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Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Reducing Transaction Costs for 

Energy Efficiency Investments 

and Analysis of Economic Risk 

Associated With Building 

Performance Uncertainties

Small Buildings and Small Portfolios 

Program

Rois Langner, Bob Hendron, and Eric Bonnema

Technical Report

NREL/TP-5500-60976

August 2014
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Reducing Transaction Costs 

for Energy Efficiency 

Investments and Analysis of 

Economic Risk Associated 

With Building Performance 

Uncertainties

Small Buildings and Small Portfolios 

Program

Rois Langner, Bob Hendron, and Eric 

Bonnema

Prepared under Task No. BE4C.8101

Technical Report

NREL/TP-5500-60976

August 2014
NOTICE

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express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

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trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

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Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.

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iii

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Acknowledgments

The authors would like to thank Glenn Schatz, Arah Schuur, and the U.S. Department of Energy 

(DOE) Building Technologies Office for supporting the project. This report was prepared by the 

National Renewable Energy Laboratory (NREL) under Project ID NREL-FY13-01.

We would also like to thank our key pilot project partners, Mary Templeton at Michigan Saves;

Angela Ferrante (formerly at Energi); Kevin Kaminski, Patrick Grogan, and Chris Lohmann at 

Energi; as well as their partners who have greatly contributed to this research, including Anne 

Saliers at Holland Board of Public Works. Lastly, we would like to acknowledge Brian Ball, 

Nick Long, and Katherine Fleming at NREL for technical support, and Jennifer Scheib, Kristin 

Field, Stefanie Woodward, and the NREL communications team for reviewing and editing the 

document. 
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Nomenclature

AEDG Advanced Energy Design Guide

AERG Advanced Energy Retrofit Guide

ASHRAE American Society of Heating, Refrigerating and Air-Conditioning 

Engineers

CBECS Commercial Buildings Energy Consumption Survey

COP coefficient of performance

DOE U.S. Department of Energy

EEM energy efficiency measure

EER energy efficiency ratio

EIA U.S. Energy Information Administration

EUI energy use intensity

GSA General Services Administration

HVAC heating, ventilating, and air conditioning

IEA International Energy Agency

LBNL Lawrence Berkeley National Laboratory

LHS Latin Hypercube Sampling

LPD lighting power density

NREL National Renewable Energy Laboratory

O&M operation and maintenance

PGL Preservation Green Lab

PSZ-AC packaged single zone air conditioner

PTAC packaged terminal air conditioner

PTHP packaged terminal heat pump

SEER seasonal energy efficiency ratio

SBSP Small Buildings and Small Portfolios

TSD Technical Support Document
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Terms

Cost Effective: A net positive cash flow, meaning that the annual energy cost savings is greater 

than the annual payment of an energy efficiency investment.

Energy Cost Savings: A reduction in utility bills resulting from an investment in energy 

efficiency.

Energy Performance Guarantee: A guarantee from an energy service company or contractor 

ensuring that a certain level of energy savings is achieved.

Energy Savings Threshold: The minimum desired energy savings that a building owner or 

evaluator expects to achieve cost effectively. 

Energy Service Company: A business that provides a wide range of energy saving services to a 

building owner.

Energy Use Intensity: Normalized whole-building energy use by building square footage. EUI 

is represented in the units kBtu/ft2

/year.

Full Initial Costs: The total costs for an energy efficiency building improvement.

Incremental Costs: The additional cost of an efficiency improvement that goes beyond the cost 

of a code-minimum system.

Major Renovation: A comprehensive building renovation that includes an overhaul of the major 

building systems.

Net Cash Flow: The difference between the expected annual energy cost savings and the total 

first year cost for an energy efficiency building upgrade.

Net Initial Costs: Costs for energy efficiency building improvements that include relative first 

costs and incremental costs together.

Split Incentive: A dynamic between a building owner and a tenant where the energy savings 

benefits may not impact the person who pays for the transaction. 

Service Provider: A trade ally or a contractor hired to implement a building improvement.

Transaction Costs: All costs related to an energy efficiency building improvement, including a 

building energy audit, time spent researching energy efficiency improvements, etc.
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Executive Summary

The small buildings and small portfolios (SBSP) sector faces barriers that inhibit SBSP owners 

from adopting energy efficiency solutions. This pilot project focused on overcoming two of the 

largest barriers to financing energy efficiency upgrades in small buildings: disproportionately 

high transaction costs and unknown or unacceptable risk. Solutions to these barriers can often be 

at odds, because inexpensive turnkey solutions are often not sufficiently tailored to the unique 

circumstances of each building, reducing confidence that the expected energy savings will be 

achieved. To address these barriers, NREL worked with two lead partners, Michigan Saves and 

Energi Insurance Services, to develop technical solutions that provide a quick and easy process 

to encourage energy efficiency investments while managing risk. 

The pilot project included two stages: the first stage focused on reducing transaction costs, and 

the second stage focused on reducing performance risk. In the first stage, NREL worked with the 

nonprofit organization, Michigan Saves, to analyze the effects of 11 energy efficiency measures 

(EEMs) on 81 baseline small office building models, using the OpenStudio energy modeling 

software, in Holland, Michigan (climate zone 5A). The results of this analysis (totaling more 

than 30,000 cases) are summarized in a simple spreadsheet tool, called the EEM Selection & 

Cost Evaluation Tool

(http://www.nrel.gov/buildings/docs/2014_eemselection_costevaltool_smoffice.xlsm), which 

enables users to easily find low-risk small office EEM packages that meet a particular energy 

savings threshold and are likely to be cost effective. Snapshots of the tool can be found in 

Appendix A. In summary, the spreadsheet tool displays:

• More than 1,500 EEM package options that are expected to achieve 20% energy savings 

cost effectively, for projects where the EEM package is part of a major renovation

• Approximately 30 EEM package options that achieve greater than 15% energy savings 

cost effectively, for projects that are not planning major renovations

• More than 150 EEM package options that achieve greater than 10% energy savings cost 

effectively, for projects that are not planning major renovations

• Energy and cost savings predictions for more than 30,000 EEM/baseline combinations.

If a low-risk EEM package is identified and recommended for a particular building, it should be 

noted that the investment may still present some uncertainty. To increase the chances of 

achieving the estimated savings, a number of risk mitigation strategies are recommended to 

control and optimize building operation in a manner that mitigates performance risk. The 

recommended strategies include policies and controls to better manage building operation, 

maximizing occupant density to reduce the need to condition and power unused office space, and 

retrocommissioning/recommissioning to ensure that building equipment is operating at its 

maximum efficiency. These strategies are described in detail throughout the report and 

summarized in Appendix B. 

NREL also looked at the energy savings effects of individual EEMs. The results, displayed in 

Appendix C, show the effects of individual EEMs on building energy use averaged over all the 

energy models, averaged for stand-alone buildings and buildings adjacent to other buildings, and 
vii

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averaged for each heating, ventilation, and air-conditioning (HVAC) system type considered in 

the analysis. The key trends of this analysis include:

• Lighting, daylighting controls, and plug load EEMs have the greatest effect on reducing 

small office building energy use.

• Occupancy sensors also show strong energy savings in most of the small office building 

models.

• Reducing building leakage is highly impactful in older buildings, primarily pre-1980 

vintages.

• Upgrading to high-efficiency windows has the least impact on building energy use.

• Upgrades of buildings with existing packaged terminal air-conditioner systems tend to 

see higher energy savings than the other HVAC system types considered in this analysis.

In the second stage of the pilot project, NREL worked with the energy insurance company, 

Energi, to quantify performance risk and the uncertainty in cash flow associated with EEM 

packages that are designed and installed correctly. The purpose of this analysis was to quantify 

the effects of uncontrollable uncertainties that go beyond typical performance guarantees. The 

uncertainties include variations in weather, occupant behavior, fuel escalation rates, and quality 

of preventative maintenance. To quantify performance risk associated with these uncertainties, 

NREL used the software R to generate a random sample for the key input variables according to 

a probability distribution that were applied to a subset of the energy models analyzed in the first 

stage. The key findings include calculated “buffers” that owners and lenders can apply to 

projected energy cost savings (defined as the reduction in utility bills resulting from an 

investment in energy efficiency), in an effort to control performance risk to an acceptable level. 

In summary, the following observations were made: 

• Assuming that measures are designed and installed correctly, and that the building does 

not implement risk mitigation strategies to optimize building operation, we can be 

95% confident that the energy cost savings will exceed 66% of the expected value over a 

1-year time period.

• Over a longer time period, variations in energy savings lessen when averaged over 

multiple years and results improve. For a 3-year time period, we can be 95% confident 

that the energy cost savings will exceed 80% of the expected value. For a 5-year time 

period, we can be 95% confident that the energy cost savings will exceed 85% of the 

expected value.

• For buildings that implement risk mitigation strategies to optimize building operation, we 

again see improved results. In this case, it should be noted that NREL reduced the range 

of input values for performance risk variables related to occupant behavior by 50%, to 

show a correlation between more stringent building operation and energy cost savings. 

The results show that we can be 95% confident that the energy cost savings will exceed 

81% of the expected value over a 1-year time period.

• Looking at 3- and 5-year projections for projects that implement risk mitigation 

strategies, we see the least performance risk. For a 3-year time period, we can be 
viii

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95% confident that the energy cost savings will exceed 89% of the expected value. For a 

5-year time period, we can be 95% confident that the energy cost savings will exceed 

91% of the expected value.

NREL also looked at the effects of individual performance risk variables on energy cost savings. 

This study shows that the greatest uncertainties associated with the performance risk variables 

are due to fluctuations in occupant density, control of heating and cooling temperature set points 

that go beyond recommended bounds during occupied hours, and plug loads left on after 

working hours. Lights being left on after work hours and effects of weather have similar 

uncertainty ranges, but have lesser effects than the other variables that were analyzed. 

In summary, this pilot project enabled NREL to identify numerous low-risk EEM packages that 

are likely to be cost effective in small office buildings in Michigan. To further reduce the risk in 

each investment, we strongly recommend that owners install controls for building equipment, 

enforce policies surrounding building energy management, optimize space utilization to reduce 

the amount of energy used to condition unoccupied space, and use retrocommissioning to ensure 

that the building equipment is operating at its maximum efficiency.
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Table of Contents

1 Introduction........................................................................................................................................... 1

1.1 Energy Efficiency Packages That Achieve Energy Savings Cost Effectively for Small Office 

Buildings.......................................................................................................................................... 2

1.2 Available Literature on Small Office Characteristics and Efficiency Measures ............................. 3

1.2.1 Commercial Reference Building Models.......................................................................... 3

1.2.2 179D DOE Calculator....................................................................................................... 3

1.2.3 Advanced Energy Retrofit Guide for Office Buildings .................................................... 4

1.2.4 Advanced Energy Design Guides..................................................................................... 4

1.2.5 Technical Support Document: Large Office Buildings..................................................... 4

1.2.6 Asset Score Sensitivity Analysis....................................................................................... 5

1.2.7 Data From the Michigan Saves Program .......................................................................... 5

1.2.8 Commercial Building Energy Consumption Survey......................................................... 5

1.2.9 Data From Preservation Green Lab................................................................................... 5

1.3 Energy Model Development............................................................................................................ 6

1.3.1 Baseline Building Models................................................................................................. 6

1.3.2 Decision Tree/Energy Efficiency Measure Screening Questions..................................... 8

1.3.3 Small Office Energy Efficiency Measures...................................................................... 10

1.4 Energy Efficiency Measure Packages and Cost Data for Small Office Buildings......................... 13

1.5 Energy Savings for Individual Energy Efficiency Measures......................................................... 15

2 Analysis of Economic Risk Associated With Building Performance Uncertainties Not Covered 

by Performance Guarantees.............................................................................................................. 16

2.1 Data Sources for Performance Risk Analysis................................................................................ 16

2.1.1 Weather Data................................................................................................................... 19

2.1.2 Operating Conditions Data.............................................................................................. 19

2.1.3 Utility Price Escalation ................................................................................................... 21

2.1.4 Quality of Preventative Maintenance.............................................................................. 22

2.2 Statistical Analysis......................................................................................................................... 23

2.3 Results............................................................................................................................................ 24

3 Conclusions and Next Steps............................................................................................................. 40

References................................................................................................................................................. 41

Appendix A: EEM Selection & Cost Evaluation Tool Snapshot........................................................... 44

Appendix B: Recommended Risk Mitigation Strategies....................................................................... 48

Appendix C: Effects of Individual EEMs on Percent Energy Savings................................................. 49
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List of Figures

Figure 1. Range of heating and cooling temperature values used in this analysis........................... 20

Figure 2. Historical electricity and natural gas prices for Michigan.................................................... 22

Figure 3. Example of how a triangle distribution approximates a normal distribution ..................... 23

Figure 4. Illustration of output distribution curves for different sample sizes................................... 25

Figure 5. Results showing the average energy cost savings (red) and standard deviations (blue) 

resulting from building performance uncertainties not covered by performance guarantees for 

various EEM packages developed for small commercial office buildings. The black line 

illustrates the density curve fit to each histogram. ........................................................................ 27

Figure 6. Results showing the average energy cost savings (red) and standard deviation (blue) 

resulting from a tighter range in building performance uncertainties not covered by 

performance guarantees for various EEM packages developed for small commercial office 

buildings. The black line illustrates the density curve fit to each histogram. ............................. 31

