A Tool to Prioritize Energy 

Efficiency Investments 

Philip Farese, Rachel Gelman, and 

Robert Hendron 

National Renewable Energy Laboratory (NREL) 

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. 

Technical Report 

NREL/TP-6A20-54799 

August 2012 

Contract No. DE-AC36-08GO28308 
 

 

 

National Renewable Energy Laboratory 

15013 Denver West Parkway 

Golden, Colorado 80401 

303-275-3000 • www.nrel.gov 

A Tool to Prioritize Energy 

Efficiency Investments 

Philip Farese, Rachel Gelman, and 

Robert Hendron 

National Renewable Energy Laboratory (NREL) 

Prepared under Task No. SA10.1072 

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. 

Technical Report 

NREL/TP-6A20-54799 

August 2012 

Contract No. DE-AC36-08GO28308 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Table of Contents 

Table of Contents...........................................................................................................................iii
 

List of Figures................................................................................................................................ iv
 

List of Tables ................................................................................................................................. iv
 

Acknowledgements......................................................................................................................... v
 

Executive Summary....................................................................................................................... vi
 

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

2 Measure Design Philosophy ................................................................................................... 6
 

2.1 Approach to Data Collection .......................................................................................... 6
 

2.2 Data Required of a Measure for Its Inclusion in the Selection Set................................. 7
 

2.2.1 Energy Efficiency Data............................................................................................. 7
 

2.2.2 Cost Data................................................................................................................... 7
 

2.2.3 Market Data .............................................................................................................. 7
 

2.3 Overview of Data Used to Create Baseline Energy Use................................................. 8
 

3 Baseline Energy Use and Technical Potential Calculation................................................... 11
 

3.1 Mathematical Formulation and Baseline Definition..................................................... 11
 

3.2 Baseline Energy Use and Technical Potential in the Engine........................................ 12
 

3.2.1 “Existing” Mix of Alternatives ............................................................................... 13
 

3.2.2 “Measure” To Be Evaluated ................................................................................... 13
 

3.3 Calculating the Baseline Energy Use............................................................................ 14
 

3.4 Calculating the Technical-Minimum Energy Use and Technical Potential.................. 17
 

4 Stock-and-Flow Model and Full-Adoption-Based Potential Calculation............................. 19
 

4.1 The Stock-and-Flow Model .......................................................................................... 19
 

4.1.1 Design ..................................................................................................................... 19
 

4.1.2 Limitations of Our Data and Methodology............................................................. 21
 

4.1.3 Stock Flow Calculations ......................................................................................... 21
 

4.2 Calculating the Full-Adoption-Based Energy Use and Economic Potential ................ 25
 

4.3 Treatment of Cost Compression ................................................................................... 25
 

4.4 Example Full-Adoption-Based Potential Calculation................................................... 25
 

5 Adoption Logic and Adoption-Based Potential.................................................................... 28
 

5.1 Description of the Bass Adoption Curve ...................................................................... 28
 

5.2 Estimation of “p” and “q” Parameters .......................................................................... 28
 

5.3 Extensions and Alternatives to the Bass Adoption Model............................................ 29
 

5.4 Limitations .................................................................................................................... 32
 

5.5 Defining the “Bass-Adoption-Based” Potential............................................................ 33
 

6 Developing a Portfolio: Staging Logic ................................................................................. 36
 

6.1 Staging by Cost of Conserved Energy.......................................................................... 36
 

6.1.1 Identifying the Discount Factor to Value Future Costs and Benefits ..................... 38
 

6.2 Evaluating Market Overlap........................................................................................... 38
 

6.2.1 Defining the “Micro-segments”.............................................................................. 39
 

6.2.2 Example Micro-segment Calculations.................................................................... 42
 

6.2.3 Evaluating Market Overlap..................................................................................... 43
 

6.3 Determining the Nature of Interaction Between Measures........................................... 44
 

6.4 Calculating the “Staged” Savings ................................................................................. 46
 

6.5 Iteration......................................................................................................................... 46
 

iii 
 

 

7 Outputs and Their Uses......................................................................................................... 48
 

7.1 Technology Limits........................................................................................................ 48
 

Efficiency “Potential” Studies .................................................................................................. 50
 

7.1.1 Technology Selection Set- reproducing Related Work .......................................... 52
 

7.1.2 Variations in Discount Factor Significantly Change the Potential ......................... 53
 

7.1.3 Increasing Energy Prices Drive a Corresponding Increase in Potential ................. 54
 

7.1.4 Uncertainty in Savings Can Impact Measure Selection.......................................... 55
 

7.1.5 Uncertainty in Measure Cost can Also Impact Measure Selection and Total Savings 

Potential ................................................................................................................................ 55
 

7.1.6 Rebound Effects Convert Savings to Value............................................................ 55
 

8 Conclusions........................................................................................................................... 57
 

Works Cited .................................................................................................................................. 58
 

Appendix A: Baseline Assumptions ............................................................................................. 61
 

Appendix B: Input Table .............................................................................................................. 63
 

Additional Resources.................................................................................................................. 106
 

List of Figures 

Figure 1. Baseline energy use ......................................................................................................... 2
 

Figure 2. Categorization of equipment into nine classifications according to efficiency and 

potential action they undergo when flowing stock from year y-1 to year y. ................. 20
 

Figure 3. Identification of how stock-and-flow calculation treats each equipment classification.22
 

Figure 4. Graphical depiction of our stock-and-flow calculation. ................................................ 24
 

Figure 5. Primary energy use........................................................................................................ 26
 

Figure 6. Adoption rate uncertainty arising from variations in adoption model selected............. 31
 

Figure 7. Primary energy use........................................................................................................ 34
 

Figure 8. Energy efficiency supply curve showing annual economic savings in 2030. ............... 52
 

List of Tables 

Table 1. Baseline Energy Use in the Prioritization Tool to Analyze the BTP Portfolio .............. 10
 

Table 2. Bass Adoption Coefficients from Fit to the Baseline Energy Use for Measures in Full
 

Data Set.......................................................................................................................... 34
 

Table 3. Interaction Between Components Describing Measures in Tool ................................... 45
 

Table 4. Staged and "Un-staged" Energy Savings........................................................................ 47
 

Table 5. Technical Energy Savings Possible in 2030 with R&D and Full Deployment .............. 50
 

Table 6. Energy Savings Identified in Five Analyses................................................................... 53
 

Table 7. Variation of Economic-potential Scenario with Changes in Discount Factor................ 54
 

Table 8. Variation of Economic-potential Scenario with Changes in Energy Price..................... 54
 

Table 9. Direct and Indirect Rebound Effects Likely to Reduce Energy Savings........................ 56
 

iv 
 

 

 

 

 

 

Acknowledgements 

The following alphabetical list represents all of the organizations and individuals who have 

contributed to this work either by adding measures that met our requirements, reviewing the 

aggregated measure in their area of expertise, reviewing our methodology, or providing other 

useful conversations or contributions to our work. We are deeply indebted to them and thankful 

for their contributions. Despite having used their data and perspectives, the ultimate 

responsibility for these findings rests with the authors of this report. We thank Alexis Abramson 

[U.S. Department of Energy (DOE)], Dave Anderson [Pacific Northwest National Laboratory 

(PNNL)], Ren Anderson [National Renewable Energy Laboratory (NREL)], Edward Barbour 

(Navigant Consulting), Van Baxter [Oak Ridge National Laboratory (ORNL)], Marcy Beck 

[Lawrence Berkeley National Laboratory (LBNL)], David Belzer (PNNL), Peter Biermayer 

(LBNL), Antonio Bouza (DOE), Sara Lynn Bunch (DOE), Kymisha Carey (DOE), Craig 

Cristensen (NREL), John Cymbalsky (DOE), Omer Andre Desjarlais (ORNL), Louis-Benoit 

Desroches (LBNL), Anne Dillon (NREL), James Dirks (PNNL), Michael Erbesfeld (DOE), 

Doug Elliot (PNNL), Bill Goetzler (Navigant Consulting), Rebecca Gordon (Energetics), 

Bahman Habibzadeh (DOE), Donna Hostick (PNNL), Kate Hudon (NREL), Patrick Hughes 

(ORNL), Ron Judkoff (NREL), Marc LaFrance (DOE), David Lee (DOE), Ronnen Levinson 

(LBNL), Xiaobing Liu (ORNL), Deborah Karpay (CLASP), Paul Matthew (LBNL), Alan Meier 

(LBNL), Michael Myer (LBNL), Sean McDonald (PNNL), Jim McMahon (LBNL), Andrew 

Nicholls (PNNL), Bruce Nordman (LBNL), Shanti Pless (NREL), Sam Rashkin (DOE), Jeremy 

Richardson (New West Technology), Roland Risser (DOE), Dave Roberts (NREL), Steven 

Selkowit (LBNL), Arah Schuur (DOE), Timothy Snow (NREL), Therese Stoval (ORNL), Cody 

Taylor (DOE), Robert Tenent (NREL), Paul Torcellini (NREL), Robert VanBuskirk (LBNL), Ed 

Vineyard (ORNL), Subid Wagley (DOE), Eric Werling (DOE), and Jeremy Williams (DOE 

fellow). 

v 
 

 

 

 

 

 

 

Executive Summary 

To provide analytic support of the U.S. Department of Energy’s (DOE) Office of the Building 

Technology Program (BTP) we developed a comprehensive, straightforward Microsoft Excelbased tool to provide an open and objective comparison of the hundreds of investment 

opportunities available to BTP. This tool uses established methodologies to evaluate the energy 

savings and cost of those savings of these investment opportunities. Specifically the 

methodologies include: 

•	 Calculating the levelized cost of conserved energy (CCE) using net present value 

calculations of the costs and benefits of each opportunity 

•	 Tracking energy use through stock-and-flow calculations to determine the “economic 

potential”: that energy savings where the benefits of investments made exceed the 

cost of those investments 

•	 Evaluating the savings the market can adopt using Bass-model technology diffusion 

calculations. 

