 15188 14605 13976 13714 14823 12387

CHW Savings (MMBtu/yr) Baseline 777 1392 2056 2331 1161 3732

CHW Savings (GJ/yr) Baseline 736 1319 1949 2209 1100 3537

CHW % Savings Baseline 4.62 % 8.28 % 12.24 % 13.87 % 6.91 % 22.21 %

Standard IPMVP Weather Normalization Approach

Baseline

Consumption 

with Post-Cx 

Weather

CHW Use (MMBtu/yr) 18874 17017 18610 17571 17839 17685 17983

CHW Use (GJ/yr) 17889 16129 17639 16654 16908 16762 17045

Post-Cx

Consumption 

with Own 

Period’s 

Weather

CHW Use (MMBtu/yr) 18874 16253 17066 15685 15305 16164 13819

CHW Use (GJ/yr) 17889 15405 16175 14866 14506 15320 13098

CHW Savings (MMBtu/yr) Baseline 764 1544 1886 2534 1522 4164

CHW Savings (GJ/yr) Baseline 724 1463 1788 2402 1443 3947

CHW % Savings Baseline 4.05 % 8.18 % 9.99 % 13.43 % 8.06 % 22.06 %

CHW % Savings 

Difference Baseline 0.58 % 0.10 % 2.24 % 0.45 % 1.15 % 0.15 %
Page 73 of 239

Table 3.17 Chilled water consumption and savings results using Option C of IPMVP with 

regression models to compare NAC and standard IPMVP weather normalization approaches. The 

long-term average weather year is used for the NAC weather normalization approach.

Year/Period

1996 

PreCx1997 1998 1/1999-

4/2000 2001 1/2002-11/2002

9/2003-

6/2004

NAC Weather Normalization Approach

Normalized to LongTerm Average

Weather

CHW Use 

(MMBtu/yr) 17356 16491 15849 14890 14767 15822 13256

CHW Use 

(GJ/yr) 16450 15630 15022 14113 13996 14996 12564

CHW Savings 

(MMBtu/yr)

Baseline 864 1507 2466 2589 1534 4100

CHW Savings 

(GJ/yr)

Baseline 819 1428 2337 2454 1454 3886

CHW % Savings Baseline

4.98

%

8.68

% 14.21 % 14.92% 8.84 % 23.62 %

Standard IPMVP Weather Normalization Approach

Baseline 

Consumption with 

Post-Cx. Weather

CHW Use 

(MMBtu/yr) 18860 17142 18840 18034 18058 17841 18094

CHW Use

(GJ/yr) 17876 16247 17857 17093 17116 16910 17150

Post-Cx

Consumption with 

Own Period’s 

Weather

CHW Use 

(MMBtu/yr) 18860 16253 17059 15685 15298 16125 13734

CHW Use 

(GJ/yr) 17876 15405 16169 14866 14500 15283 13017

CHW Savings 

(MMBtu/yr)

Baseline 889 1780 2349 2760 1716 4360

CHW Savings 

(GJ/yr)

Baseline 843 1687 2226 2616 1626 4132

CHW % Savings Baseline

4.71

%

9.44

% 12.45 % 14.64% 9.10 % 23.12 %

CHW % Savings 

Difference

Baseline0.27

%

0.76

% 1.76 % 0.28% 0.26 % 0.50 %

While the results in Tables 3.16 and 3.17 do not show any striking differences in savings between the 

NAC and standard IPMVP weather normalization approaches, they do not show which approach has less 

variability. Figure 3.8 compares chilled water plus hot water percent savings quartiles of the NAC and 

standard IPMVP weather normalization approaches side by side when using the 29 different College 

Station weather years and random runs. Option D (MBE adjusted) of the IPMVP is used for both weather 

normalization approaches in Fig. 3.9. The minimum, median and maximum values are shown. Figure 3.8

is significant in that it shows an appreciably smaller variability in savings for the NAC weather 

normalization approach than the standard IPMVP approach for several of the post-commissioning time 

periods. It shows that depending on the weather years used for the standard IPMVP weather 

normalization approach, there may be considerably less persistence in savings over time than there 

would be if the NAC weather normalization approach is used. 
Page 74 of 239

Figure 3.8 Post Commissioning Period, chilled water plus hot water percent savings variability 

(min, median and max) comparison between NAC (left set) and standard IPMVP (right set) weather 

normalization approaches when using Option C of IPMVP with regression.

Figure 3.9 Post Commissioning Period, chilled water plus hot water percent savings variability 

comparison between NAC (left set) and standard IPMVP (right set) weather normalization 

approaches when using Option D (MBE adjusted) of IPMVP.

0%

5%

10%

15%

20%

25%

30%

19971998

1/99-4/24/00

2001

1/02-11/02

9/03-6/04

(CHW + HW) % Savings

0%

5%

10%

15%

20%

25%

30%

1997199719981998

1/99-4/24/001/99-4/24/00

20012001

1/02-11/021/02-11/02

9/03-6/049/03-6/04

(CHW + HW) % Savings
Page 75 of 239

Chilled and hot water savings ranges and averages across all weather years with the NAC weather 

normalization approach and all random runs with the standard IPMVP weather normalization approach 

are listed in Table 3.18. Option D (MBE adjusted) of the IPMVP is used for both weather normalization 

approaches in this table. The results show that the NAC weather normalization approach has a smaller 

average range in savings across all post-commissioning periods than the standard IPMVP weather 

normalization approach for both chilled and hot water. For chilled water, the mean percent savings range 

is 1.32 % for the NAC weather normalization approach and 2.64 % for the standard IPMVP weather 

normalization approach. For hot water, the mean percent savings range is 3.30 % for NAC and 5.04 %

for standard IPMVP. Despite these differences, the mean of the post-commissioning period average 

savings is quite similar. For chilled water, the mean of the average percent savings is 11.38 % for the 

NAC weather normalization approach versus 11.40 % for the standard IPMVP weather normalization 

approach. For hot water, the mean of the average percent savings is 15.99 % for both the NAC and 

standard IPMVP weather normalization approaches. The differences between the two weather 

normalization approaches in average percent savings across each of the post-commissioning periods are 

also relatively small. For chilled water, the largest difference in average savings between the two weather 

normalization approaches in a post-commissioning period is 0.25 %, occurring in the 9/03-6/04 period. 

For hot water, it is 0.28 %, occurring in 2001.

Table 3.18 Chilled and hot water percent savings range and average across all College Station 

weather years under NAC weather normalization approach and across all random runs under 

standard IPMVP weather normalization approach. Both approaches use Option D (MBE adjusted)

CHW %

Savings 

Range

1997 1998 1/99-

4/24/00 2001 1/02-11/02 9/03-6/04 Average

NAC 1.30 % 0.81 % 1.49 % 1.04 % 1.42 % 1.84 % 1.32 %

Standard 

IPMVP 0.82 % 1.67 % 2.62 % 3.47 % 1.64 % 5.62 % 2.64 %

HW %

Savings 

Range

1997 1998 1/99-

4/24/00 2001 1/02-11/02 9/03-6/04 Average

NAC 4.43 % 3.53 % 2.76 % 2.70 % 3.29 % 3.09 % 3.30 %

Standard 

IPMVP 4.31 % 5.02 % 9.00 % 6.11 % 2.77 % 3.03 % 5.04 %

CHW %

Savings 

Average

1997 1998 1/99-

4/24/00 2001 1/02-11/02 9/03-6/04 Average

NAC 4.33 % 8.21 % 12.12 % 14.02 % 7.18 % 22.41 % 11.38 %

Standard 

IPMVP 4.32 % 8.14 % 12.03 % 14.12 % 7.16 % 22.66 % 11.40 %

HW %

Savings 

Average

1997 1998 1/99-

4/24/00 2001 1/02-11/02 9/03-6/04 Average

NAC 18.71 % 23.50 % 25.36 % 16.04 % -3.20 % 15.53 % 15.99 %

Standard 

IPMVP 18.87 % 23.77 % 25.19 % 15.76 % -3.18 % 15.52 % 15.99 %
Page 76 of 239

Option C with Regression Models Versus Option D of IPMVP

The previous section compares the variability in savings persistence between the NAC and standard 

IPMVP weather normalization approaches. It is also valuable to know whether Option C with regression 

models or Option D of the IPMVP shows less variability in the persistence of savings and by how much. 

Knowing this may influence how much time one is willing to invest calibrating simulations under Option D 

when one could quickly create regression models.

Table 3.19 quantifies the variation in percent savings across the different weather years using the NAC 

weather normalization approach for both Option C with regression models and Option D (MBE adjusted) 

of the IPMVP. The range and average percent savings across all 29 College Station weather years for 

each post-commissioning period are shown. Table 3.19 shows that each of the post-commissioning 

period calibrated simulations (Option D, MBE adjusted) has less variation (smaller range) than the 

corresponding regression models (Option C) for both chilled and hot water, although some of the chilled 

water post-commissioning periods are quite similar. The smallest chilled water range difference between 

Option C and Option D is 0.03 % (1997) while the largest is 1.56 % (2001). The average chilled water 

savings range over all post-commissioning periods for Option D (MBE adjusted) is 1.32 % while the 

average for Option C with regression models is 2.14 %. The hot water range differences are larger, the 

smallest being 1.80 % (2001) and the largest being 6.22 % (1/99-4/24/00). The mean hot water savings 

range over all post-commissioning periods for Option D (MBE adjusted) is 3.30 % and 7.16 % for Option 

C with regression models. Even though Option D (MBE adjusted) exhibits an overall lower savings range 

than Option C with regression models across all weather years when using the NAC weather 

normalization approach, the average chilled water savings from the different weather years over all postcommissioning periods are somewhat similar for Option D (MBE adjusted) and Option C with regression 

models. The differences for average chilled water percent savings vary from 0.48 % in 1997 to 1.81 % in 

1/02-11/02. For hot water, the differences for average savings during the post-commissioning periods 

vary as two of the periods show differences between Option D (MBE adjusted) and Option C with 

regression models of less than 1 % (1997 and 1/02-11/02) and three show differences greater than 3 %

(1998, 2001, and 9/03-6/04). The differences for average hot water percent savings vary from 0.54 % in 

1997 up to 3.48 % in 2001.
Page 77 of 239

Table 3.19 Chilled and hot water percent savings range and average across all College Station 

weather years under NAC weather normalization approach for Option D (MBE adjusted) and 

Option C with regression models.

CHW %

Savings 

Range 1997 1998

1/99-

4/24/00 2001

1/02-

11/02 9/03-6/04 Average

Option D 1.30 % 0.81 % 1.49 % 1.04 % 1.42 % 1.84 % 1.32 %

Option C 

with 

Regression 1.33 % 1.82 % 1.82 % 2.59 % 2.69 % 2.60 % 2.14 %

HW %

Savings 

Range 1997 1998

1/99-

4/24/00 2001 1/02-11/02 9/03-6/04 Average

Option D 4.43 % 3.53 % 2.76 % 2.70 % 3.29 % 3.09 % 3.30 %

Option C 

with 

Regression 9.65 % 7.14 % 8.97 % 4.49 % 6.01 % 6.70 % 7.16 %

CHW %

Savings 

Average 1997 1998

1/99-

4/24/00 2001 1/02-11/02 9/03-6/04 Average

Option D 4.33 % 8.21 % 12.12 % 14.02 % 7.18 % 22.41 % 11.38 %

Option C 

with 

Regression 4.81 % 8.79 % 13.82 % 14.91 % 9.00 % 24.08 % 12.57 %

HW %

Savings 

Average 1997 1998

1/99-

4/24/00 2001 1/02-11/02 9/03-6/04 Average

Option D 18.71 % 23.50 % 25.36 % 16.04 % -3.20 % 15.53 % 15.99 %

Option C 

with 

Regression 18.16 % 20.07 % 26.87 % 12.56 % -4.20 % 18.87 % 15.39 %

Variability of Savings Statistical Measures

Two statistical measures are now shown to better quantify the variability of savings for each of the four 

different combinations of savings determination methods and weather normalization approaches (Option 

D using the NAC weather normalization approach, Option C with regression models using the NAC 

weather normalization approach, Option D using the standard IPMVP weather normalization approach, 

and Option C with regression models using the standard IPMVP weather normalization approach). This 

allows for simple comparison of savings variability between the four different combinations of sets of 

savings obtained from the 29 different College Station weather years and random runs for the NAC and 

standard IPMVP weather normalization approaches, respectively. The two statistical measures used 

here are the standard deviation and the coefficient of variation of the percent savings. 

Chilled water savings variability results are listed in Table 3.20. Both the standard deviation and 

coefficient of variation results show Option D (MBE adjusted) with the NAC weather normalization 

approach to have the least amount of savings variability (0.32 % savings average standard deviation and 

3.50 % average coefficient of variation for chilled water savings across all post-commissioning periods) 

while Option C with regression models using the standard IPMVP weather normalization approach has 

the most amount of savings variability (1.01 % savings average standard deviation and 8.36 % average 
Page 78 of 239

coefficient of variation for chilled water savings across all post-commissioning periods). The average 

chilled water savings variability measures are similar for Option D (MBE adjusted) with the standard 

IPMVP weather normalization approach (0.68 % savings average standard deviation and 5.78 % average 

coefficient of variation for chilled water savings across all post-commissioning periods) and Option C with 

regression models with the NAC weather normalization approach (0.57 % savings average standard 

deviation and 5.29 % average coefficient of variation for chilled water savings across all postcommissioning periods). Thus the NAC weather normalization approach shows less variability in chilled 

water savings than the standard IPMVP weather normalization approach when comparing results using 

the same IPMVP savings determination method. Additionally, Option D (MBE adjusted) shows less 

variability in chilled water savings than Option C with regression models when comparing results using 

the same weather normalization approach.

Table 3.20 Chilled water savings variability quantification for each of the four combinations of the 

NAC and standard IPMVP weather normalization approaches and Option C and Option D (MBE 

adjusted) savings determination methods.

Option D (MBE Adjusted) with NAC Weather Normalization Approach

1997 1998

1/99-

4/24/00 2001 1/02-11/02 9/03-6/04 Average

stdev 0.29 % 0.20 % 0.39 % 0.25 % 0.36 % 0.43 % 0.32 %

coeff var 6.76 % 2.41 % 3.19 % 1.76 % 4.95 % 1.92 % 3.50 %

Option D (MBE Adjusted) with Standard IPMVP Weather Normalization Approach

1997 1998

1/99-

4/24/00 2001 1/02-11/02 9/03-6/04 Average

stdev 0.20 % 0.41 % 0.61 % 0.93 % 0.49 % 1.46 % 0.68 %

coeff var 4.70 % 5.06 % 5.08 % 6.57 % 6.82 % 6.44 % 5.78 %

Option C with Regression Models with NAC Weather Normalization Approach

1997 1998

1/99-

4/24/00 2001 1/02-11/02 9/03-6/04 Average

stdev 0.35 % 0.47 % 0.48 % 0.70 % 0.73 % 0.71 % 0.57 %

coeff var 7.20 % 5.37 % 3.46 % 4.68 % 8.06 % 2.96 % 5.29 %

Option C with Regression Models with Standard IPMVP Weather Normalization Approach

1997 1998

1/99-

4/24/00 2001 1/02-11/02 9/03-6/04 Average

stdev 0.40 % 0.69 % 0.97 % 1.27 % 1.02 % 1.74 % 1.01 %

coeff var 8.75 % 7.79 % 7.09 % 8.41 % 10.97 % 7.14 % 8.36 %

Hot water savings variability results are listed in Table 3.21. When not considering the coefficient of 

variation of the 1/02-11/02 where negative savings occur, hot water savings variability results are similar 

to those of the chilled water except that the savings variability measures are much closer for the NAC and 

standard IPMVP weather normalization approaches when using Option C with regression models. The 

average standard deviation across all post-commissioning periods is 1.83 % savings for Option C with 

regression models using the NAC weather normalization approach versus 1.85 % savings for Option C 

with regression models using the standard IPMVP weather normalization approach. When using Option 

D (MBE adjusted), however, the NAC weather normalization approach clearly shows less savings 

variability than the standard IPMVP weather normalization approach. The average standard deviation 

across all post-commissioning periods is 0.79 % savings for Option D (MBE adjusted) using the NAC 

weather normalization approach versus 1.32 % savings for Option D (MBE adjusted) using the standard 

IPMVP weather normalization approach. When considering the average of the coefficient of variation 

across all post-commissioning periods excluding the 1/02-11/02 period, the same pattern seen with the 

standard deviation exists. The NAC weather normalization approach (4.20 % average coefficient of 

variation) shows less variability in savings than the standard IPMVP weather normalization approach 
Page 79 of 239

(7.36 % average coefficient of variation) when using Option D (MBE adjusted) of the IPVMP. Also, the 

NAC weather normalization approach (9.83 % average coefficient of variation) shows similar variability in 

savings to the standard IPMVP weather normalization approach (9.92 % average coefficient of variation) 

when using Option C with regression models of the IPMVP. 

Table 3.21 Hot water savings variability quantification for each of the four combinations of the 

NAC and standard IPMVP weather normalization approaches and Option C and Option D (MBE 

adjusted) savings determination methods.

Option D (MBE Adjusted) with NAC Weather Normalization Approach

1997 1998

1/99-

4/24/00 2001

1/02-

11/02

9/03-

6/04 Average

Avg 

without 

1/02-

11/02

stdev 1.06 % 0.82 % 0.62 % 0.67 % 0.76 % 0.80 % 0.79 % 0.80 %

coeff var 5.69 % 3.50 % 2.46 % 4.18 % -23.74 % 5.15 % -0.46 % 4.20 %

Option D (MBE Adjusted) with Standard IPMVP Weather Normalization Approach

1997 1998

1/99-

4/24/00 2001

1/02-

11/02

9/03-

6/04 Average

Avg 

without 

1/02-

11/02

stdev 1.14 % 1.34 % 2.11 % 1.70 % 0.69 % 0.93 % 1.32 % 1.44 %

coeff var 6.05 % 5.62 % 8.39 % 10.76 % -21.82 % 5.96 % 2.49 % 7.36 %

Option C with Regression Models with NAC Weather Normalization Approach

1997 1998

1/99-

4/24/00 2001

1/02-

11/02

9/03-

6/04 Average

Avg 

without 

1/02-

11/02

stdev 2.54 % 1.80 % 2.09 % 1.13 % 1.61 % 1.78 % 1.83 % 1.87 %

coeff var 14.00 % 8.95 % 7.79 % 8.96 % -38.48 % 9.46 % 1.78 % 9.83 %

Option C with Regression Models with Standard IPMVP Weather Normalization Approach

1997 1998

1/99-

4/24/00 2001

1/02-

11/02

9/03-

6/04 Average

Avg 

without 

1/02-

11/02

stdev 2.49 % 1.72 % 2.38 % 1.58 % 1.70 % 1.22 % 1.85 % 1.88 %

coeff var 13.45 % 8.31 % 8.91 % 12.60 % -47.54 % 6.32 % 0.34 % 9.92 %

Variability of Persistence of Savings Statistical Measures

Just as it is important to have some quantifiable measure of the variability of commissioning savings for 

each of the four different combinations of savings determination methods and weather normalization 

approaches, it is also valuable to quantify the variability of commissioning persistence of savings for each 

of these four different combinations. Persistence of savings in this case is defined as the percent savings 

difference between a post-commissioning period after 1997 (the first post-commissioning period) and the 

1997 post-commissioning period. 

Table 3.22 shows the chilled water savings and persistence of savings for both the NAC and standard 

IPMVP weather normalization approaches using Option D (MBE adjusted) of the IPMVP. The long-term 

average College Station weather year is used for the NAC weather normalization approach. The normal 

procedure for the standard IPMVP weather normalization approach is employed that uses the actual 
Page 80 of 239

weather data of the post-commissioning periods. Table 3.22 indicates that persistence of savings results 

do vary depending on the weather normalization approach used just as savings results do. For two of the 

post-commissioning periods (1/99-4/24/00 and 1/02-11/02), chilled water persistence of savings differs by 

more than 1.5 % between the NAC and standard IPMVP weather normalization approaches. 

Table 3.22 Chilled water savings and persistence of savings results using Option D (MBE 

adjusted) of IPMVP for both NAC and standard IPMVP weather normalization approaches. The 

long-term average weather year is used for the NAC weather normalization approach.

1997 1998 1/99-4/24/00 2001 1/02-11/02 9/03-6/04

NAC Weather Normalization Approach

CHW %

Savings 4.62 % 8.28 % 12.24 % 13.87 % 6.91 % 22.21 %

CHW 

Persistence n/a 3.66 % 7.62 % 9.25 % 2.29 % 17.59 %

Standard IPMVP Weather Normalization Approach

CHW %

Savings 4.05 % 8.18 % 9.99 % 13.43 % 8.06 % 22.06 %

CHW 

Persistence n/a 4.13 % 5.94 % 9.38 % 4.01 % 18.01 %

CHW %

Savings 

Difference

0.57 % 0.10 % 2.25 % 0.44 % 1.15 % 0.15 %

CHW 

Persistence 

Difference

n/a 0.47 % 1.68 % 0.13 % 1.72 % 0.42 %

To quantify the variability of persistence of savings, sets of savings from the 29 different College Station 

weather years and 29 different random runs are again used for the NAC and standard IPMVP weather 

normalization approaches, respectively. From these sets of persistence of savings results, the standard 

deviation and the coefficient of variation of the persistence are found. Table 3.23 quantifies the chilled

water variability of persistence of savings. Results of the variability of chilled water persistence of savings 

differ somewhat from the results of the variability of chilled water savings. Option C with regression 

models using the NAC weather normalization approach (0.38 % persistence average standard deviation 

and 5.12 % average coefficient of variation across all post-commissioning periods) shows slightly less 

overall variability than Option D (MBE adjusted) using the NAC weather normalization approach (0.42 %

persistence average standard deviation and 7.37 % average coefficient of variation across all postcommissioning periods). Option D (MBE adjusted) with the standard IPMVP weather normalization 

approach (0.68 % persistence average standard deviation and 9.04 % average coefficient of variation 

across all post-commissioning periods), however, still shows less variability than Option C with regression 

models using the standard IPMVP weather normalization approach (1.20 % persistence average 

standard deviation and 15.03 % average coefficient of variation across all post-commissioning periods). 

As with chilled water variability of savings results, the NAC weather normalization approach shows less 

chilled water variability of persistence than the standard IPMVP weather normalization approach when 

comparing results using the same IPMVP savings determination method (both Option C with regression 

models and Option D).
Page 81 of 239

Table 3.23 Chilled water persistence of savings variability quantification for each of the four 

combinations of the NAC and standard IPMVP weather normalization approaches and Option C 

and Option D (MBE adjusted) savings determination methods.

Option D (MBE Adjusted) with NAC Weather Normalization Approach

1998 1/99-4/24/00 2001 1/02-11/02 9/03-6/04 Average

stdev 0.17 % 0.22 % 0.42 % 0.61 % 0.70 % 0.42 %

coeff var 4.38 % 2.84 % 4.29 % 21.49 % 3.87 % 7.37 %

Option D (MBE Adjusted) with Standard IPMVP Weather Normalization Approach

1998 1/99-4/24/00 2001 1/02-11/02 9/03-6/04 Average

stdev 0.30 % 0.46 % 0.80 % 0.45 % 1.38 % 0.68 %

coeff var 7.78 % 6.02 % 8.21 % 15.65 % 7.53 % 9.04 %

Option C with Regression Models with NAC Weather Normalization Approach

1998 1/99-4/24/00 2001 1/02-11/02 9/03-6/04 Average

stdev 0.18 % 0.17 % 0.44 % 0.49 % 0.60 % 0.38 %

coeff var 4.61 % 1.85 % 4.37 % 11.68 % 3.10 % 5.12 %

Option C with Regression Models with Standard IPMVP Weather Normalization Approach

1998 1/99-4/24/00 2001 1/02-11/02 9/03-6/04 Average

stdev 0.84 % 1.01 % 1.38 % 1.07 % 1.71 % 1.20 %

coeff var 19.39 % 11.12 % 13.07 % 22.91 % 8.65 % 15.03 %

When assessing hot water variability of persistence of savings results (see Table 3.24), the coefficient of 

variation should not be considered. Due to very small average persistence values in multiple postcommissioning periods, many of the results for the coefficient of variation cannot accurately assess the 

variability of hot water persistence of savings. The standard deviation appears better suited as a 

measure of variability of hot water persistence of savings. When only considering the average value 

across all post-commissioning periods of the standard deviation for hot water persistence of savings, 

Option D (MBE adjusted) using the NAC weather normalization approach has the least variability (0.98 %

persistence average standard deviation across all post-commissioning periods). This is followed by 

Option D (MBE adjusted) using the standard IPMVP weather normalization approach (1.12 % persistence 

average standard deviation across all post-commissioning periods), Option C with regression models 

using the NAC weather normalization approach (1.75 % persistence average standard deviation across 

all post-commissioning periods), and Option C with regression models using the standard IPMVP weather 

normalization approach (2.61 % persistence average standard deviation across all post-commissioning 

periods).
Page 82 of 239

Table 3.24 Hot water persistence of savings variability quantification for each of the four 

combinations of the NAC and standard IPMVP weather normalization approaches and Option C 

and Option D (MBE adjusted) savings determination methods.

Option D (MBE Adjusted) with NAC Weather Normalization Approach

1998 1/99-4/24/00 2001 1/02-11/02 9/03-6/04 Average

stdev 0.32 % 1.09 % 1.24 % 1.69 % 0.56 % 0.98 %

coeff var 6.78 % 16.43 % -46.25 % -7.71 % -17.61 % -9.67 %

Option D (MBE Adjusted) with Standard IPMVP Weather Normalization Approach

1998 1/99-4/24/00 2001 1/02-11/02 9/03-6/04 Average

stdev 0.80 % 1.59 % 1.16 % 1.61 % 0.44 % 1.12 %

coeff var 16.30 % 25.21 % -37.33 % -7.30 % -12.98 % -3.22 %

Option C with Regression Models with NAC Weather Normalization Approach

1998 1/99-4/24/00 2001 1/02-11/02 9/03-6/04 Average

stdev 0.83 % 1.31 % 2.10 % 2.98 % 1.51 % 1.75 %

coeff var 43.68 % 15.09 % -37.40 % -13.31 % 214.89 % 44.59 %

Option C with Regression Models with Standard IPMVP Weather Normalization Approach

1998 1/99-4/24/00 2001 1/02-11/02 9/03-6/04 Average

stdev 2.42 % 3.03 % 2.36 % 3.25 % 1.98 % 2.61 %

coeff var 111.63 % 37.05 % -39.69 % -14.71 % 271.96 % 73.25 %

Summary Comparison of Savings Using Four Different Normalization Approaches

The variability of savings and persistence of savings results from the commissioning of CE/TTI using the 

NAC and standard IPMVP weather normalization approaches, as well as Option C with regression 

models and Option D of the IPMVP are presented in this chapter. It has been shown that the savings and 

persistence of savings may vary greatly depending on which weather normalization approach, IPMVP 

Option, and weather year selected as the “normal” weather year are used. 

Overall, the NAC weather normalization approach shows less variability in savings and persistence of 

savings than the standard IPMVP weather normalization approach. Additionally, Option D of the IPMVP 

generally shows less variability in savings and persistence of savings than Option C with regression 

models.These statements hold true when considering chilled water savings. However, for hot water 

savings, results for the variability in the persistence of savings are mixed. Chilled water persistence of 

savings results differ in that Option C with regression models using the NAC weather normalization 

approach shows slightly less overall variability of persistence than Option D using the NAC weather 

normalization approach. Finally, the hot water persistence of savings shows mixed variability. 

For chilled water savings, the average standard deviation across all post-commissioning periods is 0.32

% savings for Option D (MBE adjusted) using the NAC weather normalization approach, 0.68 % savings 

for Option D (MBE adjusted) using the standard IPMVP weather normalization approach, 0.57 % savings 

for Option C with regression models using the NAC weather normalization approach, and 1.01 % savings 

for Option C with regression models using the standard IPMVP weather normalization approach. 

For hot water savings, the average standard deviation across all post-commissioning periods is 0.79 %

savings for Option D (MBE adjusted) using the NAC weather normalization approach, 1.32 % savings for 

Option D (MBE adjusted) using the standard IPMVP weather normalization approach, 1.83 % savings for 

Option C with regression models using the NAC weather normalization approach, and 1.85 % savings for 

Option C with regression models using the standard IPMVP weather normalization approach. 

For chilled water persistence of savings, the average standard deviation across all post-commissioning 

periods is 0.42 % persistence for Option D (MBE adjusted) using the NAC weather normalization 
Page 83 of 239

approach, 0.68 % persistence for Option D (MBE adjusted) using the standard IPMVP weather 

normalization approach, 0.38 % persistence for Option C with regression models using the NAC weather 

normalization approach, and 1.20 % persistence for Option C with regression models using the standard 

IPMVP weather normalization approach. 

For hot water persistence of savings, the average standard deviation across all post-commissioning 

periods is 0.98 % persistence for Option D (MBE adjusted) using the NAC weather normalization 

approach, 1.12 % persistence for Option D (MBE adjusted) using the standard IPMVP weather 

normalization approach, 1.75 % persistence for Option C with regression models using the NAC weather 

normalization approach, and 2.61 % persistence for Option C with regression models using the standard 

IPMVP weather normalization approach. 

3.4 Recent Persistence Studies

3.4.1 Persistence of Commissioning Measures in Four Buildings in Japan

Yamaha (2007) examined information available in Japanese for 14 buildings that had received “10 Year 

Awards” from the Society of Heating, Air-Conditioning and Sanitary Engineers of Japan (SHASE) for 

conducting system inspections and improvements over a period of at least ten years. He concluded there 

was sufficient information regarding energy-saving measures implemented and subsequent energy use in 

three of these buildings to be valuable for examining the persistence of energy savings in buildings. In 

these cases, the measures were primarily energy efficiency measures incorporated into the design of the 

buildings. 

He discussed the Chubu Electric Power Co. (CEPCO) Okazaki Building, the Tokyo Electric Power Co. 

(TEPCO) Higashi-Murayama Building and the TEPCO R&D Center, all of which received SHASE Ten 

Year Awards. These buildings used 20 % to 40 % less energy than reference designs. He also presents 

the performance of the Tonets Corp. (TONETS) Shinkawa Building.

The CEPCO Okazaki Building incorporated high levels of insulation, sun shading with controls and cubic 

shape to minimize surface area. The building also uses thermal energy storage, variable water volume 

and variable air volume control strategies and heat recovery. Figure 3.10 shows the disaggregated 

energy consumption of the CEPCO Okazaki Building for 20 years. The energy use ranges from a low of 

50 % of the reference consumption to a high of about 80 % of the reference consumption. During the 

latter half of the 1980s, building consumption increased substantially, primarily due to increases in lighting 

consumption, fan energy and pumping power. But even after these increases, the building still used 20 %

less than the reference design.

The TEPCO Higashi-Murayama Building incorporates ventilation windows, sun shade controls, data 

visualization tools, thermal energy storage and large temperature differences in the distribution systems. 

As shown in Fig. 3.11, the consumption was relatively stable over the last 11 years shown, with 

consumption between a low of about 75 % of the reference building (in a year with a cool summer) to a 

high of about 85 % of the reference. Consumption was very low in 1988 since the building was not 

operational for the entire year. 