Figure 7. Results showing the average energy cost savings (red) and standard deviation (blue) 

resulting from individual performance uncertainties on energy cost savings. This study was 

conducted on the post-1980, 25,000-ft2

, stand-alone, small office building model. All EEMs 

were applied to this model. ............................................................................................................... 33

Figure 8. Results showing the average energy cost savings (red) and standard deviation (blue) 

over a 3-year time period resulting from building performance uncertainties not covered by 

performance guarantees for various EEM packages developed for small commercial office 

buildings. The black line illustrates the density curve fit to each histogram. ............................. 36

Figure 9. Results showing the average energy cost savings (red) and standard deviation (blue) 

over a 5-year time period resulting from building performance uncertainties not covered by 

performance guarantees for various EEM packages developed for small commercial office 

buildings. The black line illustrates the density curve fit to each histogram. ............................. 37

Figure 10. Results showing the average energy cost savings (red) and standard deviation (blue) 

over a 3-year time period, resulting from a tighter range in building performance uncertainties 

not covered by performance guarantees for various EEM packages developed for small 

commercial office buildings. The black line illustrates the density curve fit to each histogram.38

Figure 11. Results showing the average energy cost savings (red) and standard deviation (blue) 

over a 5-year time period, resulting from a tighter range in building performance uncertainties 

not covered by performance guarantees for various EEM packages developed for small 

commercial office buildings. The black line illustrates the density curve fit to each histogram.39

Figure 12. Snapshot of the EEM Selection & Cost Evaluation Tool’s graphical user interface ....... 45

Figure 13. Snapshot of the EEM Selection & Cost Evaluation Tool’s graphical user interface, where 

the user can modify estimated energy efficiency improvement costs......................................... 46

Figure 14. Snapshot of the EEM Selection & Cost Evaluation Tool’s graphical user interface, where 

the user can modify utility costs and loan information.................................................................. 47

Figure 15. Snapshot of the results of the EEM Selection & Cost Evaluation Tool after running the 

built-in macro...................................................................................................................................... 47
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List of Tables

Table 1. Major Sources of Performance Risk That Are and Are Not Typically Covered by a 

Performance Guarantee....................................................................................................................... 2

Table 2. Baseline Building Characteristics That Remained Constant Throughout the EEM Analysis7

Table 3. Baseline Building Characteristics That Were Varied for the EEM Analysis........................... 7

Table 4. Baseline HVAC System Efficiencies........................................................................................... 8

Table 5. Screen Questions To Aid in the Selection of Appropriate EEM Packages............................. 9

Table 6. Building Envelope EEMs Applied to Baseline Building Models............................................ 10

Table 7. LPD EEMs Applied to Baseline Models.................................................................................... 11

Table 8. HVAC System Cooling Efficiency Comparison for Baseline and High-Efficiency Cases... 12

Table 9. HVAC System Heating Efficiency Comparison for Baseline and High-Efficiency Cases... 12

Table 10. Cost Data for Individual EEMs ................................................................................................ 15

Table 11. Summary of Models Used for the Performance Risk Analysis............................................ 16

Table 12. Summary of Performance Risk Variables, Associated Ranges, and Probability 

Distribution Used in This Analysis ................................................................................................... 18

Table 13. Buffers To Be Applied to Energy Cost Savings, Calculated at Various Confidence Levels26

Table 14. Summary of Performance Risk Variables With Tighter Distribution Ranges, Accounting 

for Best Practices and Efficient Building Control. The Reduced Values are Highlighted in Red 

and Can Be Compared to Table 12. .................................................................................................. 29

Table 15. Buffers Calculated From the Tighter Ranges of Performance Risk Variables, To Be 

Applied to Energy Cost Savings at Various Confidence Levels ................................................... 30

Table 16. Buffers Calculated Using the Original Distribution Ranges Over 3- and 5-Year Time 

Periods................................................................................................................................................. 34

Table 17. Buffers Calculated Using the Reduced Distribution Ranges Over 3- and 5-Year Time 

Periods................................................................................................................................................. 35
1

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1 Introduction

A number of barriers have been reported that inhibit small business and small portfolio (SBSP)

owners from adopting energy efficiency solutions (Langner et al. 2013). The largest of these 

barriers include:

• Limited capital

• Higher transaction costs relative to energy cost savings

• Lack of time to research and implement energy efficiency solutions

• Split incentive obstacles between owners and tenants

• Lack of available sector-specific resources and technologies.

In an effort to help overcome some of these barriers and encourage energy efficiency

investments within the SBSP sector, the National Renewable Energy Laboratory (NREL) worked 

with two pilot partners to develop a quick and easy process to determine appropriate energy 

efficiency solutions, reduce transaction costs, provide greater access to capital, and manage the 

risks associated with the investment. 

As part of the first stage of this pilot project, NREL focused on overcoming the 

disproportionately high transaction costs that the SBSP sector faces. To address this barrier, 

NREL worked with Michigan Saves, a nonprofit dedicated to making energy improvements 

easier for Michigan energy consumers, to develop low-risk and cost-effective energy efficiency 

measure (EEM) packages that achieve 20% energy savings for one specific small building type. 

The team chose to focus the pilot project efforts on small commercial office buildings in 

Holland, Michigan (defined as climate zone 5A by ASHRAE). EEM packages developed as part 

of the pilot project will be pre-approved by Michigan Saves and its principal lender, allowing 

small commercial office building owners who meet a small number of specified criteria to obtain 

funding with minimal paperwork and without a detailed energy audit. 

The second stage of the project addressed quantification of performance risk, which can deter 

building owners and lenders from investing in energy efficiency projects. For a retrofit project 

that receives a detailed energy audit, much of the performance risk can be borne by an energy 

insurance company or performance contractor, where the service provider or energy service 

company guarantees energy savings to the building owner and the insurance policy protects the 

service provider. However, not all risk to the owner is covered by a typical performance 

guarantee or energy insurance policy, both of which incorporate certain assumptions about how 

the building will be operated and how the savings will be verified. If these assumptions prove 

incorrect for reasons outside the service provider’s control, and the owner’s utility bills thus 

decreases by less than the expected amount, the cash flow available to the building owner to pay 

off the loan may be jeopardized, creating a risk for the owner and lender. As part of the second 

stage of this pilot project, NREL worked with the energy insurance company, Energi, to quantify 

the uncertainty in building energy costs caused by performance risks that go beyond typical 

performance guarantees. Performance risks considered in this analysis are associated with 

variations in weather conditions, operating conditions, utility rates, and quality of preventative 

maintenance. Using a sample of the pre-approved, cost-effective EEM packages for commercial 
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office buildings up to 25,000 ft2 (developed as part of the first stage of this project with Michigan 

Saves), NREL quantified the uncertainty in cash flow associated with EEM packages that are 

designed and installed correctly to allow owners and lenders to estimate a “buffer” needed to 

control performance risk to an acceptable level. In use, the predicted energy cost savings for a 

particular project should be multiplied by the buffers to calculate a de-rated savings, with higher 

confidence and certainty that the savings will actually be achieved. Table 1 categorizes the major 

sources of performance risk as those that are and are not typically covered by a performance 

guarantee.

Table 1. Major Sources of Performance Risk That Are and Are Not

Typically Covered by a Performance Guarantee

Sources of Uncertainty Typically Covered by 

a Performance Guarantee

(Not Addressed in This Study)

Sources of Uncertainty Typically Not Covered 

by a Performance Guarantee

(Addressed in This Study)

Design and modeling errors Uncontrollable variations in weather

Operating schedules and set points that deviate 

within specified limits from the assumed 

operational parameters

Operating schedules and set points that deviate 

beyond specified limits from the assumed 

operational parameters

Variations in equipment performance Changes in utility rates

Substandard workmanship and ineffective 

commissioning Inadequate preventative maintenance practices

Installation errors –

This report is divided into two major efforts:

• Research conducted to develop EEM packages for small commercial office buildings that 

achieve 20% energy savings cost effectively. The results of this study are summarized in 

a simple spreadsheet tool (the EEM Selection & Cost Evaluation Tool

[www.nrel.gov/buildings/docs/2014_eemselection_costevaltool_smoffice.xlsm]) and are 

intended to be used to engage small building customers in energy efficiency discussions 

by providing a business case (energy savings, energy cost savings, and an analysis of 

cost) for more comprehensive EEM packages that achieve higher energy savings cost

effectively. 

• Analyzing economic risk associated with building performance uncertainties that are not 

typically covered by performance guarantees. The result of this analysis are calculated 

buffers that building owners and lending institutions can apply to projected energy cost 

savings, in an effort to control performance risk to an acceptable level. The pilot partners 

have reviewed the results of both analyses and feedback has been incorporated into this 

final report.

1.1 Energy Efficiency Packages That Achieve Energy Savings Cost 

Effectively for Small Office Buildings

The first section of this report summarizes the analysis, results, and steps conducted by NREL to 

develop cost-effective EEM packages for existing small commercial office buildings to achieve 

20% energy savings. We define cost effective as net positive cash flow, meaning that the annual 

energy cost savings are greater than the annual payment of the investment. The intent of 
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developing these packages and providing associated cost data is to encourage the bundling of 

EEMs (which promotes more comprehensive building upgrades that can result in higher energy 

savings), while making the business case for the building owner and enabling an easier financing 

process. The results are not intended to replace a detailed audit when desired by the building 

owner or justified by the size of the investment.

Section 1.2 walks through the literature reviewed to identify data on typical small commercial 

office building characteristics, appropriate EEMs, and characteristics of the representative 

building energy models used to develop the EEM packages. Office buildings were chosen as the 

target building type for this pilot project in collaboration with Michigan Saves and Energi based 

on the large number of small commercial office buildings in their project bases and the 

feasibility of cost-effectively achieving 20% energy savings.

1.2 Available Literature on Small Office Characteristics and 

Efficiency Measures

A number of tools and resources have supported the analysis for this pilot project. The most 

prominent resources are described below and were used to define typical building characteristics 

for the representative baseline building energy models, and to determine appropriate EEMs to 

apply to the baseline energy models for small office buildings.

1.2.1 Commercial Reference Building Models

The U.S. Department of Energy (DOE) and the national laboratories developed standard or 

reference energy models for the most common commercial buildings to serve as starting points 

for energy efficiency research (Deru et al. 2011). The reference building models represent 

reasonably realistic building characteristics and construction practices, and are intended to be 

used for research to assess new technologies; optimize designs; analyze advanced controls; 

develop energy codes and standards; and conduct lighting, daylighting, ventilation, and indoor 

air quality studies (Deru et al. 2011). For this pilot project, we chose the small office 

Commercial Reference Building models for climate zone 5A (the same climate zone as Holland, 

Michigan, where the pilot project was implemented) as our baseline starting point. Three models 

are available for the small office Commercial Reference Buildings; the models vary by vintage 

and comply with typical construction practices for each vintage. The vintages include pre-1980, 

post-1980, and “new” construction that comply with ASHRAE Standard 90.1-2004. To 

accommodate a wider range of typical small commercial office buildings, NREL manipulated 

the three reference building models by varying characteristics such as square footage, heating, 

ventilation, and air-conditioning (HVAC) system type, and boundary conditions (stand-alone 

structures versus structures that adjoin other buildings). EEMs were then applied to the various 

baseline models to determine energy savings per EEM and develop appropriate EEM packages 

that achieve 20% energy savings.

1.2.2 179D DOE Calculator

The 179D DOE Calculator was developed to provide a fast and efficient solution for estimating 

savings from EEMs that meet the requirements of the 179D Federal Tax Code (NREL 2012). 

The 179D Federal Tax Code provided a tax deduction of up to $1.80/ft2 to encourage EEMs in 

new construction and major renovations in commercial buildings (NREL 2012). We define the 

term major renovation as a fairly comprehensive building renovation, including an overhaul of 
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the major building systems. The results displayed in the 179D DOE Calculator tool are based on 

nearly 250,000 whole-building energy simulations of 12 common building types and EEMs 

designed to cover a broad range of scenarios and locations (NREL 2012). The initial 12 common 

building type models used in this tool were based on the Commercial Reference Buildings, as 

described above. Leveraging the work and modeling processes developed for this tool, NREL 

used code developed for the 179D DOE Calculator to manipulate and manage the baseline small 

commercial office building models for this pilot project (in climate zone 5A). The 179D 

modeling processes were also used to apply EEMs and combinations of EEMs to each baseline 

model, allowing NREL to optimize the recommended EEM packages by analyzing every 

possible combination of baseline models and applied EEMs. This analysis was also used to 

understand the effects of individual EEMs and packages of EEMs on building energy use. 

Results showing the effects of individual EEMs on percent energy savings are displayed in 

Appendix C. Using the 179D modeling process, 81 baseline models were developed and 384 

EEM combinations were evaluated against each baseline. In total, 31,104 simulations were run 

using NREL’s supercomputer (the cloud is another computing option), each averaging about 

1 minute of computer run time for a simulation of annual energy consumption.