We identified over 770 energy efficiency measures through literature review and expert 

interviews each of which was supported by peer-reviewed publication, laboratory testing, in-situ 

(i.e., “pilot”) testing, engineering-macroeconomic analysis (e.g., through the technical support 

documents of BTP’s Equipment and Appliance Standards Program), building-energy use 

modeling, or verified savings through mass adoption (e.g., utility or other energy efficiency 

programs). 

Finally, each of these measures was analyzed with the tool both individually and in the context 

of the full portfolio of possible measures. This work demonstrates multiple pathways to achieve 

approximately 50% savings from the baseline projected building energy use in 2030. It further 

demonstrates that the average cost of these savings is less than half the production cost of energy 

(as compared to currently available or near-term resources). We also reproduce findings of recent 

reports showing that approximately 30% energy savings remains possible with currently 

available and cost effective efficiency measures. Finally, we identify that emerging technologies 

(i.e., with expected market availability in 5-10 years) can increase this to approximately 65% 

savings while including technologies in earlier stages of development increase savings to 

approximately 80%. 

We anticipate that this analysis, made possible through the objective comparison provided by the 

evaluation tool, will provide perspectives on energy savings possible with R&D and identify 

promising technologies to research. This tool, developed to support BTP in portfolio design, will 

be made available for continued public use in the future. 

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1 Introduction 

Attempts to understand, analyze, and seize the opportunity energy efficiency presents date back 

nearly four decades to the oil crises the U.S. faced in 1973 and 1979. Early researchers (Meier 

1982; Meier Wright Rosenfeld 1983) developed a methodology for characterizing energy saving 

opportunities, which we term herein “measures” according to their potential “economical 

savings” and the “levelized cost of conserved energy”. These quantities represent, respectively: 

•	 The reduction in U.S. annual energy use that could be captured if 100% of the market 

adopts the energy-using technology that provides services at the lowest lifecycle cost 

rather than a standard unit as forecast in an accepted baseline 

•	 The present value of the incremental investment in this technology divided by the 

present value of energy saved over the baseline energy use. Discount rates used to 

calculate present values vary from approximately 3% (i.e., inflation) to 6-10% (i.e., 

historic rates of returns on bonds or securities) to even 20-40% (i.e., rates observed to 

govern some decisions). 

These works also developed the concept of an “accounting framework” to insure that an 

organization pursuing multiple measures accounts for the energy captured by each measure 

pursued without “double counting” any energy savings. 

Other works (SERI 1982; Interlaboratory Working Group on Energy-Efficient and Clean-Energy 

Technologies 2000; National Academy of Sciences 2009; Choi-Granade 2009) have used these 

techniques to understand the possibility to meet future demand for energy services with 

advanced technologies that use less energy to provide those services. These works have had 

several design principles in common, notably considering economic potential, focusing on a 10-

20 year time horizon, and limiting measures considered to those available at the time of their 

writing. They consistently identified the opportunity to reduce energy use over a 10 year horizon 

by 16-28% (Choi-Granade 2009) and over a 20-year horizon by 23-32% (National Academy of 

Sciences 2009) through commercially available energy efficiency measures. The most 

aggressive of these (SERI 1982) also identified a 57% technical potential reduction in the 

residential sector and a technical savings of 50% in commercial retrofit measures and 75% 

commercial new building measures. 

Building on this legacy, we developed a prioritization tool to aggregate and analyze (with a 

consistent methodology) the many building efficiency measures available for investment by the 

U.S. Department of Energy’s (DOE) Energy Efficiency and Renewable Energy (EERE) Office 

of the Building Technologies Program (BTP). BTP can consider a very large number of 

investment opportunities that cover technologies in all major end-use areas. Projected energy 

consumption by end use in 2030, expressed in primary1 energy use in quadrillion British thermal 

units (Btu) annually (quads) in 2030 in both residential and commercial buildings (respectively) 

include (Energy Information Agency 2011): 

1 Primary or “source” energy represents energy in the form it is first accounted (e.g., Btu of coal, oil, or natural gas) 

before transformation to secondary or tertiary forms (e.g., electricity). End-use, or “site” energy refers to energy 

used in buildings settings (e.g., to light, heat, or cool buildings). 

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•	 Heating (Residential: 5.2 Quads, Commercial: 2.2 Quads) 

•	 Cooling (Residential: 3.1 Quads, Commercial: 2.0 Quads) 

•	 Ventilation (Residential: 0.6 Quads, Commercial: 2.0 Quads) 

•	 Lighting (Residential: 1.4 Quads, Commercial: 3.9 Quads) 

•	 Water heating (Residential: 3.1 Quads, Commercial: 0.9 Quads) 

•	 Refrigeration (Residential: 1.5 Quads, Commercial: 1.6 Quads) 

•	 Washing and drying (Residential: 1.5 Quads, Commercial: 0.4 Quads) 

•	 Cooking (Residential: 0.8 Quads, Commercial: 0.3 Quads) 

•	 Miscellaneous loads, mostly electrical (Residential: 6.2 Quads, Commercial: 6.1 

Quads). 

Figure 1. Baseline energy use adapted from Annual Energy Outlook, 2010 

The aggregate of these technologies represents a total of 42.6 quads expected annual primary 

energy use by building-related activities in 2030 under the scope2 addressable by BTP. The 

inclusion into our scope of two major technology element, data centers (forecast to represent 2.2 

quads in 2030) and community infrastructure (including distribution transformers, waste water 

treatment, irrigation that are forecast to represent cumulatively 2.2 quads in 2030), remains 

unclear; excluding these reduces the scope of our analysis to 38.2 quads expected energy use in 

2030 absent BTP involvement. 

2 Primary energy use estimated from the Energy Information Agency’s Annual Energy Outlook, 2010. 

Miscellaneous loads exclude commercial unspecified loads which lack sufficient data for analysis. Washing and 

drying include only machine energy (i.e., not hot water) with commercial washing and drying energy use estimated 

from other sources. Residential ventilation represents furnace fans and boiler circulation pumps. 

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BTP has multiple strategies at their disposal to capture the available energy savings in these 

areas including researching and developing emerging technologies (i.e., those that can be 

commercialized within five years), deploying those technologies using multiple approaches (e.g., 

labeling efficient homes and products, developing specification and voluntary agreements 

through stakeholder alliances, advancing building science to demonstrate savings and 

performance in situ), and developing building codes and appliance standards to lock in savings 

as required by statute. The prioritization tool provides an objective comparison of new and 

existing measures and is being used to inform decision-making with respect to BTP’s portfolio of 

projects. Four criteria drove the tool’s design at the outset of its development, namely that the 

tool would be: 

•	 Comprehensive. Include most (i.e., ideally all, but realistically 90% or more) known 

energy efficiency measures that have been proven to save energy in residential and/or 

commercial buildings. 

•	 Open. Though “proving” savings is a challenge (and a research area in its own right) 

we include in the analysis any measure that has laboratory demonstrated, field tested, 

or analytically derived (if peer reviewed) savings. 

•	 Straightforward. We limit input, output, and applied analytical techniques to firmly 

established methods that can be empirically explained and avoided “machine logic” 

elements. As a result, the calculations were limited to stock-and-flow and 

technology-diffusion-based logic rather than simulation such as probabilistic, agentbased, or discrete choice modeling. 

•	 Objective. Integrating inputs from hundreds of sources and dozens of expert 

interviews placed significant requirements on applying “judgment” regarding what 

inputs to admit. As expected for such work we hold all experts and sources to the 

same objective bar regardless of their reputation or our opinions regarding the 

efficacy or cost effectiveness of the measures they submitted. 

This report and a forthcoming journal publication describe the results of the following process: 

•	 Build the “engine”. Devise a stock-and-flow technology-diffusion model to apply 

consistent baseline assumptions and analysis techniques to estimate energy savings 

and cost of conserved energy as described in sections 2-6. 

•	 Populate the “database”. Use journal publications, reports, technical support 

documents (from the BTP Appliance and Equipment Efficiency Standards Program), 

and tools supporting the Energy Information Administration’s (EIA’s) Annual Energy 

Outlook to create an initial measure list. To evaluate the potential we aggregated 

~400 measures as an initial set. 

•	 Conduct a review with lab experts. Engage in a first review where National 

Laboratory program managers reviewed the preliminary findings and database to 

determine any data gaps or inaccuracies. 

•	 Share findings with BTP staff. Engage in multiple rounds of review and application 

of the tool to provide analysis in support of strategic planning demonstrated the 

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usefulness of the tool while refining its outputs. The goal is as an open source, 

evergreen tool that can inform program planning and benefit analysis. 

•	 Review inputs and expand the measure list. Conducted a more comprehensive and 

detailed review including 65 experts involved in buildings research and deployment 

who further challenged, refined, and improved the dataset and other inputs to the tool. 