The TEPCO R&D Center utilizes data visualization tools to help achieve optimal operation and 

incorporates thermal storage and large temperature differences in the distribution systems. The energy 

consumption shown in Fig. 3.12 is quite stable for four of the nine years shown, has three years of 

abnormally high consumption (1997 to 1999) due to increased consumption of an experimental fuel cell 

installation in the building. During 2002 and 2003 consumption decreased by 15 % and 26 % due to the 

removal of the experimental fuel cell installation and a cool summer in 2003.
Page 84 of 239

Figure 3.13 shows the savings in MJ/m2-year of the CEPCO Okazaki Building, the TEPCO HigashiMurayama Building and the TONETS Shinkawa Building. The first two saved significant energy 

throughout the entire period of observation along with the TEPCO R&D building. The TONETS Shinkawa 

Building saved significant energy for seven years, then over the next seven years shows savings that are 

negative in aggregate due to a 25 % increase in operating hours and additional computers and office 

machines.

As a group, these buildings are consistent with the commissioned buildings that have been examined in 

Annex 47 work – most continue to show savings over periods that in this case run from 10 years to 20

years, while generally showing some decrease in savings over time.

Figure 3.10 Annual energy consumption of the CEPCO Okazaki Building for years 1979 to 1998 

(Source: Yamaha 2007).

 

0

20

40

60

80

100

120

140

160

180

200

Reference

19791980198119821983198419851986198719881989199019911992199319941995199619971998

Year

kWh/(m2 year)

Others

Lightings

Pump

Fan 

Heat Source
Page 85 of 239

Figure 3.11 Energy consumption of the TEPCO Higashi Murayama Building for 1988 to 1999 

(Source: Yamaha 2007).

Figure 3.12 Energy consumption of the TEPCO R&D for 1995 to 2003 (Source: Yamaha 2007).

0

200

400

600

800

1000

1200

1400

1600

1800

Reference

198819891990

1991

19921993

1994

1995

19961997

1998

1999

MJ/(m2 year)

heat source

Pumps

fans

lighting

others
Page 86 of 239

Figure 3.13. Persistence of energy savings for three buildings over a 20 year period (Source: 

Yamaha 2007).

3.4.2 Persistence of Commissioning Measures in ten Buildings at Texas A&M –

An Update

Results of Previous Study

As noted earlier, the previous study of the ten buildings by Turner et al. (2001) and Cho (2002) compared 

the normalized energy savings of each building over a period of four years following retro-commissioning. 

These results have been updated by Toole (2009). Table 3.25 details the results of this study, with the 

chilled water, hot water, and electricity consumption and savings shown on a yearly basis.
Page 87 of 239

Table 3.25 Energy savings results for the years examined in the previous study (Cho, 2002).

CHW 22,955 16,723 27 19,530 15 20,164 12 21,083 8

HW 8,735 4,093 53 1,676 81 3,330 62 4,344 50

Elec 4,832 3,773 22 3,883 20 3,936 19 3,859 20

CHW 30,625 18,846 38 18,660 39 19,012 38 20,360 34

HW 7,584 2,578 66 1,154 85 1,831 76 4,712 38

Elec 4,891 3,698 24 3,675 25 3,823 22 3,874 21

CHW 18,872 8,717 54 8,511 55 14,548 23 15,858 16

HW 21,155 6,091 71 549 97 4,923 77 10,111 52

Elec 1,480 1,297 12 1,168 21 1,171 21 1,291 13

CHW 14,179 7,109 50 8,420 41 7,660 46 9,032 36

HW 6,896 2,603 62 914 87 1,629 76 3,519 49

Elec 1,666 1,297 22 1,336 20 1,341 20 1,353 19

CHW 59,271 34,864 41 34,969 41 36,731 38 41,965 29

HW 40,812 6,523 84 1,215 97 8,030 80 10,591 74

Elec 5,511 5,458 1 5,067 8 4,778 13 4,684 15

CHW 21,964 12,177 45 12,988 41 12,740 42 11,804 46

HW 2,103 704 67 399 81 634 70 649 69

Elec 2,850 2,511 12 2,597 9 2,624 8 2,592 9

CHW 28,526 13,599 52 15,637 45 15,078 47 17,702 38

HW 18,227 6,565 64 5,588 69 5,098 72 2,171 88

Elec 1,933 1,898 2 1,914 1 1,991 -3 2,153 -11

CHW 40,892 23,115 43 24,080 41 22,915 44 23,307 43

HW 3,569 887 75 2,041 43 2,097 41 2,051 43

Elec 4,186 3,996 5 4,140 1 4,236 -1 4,056 3

CHW 19,193 12,327 36 13,339 31 12,530 35 11,609 40

HW 13,393 10,876 19 9,715 27 6,581 51 6,350 53

Elec 2,555 2,410 6 2,446 4 2,552 0 2,581 -1

CHW 40,824 16,737 59 17,377 57 18,148 56 20,225 50

HW 7,676 1,630 79 3,230 58 2,226 71 4,271 44

Elec 7,502 6,762 10 6,793 9 7,099 5 6,955 7

Total Total Average Total Average Total Average Total Average

 297,298 164,215 44.8 173,509 41.6 179,527 39.6 192,946 35.1

 130,149 42,549 67.3 26,482 79.7 36,380 72.0 48,768 62.5

 37,407 33,100 11.5 33,018 11.7 33,552 10.3 33,399 10.7

Chilled Water

Hot Water

Electricity

Type

Wehner CBA

Zachry Engr. 

Center

Rich. 

Petroleum

VMC Addition

G.R.White 

Coliseum

Harrington 

Tower

Kleberg 

Building

Koldus 

Building

Blocker

Eller O&M

Building 

Name Type

Baseline 

Use 

(MMBtu) 

(MWh) / yr

1997 1998 1999 2000

Use 

(MMBtu) 

(MWh) / yr

Saving 

(%)

Use 

(MMBtu) 

(MWh) / yr

Saving 

(%)

Use 

(MMBtu) 

(MWh) / yr

Saving 

(%)

Use 

(MMBtu) 

(MWh) / yr

Saving 

(%)

**

*

*

**

* The baseline energy use for these buildings was estimated from the average savings of the other 

buildings because insufficient data was available to create reliable baselines.

** The Blocker building had insufficient chilled water and hot water energy use data in 2000 to determine 

normalized annual consumption. So the savings were estimated from the average degradation that 

occurred between 1999 and 2000 in the other nine buildings.

New Findings

The results of the previous study were expanded upon to include normalized consumption data and 

savings calculations for additional years following the completion of the original study. For seven of the 

buildings, reliable energy consumption data were available from as recently as 2006-2007. For the other 

three buildings, the last year of reliable consumption data ranged from 2002 to 2004. Table 3.26 shows 

the combined results of the previous study with the additional years of data for each building.
Page 88 of 239

Table 3.26 Updated results of energy savings analysis, normalized to common weather year.

Note: The consumption data used for the time period labeled “2005-2006” were from 7/25/2005 – 7/24/2006 for all of the buildings with data for 

this period. For the period labeled “2006-2007,” the consumption data were from 7/25/2006 – 7/24/2007 for G.R. White Coliseum, Harrington 

Tower, and Kleberg, and were from 10/16/2006 – 10/15/2007 for Blocker, Wehner, and Zachry. These time periods were chosen due to the 

availability of reliable energy consumption data.

CHW 22,955 16,723 27 19,530 15 20,164 12 21,083 8 19,082 17 17,887 22 20,850 9 21,179 8

HW 8,735 4,093 53 1,676 81 3,330 62 4,344 50 4,623 47 2,654 70 6,367 27 4,409 50

Elec 4,832 3,773 22 3,883 20 3,936 19 3,859 20 3,639 25 3,516 27 3,583 26 3,511 27

CHW 30,625 18,846 38 18,660 39 19,012 38 20,360 34 24,002 22 21,120 31 19,948 35 21,805 29

HW 7,584 2,578 66 1,154 85 1,831 76 4,712 38 4,488 41

Elec 4,891 3,698 24 3,675 25 3,823 22 3,874 21 3,972 19 3,732 24 3,745 23 3,861 21

CHW 18,872 8,717 54 8,511 55 14,548 23 15,858 16 6,837 64 11,134 41

HW 21,155 6,091 71 549 97 4,923 77 10,111 52 3,276 85 2,216 90

Elec 1,480 1,297 12 1,168 21 1,171 21 1,291 13 1,102 26 1,028 31 1,015 31 1,109 25 1,028 31 956 35

CHW 14,179 7,109 50 8,420 41 7,660 46 9,032 36 8,380 41 9,267 35 8,614 39 7,817 45 7,103 50 6,927 51

HW 6,896 2,603 62 914 87 1,629 76 3,519 49 3,921 43 3,538 49 2,966 57 2,807 59

Elec 1,666 1,297 22 1,336 20 1,341 20 1,353 19 1,319 21 1,331 20 1,390 17 1,293 22 1,220 27

CHW 59,271 34,864 41 34,969 41 36,731 38 41,965 29 45,187 24 37,180 37 31,911 46 33,560 43 20,964 65 28,831 51

HW 40,812 6,523 84 1,215 97 8,030 80 10,591 74 7,421 82 12,989 68

Elec 5,511 5,458 1 5,067 8 4,778 13 4,684 15 4,539 18 4,564 17 4,832 12 4,666 15 3,320 40 3,533 36

CHW 21,964 12,177 45 12,988 41 12,740 42 11,804 46 12,735 42 12,487 43 13,784 37

HW 2,103 704 67 399 81 634 70 649 69 390 81 3,488 -66 4,225 -101

Elec 2,850 2,511 12 2,597 9 2,624 8 2,592 9 2,603 9 2,667 6 2,682 6 2,553 10 2,546 11

CHW 28,526 13,599 52 15,637 45 15,078 47 17,702 38 13,937 51 15,587 45 17,023 40 17,625 38

HW 18,227 6,565 64 5,588 69 5,098 72 2,171 88 6,568 64 6,994 62 7,391 59 8,882 51

Elec 1,933 1,898 2 1,914 1 1,991 -3 2,153 -11 2,039 -5 2,026 -5 2,110 -9 2,155 -11

CHW 40,892 23,115 43 24,080 41 22,915 44 23,307 43 24,380 40 25,849 37

HW 3,569 887 75 2,041 43 2,097 41 2,051 43 1,881 47 3,203 10

Elec 4,186 3,996 5 4,140 1 4,236 -1 4,056 3 4,219 -1 4,169 0

CHW 19,193 12,327 36 13,339 31 12,530 35 11,609 40 13,490 30 13,283 31

HW 13,393 10,876 19 9,715 27 6,581 51 6,350 53 7,309 45 1,723 87

Elec 2,555 2,410 6 2,446 4 2,552 0 2,581 -1 2,529 1 2,342 8

CHW 40,824 16,737 59 17,377 57 18,148 56 20,225 50 19,794 52 20,440 50 24,296 40

HW 7,676 1,630 79 3,230 58 2,226 71 4,271 44 4,467 42 3,623 53 4,694 39

Elec 7,502 6,762 10 6,793 9 7,099 5 6,955 7 6,597 12 6,516 13 6,456 14 4,377 42 4,662 38

2005-2006

Building 

Name Type

Baseline 

Use 

(MMBtu) 

(MWh) / yr

1997 1998 1999

Saving 

(%)

Use 

(MMBtu) 

(MWh) / yr

2000 2001 2002 2003

Saving 

(%)

Use 

(MMBtu) 

(MWh) / yr

2004 2006-2007

Use 

(MMBtu) 

(MWh) / yr

Saving 

(%)

Use 

(MMBtu) 

(MWh) / yr

Saving 

(%)

Use 

(MMBtu) 

(MWh) / yr

Saving 

(%)

Use 

(MMBtu) 

(MWh) / yr

Saving 

(%)

Use 

(MMBtu) 

(MWh) / yr

Saving 

(%)

Use 

(MMBtu) 

(MWh) / yr

Saving 

(%)

Blocker

Saving 

(%)

Use 

(MMBtu) 

(MWh) / yr

Saving 

(%)

Use 

(MMBtu) 

(MWh) / yr

Eller O&M

G.R.White 

Coliseum

Harrington 

Tower

Kleberg 

Building

Koldus 

Building

Rich. 

Petroleum

VMC Addition

Wehner CBA

Zachry Engr. 

Center

**

**
Page 89 of 239

The overall trends in chilled water, hot water, and electricity savings over the period sampled for the ten 

buildings are diagrammed in Figures 3.14, 3.15, and 3.16 that follow. Specifics about the savings 

patterns of each building are discussed thereafter.

Figure 3.14 Chilled water savings trends over time for the ten buildings studied (some data points 

not available).
Page 90 of 239

Figure 3.15 Hot water savings trends over time for the ten buildings studied.

Figure 3.16 Electricity savings trends over time for the ten buildings studied.

Blocker, 1997, 53

Blocker, 1998, 81

Blocker, 1999, 62

Blocker, 2000, 50 Blocker, 2001, 47

Blocker, 2002, 70

Blocker, 2003, 27

Blocker, 2006-07, 

50

Eller O&M, 1997, 

66

Eller O&M, 1998, 

85Eller O&M, 1999, 

76

Eller O&M, 2000, 

38

Eller O&M, 2001, 

41

G. Rollie White 

Coliseum, 1997, 71

G. Rollie White 

Coliseum, 1998, 97 G. Rollie White 

Coliseum, 1999, 77

G. Rollie White 

Coliseum, 2000, 52

G. Rollie White 

Coliseum, 2005-06, 

85

G. Rollie White 

Coliseum, 2006-07, 

90 Harrington Tower, 

1997, 62

Harrington Tower, 

1998, 87 Harrington Tower, 

1999, 76

Harrington Tower, 

2000, 49 Harrington Tower, 

2002, 43

Harrington Tower, 

2003, 49

Harrington Tower, 

2005 2006-

-06, 57 07, 59

Kleberg, 1997, 84

Kleberg, 1998, 97

Kleberg, 1999, 80 Kleberg, 2000, 74

Kleberg, 2005-06, 

Kleberg, 2006 8 -07, 

68 Koldus, 1997, 67

Koldus, 1998, 81

Koldus, 1999, 70 Koldus, 2000, 69

Koldus, 2001, 81

Koldus, 2005-06, -

66

Koldus, 2006-07, -

101

Richardson 

Petroleum, 1997, 

64

Richardson 

Petroleum, 1998, 

69

Richardson 

Petroleum, 1999, 

72

Richardson 

Petroleum, 2000, 

88 Richardson 

Petroleum, 2001, 

64

Richardson 

Petroleum, 2002, 

62

Richardson 

Petroleum, 2003, 

59

Richardson 

Petroleum, 2004, 

51

Veterinary Medical 

Center Addition, 

1997, 75 Veterinary Medical 

Center Addition, 

1998, 43

Veterinary Medical 

Center Addition, 

1999, 41

Veterinary Medical 

Center Addition, 

2000, 43

Veterinary Medical 

Center Addition, 

2001, 47

Veterinary Medical 

Center Addition, 

2002, 10

Wehner, 1997, 19 Wehner, 1998, 27

Wehner, 1999, 51 Wehner, 2000, 53 Wehner, 2001, 45

Wehner, 2006-07, 

87 Zachry Engineering 

Center, 1997, 79 Zachry Engineering 

Center, 1998, 58

Zachry Engineering 

Center, 1999, 71

Zachry Engineering 

Center, 2000, 44

Zachry Engineering 

Center, 2001, 42

Zachry Engineering 

Center, 2005-06, 

53

Zachry Engineering 

Center, 2006-07, 

39

% Savings

HW Savings

Blocker

Eller O&M

G. Rollie White Coliseum

Harrington Tower

Kleberg

Koldus

Richardson Petroleum

Veterinary Medical Center

Addition

Wehner

Zachry Engineering Center

Blocker, 1997, 22 Blocker, 1998, 20 Blocker, 1999, 19 Blocker, 2000, 20

Blocker, 2001, 25

Blocker, 2002, 27 Blocker, 2003, 26

Blocker, 2006-07, 

Eller O&M, 1997, 27

24

Eller O&M, 1998, 

25Eller O&M, 1999, 

22

Eller O&M, 2000, 

21Eller O&M, 2001, 

19

Eller O&M, 2002, 

24

Eller O&M, 2003, 

23Eller O&M, 2004, 

21

G. Rollie White 

Coliseum, 1997, 12

G. Rollie White 

Coliseum, 1998, 21

G. Rollie White 

Coliseum, 1999, 21

G. Rollie White 

Coliseum, 2000, 13

G. Rollie White 

Coliseum, 2001, 26

G. Rollie White 

Coliseum, 2002, 31

G. Rollie White 

Coliseum, 2003, 31

G. Rollie White 

Coliseum, 2004, 25

G. Rollie White 

Coliseum, 2005-06, 

31

G. Rollie White 

Coliseum, 2006-07, 

35

Harrington Tower, 

1997, 22 Harrington Tower, 

1998, 20

Harrington Tower, 

1999, 20

Harrington Tower, 

2000, 19

Harrington Tower, 

2001, 21

Harrington Tower, 

2002, 20 Harrington Tower, 

2003, 17

Harrington Tower, 

2005-06, 22

Harrington Tower, 

2006-07, 27

Kleberg, 1997, 1

Kleberg, 1998, 8

Kleberg, 1999, 13 Kleberg, 2000, 15

Kleberg, 2001, 18 Kleberg, 2002, 17

Kleberg, 2003, 12

Kleberg, 2004, 15

Kleberg, 2005-06, 

Kleberg, 2006 40 -07, 

36

Koldus, 1997, 12

Koldus, 1998, 9 Koldus, 1999, 8 Koldus, 2000, 9 Koldus, 2001, 9

Koldus, 2002, 6 Koldus, 2004, 6

Koldus, 2005 Koldus, 2006--06, 10 07, 11

Richardson 

Petroleum, 1997, 2

Richardson 

Petroleum, 1998, 1 Richardson 

Petroleum, 1999, -

3 Richardson 

Petroleum, 2000, -

11

Richardson 

Petroleum, 2001, -

5

Richardson 

Petroleum, 2002, -

5

Richardson 

Petroleum, 2003, -

9

Richardson 

Petroleum, 2004, -

11

Veterinary Medical 

Center Addition, 

1997, 5

Veterinary Medical 

Center Addition, 

1998, 1

Veterinary Medical 

Center Addition, 

1999, -1

Veterinary Medical 

Center Addition, 

2000, 3

Veterinary Medical 

Center Addition, 

2001, -1

Veterinary Medical 

Center Addition, 

2002, 0

Wehner, 1997, 6 Wehner, 1998, 4

Wehner, 1999, 0 Wehner, 2000, -1 Wehner, 2001, 1

Wehner, 2006-07, 

8

Zachry Engineering 

Center, 1997, 10

Zachry Engineering 

Center, 1998, 9 Zachry Engineering 

Center, 1999, 5

Zachry Engineering 

Center, 2000, 7

Zachry Engineering 

Center, 2001, 12

Zachry Engineering 

Center, 2002, 13

Zachry Engineering 

Center, 2003, 14

Zachry Engineering 

Center, 2005-06, 

42

Zachry Engineering 

Center, 2006-07, 

38

% Savings

Electricity Savings

Blocker

Eller O&M

G. Rollie White Coliseum

Harrington Tower

Kleberg

Koldus

Richardson Petroleum

Veterinary Medical Center

Addition

Wehner

Zachry Engineering Center
Page 91 of 239

Blocker

The savings trends for chilled water, hot water, and electricity consumption for the Blocker Building are 

shown in bar graph form in Fig. 3.17.

Figure 3.17 Normalized energy savings patterns for the Blocker Building.

The chilled water savings achieved in 1997 was 27 %, but had degraded to 8 % by 2000. However, over 

the next two years the chilled water savings increased, then fell again, so that by 2006-07 it had again 

fallen to 8 %. The hot water savings achieved in 1997 was 53 %, and in 2006-07 was a close 50 %. 

During the years between, however, it rose as high as 81 %, while dropping as low as 27 %. The 

electricity savings remained fairly constant in the ten year period, even rising some from 22 % in 1997 to 

27 % in 2006-07.

CHW, 1997, 27

CHW, 1998, 15

CHW, 1999, 12

CHW, 2000, 8

CHW, 2001, 17

CHW, 2002, 22

CHW, 2003, 9 CHW, 2006-07, 8

HW, 1997, 53

HW, 1998, 81

HW, 1999, 62

HW, 2000, 50

HW, 2001, 47

HW, 2002, 70

HW, 2003, 27

HW, 2006-07, 50

Electricity, 1997, 22

Electricity, 1998, 20

Electricity, 1999, 19

Electricity, 2000, 20

Electricity, 2001, 25

Electricity, 2002, 27

Electricity, 2003, 26

Electricity, 2006-

07, 27

% Savings

CHW HW Electricity
Page 92 of 239

Eller O&M

The savings trends for chilled water, hot water, and electricity consumption for the Eller O&M Building are 

shown in bar graph form in Fig. 3.18.

Figure 3.18 Normalized energy savings patterns for the Eller O&M Building.

The chilled water savings achieved in 1997 was 38 %. By 2004, the last year of available data, the 

savings had degraded slightly to 29 %. The hot water savings achieved in 1997 was 66 %, increased to 

85 % the next year and 76 % the next, and then declined sharply to 38 % and 41 % in the final two years 

of available data. The electricity savings remained fairly constant in the eight year period of available 

data, beginning at 24 % in 1997 and falling slightly to 21 % by 2004.

CHW, 1997, 38

CHW, 1998, 39

CHW, 1999, 38

CHW, 2000, 34

CHW, 2001, 22

CHW, 2002, 31

CHW, 2003, 35

CHW, 2004, 29

HW, 1997, 66

HW, 1998, 85

HW, 1999, 76

HW, 2000, 38

HW, 2001, 41

Electricity, 1997, 24

Electricity, 1998, 25

Electricity, 1999, 22

Electricity, 2000, 21

Electricity, 2001, 19

Electricity, 2002, 24

Electricity, 2003, 23

Electricity, 2004, 21

% Savings

CHW HW Electricity
Page 93 of 239

G. Rollie White Coliseum

The savings trends for chilled water, hot water, and electricity consumption for the G. Rollie White 

Coliseum are shown in bar graph form in Fig. 3.19.

Figure 3.19 Normalized energy savings patterns for the G. Rollie White Coliseum.

The G. Rollie White Coliseum experienced some rather dramatic swings in the level of savings in both 

chilled water and hot water consumption, particularly in the first few years after retro-commissioning. 

However, by the later years (2005-2007), the level of savings for hot water was close to or exceeded 

previous savings, and the level of chilled water savings had risen again and settled out at a level of 41 %, 

or 13 % lower than the 1997 value. The electricity savings actually increased fairly steadily over time, 

rising from 12 % in 1997 to 35 % in 2006-07.

CHW, 1997, 54

CHW, 1998, 55

CHW, 1999, 23

CHW, 2000, 16

CHW, 2005-06, 64

CHW, 2006-07, 41

HW, 1997, 71

HW, 1998, 97

HW, 1999, 77

HW, 2000, 52

HW, 2005-06, 85

HW, 2006-07, 90

Electricity, 1997, 12

Electricity, 1998, 21

Electricity, 1999, 21

Electricity, 2000, 13

Electricity, 2001, 26

Electricity, 2002, 31

Electricity, 2003, 31

Electricity, 2004, 25

Electricity, 2005-

06, 31

Electricity, 2006-

% Savings

07, 35

CHW HW Electricity
Page 94 of 239

Harrington Tower

The savings trends for chilled water, hot water, and electricity consumption for the Harrington Tower are 

shown in bar graph form in Fig. 3.20.

Figure 3.20 Normalized energy savings patterns for the Harrington Tower.

Harrington Tower demonstrated remarkable levels of savings persistence in both chilled water and 

electricity consumption, actually increasing slightly in the level of savings of each in a ten year period. 

While the hot water savings ended up considerably lower than the peak level achieved (down to 59 %

from 87 %), it had risen in later years, and ended very close to the level achieved originally in 1997 (62

%).

CHW, 1997, 50

CHW, 1998, 41

CHW, 1999, 46

CHW, 2000, 36

CHW, 2001, 41

CHW, 2002, 35

CHW, 2003, 39

CHW, 2004, 45

CHW, 2005-06, 50

CHW, 2006-07, 51

HW, 1997, 62

HW, 1998, 87

HW, 1999, 76

HW, 2000, 49

HW, 2002, 43

HW, 2003, 49

HW, 2005-06, 57

HW, 2006-07, 59

Electricity, 1997, 22

Electricity, 1998, 20

Electricity, 1999, 20

Electricity, 2000, 19

Electricity, 2001, 21

Electricity, 2002, 20

Electricity, 2003, 17 Electricity, 2005-

06, 22

Electricity, 2006-

07, 27

% Savings

CHW HW Electricity
Page 95 of 239

Kleberg

The savings trends for chilled water, hot water, and electricity consumption for the Kleberg Building are 

shown in bar graph form in Fig. 3.21.

Figure 3.21 Normalized energy savings patterns for the Kleberg Building.

The level of hot water savings in Kleberg decreased from a peak of 97 % in 1998 to 68 % in 2006-07. 

However, the chilled water savings increased during the same period, rising from 41 % in 1997 to as high 

as 65 % in 2005-06, and settling back to 51 % in the most recent year. Electricity savings were higher in 

every subsequent year following 1997, beginning at just 1 % and ending at 36 %.

CHW, 1997, 41

CHW, 1998, 41

CHW, 1999, 38

CHW, 2000, 29

CHW, 2001, 24

CHW, 2002, 37

CHW, 2003, 46

CHW, 2004, 43

CHW, 2005-06, 65

CHW, 2006-07, 51

HW, 1997, 84

HW, 1998, 97

HW, 1999, 80

HW, 2000, 74

HW, 2005-06, 82

HW, 2006-07, 68

Electricity, 1997, 1

Electricity, 1998, 8

Electricity, 1999, 13

Electricity, 2000, 15

Electricity, 2001, 18

Electricity, 2002, 17

Electricity, 2003, 12

Electricity, 2004, 15

Electricity, 2005-

06, 40

Electricity, 2006-

07, 36

% Savings

CHW HW Electricity
Page 96 of 239

Koldus

The savings trends for chilled water, hot water, and electricity consumption for the Koldus Building are 

shown in bar graph form in Fig. 3.22.

Figure 3.22 Normalized energy savings patterns for the Koldus Building.

The Koldus building demonstrated high levels of persistence in both chilled water and electricity savings 

over a ten year period. However, it also experienced a huge increase in hot water consumption in the 

most recent years, even doubling pre-retro-commissioning consumption levels. This was by far the most 

significant example of savings degradation noted in the ten buildings during the period studied. However, 

as will be discussed later, this may have been due to metering issues and not actual degradation.

CHW, 1997, 45

CHW, 1998, 41

CHW, 1999, 42

CHW, 2000, 46

CHW, 2001, 42

CHW, 2005-06, 43

CHW, 2006-07, 37

HW, 1997, 67

HW, 1998, 81

HW, 1999, 70 HW, 2000, 69

HW, 2001, 81

HW, 2005-06, -66

HW, 2006-07, -101

Electricity, 1997, 12

Electricity, 1998, 9 Electricity, 1999, 8

Electricity, 2000, 9

Electricity, 2001, 9

Electricity, 2002, 6

Electricity, 2004, 6

Electricity, 2005-

06, 10

Electricity, 2006-

07, 11

% Savings

CHW HW Electricity
Page 97 of 239

Richardson Petroleum

The savings trends for chilled water, hot water, and electricity consumption for the Richardson Petroleum 

Building are shown in bar graph form in Fig. 3.23.

Figure 3.23 Normalized energy savings patterns for the Richardson Petroleum Building.

The chilled water savings for the Richardson Petroleum Building remained fairly steady, and in an eight 

year period fell only from 52 % to 38 %. The hot water savings increased in each of the first four years 

after retro-commissioning, peaking at 88 %, but then fell in succeeding years to a level of 51 % in 2004. 

Electricity savings had fallen to the negative range by the third year after commissioning, and ended in 

2004 at -11 %, from the 2 % level in 1997.

CHW, 1997, 52

CHW, 1998, 45

CHW, 1999, 47

CHW, 2000, 38

CHW, 2001, 51

CHW, 2002, 45

CHW, 2003, 40

CHW, 2004, 38

HW, 1997, 64

HW, 1998, 69

HW, 1999, 72

HW, 2000, 88

HW, 2001, 64

HW, 2002, 62

HW, 2003, 59

HW, 2004, 51

Electricity, 1997, 2

Electricity, 1998, 1

Electricity, 1999, -3

Electricity, 2000, -

11 Electricity, 2001, -5

Electricity, 2002, -5

Electricity, 2003, -9 Electricity, 2004, -

11

% Savings

CHW HW Electricity
Page 98 of 239

Veterinary Medical Center Addition

The savings trends for chilled water, hot water, and electricity consumption for the Veterinary Medical 

Center Addition are shown in bar graph form in Fig. 3.24.

Figure 3.24 Normalized energy savings patterns for the Veterinary Medical Center Addition.

The Veterinary Medical Center Addition had the least amount of reliable energy data available, but a six 

year period following retro-commissioning was able to be examined. During this time chilled water 

savings remained consistent, falling only to 37 % in 2002 from 43 % in 1997. Electricity savings 

essentially degraded to none after a 5 % level initially. Hot water savings was 75 % in 1997, fell sharply 

to 43 % in 1998, remained very close to that level for the next three years, then fell sharply again to just 

10 % in 2002.

CHW, 1997, 43

CHW, 1998, 41

CHW, 1999, 44

CHW, 2000, 43

CHW, 2001, 40

CHW, 2002, 37

HW, 1997, 75

HW, 1998, 43

HW, 1999, 41

HW, 2000, 43

HW, 2001, 47

Electricity, 1997, 5 HW, 2002, 10

Electricity, 1998, 1

Electricity, 1999, -1

Electricity, 2000, 3

Electricity, 2001, -1

Electricity, 2002, 0

% Savings

CHW HW Electricity
Page 99 of 239

Wehner

The savings trends for chilled water, hot water, and electricity consumption for the Wehner Building are 

shown in bar graph form in Fig. 3.25.

Figure 3.25 Normalized energy savings patterns for the Wehner Building.

The Wehner Building experienced good persistence in chilled water savings over time. Hot water savings 

increased in the years following retro-commissioning, and remained high in the most recent data year, 

based on the calibrated simulation model used. Electricity savings degraded some in the years following 

commissioning, but increased back to a level slightly higher than the 1997 level in the most recent data 

year, based on the calibrated simulation model.

CHW, 1997, 36

CHW, 1998, 31

CHW, 1999, 35

CHW, 2000, 40

CHW, 2001, 30 CHW, 2006-07, 31

HW, 1997, 19

HW, 1998, 27

HW, 1999, 51

HW, 2000, 53

HW, 2001, 45

HW, 2006-07, 87

Electricity, 1997, 6

Electricity, 1998, 4

Electricity, 1999, 0

Electricity, 2000, -1

Electricity, 2001, 1

Electricity, 2006-

07, 8

% Savings

CHW HW Electricity
Page 100 of 239

Zachry Engineering Center

The savings trends for chilled water, hot water, and electricity consumption for the Zachry Engineering 

Center are shown in bar graph form in Fig. 3.26.

Figure 3.26 Normalized energy savings patterns for the Zachry Engineering Center.

The chilled water savings in the Zachry Engineering Center degraded from 59 % in 1997 to 40 % in 2006-

07. The hot water savings fluctuated in the first few years after retro--commissioning. In 1997 it was at its 

highest level, 79 %, but had degraded to 39 % by 2006-07. Electricity consumption was a different story, 

however, beginning at 10 % in 1997, holding fairly constant for several years thereafter, then jumping to 

42 % in 2005-06, and ending at 38 % in the most recent year.