1.2.3 Advanced Energy Retrofit Guide for Office Buildings

The Advanced Energy Retrofit Guide (AERG) for Office Buildings, developed by Pacific 

Northwest National Laboratory, was used to understand retrofit strategies that are appropriate for 

commercial office buildings (DOE 2011). The building-type-specific AERGs (five in total) work 

to achieve a common goal of improving existing building energy efficiency across the 

commercial building sector by providing project planning guidance as well as example financial 

payback metrics for the most common EEMs (DOE 2011). The primary audience for the AERG 

for Office Buildings consists of facility and energy managers of large existing office buildings 

(>100,000 ft2

), but also includes considerations for small and medium size office buildings (DOE 

2011). These considerations were evaluated as part of operations and maintenance (O&M) 

strategies, EEM recommendations, and cost estimation for this pilot project. 

1.2.4 Advanced Energy Design Guides

The Advanced Energy Design Guides (AEDGs) were developed to provide design strategies and 

recommendations that achieve 30% and 50% energy savings over the minimum code 

requirements of ASHRAE Standard 90.1-2004 for new construction (ASHRAE 2011). The 30% 

AEDG for office buildings targets small office buildings; the 50% AEDG applies to small and 

medium size office buildings with gross floor areas up to 100,000 ft2 (ASHRAE 2011). Although 

these guides are for new construction, certain design recommendations can be applied to 

retrofits. These recommendations were considered as part of the EEMs analyzed in this project. 

1.2.5 Technical Support Document: Large Office Buildings

NREL developed the Technical Support Document (TSD) for large office buildings to evaluate 

the potential for new large office buildings in the United States to achieve 50% energy savings 

compared to ASHRAE Standard 90.1-2004 (Leach et al. 2010). Detailed design 

recommendations are not provided in the Large Office TSD in recognition that they are outlined 

in the AEDGs (Leach et al. 2010). The intended audience for the TSD includes energy modelers 

and engineers who aim to simulate low-energy large office buildings as part of the design 

process and understand assumptions that inform low-energy building design (Leach et al. 2010). 
5

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As part of this pilot project, the TSDs were primarily used to understand the details and 

associated costs of specific EEMs applied to office buildings. Cost data were included as part of 

the TSDs to evaluate the feasibility of EEMs. NREL considered the same cost assumptions when 

appropriate (taking building size into consideration) for evaluating the cost effectiveness of 

recommended retrofit EEM packages for small commercial office buildings. 

1.2.6 Asset Score Sensitivity Analysis

The Commercial Building Energy Asset Scoring Tool developed by DOE is intended to guide 

data collection, store building information, and generate Asset Scores and system evaluations of 

building envelopes and building systems (DOE 2013). To generate the Asset Score, the Asset 

Scoring Tool uses OpenStudio to perform whole-building energy simulations and generate a 

score, based on building envelopes and building systems (HVAC, lighting, and service hot 

water) (DOE 2013). The output of these models depends on the accuracy of user-supplied data, 

the sensitivity of the applied modeling assumptions, and the accuracy of the underlying 

simulation engine (NREL 2013). 

The Asset Score sensitivity analysis screened 35 input variables in the Asset Scoring Tool for 

four building types, including small office buildings (NREL 2013). The results were useful in 

determining which input variables have negligible impact on whole building energy use 

intensities (EUIs) (NREL 2013). Conversely, the results also provided input on variables that 

have considerable effect on building EUIs. These variables were taken into consideration as part 

of this pilot project to help specify EEMs to apply to small office buildings. The variables with 

greater effects on EUIs include roof insulation, window-to-wall ratios, and interior lighting 

power density (LPD). 

1.2.7 Data From the Michigan Saves Program

Michigan Saves has provided project data from participants of the Michigan Saves program 

since 2011. These data include information on building types and implemented EEMs for 

approximately 30 projects that participated in the Michigan Saves program. NREL used these 

data to determine the most common EEMs implemented in these projects, and considered these 

EEMs as part of the analysis to determine EEM packages most likely to be cost effective for 

small office buildings. The most common EEMs that were implemented by Michigan Saves 

program participants were upgrades to lighting, rooftop units, and gas furnaces.

1.2.8 Commercial Building Energy Consumption Survey

Data from the Commercial Building Energy Consumptions Survey (CBECS) 2003 were used to 

understand basic occupant density and hours of operation for small commercial office buildings. 

These data were used to inform the baseline building energy models and support the decision 

tree developed to aid in the selection of appropriate EEM packages. Although CBECS is a 

decade old, the data still provide valuable information on typical building use and energy 

consumption that is unrivaled in breadth by any other dataset.

1.2.9 Data From Preservation Green Lab

Preservation Green Lab (PGL), a subdivision of the National Trust for Historic Preservation, 

provided data for small office buildings in climate zone 5A from its survey of 800 small 

buildings across the United States. PGL conducted the building survey to establish a 
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comprehensive building typology, adding details to building types derived from the CBECS 

database and identifying regional variations that influence small building energy use (PGL 

2013). The survey did not include buildings in Michigan, but it did include buildings in Chicago, 

Illinois, which falls under the same climate zone as Holland, Michigan. We assumed that 

building construction was similar because the climate zone is the same and the cities are 

geographically proximate; therefore, the data could be used interchangeably. We then used the 

building typology data for Chicago to understand typical small office building characteristics and 

formulate the baseline building energy models.

1.3 Energy Model Development

In total, 31,104 OpenStudio models were run to complete the analysis. The following section 

discusses the modeling approach, model inputs, and cost data used to derive the results. The 

results were composed into a summary spreadsheet, called the EEM Selection & Cost Evaluation 

Tool, which can be easily filtered to evaluate the energy savings and cost effectiveness of EEM 

packages that are appropriate for particular categories of small office buildings.

1.3.1 Baseline Building Models

A variety of baseline building models were developed using the DOE Commercial Reference 

Building EnergyPlus models for small offices. As previously mentioned, the Commercial 

Reference Building models represent realistic building characteristics and construction practices 

for three vintages: pre-1980, post-1980, and new construction practices that comply with 

ASHRAE Standard 90.1-2004. 

To accommodate typical variations in small office buildings, NREL manipulated the baseline 

reference building models to account for a range of sizes, HVAC system types, and boundary 

conditions. PGL determined that the average size of small commercial buildings (across all 

building types) is approximately 8,000 ft2 (PGL 2013). In setting some boundaries on this pilot 

project we assumed that because the average small commercial building is relatively small, 

certain building characteristics would probably have little variation from building to building. 

These particular characteristics remained constant in our models and include the aspect ratio, the 

number of floors, the floor-to-floor height, and the window-to-wall ratio, which are described in

Table 2. Even though window-to-wall ratio can have a large effect on building EUI, we assumed 

that the average value used in the Commercial Reference Building model was appropriate for 

most small office buildings, and that in general, asking a small building owner to provide an 

accurate window-to-wall ratio may be too time intensive or difficult, and detract from the 

simplicity and celerity of the tool. We also assumed that typical small office buildings are 

operated from 9:00 a.m. to 5:00 p.m. Monday through Friday, and with reduced occupancy, from 

9:00 a.m. to 12:00 p.m. on Saturdays. This operation schedule is the same as the schedule 

represented in the Commercial Reference Building models. The baseline building characteristics 

that remained constant throughout the analysis are summarized in Table 2.
7

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Table 2. Baseline Building Characteristics That Remained Constant Throughout the EEM Analysis

Building Characteristic Value

Aspect Ratio 1.5

Number of Floors 1

Floor-to-Floor Height 10 ft

Window-to-Wall Ratio 21%

Building Occupancy 9:00 a.m. to 5:00 p.m. Monday through Friday, 

9:00 a.m. to 12:00 p.m. Saturday

The building occupancy schedule assumption was also validated by CBECS 2003 data, which 

indicate that the average number of weekly operating hours for an office building is 55 (EIA 

2003). This, however, includes buildings of all sizes. Assuming that many small office buildings 

are occupied by small businesses, as derived in an NREL technical report that characterizes the 

SBSP sector (Langner et al. 2013), we assumed that it was appropriate for the small office 

occupancy schedule to be about 11 hours shorter than that of a larger office building, and that the 

9:00 a.m. to 5:00 p.m. Monday through Friday operation schedule was representative of most 

small office buildings. If a building’s occupancy schedule extends beyond our assumption, our 

modeling experience indicates that a small change of an hour or two in the schedule is not likely 

to significantly affect the EEM package recommendations or the resulting energy savings.

The building characteristics that varied between the baseline models include vintage, building 

square footage, HVAC system type, and the exterior wall boundary condition (stand-alone 

structure versus structures that adjoin other buildings). These characteristics are summarized in 

Table 3 and described in the following paragraphs.

Table 3. Baseline Building Characteristics That Were Varied for the EEM Analysis

Building Characteristic Value

Vintage

Pre-1980

Post-1980

New – ASHRAE Standard 90.1-2004

Building Size

5,500 ft2

10,000 ft2

25,000 ft2

HVAC System Type

Packaged single zone air-conditioners (PSZ-ACs: rooftop 

unit with gas furnace)

Packaged terminal air-conditioners (PTACs: split system 

air-conditioner with gas furnace)

Packaged terminal heat pumps (PTHPs: heat pump)

Exterior Wall Boundary Condition

Stand-alone

Adjacent building on 1 side

Adjacent building on 2 sides

The size of the small office Commercial Reference Building models is 5,500 ft2. The medium 

office reference model is approximately 50,000 ft2

. For a next step of this project, it may be 

beneficial to expand this research to include the medium office Commercial Reference Building; 
8

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however, for this pilot project, we focused efforts on EEM recommendations specifically for 

small office buildings. To account for differences in small office sizes, we varied the small office 

building models to accommodate three size ranges that match size categories recorded by 

CBECS 2003: 5,500 ft2, 10,000 ft2, and 25,000 ft2. 

NREL also modeled three variations of HVAC system types for each small office baseline 

model. The modeled HVAC system types are typical of small office buildings across the nation 

and include PSZ-ACs – a single system rooftop unit with direct expansion cooling and a gas 

forced air furnace, and two split systems: PTACs – direct expansion air conditioner with gas 

forced-air furnace and PTHPs – heat pump with electric supplemental heat. The baseline heating, 

cooling, and fan efficiencies for these units were derived from ASHRAE Standard 90.1-2004. 

The efficiencies for the pre-1980 and post-1980 HVAC systems were de-rated from the 

ASHRAE Standard 90.1-2004 values, to simulate HVAC efficiencies typical to systems found in 

those building vintages (Deru et al. 2011). Efficiencies for pre-1980 buildings are higher than 

post-1980 based on the assumption that the HVAC equipment has been more recently replaced. 

The efficiencies for the baseline units are noted below in Table 4. 

Table 4. Baseline HVAC System Efficiencies

System 

Type

Pre-1980 Post-1980 90.1-2004

Cooling 

Efficiency 

(COP*)

Heating 

Efficiency

Cooling 

Efficiency 

(COP)

Heating 

Efficiency

Cooling 

Efficiency 

(COP)

Heating 

Efficiency

PSZ-AC 2.81 78% 2.55 80% 3.05 80%

PTAC 2.92 78% 2.65 80% 3.17 80%

PTHP 2.92 1.95 (COP) 2.65 2.00 (COP) 3.17 2.00 (COP)

* Coefficient of performance

Because many small office buildings reside in downtown or main street districts, we varied the 

boundary conditions of the baseline models to accommodate structures that adjoin other 

buildings and buildings that are stand-alone. For stand-alone buildings, the reference building 

models were left unchanged. For those that adjoin other buildings, we changed the boundary 

conditions from exterior wall to adiabatic wall (which mimics an interior wall) on either one 

exterior side (if the building adjoins another building on one side), or on two exterior sides (if the 

building is between two other buildings). For adjacent walls, windows were also removed. The 

configurations of these buildings greatly influence the building’s heating and cooling loads. 

1.3.2 Decision Tree/Energy Efficiency Measure Screening Questions

The variations in the baseline building models were used to develop a very simple decision tree, 

or list of screening questions, that are used in the EEM Selection & Cost Evaluation Tool to help

users select appropriate EEM packages for their specific small office buildings. The screening 

questions, listed in Table 5, are necessary to understand the basic characteristics of the 

participating small office building, such as vintage, size, boundary conditions, HVAC system, 

and age of equipment, and to recommend appropriate EEM packages. The screening questions 

also include questions about whether the building owner plans to conduct any major renovations 

or equipment upgrades. As previously mentioned, we define the term major renovation as a 

fairly comprehensive building renovation, including an overhaul of the major building systems. 

In this case of a major renovation, the EEM Selection & Cost Evaluation Tool considers 
9

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incremental costs instead of full first costs when analyzing the cost effectiveness of the EEM 

packages. Incremental costs include the additional costs for an energy efficient product, when 

compared to a standard product. For example, if a building owner is already planning to replace a 

defective roof-top unit, the incremental cost would be the additional cost to purchase a highefficiency model compared to a standard-efficiency model. The full first cost assumes that a 

major renovation is not planned, and includes the entire cost of the energy-efficient piece of 

equipment. The output of this tool is also intended to help the reviewer or financial institution to 

choose an EEM package that is likely to achieve a certain threshold of energy savings (specified 

by the user) cost effectively. Conversely, if there is no cost-effective EEM package for a 

building, or the building characteristics are not consistent with any baseline, the analysis results 

will inform the reviewer or financial institution that a more detailed energy audit is required.

Table 5. Screen Questions To Aid in the Selection of Appropriate EEM Packages

EEM Package Screening Questions:

When was your building built?