•	 Publicize the tool and findings. This report represents the first step in a multi-step 

publication process. We will detail the methodology (this report), share the most 

significant findings (journal publications in preparation), and plan to make the fully 

operational spreadsheet and supporting spreadsheet tool available for public use and 

refinement. 

Given the limited funding, aggressive timescale, and simplifying assumptions required, we 

provide the following caveats about this analysis: 

•	 An 80/20 solution. This work was originally designed to provide 80% of “the 

answer” using 20% of the time required to arrive at the full answer. The results likely 

represent more of a 55/5 solution: 55% of “the answer” using 5% of the time required 

for the full answer. Given the vast challenge of optimizing the built environment we 

anticipate this work has invested no more than 5% of the total resources needed to 

achieve “the full answer” to this problem. 

•	 Limited precision. Though many of the resources cited have used detailed energy 

modeling to estimate the cost and impact of the measures, we performed no 

supplemental energy modeling to determine input values. As such we estimate 

national, or at best climate-regional, impacts without exploring the potential impacts 

for more granular geographic regions. 

•	 Sufficient accuracy. Estimating the accuracy of each measure is beyond the scope of 

work. Few of the resources used provide an uncertainty estimate on their benefits. 

However, by performing sensitivity analysis as discussed in Section 7.3, we believe 

our ultimate estimate of total cost-effective energy savings is accurate to +/-10% as 

detailed in section 7. 

•	 Ultimate responsibility. Despite having used the data and perspectives of many 

excellent researchers the ultimate responsibility for these findings rests with the 

authors of this report. 

This report contains the following sections: 

•	 Introduction. Provides the objectives, design philosophy, process, and caveats of the 

work 

•	 Measure design philosophy. Outlines the approach to data collection, measure 

refinement, and baseline development 

•	 Baseline energy use and technical potential calculation. Details the mathematical 

underpinnings for the calculations of energy efficiency benefits 

4
 
 

 

 

 

 

•	 Stock-and-flow model and economic potential calculation. Further details our 

stock-and-flow methodology and the resulting definition of “economic energy 

efficiency potential” 

•	 Adoption logic. Discusses the application of technology-diffusion (i.e., Bass-model), 

presents some alternatives, develops multiple means for parameter estimation, and 

discusses limitations of this approach 

•	 Staging logic. Details how our model moved from single-measure evaluation to a 

portfolio of multiple measures by attributing savings to the least costly measures 

while aggregating benefits and evaluation costs 

•	 Outputs and their uses. Provides an overview of the main outputs of this work, 

including evaluation of the potential of individual measures, de-selection of measures 

to include in a program portfolio, identification of long-term vision targets, and 

evaluation of technical limits to individual end uses 

•	 Conclusion. Shares information about the concurrent publications that are underway 

as well as planned and potential future work 

•	 Appendices. Provide additional detail regarding the researchers’ baseline 

assumptions, inputs for each measure, and a full bibliography of all sources 

consulted. 

5
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2 Measure Design Philosophy 

This section focuses on three topics related to data gathering and characterization in the 

prioritization tool: selection criteria for measures to admit to the database, characterization of the 

data elements required, and a brief discussion of the baseline used to characterize current and 

future energy use. 

2.1 Approach to Data Collection 

As discussed in the introduction we sought to gather credible data on any building-related energy 

efficiency solution including technologies, system solutions, business models, and enabling 

elements (e.g., behavioral “nudges” and building energy modeling).3 We remained open-minded 

to the measures that were considered but also thoughtful regarding what data were sufficiently 

credible for inclusion. 

As a result, to date, we have identified 770 energy efficiency measures to consider. The measures 

were drawn from literature review and expert interviews; each supported by peer-reviewed 

publication, laboratory testing, in-situ (i.e., “pilot”) testing, engineering-macroeconomic analysis 

(e.g., through the technical support documents of BTP’s Appliance and Equipment Efficiency 

Standards Program), building-energy use modeling, or verified savings through mass adoption 

(e.g., utility or other energy efficiency programs). These 770 measures include multiple views on 

a single measure (e.g., different Energy Factors of tankless water heaters, different costs for 

dynamic windows as either new or replacement windows, “max-tech” appliances vs. individual 

component upgrades). Eliminating all such overlapping measures reduces our dataset to 497 

possible measures. Of these 411 presented sufficiently credible data for inclusion in the analysis. 

Appendix 2 characterizes these 411 measures by providing their most significant inputs and 

primary sources for those inputs. As mentioned in the introduction, the tool and measures 

therein went through two rounds of multiple laboratory and stakeholder review,

4 including: 

1.	 Building energy experts at Pacific Northwest National Laboratory, Lawrence Berkeley 

National Laboratory, Oak Ridge National Laboratory, and NREL reviewed the initial 

selection set, methodology, and preliminary findings. The experts provided extremely 

helpful direction on ways to refine and expand the work. 

2.	 BTP “performers” (i.e., researchers funded by BTP) provided feedback on the inputs 

used. We presented them the opportunity to share their perspective on the sources 

consulted and inputs used and to provide additional or alternate inputs supported by 

credible sources as outlined above. 

We envision continuing this process of expanding the measures considered, refining the inputs, 

and updating inputs as technologies and markets develop. This approach provides a 

comprehensive and internally consistent dataset of energy efficiency measures which, in itself, is 

of value. 

3 The tool is also capable of representing policy actions or other programs through the adoption coefficient approach 

(see Section 5); however, policy analysis was outside the scope of our work. 4 Despite having used the data and perspectives of many excellent researchers, the ultimate responsibility for these 

findings rests with the authors of this report.
 

6
 
 

 

 

 

 

 

 

2.2 Data Required of a Measure for Its Inclusion in the Selection Set 

Three pieces of data characterize any measure to be included in the dataset: energy efficiency, 

cost, and market. This section details these data requirements and concludes with a list of other 

meta-data used to characterize each measure. 

2.2.1 Energy Efficiency Data 

Energy efficiency data represents the change in energy use the measure causes. Typically this 

results in a percentage decrease in site (end use) and source (primary) energy use over the 

existing baseline technology mix. Savings are expressed in a variety of units such as the 

equipment efficiency level (e.g., Seasonal Energy Efficiency Rating (SEER) or Energy 

Efficiency Rating (EER) for air conditioning, Annual Fuel Utilization Efficiency (AFUE) for 

furnaces, Energy Factor (EF) for water heating, and R-value for windows or insulation) or as the 

aggregate energy savings for the end-uses impacted. However, this can include fuel switch 

measures (e.g., moving from an electric resistance water heater to a tankless gas water heater or 

from an oil boiler/furnace to an electric heat pump) which may increase or decrease site or 

source energy. We include fuel switching measures that decrease source energy (even if they 

increase site energy) but exclude those measures that increase source energy (even if they 

decrease site energy). Section 3 details how the model applies this efficiency data to determine 

the total energy savings and cost of that energy savings. 

2.2.2 Cost Data 

Cost data represents the difference in capital, operations, and maintenance costs between the 

measure being analyzed and the typical unit in the baseline. This represents the most challenging 

piece of data to acquire given complicating factors such as regional variations in labor and 

equipment prices, volatility in raw material prices, uncertainty in costs of products not yet 

brought to market, profit margins and other business expenses, and compression of 

manufacturing costs and other costs as production increases. Our goal is to present the retail 

price of installed measures at the time of significant market adoption. We define this as when 

sales volume is sufficient to support full production of a manufacturing facility; we approximate 

this as 5% of the total market size. Undoubtedly, the cost estimation represents the value with the 

greatest uncertainty in the analysis; as such we are careful to test the sensitivity of our 

conclusions to larger (+/-50%) variations in the cost estimates. Note that we reduce the marginal 

cost over time as described in section 4.3. 

2.2.3 Market Data 

Market data represents the total equipment stock and energy use that the measure strives to 

address. This will typically be characterized by what end-uses (e.g., cooling, heating, lighting), 

building types (e.g., new, existing, single family homes, supermarkets, etc.), and other measurespecific characteristics (e.g., cooking devices, climate zones) the measure can impact. It requires 

identifying three elements for each year of the analysis 2010-2035: 

•	 End-use (also known as site) energy indicates how much energy, characterized as 

described above, each measure can impact
 

•	 Fuel type specifies what fuel or fuel mix is used to supply that end-use energy demand; 

this is required to convert from end-use to primary energy impacts 

7
 
 

 

 

 

 

 

 

 

 

 

•	 Equipment stock represents the total number of appliances, pieces of equipment, number 

of buildings, or total square footage of building space each measure must impact to 

capture its full market potential. 

We use two methods to estimate the market size; these are detailed in Section 6.2. The first 

draws directly from the data source’s own description of the market and such resources as the 

Building Energy Data Book. 

2.3 Overview of Data Used to Create Baseline Energy Use 

The prioritization tool uses EIA’s Annual Energy Outlook 2010 (AEO 2010) as the basis for its 

baseline energy use. The AEO has been providing projections of energy supply and demand 

since the 1980s. It typically forecasts 20 years or more, is currently updated annually, and 

represents the most commonly used resource for energy use projections. It is straightforward, but 

somewhat time consuming, to update this baseline to future AEO editions. 