3.4.3 Summary of Recent Persistence Studies

As a group, the four Japanese buildings studied are consistent with the commissioned buildings that have 

been examined in the literature review – most continue to show savings over periods that in this case run 

from 10 to 20 years, while generally showing some decrease in savings over time.

The fundamental conclusion from the additional study of ten buildings at Texas A&M is that cooling and 

heating savings did not change appreciably during the up to seven years of additional data following the 

year 2000. The average cooling savings were 45 % in 1997, 34 % in 2000 and 36 % in the last year of 

good data for each building (average of 2005.9). Heating savings averaged 63 % in 1997, 56 % in 2000 

and 55 % in the last year of available data (average 2005-06). It should be noted that follow-up 

commissioning was conducted in most of these buildings at least once.

CHW, 1997, 59

CHW, 1998, 57

CHW, 1999, 56

CHW, 2000, 50

CHW, 2001, 52 CHW, 2005-06, 50

CHW, 2006-07, 40

HW, 1997, 79

HW, 1998, 58

HW, 1999, 71

HW, 2000, 44 HW, 2001, 42

HW, 2005-06, 53

HW, 2006-07, 39

Electricity, 1997, 10

Electricity, 1998, 9

Electricity, 1999, 5

Electricity, 2000, 7

Electricity, 2001, 12

Electricity, 2002, 13

Electricity, 2003, 14

Electricity, 2005-

06, 42

Electricity, 2006-

07, 38

% Savings

CHW HW Electricity
Page 101 of 239

3.5 References

ASHRAE. 1996. ASHRAE Guideline 1-1996: The HVAC Commissioning Process. Atlanta: American 

Society of Heating, Ventilating, and Air Conditioning Engineers, Inc.

Bourassa, N.J., M.A. Piette, N. Motegi., “Evaluation of Persistence of Savings from SMUD Retrocommissioning Program – Final Report,” Lawrence Berkeley National Laboratory, May 2004. LBNL54984 

Chen, H., S. Deng, and H. Bruner. 2004. Continuous CommissioningSM Report for Civil Engineering & 

Texas Transportation Institute (CE/TTI) Building. Energy Systems Laboratory, Texas A&M University, 

College Station, TX. 

Chen, Hui, Song Deng, Homer Bruner, David Claridge and W.D. Turner, “Continuous CommissioningSM

Results Verification And Follow-Up For An Institutional Building – A Case Study,” Proc. 13th Symposium 

on Improving Building Systems in Hot and Humid Climates, May 20-23, 2002, Houston, TX, pp. 87-95. 

Cho, Sool Yeon, “The Persistence of Savings Obtained from Commissioning of Existing Buildings,” M.S. 

Thesis, Mechanical Engineering Department, Texas A&M University, ESL-TR-02-05-01, May, 2002, 347 

pp.

Claridge, D. E., N. Bensouda, S. Lee, G. Wei, K. Heinemeier, and M. Liu. 2003. Manual of Procedures 

for Calibrating Simulations of Building Systems. Prepared for Lawrence Berkeley National Laboratory, 

Berkeley, CA.

Claridge, D.E., Turner, W.D., Liu, M., Deng, S., Wei, G., Culp, C., Chen, H. and Cho, S.Y., “Is 

Commissioning Once Enough?,” Solutions for Energy Security & Facility Management Challenges: Proc. 

Of the 25th WEEC, Atlanta, GA, pp. 29-36, Oct. 9-11, 2002.

Claridge, D.E., Turner, W.D., Liu, M., Deng, S., Wei, G., Culp, C., Chen, H., and Cho, S.Y., “Is 

Commissioning Once Enough?” Energy Engineering, Vol. 101, No. 4, 2004, pp. 7-19.

Fels, M. 1986. PRISM: An Introduction. Energy and Buildings 9(1 and 2): 5-18.

Frank, M., Friedman, H., Heinemeier, K., Toole, C., Claridge, D., Castro, N., and Haves, P., 2005,

“Existing Cost/Benefit and Persistence Methodologies and Data, State of Development of Automated 

Tools, and Assessment of Needs for Commissioning ZEB,” Draft report submitted to U.S. Department of 

Energy under Contract No. DE-AC02-05CH11231, 78 pp., August.

Friedman, H., Potter, A., Haasl, T., and Claridge, D., “Persistence of Benefits from New Building 

Commissioning,” Proceedings of the 2002 ACEEE Summer Study on Energy Efficiency in Buildings, 

Pacific Grove, CA, Aug. 19-23, 2002, pp. 3.129 – 3.140.

Friedman, H., Potter, A., Haasl, T., Claridge, D and Cho, S., “Persistence of Benefits from New Building 

Commissioning,” Proc. Of 11th National Conference on Building Commissioning, Palm Springs, CA, May 

20-22, 2003a, 15 pp., CD. 

Friedman, H., A. Potter, T. Haasl, and D. Claridge, “Report on Strategies for Improving Persistence of 

Commissioning Benefits – Final Report,” July 2003b, 47 pp. Lawrence Berkeley National Laboratory. 

http://buildings.lbl.gov/hpcbs/pubs/E5P22T5c-Final.pdf

IPMVP 2001. IPMVP Committee, International Performance Measurement & Verification Protocol: 

Concepts and Options for Determining Energy and Water Savings, Vol. 1, U.S. Dept. of Energy, 

DOE/GO-102001-1187, 86 pp., January.
Page 102 of 239

IPMVP Technical Committee. 2002. International Performance Measurement & Verification Protocol 

Volume 1: Concepts and Options for Determining Energy and Water Savings. U.S. Dept. of Energy: 86.

Katipamula, S., T.A. Reddy, and D.E. Claridge. 1995. Effect of Time Resolution on Statistical Modeling 

of Cooling Energy Use in Large Commercial Buildings. ASHRAE Transactions 101(2).

Liu, M. 1995. Manual for AirModel. Energy Systems Laboratory, Texas A&M University, College Station, 

TX.

Liu, M., Claridge, D. E. and Turner, W.D., 2002, Continuous Commissioning SM Guidebook: Maximizing 

Building Energy Efficiency and Comfort, Federal Energy Management Program, U.S. Dept. of Energy, 

144 pp., Available at 

http://www.eere.energy.gov/femp/operations_maintenance/commissioning_guidebook.cfm

Liu, C., Turner, W.D., Claridge, D., Deng, S. and Bruner, H.L., “Results of CC Follow-Up in the G. Rollie 

White Building,” Proc. 13th Symposium on Improving Building Systems in Hot and Humid Climates, May 

20-23, 2002, Houston, TX, pp. 96-102. 

Mills, E., H. Friedman, T. Powell, N. Bourassa, D. Claridge, T. Haasl, and M. Piette, “The CostEffectiveness of Commercial-Buildings Commissioning: A Meta-Analysis of Energy and Non-Energy 

Impacts in Existing Buildings and New Construction in the United States,” December 2004. LBNL-56637. 

Mills, E., N. Bourassa, M.A. Piette, H. Friedman, T. Haasl, T. Powell, and D. Claridge. "The CostEffectiveness of Commissioning New and Existing Commercial Buildings: Lessons from 224 Buildings,"

Proceedings of the 2005 National Conference on Building Commissioning, Portland Energy Conservation, 

Inc., New York, New York, May, 2005.

http://www.peci.org/ncbc/proceedings/2005/19_Piette_NCBC2005.pdf

National Climatic Data Center (NCDC). Viewed 21 September 2006. 

http://cdo.ncdc.noaa.gov/pls/plcimprod/cdomain.abbrev2id

Peterson, Janice, “Evaluation of Retro-commissioning Results After Four Years: A Case Study,” 

Proceedings of the 2005 National Conference on Building Commissioning, Portland Energy Conservation, 

Inc., New York, New York. 

Selch, M. and J. Bradford, “Re-commissioning Energy Savings Persistence,” Proceedings of the 2005 

National Conference on Building Commissioning, Portland Energy Conservation, Inc., New York, New 

York, May, 2005.

Shao, X. 2005. First Law Energy Balance as a Data Screening Tool. M.S. Thesis, Texas A&M 

University, Department of Mechanical Engineering, College Station, TX.

Toole, C., “The Persistence of Retrocommissioning Savings in 10 University Buildings,” M.S. Thesis, 

Mechanical Engineering Department, Texas A&M University, Dec, 2009,

Turner, W.D., Claridge, D.E., Deng, S., Cho, S., Liu, M., Hagge, T., Darnell, C., Jr., and Bruner, H., Jr., 

"Persistence of Savings Obtained from Continuous CommissioningSM," Proc. of 9th National Conference 

on Building Commissioning, Cherry Hill, NJ, p. 20-1.1 - 20-1.13, May 9-11, 2001.

Wei, G., M. Liu, and D.E. Claridge. 1998. Signatures of Heating and Cooling Energy Consumption for 

Typical AHUs. Proceedings of the Eleventh Symposium on Improving Building Systems in Hot and 

Humid Climates. Fort Worth, TX.

Yamaha, Motoi, “Persistence Surveys in Japan,” Powerpoint presentation A47-C2-M5- JP-CU-1 at IEA 

Annex 47 Meeting 5, Kyoto, Japan, October, 2007.
Page 103 of 239

4. TOOLS TO ENHANCE PERSISTENCE

4.1 An Automated Building Commissioning Analysis Tool (ABCAT)

4.1.1 Introduction

In the United States, slightly more than one-third of the total primary energy consumption is used in the 

building sector. Commercial buildings alone cost 18 % of the total energy use in the U.S. in 2007 (Energy 

Information Administration. 2007). Energy conservation programs for the building sector would contribute 

to the reduction of energy sources waste. Building commissioning services, which either ensure that 

building systems are installed and operated to provide the performance envisioned by the designer or 

identify and implement optimal operating strategies for buildings as they are currently being used, have 

proven to be successful in saving building energy consumption. A broad and major study of 224 new and 

existing commercial buildings in 21 states across the country, commissioned by 18 different 

commissioning service providers, netted a median savings of 15 % of whole building energy use (Mills et 

al. 2005). The Energy Systems Laboratory at Texas A&M University (TAMU) started Continuous 

Commissioning®1 (CC®

) in 1996. The CC® process has produced average energy savings of about 20 

percent without significant capital investment in over 150 large buildings in which it has been 

implemented (Claridge et al. 2004).

Though commissioning services are effective in reducing building energy consumption, the optimal 

energy performance obtained by commissioning may subsequently degrade, as described in Chapter 3. 

The persistence of savings is a significant topic of concern. Claridge et al. (2004) presented the results of 

a study of the persistence of savings in ten university buildings that averaged an increase of chilled water 

(CHW) and hot water (HW) costs by 12.1 % over a two year period post-commissioning. Almost 75 % of 

this increase was caused by significant component failures and/or control changes that did not 

compromise comfort but caused large changes in consumption. The remainder was due to control 

changes implemented by the operators (Claridge et al. 2004, Turner et al. 2001). The major increases 

were not identified until two years had passed, and hundreds of thousands of dollars in excess energy 

costs had already occurred. Obviously there is a need for a simple, cost efficient automated system that 

can continuously monitor building energy consumption, alert operations personnel early upon the onset of 

significant increases in consumption and assist them in identifying the problem. The Automated Building 

Commissioning Analysis Tool (ABCAT) is one of several such tools for maintaining the optimal energy 

performance in a building.

ABCAT was originally initiated by Lee and Claridge (2003), and has developed to an advanced prototype 

by Curtin et al. (2007), along with demonstrating its effectiveness in live and retrospective building 

implementations. This report describes the functions of the advanced prototype ABCAT tool and provides 

a summary of its live testing results on six buildings and retrospective testing results on five buildings. 

The tool has not yet been commercialized. 

ABCAT Description

The fault detection and diagnosis approach to be undertaken in ABCAT will be applied to the whole 

building energy consumption level and is simplified to aid in the practicality of its implementation outside 

of the university and research lab setting. First, a building energy simulation model using the American 

Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) simplified energy analysis 

procedure (Knebel et al. 1983) is established and calibrated based on the building CHW and HW 

consumption in the baseline period chosen from a post-commissioning time period when the building’s 

operation is considered to be optimal. Second, subsequent CHW and HW consumption is predicted by 

the model using future weather data and building electricity consumption. Third, both the simulated and 

measured consumption are passed to the data analysis routine that generates building performance 
Page 104 of 239

plots, compares and performs calculations on the simulated and measured consumption data, applies 

fault detection methods, and reports diagnostic and energy consumption statistics. Finally, the user of the 

tool evaluates the data presented and determines whether or not there is a fault that requires action. If a 

fault is identified, the user or other experts can use the diagnostic information provided by ABCAT to help 

identify and correct the fault, and follow up observations should observe a return to expected 

performance. 

The ABCAT is initially setup in a building through the following sequence of steps:

1. Define a Baseline Consumption Period and Collect Baseline Measurements

The baseline period should correspond to a time when the building mechanical systems are known to be 

operating correctly, typically post new building commissioning (Cx) or existing building commissioning 

(EBCx). The length of baseline can be a minimum of four weeks if during the swing seasons where a 

wide range of outside air temperatures is experience and heating and cooling systems are both operating. 

Required measurements include whole building heating (WBHeat) whole building cooling (WBCool), 

whole building electric (WBElec), ambient outside air temperature and relative humidity or dew point 

temperature, all recorded in hourly intervals. 

Figure 4.1 describes the consumption monitoring that is required for the ABCAT. Ideally the WBHeat and 

the WBCool would be obtained by Btu metering of chilled and hot water, but these values could also be 

obtained by modeling the chiller and boiler if interval meters exist that monitor chiller electric loads and 

natural gas consumption. 

Figure 4.1. Consumption metering requirements for ABCAT

Chiller

Boiler

BUILDING

Building + 

Plant Elec

WBElec

WBCool

WBHeat

F

Natural 

Gas

ABCAT Whole Building Analysis

Primary Energy Metering

Chilled 

Water

Hot 

Water

kwh

1 0 0 4

kwh

1 0 0 4

kwh

1 0 0 4

Outdoor

Tdb Hum

PLANT

Alternative 

Energy 

Metering

dT

dT

Btu

Btu
Page 105 of 239

2. Obtain Building and Air Handling Unit System Details

The key characteristics of the building and its heating ventilation and air-conditioning (HVAC) systems 

that must be included in the model are:

(1) Envelope Area and Heat Transfer Coefficient

(2) Solar Radiation Load

(3) Internal Heat Gain from Equipment and Lighting (taken as fraction of measured electric load)

(4) Deck Temperature Schedules

(5) Maximum and Minimum Air Flow Rates,

(6) Outside Air Intake and Economizer Settings

(7) Occupancy Schedule

(8) AHU Operation Schedule

(9) Humidification Operations.

Liu et al. (1998) describes the steps for an initial value selection of these parameters in a model with 

similar input requirements as the ABCAT. 

3. Establish Initial Values of Inputs for the Simulation Model and Calibrate the Model

Generate an input file for simulation based on measured data and system information, and calibrate or 

tune the model inputs until desired accuracy is achieved.

4. Correct for Bias in Model

Provide a final adjustment to simulation model by calculating the mean bias error (MBE) and subtracting 

this amount from to the model so that the MBE of the model is zero for the baseline period. Even a small 

systematic bias in the simulation will decrease the sensitivity of the fault detection process.

5. Program Regular Data Transfer to ABCAT

Develop a method by which the required measured inputs can regularly be updated and passed to the 

ABCAT program. In the current test facilities, Visual Basic for Applications programs link the ABCAT with 

consumption data files, sorts, fills missing data with linear interpolation when applicable, summarizes and 

imports the data into the ABCAT program in its required format.

Once the ABCAT is configured for the particular building through steps 1 to 4, the program is ready for 

execution. 

Figure 4.2 is a process flow diagram which visually describes the following five steps to the ABCAT 

methodology:

1. Import Measured Data 

Evoke the program developed in step 5 of the initial setup steps 1 to 5 from the ABCAT program. 

2. Simulate CHW and HW Consumption

The required inputs are passed to the energy simulation routine, where the CHW and HW consumption is 

simulated. A detailed flow diagram of the ABCAT process through to the execution of the simulation is 

summarized in Appendix A.

3. Data Analysis

The simulated consumption and measured consumption are passed to the data analysis routine that 

generates the building performance plots, compares and performs calculations on the two values, applies 

fault detection methods, and reports diagnostic and energy consumption statistics. The current graphical 

layout of the ABCAT is detailed in Appendix B.
Page 106 of 239

4. Evaluation

The user of the tool is to evaluate the data presented and arrive at the conclusion as to whether or not a 

fault of enough significance exists such that action is required. The user plays an important role in 

defining fault triggers and manipulating the plotted data with easily adjustable parameters to suit their site 

specific preferences. One of the primary metrics established to aid in the user decision is the “Cumulative 

Energy (or Cost) Difference” plot, previously used by Haberl and Vajda (1988), which accumulates the 

energy residuals of persistent deviations from measured consumption by adding them to that of the total 

of the previous day (multiplies the accumulated energy by a user specified utility cost for the Cost 

Difference plot). The cost plot presents the deviations in the universally understood language of dollars 

and cents, which is expected to help compel users of the ABCAT to act in the case of a fault.

5.Action

If action is deemed necessary, the type of action taken will depend upon if the faulty condition observed is 

determined to be a result of a required change in operations (where the simulation model would have to 

be recalibrated) or if it was caused by a system or component failure or a change in control to a less than 

optimal setting (where repair, maintenance or a control change may be in order). If a fault is identified, the 

user or other experts can use the diagnostic information provided by ABCAT to help identify and correct 

the fault, and follow up observations should observe a return to expected performance.

Figure 4.2 ABCAT flow diagram
Page 107 of 239

Project Results

Live Test Cases

The ABCAT was implemented in six live building situations, with various levels of automation and file 

manipulation were built into a specific data collection process for each building based on its unique 

conditions regarding data availability and format. The testing of the ABCAT in the six buildings provided a 

live learning scenario that helped to influence continued developments, and a summary of these test 

cases is provided in Table 4.1. 

Table 4.1 Test buildings, results and findings from live ABCAT implementation

Building 

Description

Location Test Period Results and Findings

7618 m2

82,000 ft2

university dining 

facility 

College Station,

Texas 

Mar 2005 –

July 2007

• Detected excess cooling energy fault related 

to excessive latent cooling from low 

discharge air temperature on 2 of 3 Outside 

Air Handling Units – Summer 2006 shown in 

Fig. 4.3.

44779 m2

482,000 ft2

computing 

services facility

Austin,

Texas

May 2005 –

July 2007

• Detected significant decrease in measured 

cooling energy due to meter calibration –

Oct 2005 (Figure 4.4). 

• A second fault, significant excess cooling 

energy was detected in Nov 2006 Fig. 4.5). 

• Also demonstration of successful short-term 

adaptation of simulation to multiple baseline 

changes.

16723 m2

180,000 ft2

office building 

Albany,

New York

Jan 2007 –

July 2007

• Successful monitoring of heating energy 

savings following implementation of EBCx 

measures Fig. 4.6).

• Training and support for two ABCAT testers.

16723 m2

190,000 ft2

high-rise office 

building 

Omaha,

Nebraska

Feb 2007 –

July 2007

• Confirmation of optimal heating and cooling 

energy through continued tracking. 

• Identification of HW metering failure (Figure 

4.7).

12356 m2

133,000ft2

university 

teaching building

College Station,

Texas

June 2007-

May 2009

• Confirmed excess outside air increased 

CHW use from 06/2008-08/2008

• CHW increase from 9/15/2008-12/05/2008 

and 02/11/2009-05/18/2009 observed.

6625 m2

67,000ft2

university office 

building

College Station,

Texas

Nov 2007- May 

2009

• Identified apparent CHW meter problem 

during 11/2008.

• Noted apparent HW meter recalibration 

during 2008.
Page 108 of 239

Figure 4.3 Sbisa Dining Hall cumulative energy difference meas – sim (MMBtu) with simulation 

calibrated to period of 5/01/2004 to 06/27/2006

Figure 4.4 Computing Services Facility ABCAT cumulative energy difference meas – sim (MMBtu) 

with simulation calibrated to period of 12/01/2004 to 10/27/2005

-400

-200

0

200

400

600

800

1000

1200

1400

4/23/04 8/1/04 11/9/04 2/17/05 5/28/05 9/5/05 12/14/05 3/24/06 7/2/06

Cumulative Energy Difference

 [Meas - Sim] (MMBtu)

CHW

HW

-9000

-8000

-7000

-6000

-5000

-4000

-3000

-2000

-1000

0

1000

9/20/04 2/17/05 7/17/05 12/14/05 5/13/06 10/10/06 3/9/07

Cumulative Energy Difference

 Meas - Sim (MMBtu)

CHW

STM

7651 MMBtu 

or

$76,510 @ 

$10/MMBtu

$9,500+ of Additional 

Cooling in 10 Weeks 

@ $13.44/MMBtu
Page 109 of 239

Figure 4.5 Computing Services Facility cumulative energy difference for period starting 

04/29/2006 for 1 year after simulation recalibrated to period of 10/27/2005 – 5/19/2006

Figure 4.6 DASNY cumulative cost difference ($15/MMBtu heating, $10/MMBtu cooling)

-3000

-2500

-2000

-1500

-1000

-500

0

500

1000

3/4/06 5/13/06 7/22/06 9/30/06 12/9/06 2/17/07 4/28/07 7/7/07

Cumulative Energy Difference

 [Meas - Sim] (MMBtu)

CHW

HW

-$3,500

-$3,000

-$2,500

-$2,000

-$1,500

-$1,000

-$500

$0

$500

11/29/06 1/18/07 3/9/07 4/28/07 6/17/07

Cumulative Cost Difference

 [Meas - Sim] ($)

CHW

HW

2906 MMBtu

or

$29,006 @ 

$10/MMBtu

Short-term flow 

meter problem. 

Identified and 

resolved by 

utility provider.

Observed heating 

savings due to 

implementation of 

new EBCx 

measures
Page 110 of 239

Figure 4.7 OPPD energy plaza cumulative cost difference 

($15/MMBtu heating, $10/MMBtu cooling)

The testing of the ABCAT in these six buildings, the identification of the faults and the diagnostic 

reasoning that followed, helped shaped some of the specific ideas as to the developmental direction of 

the ABCAT. Some of the keys points to take away from these test experiences are the following:

• Whole building analysis can provide valuable diagnostic information

• Accumulated deviations from optimal performance provide the good indicator of significant faults 

that persist, and cost information 

• The value of ABCAT does not appear to lay in daily short-term observations, but rather 

observations on the order of weeks to months.

• The advantage of using a first principles simulation model can be seen with occasional 

recalibrating requirements due to changes in building operations

Retrospective Test Cases

In order to further test the capabilities of ABCAT, a multiple building retrospective test is performed. Five 

buildings on the Texas A&M University campus which had previously been studied in a commissioning 

persistence study (for the years of 1996 to 2000), had fairly complete consumption data sets, historical 

documentation as to commissioning measures implemented, and documentation of some control system 

set point changes during the period analyzed. It was expected that an analysis with ABCAT of a span of 

more than 15 building years, would provide some immediate feedback into the fault detection and 

diagnostic capability of the tool.

The “Cumulative Energy (or Cost) Difference” plot can visually detect a fault and show how the fault 

influences energy cost. Because visual fault detection depends heavily on personal subjective 

experience, the “Days Exceeding Threshold” plot was developed and added into ABCAT to detect faults 

analytically. It is drawn based on the simple standard that identifies a fault if the deviation between the 

measured and simulated consumption is greater than one standard deviation in the baseline period and 

persists for at least 30 d. The reason for choosing 30 d as the fault definition is that the typical utility meter 

reading interval is one month. Every point in the plot represents the number of days in the next 30 d

(including the day on which the point is plotted) where consumption has been at least one standard 

deviation above or below expected consumption. For example, a point at ±10 means there are 10 d of the 

next 30 d when the measured consumption is more than one standard deviation above or below the 

simulated consumption. Thus a fault period appears as one or more points at ±30 on the plot. Compared 

with the “Cumulative Energy (or Cost) Difference” plot, the “Days Exceeding Threshold” plot permits 

-$16,000

-$14,000

-$12,000

-$10,000

-$8,000

-$6,000

-$4,000

-$2,000

$0

$2,000

12/9/06 12/29/06 1/18/07 2/7/07 2/27/07 3/19/07 4/8/07

Cumulative Cost Difference

 [Meas - Sim] ($)

CHW

HW

Hot water return 

sensor failure 

resulting in 

significantly 

lower measured 

heating energy
Page 111 of 239

relatively precise identification of the time that a fault starts or ends and provides more objective fault 

detection metrics. In the retrospective cases, the “Days Exceeding Threshold” plot is used as the chief 

fault detection criterion.

Eighteen faults were detected in 15 building-years of consumption data with the “Days Exceeding 

Threshold” plot. One of the eight detected CHW faults and six of the ten detected HW faults are verified 

by the historical information. The remaining fault diagnoses remain unconfirmed due to data quality 

issues and incomplete information on maintenance performed in the buildings. A summary of these test 

cases is provided in Table 4.2.

Table 4.2 Building Faults Detected in five buildings

Building 

Description

Test Period Results and Findings

192,000 ft2

university 

teaching building 

Jan 1997 –

Dec 2000

• Detected two excess heating energy faults (HW Fault #1 and 2 in

Fig. 4.8) which might be related to scaling problems on the HW 

meter.

• Detected one decrease in measured cooling energy (CHW Fault in

Fig. 4.8) which might be caused by an increase in the cold deck 

temperature.

165,000 ft2

university 

teaching building

Nov 1996 –

Dec 2000

• Detected significant decrease in measured heating energy (HW 

Faults #1 to #4, and #6 in Fig. 4.9) due to a HW meter problem. 

• Detected one excess heating energy fault (HW Fault # 5 in Fig. 

4.9) due to the problems the Kleberg Center experienced after April 

1999 as documented in Chen et al (2002)

• Detected five excess cooling energy faults (CHW Faults #1 to #5 in

Fig. 4.9). CHW Fault # 1 to #3 and #5 can’t be diagnosed because 

of the data quality issues. The reasons for CHW Fault #4 were the 

same as for HW Fault # 5.

180,000 ft2

university 

teaching building

Mar 1997 –

Dec 2000

• Detected a significant decrease in measured heating energy (HW 

Fault # 1 in Fig. 4.10) which may be related to a HW meter 

problem.

• Detected one excess heating energy fault (HW Fault # 2 in Fig. 

4.10) which may be related to an increase in minimum airflow ratio 

and hot deck temperature.

• The “Cumulative Cost Difference” plot (Figure 4.11) shows that the 

CHW consumption deviation over four years The maximum CHW 

consumption deviation over four years is approximately 1 % of the 

cumulative consumption. This indicates that the simulation is 

capable of accurately predicting consumption if there are no 

significant changes in the building.

115,000 ft2

university 

teaching building

Jan 1998 –

Dec 2000

• Neither a CHW fault nor a HW fault was detected on the “Days 

Exceeding Threshold” plot (Figure 4.12).

131,000 ft2

university 

teaching building

Aug 1996 –

Dec 2000

• Detected two excess cooling energy faults (CHW Fault # 1 and #2 

in Fig. 4.13) which can’t be diagnosed because of data quality 

issues.
Page 112 of 239

Figure 4.8 Days exceeding threshold in 30 d periods 

from 01/01/1997 to 12/31/2000 for the Wehner Building

Figure 4.9 Days exceeding threshold in 30 d periods 

from 11/01/1996 to 12/31/2000 for the Kleberg Center

-40

-30

-20

-10

0

10

20

30

40

1/1/97 7/20/97 2/5/98 8/24/98 3/12/99 9/28/99 4/15/00 11/1/00 5/20/01

Days Exceeding Threshold

CHW HW

HW Fault #1 HW Fault #2

CHW Fault

-40

-30

-20

-10

0

10

20

30

40

11/1/96 5/20/97 12/6/97 6/24/98 1/10/99 7/29/99 2/14/00 9/1/00 3/20/01

Days Exceeding Threshold

CHW HW

HW Fault #1

HW Fault #4

HW Fault

#5

HW Fault #6

CHW Fault #1

CHW Fault #3 CHW Fault #4

CHW

Fault

 #5

HW Fault #2

HW Fault #3

CHW Fault #2
Page 113 of 239

Figure 4.10 Days exceeding threshold in 30 d periods

from 03/19/1997 to 12/31/2000 for the Eller O&M Building

Figure 4.11 Cumulative heating and cooling cost differences for the period

of 03/19/1997 to 12/31/2000 for the Eller O&M Building 

(Assuming $10 and $15/MMBtu for CHW and HW respectively)

-40

-30

-20

-10

0

10

20

30

40

12/1/96 6/19/97 1/5/98 7/24/98 2/9/99 8/28/99 3/15/00 10/1/00 4/19/01

Days Exceeding Threshold

CHW HW

HW Fault #1

HW Fault #2

-$60,000

-$50,000

-$40,000

-$30,000

-$20,000

-$10,000

$0

$10,000

3/19/97 10/5/97 4/23/98 11/9/98 5/28/99 12/14/99 7/1/00 1/17/01

Cumulative Cost Difference

 [Meas - Sim] ($)

CHW HW

HW

Fault #1

HW Fault #2
Page 114 of 239

Figure 4.12 Days exceeding threshold in 30 d periods

from 01/01/1998 to 12/31/2000 for the Veterinary Research Building

Figure 4.13 Days exceeding threshold in 30 d periods

from 08/16/1996 to 12/31/2000 for Harrington Tower

The retrospective test cases provided an opportunity to test the simulation capabilities of the ABCAT in 

five additional buildings of varying types and functions, and indicate ABCAT is a promising fault detection 

and diagnosis tool for post-commissioning use in buildings. 

-30

-20

-10

0

10

20

30

40

1/1/98 7/20/98 2/5/99 8/24/99 3/11/00 9/27/00 4/15/01

Days Exceeding Threshold

CHW HW

-40

-30

-20

-10

0

10

20

30

40

8/16/96 3/4/97 9/20/97 4/8/98 10/25/98 5/13/99 11/29/99 6/16/00 1/2/01 7/21/01

Days Exceeding Threshold

CHW HW

CHW Fault #1 CHW Fault #2
Page 115 of 239

ABCAT Layout

Interface

The ABCAT is laid out as any typical Microsoft Excel file, with multiple worksheets and chart sheets 

accessible by the colored tabs at the bottom of the screen. The Interface sheet (Fig. 4.14) is the gateway 

of communication between the user and the tool, and includes the following: 

• The dates of the periods analyzed can be adjusted

• Various alarm thresholds can be modified to user preferred levels 

• Utility cost information can be specified

• Folder and file locations can be setup for importing and saving data files

• The calibrated simulation statistical results for the baseline

• Consumption totals and diagnostic summary of the period analyzed

Figure 4.14 The ABCAT user interface

Other Features

• Multiple plots on each of five chart sheets providing performance data comparing measured and 

simulated or two periods of measured data. 

• Quick day-type and date association for all plotted points with double point click

• Scroll through time with the scroll bars
Page 116 of 239

• Daily data summarized and stored in the tool such that the simulation can run for any period 

without user concern of reprocessing or collecting required inputs.

Analysis of Comments and Feedback

In the developmental process of the ABCAT, informal comments and feedback from users, potential 

users, as well as management and advisory committee members involved in the project were used to 

guide many choices about the features implemented in the advanced prototype tool. Although no 

statistical significance can be assigned to this feedback due to the small number of participants involved 

and no formal collection methodology was used, this feedback has nonetheless been beneficial in 

implementing upgrades to the tool, and setting the course for future developmental and testing steps.