Post-2004

1980–2004

Pre-1980

Roughly, what size is your building?

5,500 ft2

10,000 ft2

25,000 ft2

Is your building:

A stand-alone structure?

Adjacent to another building on 1 side?

Adjacent to buildings on 2 sides?

What type of HVAC system do you have?

Packaged single zone air-conditioner (rooftop unit with gas furnace)

Packaged terminal air-conditioner (split system air-conditioner with gas furnace)

Packaged terminal heat pump (heat pump with electric supplemental heat)

Are you planning for any of the following actions in the near future?

Major renovation of building

Roof replacement

HVAC equipment replacement

Even if a building complies with the screening questions, the results may not fully conform to 

the particular office building under consideration. The spreadsheet tool is intended to be used as 

an engagement tool, informing the user of possible low-risk EEM combinations where there is 

high confidence of cost effectiveness. These packages are recommended for financing, yet it 

must also be understood that the investment still presents some uncertainty. As part of the second 

part of this study, uncertainty that goes beyond typical performance guarantees is analyzed, and 

risk mitigation strategies are recommended to increase the chances of achieving the estimated 

savings. These strategies are noted in upcoming sections of this report, and are summarized in 

Appendix B. The building owner should ensure that the building is being operated in a manner 

that uses the least amount of energy and still maintain operating goals. Retrocommissioning is 

also recommended to ensure that the building equipment operates at its maximum efficiency 

potential following a retrofit. Commissioning has been shown to reduce energy consumption by 
10

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an average of 5% at very little cost, providing a buffer for energy savings estimates in our 

analysis (Mills 2009).

If a particular building’s characteristics fall outside the decision tree options, we recommend that 

the building owner pursue a detailed building audit to determine appropriate EEM packages and 

cost information. In a parallel effort, if the building has HVAC equipment older than 10 years 

and the building owner is not planning a major renovation or HVAC equipment replacement, we 

also recommend that the financial institution or program administrator work with the building 

owner to recommend a high-efficiency HVAC equipment replacement when the equipment must 

be replaced.

1.3.3 Small Office Energy Efficiency Measures

Most EEMs evaluated in this analysis were derived from the AERG for Office Buildings, the 

30% and 50% AEDGs for Office Buildings, the TSD for Large Office Buildings, the Asset Score 

Sensitivity Analysis, and data collected by Michigan Saves through its project participants. 

Additional design sources were evaluated to determine lighting and plug load retrofit measures, 

as well as HVAC efficiencies. Each measure and the design sources are noted in the following 

sections.

1.3.3.1 Building Envelope

Results from the Asset Score Sensitivity Analysis for small office buildings show the 

significance of various building characteristics on overall building EUI (NREL 2013). From 

these results, along with recommendations from the AERG for office buildings, data provided by 

Michigan Saves on past projects, and design values from the 50% Office AEDG, we concluded 

that increasing roof insulation, replacing windows, and reducing building leakage were 

appropriate EEMs to consider for small office efficiency retrofit projects. These EEMs are 

summarized in Table 6, along with the baseline assumptions (found in the Commercial 

Reference Building models) for reference.

Table 6. Building Envelope EEMs Applied to Baseline Building Models

EEM Baseline Value Efficient Value

Increase Roof 

Insulation

R-30 for attic (post-1980 and 90.1-2004 

construction)

R-14 for pre-1980 construction

R-49 for attic (post-1980 and 90.1-2004 

construction)

R-30 continuous insulation for insulation 

entirely above deck (pre-1980 

construction)

Replace 

Windows

U-0.62 (pre-1980)

U-0.59 (post-1980)

U-0.57 (90.1-2004 construction)

Improved windows U-0.45, SHGC 0.25: 

double-glazed, low-e, spectrally selective, 

aluminum frame with thermal break

Reduce 

Building 

Leakage

1.5 cfm/ft2 (average leakage rate (per ft2

of exterior wall) for pre-1980 and post1980 construction)

0.4 cfm/ft2 (average leakage rate (per ft2

of exterior wall) for 90.1-2004 

construction) 

Reduce infiltration by:

40%, pre-1980 construction

25%, post-1980 construction

10%, 90.1-2004 construction
11

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A number of references were reviewed to determine appropriate values for improving the air 

sealing of a building (reducing building leakage). These references included the Large Office 

TSD, retrofit percent reduction goals proposed by Xcel Energy, and the experience of the 

authors. 

1.3.3.2 Lighting 

A number of lighting EEMs were applied to the baseline models that include LPD reductions, 

lighting controls, and adding daylighting sensors. Based on LPD design recommendations for 

new construction noted in the Office AEDG, and consultation with NREL’s lighting experts, we 

analyzed two options for reducing LPD. Alternative, cost-effective lighting approaches may also 

be used to reach the energy savings targets that we recommend; however, these approaches 

should be determined from an energy audit. In both lighting recommendations, we assumed that 

0.5 W/ft2 of the current office lighting would remain in place, and the rest would be reduced by 

retrofitting lamps to high-performance lamps (as an example, we analyzed T8s) and either 

maintaining a lighting level of 40 fc, or delamping or increasing the fixture spacing to achieve an 

ambient illuminance of 25 fc plus task lights. Also, the 25-fc case assumes that the project 

installs new, high-efficiency fixtures that require fewer fixtures per square foot to meet the 

illuminance criteria. This retrofit option is more expensive because it involves overhauling the 

lighting system. The LPD reductions and illuminance criteria for both options are summarized in

Table 7.

Table 7. LPD EEMs Applied to Baseline Models

Vintage

Reference 

Building LPD 

(W/ft2)

Retrofit to T8s, 40 fc 

(W/ft2)

Retrofit to T8 and Change Design 

(e.g., delamp fixtures, increase 

fixture spacing), 25 fc (W/ft2)

Pre-1980 1.8 1.4* 1.1**

Post-1980 1.8 1.4* 1.1**

90.1-2004 1.0 1.0 0.8

*Assume 0.5 W/ft2 of the office lighting will remain in place (office ambient LPD moves from 1.3 W/ft2 to 0.9 W/ft2).

**Assume 0.5 W/ft2 of the office lighting will remain in place (office ambient LPD moves from 0.9 W/ft2 to 

0.6 W/ft2

). For the ASHRAE 90.1-2004 model, the percent of lighting that would remain in place was prorated.

Lighting controls were applied to the baseline models as EEMs. The baseline lighting schedule 

remained the same as the assumptions in the small office Commercial Reference Building 

models, and occupancy sensors were applied to the entire building model, assuming that they 

reduce the total power associated with the daytime lighting schedule by 20% in open office and 

40% in auxiliary spaces. This is an aggressive assumption, assuming that vacancy, manual-on 

sensors are used. 

We assumed that photosensors for daylight response also alter the baseline lighting schedule, 

reducing the total lighting power to 50% in perimeter zones of the building. The daylight sensors 

were assumed to be continuous dimming with a daylighting set point of 25 fc. This measure

added dimming ballasts in perimeter zones.

1.3.3.3 Plug Loads

Recent research has shown that advanced power strips can reduce office building plug load 

power to approximately 10% at night (Metzger et al. 2012). Advanced power strips offer an 

affordable option to reduce energy consumption in an office building. Costing $15–$30 each, the 
12

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advanced power strips allow occupants to easily turn off all the office equipment in their 

workspaces when they leave at the end of the day. For our EEM analysis, we assumed that the 

plug load equipment schedule was reduced to 10% total power at night.

1.3.3.4 HVAC Systems

Pending whether a building is due for HVAC system equipment upgrades, we analyzed energy 

savings for replacing existing HVAC equipment with high-efficiency equipment. Leveraging 

efforts associated with the DOE High Performance Rooftop Unit Challenge, we used 

recommended efficiencies proposed in the Tier 2 category of the Consortium for Energy 

Efficiency Commercial Unitary AC and HP Specification (Jensen 2012). Cooling and heating 

efficiencies for the baseline and high-efficiency systems are noted in Table 8 and Table 9, 

respectively.

Table 8. HVAC System Cooling Efficiency Comparison for Baseline and High-Efficiency Cases

System 

Type

Pre-1980 Post-1980 90.1-2004

Baseline 

(COP)

Low-Energy 

(COP)

Baseline 

(COP)

Low-Energy 

(COP)

Baseline 

(COP)

Low-Energy 

(COP)

PSZ-AC 2.81 3.52 2.55 3.52 3.05 3.52

PTAC 2.92 3.66 2.65 3.66 3.17 3.66

PTHP 2.92 3.66 2.65 3.66 3.17 3.66

Table 9. HVAC System Heating Efficiency Comparison for Baseline and High-Efficiency Cases

System 

Type

Pre-1980 Post-1980 90.1-2004

Baseline Low-Energy Baseline Low-Energy Baseline Low-Energy

PSZ-AC 78% 80% 80% 80% 80% 80%

PTAC 78% 90% 80% 90% 80% 90%

PTHP (COP) 1.95 2.64 2.00 2.64 2.00 2.64

NREL assumed the cooling seasonal energy efficiency ratio (SEER) for the PTAC and PTHP 

system types to be 12.0 SEER, as listed in ASHRAE Standard 90.1-2004 (tables 6.8.1A and 

6.8.1B) – also assuming that the units were installed after January 23, 2006. To convert between 

SEER and the energy efficiency ratio (EER), we used the methodology noted by Deru et al.

(2011). The equation used is noted below:

EER = SEER * 0.697 + 2.0394

The COP values for the PSZ-AC system noted in Table 8 apply to the entire unit. Because this is 

not a split system, and the EnergyPlus input for COP is for the compressor COP only, we had to 

remove the fan efficiency from the overall COP to model it correctly in EnergyPlus. To do so, 

we again used the methodology noted by Deru et al. (2011). The equation is as follows:

COPcomp = ((EER / 3.413) + R) / (1 + R),

where R is assumed to be 0.12, a reasonable value to represent a broad class of products 

(Deru et al. 2011).
13

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Lastly, the baseline COP values for the PTAC and the PTHP systems were de-rated to account 

for building vintage in the same proportion that the PSZ-AC system was de-rated in the 

Commercial Reference Building models. 

1.4 Energy Efficiency Measure Packages and Cost Data for

Small Office Buildings

The results of this analysis are summarized in a simple spreadsheet tool (the EEM Selection & 

Cost Evaluation Tool) that based on answers to the screening questions, provides users with 

bundled EEM packages that meet a particular energy savings threshold and are likely to be cost 

effective. The tool includes a macro to facilitate the process and the user specifies the desired 

energy savings threshold. If there is no EEM package for a particular building, the building is 

likely already energy efficient and it should be re-evaluated at a later date. If the screening 

questions indicate that the building is not consistent with any baseline, an energy audit may be 

necessary to identify cost-effective EEM packages. The tool contains energy savings data from 

each EnergyPlus simulation for every combination of EEMs applied to the 81 baseline building 

models, totaling more than 30,000 scenarios. It also includes estimated cost data for each EEM 

and EEM package; however, it is highly recommended that the user enter updated cost data for 

their specific region and available products. The estimated cost data in the tool were accumulated 

from a number of resources, including the Large Office TSD, RSMeans, and the AERGs. The 

cost data include full costs, incremental costs, and costs associated with O&M, which can all be 

edited by the user in the spreadsheet tool if better cost data are available. The cost data were then 

used to determine:

• Net initial cost (adjusted for equipment replacement costs that would have occurred 

without the efficiency upgrade) and full initial cost for each EEM package

• Quality assurance fees associated with the Michigan Saves program

• Annual lease payments

• Measurement and verification costs

• Total first year investment costs

• Net cash flow during a typical 5-year lease period. 

Depending on the approved loan amount and building characteristics, the EEM Selection & Cost 

Evaluation tool outputs EEM packages that achieve energy savings above the user-specified 

energy savings threshold and offer a net positive cash flow. The net cash flow is based on the 

annual energy cost savings (calculated using average electricity and gas prices for Michigan) 

versus the total first-year costs of implementing the selected EEM package. The average 

electricity and gas prices for Michigan were determined using the U.S. Energy Information 

Administration (EIA) 2010 average rates for electricity and natural gas for commercial buildings 

(EIA 2012a, 2012b). Those rates are $0.10/kWh for electricity and $9.14/1,000 ft3

(approximately $0.03/kWh) for gas. 

The total first-year costs equate to the sum of the annual energy-related lease payment (which 

includes annualized fees and rebates), O&M costs, and measurement and verification costs. 
14

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Total 1st Year Costs = Annual Payment + Operation & Maintenance Costs + Measurement & 

Verification Costs

The annual energy-related lease payment is a factor of the net initial cost, the interest rate (set at 

5.9%), a 5-year lease period, fees associated with the loan transaction, and any potential rebates. 

Annual Energy-Related Lease Payment = (Net Initial Cost + Quality Assurance Fee – Rebate) * 

(Monthly Interest/(1-(1+ Monthly Interest) -(Number of Months))The net initial costs are used to 

calculate cost effectiveness of the investment, and include incremental costs when equipment 

would have to be replaced even without the energy efficiency upgrades. The full initial costs are 

used to quantify total project costs covered by the lease. The net and full initial costs are 

calculated by summing the relevant first cost or incremental costs together for each measure 

noted in the EEM package. 