We base much of the market information on the detailed output tables available from the EIA by 

request. These files provide more detailed data than the AEO publications; specifically we use 

the pivot tables indicating the energy use and stock for each enduse, fuel type, equipment class, 

building type, and census region. We develop our baseline by identifying the energy uses that 

BTP can impact and supplement this EIA data with other research as necessary. In total the 

baseline used for most of the analysis conducted represents annual primary energy use of 40.5 

quads in 2030 which, as explained below, is 5.9 quads less than the full AEO 2010 forecast. The 

tool can vary this baseline if needed to analyze other opportunities such as lighting energy use in 

industrial establishments or outdoor spaces. In particular, our 40.5 Quads of energy use include 

the following details, here aggregated into commonly used end-use groups: 

•	 Heating (Residential: 5.2 Quads, Commercial: 2.2 Quads): contains all primary and 

secondary heating for commercial and residential applications including all 

technologies (e.g., furnaces, boilers, heat pumps, roof top units) and all fuels (e.g., 

natural gas, electricity, heating oil/distillate fuel, wood). 

•	 Cooling (Residential: 2.9 Quads, Commercial: 2.0 Quads): represents all energy used 

to cool and air condition buildings; though more than 9% electricity natural gas and 

other fuel fired cooling equipment is allowed. 

•	 Ventilation (Residential: 0.6 Quads, Commercial: 2.0 Quads): includes both 

heating/cooling distribution energy (i.e, furnace fans and circulation pumps) and airquality requirements; this is entirely fueled by electricity. 

•	 Lighting (Residential: 1.4 Quads, Commercial: 3.9 Quads): includes all lighting 

technologies and applications in homes and commercial buildings. The tool can also 

optionally analyze 0.6 Quads of commercial lighting and 0.8 Quads of outdoor 

lighting as appropriate to the baseline for a given scenario. 

•	 Water heating (Residential: 2.9 Quads, Commercial: 0.7 Quads): represents 

consumption of all fuels (primarily natural gas and electricity) for all uses of hot 

water including clothes washing, dish washing, hygiene, and others. 

8
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

•	 Refrigeration (Residential: 1.5 Quads, Commercial: 1.6 Quads): contains all 

residential and commercial refrigeration applications (other than space cooling) 

including home refrigerators and freezers, supermarkets, and other commercial 

refrigeration applications such as food service, food sales and laboratories. It is 

important to note this does not include “industrial” applications such as food 

processing or transportation; also it exceeds the AEO as more detailed resources were 

available (Goetzler, et al., 2009). 

•	 Washing and drying (Residential: 1.5 Quads, Commercial: 0.3 Quads): includes 

dishwashing machine energy (0.4 Quads), clothes washing machine energy (0.1 

Quads), and clothes drying machine energy (1.25 Quads) but excludes hot water; 

machine hot water use, representing 0.4 Quads for dishwashing and 0.6 Quads for 

clothes washing, is captured and categorized under water heating. Note that that 

energy use here also departs from the AEO estimate as a similarly more detailed 

resource is available (Goetzler 2009) and will likely be incorporated into future AEo 

estimates. 

•	 Cooking (Residential: 0.8 Quads, Commercial: 0.3 Quads): represents conventional 

(i.e., range and oven) and microwave cooking in residential homes and all food 

preparation in commercial buildings. 

•	 Miscellaneous loads (Residential: 6.2 Quads, Commercial: 4.6 Quads): includes only 

energy use we were able to characterize in detail. Enduses in the detailed output 

tables include ceiling fans, coffee makers, DVD players, home audio, PCs, 

“rechargeable” devices, Spas, security systems, TVs, and “electric other”. We also 

identified and include in our baseline energy use for dehumidifiers, pool pumps, 

video game consoles, vacuum cleaners, elevators (i.e., in multi-family homes) and 

“vampire loads” representing standby energy use from not-otherwise-identified 

devices. Commercial AEO enduses include PCs and non-PC office equipment (2.1 

Quads combined) and 2.5 Quads of commercial energy classified by the AEO as 

“Other uses”.

6 Measures we were able to characterize include pumps, transformers, 

elevators and escalators, water treatment and irrigation, and fume hoods. 

•	 Typically excluded (Residential 0.4 Quads, Commercial: 5.5 Quads): excludes from 

our baseline (in most analysis) energy uses that lack sufficiently detail to analyze and 

those outside the scope of BTPs influence. The detailed AEO tables for residential 

categorize 3.0 Quads as “Electric Other”; we were only able to detail 2.6 Quads as 

described in “Miscellaneous loads” above. The detailed AEO tables for commercial 

include two categories difficult to analyze, namely “Other” (4.1 Quads electricity, 0.5 

Quads of other fuels) and “Unspecified” (3.1 Quads of electricity, 1.2 Quads of other 

fuels). All 1.7 Quads of other fuels lie outside BTP’s scope (and most lack sufficient 

detail for analysis); the remaining 7.1 Quads of electricity break down as follows. 

One major technology element, data centers (forecast to represent 2.0 Quads in 2030), 

5 Numbers do not total due to rounding.
 6 The AEO indicates this includes miscellaneous uses, such as service station equipment, automated teller machines,
 

telecommunications equipment, medical equipment, pumps, emergency generators, combined heat and power in 

commercial buildings, manufacturing performed in commercial buildings, and cooking (distillate), plus residual fuel
 

oil, liquefied petroleum gases, coal, motor gasoline, and kerosene.
 

9
 
 

 

 

 

 

 

 

 

are typically excluded from the baseline because they are outside BTP’s current 

scope, though they are included in the baseline for technical analysis and other 

applications of the tool. The 1.8 Quads of the electricity lacks sufficient detail to 

analyze, 2.5 is classified in miscellaneous loads above, and 0.9 has been identified 

using detailed resources as described in the “refrigeration” and “washing and drying” 

uses described above. 

These data are summarized in Table 1. 

In conclusion, it is worth noting that there is significant flexibility in defining the baseline for 

the tool to allow additional analysis. For example, identifying a citable data source that 

specifies only federal building energy use would allow us to tailor the analysis to solely that 

segment. Similarly, the tool could perform analysis for specific states or localities, other 

nations, or even the entire world if sufficiently reliable and detailed energy use information 

becomes available. 

Table 1. Baseline Energy Use in the Prioritization Tool to Analyze the BTP Portfolio 

2030 Primary energy use in baseline (Quads) 

Enduse Residential Commercial Total 

Heating 5.2 2.2 7.4 

Cooling 2.9 2.0 4.9 

Ventilation 0.6 2.0 2.6 

Lighting 1.4 3.9 5.3 

Water heating 2.9 0.7 3.6 

Refrigeration 1.5 1.6 3.1 

Washing & Drying 1.5 0.3 1.8 

Cooking 0.8 0.3 1.1 

Misc. loads 6.2 4.6 10.8 

Typical baseline 23.0 17.6 40.6 

Data centers N/A 2.0 2 

Electric, insufficient data 0.4 1.8 2.2 

Other insufficient data 0.0 1.7 1.7 

Total excluded 0.4 5.5 5.9 

AEO 2010 forecast 23.4 23.1 46.5 

In Table 1, data are detailed by standard end-use categories with exclusions identified and compared to the Annual 

Energy Outlook 2010 forecast for 2030. 

10
 
 

 

 

 

 

 

 

 

 

3 Baseline Energy Use and Technical Potential Calculation 

This section provides information about our treatment of the tools methodology and describes 

the mathematical formulation of energy use, efficiency, and service demand. This section also 

shows the formulas used in the engine that performs the energy use and cost calculations and 

introduces the technical potential of an energy efficiency measure, including its calculation. 

3.1 Mathematical Formulation and Baseline Definition 

No commonly accepted definition for energy efficiency exists. For the purposes of identifying 

opportunities to improve the annual energy use associated with buildings on a macroeconomic 

scale we define for each “service” delivered (e.g., maintain an occupied space at a comfortable 

temperature, providing sufficient light): 

U = S × E−1 × D

[BTU/year] = [Units] x [BTU/service demand] x [Service demand/unit/year] 

Where: 

•	 U is the total energy use associated with the service (e.g., trillion Btu primary energy 

per year) 

•	 S is the equipment stock associated with the service (e.g., number of furnaces in use 

in homes in 2010, square feet of floor space) 

•	 E is the efficiency (or more formally intensity) indicating the energy required to meet 

the service demand (e.g., Btu of heat output per Btu of fuel input or lumens per watt 

per square foot of floor space); note that this quantity divides the other quantities on 

the right-hand side of the equation (i.e., the superscript denotes inverse) 

•	 D is the annual service demand per unit stock (e.g., Btu of heating required to 

maintain occupant comfort per home, lumens per year per square foot of building 

space). 

The tool analyzes the opportunity for an energy savings measure to reduce energy use (U in the 

formula above) in a specific market. We define: 

•	 Measure: a change in the technology, system, behavior, or other aspects of energy 

used to provide a given service. Examples applicable to the BTP include researching 

and developing light emitting diodes to replace existing, less efficient light sources; 

developing technical specifications for rooftop units; and developing and enforcing 

minimum efficiency standards for home refrigerators. 

•	 Market: the specific (macroeconomic) subset of building energy use applicable to a 

given measure. Examples include all high-quality lighting applications in homes and 

commercial buildings; commercial cooling in all buildings (or specific climate zones 

or building types); and all home refrigerators to be purchased after a certain year. 

The technical potential represents the first analytic quantity of interest. It corresponds to the 

change in energy use in a market realized by implementing a given measure in that market. It 

11
 
 

 

 

 

 

 

 

 

 

 

 

 

 

also represents the theoretical maximum change in energy available as it is free of practical 

constraints such as financing and deployment considerations. The model defines the technical 

potential as measured from a baseline specific to the measure and the market. Given the intimacy 

between the baseline and technical potential, we define them both here simultaneously. 