General Approach

A common theme that has resonated almost universally through all those involved in testing or evaluating 

the ABCAT is that there is interest and a market for simple energy monitoring and fault detection and 

diagnostic tools. Over-complication of a tool can immediately lead to reservations from any potential 

future user that is not about ready to quit his day job to learn how to operate and continually manage the 

tool. Participants expressed general support for an adequate modeling system that could improve upon 

the setup complications of programs like DOE-2 or Energy-Plus.

Several positive remarks came with the presentation of the cumulative cost difference chart in the 

ABCAT. Thoughts were that this would provide a motive for users to act, or at least help the user in 

providing justification if higher level approval would first be required. It was also stated that detection, and 

clearly expressing the significance of faults carries a greater weight than the diagnostics, since experts 

will be able to find the fault if called upon, but if they don’t know it exists they might not ever move to 

address it. One comment that is fitting for the future development is “Do you put the expert in the tool or 

leave the expert in the field?”

Summary of New York Pilot Feedback

The protoype tool was installed in the headquarters building of the Dormitory Authority of New York and 

used by the building operator and the commissioning engineers working on the building for several 

months. The tool was found to be “very helpful and beneficial for tracking energy consumption on a 

higher level” and “good for both building owners and operators”, although it was stated that the tool 

“cannot take the place of on-site diagnostics”. On weekly time intervals the required consumption data 

was imported into the tool, and it was perceived by one user that the optimal time interval for using the 

tool is weekly. Microsoft Excel as the host program was considered “Good” as far as file size, speed of 

execution, graphical capabilities, data storage general file layout, familiarity and ease of operation were 

concerned. A preference was expressed for greater clarity with labels on the Interface sheet for user 

manipulated fields, consumption period totals for both defined periods on the Interface sheet, and a 

linking to greater granularity (hourly) than daily data. One user expressed an interest in a lesson to 

calibrate the tool.

Software Layout and Performance

With the use of Microsoft Excel as a host to the ABCAT, speed of execution, program flow and the size of 

the program were concerns, although current performance capabilities were viewed as favorable. 

Recommendations for linking to Microsoft Access were made, which could strengthen data storage 

capabilities, and allow for storage of smaller time interval and supporting data that would not be feasible 

to manage within Excel alone. The familiarity of most users with the general function of Excel was seen 

as a bonus.

As far as the graphical presentation of the tool was concerned, positive feedback was received from the 

multiple plots per chart sheet layout, scroll bars for zooming, and pop-up window feature for identifying 

day type and date of specific data points. The Interface sheet of the tool was upgraded in response to 

recommendations for including data summary tables, and ease of identifying user control options. 
Page 117 of 239

Additional recommendations of including day typing, highlighting the most recent data on plots, and 

general “cleaning” of the plot areas were found to be valuable. Due to the variety of viewing preferences 

by user, options are provided in the ABCAT for rearranging the existing or creating new plots

Conclusions

ABCAT is a simple, cost efficient automated tool for maintaining the optimal energy performance in a 

building. It can continuously monitor building energy consumption, alert operations personnel early upon 

the onset of significant increase in consumption and assist them in identifying the problem. 

In the six live building implementations, over eight building-years of operation, the “Cumulative Energy (or 

Cost) Difference” plot in ABCAT identified eight periods where significant energy consumption changes 

occurred that otherwise went undetected by the building energy management personnel. In the five 

retrospective building test cases, ABCAT detected 18 faults detected with “Days exceeding threshold” 

plots based on the simple standard that deviations greater than +/- one standard deviation (as determined 

from the statistics of the calibrated simulation) that persisted for a period of at least one month constituted 

a fault. 

The potential future success of the ABCAT is strongly tied to the ability of future users to obtain accurate 

and reliable measurements. A strong emphasis in sound engineering practices of installation, data 

management, calibration and data prescreening must accompany the ABCAT to ensure verification of 

data quality, and the likelihood for success in implementing the tool.

In addition to the originally targeted goals of tracking and ensuring energy optimization in commissioned 

buildings, through the course of implementing and testing the ABCAT, several other added benefits or 

alternative functional approaches have been identified. These include use of the ABCAT as a 

commissioning savings tracking tool, a simple whole building energy analysis tool (even without the 

simulated consumption), and providing verification of, or use in filling missing metered or billing data, both 

important for customers of district utility providers, and the providers themselves.

4.2 The Diagnostic Agent for Building Operation (DABOTM): a BEMS 

Assisted On-going Commissioning Tool15

4.2.1 Introduction

It is accepted that the initial, retro, or ongoing commissioning of HVAC systems are proven processes that 

reduces energy consumption and improves occupant comfort in buildings. Claridge et al. (1998) have 

shown that the use of the existing building control system for commissioning resulted in 25 % energy cost 

savings. In spite of documented benefits, commissioning is still regarded by many building owners as a 

minor activity in building operation and remains a one-time task that is performed during the building 

construction phase. Building professionals and owners attribute this situation principally to the cost 

associated with the commissioning process as well as the related difficulty of finding qualified resources 

to execute it.

The evolving capabilities of Building Energy Management Systems16 (BEMS) can help to circumvent the 

barriers to commissioning by offering opportunities to automate some parts of the commissioning 

process. Automation or semi-automation of certain aspects of the commissioning process has the 

potential to reduce costs for commissioning, thereby leading to more widespread application of the 

process. Furthermore, automating this essentially manual process could allow its application on a regular 

basis, generating benefits over the entire life of a building. Developing a detailed systematic automated 

 15 This section is abridged and edited from Choiniere and Corsi (2003) and Choiniere (2004). 16 Building Energy Management System may also be referred to as an Energy Management Control System (EMCS)
Page 118 of 239

approach will improve the quality assurance process and could even integrate energy audit capabilities 

that improve the overall performance of buildings. In this context, an on-going BEMS assisted 

commissioning tool, Diagnostic Agent for Building Operators (DABO) that verifies and optimizes the 

performance of building HVAC systems using the capabilities of BEMS was developed by the CANMET 

Energy Technology Centre. This tool is applicable mainly to commercial and institutional buildings. 

Commercialization is being considered.

On-going commissioning (IEA 2001a) is defined as a systematic approach used to inspect, verify and 

document the installations and operation of building systems to ensure that they operate at their optimum 

energy performance levels. This state is only achieved when buildings consume the minimum energy at 

the lowest cost while simultaneously considering the building’s function and comfort level, available 

energy source(s), building energy systems and energy rates. To be efficient, many tasks must be 

performed continuously, an undertaking that can be facilitated by monitoring the condition of HVAC 

systems and building energy consumption using a BEMS.

Since 1998, the Canada Centre For Mineral And Energy Technology(CANMET) Energy Building has 

been used to test and demonstrate various tools developed in the context of the CANMET Intelligent 

Building Operating Technologies R&D plan (Jean, G. 2004) of which DABO is the central component. In 

this context and principally for the last eight years an on-going 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 summarized as follows:

Energy savings Improved performance of mechanical systems

Reductions in greenhouse gas emissions Rapid and automatic fault detection

Improved occupant comfort Energy performance monitoring

Capabilities for troubleshooting of malfunctions Reduce operation costs

This chapter builds on the paper “A BEMS-Assisted Commissioning tool to improve the energy 

performance of HVAC systems” where Choinière and Corsi (2003) described the use of BEMS assisted 

commissioning tools and have identified the potential to facilitate the application of initial, retro and ongoing commissioning processes.

In a subsequent paper (‘Four years of On-going commissioning in CTEC-Varennes Building with a BEMS 

Assisted Cx Tool’) Choinière (2004) presented results from the first four years of the on-going 

commissioning project performed in the CANMET ENERGY Building that generated a 35 % reduction in 

the energy used. DABO has largely contributed in the verification and optimization of the performance of 

the building.

This section presents the functions of DABO and the results of the first eight-years of on-going 

commissioning conducted at the CANMET Energy- Varennes building with DABO. 

DABO, A BEMS-Assisted Commissioning Tool 

DABO mainly includes two parts: the commissioning-assistant module and the fault management report.

The commissioning-assistant module, designed to assist and perform some functions described in the ongoing commissioning process section is a module of DABO (Choinière 2001) which serves as the 

interface between the end-user (e.g., building operator, commissioning agent, and energy manager) and 
Page 119 of 239

the control system (BEMS). As shown in Fig. 4.15, 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, perform additional automated tests of components and systems, identify faults and diagnose them, 

and evaluate potential improvements in energy efficiency. The tool produces reports adapted to the 

different partners involved in the on-going commissioning process (building operators, service 

technicians, energy managers, commissioning agents, HVAC&R engineers).

Figure 4.15 Structure of the On-going Commissioning Tool

for HVAC Systems Embedded in DABO.
Page 120 of 239

The standardized test procedures are performed at three levels.

• 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. To 

reduce data traffic on the communication networks, the tool’s steady state detectors and zone 

fault detectors are directly embedded in controllers. Specific applications of the fault detection 

and diagnostic (FDD) methods implemented in DABO are described further in Section C of IEA 

2001b.

The Fault Management Report (FMR) is a tool embedded into the DABO platform. The FMR report 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 the subsequence 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 form. 

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 4.16 shows for all air handling units in a building a summary display of all faults detected that need 

to be repaired. Figure 4.17 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. 
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Figure 4.16 Typical Fault Management Report for an Air Handling Unit in DABO TM
Page 122 of 239

Figure 4.17 Typical Repaired Failures Report for an Air Handling Unit in DABO TM

On-going Commissioning Project

The demonstration building is the CANMET Energy Building located in Varennes, Québec, Canada. Built 

in 1992, the single floor 3600 m2 building includes office spaces for 90 people as well as 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 central controlled by a BEMS system. (Table 4.3)
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Table 4.3 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-hour 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, heating, 

cooling)

5,815 l/s Laboratories

M3 (VAV, heating, cooling) 5,500 l/s Office phase 1

M4 (CAV, heating) 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

The on-going commissioning process started in 1999 and continued until 2009 aimed at resolving 

operating problems, improving comfort, optimizing energy use and recommending retrofits where 

necessary. Delivery of the on-going commissioning project system included a series of tasks performed 

in four steps: planning, investigation, implementation and hand off (Table 4.4). 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 insure the persistence of savings and detect new 

deficiencies.

Results for the investigation and implementation period 2000 to 2006 were presented in ‘Four Years of 

On-going Commissioniong in CTEC-Varennes Building with a BEMS Assisted Cx Tool’ (Choinière 2004) 

and are summarized in the following section. 

Results for the hand off and persistence period include new deficiencies that occurred during the normal 

operation as well as deficiencies that were not detected during the commissioning of the various 

optimization projects (2006 to 2009). 
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Table 4.4 The Ongoing Commissioning Project Delivery System Followed at CETC-V

PLANNING

• Choose the team

• Define project objectives, scope and deliverables

• Review building documentation and energy bills

• Develop Commissioning plan

• Initiate cooperation with the building operation team

INVESTIGATION (continuous over 6 years, 2000 to 2006)

• 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 to 2006)

• Implement improvements identified in investigation phase

• Retest and re-monitor to confirm the results

• Adjust, if necessary, the improvements carried out during the investigation phase

• Review the 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 operation 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 on-going commissioning process and an energy management plan

o Ensure that the use of DABO is well understood by the operators so as to 

maintain the re-commissioning benefits
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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)

• Optimise 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 CDN

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 4.17 shows the impact on the energy consumption of the on-going commissioning 

project implemented at the CETC-V since 1998. During this period 1999-2006, measures 

implemented have resulted in a 35 % reduction in electricity and 45 % in natural gas 

consumption.

Energy reduction

Figure 4.18 shows the impact on the energy consumption of the on-going commissioning project 

implemented at the CETC-V since 1998.

During this period, measures implemented have resulted in a 44 % reduction in electricity and 78 % in 

natural gas consumption. For 2007to 2008, the cost savings represented $91,861 CDN, or 51 % of the 

building energy bills . The Cumulative savings since 1998 are $496,124 CDN while the global energy 

used drop from 2248 MJ/m2 to 994 MJ/ m2

. 
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Figure 4.18 On-going commissioning impact on CETC-V Building energy consumption

How to Improve the BEMS-assisted Commissioning Tool (input from 11 demonstration sites)

The demonstration project was also used to validate the tool technology, to understand the barriers and 

opportunities and to identify areas for R&D activities to improve the prototype tool.

The following sections provide feedback collected from the tool installers, the building operators, Cx 

providers and facility managers on this project as well as on 11 other buildings where the technology has 

been deployed. This section also provides some thoughts on the lessons learned from our R&D projects.

Feedback from the installers (Total: 12 installations)

Perceived Strengths:

• Friendly user interface

• Most common HVAC system configurations can be set up

• Easy to install when the building information and connection to BEMS are available 

Perceived Barriers:

• Installation is time consuming when building information is not adequate

• Remote accessibility (internet connection of the BEMS computer) is hard to get and 

frequently relatively slow. (Organizational Network department)

• Control procedures and control point naming conventions are not general (function of the 

control supplier and installer, the consultant, the owner)

• Lack and inaccuracy of the Documentation: (control and building drawings, balancing 

report)
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Feedback from the building operators, Cx providers, facility managers (Total: 6 installations)

Perceived Strengths:

• Explanation and data supply for fault detected

• Powerful analysis of monitored data

• Very useful tool to secure persistence of savings

Perceived Barriers:

• The user needs basic computer knowledge

• The user needs a training period due to the number of available functions and the various 

acronyms used in tool reports

• Complexity to classify good and deficient devices (should have a summary report of 

defective components)

From facility manager, the information seems to be disparate. 

Feedback from Tool developers

Perceived Barriers

• From the software aspect, DABO faced problems due to the obstacles inherent to the IT 

Office Network and the Building Energy Management Systems. The most common 

problems were: connectivity to Internet and/or control network and lack of resources from 

the owner for the installation of DABO. 

• Also, the lack of availability and accessibility of the information on the mechanical 

systems and the sequences of control constrained the installation of DABO. 

• From the human resources aspect, the barriers are related to the low level of knowledge 

of the mechanical system operator, and the insufficient time for training allocated to 

DABO and the turn-over of trained building operators.

• The absence of a consulting engineer and the activities related to re-commissioning pose 

a barrier to the use of DABO

• From a manager’s point of view, the disparity and the quantity of information from 

approximately 20 different reports makes the day-to-day quantification of the tool benefits 

complex.

Opportunities 

• The software prototype DABO is used and satisfies the user when the building owner accepts 

the tool and devotes satisfactory resources to ensure its installation and use

• Among the success factors we find:

– The operator of the control system devotes his time to using DABO for the detection 

of faults, diagnosis and for EBCx measures. The time devoted to DABO varies 

according to the scope of the installation.

– A consultant is hired to do the follow-up and effective management of project 

activities (The time devoted to DABO varies according to the scope of the installation) 

– The computer support staff is involved in the implementation from the very beginning 

to facilitate the set up of the DABO station and the connectivity to Internet for an 

external station. 
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– A consulting engineer is on-site and use DABO to carry out re-commissioning. 

– The documentation on the mechanical systems and the sequences of control is 

available and up to date.

– The identified remedies for the detected faults are implemented.

Conclusions

An on-going commissioning process ensures that buildings achieve and operate at their optimized energy 

cost and performance levels, while ensuring comfort conditions for occupants. The project conducted at 

CANMET Energy has generated 44 % reduction in the electric use and 78 % in the natural gas 

consumption over the 1998 to 2008 period. 

DABO, a BEMS-assisted commissioning tool has monitored the enormous amounts of data produced by 

BEMS and provided an extensive analysis of the incoming data. 

The use of a BEMS assisted commissioning tool has helped to circumvent commissioning barriers by 

automating some parts of the process, which has reduced the costs for commissioning. Developing a 

detailed systematic automated approach has improved the quality assurance process and the overall 

performance of the building. Furthermore, automating this essentially manual process has allowed its 

application on an on-going 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 ten 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. 

Demonstration projects are currently in the investigation and implementation stages; however early 

results show that DABOTM helps circumvent commissioning barriers by automating some parts of the 

process, which has reduced the cost of commissioning. Developing a detailed systematic automated 

approach has improved the quality assurance process and the overall performance of the buildings. 

Furthermore, automating this essentially manual process has allowed its application on an on-going 

basis. 

4.3 References

Energy Information Administration (EIA), 2007, Annual Energy Review 2007, Report DOE/EIA0384(2007), U.S. Department of Energy, Washington, DC, June.

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.

Choinière D, and M Corsi. 2003. A BEMS assisted commissioning tool to improve the energy 

performance of HVAC systems. Proceedings of ICEBO 2003. Berkely, CA.

Choinière D. 2004. Four years of On-going commissioning in CTEC-Varennes Building with a BEMS 

Assisted Cx Tool. Proceedings of ICEBO 2004. Paris, France.
Page 129 of 239

Claridge D. E., M. Liu, W. D. Turner, Y. Zhu, M. Abbas, and J. S. Haberl. 1998. Energy and Comfort 

Benefits of Continuous Commissioning in Buildings. Proceedings of the International Conference 

Improving Electricity Efficiency in Commercial Buildings. September 21-23.

Claridge, D. E., W. D. Turner, M. Liu, S. Deng, G. Wei, C. Culp, H. Chen and S. Y. Cho. 2004. Is 

Commissioning Once Enough? Energy Engineering: Journal of the Association of Energy Engineering, 

101(4): 7-19.

Curtin, Jonathan M., “Development and Testing of an Automated Building Commissioning Analysis Tool 

(ABCAT)” M.S. Thesis, Department of Mechanical Engineering, Texas A&M University, August, 2007.

Haberl, J. S. and E. J. Vajda. 1988. Use of Metered Data Analysis to Improve Building Operation and 

Maintenance: Early Results from Two Federal Complexes. Proceedings of the ACEEE Summer Study on 

Energy Efficiency in Building, Asilomar, California.

IEA. 2001a. International Energy Agency. Annex 40: Commissioning of Buildings and HVAC Systems for 

Improved Energy Performance. http://www.commissioning-hvac.org.

IEA. 2001b. International Energy Agency. Annex 34: Computer-Aided Evaluation of HVAC System 

Performance. Final Report. Editors Arthur Dexter and Jouko Pakanen. International Energy Agency.

Jean Gilles. 2004. A climate change solution; “Intelligent Building Operating Technologies”. 

http://cetcvarennes.nrcan.gc.ca

Knebel, D.E. 1983. Simplified Energy Analysis Using the Modified Bin Method. Atlanta: American Society 

of Heating, Refrigerating and Air-Conditioning Engineers, Inc.

Lee, S. U. and D. E. Claridge. 2003. Field Tests of Whole Building Simulation for HVAC System Fault 

Detection, draft report of Energy Systems Laboratory, Texas A&M University July 2003. 

Lee, S.U., Painter, F.L. and Claridge, D.E., “Whole Building Commercial HVAC Systems Simulation for 

Use in Energy Consumption Fault Detection,” ASHRAE Trans., Vol. 113, 2007, Pt. 2, pp. 52-61. 

Lin, G. and Claridge, D.E., “Retrospective Testing of an Automated Building Commissioning Analysis Tool 

(ABCAT),” Proc. 3rd Int. Conf. on Energy Sustainability, ASME, July 19-23, 2009, San Francisco, CA, 

USA, CD.

Turner, W. D., D. E. Claridge, S. Deng, S. Cho, M. Liu, T. Hagge, C. J. Darnell and H. J. Bruner. 2001. 

Persistence of Savings Obtained from Continuous Commissioning. Proceedings of the Ninth National

Conference on Building Commissioning, Cherry Hill, NJ.
Page 130 of 239

5. INTERNATIONAL COMMISSIONING COST-BENEFIT AND 

PERSISTENCE DATABASE

5.1 Introduction

It is generally recognized that demonstrating cost-effectiveness will remove a major barrier to the wider 

market acceptance of building Commissioning (Cx). Between 2005 and 2009, the U.S. team for the IEA 

Annex 47 led efforts to increase the diversity of Cx project data available and to quantify Cx costs and 

benefits for international Cx projects through Task 4 of the DOE-funded IEA ECBCS Annex 47 on CostEffective Cx of Existing and Low Energy Buildings, “Technical Development and Data Population of the 

Cx Database (IEA Subtask C)”.

• The project collected financial metrics and technical details from international commissioning 

projects.

• The data is intended for use to demonstrate the value of commissioning to owners and possibly to

assist in government policy-making.

Table 5.1 IEA Subtask C Objectives and Desired Impacts

Objective Impact

Improve ability to estimate costs and benefits 

of future commissioning projects based on 

results of previous, similar projects 

Increase adoption of commissioning by 

providing greater certainty about costs and 

benefits to building owners

Identify commonly occurring building problems 

or “hot spots” based on statistically significant 

data 

Increase cost-effectiveness of commissioning 

by helping providers focus attention on likely 

problem areas

Focus policy and technical R&D efforts on 

areas of opportunity for improving building 

energy efficiency and performance

Foundational work began in 2005 with an initial presentation of cost-benefit methodologies and output 

options at the Spring 2005 Annex meeting in Munich and a survey of Annex members on the subject of a 

data collection protocol at the Fall 2005 meeting in Prague (Friedman et al. 2005, see Appendix A Cost 

Benefit Protocols Report). At this meeting, Annex participants were engaged in collaborative planning for 

the project and set goals for expected participants’ annual submissions of Cx and Existing Building 

Commissioning (EBCx) projects to an international Cx database, as shown in Table 5.2.

Work continued in 2006 with a study of Cx cost and benefit methodologies Friedman et al. 200517 and the 

first draft of the data collection tools (Excel forms). In 2006 and the first half of 2007 the data collection 

tools were continuously refined in collaborative processes with Annex members at the Spring 2006 Annex 

meeting in Trondheim, the Fall 2006 Annex meeting in Shenzhen, and the Spring 2007 meeting in 

Budapest, as well as the significant contributions of a specially formed Advisory Committee. The impact 

 17 Friedman, H., M. Frank, T. Haasl, K. Heinemeier, 2006. Chapter 1 “State-of-the-Art Review for Commissioning Low Energy 

Buildings”
Page 131 of 239

of the method used to determine savings persistence was evaluated and online testing of the automated 

persistence enhancement tool ABCAT (Automated Building Cx Analysis Tool) was carried out in four 

buildings. In Spring of 2007, the final data collection tools were released, previously received data was 

transferred from earlier versions of the tools and additional data collection efforts were engaged. 

Over the following 18 months, opportunities and interest arose for additional supplementary funding to 

expand the project, first through an earmark to the National Center for Energy Management and Building 

Technologies (NCEMBT) which would have included the creation of an online searchable data base with 

many more U.S. projects. This expanded research opportunity diverted the progress of the initial project 

plan somewhat, as schedules and work plans were adjusted to accommodate the expected development 

of the online database including an online data submission tool over a 6 month period in the Summer and 

Fall of 2007. The scope of the project was revised again back to the original work plan, when it was 

discovered that the 2007 NCEMBT earmark would not be funded. Although the DOE has expressed 

strong interest as recently as July of 2008 in funding the proposed expansion of this project, including the 

creation of an online searchable Cx cost-benefit database for end users and significant U.S. project data 

additions from strategic market sectors, no additional work was funded during the Annex project. 

5.1.1 Cost-Benefit Methodology

The cost/benefit methodology evolved through collaboration between the Annex participants between 

2005 and 2007 (Refer to Appendix A for details). Through this process the goals and data collection 

format were developed, along with defining the types of data that would be collected relating to costs, 

energy-related benefits, and non-energy benefits. This process is defined in the project’s Cost-Benefit 

Protocols Report (see Appendix A).

The output of this process was the creation of two Commissioning Data Collection Forms - one for new 

buildings and one for existing buildings. The surveys were divided into four sections:

• Project

o Project Information

o Technical Information

o Cost Data

• Energy

o Resource Savings

o Resource Use after Cx (Persistence of savings data)

• Non-Energy Benefits

• Issues & Measures

There were “required” and “optional” elements. The required elements were intended to give enough 

data to perform a cost-benefit analysis, and therefore include fields such as “total cost of commissioning” 

and “annual energy savings from implemented measures”.

Additionally, there were building characteristics that are required for analysis purposes such as floor area 

and year the project was completed. 

5.1.2 This Chapter

This chapter, documenting the submission of data by participants in the IEA’s Annex 47 and including an 

analysis of this project data, is the culmination of data collection performed from May of 2007 through 

November of 2008. These data collection efforts included email and phone requests for data, support of 

Annex members in supplying data using the data collection tools, follow up contacts with the data source 
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to clarify questions related to data quality and for missing or incomplete data in required fields for costbenefit analysis.

Overview of the information presented in this chapter: 

• Summary of the most relevant findings in the body of this report, related not only 

to the Cx/EBCx projects knowledge gained, but also lessons learned about the 

process of collecting data for such a study. 

• Discussion around what we’ve learned from this study and how it relates to 

existing knowledge about Cx industry and in the Cx cost-benefit area

• Recommendations for the path forward for international commissioning costbenefit projects

• Appendices provide comprehensive analysis, data collection forms, and 

• Interim reports created throughout the project

5.2 Data Collection 

5.2.1 Data Collection Goals and Outcomes

At the Fall 2005 Annex meeting in Prague, participants agreed to goals for country submissions of new 

building Cx projects and existing building EBCx projects (See Table 5.2). At the Spring 2007 Annex 

meeting in Budapest, a schedule for submission of projects and overall goal presented called for 45 

projects to be submitted by Annex members between December of 2007 and May of 2008. 

Projects had been submitted slowly and infrequently by Annex members throughout this period, so a 

decision to extend the initial round of data collection was made by the U.S. team. A final round of data 

was received in the Fall of 2008, which brought the total number of projects submitted to 54. The final 

number of completed projects submitted by Annex members and included in this analysis are represented 

in Table 5.2. Of the surveys used to complete this study, many did not include data in all fields, and so 

the available dataset for analysis of each of the survey questions varies. 

Table 5.2 Goals and Actual Submissions of Projects by Nov. 2008

Country Goal Cx Actual Cx Goal EBCx Actual EBCx Total Projects

Belgium N/A N/A 6 6

Canada 2-6 2-6 2 2

Czech Republic 2-6 2-6

Germany 2-6 1 6-12 1

Hungary 2-6 2-6

Japan 2-6 6 2-6 7 13

Netherlands N/A 1 N/A 9 10

Norway 2-6 0 5 5

USA > 12 2 > 12 15 17

Total 24-48 10 26-48 44 54
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5.2.2 Accuracy and Completeness of Data

Cx Data Set

Only ten new building Cx surveys were returned; Most surveys contained a completed Project 

Information section; however, very few of the returns included complete information on the remaining 

sections of the survey due to data collection limitations (see Section 5.4 Data Access Limitations for 

discussion). 

In order to determine cost-effectiveness in terms of simple payback period of the submitted projects, the 

end results (savings) of the project are also required. There were six surveys with completed fields 

related to the estimated savings from Cx. Of these six, only four provided the complete cost data. 

Therefore, the cost-effectiveness of Cx was not able to be determined. An underlying issue is the 

method for determining cost savings in new construction Cx. Three of the six surveys with completed Cx 

savings included the method used for determining the savings. 

For the purposes of this analysis Cx costs were converted to US $ using the average exchange rate for 

the calendar year prior to the year of completion to estimate the timing when the decision to invest in 

commissioning was made. 

EBCx Data Set

In examining the data from the EBCx surveys, it was found that while most projects had complete data for 

the project information section, fewer had the data required in order to determine cost-effectiveness 

(EBCx cost, pre- and post-EBCx and energy usage, energy unit cost). Simple payback was calculated for 

each project, which does not require conversion of currencies and allows comparison across the data set. 

Nineteen projects had sufficient data for calculating simple payback, and 12 of these were from the USA. 

As a result, this data cannot be considered a reliable basis for establishing international costeffectiveness metrics. In this analysis, it is noted in those sections where data was not available from all 

projects and therefore a subset of the data was used.

For this analysis, EBCx costs have been converted to US $ using the average exchange rate for the 

calendar year prior to the year of project completion to estimate the timing when the decision to invest in 

commissioning was made. 

5.3 Cost-Benefit Analysis Summary

5.3.1 New Construction Commissioning

The following section highlights the key findings following analysis of data collected on new construction 

commissioning:

Project Characterization

Ten new construction commissioning surveys from 4 countries were returned, as shown in Fig. 5.1. The 

combined floor area for all of the projects totaled nearly 2 million square feet (ft2

), with the smallest project 

at 9,000 ft2 and the largest project at 508,853 ft2

. The wide variation in building size makes comparisons 

challenging, as the Cx process would be different for buildings at either end of the spectrum.
Page 134 of 239

Figure 5.1 Breakdown of Cx projects by country

Owners were asked to rate their reasons for selecting Cx on a scale of 1 to 5 in a variety of categories –

with a score of 4 or 5, a factor is considered “important” . The results are summarized in Fig. 5.2.

Figure 5.2 Owners’ reasons for performing Cx

All respondents stated that ensuring system performance was important. The next highest factors were to 

obtain energy savings, improve occupant comfort, and increase occupant productivity. It is interesting to 

note that while ensuring system performance can be quantified, the other three factors are harder to 

demonstrate for a Cx project.

Understanding the scope of the commissioning process undertaken provides a sense of how 

comprehensive the commissioning process was for the seven projects that reported this information. 

Figure 5.3 shows that scope varied widely across the small sample of projects. 

Germany, 1 Japan, 6

Netherlands, 1

United States, 2

n = 7 surveys

0% 20% 40% 60% 80% 100%

 Comply with sustainability rating

 Reduce liability

 Participate in a utility program

 Smoother process/turnover

Research/pilot project

 Ensure indoor air quality (IAQ)

 Extend equipment life

 Increase occupant productivity

 Improve occupant comfort

 Obtain energy savings

 Ensure system performance

Possible reason for performing Cx

% of respondents reporting a reason as "important"
Page 135 of 239

Figure 5.3 Items included in Cx cost

It is interesting to note that, while most building owners consider “obtain energy savings” as a main 

reason for performing Cx, only two out of the seven respondents included “evaluate energy cost savings” 

as part of their Cx process. This is likely due to the difficulties involved in evaluating energy savings for 

new construction projects, since there is no baseline energy use to compare against. 

While no conclusions can be drawn with such limited data, the least often included items in scope (upated 

as-built drawing, systems/EBCx manual, and diagnostic tools) are consistent with our understanding of 

the U.S. experience. These items have high relevance for improving the persistence of benefits from 

commissioning. 

Issues and Measures

A total of 55 issues compiled from six completed surveys were grouped by the type of system affected as 

shown in Fig. 5.4. Just over 65 % of the issues were found in the central plant and the air handling 

systems.

n = 7 surveys

0 1 2 3 4 5 6 7

Diagnostic tools/automation techniques

Systems/recommissioning manual

 Update as-built drawings

 Write Cx specifications

 Construction observation

 Evaluate energy cost savings

 Develop sequences of operation 

 Submittal review

 Oversee training

 Review O&M manuals

 Developing owner's requirements

 Design review

 Verification/pre-functional testing

 Perform trend analysis 

 Develop Cx plan

 Functional testing

 Significant issue resolution

 Final Cx report

Possible items included in Cx cost

Number of projects that included item
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Figure 5.4 Cx Issues identified, by system type

Four surveys containing 45 of the issues listed in Fig 5.4 were classified according to four additional 

categories; design, construction, O&M or capital improvement. Figure 5.5 shows the breakdown of the 

additional categories (there were no responses for “capital improvement”). It is interesting to note the 

even split between 3 categories.