Net Initial Cost = Sum of Either Incremental or Full Costs for a Project

The decision tree questions related to whether the building owner is planning a major renovation, 

roof replacement, or replacement of HVAC equipment determine whether the incremental cost 

or full cost is used. The actual cost data, including each data source, are summarized in Table 10. 

NREL reviewed these costs with Michigan Saves and several of its partners to ensure that the 

cost assumptions are realistic. Accurate cost data are difficult to obtain because they can vary 

considerably with specific building types and location. Regardless, the overall feedback on our 

cost data suggested that the costs were conservative but not unrealistic, which could mean that in 

some instances the proposed packages are more cost effective than predicted. With the partners’ 

input, we decided to refine and update the spreadsheet tool to allow the user to change the cost 

data if more accurate cost data are found. The costs used in this study and the associated data 

sources are listed in Table 10.

The decrease in O&M costs for lighting accounts for a couple of factors. If a retrofit involves 

delamping or increased fixture spacing, fewer lamps need to be maintained and replaced after the 

retrofit. Typically, the higher performance lamps have longer lives as well, so lamps are replaced 

less often. Similarly, occupancy sensors can promote longer lamp life, but the O&M savings 

from this measure are often negated because more maintenance is needed to calibrate the 

sensors, program the controls, and monitor their operation.

In the end, the EEM Selection & Cost Evaluation Tool displays 1,597 EEM package options that 

are expected to achieve 20% energy savings cost effectively for projects undergoing major 

renovations. For projects that are not undergoing major renovations, 20% energy savings are 

harder to achieve cost effectively. Only one cost-effective EEM package option is available that 

achieves 20% energy savings for small office buildings that are not planning major renovations. 

For these cases, we recommend that the user reduce the energy savings threshold. For example, 

reducing the energy savings threshold to 15% yields approximately 30 cost-effective EEM 

package options; 10% energy savings yields 164 cost-effective EEM options. Snapshots of the 

EEM Selection & Cost Evaluation Tool can be found in Appendix A.
15

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Table 10. Cost Data for Individual EEMs

EEM

Incremental 

Cost

($/ft2)

Full 

Cost 

($/ft2)

O&M Cost 

Decrease 

($/ft2)

Source

Increase Roof Insulation to R-49 (for attics, 

post-1980 and 90.1-2004 construction) $3.00 $12.15 $ – TSD Large Office

Increase Roof Insulation to R-30 Continuous 

Insulation (for insulation entirely above deck, 

pre-1980 construction)

$1.32 $10.47 $ – TSD Large Office

Replace Windows $7.18 $43.00 $ – AERGs

Reduce Building Leakage $0.43 $0.43 $ – AERGs

LPD Reduction, Retrofit to T8s, 40 fc $0.66 $2.06 $0.12 AERGs

LPD Reduction, Retrofit to T8s and change 

design (e.g., delamp) to 25 fc plus task lights $0.79 $11.15 $0.19 TSD Large Office

Daylighting Controls $0.55 $3.41 $ – TSD Large Office, 

AERGs

Occupancy Sensors $0.36 $0.36 $ – TSD Large Office

Advanced Power Strips $0.15 $0.15 $ – GSA* Plug-Load 

Control Document

Replace HVAC Equipment (PSZ-AC) $0.41 $6.09 $ – TSD Grocery 

Stores

Replace HVAC Equipment (PTAC) $0.66 $2.85 $ –

Residential 

Measures 

Database

Replace HVAC Equipment (PTHP) $1.53 $3.30 $ –

Residential 

Measures 

Database

* U.S. General Services Administration

1.5 Energy Savings for Individual Energy Efficiency Measures

To enhance our understanding of the energy savings effects of individual EEMs, NREL filtered 

the results from this analysis to understand the mean energy savings of each EEM (see Appendix 

C). It should be noted that the results take into account only the small buildings considered in 

this project and are not intended to be conveyed as an average over a larger cross-section of 

buildings. The graphs in Appendix C show the energy effects of individual EEMs on building 

energy use averaged over all energy models. They also show more detailed results that look at 

the energy effects of individual EEMs averaged for stand-alone buildings and buildings adjacent 

to other buildings, as well as averaged effects for each HVAC system type (PSZ-AC, PTAC, 

and PTHP). 

A number of trends emerged from this analysis. Overall, the lighting, daylighting sensors 

(photosensors for daylighting response), and plug load EEMs have the greatest effect on 

reducing small office building energy use. Occupancy sensors also show high energy savings in 

most building models. In older buildings, primarily pre-1980 buildings, reducing building 

leakage is highly impactful. Likewise, upgrading the HVAC system with a higher efficiency 

system shows a greater effect in older buildings, as does increasing roof insulation. In general, 

upgrading windows with high-efficiency windows shows the least effect on building energy use. 

However, the results might vary from building to building, especially for cases where the 

window-to-wall ratio is significantly larger than what was used in the energy models of this 

study. Lastly, buildings with PTACs tend to see higher savings than the other two HVAC system 

types analyzed in this study.
16

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2 Analysis of Economic Risk Associated With 

Building Performance Uncertainties Not Covered by 

Performance Guarantees

For the second stage of this pilot project, NREL worked with the energy insurance company, 

Energi, to quantify building performance risks that are not typically covered by performance 

guarantees. The performance uncertainties are associated with uncontrollable variations in 

weather, occupant behavior, fuel escalation rates, and quality of preventative maintenance. The 

following sections describe the approach, analysis, and results of the study quantifying the

performance uncertainties, and provide recommendations to help mitigate these risks.

2.1 Data Sources for Performance Risk Analysis

The result of this analysis quantifies uncertainties in building performance that are outside the 

control of designers and installers. The results are intended to be used by contractors that provide 

energy savings guarantees, or by lending institutions and other loan managers beyond energy 

insurance companies. The starting point of this analysis began with a sample of the pre-approved 

EEM packages that achieve close to 20% energy savings developed in the first stage of this 

project with Michigan Saves. As discussed earlier in this report, these energy models were 

generated using the small office Commercial Reference Building models as a starting point 

(Deru et al. 2011). Table 11 provides a summary of the models. 

Table 11. Summary of Models Used for the Performance Risk Analysis

Baseline Office Building 

Model

Bundled EEMs Applied to

Model

Energy 

Savings

Net 

Cash 

Flow

Planned 

Major 

Renovation?

Post-1980, 5,500-ft2, PTAC 

HVAC System, Stand-Alone 

Building

Reduced LPD, daylight 

sensors, occupancy sensors, 

advanced power strips for plug 

loads

17% $1,172 Yes

Post-1980, 5,500 ft2, PTAC 

HVAC System, Adjacent to 

Buildings on 2 Sides

Reduced infiltration, reduced 

LPD, daylight sensors, 

occupancy sensors, advanced 

power strips for plug loads

19% $1,138 Yes

Post-1980, 25,000-ft2, PTAC 

HVAC System, Stand-Alone 

Building

Reduced LPD, occupancy 

sensors, advanced power 

strips for plug loads

21% $6,658 Yes

Post-1980, 25,000-ft2, PTAC 

HVAC System, Stand-Alone 

Building

Reduced LPD, daylight 

sensors, advanced power 

strips for plug loads

23% $6,792 Yes

Post-1980, 25,000-ft2, PTAC 

HVAC System, Adjacent to 

Buildings on 2 Sides

Reduced infiltration, reduced 

LPD, occupancy sensors, 

advanced power strips for plug 

loads

21% $6,850 Yes

Post-1980, 25,000-ft2, PTAC 

HVAC System, Adjacent to 

Buildings on 2 Sides

Reduced LPD, daylight 

sensors, advanced power 

strips for plug loads

22% $7,273 Yes

Pre-1980, 25,000-ft2, PTAC 

HVAC System, Stand-Alone 

Building

Reduced infiltration, reduced 

LPD, advanced power strips 

for plug loads

20% $7,380 Yes
17

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For the statistical analysis of the performance risk, NREL used the free software, R, for statistical 

computing and graphics to generate a random sample of inputs for the key input variables 

according to a probability distribution. The sample of inputs were then applied to each 

OpenStudio model to determine the range and standard deviation in energy cost savings caused 

by uncontrollable variations in weather, operating conditions, unexpected utility rate 

fluctuations, and the quality of preventative maintenance. The data sources described in the 

following subsections were used to determine realistic ranges (minimum, maximum, and 

mean/mode values) for each variable, which were then input into R to generate the random 

sample of realistic inputs. Table 12 provides a summary of the variables considered in this 

analysis, their associated ranges, and probability distribution. Normal distributions were 

approximated by a triangular distribution to ensure that random variables would not deviate 

beyond realistic maximum and minimum values.
18

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Table 12. Summary of Performance Risk Variables, Associated Ranges, and Probability Distribution Used in This Analysis

Units

Weather

Heating 

Set Point 

(Occupied 

Hours)

Cooling 

Set Point 

(Occupied 

Hours)

Lighting 

Schedule 

(Unoccupied 

Hours)

Equipment 

Schedule 

(Unoccupied 

Hours)

Occupant 

Density

Utility 

Price 

Escalation

Electricity

Utility 

Price 

Escalation

Gas

Quality of 

Preventative 

Maintenance

Year of 

weather 

data used 

in 

simulation

°F °F

Multiplier 

applied to 

schedule

Multiplier 

applied to 

schedule

Multiplier 

applied 

to 

occupied 

density

Multiplier 

applied to 

calculated 

electricity 

cost

Multiplier 

applied to 

calculated 

gas cost

Multiplier 

applied to 

whole 

building 

energy use

Probability 

Distribution Uniform Triangular Triangular Discrete Triangular Triangular Triangular Triangular Triangular

Range: 

Minimum 1988 66.56 72.86 N/A 0.10 0.75 0.94 0.85 0.96

Range: 

Maximum 2012 72.32 77.90 N/A 0.30 1.25 1.07 1.15 1.04

Mode N/A 69.80 75.20 N/A 0.20 1.00 1.00 1.00 1.003

Scenario 1 N/A N/A N/A 1.00 N/A N/A N/A N/A N/A

Scenario 2 N/A N/A N/A 1.63 N/A N/A N/A N/A N/A

Scenario 3 N/A N/A N/A 0.38 N/A N/A N/A N/A N/A

Probability of 

Scenario 1 N/A N/A N/A 0.90 N/A N/A N/A N/A N/A

Probability of 

Scenario 2 N/A N/A N/A 0.05 N/A N/A N/A N/A N/A

Probability of 

Scenario 3 N/A N/A N/A 0.05 N/A N/A N/A N/A N/A
19

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

2.1.1 Weather Data

NREL purchased 25 years of Actual Meteorological Year climate files from Weather Analytics 

(www.weatheranalytics.com) for Holland, Michigan, to provide a sufficient range in data that 

portrays realistic variations in actual weather data. These data draw from a full range of weather 

data taken from meteorological stations around the country (primarily at airports). The data are 

delivered in a clean format (missing data are filled in and erroneous readings are removed) and 

packaged for direct use in OpenStudio models. The data downloaded for this analysis included 

actual weather data for Holland, Michigan between 1988 and 2012. NREL then used R to 

randomly choose the year of weather data that were then applied to the OpenStudio models.

2.1.2 Operating Conditions Data

A number of data sources and assumptions were used to define the ranges in operating 

conditions used in this analysis. To bound the scope of this pilot project, NREL focused efforts 

on a few key operating conditions that have high impact on building energy use and are 

influenced by daily occupant behavior. Those operating conditions include:

• Ranges in heating and cooling set points during occupied hours of the building

• Ranges in the percentage of lights and plug loads left on during unoccupied hours of the 

building

• Ranges in occupant density caused by typical fluctuations in employee numbers.

The following subsections describe the data sources that were used to determine the ranges of 

each variable. 

2.1.2.1 Heating and Cooling Set Points

Measured heating and cooling temperature set points for commercial buildings are not well 

documented. Many datasets record surveyed temperature set points, but few record actual 

measured data of observed temperatures. This is an important distinction for this analysis 

because surveyed temperature set points are often unreliable and do not reflect the actual 

behavior of occupants who adjust thermostat settings daily. For this study, NREL used a robust 

dataset containing measured temperature set points from 100 buildings. The study, conducted by 

Lawrence Berkeley National Laboratory (LBNL), used data from the U.S. Environmental 

Protection Agency’s Building Assessment Survey and Evaluation Study (Mendell and Mirer

2008). This study was conducted from 1994 to 1998 to evaluate parameters related to indoor air 

quality. Part of the study focused on indoor thermal factors and how temperature ranges 

influenced health symptoms reported by office workers. The study used temperature 

observations from 100 buildings, and recorded minimum, maximum, and mean values for 

heating and cooling. For this analysis, NREL applied these ranges to the baseline temperature 

schedule used in the OpenStudio office building models. Figure 1 illustrates the modeled 

baseline temperature set point schedule (assumed as the average) with the minimum and 

maximum schedule values for heating and cooling during occupied hours. NREL then input 

these ranges into R to generate a triangular distribution of temperature values that was applied to 

each model. 
20

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 1. Range of heating and cooling temperature values used in this analysis

Although temperature setback schedules are often implemented as EEMs for office building 

retrofits, temperature setback was already included as typical operation in the Commercial 

Reference Building model, so we did not change it. We also assumed that the temperature 

setback would occur at the same time of day after a retrofit as it did before the upgrades were 

made.