The following equation expresses the total baseline energy use, Ub, in a given year, (y), of a 

given market comprising the sum of annual baseline energy use of i technologies relevant to that 

market, or: 

Ub(y) = ෍Ui

b(y) = ෍Sib(y) × (E−1)ib(y) × Dib(y) = ෍Sib(y) × Ci(y) 

i i i

Where, given data available for most of the measures in the tool: 

•	 Ui

b (y) represents the annual energy use in the baseline of technology i 

•	 Si

b(y) represents the baseline stock of technology i in the market analyzed in year y 

•	 Ci(y) is the energy use (or “consumption”) per unit stock of the technology in year y. 

Additionally we can define the following two quantities that relate energy use to service demand: 

•	 Ei

b (y) represents the efficiency expressed in energy use per unit of service demand 

for technology i in year y 

•	 Di

b(y) is the service demand in the market (e.g., lumen-hours of lighting) met by 

technology i in year y. 

3.2 Baseline Energy Use and Technical Potential in the Engine 

As described in more detail below, we use a simplified stock-and-flow model composed of two 

alternatives to represent a measure’s impact. In reality an energy user will not just have two 

options but will employ a technology or procedure drawn from hundreds of alternatives. For 

example a commercial building operator replacing lighting has dozens of lamp, ballast, 

luminaire, and control choices to make a fully functioning system. Similarly a home owner 

purchasing a new refrigerator will select from dozens of models with different sizes, annual 

energy use specifications, designs (e.g., freezer on top vs. side-by-side), and features (e.g., 

icemaker, through-the-door water dispenser). The prioritization tool analyzes the potential 

impact of a single measure by representing these hundreds of choices with two alternatives: 

1.	 The “measure” to be evaluated: specifies the technology or procedure being analyzed 

that is providing energy savings (e.g., condensing furnaces for homes, retrocommissioning a building). We use the subscript “m” to denote this alternative in 

equations. 

2.	 The “existing” mix of choices: represents the average of all choices currently in use 

other than the measure to be evaluated. Typically this will represent the populationweighted average of all available technologies (e.g., the average home furnace 

12
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

efficiency of 0.82 Average Fuel Utilization Efficiency (AFUE), deciding not to retrocommission a building). The subscript “e” denotes this alternative in equations. 

Alternatives can most easily be considered technologies but can include energy services, 

policies, or any measure that intervenes in the economy to change energy use patterns. For 

example to evaluate the energy savings of the measure, “increase purchases of condensing 

furnaces for homes” then alternative 1, or “m”, represents a state-of-the-art condensing furnace 

with an AFUE of 98%; alternative 2, or “e”, represents the average of the currently installed base 

with an AFUE of 84%. Similarly the measure “Retro-commission commercial buildings, would 

take “m” as the decision to retro-commission the buildings and the corresponding “e” alternative 

would be the decision not to do so. More detail on these alternatives follows. 

3.2.1 “Existing” Mix of Alternatives 

In all cases we will evaluate placing a measure (e.g., a highly efficient 23 cubic foot home 

refrigerator) into a market (e.g., primary refrigerators in all single-family, multi-family, and 

mobile homes). Ideally we would characterize the existing mix technologies by specifying the 

efficiency of every unit in use, or (more realistically) by specifying a number (e.g., 5-20) of 

efficiency classes and the stock of each class. For example, in analyzing home furnaces we 

would need to know the current stock and purchases for all available models. With seven 

manufacturers capturing almost all of U.S. sales, each offering similar products there are 

hundreds of available models, all with slightly different real-world efficiency characteristics. In 

this case it would be possible to simplify this categorization to typical efficiencies (i.e., AFUE) 

sold in the past two decades; for example one manufacturer offers models with AFUE ratings 

including 98.5%, 96.6%, 96.5%, 95.0%, 93.0%, 92.0%, 81.0%, 80.0%, and 78.0%. For this 

example, the appliance standard program does provide a technical support document including 

this information for historical purchase and forward-looking projections. However, because these 

data are not available for most measures, we represent the existing mix of “inefficient” 

technologies by the average efficiency of the currently deployed stock. This characterizes the 

baseline in year y0, by its energy use per unit stock, Ce

b(y0), and the total stock, Si(y0). In our 

gas furnace example this corresponds to y0 = 2011, Ce

b(y0) = 83.8% AFUE, and 45 million units. 

The derivation of these parameters from available data is discussed below. 

3.2.2 “Measure” To Be Evaluated 

Measure savings are defined as “equipment upgrade” or “demand reducing” which refers to the 

principle means by which a measure saves energy. Naturally a given measure may both reduce 

(or increase) demand and upgrade (or worsen) the efficiency of the equipment. We require this 

simplifying assumption because typically data are only available for the energy use, not both the 

energy use and service demand; as such we categorize each measure as either: 

1.	 “Equipment upgrade.” The measure deployed does not impact service demand but does 

improve equipment efficiency. Replacing a home furnace with a more efficient model 

(e.g., upgrading from AFUE 0.82 to AFUE 0.92) represents such a savings. In the 

simplifying case where the baseline is either all the existing alternatives or the measure 

b we are evaluating, we note that the measure does not impact demand, i.e., Dm(y) =

De

b(y), but improves efficiency: Eeb(y) ≠ Em We reference the percentage b (y).

reduction in energy use of measures of this type to the average efficiency in the starting 

year of the analysis. Because the average stock after the starting year of the analysis 

13
 
 

 

 

 

 

 

 

 

 

 

 

 

 

changes—ideally improves in terms of efficiency over time—the percentage of energy 

savings changes (likely decreasing over time). The spreadsheet therefore calculates the 

energy use per unit in a future year y>0 as: 

b (y) = minቀ Cm(y0), Ce

b Cm (y)ቁ

We use the minimum function to be sure that energy use of the measure remains

less than or equal to the “existing” mix. 

2.	 “Demand reducing.” The measure does not change the equipment efficiency but does 

impact service demand once deployed (e.g., improved controls, improved operation 

through maintenance, and reduced waste) in which case the converse holds:

Dm b(y), but Ee b (y). The spreadsheet therefore calculates the energy b (y) ≠ De b(y) = Em

use per unit in all years as: 

b (0) b (y) = b Cm (y)

Cm

b(0) × Ce Ce

We label the latter as “demand reducing” because the opportunity to reduce service demand 

persists as the market improves the efficiency of its energy-using stock. We label the former as 

“equipment upgrade” because improvements in stock efficiency are gradually adopted into the 

baseline and thus the opportunity to improve to the specified technology level is not persistent 

over time. Relaxing the simplifying assumption that a measure either reduces demand or 

upgrades equipment would require a full stock-and-flow treatment of hundreds of technology 

options. Initial comparisons of the tool’s output to the full stock-and-flow model used for 

developing the recent refrigerator standard (Building Technology Program, 2012) indicated this 

would provide only a slight improvement in the accuracy of the calculations so is deemed 

beyond the tool’s scope. 

3.3 Calculating the Baseline Energy Use 

Given the above definitions, it is now straightforward to see that the baseline energy use in a 

particular year for a particular market is: 

Ub(y) = Sm b(y) × Ce(y) b (y) × Cm(y) + Se

Typically the tool is striving to specify these four parameters for each of 25 years, y=2011-2035, 

as such we have 100 values to determine. Given the measure’s efficiency improvement we have 

Cm(y) as described in section 2.2 above. To calculate the baseline values Se

b(y) and Ceb(y), the 

calculation requires two data elements and an equation defined by three parameters that are 

derived by minimizing an objective function (describe below). These five elements are: 

•	 Data element #1. Total energy used in the market by year, Ub(y); this is typically 

provided as site energy and then converted to source energy given data availability 

and to allow calculation of fuel switching options. Most measures use data from the 

National Energy Modeling System (NEMS) output tables available from EIA for the 

years 2010-2035 as discussed in section 2.3. 

b •	 Data element #2. Total stock in the market, S (y) + Sm b(y) = Se b (y). These data are 

also typically gathered from the detailed NEMS output tables. 

14
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

•	 Fit parameter #1. Annualized change in the per-unit energy consumption, Ce, of the 

“existing” mix of alternatives: f. This represents the average efficiency improvements 

of the enduse expressed as percent per year. In the limiting case where Sm

b (y) = 0 for 1 ቀ

(2035−2011)

ቁ

all years then f takes the value f = 1 − ቀ

Ce(2035)

ቁ Ce(2011)

•	 Fit parameters #2 and #3. Bass adoption coefficients “p” and “q” (see description in 

Section 5.5 and (Bass 1969)) used to compute the share of annual sales of the 

measure being analyzed. 

With these data and variables the engine calculates: 

Ub(y) = Sm b (y) + (Sb(y) − Sm b(y0) × (1 − f)(y−y0)) b (y) × Cm b (y)) × (Ce

where: 

•	 Ub(y) is the market specific energy use in the year y provided as a tool input as 

described above. 

•	 Sm

b (y) derives from the Bass adoption coefficients fit; as described in section 5.1, 

Sm

b (y0) is taken as 0 in the first year of the analysis, y0, (i.e., before product 

b introduction there are none in the stock) and all subsequent years Sm(y) = b (y−1) (p + q × F(y)) × (1 − F(y)), where F(y) = 

Sm . Sb(y) is the total stock in the 

Sb(y) 

market (typically taken from the detailed tables as described above), and p and q are 

the adoption coefficients described in section 5.5. 