Figure 5.5 Cx Issues identified, by fault type

n = 55 issues total

0 2 4 6 8 10 12 14 16

 Other 

 Packaged/split system DX

 Domestic hot water

 Heat pump system

 Terminal units

 Daylighting/lighting controls

 Envelope and infiltration

 HVAC system integration (EMCS/BAS)

 Thermal energy storage

 Chilled water plant/distribution system

 Heating water plant/distribution system

 Air handling/distribution: Overhead system

System affected

Number of issues

n = 45 issues total

Design Issue, 35%

Construction and 

Installation Issue, 

33%

Operational, 

Controls, or 

Maintenance Issue, 

32%
Page 137 of 239

Out of ten returned surveys, only three had information associated with documenting issues and the 

measures implemented to fix them (see Fig. 5.6). In total, there were 56 reported measures, and all 

measures were reported as implemented except for a single electrical issue that was listed as unknown. 

The categories “Other,” “Installation modifications,” and “Design change” account for 84 % of all reported 

measures. 

Figure 5.6 Implemented Cx measures

For the 17 survey answers with measure type “Other”, the corresponding equipment categories were:

• Electrical (11)

• Security (2)

• 1 each for Ductwork, Fire/Life safety, Exhaust Fans, AHU distribution (overhead)

Costs

A total of six surveys were returned with costs reported in currency. Two additional surveys conveyed the 

total cost as hours of labor. 

The Cx cost ranges from $0.06 per ft2 to $2.57 per ft2

, suggesting the Cx process varied significantly 

and/or the way costs were attributed varies. This echoes earlier comments that the data set allows for 

little cross-comparison.

Three surveys were returned with a breakdown of fees. These results are shown in Fig. 5.7.

n = 56 measures total, from 3 projects

Installation modifications

45%

Other

30%

Scheduling modified

2% Reset strategy

2%

Design Change

9%

Retrofit/equipment 

replacement

5%

Control sequence 

modification

5%

Equipment staging

2%
Page 138 of 239

*costs reported as hours of labor for one of the Japan projects 

Figure 5.7 Breakdown of fees as % of total Cx costs

The U.S. project was the only project to include architect’s fees, and one of the Japanese projects only 

included fees for the Cx leader and mechanical engineering. For the U.S. project, the Cx leader fees 

account for over 55 % of the overall cost, suggesting a very active leadership role compared to the other 

projects where the Cx leader is perhaps facilitating/supporting the work of other parties.

Energy Savings

• Determining energy “savings” for a new building is challenging, and different methods may be 

used

• Claimed energy use may not account for the full floor area of a building, hence the EUI and 

savings percentage will not be accurately represented 

5.3.2 Existing Building Commissioning (EBCx)

The complete results of the analysis of the existing building commissioning projects can be found in Fig. 

5.8. The following section highlights the key findings.

Project Characterization

In total, six countries were represented in the data set. Significantly more U.S. buildings were available 

for inclusion in the data set, however it was decided that to retain a balance to the international data set, 

only 15 of these buildings would be entered. 

n = 3 projects

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

USA Japan Japan*

Country

Fees % of Total

Controls Contractor

Mech. Contractor

Mech. Engineer

Architect

Cx Leader Fee
Page 139 of 239

Figure 5.8 Breakdown of EBCx projects by country

The floor area from all of the projects totaled to more than 12 million ft2

, with the smallest building at 

11,400 feet and the largest building at 1.4 million ft2 The category with the greatest number of buildings is 

the range of 100,000 to 200,000 ft2

The oldest building has a 1950 vintage, and the newest was built in 2006. Ten out of 44 buildings were 

less than five years old at the time of the EBCx project. This may be counter-intuitive for building owners, 

who feel that a relatively new building would not require system improvements, however it is consistent 

with the vintage of building often targeted in the U.S. for EBCx efforts since the control systems in these 

buildings often can accommodate greater energy-saving enhancements. 

Figure 5.9 Age of building at time of EBCx project

n = 44 projects

United States, 15

Japan, 7

Canada, 2

Netherlands, 9

Norw ay, 5

Belgium, 6

n = 34 EBCx projects

0

2

4

6

8

10

12

0-5 6-10 11-15 16-20 21-25 26-30 31-35 36-40 40-45 45-50

Age (years)

Number of buildings
Page 140 of 239

Publicly owned buildings comprise 49 % of the data set; 51 % are privately owned. Of the publicly owned 

buildings, all but four are universities (one public assembly, one office, one laboratory, and one office). 

The majority of the buildings - 65 % - are owner-occupied, 28 % are leased, and 7 % are both owneroccupied and leased. Historically, public owner-occupied buildings are the early adopters of Cx efforts, so 

it is encouraging to note that private owner-occupied is gaining traction. Building leases often create 

disincentives for building energy improvements if all or a portion of energy costs are simply passed 

through to the tenants. 

Figure 5.10 Reasons for performing EBCx

Internationally, the reasons for performing EBCx are consistent with expectations – to save energy, 

ensure occupant comfort, train operators, and ensure system performance. The fact that “participate in a 

research, demonstration, or pilot project” was also high-ranking reflects the state of the EBCx industry in 

many countries where EBCx is not an industry with practitioners, rather a research-grade effort. 

Thirty-three out of 42 projects were led by an independent EBCx leader or energy service company. 

EBCx is still considered a specialized process, and so the skills have not yet been absorbed into 

conventional construction and mechanical engineering disciplines.

The full EBCx process includes most of the tasks included in Fig. 5.11, with a few tasks such as 

diagnostic tools/automation and implement capital improvements generally undertaken as an additional 

benefit beyond the EBCx process. Based on this data, the scope of EBCx generally includes:

• Trend analysis

• Document master list of findings

• Present a findings and recommendations report 

• Implement operations and maintenance improvements

• Final report

n = 33 surveys

0% 20% 40% 60% 80% 100%

Comply with LEED or other sustainability rating system

 Reduce liability

 Qualify for rebate, financing, or other services

 Participate in a utility program

 Extend equipment life

 Increase occupant productivity

 Ensure adequate indoor air quality (IAQ)

 Ensure system performance

 Train and increase awareness of building operators

Participate in a research, demonstration or pilot project

 Ensure or improve occupant comfort

 Obtain energy savings

Possible reason for performing EBCx

% of respondents reporting a reason as "important"
Page 141 of 239

Almost every project presented a findings and recommendations report, and of those that did not include 

this report, all but one included a final report instead.

The low occurrence of updating documentation, development of a systems/EBCx manual, and monitoring 

persistence is a result of the cost of these activities. The lack of documentation and monitoring may 

result in a lack of persistence of benefits from these EBCx projects. 

One surprising finding about the scope of EBCx is that utility bill analysis, benchmarking, and calculating 

cost savings were only performed in about half of the projects. These are common EBCx activities in the 

U.S., but not as common in these international projects.

Figure 5.11 Tasks performed as part of EBCx project

Issues and Measures

The most common issues are as expected, related to integration and controls, air handling systems, and 

chilled and heating water plants. In order to get a better understanding of the impact of the phases of a 

building life-cycle on the problems found, a question was asked about where the issues originated. 

n = 35 surveys

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

 Final commissioning report

 Develop systems manual/recommissioning manual

 Monitor implemented measures for persistence

 Monitor and verify energy savings 

 Implement capital improvements 

 Implement O&M improvements 

 Update system documentation after implementation 

 Present a findings and recommendations report

 Calculate energy cost savings for findings

 Document master list of findings

Use of diagnostic tools and Cx automation techniques

 Building energy modeling/simulation

 Trend analysis

 Benchmarking

 Utility bill analysis

 Develop Commissioning Plan

 Document owner's project requirements

Tasks performed as part of EBCx project

Number of projects that included these tasks
Page 142 of 239

Figure 5.12 Origin of issues found through EBCx process

Here, the categories Operations and Control and Maintenance account for a large portion of the issues, 

areas where EBCx seeks to identify energy-saving improvements. Notably, 48 % of the issues were 

attributed to design or construction phase decisions, which points to the benefits of commissioning for 

new buildings.

In 38 projects, over 203 issues were reported through the survey and categorized by issue type (see 

Table 5.3 for a list of issue types available for selection by survey respondents). Design/installation 

issues included findings related to the design or installation of equipment, rather than the operation and 

control of that equipment. Figure 5.13 shows the results from completed surveys for 38 EBCx projects. 

Capital 

Improvement, 

10%

Design Issue, 

Construction 31%

and Installation 

Issue, 17%

Operations & 

Maintenance, 

42.00%
Page 143 of 239

Table 5.3 List of issue types available for selection by survey respondents

Design/Install

Design detail inadequate

Equipment selection inappropriate

Lighting - spaces over-lit

O&M access insufficient

Overpumping or throttled discharge valve

System selection inappropriate

Equipment not based on design

Equipment not properly installed

Maintenance issues

Ductwork leaky

Filtration requires modification

Flow obstructions

Poor actuator operation

Valves leaky

Scheduling

Equipment scheduling sub-optimal

Equipment staging sub-optimal

Equipment start/stop sub-optimal

Lighting scheduling sub-optimal

Controls

Control loop needs tuning

Manual changes or overrides causing problems

Sensor problem 

Sequence of operations inadequate

Simultaneous heating and cooling

Outside air

Economizer sub-optimal

Ventilation issues

Reset Strategy

Reset strategy: pressure reset strategy sub-optimal

Reset strategy: temperature reset strategy sub-optimal

Retrofit

Fan motor - no VFD

HVAC equipment inefficient

HVAC system inefficient

Lighting inefficient

Setpoints

Setpoints sub-optimal

VAV box minimums high

Variable flow

Variable flow: pump speed/flow high or constant when it should vary
Page 144 of 239

Variable flow: fan speed/flow high or constant when it should vary

Other (Controls)

Other (General)

Figure 5.13 Issue types uncovered through EBCx process

One of the most unexpected findings in this report is that “Design/Install” was clearly the most common 

issue. This issue broke down into 9 sub-categories that were selected as follows.

• Construction/installation (24 issues)

• Equipment selection inadequate (15)

• Design detail inadequate (8)

• Space over-lit (2)

• Not properly installed (2)

• O&M access insufficient (1)

• Over-pumping/throttled valve (1)

• System selection inadequate (1)

• Equipment not based on design (1)

This finding points to the need for design and construction-phase commissioning of new buildings, as the 

problems can plague a building for its life, and finally be addressed in the EBCx process.

Figure 5.14 shows the systems affected for the 203 reported issues, with more than half of the issues 

being attributed to the “Air handling/distribution: overhead” or “Multiple HVAC systems” categories. These 

responses are generally in line with expectations.

n = 203 issues, from 38 projects

55

34

24 23 22

10 9

4

1

21

0

10

20

30

40

50

60

Design/Install

Setpoints

Scheduling

Reset strategy

Controls

Maintenance

Outside air

Retrofit

Variable flow

Other (general)

Issue type

Number of Issues
Page 145 of 239

Figure 5.14 EBCx issues, system affected18

* Category “Other” includes lighting/daylighting controls, radiant heating, heat pump system, 

envelope/infiltration, plumbing, radiant cooling, thermal energy storage, all of which received 5 or fewer 

responses.

In addition to reporting the issues/problems identified through EBCx, survey respondents also reported 

the recommended fixes/measures for addressing those problems. Respondents gave the measure type, 

and for each measure indicated whether it was implemented, not implemented, or “unknown” (see Fig. 

5.15).

Out of 205 recommended measures, 121 were implemented (59 %). While 59 % of the recommended 

measures were implemented, it is not known what percentage of available energy savings this 

represents. It is assumed that measures with higher savings would be more likely to be implemented and 

that the achieved savings would be greater than 59 % of the potential savings, but this cannot be verified 

for this study.

 18 Each category shown in this chart includes its associated controls. The standalone category “controls” includes general 

controls issues that did not fit into any other specific system type.

n = 203 issues, from 38 projects

Air handling/ 

distribution: 

overhead, 79

Terminal units, 12

Other*, 35

Controls, 15

Multiple HVAC 

systems, 29

Heating water 

plant/distribution, 

16

Chilled water 

plant/distribution, 

17
Page 146 of 239

Figure 5.15 Percentage of measures implemented, by type

This data shows that a relatively small percentage of the retrofits/equipment replacements and installation 

modifications were implemented, whereas the majority of the setpoint modifications, reset strategies, and 

other modifications to the control sequence of operations were implemented. This is in line with 

expectations, as the items that were implemented are generally lower cost than those that were not 

implemented.

Costs

The cost of commissioning existing buildings in all countries was normalized into US$/ft2 and US$/m2 

using the average exchange rate for the year that the EBCx project was completed. To convert from 

US$/ft2 to US$/m2

, multiply by a conversion factor of 0.0929. Through this analysis, median cost was 

found to be $3.12/m2 (US$0.29/ft2

), with a range of US$0.11/m2 to US$17.76/m2 (US$0.01/ft2 to

US$1.65/ft2

) (see Fig. 5.16). It is important to note that cost data was not always available, especially for 

research-grade projects. In research projects, it is often difficult to separate out what might be deemed a 

typical commissioning scope. 

Notes on projects with the highest and lowest unit costs per ft2

:

Highest unit cost:

Canada, EBCx cost US$/m2 (US$1.65/ft2

)

° 465 m2 (5,000 ft2) 65 % office, 35 % “Research Areas”

° The list of tasks included in the process denote this as an example of a comprehensive 

EBCx project

° Performing comprehensive EBCx on a small building with research areas would 

understandably result in a high unit cost per ft2

Japan, EBCx cost US$9.58/m2 (US$0.89/ft2)

n = 205 measures total (121 measures implemented)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Other

Design Change

Installation modifications

Retrofit/equipment replacement

Schedule modified (for occupancy)

Mechanical fix

Sensors calibrated

Setpoint modified

Equipment staging

Schedule modified (for environment)

Control sequence modification

Reset strategy

Control loop tuned

Measures identified through EBCx project

% of measures implemented, for each type
Page 147 of 239

° 9383 m2 (101,000 ft2), office building

° There is nothing in the building size, type, or EBCx tasks list to suggest why this building 

had a high unit cost per ft2

Lowest unit cost:

Netherlands, EBCx cost US$0.11/m2 (US$0.01/ft2

)

° 19974 m2 (215,000 ft2), university

° Project cost only 2,000 Euros

° EBCx scope included only trend analysis and a final report

° The limited scope explains the low cost

Netherlands, EBCx cost US$0.65/m2 (US$0.06/ft2)

° 30935 m2 (333,000 ft2), 80 % office, 20 % restaurant

° This project also had a limited scope

Figure 5.16 EBCx projects’ cost per ft219

 19 To convert from US$/ft2 to US$/m2

, multiply by a conversion factor of 0.0929.
Page 148 of 239

Figure 5.17 EBCx project cost per ft2 vs. building size 20

Figure 5.17 shows a slight correlation between the size of a building and the EBCx cost per ft2

, but a 

larger dataset would be required in order to create a reliable correlation. Within the size range of 4645 m2

to 37160 m2 (50,000 ft2 to 400,000 ft2

), the scatter looks random.

 20 To convert from ft2 to m2

, divide by a conversion factor of 0.0929.

n = 30 projects

$-

$0.20

$0.40

$0.60

$0.80

$1.00

$1.20

$1.40

$1.60

$1.80

0

200,000400,000600,000800,000

1,000,0001,200,0001,400,000

Building floor area (sq.ft)

EBCx cost (USD per sq.ft)
Page 149 of 239

Energy Savings

Figure 5.18 Percentage electric savings from EBCx projects

The data in Fig. 5.18 show a range of 1 % to 24 % electric savings (kWh), with a median value of 8 %. 

There has been no investigation into the sources of variation in these reported savings, but they could be 

significantly affected by the following factors:

• Scope of EBCx project

• Budget limitations resulting in efficiency improvements not being implemented

• Accuracy of estimates for claimed energy savings

• Energy efficiency of building systems prior to EBCx project

Energy Use Intensity (EUI) is one measure that may indicate the energy efficiency of building systems. 

Electric EUI is measured in kWh/ft2

/yr, and Fig. 5.19 plots electric EUI against implemented kWh savings 

for this data set.

n = 20 projects

0%

5%

10%

15%

20%

25%

30%

CANCAN

JPNJPN

BEL

NORNORNORNOR

USAUSAUSAUSAUSAUSAUSAUSAUSAUSAUSA

Country

% savings from EBCx (Implemented)
Page 150 of 239

Figure 5.19 Electricity consumption vs. % savings for EBCx projects

It is logical to assume that buildings with a higher EUI would have greater savings potential, but Fig. 5.19 

suggests that there is no correlation between pre-EBCx energy use and EBCx savings percentage for this 

data set.

Cost-effectiveness

Common measures for cost-effectiveness are simple payback and ROI (return on investment). For this 

study, simple payback is used as the cost-effectiveness metric, and is calculated as the overall project 

cost divided by the claimed annual cost savings. A 2004 report summarizing data from 100 projects in the 

USA showed a median simple payback of 0.7 years, with values ranging from less than 1 month up to 

values in excess of 5 years. A summary of 19 projects from this study are included in Fig. 5.20.

n = 20 projects

0%

5%

10%

15%

20%

25%

30%

0 5 10 15 20 25 30 35 40 45 50

Pre-EBCx EUI (kWh/sq.ft./yr)

EBCx project savings (%)
Page 151 of 239

Figure 5.20 EBCx project costs vs. annual savings estimates

Project simple payback values ranged from 0.9 years to 45.7 years, with a median value of 3.7 years. 

Nine out of the 19 projects had a payback of greater than four years, and six had payback of between two 

and four years.

Higher payback is the result of either relatively high EBCx costs or relatively low resultant cost savings. 

For cases where payback was greater than four years in this study, the savings were relatively low (based 

on savings per ft2

), as opposed the costs being relatively high.

While this data is of interest and is useful as the basis for further study, it should not be used as the basis 

for predicting EBCx project savings, for a number of reasons:

• Data is from a relatively small number of projects

• There can be a wide variation in the way that cost savings are calculated, and they may not be 

comprehensive (e.g.. Electric savings calculated but not gas)

• Some projects may have been undertaken in order to address non-energy issues

• The scope of EBCx varied between projects

n = 19 projects

$-

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

$140,000

$- $50,000 $100,000 $150,000 $200,000

EBCx project cost (US $)

EBCx project savings (US $)

1-year

payback

2-year

payback

4-year

payback
Page 152 of 239

Non-Energy Benefits 

Thirty-nine surveys were completed relating to non-energy benefits; these surveys were split under three 

headings, as shown in the following figures:

• Operations & Maintenance (O&M) – See Fig. 5.21.

• Indoor Environment – See Fig. Figure 5.22.

• Asset value and liability reduction – See Figure 5.23.

Figure 5.21 Non-energy benefits – operations & maintenance (O&M)

n = 39 projects

0 2 4 6 8 10 12 14

Equipment life extended

Ongoing O&M costs

reduced

Improved system

documentation

Equipment failures

prevented

Increased ability to

operate building

O&M staff training

Non-Energy Benefit

Number of projects where benefit was reported
Page 153 of 239

Figure 5.22 Non-energy benefits – indoor environment

n = 39 projects

0 2 4 6 8 10 12 14 16 18

Humidity improvements

Air balance improvements

Indoor air quality improved

Cooling improvements

Improved occupant

productivity

Heating improvements

Improved occupant

comfort

Non-Energy Benefit

Number of projects where benefit was reported
Page 154 of 239

Figure 5.23 Non-energy benefits – asset value and liability reduction

There has been no attempt to quantify these benefits, but it does confirm claims that EBCx provides a 

number of recognizable benefits in addition to energy savings.

5.4 Discussion

Key Results 

The data collected through this research project begins to characterize the various types of 

commissioning processes that are occurring in Annex member countries internationally. While data was 

often difficult to obtain, we expanded our knowledge in the following key areas: 

• the scope of the Cx process employed for new and existing buildings 

• characterization of issues discovered through the Cx process including system type, likely origin of 

issue (design, construction/installation, O&M, or capital improvement), issue type, and measures 

implemented

While these results begin to develop a qualitative picture for how commissioning is evolving 

internationally, quantitative results were less apparent. For example, data on commissioning costs and 

energy savings were highly variable. Falling short of the data collection goals set by Annex member 

country representatives at the beginning of the project, we were not able to compile a large enough 

sample to make strong conclusions about the cost-effectiveness of Cx internationally. 

Given the relatively small sample size and variable nature of the costs and benefits, it is interesting to 

note that for existing building commissioning, the median savings value of 8 % corresponds well with 

PECI’s industry experience. Further, the 3.7 year simple payback period found in this study is much 

higher than the LBNL 2004 study which reported a median simple payback period of 0.7 years. Industry 

experience has shown that typical EBCx paybacks fall in the 1year to 2 year range, which indicates that 

the LBNL study may not have accurately accounted for costs or counted benefits from measures that 

n = 39 projects

0 1 2 3 4 5 6 7 8 9

Reduced liability

Indoor air quality (IAQ)?

Mission critical function

Improved asset value

Non-Energy Benefit

Number of projects where benefit was reported
Page 155 of 239

were not implemented. This research project simple payback is expected to be higher than the U.S. 

industry average due to the developing nature of the commissioning industry internationally, and the fact 

that many of the projects in the cost-benefit analysis were performed by researchers. 

Data Access Limitations 

One of the key barriers to obtaining a large set of international commissioning cost-benefit surveys was 

the limited data accessibility for Annex participants, especially for new building commissioning projects. 

The most difficult pieces of information to collect were data on costs and benefits, while qualitative data 

on the commissioning process and outcomes was more readily available. By setting a data collection 

methodology beforehand, we hoped to avoid retroactive data collection relying solely on documentation –

“take what you can get”. 

Costs

The cost of commissioning was difficult to obtain because many projects were research or demonstration 

projects, which have costs above and beyond what would be found in typical commissioning projects. 

Breaking out the research costs from the commissioning costs was not a clear process and in general, 

these projects reported higher costs than average. In some projects, the commissioning cost by building 

was not separately tracked due to the contracting relationship between the provider and a large campus 

of buildings. In these cases, costs by building were estimated as the percent of total fee. 

When the commissioning process is not performed by a third party, as often was the case in international 

projects, costs can be difficult to estimate as a portion of the design or construction costs and accounted 

for consistently. Even when commissioning was performed by a third-party whose costs were accurately

reported, we attempted to account for the cost of commissioning beyond simply tracking this 

commissioning provider fee. For example, the cost to the controls contractor to assist the commissioning 

provider in functional performance testing may be viewed as a cost of commissioning. However, this level 

of detailed tracking was not generally submitted.

Energy Benefits

From the outset of the research project, it was known that energy cost savings data for new construction 

commissioning is difficult to report due to lack of baseline to compare the first year energy consumption 

against. New construction commissioning savings requires a methodology for assessing the baseline, 

typically through simulation that is calibrated to actual utility bills. Alternately, savings can be estimated 

through engineering calculations based on the measures found and fixed through the commissioning 

process. With only six buildings reporting energy savings for new construction commissioning, this points 

to the need for more focused research efforts in this area. 

In tracking energy benefits, Cx providers do not always track by finding and often do not measure whole 

building energy savings as a result of EBCx projects. Owners often have less rigorous documentation 

requirements than projects that include a utility program incentive payment – utilities generally require 

detailed energy savings calculations up front prior to the decision to implement, then some kind of 

monitoring or verification that the measures were appropriately implemented. Contrasting the utilitysponsored EBCx project with an owner-sponsored one, an owner may simply provide a fee for “fixing my 

building”, without requiring measure by measure analysis. For these cases, whole building energy 

consumption may be reviewed to determine total savings, which was the method performed for 26 of the 

buildings submitted.

Tracking and reporting the persistence of energy benefits from new and existing building commissioning 

generally requires a focused persistence study. While we included a section in the methodology for 

providing persistence data, the only instances in which it was used was for projects reported by Texas 

A&M. Since those projects have already been summarized in other research findings, the results have 

not been included in this report.
Page 156 of 239

Non-energy benefits

Through this research, non-energy benefit data was gathered from a qualitative perspective, as it related 

to three areas: O&M, indoor environment, and asset value/liability. It is difficult to quantitatively assess 

these benefits without developing standard methods, and this process was out of scope for the costbenefit research.

State of the Commissioning Industry Internationally

One of the results of this cost-benefit research was to develop a greater understanding of the maturity of 

the Cx industry internationally. Table 5.4 summarizes the phase of commissioning industry adoption for 

Annex 47 member countries that participated in this research. It was developed through discussion at 

Annex meetings and is based on findings from a recent research paper on the international 

commissioning industry development (Castro et al; 2008). 

Table 5.4 Phase of commissioning industry adoption for Annex 47 member countries

Research Early adopters Developing 

industry

Different 

framework*

Cx EBCx Cx EBCx Cx EBCx

US X X X

Canada X X X

Germany* X X X

Japan X X X

Czech 

Republic X

Belgium X X X

Netherlands X X X

Norway X X

Hungary X X X

* Germany follows a different framework for implementing Cx services, where Cx activities are required by code for 

various parties. 

All Annex countries have performed Cx/EBCx research at universities and through country-sponsored 

research programs. Much of this research involved developing Cx guidelines specific to these countries 

and tools to help streamline detection of problems in buildings. A few countries can be considered “early 

adopters” for Cx, with a few practitioners offering Cx services as part of their service portfolio, but without 

any established national industry groups or guidelines.

Commissioning is developing into a recognized industry in the U.S., although Canada is moving towards 

this as it builds its infrastructure to deliver these services. It is interesting to note that Canada created the 

first guideline for the commissioning process, prior to the U.S. industry development. Existing building 

performance evaluation is also a well-developed field in Germany.

Germany follows a different framework for implementing new construction Cx services, where Cx is not 

considered as a separate activity. In Germany, 80 % to 90 % of the typical Cx tasks are regulated by 

(mandatory) German codes and standards. In no other country is the commissioning process so widely 

regulated. From the client's perspective, the quality control is part of the typical project delivery process in 

Germany. In the U.S., many clients hesitate to spend money for an additional Cx service, since they do 

not understand the value.
Page 157 of 239

For example, in Germany the contractor is required not only to perform testing, but also to document the 

results; to adjust actuators and optimize controls; to provide training; to compile systems manuals; etc. 

There is always an authority overseeing and approving these activities which is usually a construction 

supervisor from the design firm and the tasks and responsibilities are clearly defined within the 

regulations. Often, this person (or team) is on-site every day or at least several days a week during the 

entire construction phase. In projects in the U.S., the construction supervision, that is done by the design 

team (e.g. mechanical engineer), typically happens every other week or once a month, and, not every 

project actually has a separate Cx provider on board. When we consider Cx as a quality control during 

construction and start-up, this is given in Germany; however, not by an independent 3rd party, and, 

definitely depends on the skills of the construction supervisor.

Further, in Germany there is a requirement that starts after project completion for the involvement of the 

design firm for usually another two years during occupancy (or whatever the warranty period of the 

contractors/manufacturers is). The most important tasks within this phase is a walk-through to check 

functionality of the equipment and to make accordant warranty claims against the contractors. Done by 

experienced firms, this would includes the optimization of system operation.

5.5 Recommendations 

Based on the results of this research project, the following potential paths forward are recommended for 

future international as well as U.S. commissioning cost-benefit analyses.

International Commissioning Cost-Benefit Analysis

As the commissioning process is gaining momentum internationally, each country benefits from better 

definition of the value proposition. In order to obtain enough relevant data to show conclusive costeffectiveness results for new construction and existing building commissioning in countries where the 

commissioning process is in its infancy, the cost-benefit methodology developed through this research 

project should be revisited when the commissioning industry is more mature. This project has set up 

standard methods that can be tailored to local needs going forward. For instance, if commissioning is 

integrated into the new building design and construction process and typically performed by the design 

team, then appropriate break-out percentages could be developed as guidance for accounting for Cxrelated costs and benefits. 

An additional recommendation for future international commissioning cost-benefit analysis is to obtain cofunding from participating countries to have a data collection point person that is familiar with the research 

collect and input the commissioning data into the survey, rather than volunteer researchers and industry 

contacts. Each point person would be responsible for collecting and analyzing the data from their country 

according to this protocol, with issues such as variances in the commissioning process well-understood 

by the researcher. This country-level analysis would result in a well-populated international Cx costbenefit database where commonalities are analyzed. 

US Commissioning Cost-Benefit Analysis

While the international commissioning process is continuing to develop, the U.S. should continue to 

expand its collection of commissioning data to meet current needs. One of the most pressing needs is to 

develop ways to scale up existing building commissioning efforts throughout the country to meet 

aggressive energy saving goals and mandates. There is a growing need for understanding the most 

common problems found through the EBCx process across the country, and the measures that have 

been implemented to address these problems. Existing cost-benefit analyses in the U.S. have focused 

on collection of total cost and savings information, rather than measure-level details. 

Through this research project and other related work, obtaining accurate commissioning cost information 

for EBCx was difficult due to the variety of EBCx scope of work included (i.e., whole building 

commissioning, energy-focused commissioning, tune-up, etc.). While a true understanding of EBCx cost 

is important for owner acceptance of a rigorous process, we should not to let this hurdle derail our efforts 

to gather other commissioning data that is more readily available. 
Page 158 of 239

Due to these factors, it is recommended that future research focuses on the problems found in buildings 

and their solutions, whether these problems were found through a whole building or less comprehensive 

commissioning process. Through this process, commonly problematic systems and technologies are 

uncovered, where changes need to be made to decrease problems or improve training. This research 

would help develop focused programs targeting these commonly found problems and would lead to better 

guidance on how to find and fix the problems in an integrated, holistic way. Further, a solid 

understanding of EBCx findings would help define early solutions to avoid the occurrence of these issues 

in the first place. 

By focusing on cataloging building problems found through EBCx, we avoid the complexities of teasing 

out costs of EBCx when the process is applied differently by different providers and owners. It will also 

be important to collect energy savings data where it exists and clearly catalogue the methodology for 

estimating the savings (according to the existing cost-benefit protocol).

There are still complexities with this approach to tease out, including:

• Are these the most common problems because they are the easiest to find? Are there bigger 

problems that are masked?

• Are bigger problems avoided because the energy savings analysis is too difficult? Is this a 

consequence of the energy savings calculation review process in EBCx utility incentive programs?

Finally, it is worth considering this data in the context of the drivers for market growth. In the U.S. for 

example, utility programs are the major drivers for growth of existing building commissioning, and 

USGBC’s LEED certification is an emerging market driver for new construction commissioning. Engaging 

with these market drivers should improve the integrity/volume of data collected, and should also maximize 

the effectiveness of research outputs.

5.6 References

N. S. Castro, N. Nakahara, H. Yoshida, "The International State of Building System Commissioning" 2008 

ACEEE Summer Study on Energy Efficiency in Buildings , Pacific Grove, CA, (17-Aug-2008) (PubID: 

861505) 

Friedman, H., M. Frank, T. Haasl, K. Heinemeier, 2005. “Abbreviated Commissioning Cost-Benefit 

Methodology.”

Friedman, H., M. Frank, T. Haasl, K. Heinemeier, 2006. Chapter 1 “State-of-the-Art Review for 

Commissioning Low Energy Buildings”
Page 159 of 239

6. CONCLUSIONS ON COMMISSIONING COST-BENEFIT AND 

PERSISTENCE 

Commissioning Cost-Benefit 

The data collected through this research project begins to characterize the various types of 

commissioning processes that are occurring in Annex member countries internationally. While data was 

often difficult to obtain, we expanded our knowledge in two key areas: 

• The scope of the Cx process employed for new and existing buildings 

• Characterization of issues discovered through the Cx process including system type, likely origin 

of issue (design, construction/installation, O&M, or capital improvement), issue type, and 

measures implemented

For new construction Cx, most projects included developing a Cx plan, functional testing, issue resolution, 

and a final report. This finding was expected since a partial commissioning process is often employed, 

where commissioning begins during the construction phase. The least often included items in scope 

(upated as-built drawing, systems/EBCx manual, and diagnostic tools) are also consistent with our 

understanding of the U.S. experience. These items have high relevance for improving the persistence of 

benefits from commissioning. 