2.1.2.2 Lighting Loads

We reviewed a number of studies to understand the energy impact of lights being left on during 

unoccupied hours, and the potential rebound effects of people leaving lights on more frequently 

after a retrofit. A report by Roth et al. (2004) was used to determine a typical range in energy use 

caused by lights left on when a space is unoccupied. The study looked at the energy impact of 

faults in U.S. commercial buildings and concluded that lights left on after occupied hours can 

account for a 5%–10% increase in lighting energy use. 

The International Energy Agency (IEA) wrote a report quantifying rebound effects in 2005 

(IEA 2005). The rebound effect results from a tradeoff between energy savings and some other 

benefit to occupants or owners, such as greater comfort or security. The IEA study tries to 

resolve criticisms that argue against energy efficiency policies and programs; one criticism is 

that rebound effects erode most or all energy savings. The study indicates that rebound effects in 

commercial office buildings are actually quite low, and specifically for lighting, represent an 

energy savings degradation of only 0%–2% (IEA 2005). 

Combining the data from the IEA study, the Roth et al. (2004) study, and a third study on 

rebound effects by Nadel (1993), NREL concluded that 90% of post-retrofit buildings do not 

experience a change in occupant behavior associated with lighting use. For the remaining 10%, 

the studies suggest that 5% of the buildings increased their lighting use and 5% decreased their 

lighting use during unoccupied hours. 

58

60

62

64

66

68

70

72

74

76

78

80

82

1 3 5 7 9 11 13 15 17 19 21 23

Temperature (F)

Hour of Day

Cooling Setpoint Schedule

Heating Setpoint Schedule

Min Cooling Setpoint

Max Cooling Setpoint

Min Heating Setpoint

Max Heating Setpoint
21

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To model this effect, NREL first determined multipliers to apply to the lighting schedule (during 

unoccupied hours) to increase or decrease the total lighting energy by 10% in each model (based 

on the Roth 2004 study). The high bound represents rebound effects associated with lighting 

after a retrofit. The low bound represents the effects of people turning off more lights during 

unoccupied hours after a retrofit, perhaps because of increased awareness of energy efficiency 

issues or a change in policy combined with the retrofit. NREL then used R to generate a random 

but realistic sampling of inputs bound by this discrete probability distribution, that was applied to 

the OpenStudio models. 

2.1.2.3 Plug Loads

Metered plug load data are becoming more prolific as the energy industry realizes that plug loads 

consume a significant amount of energy in commercial buildings. Nevertheless, only a few 

studies record metered data of daily energy use trends for individual occupants of office 

buildings. Fortunately, the NREL Commercial Buildings Research Group has been conducting 

research in this area and has data on plug loads in individual workspaces for a number of office 

buildings, including the Research Support Facility located on the NREL campus. 

For this study, NREL chose to use plug load data from the Research Support Facility that were 

collected over 2 years, 2012 and 2013 (OpenEI 2013). The approach and methodology for 

collecting the data are summarized in a study by Sheppy et al. (2013). Based on averages of the 2 

years of data, NREL found a 5%–10% difference in the percentage of plug load use (percentage 

of the peak load) during unoccupied hours from year to year. This difference identifies normal 

changes in occupant behavior that should be expected over time. NREL assumed that major 

rebound effects are unlikely for plug load use following a retrofit; thus, the minimum and 

maximum schedule values were assumed to have a 10% decrease and 10% increase from the 

mean. The lower value for the plug loads was left at 10%, which is achievable with advanced 

power strips (Metzger et al. 2012). To achieve the assumed 10% increase and decrease from the 

mean, the mean value was then assumed to be 20% of the total peak load, and the upper value 

was bound at 30%. 

2.1.2.4 Occupant Density

A few sources of data were reviewed to determine typical year-to-year fluctuations in average 

occupant density, assuming that the building, after a retrofit, does not experience a major tenancy 

change. Unfortunately, available datasets such as CBECS 2003 have such a wide range of 

occupant density data that the statistical span of inputs was unrealistic for typical small office 

buildings (EIA 2003). To determine a more realistic range for an individual building over time, 

NREL used engineering judgment to describe a typical range of fluctuations in occupancy: a 

25% increase and decrease from the baseline occupant density was modeled. These values were 

reviewed by our partner, Energi, and deemed appropriate. Occupant loads and plug loads were 

adjusted by the same multiplier for occupant density to reflect a corresponding increase or 

decrease in plug loads caused by the building occupancy fluctuation.

2.1.3 Utility Price Escalation

Utility and fuel escalation rates were gathered from the EIA website. The EIA provides historical 

energy prices for electricity and natural gas for select states (EIA 2013a, 2013b). NREL used 20 

years of data for Michigan (1990 to 2011) to determine the minimum, maximum, and average 
22

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percent changes in electricity and natural gas prices from one year to the next over the 20-year 

time period. Electricity and natural gas prices for Michigan are illustrated in Figure 2.

Figure 2. Historical electricity and natural gas prices for Michigan

Typically, projected utility and fuel cost escalation rates are factored into the financing of an 

energy retrofit loan. Energi and its financial partners confirmed that they often factor in a 3%–

5% escalation rate every year. Based on data from the EIA, the range in fuel cost multipliers over 

the 20-year period includes a minimum value of 0.95 (or 5% price reduction), a maximum value 

of 1.08, and an average of 1.01 for electricity. For natural gas, a minimum value of 0.88, a 

maximum value of 1.18, and an average of 1.03 for natural gas were calculated. Because the 

financing of an energy retrofit loan already considers the expected increase in utility costs, 

NREL shifted the range values found in the EIA data for both electricity and gas so that the 

average value was fixed at 1.0. The new ranges, as displayed in Table 12, include a minimum 

value of 0.94, a maximum value of 1.07, and an average of 1.00 for electricity, and a minimum 

value of 0.85, a maximum value of 1.15, and an average of 1.00 for natural gas. These ranges 

were then input into R to generate a triangular distribution of the annual percent change in cost 

data for electricity and gas in Michigan. 

2.1.4 Quality of Preventative Maintenance

NREL used an LBNL study on ongoing commissioning to estimate the effects of continuous 

maintenance, and quality of maintenance, on EEMs after a retrofit (Mills 2009). The study 

summarizes the “persistence” of energy savings from commissioning projects and concludes that 

although some projects exhibit an erosion of savings over time, many do not and the tendency is 

for level—or even slightly increased—savings over time (Mills 2009). Based on the data 

presented in this report, the whole-building energy savings from the first year after a retrofit 

either increased or decreased by approximately 4%; the average was slightly higher than the 

expected energy savings. A distribution using this range was applied to each model’s wholebuilding energy use.

4

4.5

5

5.5

6

6.5

7

7.5

8

8.5

9

9.5

10

10.5

11

11.5

Average Commercial Electiricy and 

Gas Prices for Michigan

Electricity

(cents per

kWh)

Natural Gas

($/thousand

cubic feet)
23

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

2.2 Statistical Analysis

The statistical computing and graphics freeware, R, was used to generate a realistic Latin 

Hypercube Sampling (LHS) of input values for the performance risk variables analyzed in this 

study, forming a multidimensional distribution. LHS is inspired by the Latin square experimental 

design, which tries to eliminate confounding effects of various experimental factors without 

increasing the number of subjects in the experiments (Cheng and Druzdzel 2000). LHS uses an 

even sampling method that ensures that each value (or range of values) is represented in the 

samples, no matter which value might turn out to be more important (Cheng and Druzdzel 2000). 

The resulting set of random inputs yields a very smooth distribution that minimizes the number 

of runs needed to obtain an accurate distribution of output variables.

Based on the available data for each variable studied in this analysis, NREL first generated 

approximate triangular distributions for all the normally distributed variables based on the 

minimum, maximum, and mean values. For these variables, the mean value was assumed to be a 

good approximation of the modal value (or most common value), which coincides with the peak 

of the triangle. The triangular distribution characteristics were then fed into the LHS to generate 

a realistic collection of the parameter values, forming a multidimensional distribution. An 

example of how a triangular distribution approximates a normal distribution is illustrated in

Figure 3. 

Figure 3. Example of how a triangle distribution approximates a normal distribution

For this pilot study, NREL chose to keep the baseline cases as-is. For future studies, NREL could 

consider applying the a random sample of inputs to both the baseline and the energy-efficient 

cases, which would provide an estimate of performance risk associated with uncertain operating 

conditions that exist both before and after a retrofit. In this pilot study, however, NREL assumed 

that the baseline building operation before a retrofit is known based on an energy audit, but how 

that operation will change over time is unknown. 
24

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To apply the multidimensional distribution to the building energy models, NREL used 

OpenStudio to manipulate the models and manage runs. A comma-separated values worksheet 

containing the random sampling of inputs (generated by R) from distributions was fed into 

OpenStudio using Ruby scripts. The OpenStudio models were then run on one of NREL’s 

supercomputers; however, this process can be replicated by other organizations using the new 

OpenStudio feature that allows users to run multiple simulations in parallel on the cloud.

A grid sensitivity analysis was also conducted to determine the appropriate sample size of inputs; 

i.e., how many OpenStudio runs were needed to generate a smooth distribution curve that 

illustrates the magnitude of performance risk. To conduct the sensitivity analysis, NREL tested 

the sample size in the following intervals: 100 samples, 500 samples, 1,000 samples, 2,000 

samples, and 5,000 samples. Figure 4 shows an illustration of the distribution curves associated 

with each sample size. NREL ultimately chose a sample size of 5,000 based on engineering 

judgment, the smoothness of the distribution curve, and repeatability of the results. Note that the 

black line illustrates the density curve fit to each histogram.

2.3 Results

NREL generated four sets of results for this study:

• The combined effects of all performance risk variables on energy cost savings (expressed 

as the actual reduction in annual utility costs)

• The combined effects of all performance risk variables on energy cost savings, assuming 

a reduced range in uncertainties resulting from the implementation of risk mitigation 

behavior and policies that ensure optimal building operation

• The effects of individual performance risk variables on energy cost savings

• The combined effects of all performance risk variables on energy cost savings over 3-

and 5-year time periods.
25

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Figure 4. Illustration of output distribution curves for different sample sizes
26

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

The first set of results looks at how the performance risk variables analyzed in this study affect 

energy cost savings in the subset of energy-efficient models developed for Michigan Saves. 

These models, summarized in Table 11, achieve close to 20% energy savings and offer a net 

positive cash flow. The results of this analysis, illustrated in Figure 5, show the calculated mean 

(noted in red) and standard deviation (noted in blue) in energy cost savings due to the effects of 

the performance risk variables; the mean standard deviation across all of the models is 5.02%, 

which is equal to about 20% of the mean energy cost savings (approximately 25%) for the 

packages (this is also called the coefficient of variance, where the standard deviation is divided 

by the mean). The buffers calculated for this study are summarized in Table 13 and can be 

applied to the energy cost savings of a retrofit project at various confidence levels. For example, 

the average buffer of 34% across all building types at a 95% confidence level indicates that the 

actual energy cost savings will exceed 66% of the expected value 95% of the time. In other 

words, 5% of the time the savings will be less than 66% of the expected energy cost savings. 

When reviewing the results in Figure 5, note that the code “ad-e&w&flr” is for cases where the 

building is adjacent to other buildings on two sides. The black line in each graph shows the 

density curve fit to the histogram.

Table 13. Buffers To Be Applied to Energy Cost Savings, Calculated at Various Confidence Levels

Confidence Level

90%

(1.282 Standard 

Deviations)

95%

(1.645 Standard 

Deviations)

Coefficient of Variation

(ratio of the standard deviation to the 

mean)

Calculated Energy Cost Savings Buffers

Post-1980, 5,500 ft2, PTAC HVAC System, 

Stand-Alone Building: 18% 24% 30%

Post-1980, 5,500 ft2, PTAC HVAC System, 

Adjacent to Buildings on 2 Sides: 16% 21% 27%

Post-1980, 25,000 ft2, PTAC HVAC System, 

Stand-Alone Building: 23% 30% 39%

Post-1980, 25,000 ft2, PTAC HVAC System, 

Stand-Alone Building: 19% 24% 31%

Post-1980, 25,000 ft2, PTAC HVAC System, 

Adjacent to Buildings on 2 Sides: 22% 28% 36%

Post-1980, 25,000 ft2, PTAC HVAC System, 

Adjacent to Buildings on 2 Sides: 18% 23% 29%

Pre-1980, 25,000 ft2, PTAC HVAC System, 

Stand-Alone Building: 29% 38% 49%

Average: 21% 27% 34%
27

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 5. Results showing the average energy cost savings (red) and standard deviations (blue) 

resulting from building performance uncertainties not covered by performance guarantees for 

various EEM packages developed for small commercial office buildings. The black line illustrates 

the density curve fit to each histogram.
28

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The second set of results evaluates how performance risk variables affect building energy cost 

savings, assuming that risk mitigation strategies are being implemented to control and optimize 

building operation in a manner that reduces performance risk. Not all performance risk 

uncertainties can be easily mitigated; for example, weather and fuel escalation rates are 

completely uncontrollable by the building owner, and very little can be done to reduce the 

impact. However, uncertainties associated with occupant behavior and quality of preventative 

maintenance can be controlled to a certain degree. To help reduce these risks, we recommend 

that the building owner:

• Use controls and set policies around building operation to confirm that the building is 

operated within the intended bounds. These controls and policies can be placed to better 

guarantee appropriate thermostat settings during occupied hours, and to encourage lights 

and plug load equipment to be turned off at night and when not in use during the day.