•	 Sb(y) is the total stock in year y provided as a tool input as described above. 

•	 Cm

b (y) is the energy use per unit stock of the measure being analyzed. It represents a 

user input for each measure and must be specified as “equipment upgrade” or 

“demand reducing” as described above. These are taken from the data sources as 

described above and detailed in Appendix 2. 

• Ce

b(y0) is the average efficiency in the first year of analysis, y0. It is directly 

Ub (y0)

calculated from the data: Ce

b(y0) =
 

Sb (y0)

. 

•	 f is fit parameter #1: the annual improvement in the “existing” mix of alternatives. It 

represents the gradual change in the equipment stock exclusive of the measure being 

analyzed. For example, currently window stock is improving from an R-value of 

around 1 in the 1990’s to over 1.5 today; this improvement will likely continue as R3 windows represent a significant share of the purchases even if R-5 never reach 

meaningful scale in the marketplace. The fit parameter f captures this trend of 

improving window performance excluding the measure we could analyze (i.e., 

increasing R-5 adoption through volume purchase programs or ENERGY STAR most 

efficient labeling). 

This presents us with the challenge of fitting 25 data points (i.e., the energy use in each year

Ub(y) for all y 2011-2035) using three variables: f, p, and q. The tool uses Excel Solver to fit 

these parameters under reasonable constraints: 0 < p < 0.1, 0 < q < 0.25, -5% < f < 5%. We 

deem these ranges reasonable because: 

15
 
 

 

 

 

 

 

 

 

 

•	 A negative “p” or “q” coefficient in the Bass model is not well defined unless we allow 

these coefficients to vary with time. Allowing time varying coefficients was explored 

and deemed beyond the scope of this tool given the data requirements and limited data 

availability. 

•	 Literature review (Elliott 2004) of modeling using the Bass coefficient across many types 

of technologies suggests p and q rarely exceed these maximum. 

•	 The average and standard deviation of f, p, and q were 0.16%, 0.11%; 0.5%, 1.2%; and 

0.5%, 3.7% respectively. 

We include a fourth parameter, the range of years over which to fit, to the fitting procedure when 

total energy use data is particularly complicated. This allows the fit to ignore years with well 

forecast data that falls outside the functional form (e.g., a transition in appliance standards such 

as caused by the Energy Independence and Security Act of 2007). 

We use an objective function, Z, which represents the weighted sum of four terms with weights 

chosen to give similar importance to the known and forecast years. Specifically we construct: 

Z = w0 × ඥχ2 + w1 × ΔU2035 + w2 × ΔU′

2035 + w3 × ΔU′′2035

•	 ඥχ2: The square root of the sum of the squared difference between the data provided 

for Ub(y) and the fit values for all years y in the fit range. We multiply this by 

w0 = 1. 

•	 Final year (U): We add the difference between the fit and data values for the final 

w1 year, i.e., Ub(2035)

× หUb(2035) − U෩b(2035)ห, where Uෙbindicates our baseline fit. 

65 We take w1 = 

3 as this final year represents one of the three most influential 

quantities with “forecasting power” on the 65-extrapolated data points. 

•	 Slope (U′

): We add to this sum year the difference in first derivative between the data 

and the fit of the final five years of the fit period, i.e., w2 × ቚቀUb(2035) − Ub(2035) 

65 Ub(2030)ቁ − ቀ(Uෙb(2035) − U෩b(2030)ቁቚ. We also take w2 = 

3 as the slope holds 

also represents one of the three most influential quantities with “forecasting power” 

on the 65-extrapolated data points. 

•	 Curvature (U′′): We add to this sum of χ2 and the final year the difference in 

derivative between the data and the fit of the final five years of the fit period, i.e., w3

Ub(2035)

× ቚቀUb(2035) + 2 × Ub(2033) − Ub(2029)ቁ − (U෩b(2035) + 2 × 

65 U෩b(2033) − U෩b(2029))ቚ. We also take w3 = 

3 as to give this second derivative 

also one-third the “forecasting power” on the 65-extrapolated data points. 

Using a total of four three parameters introduces a high probability that the optimized value 

returned by the solver represents a best-fit solution that is local to the initial starting conditions 

rather than a global best-fit solution. We use initial conditions representing absence of the 

advanced measure from the market place, i.e., p = q = 0 with f set as the average annual 

efficient improvement of the stock. To reduce the probability of finding a local best-fit that does 

16
 
 

 

 

 

 

 

 

 

 

 

not represents the global best fit we randomize the starting positions over the allowed range and 

perform six random fit operations per measure. We determined that six randomized staring 

points was likely sufficient for finding a global minimum by performing ten random fit 

operations on 100 measures and noting that no reduction in the optimization occurred for these 

measures in random fits 7-10. We can loosely interpret the optimization function as the average 

annual uncertainty in the fit; the average and median of our optimization function are 1.4% and 

0.7% respectively. Any measure that exceeded 5% was refit with additional random iterations 

until the optimization function returned was less than this 5% threshold; this was required for 39 

measures. 

3.4 Calculating the Technical-Minimum Energy Use and Technical Potential 

In order to calculate the potential for energy efficiency we needed to move beyond the baseline 

and create alternate scenarios of energy use for the “markets” in to which we are evaluating the 

measures. The market for a measure consists of selected existing energy using technologies that 

could be displaced by the measure. The baseline energy use associated with these units is the 

existing energy use of the market. The difference between the market’s baseline use and energy 

use in some scenario X is wholly expressed by changing the stock for the existing mix of 

“inefficient” technologies and measure being evaluated, i.e., Se

X(y) ≠ Seb(y) and SmX(y) ≠

Sm

b (y). Thus, we can use the formulation given in sections 3.1 and 3.3. to define the energy 

efficiency potential of scenario X, EEX(y), as the difference in the energy consumption between 

these two scenarios, P, in the market where we are deploying our energy efficiency measure: 

PX(y) = Ub(y) − UX(y) , where:
 

Ub(y) is defined above and
 

UX(y) = Sm

X(y) × Cm(y) + SeX(y) × Ce(y)
 

The simplest quantity to calculate is the technical-minimum energy use and the corresponding 

technical potential energy efficiency savings. If we imagine replacing all the existing stock with 

the measure which impact that is being evaluated we can take for all years y: Se

X (y) = 0 (i.e., 

none of the existing stock remains) and Sm S (y) (i.e., the measure represents the entire X (y) = 

stock in each year). This represents the absolute most energy savings a measure offers in year y; 

it does not take into account any real world considerations such as equipment lifetime and turn 

over or market share/technology adoption considerations. These considerations will be addressed 

with their own energy use scenarios (“economic-minimum” and “adoption-based” respectively) 

in later sections. 

To denote the technical potential we can use the superscript A to identify the technical-minimum 

(i.e., all the potential) and S(y) to represent the total equipment stock in year y. The equations 

simplify to: 

UA(y) = S(y) × Cm(y), and
 

PA(y) = UB(y) − UA(y) = Se

B(y) × Ce(y) + SmB (y) × Cm(y) − S(y) × Cm(y),

upon simplifying: 

b P (y)] × Cm(y). A(y) = Se

b(y) × Ce(y) − [S(y) − Sm

17
 
 

 

 

 

Put simply, the energy use in the technical-minimum scenario represents replacing all stock with 

the efficient stock. The potential for energy efficiency is the difference between the energy used 

in the baseline and this technical-minimum energy use. It represents the absolute most energy 

savings offered by the efficiency-measure being analyzed as measured from the baseline 

scenario. For example, R-10 windows represent a significant improvement over the currently 

installed base of windows which, across commercial and residential buildings, average an Rvalue of R-1.8 (D&R International Ltd. 2010). As such, R-10 windows offer an ~82% reduction 

in energy lost by conduction through windows. Currently 2,460 Trillion Btu of primary energy 

is lost by conduction through windows, thus the technical potential is about 2,020 Trillion Btu 

(i.e., 82% of 2,460). It is important to note that capturing this potential requires replacing every 

window in the U.S. with an R-10 window; this would be neither a feasible nor cost-effective way 

to save energy. 

18
 
 

 

 

 

 

 

 

 

 

 

4	 Stock-and-Flow Model and Full-Adoption-Based Potential 

Calculation 

The economic-minimum energy use, and corresponding full-adoption-based efficiency potential, 

represents a second valuable scenario to consider. It represents the least expensive means to 

deploy a given efficiency measure into the marketplace. Measures therefore fall into one of two 

categories defined as follows: 

•	 End-of-life replacement. The total installed cost of efficient equipment and 

appliances is typically much larger than the value of the energy savings they provide 

(i.e., the present value of the energy savings) over currently installed stock. As such, 

the only time it may be economically rational to adopt the efficiency measure is when 

the currently deployed appliance or equipment is retired at the end of its life. This is 

typically the case for equipment and appliances such as refrigerators, furnaces, 

boilers, etc. 

•	 Accelerated replacement. The present value of the energy savings of other 

efficiency measures, such as home retrofits and many lighting technologies, exceed 

the full, installed cost of the efficiency measure. It is economically rational to replace 

all of the currently deployed stock immediately with the efficiency measure. 

We must identify each measure as either an end-of-life or accelerated replacement. Further, we 

must track over time the stock deployed to determine when units are replaced, eliminated, or 

added; this is often called a stock-and-flow model. If a measure is cost-effective, defined in 

section 6 as having a cost of conserved energy lower than the cost of energy for the fuel 

purchases the measure reduces, this potential corresponds to the economic potential or NPVpositive potential. It corresponds to the scenario where the measure being analyzed represents 

all purchase; therefore after one full equipment lifetime, 100% of the stock efficiency measure 

and the technical potential is attained. 