Based on this research, the scope of EBCx internationally generally includes trend analysis, master list of 

findings, presentation of the findings report to the owner, implementation of operations and maintenance 

improvements, and a final report. The low occurrence of updating documentation, development of a 

systems/EBCx manual, and monitoring persistence is a result of the cost of these activities. The lack of 

documentation and monitoring may result in a lack of persistence of benefits from these EBCx projects. 

The issues found through both new construction commissioning and existing building commissioning 

most often occurred in air handling systems, heating water plants, and chilled water plants. In new 

construction projects, the origin of these issues was evenly split between design, construction/installation, 

and O&M/controls. For existing building projects, more issues were related to O&M/controls (42 %), with 

the second most common being design (31 %). Additionally in existing building commissioning projects, 

27 % of issues were related to design/installation, 17 % related to setpoints, and 11 % each were related 

to scheduling, resets, and controls.

While these results begin to develop a qualitative picture for how commissioning is evolving 

internationally, quantitative results were less apparent. For example, data on commissioning costs and 

energy savings were highly variable. Falling short of the data collection goals set by Annex member 

country representatives, it was not possible to make strong conclusions about the cost-effectiveness of 

Cx internationally. However, progress was made towards understanding and categorizing the state of the 

commissioning industry for new and existing buildings in Annex member countries. While all countries 

have Cx research occurring, the majority of countries are in an early adopter phase of industry 

development. Only a few countries can be categorized as having a developing commissioning industry in 

which services are becoming more commonly obtained by owners. 

Persistence of Commissioning Benefits

A review of results of studies from five projects related to the persistence of commissioning benefits, 

either in new or existing buildings conducted prior to the beginning of Annex 47 found that the savings in 

the buildings that were retro-commissioned generally showed some degradation with time. In retrocommissioned buildings, savings generally decreased with time, but there is wide variation from building 

to building. 
Page 160 of 239

For the new buildings, well over half of the 56 commissioning fixes persisted. Hardware fixes, such as 

moving a sensor or adding a valve, and control algorithm changes that were reprogrammed generally 

persisted. Control strategies that could easily be changed, such as occupancy schedules, reset 

schedules, and chiller staging tended were less likely to persist. It was also found that the extent to which 

persistence occurs is also related to operator training.

Persistence studies conducted as part of the Annex 47 work found that as a group, four Japanese 

buildings studied are consistent with the commissioned buildings examined in the literature review – most 

continue to show savings over periods that run from 10 years to 20 years, while generally showing some 

decrease in savings over time. Additional study of 10 buildings at Texas A&M found that cooling and 

heating savings did not change appreciably during the up to seven years of additional data following the 

year 2000. The average cooling savings were 45 % in 1997, 34 % in 2000 and 36 % in the last year of 

good data for each building (average data went through 2006) Heating savings averaged 63 % in 1997, 

56 % in 2000 and 55 % in the last year of available data for each building. It should be noted that 

ongoing commissioning follow-up was conducted in most of these buildings at least once.

A preliminary study conducted during the Annex found that determination of savings using a “normalized 

annual consumption” as the basis for savings determination produced less variation in savings and 

persistence than found when the actual weather during the baseline and post-commissioning periods was 

used. It also suggested that use of calibrated simulation for baseline determination may provide more 

stable results.

Two prototype computerized tools that can assist in maintaining the persistence of commissioning 

savings have been demonstrated:

• The Automated Building Commissioning Analysis Tool (ABCAT) is a simple automated tool that uses 

calibrated simulation to maintain the optimal energy performance in a building. It can continuously 

monitor building energy consumption, alert operations personnel early upon the onset of significant 

increase in consumption and assist them in identifying the problem. In the six live building 

implementations, over eight building-years of operation, ABCAT identifed eight periods where 

significant energy consumption changes occurred that otherwise went undetected by the building 

energy management personnel. In the five retrospective building test cases (approximately 20 

building years of analysis), ABCAT detected 18 faults. 

• The Diagnostic Agent for Building Operation (DABO), a BEMS-assisted commissioning tool monitors 

the enormous amounts of data produced by BEMS and provides an extensive analysis of the 

incoming data. The use of a detailed systematic automated approach has improved the quality 

assurance process and the overall performance of the building. Furthermore, automating this 

essentially manual process has allowed its application on an on-going 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 ten projects. These projects include some of the first Canadian 

LEED buildings and the participation of major Canadian facility management firms and 

commissioning providers.

Current information on persistence of commissioning energy savings in existing buildings may be 

summarized as:

• Savings persistence at the time of the study (3 years to 20 years after commissioning) ranged from 

about 50 % to 100 % in all but a handful of buildings.

• Average savings at the time of the study were about 75 % of the original savings.

• The most dramatic savings degradation was caused by undetected mechanical or control component 

failures. 

• Follow-up when needed has demonstrated persistence of commissioning savings for 7 to 20 years in 

a small number of buildings.
Page 161 of 239

APPENDIX A: Commissioning Cost-Benefit Database

I: Commissioning Cost-Benefit Protocols Report 

II. Data Collection Form (Cx)

III: Data Collection Form (EBCx)

IV: Tabulated Survey Results (Cx)

V: Tabulated Survey Results (EBCx)
Page 162 of 239

I: Commissioning Cost-Benefit Protocols Report 

IEA Annex 47: 

Cost-Effective Commissioning for

Existing and Low Energy Buildings

Cost-benefit Protocols Report

A report documenting the cost-benefit methodology and protocols

agreed to by the Annex participants

Submitted to: 

Phil Haves, LBNL

Submitted by:

PECI

May 23, 2007
Page 163 of 239

1.0 INTRODUCTION

1.1 Background

This report, documenting the cost-benefit protocols and methodology agreed to by participants in 

the IEA’s Annex 47, represents the culmination of an 18-month process in which the goals and 

preferences of Annex members were ascertained and a series of data collections forms were 

developed, tested, revised and tested again.

2005

This work began in December 2005 with the delivery of an “abbreviated methodology,” based 

on the results of a survey of Annex members conducted at the Prague Annex meeting in October, 

2005. The abbreviated methodology specified the project goals, data collection format and types 

of cost, energy and non-energy benefit data that Annex members wanted to collect (Friedman et 

al. 2005).

2006

The first draft of the data collection form, an Excel spreadsheet, was completed in March 2006 

and a revised version – “v2.0,”was completed in July 2006. A field test of v2.0 was conducted 

between July and October 2006, in which the form was distributed to approximately 1,000 

recipients via email. They included Annex members, Evan Mills of Lawrence Berkeley National 

Laboratory and the mailing lists of the California Commissioning Collaborative, and the 

Building Commissioning Association. 

During the field test, recipients were asked to use the form to submit data on commissioning 

projects. PECI received nine completed forms. Results of the field test were presented to Annex 

members at the Trondheim Annex meeting in April 2006 and a discussion was held to resolve a 

set of outstanding issues, including terminology, system types to include and normalization 

options. 

After completion of the field test, the form was revised based on comments received and a new 

version – “v3.0,” was presented to Annex members at the Shenzhen Annex meeting in October 

2006. Another discussion was held with participants to resolve additional outstanding issues, 

including the extent of required data, numeral formatting, definitions of conditioned v. nonconditioned floor area, terminology and building and system types to include.

2007

In March 2007 the data collection form was revised for a final time reflecting all of the 

comments received to-date and the final version – “v3.1,” was presented to Annex members at 

the Budapest Annex meeting in April 2007. In comparing the final data collection form to the 

initial form developed in March 2006, one will observe a significant evolution. Major changes 

include the use of separate forms for new and existing buildings rather than a single form for 

both, the division of the form into four tabs to make completion easier and expanded sections for 

collecting data on non-energy benefits, issues and measures.
Page 164 of 239

1.2 This report

This report summarizes the data collection protocols agreed to by the Annex members. Its 

organization mirrors that of the data collection forms, and includes four sections: commissioning 

project, energy, non-energy benefits and issues and measures. The data collection forms 

themselves are attached as separate documents. 

2.0 COMMISSIONING PROJECT

2.1 Confidentiality

Respondents are asked whether the building name or any other information on the form must 

remain confidential.

2.2 Project information

This section includes the name and contact information for the person completing the form, the 

name of the commissioning provider, the building name and location, as well as other data about 

the building and the project, including:

• Design temperatures

• Building type

• Occupancy type

• Performance of O&M

• Awards received

• Year commissioning completed

• Owner’s primary and secondary reasons for commissioning

• Who led the process and who contracted with the commissioning provider

New building projects include the following additional data fields:

• Year construction completed

• Project delivery method

• Whether occupancy occurred on schedule

Existing building projects include the following additional data fields:

• Did the building undergo commissioning when constructed, and if so, in what phase did 

commissioning begin

2.3 Technical information

This section provides a technical overview of the building and the commissioning project. It is 

the same for new and existing buildings. Data fields include:

• Floor area units

• Total floor area and that to be used in an analysis of costs and benefits
Page 165 of 239

• Number of buildings (if more than one) included in the floor area

• Number of floors and their average height

• Number of operating hours per week 

• Whether the building is served by a central heating and cooling plant

This section also asks respondents to identify the number of issues found in each of the 

commissioned systems and to identify which percentage of issues originated in each phase of the 

building’s history: design, construction/installation, operational/controls/maintenance, or capital 

improvement.

2.4 Cost data

This section aims to record the various costs of the commissioning project and the activities 

represented by the costs. Data fields include:

• Currency units

• Year costs incurred

• Commissioning provider free

• Who paid the costs

• Costs incurred in each phase of the process

• Items included in the cost estimates (list differs for new and existing buildings)

The new building form also asks for the building construction cost and costs to other parties to 

conduct commissioning tasks (architect, mechanical engineer, general contractor, mechanical 

contractor, electrical contractor, controls contractor and the owner’s O&M staff).

The existing building form asks for slightly different costs, including the costs to other parties to 

conduct commissioning tasks (controls contractor and the owner’s O&M staff), the cost to 

implement measures and the cost of monitoring and verification.

3.0 ENERGY

3.1 Source, unit and cost

This section collects the unit type, energy source and average cost per unit for electricity, electric 

demand, fuel, district chilled water, district hot water, district steam and water.

3.2 Resource savings

In new buildings, respondents are asked to submit the first year annual resource use for each 

resource type and to estimate the savings obtained through commissioning, as well as the method 

used to determine the savings.

In existing buildings, respondents are asked the annual resource use before commissioning for 

each resource type, the savings from implemented measures, the estimated savings from 

measures recommended but not implemented and the method used to determine the savings.
Page 166 of 239

In both forms, respondents are asked whether data could have been affected by major changes in 

occupancy, conditioned floor area, building use and major equipment. Respondents are also 

asked whether the data submitted has been normalized for weather, changes in occupancy and 

floor area and, if so, to what year, percentage occupancy and floor area.

3.3 Resource use after commissioning

This section was developed in collaboration with David Claridge, Texas A&M University. 

Respondents are asked about the percent resource savings for each fuel type which continued to 

accrue for up to six years post-commissioning and asked to submit the starting month and year of 

the baseline period.

This section is to be completed only if multiple years of post-commissioning data are available. 

All data must be weather normalized.

4.0. NON-ENERGY BENEFITS

4.1 New buildings

This form asks for data on non-energy benefits in the following areas:

• Project cost savings

• Benefits to project design

• Change orders and warranty claims

• Construction team coordination

• Project schedule

• Building start-up and turn-over

• Operations & maintenance (O&M) 

• Indoor environment

4.2 Existing buildings

This form asks for data on non-energy benefits in the following areas:

• Asset value

• Liability reduction

• Operations & maintenance (O&M) 

• Indoor environment

5.0. ISSUES AND MEASURES

5.1 Issue data

Data is requested on issues (or problems) discovered during the commissioning process, 

including:
Page 167 of 239

• Issue description (free response)

• System affected (drop-down list)

• Equipment affected (drop-down list)

• Issue category (drop-down list)

• Issue type (drop-down list)

• Origin of issue (drop-down list)

5.2 Measure data

Data is requested on measures (or solutions) recommended as a result of the issues discovered, 

including:

• Measure description (free response)

• Whether measure was implemented (drop-down list)

• Implementation cost (free response)

• Measure cost type (drop-down list)

5.3 Resource savings

Data is requested on the resource savings resulting from each measure and for each fuel type. 

Respondents are also asked to provide:

• Savings estimation approach (drop-down list)

• Total resource cost savings (free response)

• Number of years after commissioning that 50 % or more of the resource savings 

continued to accrue (drop-down list)

• An explanation of why the savings did or did not continue to accrue (free response)

5.4 Non-energy benefits

Data is requested on the non-energy benefits resulting from each measure. For both the primary 

and secondary non-energy benefits (selected from a drop-down list), respondents are also asked 

to provide the monetary value of the benefit and the calculation method used to determine the 

monetary value.

5.5 References

Hannah Friedman, Marti Frank, Tudi Haasl and Kristin Heinemeier, “Annex 47 Abbreviated 

Commissioning Cost-Benefit Methodology,” December 9, 2005.
Page 168 of 239

II. Data Collection Form (Cx)

DATA COLLECTION FORM (Cx) – “INSTRUCTIONS” SECTION

Commissioning Data Collection Form - New Buildings

IEA Annex 47 Version 4.0 September 2007

1. Data entry instructions

 a. Enter data in white cells only. 

 b. Cells with red boxes are required information.

 c. Leave unknown fields blank. It is not expected that all data will be available for all projects. 

 d. Data entry restrictions are in place. Some cells contain dropdown lists, others require specific data formats (for example, a whole number).

2. Overview of each tab

 a. Commissioning Project: Confidentiality and contact information, data on the project's technical aspects and cost.

 b. Energy: Source units and costs, savings resulting from the project, continued savings after the project.

 c. Non-energy benefits: Qualitative and quantitative data on cost savings, benefits to project design, change orders and warranty claims, 

construction team coordination, project schedule, start-up and turnover, O&M, indoor environment.

 d. Issues and Measures: Detailed data on issues (problems) discovered during the project and measures (corrective actions).

 e. IPMVP Definitions: Explanation of five possible M&V options, to be used in completing the survey when prompted by a drop-down menu.

3. Submit completed forms:

 Please email completed forms to: Hannah Friedman, PECI (US): hfriedman@peci.org

4. Questions?

 Contact Hannah Friedman, PECI (US): 503-595-4492 or hfriedman@peci.org

Revision History: Current version is v4.0. Previous versions include v2.0 (Fall 2006) and v3.1 (Spring 2007).

Credits: Developed by PECI with comments from Annex members, based on cost-benefit methodologies from Lawrence Berkeley National Laboratory and the California Commissioning Collaborative.
Page 169 of 239

DATA COLLECTION FORM (Cx) – “COMMISSIONING PROJECT” SECTION

Commissioning Data Collection Form - New Buildings

IEA Annex 47 Version 4.0 September 2007

Notes Project Data Units Comments

CONFIDENTIALITY

 Is it necessary for the building name to remain confidential?

PROJECT INFORMATION

 Name of person completing form

 Contact information of person completing form: Phone

 Contact information of person completing form: E-mail

 Hours spent completing this form

 Who led the commissioning process?

 Name of commissioning leader

 How many other commissioning projects did the commissioning 

leader complete prior to this project? 

Include only projects 

completed by leader, not 

firm

 Who held the contract with the commissioning leader?

 Name of building/project

 Building location

 City

 State

 Country

 Year construction completed

 Total building construction cost Total cost paid by owner 

for the building or retrofit
Page 170 of 239

PROJECT INFORMATION CONTINUED

 Building ownership

 Building occupancy type

 Who performs operations and maintenance?

 Project delivery method Mouse over for 

definitions

 Commissioning project type 

 In what phase did the commissioning begin?

 Year commissioning project completed

 Was commissioning undertaken in part to achieve an award 

or certification?

 Awards/certifications received

Select "Y" for all 

awards received

List rating level in 

column E

 LEED-NC

 Energy Star (USA)

 CASBEE (Japan)

 EPBD (EU)

 Other List award name and 

rating level in column E

Indicate the owner's reasons for commissioning on a scale 

from 1 to 5, where "1" is very important and "5" is not 

important

 Ensure system performance

 Obtain energy savings

 Ensure or improve occupant comfort

 Extend equipment life

 Train and increase awareness of building operators

 Smoother process and/or turnover

 Increase occupant productivity

 Ensure adequate indoor air quality (IAQ)
Page 171 of 239

PROJECT INFORMATION CONTINUED

 Comply with LEED or other sustainability rating system

 Reduce liability

 Qualify for rebate, financing, or other services

 Participate in a research, demonstration or pilot project

 Participate in a utility program

 Other Explain in column E

 Level of commissioning

If only energy efficiency 

measures were 

commissioned, select 

"Specific systems"

TECHNICAL INFORMATION

 Floor area units

 Total floor area automatically 

calculated #N/A

 Floor area served by commissioned systems (excludes 

parking)

automatically 

calculated #N/A

 Total floor area dedicated to each of the following uses

 School #N/A

 University #N/A

 Hospital or health facility #N/A

 Laboratory #N/A

 Office #N/A

 Hotel #N/A

 Retail #N/A

 Restaurant #N/A

 Supermarket #N/A

 Residential apartment building #N/A

 Parking #N/A

 Public assembly #N/A

 Public order and safety #N/A

 Religious worship #N/A
Page 172 of 239

TECHNICAL INFORMATION CONTINUED

 Industrial building #N/A

 Service, warehouse or storage #N/A

 Vacant #N/A

 Other Explain in column E #N/A

 If the indicated floor area is for multiple buildings, how many 

buildings?

 Is the facility served by a central heating and cooling plant 

that serves multiple buildings?

 How many issues were found in each commissioned 

system? 

 If the system was not commissioned, leave cell empty. 

 If the system was commissioned but no issues were found, enter 0. 

 If the same issue occurred multiple times or places, only count as one 

 issue. 

Each system category 

below includes the 

controls related to the 

system

 HVAC system integration (EMCS/BAS)

 Chilled water plant and distribution system

 Packaged or split system DX

 Heating water plant and distribution system

 Domestic hot water

 Heat pump system

 Air handling and distribution: Overhead system

 Air handling and distribution: Underfloor system

 Terminal units

 Thermal energy storage

 Radiant heating

 Radiant cooling

 Passive heating/cooling Mouse over for 

definition

 Natural ventilation or mixed-mode ventilation

 Lighting/daylighting and lighting controls

 Electrical

 Plumbing

 Envelope and infiltration

 Fire/life safety

 Utility-related (electric, gas, water, emergency power)

TECHNICAL INFORMATION CONTINUED
Page 173 of 239

 Security

 Refrigeration

 Telecommunications

 Plug loads

 Other Explain in column E

 What percent of the issues above fall into each of the 

following categories:

automatically 

calculated

must equal 100 %

%

 Design Issue %

 Construction and installation issue %

 Operational/controls or maintenance issue %

 Capital improvement %

COST DATA

Give costs in year of 

original data; do not 

correct for inflation

 Currency

 Commissioning costs

 Cost unit (currency or labor hours)

 Total commissioning costs #N/A

 Commissioning leader fee

Please provide if 

available

Do not include 

change-order costs

#N/A

 Costs to other parties to performing commissioning functions 

 

Please provide if 

available

For example, if 

contractor writes the 

prefunctional 

checklists, include this 

cost. Do not include 

change-order costs or 

cost to implement 

measures

 Architect #N/A

 Mechanical Engineer #N/A

 General Contractor #N/A

 Mechanical Contractor #N/A
Page 174 of 239

COST DATA CONTINUED

 Electrical Contractor #N/A

 Controls Contractor #N/A

 Owner's Operation and Maintenance Staff #N/A

 Percent of cost paid by:

automatically 

calculated

must equal 100%

%

 Building owner 

%

 Utility (for example, as a rebate or incentive)

%

 Grants/tax incentives

%

 Other Explain in column E

%

 Percent of cost incurred in each phase

automatically 

calculated

must equal 100%

%

 Pre-design phase

%

 Design phase

%

 Construction phase

%

 Warranty/occupancy phase

%

 Items included in cost estimates

Select "Y" if item was 

included in cost 

estimates

 Development of owner's project requirements and basis of 

design (if not well-developed by designer)

 Building modeling

 Write commisioning specifications

 Develop commisioning plan

 Design review

 Develop sequences of operation (if not well-developed by 

mechanical or controls contractor)

 Submittal review Mouse over for 

definition

 Construction observation

 Verification checks/pre-functional testing

 Use of diagnostic tools and Cx automation techniques

List tools and/or 

methods used in 

column E

 Functional testing

 Significant involvement in issue resolution

COST DATA CONTINUED
Page 175 of 239

 Oversee training

 Review operations & maintenance manuals

 Develop systems manual/recommissioning manual

 Perform trend analysis 

 Evaluate energy cost savings

 Final commissioning report

 Update as-built drawings

 Other Explain in column E
Page 176 of 239

DATA COLLECTION FORM (Cx) – “ENERGY” SECTION

Commissioning Data Collection Form - New Buildings

IEA Annex 47 Version 4.0 September 2007

Notes Project Data Units Comments

SOURCE, UNIT & COST

 Electricity

 Unit

 Average cost per unit (in the first year after commissioning) #N/A

 Electric demand

 Unit

 Average cost per unit (in the first year after commissioning) #N/A

 Fuel

 Fuel type

 Unit

 Average cost per unit (in the first year after commissioning) #N/A

 District chilled water

 Unit

 Average cost per unit (in the first year after commissioning) #N/A

 District hot water

 Unit

 Average cost per unit (in the first year after commissioning) #N/A

 District steam

 Unit

 Average cost per unit (in the first year after commissioning) #N/A

 Water

 Unit

 Average cost per unit (in the first year after commissioning) #N/A
Page 177 of 239

WHOLE BUILDING RESOURCE SAVINGS

To calculate savings, 

subtract first year 

annual usage from 

projected usage of 

baseline building (the 

original design or 

building, before 

commissioning began)

 Electric consumption 

 First year annual usage #N/A

 Estimated annual savings from commissioning #N/A

 Describe method used to determine savings and baseline

 Electric demand 

 First year peak demand #N/A

 Estimated demand reduction from commissioning #N/A

 Describe method used to determine savings and baseline

 Fuel

 First year annual usage #N/A

 Estimated annual savings from commissioning #N/A

 Describe method used to determine savings and baseline

 District chilled water 

 First year annual usage #N/A

 Estimated annual savings from commissioning #N/A

 Describe method used to determine savings and baseline

 District hot water 

 First year annual usage #N/A

 Estimated annual savings from commissioning #N/A

 Describe method used to determine savings and baseline

 District steam

 First year annual usage #N/A

 Estimated annual savings from commissioning #N/A

 Describe method used to determine savings and baseline

 Water

 First year annual usage #N/A
Page 178 of 239

WHOLE BUILDING RESOURCE SAVINGS CONTINUED

 Estimated annual savings from commissioning #N/A

 Describe method used to determine savings and baseline

 Could data have been affected by:

 Major occupancy changes? 

 Average % occupied before commissioning %

 Average % occupied after commissioning %

 Changes in conditioned floor area?

 Average % conditioned floor area before commissioning %

 Average % conditioned floor area after commissioning %

 Changes in building use If yes, describe in 

column E

 Major equipment changes If yes, describe in 

column E

 Has submitted data been normalized for:

 Weather?

 If yes, to what year was the data normalized?

 Changes in occupancy?

 If yes, to what % occupancy was the data 

 normalized? %

 Changes in floor area? 

 If yes, to what floor area was the data normalized? #N/A

 Other If yes, describe in 

column E

 RESOURCE USE AFTER COMMISSIONING 

Please complete this 

section if multiple 

years of postcommissioning use 

data are available

 Indicate the "baseline" period (must be one full year of 

 data - usually the first full year after commissioning)

 Start month Example: 

enter January as 1

 Start year

 End date automatically 

calculated 12/1900
Page 179 of 239

 RESOURCE USE AFTER COMMISSIONING CONTINUED

 Electric consumption - weather normalized % savings from 

baseline

 Year 1 %

 Year 2 %

 Year 3 %

 Year 4 %

 Year 5 %

 Year 6 %

 Electric demand - weather normalized % savings from 

baseline

 Year 1 %

 Year 2 %

 Year 3 %

 Year 4 %

 Year 5 %

 Year 6 %

 Fuel - weather normalized % savings from baseline

 Year 1 %

 Year 2 %

 Year 3 %

 Year 4 %

 Year 5 %

 Year 6 %

 District hot water - weather normalized % savings from 

baseline

 Year 1 %

 Year 2 %

 Year 3 %

 Year 4 %

 Year 5 %

 Year 6 %

 RESOURCE USE AFTER COMMISSIONING CONTINUED
Page 180 of 239

 District chilled water - weather normalized % savings from 

baseline

 Year 1 %

 Year 2 %

 Year 3 %

 Year 4 %

 Year 5 %

 Year 6 %

 District steam - weather normalized % savings from 

baseline

 Year 1 %

 Year 2 %

 Year 3 %

 Year 4 %

 Year 5 %

 Year 6 %

 Water - weather normalized % savings from baseline

 Year 1 %

 Year 2 %

 Year 3 %

 Year 4 %

 Year 5 %

 Year 6 %

 Has submitted data been normalized for any changes 

 in:

 Occupancy?

 Schedules?

 Equipment?

 Occupied floor area?

 

DATA COLLECTION FORM (Cx) – “NON-ENERGY BENEFITS” SECTION
Page 181 of 239

Commissioning Data Collection Form - New Buildings

IEA Annex 47 Version 4.0 September 2007

Notes Project Data Units Comments

NON-ENERGY BENEFITS Describe calculations 

in column E

 Project cost savings 

 Did you achieve an overall lower project cost (or "first cost") as a 

result of commissioning? 

 What is the total value of this savings? describe in column E #N/A

 Benefits to project design 

 Were improvements to system design made as a result of 

commissioning? 

 Was equipment sized correctly as a result of commissioning? 

 What is the monetary value of these benefits? describe in column E #N/A

 Change orders and warranty claims 

 Did you reduce or avoid change orders as a result of 

commissioning? 

 How many change orders were avoided? 

 Did you reduce or avoid warranty claims as a result of 

commissioning? 

 What is the monetary value of these benefits? describe in column E #N/A

 Asset value 

 Was asset value improved as a result of commissioning? 

 What is the total value of this benefit? describe in column E #N/A

 Liability reduction 

 Was liability reduced as a result of commissioning? 

 Did this include reductions in liability related to: 

 Indoor air quality (IAQ)? 

 Fire/life safety? 

 Security? 

 The building's mission critical function? 

NON-ENERGY BENEFITS CONTINUED
Page 182 of 239

 What is the monetary value of these benefits? describe in column 

E #N/A

 Construction team coordination 

 Was coordination among team members improved as a result of 

commissioning? 

 Were disagreements among contractors reduced or resolved 

more quickly as a result of commissioning? 

 What is the monetary value of these benefits? describe in column 

E #N/A

 Project schedule 

 Did the project progress according to schedule as a result of 

commissioning? 

 Were potential problems detected and corrected earlier than they 

would have been without commissioning? 

 What is the monetary value of these benefits? describe in column 

E #N/A

 Building start-up and turn-over 

 Was the building occupied on schedule as a result of 

commissioning? 

 Were contractor call-backs reduced as a result of 

commissioning? 

 Were testing and balancing (TAB) costs reduced as a result of 

commissioning? 

 What is the monetary value of these benefits? describe in column 

E #N/A

 Operations & maintenance (O&M) 

 Was system documentation available to O&M staff as a result of 

commissioning? 

 Were O&M staff provided with training as a result of 

commissioning? 

 Do O&M staff report satisfaction with their ability to operate and 

maintain the building as a result of commissioning? 

 Was equipment life extended as a result of commissioning? 

 Were ongoing operations and maintenace costs reduced a result 

of commissioning? 

NON-ENERGY BENEFITS CONTINUED
Page 183 of 239

 What is the monetary value of these benefits? describe in column 

E #N/A

 Indoor environment 

 Was indoor air quality improved as a result of commissioning? 

 Was occupant comfort improved as a result of commissioning? 

 Did this include: 

 Improvements to heating? 

 Improvements to cooling? 

 Improvements to humidity? 

 Improvements to air balance? 

 Was occupant productivity improved as a result of 

commissioning? 

 Was occupant safety improved as a result of commissioning? 

 What is the monetary value of these benefits? describe in column 

E #N/A

 Other benefits 

 Please list any additional benefits that resulted from 

commissioning in column C, and describe the benefit in column 

E 

 What is the monetary value of these benefits? describe in column 

E #N/A
Page 184 of 239

DATA COLLECTION FORM (Cx) – “ISSUES AND MEASURES” SECTION

Commissioning Data Collection Form

IEA Annex 47 Version 4.0 September 2007

ISSUE DATA: the problem

ISSUE TYPE ISSUE TYPE 

EXAMPLE

ISSUE 

DESCRIPTION

ORIGIN OF 

ISSUE

SYSTEM 

AFFECTED

EQUIPMENT 

AFFECTED

Select from drop-down list Example populates based on 

selected ISSUE TYPE

Provide a short description of 

the problem Select from drop-down list Select from drop-down list

Select from drop-down list

List populates based on 

selected SYSTEM 

AFFECTED

1

2
Page 185 of 239

MEASURE DATA: the solution

ISSUE 

TYPE

MEASURE 

TYPE

MEASURE 

DESCRIPTION

WAS MEASURE 

IMPLEMENTED

?

IMPLEMENTATION COSTMEASURE 

COST TYPE

Select from drop-down 

list

Select from drop-down 

list

Provide a short description of 

the solution recommended to 

correct the problem

Select from drop-down list

If recommended but not 

implemented, select "No"

#N/A Select from drop-down 

list

1

2

ble 1
Page 186 of 239

RESOURCE SAVINGS: the amount of reduced resource use as a result of the measure

ISSUE 

TYPE ELECTRICITY 

PEAK 

ELECTRICAL 

DEMAND

FUEL CHILLED 

WATER 

HOT 

WATER STEAM WATER 

Select from dropdown list #N/A #N/A #N/A #N/A #N/A #N/A #N/A

1

2
Page 187 of 239

RESOURCE SAVINGS: the amount of reduced resource use as a result of the measure

ISSUE TYPE

SAVINGS 

ESTIMATION 

APPROACH

TOTAL 

RESOURCE COST 

SAVINGS

FOR HOW MANY 

YEARS AFTER 

COMMISSIONING 

DID 80% OR MORE 

of the RESOURCE 

SAVINGS 

CONTINUE?

WHY DID OR DID 

NOT RESOURCE 

SAVINGS 

CONTINUE?

Select from drop-down list Select from drop-down list

Definitions on IPMVP tab #N/A Select from drop-down list Explain 

1

2
Page 188 of 239

NON-ENERGY BENEFITS

ISSUE 

TYPE

PRIMARY 

BENEFIT:

TYPE

PRIMARY 

BENEFIT:

MONETARY 

VALUE

PRIMARY 

BENEFIT:

CALCULATION 

METHOD

SECONDARY 

BENEFIT: TYPE

SECONDARY 

BENEFIT:

MONETARY 

VALUE

SECONDARY 

BENEFIT:

CALCULATION 

METHOD

Select from 

drop-down list

Select from drop-down 

list #N/A Describe method used to calculate value of benefit

Select benefit type from 

drop-down menu #N/A Describe method used to calculate value of benefit

1

2
Page 189 of 239

III: Data Collection Form (EBCx)

EBCx DATA COLLECTION FORM – “INSTRUCTIONS” SECTION

Commissioning Data Collection Form - Existing Buildings

IEA Annex 47 Version 4.0 September 2007

1. Data entry instructions

 a. Enter data in white cells only. 

 b. Cells with red boxes are required information.

 c. Leave unknown fields blank. It is not expected that all data will be available for all projects. 

 d. Data entry restrictions are in place. Some cells contain dropdown lists, others require specific data formats (for example, a whole number).