• Regularly check and confirm that the building is being operated within intended bounds, 

and always look for operational improvements. Maximizing occupant density by 

maintaining appropriate occupancy levels or, if possible, consolidating employee 

workspaces to a particular floor or wing of a building can also help reduce energy 

consumption by reducing the need to condition and power unused office space. 

• Schedule retrocommissioning to ensure major building equipment is being operating at 

its maximum efficiency. 

• If building owners have tenants, it may be appropriate to shift some of the performance 

risk to tenants that have control over building operations and occupancy levels. Green 

leases are one way to achieve this. These risk mitigation strategies are summarized in 

Appendix A. 

For this particular study, we assumed that controls, policies, and risk mitigation strategies such 

as retrocommissioning reduce the ranges in uncertainties associated with heating and cooling 

temperature set points, lighting and plug load equipment schedules, and the quality of 

preventative maintenance. We also assumed that the building owner would take steps to optimize 

space utilization in the building, thus reducing the need to heat, cool, light, and power unused 

office space. Because there are limited data on how risk mitigation strategies truly affect the 

range of operational values for each performance risk variable, NREL chose to reduce the range, 

somewhat arbitrarily, by 50% to illustrate the correlation between a building with more stringent 

controls and the resulting distribution of energy savings. The 50% reduction can also be viewed 

as an operational target for the building owner. If the building owner can operate the building 

within the tighter ranges, there is high confidence that greater energy savings will be achieved. 

The tighter distribution ranges used in this particular study are highlighted in red in Table 14.
29

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Table 14. Summary of Performance Risk Variables With Tighter Distribution Ranges, Accounting for Best Practices and Efficient 

Building Control. The Reduced Values are Highlighted in Red and Can Be Compared to Table 12.

Reduced 

Ranges 

(Reduced by 

50%)

Weather

Heating 

Set Point 

(Occupied 

Hours)

Cooling 

Set Point 

(Occupied 

Hours)

Lighting 

Schedule 

(Unoccupied 

Hours)

Equipment 

Schedule 

(Unoccupied 

Hours)

Occupant 

Density

Utility 

Price 

Escalation

Electricity

Utility 

Price 

Escalation

Gas

Quality of 

Preventative 

Maintenance

Units

Year of 

weather 

data used 

in 

simulation

°F °F

Multiplier 

applied to 

schedule

Multiplier 

applied to 

schedule

Multiplier 

applied 

to 

occupied 

density

Multiplier 

applied to 

calculated 

electricity 

cost

Multiplier 

applied to 

calculated 

gas cost

Multiplier 

applied to 

whole 

building 

energy use

Probability 

Distribution Uniform Triangular Triangular Discrete Triangular Triangular Triangular Triangular Triangular

Range: 

Minimum 1988 68.18 74.03 N/A 0.1 0.88 0.94 0.85 0.98

Range: 

Maximum 2012 71.06 76.55 N/A 0.2 1.13 1.07 1.15 1.02

Mean N/A 69.80 75.20 N/A 0.15 1.00 1.00 1.00 1.003

Scenario 1 N/A N/A N/A 1.00 N/A N/A N/A N/A N/A

Scenario 2 N/A N/A N/A 1.32 N/A N/A N/A N/A N/A

Scenario 3 N/A N/A N/A 0.69 N/A N/A N/A N/A N/A

Probability of 

Scenario 1 N/A N/A N/A 0.90 N/A N/A N/A N/A N/A

Probability of 

Scenario 2 N/A N/A N/A 0.05 N/A N/A N/A N/A N/A

Probability of 

Scenario 3 N/A N/A N/A 0.05 N/A N/A N/A N/A N/A
30

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

The results of this analysis, illustrated in Figure 6, show the calculated mean (noted in red) and 

standard deviation (noted in blue) in energy cost savings due to the effects of the tighter 

distribution of the performance risk variables. The average standard deviation across the models, 

in this case, is 3.08%, which is equal to about 11% of the mean energy cost savings 

(approximately 27%) for the packages. Again, expressed another way, the results indicate that 

we can be 95% confident that energy cost savings will exceed 81% of the expected value. This 

means that only 5% of the time, the savings will be less than 81% of the expected value, 

indicating that energy savings are much more predictable than the first set of results. The buffers 

calculated for the tighter uncertainty ranges are summarized in Table 15 and can be applied to 

the energy cost savings of a retrofit project at various confidence levels.

Table 15. Buffers Calculated From the Tighter Ranges of Performance Risk Variables,

To Be Applied to Energy Cost Savings at Various Confidence Levels

Confidence Level

90%

(1.282 Standard 

Deviations)

95%

(1.645 Standard 

Deviations)

Coefficient of Variation

(ratio of the standard deviation to the mean) Calculated Energy Cost Savings Buffers

Post-1980, 5,500 ft2, PTAC HVAC System, 

Stand-Alone Building: 11% 14% 18%

Post-1980, 5,500 ft2, PTAC HVAC System, 

Adjacent to Buildings on 2 Sides: 10% 13% 16%

Post-1980, 25,000 ft2, PTAC HVAC System, 

Stand-Alone Building: 12% 16% 21%

Post-1980, 25,000 ft2, PTAC HVAC System, 

Stand-Alone Building: 10% 13% 17%

Post-1980, 25,000 ft2, PTAC HVAC System, 

Adjacent to Buildings on 2 Sides: 12% 15% 20%

Post-1980, 25,000 ft2, PTAC HVAC System, 

Adjacent to Buildings on 2 Sides: 10% 13% 16%

Pre-1980, 25,000 ft2, PTAC HVAC System, 

Stand-Alone Building: 16% 21% 27%

Average: 12% 15% 19%
31

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 6. Results showing the average energy cost savings (red) and standard deviation (blue) 

resulting from a tighter range in building performance uncertainties not covered by performance 

guarantees for various EEM packages developed for small commercial office buildings. The black 

line illustrates the density curve fit to each histogram.
32

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

A third study was conducted to determine the effects of each performance risk variable on 

energy cost savings. For this study, we used the post-1980, stand-alone, 25,000-ft2 office 

building model. All EEMs were applied to the model to avoid showing preference to any 

particular measure or group of measures. The results, as seen in Figure 7, highlight the impact of 

each uncertainty on energy cost savings. The performance risk variable with the highest impact 

on energy cost savings is occupant density, which makes logical sense because occupants drive 

the major loads within the building. Following occupant density are the heating and cooling 

temperature set points, plug loads left on at night, lights left on at night, and lastly, weather. 

These results pose an interesting perspective: if a building is controlled and managed correctly, 

the risk associated with these performance uncertainties can decrease. Paired with the previous 

results, by implementing risk mitigation strategies (summarized in Appendix A), the owner and 

lender can have much higher confidence in achieving the estimated energy savings. 
33

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 7. Results showing the average energy cost savings (red) and standard deviation (blue) 

resulting from individual performance uncertainties on energy cost savings. This study was 

conducted on the post-1980, 25,000-ft2

, stand-alone, small office building model. All EEMs were 

applied to this model.
34

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Going a step further, NREL looked at the effects of the uncertainties on energy cost savings over 

3- and 5-year time periods, because most performance agreements look at savings over multiple 

years. To do so, NREL randomly selected and averaged groups of 3 or 5 of the 5,000 results of 

the study (while maintaining the same sample size of 5,000 outputs) to generate a distribution of 

the uncertainty effects on energy cost savings over 3- and 5-year time periods, respectively. The 

results are summarized in the following four figures. Figure 8 and Figure 9 show the effects of 

the uncertainties using the original distribution ranges over 3- and 5-year time periods, 

respectively. Likewise, Figure 10 and Figure 11 show the effects of the uncertainties using the 

tightened distribution ranges over a 3-year and a 5-year time period, respectively. Overall, the 

longer-term results indicate that for a given confidence level, the energy cost savings are likely to

exceed a much higher percentage of the expected value than the results from the 1-year intervals. 

With these results, we encourage financial institutions and building owners to consider the 

energy cost savings over a longer time period, because the effects of the uncertainties tend to 

lessen over time. The buffers in this study are summarized in Table 16 and Table 17. Figure 8

through Figure 11 follow.

Table 16. Buffers Calculated Using the Original Distribution Ranges

Over 3- and 5-Year Time Periods

Effects of Uncertainties Over 3- and 5-

Year Time Periods, Original Distribution 

Ranges

Confidence Level

90%

(1.282 Standard 

Deviations)

95%

(1.645 Standard 

Deviations)

Coefficient of 

Variation

(% of mean)

Calculated Energy Cost Savings 

Buffers

3-Year 5-Year 3-Year 5-Year 3-Year 5-Year

Post-1980, 5,500-ft2, PTAC HVAC System, 

Stand-Alone Building 11% 8% 14% 11% 18% 14%

Post-1980, 5,500-ft2, PTAC HVAC System, 

Adjacent to Buildings on 2 Sides 9% 7% 12% 9% 16% 12%

Post-1980, 25,000-ft2, PTAC HVAC 

System, Stand-Alone Building 14% 11% 17% 14% 22% 17%

Post-1980, 25,000-ft2, PTAC HVAC 

System, Stand-Alone Building 11% 8% 14% 11% 18% 14%

Post-1980, 25,000 ft2, PTAC HVAC 

System, Adjacent to Buildings on 2 Sides 13% 10% 16% 13% 21% 16%

Post-1980, 25,000-ft2, PTAC HVAC 

System, Adjacent to Buildings on 2 Sides 10% 8% 13% 10% 17% 13%

Pre-1980, 25,000-ft2, PTAC HVAC System, 

Stand-Alone Building 17% 13% 22% 17% 28% 22%

Average 12% 9% 16% 12% 20% 15%
35

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 17. Buffers Calculated Using the Reduced Distribution Ranges

Over 3- and 5-Year Time Periods

Effects of Uncertainties Over 3- and 5-

Year Time Periods, Reduced 

Distribution Ranges

Confidence Level

90%

(1.282 Standard 

Deviations)

95%

(1.645 Standard 

Deviations)

Coefficient of 

Variation

(% of mean)

Calculated Energy Cost Savings 

Buffers

3-Year 5-Year 3-Year 5-Year 3-Year 5-Year

Post-1980, 5,500-ft2, PTAC HVAC System, 

Stand-Alone Building 6% 5% 8% 6% 10% 8%

Post-1980, 5,500-ft2, PTAC HVAC System, 

Adjacent to Buildings on 2 Sides 6% 4% 7% 6% 9% 7%

Post-1980, 25,000-ft2, PTAC HVAC 

System, Stand-Alone Building 7% 6% 9% 7% 12% 9%

Post-1980, 25,000-ft2, PTAC HVAC 

System, Stand-Alone Building 6% 5% 7% 6% 10% 8%

Post-1980, 25,000-ft2, PTAC HVAC 

System, Adjacent to Buildings on 2 Sides 7% 5% 9% 7% 11% 9%

Post-1980, 25,000-ft2, PTAC HVAC 

System, Adjacent to Buildings on 2 Sides 6% 4% 7% 6% 9% 7%

Pre-1980, 25,000-ft2, PTAC HVAC System, 

Stand-Alone Building 9% 7% 12% 9% 15% 12%

Average 7% 5% 9% 7% 11% 9%
36

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 8. Results showing the average energy cost savings (red) and standard deviation (blue) 

over a 3-year time period resulting from building performance uncertainties not covered by 

performance guarantees for various EEM packages developed for small commercial office 

buildings. The black line illustrates the density curve fit to each histogram.
37

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 9. Results showing the average energy cost savings (red) and standard deviation (blue) 

over a 5-year time period resulting from building performance uncertainties not covered by 

performance guarantees for various EEM packages developed for small commercial office 

buildings. The black line illustrates the density curve fit to each histogram.
38

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 10. Results showing the average energy cost savings (red) and standard deviation (blue) 

over a 3-year time period, resulting from a tighter range in building performance uncertainties not 

covered by performance guarantees for various EEM packages developed for small commercial 

office buildings. The black line illustrates the density curve fit to each histogram.
39

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 11. Results showing the average energy cost savings (red) and standard deviation (blue) 

over a 5-year time period, resulting from a tighter range in building performance uncertainties not 

covered by performance guarantees for various EEM packages developed for small commercial 

office buildings. The black line illustrates the density curve fit to each histogram.
40

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

3 Conclusions and Next Steps

This pilot project targeted two of the largest barriers to financing energy efficiency projects in 

small commercial buildings: disproportionately high transaction costs and unknown performance 

risk. NREL collaborated with two lead partners, Michigan Saves and Energi Insurance Services, 

along with several contractors, service providers, utilities, and lenders, to develop technical 

products that address both barriers in a complementary manner. 

To reduce transaction costs, NREL assisted Michigan Saves in the development of low-risk EEM 

packages for one specific building type: small office buildings. These packages can be integrated 

into third-party service provider offerings and coordinated with utility rebates to create preapproved turnkey solutions targeting a specific level of energy savings. The EEMs included in 

each recommended EEM package, summarized in the EEM Selection & Cost Evaluation Tool, 

were selected to maximize the predictability of energy savings and financial returns and meet a 

particular energy savings target. The EEM Selection & Cost Evaluation was developed for the 

project, providing the complete set of more than 30,000 simulations, and allowing flexibility to 

change the cost assumptions and financing parameters. 