4.1 The Stock-and-Flow Model 

Ideally one would know the date of purchase and retirement of each and every appliance or piece 

of equipment in the market and over the time period analyzed. Clearly this level of data is not 

generally available and would be very costly and logistically difficult to develop. Failing that one 

could use the recent (e.g., the last 10-20 year) history of purchases and existing stock of classes 

of efficiency; this approach is taken by both NEMS and the Building Technologies Program’s 

Appliance and Equipment Standards Program. Future purchases are then based on an expected 

lifetime that determines retirement and replacement of existing stock, growth (potentially) that 

determines introduction of new stock, and elimination (potentially) that represents retirement 

without replacement. 

4.1.1 Design 

The team developed a simple stock-and-flow model in Microsoft Excel that operates on nine 

families of stock categorized according to their state in the previous year as follows. Each family 

can be either new additions, up for replacement, or existing (i.e., not up for replacement but 

possibly eligible for early retirement). New additions require no further characterization while 

19
 
 

 

 

 

 

 

 

the other two categories are each divided into four families by splitting on two additional 

variables: 

•	 Potential action. These variables can be either eliminated from the stock or replaced 

(i.e., at end-of-life) or considered for early replacement. 

•	 Efficiency. These variables are either the existing mix of “inefficient” technologies or 

the measure being analyzed (i.e., efficient). 

Figure 2. Categorization of equipment into nine classifications according to efficiency and 

potential action they undergo when flowing stock from year y-1 to year y. 

This presents us with the nine families each having its own treatment in the stock-and-flow 

calculation. If we assume an existing share of stock in year y=0 (typically this will be all, or 

nearly all, the existing mix of “inefficient” technologies) we can see that for a given year y>0 the 

change in stock by type for the full-adoption-based potential is as follows: 

1.	 Eliminated, end of life, efficient: are removed from the stock in year y 

2.	 Eliminated, end of life, inefficient: are also removed from the stock in year y 

3.	 Replaced, end of life, efficient: remain efficient in year y 

4.	 Replaced, end of life, inefficient: are replaced with efficient stock in year y 

5.	 Eliminated, existing, efficient: are removed from the stock in year y 

6.	 Eliminated, existing, inefficient: are also removed from the stock in year y 

20
 
 

 

 

 

 

 

 

 

 

 

 

 

 

7.	 Replaced, existing, efficient: remain efficient in year y 

8.	 Replaced, existing, inefficient: are replaced with efficient stock in year y only if the 

measure is of the accelerated replacement type and is available in year y but not in year 

y-1 

9.	 “New”: enter the economic scenario stock as efficient stock; did not exist in year y-1. 

4.1.2 Limitations of Our Data and Methodology 

Because the information used is the total stock and total energy beginning in 2009, it is worth 

noting the following assumptions and limitations. If we use the subscript on the stock to denote 

the family (using T to denote the total), then the stock-and-flow model is defined by the 

following rules: 

•	 “New” and “eliminated” are net additions and eliminations. If the total stock in some 

year y is greater than in year the previous year, y-1, we assume that the difference 

∆S = ST − SN−1 ≥ 0 are all new (i.e., family 9) and there is no stock eliminated (i.e., 

families 1, 2, 5, and 6 are all unpopulated): S9 = ∆S; S1 = S2 = S5 = S6 = 0. This 

assumption introduces no inaccuracy, uncertainty, or information loss into the tool as 

typically, such as with “pre-70’s wall retrofits” and other measures targeting existing 

buildings only, it is clear that there is no “new” stock entering. 

•	 Similarly, when stock are eliminated (i.e., ∆S < 0), we assume that S9 = 0; S1 + 

S2 + S5 + S6 = −∆S. 

•	 Finally, our limited history and detail require two approaches to determine the 

number of efficient and existing stock eligible for end-of-life replacement (i.e.,

Seol,m(N) = S1(N) + S3(N); Seol,e(N) = S2(N) + S4(N) as determined by the 

lifetime of the measure being analyzed, L, and the year, y: 

o	 If y − L < 2009 then the total stock eligible for end-of-life replacement is 

calculated by dividing the stock in 2009 (assumed to be all the existing mix of 

“inefficient” technologies) by the lifetime L; i.e., S1(N) + S3(N) = 

Sm(2009)	 Se(2009) = 0; S2(N) + S4(N) = . This represents the assumption that Lm	 Le

the total stock was constant in all previous years and equal to the stock in 

2009. This does introduce uncertainty and a potential inaccuracy as stock 

likely grew or shrank by some amount in the previous years. 

o	 If y − L ≥ 2009 then the total stock eligible for end-of-life replacement 

equals the purchases of stock in year y-L; calculation of this quantity is 

defined below. 

4.1.3 Stock Flow Calculations 

In the general case each of these nine families has one of four calculations performed on it in 

transferring its stock from one year to the next (i.e., “flowing” the stock over time). With the 

initial conditions established this rule-set completely defines the flow calculations needed to 

determine the share of each stock, thus fully defining any scenario energy use and savings 

potential. The four calculations and where they apply are described below and shown in 

Figure 3. 

21
 
 

 

 

 

 

 

 

 

 

 

 

Figure 3. Identification of how stock-and-flow calculation treats each equipment classification. 

A.	 “Competed”: families 9 (New) and 4 (Replaced, end of life, inefficient) in year y-1 

are divided according to the scenario-specific rules (as described for the maximumadoption potential in section 4.2 or the bass-adoption potential in 5.5) to determine 

the fraction that are the existing mix of “inefficient” technologies versus the 

measure. In the case of the full-adoption-based scenario, the efficient measure 

captures 100% of this stock as explained above. 

B.	 “Flow-in”: families 3 (Replaced, end of life, efficient) and 7 (Replaced, existing, 

efficient) remain 100% the efficient measure from year y-1 to year y. Typically, 

family 7 will be unused, as early retirement of efficient stock is an unnecessary (and 

undefined) operation (i.e., the original stock was already retired and replaced with 

efficient stock). Assuming family 3 remains efficient implies that once a customer 

has adopted an efficient measure that consumer will always repurchase that measure 

(or potentially purchase an even more advanced measure). 

C.	 “Eliminated”: families 1, 2, 5, and 6 are removed from the stock in year y. The 

number of stock in other families requires first calculating the stock in these four as 

follows. If ∆S < 0: 

a.	 Family 2 (Eliminated, end of life, inefficient) are eliminated first, up to the 

total number of the existing mix of “inefficient” technologies that are 

eligible for end-of-life replacement; i.e.,

S2(N − 1) = min൫−∆S(N), Seol,e(N − 1)൯. 

22
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

b.	 Family 1 (Eliminated, end of life, efficient) are eliminated next, up to the 

total number of the measure being analyzed that are eligible for end-of-life 

replacement, i.e., S1(N − 1) = min൫−∆S(N) − S2(N − 1), Seol,m(N −

1)൯. 

c.	 Family 6 (Eliminated, existing, inefficient) are eliminated third. Note that 

stock in this family represent the existing mix of “inefficient” technologies 

of stock removed from service before the end of their useful life and is 

required to be less than the total number of existing mix of “inefficient” 

technologies of stock in service in the previous year: S6(N − 1) = 

min(−∆S (N)−S2(N − 1)−S1(N − 1), Se(N − 2)). 

d.	 Finally, family 5 (eliminated, existing, efficient) are eliminated last. 

Similar to family 6, stock in this family represent previously purchased 

stock of the measure being analyzed that are retired before the end of their 

useful life. Also similar to family 6, they are calculated by taking: 

S5(N − 1) = min(−∆S (N)−S2(N − 1)−S1(N − 1)−S6(N − 1),

Sm(N − 2)) 

D.	 “Possibly switched”: Family 8 (replaced, existing, inefficient) represents stock from 

the existing mix of “inefficient” technologies eligible to be retired early and 

simultaneously replaced with the measure being analyzed. Typically this represents 

some type of retrofit activity such as retro-commissioning an existing commercial 

building or blowing insulation into un-insulated wall cavities in existing buildings. It 

includes all stock from the existing mix of “inefficient” technologies that are not 

eliminated or eligible for end-of-life replacement; i.e., S8(N − 1) = Se(N − 1) −

S2(N − 1) − S4(N − 1) − S6(N − 1). This calculation is only performed if the 

measure is suitable for accelerated replacement; otherwise, S8(N − 1) = 0. 

Individual scenarios are defined by changing the operations performed on each of these four sets 

of families. For most measures in our current database, only the performance calculation for 

“competed” and “possibly switched” families changes. Specifically, for the full-adoption-based 

scenario, all stock in these families (i.e., 4, 8, and 9) are replaced with the measure being 

analyzed (i.e., efficient) at the end of their lifetime (or as soon as the technology is available if 

eligible for “early retirement”). A second scenario, the bass-adoption-based energy use scenario 

(discussed in section 5), applies a Bass adoption model to the “competed” and “possibly 

switched” families to determine the share of purchases that are efficient. 

This procedure fully defines the stock-and-flow calculation for a two-family model with known 

total stock: we classify the stock in year y-1 into these nine families which then determines the 

mix of stock in year y. We repeat this classification in year y to determine the mix of stock in 

year y+1, and so on. The tool implements this procedure in Excel as described above and as 

documented in Figure 4. 