2. Overview of each tab

 a. Commissioning Project: Confidentiality and contact information, data on the project's technical aspects and cost.

 b. Energy: Source units and costs, savings resulting from the project, continued savings after the project.

 c. Non-energy benefits: Qualitative and quantitative data on benefits to O&M, indoor environment, asset value, liability reduction.

 d. Issues and Measures: Detailed data on issues (problems) discovered during the project and measures (corrective actions).

 e. IPMVP Definitions: Explanation of five possible M&V options, to be used in completing the survey when prompted by a drop-down menu.

3. Submit completed forms:

 Please email completed forms to: Hannah Friedman, PECI (US): hfriedman@peci.org

4. Questions?

 Contact Hannah Friedman, PECI (US): 503-595-4492 or hfriedman@peci.org

Revision History: Current version is v4.0. Previous versions include v2.0 (Fall 2006) and v3.1 (Spring 2007).

Credits: Developed by PECI with comments from Annex members, based on cost-benefit methodologies from Lawrence Berkeley National Laboratory and the California Commissioning Collaborative.
Page 190 of 239

EBCx DATA COLLECTION FORM – “COMMISSIONING PROJECT” SECTION

Commissioning Data Collection Form - Existing Buildings

IEA Annex 47 Version 4.0 September 2007

Notes Project Data Units Comments

CONFIDENTIALITY

 Is it necessary for the building name to remain confidential?

PROJECT INFORMATION

 Name of person completing form

 Contact information of person completing form: Phone

 Contact information of person completing form: E-mail

 Hours spent completing this form

 Who led the commissioning process?

 Name of commissioning leader

 How many other commissioning projects did the commissioning 

leader complete prior to this project? 

Include only projects 

completed by leader, 

not firm

 Name of building/project

 Building location

 City

 State

 Country

 Building ownership

 Building type

 Year built 
Page 191 of 239

PROJECT INFORMATION CONTINUED

 Year commissioning project completed

 Building occupancy type

 Who performs operations and maintenance?

 Was commissioning undertaken in part to achieve an award or 

certification?

 Awards/certifications received

Select "Y" for all 

awards received

List rating level in 

column E

 LEED-NC

 LEED-EB

 Energy Star (USA)

 CASBEE (Japan)

 EPBD (EU)

 Other 

List award name and 

rating level in column 

E

 Indicate the owner's reasons for commissioning on a scale 

from 1 to 5, where "1" is very important and "5" is not 

important

 Ensure system performance

 Obtain energy savings

 Ensure or improve occupant comfort

 Extend equipment life

 Train and increase awareness of building operators

 Increase occupant productivity

 Ensure adequate indoor air quality (IAQ)

 Comply with LEED or other sustainability rating ystem

 Reduce liability

 Qualify for rebate, financing, or other services

 Participate in a research, demonstration or pilot project

PROJECT INFORMATION CONTINUED
Page 192 of 239

 Participate in a utility program

 Other Explain in column E

 Level of commissioning

If only energy 

efficiency measures 

were commissioned, 

select "Specific 

systems"

 Did the building undergo a new building commissioning 

process? 

 If yes, in what phase did commissioning begin? 

TECHNICAL INFORMATION

 Floor area units

 Total floor area automatically 

calculated #N/A

 Floor area served by commissioned systems (excludes parking) automatically 

calculated #N/A

 Total floor area dedicated to each of the following uses

 School #N/A

 University #N/A

 Hospital or health facility #N/A

 Laboratory #N/A

 Office #N/A

 Hotel #N/A

 Retail #N/A

 Restaurant #N/A

 Supermarket #N/A

 Residential apartment building #N/A

 Parking #N/A

 Public assembly #N/A

 Public order and safety #N/A

 Religious worship #N/A

 Industrial building #N/A

 Service, warehouse or storage #N/A

TECHNICAL INFORMATION CONTINUED

 Vacant #N/A
Page 193 of 239

 Other Explain in column E #N/A

 If the indicated floor area is for multiple buildings, how many 

buildings?

 Is the facility served by a central heating and cooling plant 

that serves multiple buildings?

 How many issues were found in each commissioned system? 

 If the system was not commissioned, leave cell empty. 

 If the system was commissioned but no issues were found, 

enter 0. 

 If the same issue occurred multiple times or places, only 

count as one issue. 

Each system category 

below includes the 

controls related to the 

system

 HVAC system integration (EMCS/BAS)

 Chilled water plant and distribution system

 Packaged or split system DX

 Heating water plant and distribution system

 Domestic hot water

 Heat pump system

 Air handling and distribution: Overhead system

 Air handling and distribution: Underfloor system

 Terminal units

 Thermal energy storage

 Radiant heating

 Radiant cooling

 Passive heating/cooling Mouse over for 

definition

 Natural ventilation or mixed-mode ventilation

 Lighting/daylighting and lighting controls

 Electrical

 Plumbing

 Envelope and infiltration

 Fire/life safety

 Utility-related (electric, gas, water, emergency power)

TECHNICAL INFORMATION CONTINUED

 Security

 Refrigeration
Page 194 of 239

 Telecommunications

 Plug loads

 Other Explain in column E

 What percent of the issues above fall into each of the 

following categories:

automatically 

calculated

must equal 100%

%

 Design Issue %

 Construction and installation issue %

 Operational/controls or maintenance issue %

 Capital improvement %

COST DATA

Give costs in year of 

original data; do not 

correct for inflation

 Currency

 Commissioning costs

 Cost unit (currency or labor hours)

 Total commissioning costs #N/A

 Commissioning leader fee/investigation costs

Please provide if 

available

Do not include 

leader's cost to assist 

with implementation

Do not include 

change-order costs

#N/A

 Costs for other parties to perform commissioning investigation

functions 

Please provide if 

available

Do not include costs 

to implement 

measures

 Controls contractor #N/A

 Owner's operation and maintenance staff #N/A

COST DATA CONTINUED

 Cost to implement measures (include cost of all labor and 

materials)

Please provide if 

available #N/A
Page 195 of 239

 Cost for monitoring and verification of savings Please provide if 

available #N/A

 Percent of cost paid by:

automatically 

calculated

must equal 100%

%

 Building owner %

 Utility (for example, as a rebate or incentive) %

 Grants/tax incentives %

 Other Explain in column E %

 Tasks performed Select "Y" if task was 

performed

 Document owner's project requirements

 Develop Commissioning Plan

 Utility bill analysis

 Benchmarking

 Trend analysis (for example, EMCS, data logging,etc)

 Building energy modeling/simulation

 Use of diagnostic tools and Cx automation techniques

List tools and/or 

methods used in 

column E

 Document master list of findings

 Calculate energy cost savings for findings

 Present a findings and recommendations report

 Update system documentation after implementation 

 Implement operations and maintenance (O&M) improvements 

 Implement capital improvements 

 Monitor and verify energy savings 

 Monitor implemented measures for persistence of benefits

 Develop systems manual/recommissioning manual

 Final commissioning report

 Other Explain in column E
Page 196 of 239

EBCx DATA COLLECTION FORM – “ENERGY” SECTION

Commissioning Data Collection Form - Existing Buildings

IEA Annex 47 Version 4.0 September 2007

Notes Project Data Units Comments

SOURCE, UNIT & COST

 Electricity

 Unit

 Average cost per unit (in the first year after commissioning) #N/A

 Electric demand

 Unit

 Average cost per unit (in the first year after commissioning) #N/A

 Fuel

 Fuel type

 Unit

 Average cost per unit (in the first year after commissioning) #N/A

 District chilled water

 Unit

 Average cost per unit (in the first year after commissioning) #N/A

 District hot water

 Unit

 Average cost per unit (in the first year after commissioning) #N/A

 District steam

 Unit

 Average cost per unit (in the first year after commissioning) #N/A
Page 197 of 239

RESOURCE SAVINGS

 Water

 Unit

 Average cost per unit (in the first year after commissioning) #N/A

 Electricity 

 Annual usage before commissioning #N/A

 Annual savings from implemented measures #N/A

 Additional annual savings from measures recommended lbut not 

implemented #N/A

 Method used to determine savings Definitions on IPMVP 

tab

 Electric demand 

 Peak demand before commissioning #N/A

 Demand reduction from implemented measures #N/A

 Additional annual savings from measures recommended lbut not 

implemented #N/A

 Method used to determine savings Definitions on IPMVP 

tab

 Fuel

 Annual usage before commissioning #N/A

 Annual savings from implemented measures #N/A

 Additional annual savings from measures recommended lbut not 

implemented #N/A

 Method used to determine savings Definitions on IPMVP 

tab

 District chilled water 

 Annual usage before commissioning #N/A

 Annual savings from implemented measures #N/A

 Additional annual savings from measures recommended lbut not 

implemented #N/A

RESOURCE SAVINGS CONTINUED
Page 198 of 239

 Method used to determine savings Definitions on IPMVP 

tab

 District hot water 

 Annual usage before commissioning #N/A

 Annual savings from implemented measures #N/A

 Additional annual savings from measures recommended lbut not 

implemented #N/A

 Method used to determine savings Definitions on IPMVP 

tab

 District steam

 Annual usage before commissioning #N/A

 Annual savings from implemented measures #N/A

 Additional annual savings from measures recommended lbut not 

implemented #N/A

 Method used to determine savings Definitions on IPMVP 

tab

 Water

 Annual usage before commissioning #N/A

 Annual savings from implemented measures #N/A

 Additional annual savings from measures recommended lbut not 

implemented #N/A

 Method used to determine savings Definitions on IPMVP 

tab

 Could data have been affected by:

 Major occupancy changes? 

 Average % occupied before commissioning %

 Average % occupied after commissioning %

 Changes in conditioned floor area?

 Average % conditioned floor area before commissioning %

 Average % conditioned floor area after commissioning %

 Changes in building use If yes, describe in 

column E

 Major equipment changes If yes, describe in 

column E

RESOURCE SAVINGS CONTINUED

 Has submitted data been normalized for:
Page 199 of 239

 Weather?

 If yes, to what year was the data normalized?

 Changes in occupancy?

 If yes, to what % occupancy was the data normalized? %

 Changes in floor area? 

 If yes, to what floor area was the data normalized? #N/A

 Other If yes, describe in 

column E

 RESOURCE USE AFTER COMMISSIONING 

Please complete this 

section if multiple 

years of postcommissioning use 

data are available

 Indicate the "baseline" period (must be one full year of data -

usually the first full year after commissioning)

 Start month Example: enter 

January as 1

 Start year

 End date automatically 

calculated 12/1900

 Electric consumption - weather normalized % savings from 

baseline

 Year 1 %

 Year 2 %

 Year 3 %

 Year 4 %

 Year 5 %

 Year 6 %

 Electric demand - weather normalized % savings from baseline

 Year 1 %

 Year 2 %

 Year 3 %

RESOURCE USE AFTER COMMISSIONING CONTINUED

 Year 4 %

 Year 5 %

 Year 6 %
Page 200 of 239

 Fuel - weather normalized % savings from baseline

 Year 1 %

 Year 2 %

 Year 3 %

 Year 4 %

 Year 5 %

 Year 6 %

 District hot water - weather normalized % savings from 

baseline

 Year 1 %

 Year 2 %

 Year 3 %

 Year 4 %

 Year 5 %

 Year 6 %

 District chilled water - weather normalized % savings from 

baseline

 Year 1 %

 Year 2 %

 Year 3 %

 Year 4 %

 Year 5 %

 Year 6 %

 District steam - weather normalized % savings from baseline

 Year 1 %

 Year 2 %

RESOURCE USE AFTER COMMISSIONING CONTINUED

 Year 3 %

 Year 4 %

 Year 5 %

 Year 6 %
Page 201 of 239

 Water - weather normalized % savings from baseline

 Year 1 %

 Year 2 %

 Year 3 %

 Year 4 %

 Year 5 %

 Year 6 %

 Has submitted data been normalized for any changes in:

 Occupancy?

 Schedules?

 Equipment?

 Occupied floor area?

 
Page 202 of 239

EBCx DATA COLLECTION FORM – “NON-ENERGY BENEFITS” SECTION

Commissioning Data Collection Form - Existing Buildings

IEA Annex 47 Version 4.0 September 2007

Notes Project Data Units Comments

NON-ENERGY BENEFITS Describe calculations in 

column E

 Operations & maintenance (O&M) 

Was improved system documentation available to O&M staff 

as a result of commissioning? 

Were O&M staff provided with training as a result of 

commissioning? 

Do O&M staff report increased ability to operate and maintain 

the building as a result of commissioning? 

Was equipment life extended as a result of commissioning? 

Were unexpected equipment failures likely prevented as a 

result of commissioning? 

Were ongoing operations and maintenace costs reduced a 

result of commissioning? 

 What is the monetary value of these benefits? describe in column E #N/A

 Indoor environment 

Was indoor air quality improved as a result of commissioning? 

Was occupant comfort improved as a result of commissioning? 

 Did this include: 

 Improvements to heating? 

 Improvements to cooling? 

 Improvements to humidity? 

 Improvements to air balance? 

Was occupant productivity improved as a result of 

commissioning? 

Was occupant safety improved as a result of commissioning? 

 What is the monetary value of these benefits? describe in column E #N/A
Page 203 of 239

NON-ENERGY BENEFITS CONTINUED

 Asset value 

Was asset value improved as a result of commissioning? 

 What is the total value of this benefit? describe in column E #N/A

 Liability reduction 

Was liability reduced as a result of commissioning? 

 Did this include reductions in liability related to: 

 Indoor air quality (IAQ)? 

 Fire/life safety? 

 Security? 

 The building's mission critical function? 

 What is the monetary value of these benefits? describe in column E #N/A

 Other benefits 

Please list any additional benefits that resulted from 

commissioning in column C, and describe the benefit in column 

E 

 What is the monetary value of these benefits? describe in column E #N/A
Page 204 of 239

EBCx DATA COLLECTION FORM – “ISSUES AND MEASURES” SECTION

Commissioning Data Collection Form

IEA Annex 47 Version 4.0 September 2007

ISSUE DATA: the problem

ISSUE TYPE ISSUE TYPE 

EXAMPLE

ISSUE 

DESCRIPTION

ORIGIN OF 

ISSUE

SYSTEM 

AFFECTED

EQUIPMENT 

AFFECTED

Select from drop-down list Example populates based on 

selected ISSUE TYPE

Provide a short description of 

the problem

Select from drop-down 

list Select from drop-down list

Select from drop-down list

List populates based on 

selected SYSTEM 

AFFECTED

1

2
Page 205 of 239

MEASURE DATA: the solution

ISSUE 

TYPE

MEASURE 

TYPE

MEASURE 

DESCRIPTION

WAS MEASURE 

IMPLEMENTED

?

IMPLEMENTAT

ION COST

MEASURE 

COST TYPE

Select from drop-down 

list

Select from drop-down 

list 

Provide a short description of 

the solution recommended to 

correct the problem

Select from drop-down list

If recommended but not 

implemented, select "No" 

#N/A Select from drop-down 

list

1

2
Page 206 of 239

RESOURCE SAVINGS: the amount of reduced resource use as a result of the measure

ISSUE 

TYPE ELECTRICITY 

PEAK 

ELECTRICAL 

DEMAND

FUEL CHILLED 

WATER 

HOT 

WATER STEAM WATER 

Select from dropdown list #N/A #N/A #N/A #N/A #N/A #N/A #N/A

1

2
Page 207 of 239

RESOURCE SAVINGS: the amount of reduced resource use as a result of the measure

ISSUE TYPE

SAVINGS 

ESTIMATION 

APPROACH

TOTAL 

RESOURCE COST 

SAVINGS

FOR HOW MANY 

YEARS AFTER 

COMMISSIONING 

DID 80% OR MORE 

of the RESOURCE 

SAVINGS 

CONTINUE?

WHY DID OR DID 

NOT RESOURCE 

SAVINGS 

CONTINUE?

Select from drop-down list Select from drop-down list

Definitions on IPMVP tab #N/A Select from drop-down list Explain 

1

2
Page 208 of 239

NON-ENERGY BENEFITS

ISSUE 

TYPE

PRIMARY 

BENEFIT:

TYPE

PRIMARY 

BENEFIT:

MONETARY 

VALUE

PRIMARY 

BENEFIT:

CALCULATION 

METHOD

SECONDARY 

BENEFIT: TYPE

SECONDARY 

BENEFIT:

MONETARY 

VALUE

SECONDARY 

BENEFIT:

CALCULATION 

METHOD

Select from 

drop-down list

Select from dropdown list #N/A Describe method used to 

calculate value of benefit

Select benefit type from dropdown menu #N/A Describe method used to 

calculate value of benefit

1

2
Page 209 of 239

IV: Tabulated Survey Results (Cx)

TABULATED SURVEY RESULTS (Cx) – “Cx PROJECT” SECTION

Project 

ref 

number

PROJECT 

INFORMATION 

 Building 

location City State Country

 Year 

commissioning 

project 

completed

TECHNICAL 

INFORMATION

 Floor area 

units

 How many issues were 

found in each commissioned 

system? 

 If the system was not 

commissioned, leave cell 

empty. 

 If the system was 

commissioned but no issues 

were found, enter 0. 

 If the same issue occurred 

multiple times or places, only 

count as one issue. 

 HVAC 

system 

integration 

(EMCS/BAS)

 Chilled water 

plant and 

distribution 

system

1 Stuttgart BadenWürttemberg Germany 2004 m2 

2 Zwolle Netherlands 2007 m2 1

3

Davenport Iowa Iowa USA 2006

ft2 

4 Pittsburgh PA USA 2004 ft2 

5 Tachikawa Tokyo Japan 2005 m2 4 4

6 Oita Oita Japan 2000 m2 1

7 Okazaki Aichi Japan m2 

8 Yokohama Kanagawa Japan m2 0 1

9

1-5-1 Higasi 

Shinbashi, 

Minatoku

Tokyo Japan 2006 m2 0 0

10 kobei Wakayama Japan 2006 m2
Page 210 of 239

Project 

ref 

number

TECHNICAL 

INFORMATION 

CONTINUED

 Packaged or 

split system DX

 Heating 

water plant and 

distribution 

system

 Domestic 

hot water

 Heat pump 

system

 Air handling 

and distribution: 

Overhead 

system

 Terminal 

units

 Thermal 

energy storage

 

Lighting/daylighti

ng and lighting 

controls

 Envelope 

and infiltration Other 

1

2 1 1 1

3 1 3 12 7

4

5 6 2

6 1

7

8 1 1 1 1 0 1 1 1

9 0 0 0

10 1 1
Page 211 of 239

Project 

ref 

number

COST DATA Currency

 Cost unit 

(currency or 

labor hours)

 Total 

commissioning 

costs 

 Items 

included in 

cost 

estimates

 Development 

of owner's 

project 

requirements 

and basis of 

design (if 

 not welldeveloped by 

designer)

 Write 

commisioning 

specifications

 Develop 

commisioning 

plan

 Design 

review

 Develop 

sequences of 

operation (if not 

well-developed 

by mechanical 

or 

 controls 

contractor)

 

Submittal 

review

 

Construction 

observation

1 EUR (euro) Currency 175000 Y Y

2 EUR (euro) Currency 15000 Y Y Y

3 USD (US dollar) Currency 15000 y y y y y y

4 USD (US dollar) Currency 18000 Y Y

5 JPY (Japanese 

yen) Labor 1104 Y Y Y Y Y Y Y

6 JPY (Japanese 

yen) Currency 30,000,000.00 

7 JPY (Japanese 

yen)

8 JPY (Japanese 

yen) Labor 432

9 JPY (Japanese 

yen) Currency 36,700,000.00 

10 1000 y y y
Page 212 of 239

Project 

ref 

number

COST DATA 

CONTINUED

 Verification 

checks/prefunctional testing

 Use of 

diagnostic tools 

and Cx 

automation 

techniques

 

Functional 

testing

 Significant 

involvement in 

issue resolution

 

Oversee 

training

 Review 

operations & 

maintenance 

manuals

 Develop 

systems 

manual/recommi

ssioning manual

 Perform trend 

analysis 

 Evaluate 

energy cost 

savings

 Final 

commissioning 

report

 Update asbuilt drawings

1 Y Y Y

2 Y Y

3 y y y y y y

4 Y Y Y Y Y

5 Y Y Y Y Y Y Y Y

6

7

8

9 Y Y Y Y Y Y

10 y y y y y y y
Page 213 of 239

TABULATED SURVEY RESULTS (Cx) – “Cx ENERGY” SECTION

Project 

Ref 

number

 Country

SOURCE, 

UNIT & 

COST 

 Electricity Unit Electric 

demand Unit Fuel Fuel type Unit

 District 

chilled 

water

 Unit District 

steam Unit Water Unit

 Average 

cost per unit 

(in the first 

year after 

commissioni

ng)

1

Germany Kilowatt-hour 

(kWh)

Kilowatt 

(kW) Natural gas

Cubic 

meters gas 

(m3)

2 Netherlands

3

USA

Kilowatt-hour 

(kWh)

Kilowatt 

(kW) Natural gas

Hundred 

cubic feet 

(CCF)

4 USA Kilowatt-hour 

(kWh)

Kilowatt 

(kW) Gallons

5 Japan Kilowatt-hour 

(kWh)

Kilowatt 

(kW)

Not used at 

this facility Liters 0.37

6 Japan Kilowatt-hour 

(kWh)

Kilowatt 

(kW)

Not used at 

this facility Liters

7

Japan Kilowatt-hour 

(kWh)

Kilowatt 

(kW)

Not used at 

this facility Liters

8 Japan Kilowatt-hour 

(kWh)

Kilowatt 

(kW)

Not used at 

this facility Liters 0.37

9 Japan Megajoule 

(MJ)

Kilowatt 

(kW)

Megajoule 

(MJ)

Megajoule 

(MJ) Liters

10

Japan
Page 214 of 239

V: Tabulated Survey Results (EBCx)

Project 

Ref 

Number

PROJECT 

INFORMATION 

Name of 

person 

completing 

form

Contact information of 

person completing 

form: Phone

Contact 

information of 

person 

completing 

form: E-mail

Building 

location City State Country Building 

ownership Building type Year 

built 

Year 

commissioning 

project completed

1 Alexis Versele 0032(0)9/265.86.10

alexis.versele@

kahosl.be

Oud 

Borgerhout Antwerp Belgium

Other (explain 

in column E) 1930

2 Alexis Versele 0032 (0)9 222 22 73

alexis.versele@

kahosl.be Wondelgem

OostVlaanderen Belgium School 1977

3 Alexis Versele 0032 (0)9 222 22 73

alexis.versele@

kahosl.be Wondelgem

OostVlaanderen Belgium School 1985

4

Stijn Van den 

Broecke 32496729090

stijnvdbroecke

@hotmail.com Mechelen

provincie 

Antwerpen Belgium Private Office 2006 2008

5

Prof. M. De 

Paepe 3292643294

michel.depaepe

@ugent.be Gent Flanders Belgium Public Laboratory 2003 2007

6 Hilde Breesch

hilde.breesch@

kahosl.be Gent

OostVlaandere Belgium Public School 2008

7 André Chalifour 5147659886

achalifour@vide

otron.ca Montréal Québec Canada

Other (explain 

in column E) 1983 2003

8

Daniel 

Choiniere 450-652-4874

dchoinie@nrcan

.gc.ca Varennes Québec Canada Public

Other (explain 

in column E) 1991 2006

9

Shigehiro 

Ichinose +81-52-973-317

Ichinose 

Shigehiro@chu

den.co.jp Nagoya Aichi Japan Office 1971 2007

10

Kazuhiro 

Nakazawa +81-6-7501-0423

nakazawa.kazu

hiro@d5.kepco.

co.jp Osaka Osaka Japan Private

Other (explain 

in column E) 2005 2007

11

Katsuhiro 

KAMITANI 81-47-359-1033

kkamitani@tone

ts.co.jp

nihonbashi,chu

o-ku Tokyo Japan Private Office 1968 2004

12

Katsuhiro 

KAMITANI 81-47-359-1033

kkamitani@tone

ts.co.jp

sinkawa,chuoku Tokyo Japan Private Office 1988 1999

13

Masahiro 

Shinozaki 81-92-726-1305

Masahiro_Shino

zaki@kyuden.c

o.jp Oita Oita Japan Private Office 1997 2007

14 Hirobumi UEDA +81-6-6205-4592

hirobumiueda@osakaga

s.co.jp Nishiku Osaka Japan Private Office 1996 2008

15 Henk Peitsman 31152763328

henk.peitsman

@tno.nl Amsterdam n.a. Netherlands Office 1999 2005

16

Henk C. 

Peitsman 31152763328

henk.peitsman

@tno.nl Rotterdam n.a. Netherlands Private Office 1991 1996

17

Henk C. 

Peitsman 31152763328

henk.peitsman

@tno.nl The Hague Netherlands Private Office 2005

18

Henk C. 

Peitsman 31152763328

henk.peitsman

@tno.nl Maastricht Netherlands Private University 2005 2007

19

Henk C. 

Peitsman 31152763328

henk.peitsman

@tno.nl Driebergen n.a. Netherlands Public Office 1989 2008

20

Henk C. 

Peitsman 31152763328

henk.peitsman

@tno.nl Hoevelaken n.a. Netherlands Private Office 1969 2008

21

Henk C. 

Peitsman 31152763328

henk.Peitsman

@tno.nl Amsterdam n.a. Netherlands Private Office 2002 2006
Page 215 of 239

Project 

Ref 

Number

PROJECT 

INFORMATION 

Name of 

person 

completing 

form

Contact 

information of 

person completing 

form: Phone

Contact information of 

person completing form: Email

Building 

location City State Country Building ownership Building type Year built 

Year 

commissioning 

project completed

22

Henk C. 

Peitsman 31152763328 henk.peitsman@tno.nl Delft n.a. Netherlands Public Office 2002 2008

23 Henk Peitsman 31152763328 henk.peitsman@tno.nl The Hague n.a. Netherlands Private Office 1969 2008

24 Natasa Djuric +477359338 natasa.djuric@ntnu.no Trondheim Norway

 

in column E) 1958 2005

25 Natasa Djuric 4748061848 natasa.djuric@ntnu.no

South Norway, 

Cost Norway Private Hotel 2002 2005

26 Natasa Djuric 4748061848 natasa.djuric@ntnu.no

West Norway, 

Cost Norway Private Hotel 1987 2005

27 Natasa Djuric 4748061848 natasa.djuric@ntnu.no

South Norway, 

Cost Norway Private Hotel 2000 2005

28 Natasa Djuric 4748061848 natasa.djuric@ntnu.no

East Norway, 

Inland Norway Private Hotel 1992 2005

29 Omer Akin (412)268-3594 oa04@andrew.cmu.edu Pittsburgh PA USA University 2004 2005

30 Ken Engan 505-397-0328 kenengan@gmail.com College Station TX USA University 1950 1997

31 Marti Frank 503.819.4789 mfrank@peci.org La Mesa California USA Private Office 1983 2001

32

Hannah 

Friedman 503-595-4492 hfriedman@peci.org Portland OR USA Public

Public 

assembly 1997 2005

33 Cory Toole 979-458-0182 corytoole@tees.tamus.edu College Station TX USA Public University 1978 1997

34 Cory Toole 979-458-0182 corytoole@tees.tamus.edu College Station TX USA Public University 1973 1997

35 Cory Toole 979-458-0182 corytoole@tees.tamus.edu College Station TX USA Public University 1955 1997

36 Cory Toole 979-458-0182 corytoole@tees.tamus.edu College Station TX USA Public University 1973 1996

37 Ken Engan 505-397-0328 kenengan@gmail.com College Station TX USA Public University 1977 1999

38 Cory Toole 979-458-0182 corytoole@tees.tamus.edu College Station TX USA Public University 1978 1996

39 Cory Toole 979-458-0182 corytoole@tees.tamus.edu College Station TX USA Public University 1997

40 Cory Toole 979-458-0182 corytoole@tees.tamus.edu College Station TX USA Public University 1997

41 Cory Toole 979-458-0182 corytoole@tees.tamus.edu College Station TX USA Public University 1996

42 Cory Toole 979-458-0182 corytoole@tees.tamus.edu College Station TX USA Public University 1995 1997

43 Cory Toole 979-458-0182 corytoole@tees.tamus.edu College Station TX USA Public University 1971 1997
Page 216 of 239

Project 

Ref 

Number

TECHNICAL 

INFORMATION 

Floor 

area 

units

Total floor 

area 

# issues found 

in each Cx'd 

system? 

- If no Cx, leave cell 

empty.

- If Cx but no issues 

were found, enter 0. 

- If same issue 

occurred multiple 

times or places, 

count as 1 issue. 

System categories 

below include 

controls related to 

the system

HVAC 

system 

integration 

(EMCS/ 

BAS)

Chilled 

water plant 

and 

distribution 

system

Heating 

water plant 

and 

distribution 

system

Domestic 

hot water

Heat 

pump 

system

Air handling 

and 

distribution: 

Overhead 

system

Air handling 

and 

distribution: 

Underfloor 

system

Terminal 

units

Thermal 

energy 

storage

Radiant 

heating

Radiant 

cooling

Passive 

heating/ 

cooling

1 m2 1149

2 m2 1524 6 Y Y Y

3 m2 1512 6 Y Y Y

4 m2 1636 7 Y Y Y Y Y Y 0

5 m2 12900 4 Y Y Y

6 m2 3472 5 Y Y

7 ft2 1431500 7 Y Y Y Y Y

8 m2 4189

9 m2 9446

10 m2 106363 2 Y Y 0

11 m2 6 Y Y Y Y Y

12 m2 5400 6 Y Y Y Y Y

13 m2 29869 2 Y Y

14 m2 41000 4 Y Y Y Y

15 m2 120000

16 m2 100000 4 Y Y Y Y

17 m2 90000 4 Y Y Y Y

18 m2 20000 3 Y Y Y

19 m2 18000 2 Y 0 0 Y

20 m2 20000 6 Y Y Y Y Y

21 m2 31000 4 Y Y 0 Y Y

22 m2 10000 3 Y Y Y
Page 217 of 239

Project 

Ref 

Number

TECHNICAL 

INFORMATION 

Floor 

area 

units

Total floor 

area 

# issues found 

in each Cx'd 

system? 

- If no Cx, leave cell 

empty.

- If Cx but no issues 

were found, enter 0. 

- If same issue 

occurred multiple 

times or places, 

count as 1 issue. 