To reduce performance risk, NREL partnered with Energi to quantify the drivers of uncertain 

energy savings and provide direction for mitigating that uncertainty. Although much of the 

performance risk can be controlled through contractor guarantees and insurance against 

performance risk stemming from equipment, design, modeling, and workmanship, lenders 

currently have no parameters or approach to understand risks outside these sources. NREL 

performed this analysis of performance risk using the same building type, climate zone, and 

EEMs selected for the Michigan Saves recommended retrofit packages. NREL also 

recommended methods for managing uncertainty throughout the life of the loan. By providing 

greater confidence in the actual range of cash flows that can be expected for an energy efficiency 

project, more lenders may be willing to provide affordable financing to small building owners 

who can effectively control the performance risk. The results can also be applied to rating and 

securitization of energy efficiency loans, providing a consistent buffer range and approach for 

analysis and control of building performance risk that falls outside of typical performance 

guarantees. Although this study was conducted in the context of the Energi collaboration, it is a 

very challenging source of risk for all financed projects, and even those that are paid for using 

the owner’s capital funds. 

The methodology used for this pilot project can be replicated in other small building sectors, in 

other locations, and with other financing models to help increase lender confidence, reduce 

investment risk and transaction costs, and motivate small building and business owners to invest 

in efficiency upgrades. This report documents the processes and resources used to perform the 

analysis of low-risk, pre-approved packages, as well as the uncertainty analysis of building 

performance risk, allowing other organizations to expand on the results in a consistent, efficient, 

and repeatable manner.
41

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

References

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Association for Artificial Intelligence. Pittsburgh, PA: University of Pittsburgh. Accessed 

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Deru, M.; Field, K.; Studer, D.; Benne, K.; Griffith, B.; Torcellini, P.; Liu, B.; Halverson, M.; 

Winiarski, D.; Rosenberg, M.; Yazdanian, M.; Huang, J.; Crawley, D. (2011). U.S. Department 

of Energy Commercial Reference Building Models of the National Building Stock. Golden, CO: 

National Renewable Energy Laboratory, NREL/TP-5500-46861. February 2011. Accessed 

August 2013 from www.nrel.gov/docs/fy11osti/46861.pdf.

DOE. (2011). Advanced Energy Retrofit Guide, Office Buildings. Washington, DC: U.S. 

Department of Energy. Accessed September, 2013 from https://www.ashrae.org/standardsresearch--technology/advanced-energy-design-guides/50-percent-aedg-free-download. 

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Department of Energy. Accessed September 2013 from 

https://www1.eere.energy.gov/buildings/commercial/assetscore_tool.html.

EIA. (2003). Commercial Building Energy Consumption Survey, 2003. Washington, DC: Energy 

Information Administration. Accessed September 2013 from 

www.eia.gov/consumption/commercial/data/2003/

EIA. (2012a). Electricity Average Price by State 1990-2010. Washington, D.C.: Energy 

Information Administration. Accessed March 2012 from www.eia.gov/electricity/data.cfm#sales.

EIA. (2012b). Natural Gas Prices by State 2010. Washington, D.C.: Energy Information 

Administration. Accessed March 2012 from www.eia.gov/naturalgas/data.cfm#sales.

EIA. (2013a). Electricity Data. Washington, DC: Energy Information Administration. Accessed 

October 2013 from www.eia.gov/electricity/data.cfm#sales. 

EIA. (2013b). Average Price of Natural Gas Delivered to Residential and Commercial 

Consumers by Local Distribution and Marketers in Selected States. Washington, DC: Energy 

Information Administration. Accessed October, 2013 from 

www.eia.gov/dnav/ng/ng_pri_rescom_a_EPG0_PRS_DMcf_a.htm.

IEA. (2005). The Experience with Energy Efficiency Policies and Programmes in IEA Countries.

International Energy Agency. Accessed October 2013 from 

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22467c12571da0032d460/$file/the+experience+with+energy+efficiency+policies+and+program

mes+in+iea+countries.pdf.
42

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Jensen, B. (2012). High Efficiency Commercial Air Conditioning and Heat Pump Initiative. 

Boston, MA: Consortium for Energy Efficiency. Accessed September 2013 from 

http://library.cee1.org/sites/default/files/library/5347/CEE_CommHVAC_HECAC__InitDescip.

pdf.

Langner, R.; Hendron, B.; Pless, S.; Huppert, M.; Cochrane, R. (2013). Industry Research and 

Recommendations for Small Buildings and Small Portfolios. Golden, CO: National Renewable 

Energy Laboratory NREL/TP-5500-57776. Accessed May 19, 2014 from 

www.nrel.gov/docs/fy14osti/57776.pdf. 

Leach, M.; Lobato, C.; Hirsch, A.; Pless, S.; Torcellini, P. (2010). Technical Support Document: 

Strategies for 50% Energy Savings in Large Office Buildings. Golden, CO: National Renewable 

Energy Laboratory, NREL/TP-550-49213. September, 2010. Accessed August 2013 from 

www.nrel.gov/docs/fy10osti/49213.pdf.

Mendell, M.J.; Mirer A. (2008). Indoor Thermal Factors and Symptoms in Office Workers: 

Findings from the U.S. EPA BASE Study. Berkeley, CA: Lawrence Berkeley National 

Laboratory. Accessed September 2013 from www.iaqscience.lbl.gov/pdfs/benefits-3.pdf. 

Metzger, I.; Cutler, D.; Sheppy, M. (2012). Plug-Load Control and Behavior Change Research 

in GSA Office Buildings. Golden, CO: National Renewable Energy Laboratory. Accessed 

September 2013 from http://www.gsa.gov/graphics/pbs/GSA-GPG-PlugLoadsReportFINAL.pdf.

Mills, E. (2009). Building Commissioning: A Golden Opportunity for Reducing Energy Costs 

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Nadel, S. (1993). The Take Back Effect: Fact or Fiction? American Council for an Energy 

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Roth, K.W.; Westphalen, D.; Llana, P.; Feng, M. (2004). The Energy Impact of Faults in U.S. 

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Renewable Energy Laboratory’s Research Support Facility. Golden, CO: National Renewable 

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www.nrel.gov/docs/fy13osti/57785.pdf. 
44

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix A: EEM Selection & Cost Evaluation Tool 

Snapshot

Appendix A provides a snapshot of the EEM Selection & Cost Evaluation Tool

(www.nrel.gov/buildings/docs/2014_eemselection_costevaltool_smoffice.xlsm). This tool is 

available to the public upon request. The tool has a simple user interface, where the user can 

input basic building data, run a simple built-in macro, and see applicable EEM packages that are 

appropriate and cost effective for a particular building. The building data required to run the tool 

include the building vintage, square footage, HVAC system type, and boundary condition 

(whether the building is a stand-alone building, or attached to other buildings on either one or 

two sides). The user can also select an energy savings threshold, defined as the minimum energy 

savings that the building owner or evaluator expects to achieve cost effectively. By running the 

built-in macro (the user selects the “Run” button), the tool filters presimulated results to select 

EEM bundles that meet the energy savings threshold and are calculated to be cost effective. 

Again, cost effective is defined as a net positive cash flow, where the annual energy cost savings 

is greater than the annual payment of an energy efficiency investment. 

A few things should be noted about the tool:

1. The predicted energy and energy cost savings represented in the EEM selection and Cost 

Evaluation Tool may not be accurate for all small office buildings. It is intended to be 

used by building owners to gain understanding of a wider range of possible EEM options 

that can be implemented within a building cost effectively.

2. There may be other EEMs not represented in this tool that are also cost effective and 

appropriate for a particular building. The EEMs included in this tool represent “typical”

EEMs that are commonly applied to small office buildings.

3. The full set of EEM combinations, energy savings results, and energy cost savings results 

(used as input for this tool) are located in the Results tab. It is not intended that the user 

use this tab; however, these results are provided in case the user wants to understand or 

modify how the cost savings calculations are calculated.

Figure 12 shows the main user interface of the EEM Selection & Cost Evaluation Tool. The user 

inputs information about a particular building in the “Input Building Data” section. After hitting 

the “Run” button, the built-in macro sorts the results and displays cost-effective EEM package 

options under the gold-colored column headers. Each row of results describes a particular EEM 

package that is cost effective. The energy savings and cost analysis is also provided as part of 

this output.
45

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 12. Snapshot of the EEM Selection & Cost Evaluation Tool’s graphical user interface

Because the costs for energy efficiency improvements can vary per building project and location, 

the tool allows the user to modify cost values. If a cost value is modified, the results and cost

analysis will reflect the updated cost value. A snapshot of the tool’s graphical user interface 

where the user can modify efficiency measure costs is shown in Figure 13. 

Screening questions to filter 

results Results: EEM package options Run the macro, or clear 

results
46

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 13. Snapshot of the EEM Selection & Cost Evaluation Tool’s graphical user interface, where 

the user can modify estimated energy efficiency improvement costs

Similarly, the user can modify the utility costs (for electricity and gas) and specifics for a 

particular loan (interest rate and loan term). A snapshot of this part of the EEM Selection & Cost 

Evaluation Tool is shown in Figure 14.
47

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 14. Snapshot of the EEM Selection & Cost Evaluation Tool’s graphical user interface,

where the user can modify utility costs and loan information

Once the macro has been run, the tool lists descriptions of possible EEM package options that 

meet the user-specified energy savings threshold cost effectively. Each row provides a different 

EEM package with data regarding the energy and energy cost savings, and complete cost 

analysis to implement each package. A snapshot of this part of the tool is shown in Figure 15.

Figure 15. Snapshot of the results of the EEM Selection & Cost Evaluation Tool

after running the built-in macro

Description of cost effective 

EEM package options that meet 

an energy savings threshold

Energy savings for each EEM 

package (sorted highest to 

lowest)

Cost information 

for each EEM 

package
48

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix B: Recommended Risk Mitigation Strategies

To increase the chances of achieving the estimated savings for a recommended EEM package, 

NREL has recommended a number of building performance risk mitigation strategies, based on 

the statistical analysis looking at the effects of individual performance risk variables on energy 

cost savings. The recommended strategies are intended to control and optimize building 

operation in a manner that mitigates performance risk. The strategies encourage building owners 

to:

• Use controls and set policies around building operation:

o Turn off lights and plug loads at night, and throughout the day when not needed.

o Control heating and cooling temperature set points within appropriate ranges.

o Regularly check and confirm that the building is being operated within intended 

bounds, and look for operational improvements.

o For building owners with tenants, use leasing language to shift relevant 

components of performance risk to tenants who are in control of building 

operations and occupancy levels, and consider green leases.

• Optimize the use of building space, reducing the need to condition and power unused 

office space.

• Recommission the building regularly to ensure the building equipment is operating at its 

maximum efficiency.
49

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix C: Effects of Individual EEMs on Percent 

Energy Savings

Appendix B shows results for the study conducted to determine the effects of individual EEMs 

on percent energy savings. The first set of results is averaged over all the small office building 

models used in this pilot project. The subsequent sets of results show the effects of each EEM on 

percent energy savings averaged over HVAC system type (for stand-alone buildings and 

buildings that adjoin buildings on two sides), and averaged over boundary condition (for 

buildings with PSZ-AC, PTAC, and PTHP HVAC system types).

Results averaged over all small office building models:

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Percent Energy Savings

All Small Office Building Models

pre1980

post1980

new90.1-2004
50

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Stand-alone office buildings, averaged over HVAC system type:

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Percent Energy Savings

5,500 ft2 Small Office

pre1980

post1980

new90.1-2004

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Percent Energy Savings

10,000 ft2 Small Office

pre1980

post1980

new90.1-2004

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Percent Energy Savings

25,000 ft2 Small Office

pre1980

post1980

new90.1-2004
51

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Office buildings that adjoin buildings on two sides, averaged over HVAC system type:

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Percent Energy Savings

5,500 ft2 Small Office

pre1980

post1980

new90.1-2004

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Percent Energy Savings

10,000 ft2 Small Office

pre1980

post1980

new90.1-2004

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Percent Energy Savings

25,000 ft2 Small Office

pre1980

post1980

new90.1-2004
52

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Office buildings with PSZ-ACs, averaged over boundary conditions:

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Percent Energy Savings

5,500 ft2 Small Office

pre1980

post1980

new90.1-2004

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Percent Energy Savings

10,000 ft2 Small Office

pre1980

post1980

new90.1-2004

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Percent Energy Savings

25,000 ft2 Small Office

pre1980

post1980

new90.1-2004
53

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Office buildings with PTACs, averaged over boundary conditions:

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Percent Energy Savings

5,500 ft2 Small Office

pre1980

post1980

new90.1-2004

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Percent Energy Savings

10,000 ft2 Small Office

pre1980

post1980

new90.1-2004

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Percent Energy Savings

25,000 ft2 Small Office

pre1980

post1980

new90.1-2004
54

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Office buildings with PTHPs, averaged over boundary conditions:

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Percent Energy Savings

5,500 ft2 Small Office

pre1980

post1980

new90.1-2004

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Percent Energy Savings

10,000 ft2 Small Office

pre1980

post1980

new90.1-2004

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

Percent Energy Savings

25,000 ft2 Small Office

pre1980

post1980

new90.1-2004