System categories 

below include 

controls related to 

the system

HVAC 

system 

integration 

(EMCS/ 

BAS)

Chilled 

water plant 

and 

distribution 

system

Heating 

water plant 

and 

distribution 

system

Domestic 

hot water

Heat 

pump 

system

Air handling 

and 

distribution: 

Overhead 

system

Air handling 

and 

distribution: 

Underfloor 

system

Terminal 

units

Thermal 

energy 

storage

Radiant 

heating

Radiant 

cooling

Passive 

heating/ 

cooling

23 m2 32000 4 Y Y 0 Y 0 Y

24 m2 13702 2 Y Y

25 m2 17000 3 Y

26 m2 12000 5 Y Y

27 m2 12500 4 Y Y Y

28 m2 10000 4 Y Y Y

29 ft2 11400

30 ft2 200460 1 Y

31 ft2 125000 1

32 ft2 589140 0

33 ft2 255490 0

34 ft2 180316 0

35 ft2 177838 0

36 ft2 130844 0

37 ft2 158979 4 Y Y Y

38 ft2 165030 0

39 ft2 97920 0

40 ft2 113699 0

41 ft2 114666 0

42 ft2 192001 0

43 ft2 258600 0
Page 218 of 239

Project 

Ref 

Number

TECHNICAL 

INFORMATION 

CONTINUED 

Natural 

ventilation 

or mixedmode 

ventilation

Lighting/ 

daylighting 

and lighting 

controls

Electrical

Envelope 

and 

infiltration

Utilityrelated 

(electric, 

gas, water, 

emergency 

power)

Other - 

Explain in 

column E

COST DATA 

CONTINUED - 

Give costs in 

year of original 

data; do not 

correct for 

inflation

Currency Commissioning 

costs

Cost unit 

(currency or 

labor hours)

 Total Cx 

costs 

 Tasks 

performed - 

Select "Y" if 

task was 

performed

Document 

owner's project 

requirements

1 EUR (euro) Currency

2 Y Y Y EUR (euro) Currency 5000

3 Y Y Y EUR (euro) Currency 5000

4 0 Y EUR (euro) Labor 150

5 Y EUR (euro) Y

6 Y Y Y EUR (euro) Currency

7 Y Y CAD (Canadian dollar) Currency 199300 Y

8 CAD (Canadian dollar) Currency 90000

9 JPY (Japanese yen) Currency 10480000 Y

10 JPY (Japanese yen) Currency 19800000

11 Y JPY (Japanese yen) Labor 1200 Y

12 Y JPY (Japanese yen) Labor 5000

13 JPY (Japanese yen) Currency 20000000

14 JPY (Japanese yen) Labor 200

15 EUR (euro) Currency 185000

16 0 EUR (euro) Currency 26000 Y

17 EUR (euro) Currency 27000 Y

18 EUR (euro) Currency 2000

19 EUR (euro) Currency 15200 y

20 Y EUR (euro) Currency 51000 y

21 EUR (euro) Currency 15200 y

22 EUR (euro) Currency 22000 y
Page 219 of 239

Project 

Ref 

Number

TECHNICAL 

INFORMATION 

CONTINUED 

Natural 

ventilation 

or mixedmode 

ventilation

Lighting/ 

daylighting 

and lighting 

controls

Electrical

Envelope 

and 

infiltration

Utilityrelated 

(electric, 

gas, water, 

emergency 

power)

Other - 

Explain in 

column E

COST DATA 

CONTINUED - 

Give costs in 

year of original 

data; do not 

correct for 

inflation

Currency Commissioning 

costs

Cost unit 

(currency or 

labor hours)

 Total Cx 

costs 

 Tasks 

performed - 

Select "Y" if 

task was 

performed

Document 

owner's project 

requirements

23 EUR (euro) Currency y

24 NOK (Norwegian krone) Currency 33000

25 Y Y NOK (Norwegian krone) Currency 110000

26 Y Y Y NOK (Norwegian krone) Currency 110000

27 Y NOK (Norwegian krone) Currency 110000

28 Y NOK (Norwegian krone) Currency 110000

29 USD (US dollar) Currency 3000

30 USD (US dollar) Currency

31 Y USD (US dollar) Currency 71693

32 USD (US dollar) Currency 180554 Y

33 USD (US dollar) Currency 77324

34 USD (US dollar) Currency 99050

35 USD (US dollar) Currency 49525

36 USD (US dollar) Currency 27344

37 Y USD (US dollar) Currency 53035

38 USD (US dollar) Currency 59054

39 USD (US dollar) Currency 41894

40 USD (US dollar) Currency 44541

41 USD (US dollar) Currency 52314

42 USD (US dollar) Currency 66423

43 USD (US dollar) Currency 105591
Page 220 of 239

Project 

Ref 

Number

COST DATA 

CONTINUED - 

Give costs in 

year of original 

data; do not 

correct for 

inflation

Develop 

Commissio

ning Plan

Utility bill 

analysis

Bench 

marking

Trend 

analysis (for 

example, 

EMCS, data 

logging,etc)

Building 

energy 

modeling/ 

simulation

Use of 

diagnostic 

tools and 

Cx 

automation 

techniques. 

List tools 

and/or 

methods 

used in 

column E

Document 

master list 

of findings

Calculate 

energy 

cost 

savings 

for 

findings

Present a 

findings and 

recommendati

ons report

Update 

system 

documentati

on after 

implementat

ion 

Implement 

operations and 

maintenance 

(O&M) 

improvements 

Implement 

capital 

improvements 

Monitor 

and 

verify 

energy 

savings 

Monitor 

implemented 

measures for 

persistence 

of benefits

Develop 

systems 

manual/ 

recommissio

ning manual

Final 

commission

ing report

Other - 

Explain in 

column E

1 Y Y Y Y Y Y Y Y Y Y Y Y

2 Y Y Y Y Y Y

3 Y Y Y Y Y Y

4 Y Y Y Y

5 Y Y Y Y Y Y Y Y

6

7 Y Y Y Y Y Y Y Y Y Y Y Y

8 y Y Y Y Y Y Y Y Y Y Y Y

9 Y Y Y Y

10 Y Y Y Y Y Y Y

11 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

12 Y Y Y Y Y Y Y Y Y Y

13

14 Y Y Y Y Y Y Y Y Y

15 Y Y Y Y

16 Y Y Y Y Y Y Y

17 Y Y Y

18 Y Y

19 y y y y

20 y y y y y y

21 y y y y

22 y y y y
Page 221 of 239

Project 

Ref 

Number

COST DATA 

CONTINUED - 

Give costs in 

year of original 

data; do not 

correct for 

inflation

Develop 

Commissio

ning Plan

Utility bill 

analysis

Bench 

marking

Trend 

analysis (for 

example, 

EMCS, data 

logging,etc)

Building 

energy 

modeling/ 

simulation

Use of 

diagnostic 

tools and 

Cx 

automation 

techniques. 

List tools 

and/or 

methods 

used in 

column E

Document 

master list 

of findings

Calculate 

energy 

cost 

savings 

for 

findings

Present a 

findings and 

recommendati

ons report

Update 

system 

documentati

on after 

implementat

ion 

Implement 

operations and 

maintenance 

(O&M) 

improvements 

Implement 

capital 

improvements 

Monitor 

and 

verify 

energy 

savings 

Monitor 

implemented 

measures for 

persistence 

of benefits

Develop 

systems 

manual/ 

recommissio

ning manual

Final 

commission

ing report

Other - 

Explain in 

column E

23 y y y y

24 y Y Y Y Y Y Y Y

25 Y Y Y Y Y Y Y Y

26 Y Y Y Y Y Y Y

27 Y Y Y Y Y Y Y

28 Y Y Y Y Y Y Y

29

30 Y Y Y Y

31 Y Y Y Y Y Y Y Y Y Y

32 Y Y Y Y Y Y Y Y Y Y Y

33

34 Y Y

35

36 Y Y

37 Y Y Y Y Y Y

38 Y Y

39

40

41 Y Y

42

43
Page 222 of 239

 Average cost 

per unit (in the 

first year after 

commissioning)

Unit

 Average cost 

per unit (in the 

first year after 

commissioning)

Unit Fuel type

Avg cost/unit 

(in the first 

year after Cx)

Unit

Avg cost/unit 

(in the first 

year after Cx)

Unit

Avg 

cost/unit (in 

the first year 

after Cx)

Unit

 Average cost per unit 

(in the first year after 

commissioning)

Unit

Project Data 14 EUR/kWh

Project Data 0.155 EUR/kWh Natural gas 0.019444444 EUR/MJ

Project Data 0.155 EUR/kWh Natural gas 0.019444444 EUR/MJ

Project Data Natural gas

Project Data 0.0526 CAD/kWh 11.85 CAD/kW Natural gas 0.48 CAD/m3

Project Data 0.074 CAD/kWh Natural gas 0.54 CAD/m3

Project Data 9.98 JPY/kWh 1774.5 JPY/kW

Project Data 8.99 JPY/kWh 1517 JPY/kW 0.157 JPY/Liters

Comments

on Cx (but 9.890 

in summer 

daylight & 4.500 

at night) Average in 2005

Project Data 0.7 NOK/kWh 0.7 NOK/kWh

Project Data 0.078 USD/kWh 0.121 USD/kW Natural gas

Project Data 0.55 NOK/kWh 460 NOK/kW 15 NOK/Other

Comments Incoming + outlet water 

Project Data 0.55 NOK/kWh 460 NOK/kW Fuel oil 0.5 NOK/Therms 15 NOK/Other

Comments

The cost is per 

kWh Incoming + outlet water 

Project Data 0.55 NOK/kWh 460 NOK/kW Fuel oil 0.5 NOK/Therms 15 NOK/Other

Comments Incoming + outlet water 

Project Data 0.02788 USD/kWh 4.67 #N/A 4.75 USD/MMBTU

Project Data 0.02788 USD/kWh 4.67 #N/A 4.75 USD/MMBTU

Project Data 0.02788 USD/kWh 4.67 #N/A 4.75 USD/MMBTU

Project Data 0.02788 USD/kWh 4.67 #N/A 4.75 USD/MMBTU

Project Data 0.02788 USD/kWh 4.67 #N/A 4.75 USD/MMBTU

Project Data 0.02788 USD/kWh 4.67 #N/A 4.75 USD/MMBTU

Project Data 0.02788 USD/kWh 4.67 #N/A 4.75 USD/MMBTU

Project Data 0.02788 USD/kWh 4.67 #N/A 4.75 USD/MMBTU

Project Data 0.02788 USD/kWh 4.67 #N/A 4.75 USD/MMBTU

43 Project Data 0.02788 USD/kWh 4.67 #N/A 4.75 USD/MMBTU

Electricity Electric demand Fuel District hot water Water

District chilled 

water

Project 

Ref 

Number

SOURCE, 

UNIT & 

COST

30

1

31

32

9

7

2

3

4

42

36

38

39

40

41

33

34

35

10

26

24

8
Page 223 of 239

RESOURCE 

SAVINGS Savings from implemented measures (kWh)

 Savings from 

implemented measures 

(kW)

 Savings from 

implemented measures 

(m3)

 Savings from implemented 

measures (MJ)

 Savings from 

implemented measures 

(MMBTU)

 Savings from 

implemented 

measures (liters)

Project Data 8310 73443

Project Data 4047000 949 178800

Project Data 60622 164 59898

Project Data 283410

Project Data 22100 41

Comments 468500-4464000 1895-1854

Project Data 29570

Project Data 30

Project Data 113000 1800 247000 1424000 2600000

Project Data 142382

Project Data 334167

Project Data 126523

Comments

In 2004, there is no proof that all the suggested 

measures have been implemented

Project Data 173288

Project Data 67059

Project Data 1059000 6232 4642

Comments Based on 1st year after RCx

 

after RCx Based on 1st year after RCx

Project Data 1193000 11779 5006

Comments Based on 1st year after RCx

MMBTU - Based on 1st year 

after RCx Based on 1st year after RCx

Project Data 183000 10155 15064

Project Data 369000 7070 4293

Project Data 2545 781 -244

Project Data 53000 24407 34289

Project Data 339000 9787 1399

Project Data 35000 14927 11662

Project Data 220000 17777 2682

Project Data 145000 6866 2517

43 Project Data 740000 24087 6046000

Project 

Ref 

Number

Electric demand Fuel District chilled water District hot water Water

30

Electricity

1

31

32

9

7

42

36

37

38

39

40

41

33

34

35

10

12

13

26

24

14

8
Page 224 of 239

APPENDIX B: The State of Building Systems Commissioning

The following sections on the Asia Pacific, Europe, and North America regions provide an 

overview of the state of building systems commissioning. Each national assessment aims to 

present the national drivers, leading efforts and an indication of their success (if known). Fifteen 

countries are represented in this review. 

Asia Pacific

In the Asia Pacific region, the construction industry is experiencing unparalleled growth. 

China alone reports the annual construction of two billion m2

s of floor space, (Qiu, 2008). The 

environmental impact of this growth has global repercussions. It is therefore critical that the 

building industry adopt building commissioning as a standard practice to improve the quality and 

efficiency of design, construction, and operations. 

Asia Pacific countries are leveraging national energy laws, institutional mandates, and non-profit 

organizations to foster development of building systems commissioning. For several countries in 

the region, recent exposure through international symposia and conferences sparked interest that 

has developed into national research projects. This exchange of information is particularly 

useful for countries that have natural synergies, such as climate, that facilitate transfer of 

technologies or guidelines. Through these cooperative exchanges, countries are able to leapfrog 

technologies and accelerate adoption. 

Australia: Indoor environmental quality assessments, rising energy costs, and a greater 

understanding of the link between comfort and productivity are driving the demand for improved 

energy efficiency of buildings in Australia. At present, most of the commissioning projects are 

associated with major contracts and do not address smaller projects. Furthermore, the fully 

integrated approaches to project commissioning, promoted by the Chartered Institution of 

Building Services Engineers (CIBSE) and American Society of Heating, Refrigerating and Airconditioning Engineers (ASHRAE) commissioning guidelines, are not well understood by the 

commercial sector, and this educational need is yet to be addressed (EcoLibrium 2006). 

Awareness of the importance of the commissioning process is increasing, largely due to the 

market influence of voluntary, performance-based rating schemes such as the National 

Australian Built Environment Rating System and the Green Building Council’s Green Star 

program. Through this mechanism, several large commercial real estate owners and 

governmental agencies have made commitments to achieve a certain rating in their building 

stock. The implementation and promotion of these rating schemes have the potential to trigger 

the demand for energy audits and commissioning in the existing building stock. Deakin 

University is developing rapid reporting techniques to facilitate building performance 

improvement in the commissioning process (Nakahara et al. 2007).

China: Mainland China has seen several construction booms. In the 1980’s the rapid 

growth of residential construction prompted the Ministry of Construction to develop energyefficient building codes. These codes did not include commissioning and quality, in general, was 

considered poor. In the 1990s, testing and commissioning concepts diffused into the Chinese 
Page 225 of 239

buildings industry as part of the western management techniques from numerous overseas, joint 

venture construction projects (Chow et al. 2006). International partnerships now also extend to 

academic research. Chinese universities, including Tsinghua University, are conducting research 

on building optimization, building commissioning and other related work. In 2008, China’s 

Ministry for Construction released acceptance codes for building services equipment and 

building systems, though no information could be obtained regarding the administration and 

enforcement of these requirements.

Hong Kong was one of the first adopters and developers of the building commissioning 

process due to early exposure to the United Kingdom’s commissioning model. As early as 1990, 

the Hong Kong Government published twelve booklets based on the CIBSE Commissioning 

Codes. The documents were developed into commissioning specifications released in 2002 and 

are intended to be incorporated into government building project contracts (Chow et al. 2006). 

Furthermore, in 2004, Hong Kong introduced a new voluntary scheme, termed the Consolidated 

Environmental Performance Assessment Scheme (CEPAS), to promote the design of 

environmentally-friendly buildings. CEPAS incorporates building commissioning as a major 

element for performance assessment. 

The Hong Kong Building Commissioning Centre (HKBCxC) was established in 

December 2004 as a non-profit organization whose mission is to promote the establishment of a 

standardized approach to building commissioning in Hong Kong. The HKBCxC organizes 

programs for continuing professional development, certification services and publishes 

guidebooks on the management of building commissioning, field measurements, and system 

tuning. In the “Practical Guide to Building Commissioning Management”, Chow et al (2006) 

report that independent contracts for commissioning and retro-commissioning are soaring in 

Hong Kong.

Japan: In 2006, building energy performance reporting became mandatory under the Energy 

Conservation Law. The reports are based on simple performance tests of the components and 

systems that have the largest impact on the energy consumption of heating, ventilation, and airconditioning systems. In practice, there is significant variation in the implementation approach 

because no standard test procedures are specified and it is unclear whether relevant problems 

could be investigated adequately through the data contained in the reports. Hence, aspects of the 

commissioning process are drawing more attention in Japan’s building sector. In existing 

buildings, various approaches for retro-commissioning are commonly implemented, but initial 

commissioning for new construction is not common. 

In 2005 the Society of Heating, Air-Conditioning and Sanitary Engineers of Japan (SHASE) 

technical committee on commissioning issued a guideline on the building services 

commissioning process. The Building Services Commissioning Association (BSCA), a nonprofit organization launched in 2004, provides seminars about commissioning technologies in 

major cities and has undertaken cooperative activities with Asian countries such as China 

(including Hong Kong), Taiwan, and Korea. It also continues to compile commissioning 

documentation and tools through actual commissioning projects and research. BSCA’s strategy 

is to establish a certification program for commissioning engineers, including the 

Commissioning Authority, and to educate the construction industry and related government 

sectors.
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Energy policy is playing an important role. The Ministry of Economy, Trade and Industries is 

interested in a new business model, based on building commissioning, to enhance energy 

efficiency of new and existing buildings and The Ministry of Land, Infrastructure and 

Transportation is promoting use of lifecycle energy management with a newly developed 

simulation tool. Market demand for commissioning is believed to be strong but mandates, based 

on energy and environmental policy, are needed for building owners to apply building 

commissioning to new construction.

Taiwan: In Taiwan, building commissioning for new construction is considered established 

practice. The Taiwanese government has issued a mandate requiring that all public projects with 

a budget over 15 million U.S. dollars acquire the Green Building Label before a construction 

permit can be granted. Testing, adjusting and balancing (TAB), commissioning, and the use of a 

building energy management system (BEMS) are prerequisites. However, the process 

implemented in Taiwan does not represent the full range of actions from design to operations. 

The commissioning role in Taiwan for existing buildings is typically similar to that served by 

energy service companies in Japan and the USA. Taiwan is involved in international activities to 

expand national practices as deemed necessary. For example, a review of the Green Building 

Label (similar to Leadership in Energy and Environmental Design (LEED) for new construction 

in the USA and CASBEE in Japan) suggested that a commissioning plan be added. 

A national-scale project was launched in 2003, by the Architecture and Building Research 

Institute, Ministry of the Interior, to renovate all the central heating, ventilation, and airconditioning (HVAC) systems in governmental buildings for energy conservation. The 

renovation process includes system diagnostics, remedial strategies, establishing engineering 

jobs, contracting, TAB, commissioning, and system performance validation through the BEMS. 

To date, 22 million USD have been spent with an overall energy-savings of 22 % and an average 

payback of five years. The success has since led to a series of demonstration projects for civil 

and governmental buildings. In 2008, Taiwan will launch another five-year program where the 

energy savings effort will be enhanced with greater system fine-tuning and commissioning as a 

means to support the Kyoto Protocol and global CO2 emission reduction efforts.

Korea: The Korean Institute for Energy Research (KIER) is working to promote the 

commissioning process through energy conservation and quality assurance measures for new 

construction. Commissioning projects have been carried out by KIER as part of their research 

work, and have also been implemented in several buildings financed by foreigners. However, 

there is no recognized national standard and the Korean government has no intention to mandate 

the commissioning process. 

Europe: The countries of the European region present significant differences in their building 

delivery processes as well as their emphasis on energy efficiency and measures for quality 

assurance. For most, with the exception of the UK, the commissioning process is quite new. 

However, the European Commission established the European Performance of Buildings 

Directive, EPBD (EC 2002), to promote the improvement of energy efficiency and building 

performance. Four requirements to be implemented by the Member States are to: 1) develop a 

framework for a methodology to calculate the integrated performance of buildings, 2) set 
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minimum standards in new and existing buildings, 3) certify the energy performance of 

buildings, and 4) inspect and assess heating and cooling installations.

According to reports from the member states, the EPBD poses significant challenges in terms of 

its practical implementation, including difficulties associated with the transfer of requirements 

into existing building practices under a range of climates. However, because the commissioning 

process is well aligned with the goals of the EPBD, several national research programs are 

introducing commissioning tools as a means to address the requirements of the directive. In 

many countries, commissioning tasks are focused on the building handover, or performed as part 

of the facilities management. However, for commissioning to have a real impact on savings, the 

review must begin at the pre-design phase, where changes are easier and more cost-effective to 

make. It is anticipated that the increased attention to energy efficiency in buildings will lead to 

greater application and consistency of commissioning through the building lifecycle.

Belgium: Commissioning research has been underway in Belgium for several years, and 

mainstream awareness of the importance of commissioning of low-energy buildings has 

increased due to the implementation of the EPBD and the introduction of the passive house 

concept for very low energy building. Energy performance laws, which set requirements for the 

energy performance and indoor climate for most buildings requiring a building permit, exist in 

the Flemish Region and are under development in the Brussels and Walloon regions. 

Czech Republic: Building commissioning is a new concept in the Czech Republic. Only some 

aspects of building commissioning are implemented as a part of the facility management and 

energy auditing processes that are related to EPBD implementation. Under the IEA Annex 47 

project, researchers have developed tools that support additional aspects of the broader 

commissioning process, including new control system energy services.

Finland: Historically, individual contractors and builders in Finland have managed 

commissioning-related activities as part of their quality assurance measures. More recently, 

emphasis has been placed on the development and implementation of "energy auditing" 

procedures. In 2002, a Finnish national research program called CUBE was launched to improve 

the performance of building services. This program includes a national R&D project to develop 

Finnish procedures for building commissioning, focusing on the indoor air quality and energy 

efficiency of buildings. The Finnish term and concept of "toimivuuden varmistaminen (ToVa)," 

an adaptation of building systems commissioning, is being promoted. Practical testing and 

further development of the guidebook and tools are underway. Methods and tools to support the 

commissioning of buildings and their subsystems throughout the phases of the building life-cycle 

are being developed.

France: The commissioning process is just beginning to take hold in France and a national 

guideline on commissioning is under development. In practice, commissioning is implemented in 

the operations phase, though there is greater interest from large building owners for more 

complete commissioning plans as a result of ongoing national research and new requirements 

under the EPBD. Current research is focused on automating the commissioning process to 

improve performance, pushing for early implementation of the commissioning process from 
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design through certification, and developing tools and procedures for specific building 

applications (e.g., schools).

Germany: Commissioning for new construction is not established or even required as a third 

party service in Germany. German law (HOAI) dictates that architects and design engineers 

perform the following tasks within the construction administration and construction supervision:

• supervising acceptance and performance tests and statement of deficiencies;

• collecting/compiling and delivering as-built documentation, operating manuals, and 

acceptance protocols; and

• supervising the rectification of deficiencies that fall under the two-year contractor’s 

warranty period or the five-year design team warranty period from date of acceptance.

This approach, initiated in 1976, presents a model for internal commissioning during the 

construction phase and, in conjunction with energy conservation laws, has resulted in higher 

performing buildings. Efforts to implement the full commissioning process beginning in predesign have been more recent. In one German national research program on energy optimization 

in buildings (ENOB), more than twenty demonstration buildings surpassed national energy 

consumption standards by 50 % without incurring additional building costs. The program has 

been extended and now supports the design process, commissioning, and monitoring of the first 

two years of operation. ENOB also supports several projects focusing on improved 

commissioning and operation of innovative buildings. Energy agencies and utilities support 

energy efficiency through contracting and public private partnerships. Two other projects, 

ModBen and Building EQ, deal with performance evaluation of existing buildings.

The Netherlands: In the Netherlands, practitioners agree on the importance of building 

commissioning. However, it is currently only implemented in the installation and formal 

handover of selected buildings. A pilot project on functional performance tests conducted in 

forty buildings identified that 70 % of the systems tested were malfunctioning, leading to 

increased energy use and reduced comfort. To improve system operation, large-building owners 

are investigating performance contracting for operations and maintenance, based on well-defined 

criteria. The commissioning process plays an important role. The Netherlands Organization for 

Applied Scientific Research is involved in the development of national standards concerning the 

energy performance of new buildings to promote the implementation of the whole 

commissioning process. Norway: Building commissioning is not an established practice in 

Norway. A draft national commissioning guideline was developed in 2007 to promote the lifelong commissioning of building HVAC systems. The guideline is currently under review and 

several large governmental and private building owners are involved in the effort to verify, 

document and implement suitable tools to provide continuous control of energy and indoor 

environment during the life of the building.

United Kingdom: The UK developed the earliest commissioning codes and provided the basis 

for similar work in many other countries. CIBSE published the first commissioning code on airdistribution systems in 1960 and subsequently released codes for other types of equipment. The 

emphasis of the commissioning codes was originally post-construction commissioning. Bordass 

(2008) reports that independent commissioning engineers were usually appointed as part of the 

design and build team in the late 1970’s and early 1980’s. However, as markets became more 

competitive, in the late 1980’s and 1990’s, commissioning was less commonly sold as a separate 
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service and eventually became a subcontractor role, which had a negative impact on quality due 

to cost-cutting pressures. Today, initial commissioning has become routine for large projects 

and quality has once again improved but there are still major challenges in that the 

commissioning period is often squeezed when other delays impact the delivery date. The current 

set of CIBSE commissioning codes includes: air distribution systems, boilers, automatic controls, 

lighting, management (Code M), refrigeration, and water distribution systems. Code M shows 

an important change in the approach to the initial commissioning process by stating that the 

commissioning manager should be appointed early in the design phase in order for the system to 

be designed as commissionable. 

Other organizations have also been directly involved in improving the commissioning industry. 

The Building Services Research and Information Association (BSRIA), a non-profit consulting 

design, construction, and operations organization is leading the “Soft landings” development 

with support from the Usable Buildings Trust, a non-profit organization dedicated to improving 

building performance through the better use of feedback. The Soft landings procedure aims to 

reduce the loss of information by extending the service of the design and building (and 

commissioning) team to facilitate fine-tuning and debugging at building handover in order to 

achieve a closer match between design targets and building operation, and to allow individuals to 

learn from the experience (BSRIA 2008). The Commissioning Specialists Association (CSA) is 

focused on career development, including training and certification of individuals. 

Although retrocommissioning is not widespread, there is expected to be a major increase in its 

implementation as demand for improved energy and carbon performance increases, driven by 

new requirements of the EPBD, occupants, and building owners.North America

In North America, the concept of building commissioning began with the Code of Practice for 

Commissioning Mechanical Systems in Buildings that was developed in 1986 by the Standing 

Committee of Consulting Engineers and Mechanical Contractors of British Columbia. Today 

there are several, industry-recognized guidelines on the commissioning process: ASHRAE 

Guideline 1-1989 The HVAC Commissioning Process (revised in 1996), and ASHRAE 

Guideline 0-2005, The Commissioning Process. Although building systems commissioning is 

established practice in both Canada and the USA, the process is not widespread. Many of the 

existing resources are focused on conventional HVAC systems and there is need for information 

on other types of systems, particularly due to increased interest in non-conventional, low energy 

systems.

In Canada and the USA, awareness has increased through professional organizations, 

certification programs (e.g., LEED), large-owner mandates, and energy-efficiency initiatives. 

However, market barriers are significant. There is a need for greater awareness of the benefits 

and cost of commissioning to increase demand, and a need for more training and certification of 

commissioning providers to increase the supply. At present, building owners lack the access to 

experience and lessons learned in easily accessible, creditable and persuasive formats that would 

facilitate their investment decisions. Furthermore, tools and standardization are needed to reduce 

the cost of commissioning and improve the cost-benefit ratio for greater uptake. This includes 

automated tools, data on cost-benefits, and clear specifications for key building performance 

metrics, monitoring methods, and energy calculation methods. Details of national initiatives 

follow.
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Canada: Existing building commissioning in Canada is at the early stage of its development. 

While the demand for commissioning services remains stagnant, there are encouraging signs of 

activity transformation favorable to the creation of a better structured and more efficient market 

place. As more and more building owners and managers strive for energy conservation and 

building sustainability, their first step consists to improve their building operation efficiency. 

With the increase of energy prices, low cost technology availability and their practical 

advantages have strengthened the demand for energy-saving HVAC controls. 

In order to support institutional and commercial businesses in this shifting market, the Ministry 

of Natural Resources Canada and its energy science and technology organization, 

CanmetENERGY, provides knowledge, services and tools for recommissioning, commissioning 

and ongoing commissioning projects. They offer to building engineers, technicians, owners, 

managers and other similar stakeholders a suite of capacity-building and decision-making tools, 

including guides, training material, awareness seminars, pre-screening and benchmarking tools 

and case studies. CanmetENERGY developed an application software tool (DABOTM) to 

continuously monitor, detect and diagnose the operation inconsistencies and poor performances 

of a building’s electromechanical systems. 

Furthermore, the Demand Side Management Working Group (DSMWG) created by Natural 

Resources Canada collaborates with utilities, public works, green building associations and 

standard organizations on energy management to accelerate the training and certification of 

commissioning providers and to raise stakeholders’ awareness on the benefits of commissioning 

work. The persistence of energy savings has also attracted the interest of DSMWG and action 

has been undertaken to implement a new Energy Management Information System (EMIS) 

component to the recommissioning process.

It is also worth mentioning that the Canadian Standard Association is preparing a new standard 

on commissioning that should come into effect in 2011. 

USA: Rising energy costs and a shift in public policy that emphasizes the need for energy 

independence are driving energy conservation. In the absence of national commissioning 

requirements, individual state codes and institutional mandates have proven to be strong market 

drivers, though enforcement mechanisms have been problematic.

The buildings industry is working to address market barriers and improve the quality of its 

services. Non-profit organizations, including ASHRAE, Portland Energy Conservation, Inc. 

(PECI), the California Commissioning Collaborative and the Building Commissioning 

Association (BCA), provide access to industry resources for both providers and building owners, 

including: 

A library of published papers and commissioning guides for retro-, initial, and on-going 

commissioning; tools: design guides, operations and maintenance best practices, case studies 

database; Sample documents: commissioning plans, specifications, functional tests, checklists; 

and training and certification programs, career and provider directories.

In recent years, market indicators show growth in the number of firms offering commissioning 

services. In 2008, 267 commissioning provider firms and 665 members registered with the BCA. 

There has also been a dramatic increase in commissioning certification and training provided by 
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professional organizations, which indicate a demand by engineering professionals for more 

knowledge. 

Utilities and government agencies have also invested resources on research and technology 

demonstration activities in an effort to stimulate the market for commissioning. More 

investment is needed to significantly improve the energy efficiency of the existing building 

stock, to meet the low energy targets for new construction, and to address the projected shortage 

of skilled and certified providers. 
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APPENDIX C: Relevant Papers and Presentations 

Claridge, D.E., “Methodologies for Determining Persistence of Commissioning Benefits,” Proc. of 

7th International Conference for Enhanced Building Operations – Maximizing Building Energy 

Efficiency and Comfort Permission to reprint granted on 12/1/11.

Toole, C. and Claridge, D.E., “Review on Persistence of Commissioning Benefits in New and 

Existing Buildings”, Proc. of 7th International Conference for Enhanced Building Operations –

Maximizing Building Energy Efficiency and Comfort, Part II, Paper VI-4-4, Shenzhen, China, 

November 6-8, 2006, 15 pp. Permission to reprint granted on 12/1/11.

Lin, G. and Claridge, D.E., “Retrospective Testing Of An Automated Building Commissioning 

Analysis Tool (ABCAT),” Proceedings of the 3rd International Conference On Energy 

Sustainability ASME, July 19-23, 2009, San Francisco, CA, USA. Copyrighted by ASME. 

Permission to reprint granted on 12/1/11.
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Insert PDF files of 3 papers and check page numbering.
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Page 235 of 239

Appendix D: 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
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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.
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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