23
 
 

 

 

Figure 4. Graphical depiction of our stock-and-flow calculation. 

Blue lines indicate flow of stock before it is identified as efficient or inefficient; red, dashed show a “negative” flow 

to stock (i.e., elimination); yellow and green show inefficient and efficient stock flows (respectively). 

24 
 

 

 

 

 

 

 

 

 

 

4.2 Calculating the Full-Adoption-Based Energy Use and Economic Potential 

The process of calculating the economic-minimum energy use and economic potential follows 

the same procedure used above to calculate the technical-minimum energy use and technical 

potential. Using the superscript G to represent the full-adoption-based scenario we express the 

energy use, U, in this scenario as: 

UG(y) = Sm G(y) × Ce(y), G (y) × Cm(y) + Se

Where we recall that for each year, y: 

•	 S indicates the equipment stock 

•	 C indicates the energy use per unit stock, and 

•	 The subscripts, m and e represent the measure being analyzed and the existing mix of 

“inefficient” technologies of alternatives, respectively. 

We can now calculate the energy savings potential, P, as: 

PG(y) = [Se

G(y) × Ce(y) + Sm b(y) × Ce(y) + Sm G (y) × Cm(y)] − [Se b (y) × Cm(y)], 

Or equivalently: 

PG(y) = [Se

b(y) − Se b (y) − Sm G(y)] × Ce(y) + [Sm G (y)] × Cm(y) 

The quantities Se

G(y) and SmG (y) are determined using the stock-and-flow calculation described 

above. 

4.3 Treatment of Cost Compression 

The stock-and-flow model also approximates the “learning curve” effect: the empirical fact that 

technology costs decrease over time in proportion to the cumulative volume of sales. Specifically 

we characterize each measure by its incremental price at the time of commercialization. “Time of 

commercialization” roughly corresponds to production volumes from one full factory (or one atscale workforce) where economies of scale are captured. We apply a learning curve that reduces 

cost by 30% (F. Ferioli, 2009); (Weiss, Patel, & Junginger, 2010) per doubling of cumulative 

purchases of the efficiency measure. 

4.4 Example Full-Adoption-Based Potential Calculation 

Continuing on the example that concluded our treatment of the technical potential in section 3, 

we can calculate the full-adoption-based potential for R-10 windows. The lifetime for windows 

currently installed is approximately 30 years. Because the tremendous cost of replacement 

windows in a home (typically $10,000-$20,000) does not justify their lifetime energy savings our 

measure, “upgrading windows to R-10” is an “end-of-life” stock-and-flow calculation (i.e., 

family 8, “early-retired existing inefficient”, representing calculation “D” from Figure 3, is not 

upgraded). 

Our analysis neatly breaks into two time periods; the first is the “transition” period: the first 30 

years after R-10 windows enter the market during which 2.5% (i.e., 1/40th) of previouslyinstalled windows are replaced with R-10 windows and all new purchases are R-10 windows. 

During this period purchases in each family for each calculation performed as described above 

are as follows: 

25
 
 

 

 

 

A. Competed: Families 4 and 9 become R-10 windows 

B. Flow: Family 3 is unoccupied (i.e., no R-10 windows have reached the end of their life) 

and family 7 unused (i.e., there is no reason to early-replace R-10 windows) 

C. Eliminated: Families 1, 2, 5, and 6 are vacant because our data source indicates the stock 

(i.e., number of windows or number of homes) is increasing 

D. Possibly switched: Family 8 is unoccupied because this is not an early-retirement 

measure. 

Once the transition period has passed we are in a steady state where all windows, existing, endof-life, and new, are R-10 windows. Only new purchases (family 9) and replaced, end-of-life 

efficient (family 3) are non-zero and all purchases are of R-10 windows. 

Figure 5 shows the energy use scenarios for the baseline case (blue), technical (red), and fulladoption-based (green) scenarios. The difference between the red and blue lines presents the 

technical potential in a given year while the difference between the green and blue lines provides 

the economic potential in a given year. Note the transition phase (2011-2041) for the fulladoption-based scenario that shows the stock turn-over as the efficiency is captured and the 

steady state phase where the technical and full-adoption-based scenarios are identical (i.e., all 

stock is R-10 windows). While this analysis provides a more realistic calculation of the potential 

Figure 5. Primary energy use 

R: R-10 windows 

Technical, Full-adoption-based, and Baseline energy scenarios for R-10 windows. 

26
 
energy benefits of R-10 windows, it still does not provide information on the cost effectiveness 

(i.e., at current and future prices does the cost of R-10 windows justify their investment by 

homeowners) nor the likely uptake (i.e., what fraction of the 100% of purchases assumes are 

likely to be R-10 windows). Cost effectiveness in particular should be analyzed in concert with 

this analysis. 

27
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

5 Adoption Logic and Adoption-Based Potential 

This section describes the standard approach to technology diffusion as introduced by Frank 

Bass (Bass 1969) and our implementation of the Bass model described. We proceed to support 

our choice of this model by discussing alternatives, reviewing procedures to determine model 

parameters, and discussing model limitations. We conclude by describing how we construct an 

energy use scenario and its energy efficiency potential; we also describe how to apply the Bass 

model to these equations to define the adoption-based potential. 

5.1 Description of the Bass Adoption Curve 

The Bass adoption model (Bass 1969) describes the diffusion of a new technology introduced 

into an existing market using the fraction of sales in a given year y: F(y). It models technology 

adoption in a manner similar to disease infection. It is defined by two parameters, p, and q, using 

the differential equation: 

dF(y) 

= (p + q × F(y)) × (1 − F(y)) dt

The coefficients “p” and “q” represent the effectiveness of communication on the adoption of the 

technology. They typically take values between 0 and 1 and can be interpreted as: 

•	 “p”: The influence of mass-media, sales and marketing, and other “external” factors. 

In management and marketing communities it is often called the external parameter 

because it is used to represent the positive effect that marketing has on driving 

“innovators” to adopt a new technology. It is helpful to think of this as the first year 

sales (F(y0)=p) which shows that, at low adoption (F(y) << 1) p represents the annual 

growth in market share. 

•	 “q”: Word-of-mouth communication in the (social) marketplace. This is often termed 

the “word-of-mouth” effect on the “imitators” and represents the empirically 

observed increase in sales as “innovators” interact with non-innovators to increase 

awareness and drive product adoption. It is helpful to think of “q” as the annual 

growth rate of F. This can be seen by noting that with p<<q and with (F(y) << 1) we 

∆F(y) can approximate 1-F(y) = 1to note that ∆F(y) ~(q × F(y)), or ~q. F(y) 

Note that we take the initial condition that for all years prior to market introduction, y0, F(y) = 0. 

Our tool implements this (computationally) by taking ∆F(y) as the change in sales fraction from 

year y-1 to year y as: 

∆F(y) = (p + q × F(y − 1)) × ൫1 − F(y − 1)൯

5.2 Estimation of “p” and “q” Parameters 

As mentioned in the baseline discussion above, our procedure of fitting to the NEMS forecast 

provides the baseline “p” and “q” coefficients. Defining alternate cases (other than the technicalminimum and adoption-based scenarios that do not use the adoption logic) requires modifying 

these “p” and “q” coefficients. We use an existing set of ten pairs of “p” and “q” coefficients 

showing the adoption of energy efficient technologies after intervention in the marketplace by 

DOE (Anderson 2004). These coefficients are binned into five ranges of “p” and “q” to derive a 

28
 
 

 

 

 

 

 

 

 

 

 

 

bin spacing of 0.0139 and 0.117 for “p” and “q” respectively. The scenario then defines the 

impact depending on the type of intervention as follows: 

•	 Research and development (R&D) efforts increase the “q” coefficient by the greater 

of one bin-spacing and 50% of the baseline value. This represents improvement in 

product quality, cost reduction, and other factors that will accelerate the network 

effect. R&D can also accelerate the year of market introduction. Per the National 

Research Council (National Research Council 2004) we default to a value of five 

years and introduce variations when the advanced technologies seem more or less 

likely to succeed. 

•	 Deployment efforts increase the “p” coefficient by the greater of one bin-spacing and 

50% of the baseline value. This represents the market transformation effort induced 

by programs such as ENERGY STAR. 

•	 Standards and codes efforts set adoption to 100% in the year they take effect. 

A second approach would determine the coefficients in light of the market environment (e.g., 

complexity of supply chain, means of product distribution) and technology attributes (e.g., 

auxiliary benefits, payback times, capital requirements) the measure strives to change. A third 

approach could focus on the historic impact of different program types (e.g., ENERGY STAR 

impacts) rather than the markets the measures seek to impact. We pursued neither approach to 

completion given data and time requirements. 

5.3 Extensions and Alternatives to the Bass Adoption Model 

Extension and alternatives to the Bass adoption approach exist (Mahajan Muller and Bass 1990); 

we review them briefly to demonstrate that the Bass model is a sufficiently accurate 

representation of consumer behavior for the data we have gathered. Many researchers have 

contributed to the field begun by Bass in 1969. Recent reviews by Peres et al. (2010) and 

Mahajan, Muller, and Bass (1990) have highlighted the pros, cons, and challenges of the various 

extensions (e.g., multiple levels of “early adopter,” and addition of price) and alternatives [e.g., 

affordability and marketing impacts (Golder and Tellis 1998)]. The majority of this work has 

focused on private-sector marketing analysis, including sales forecasting to accelerate adoption 

and maximize sales of a given product. Estimating potential future benefits of actions (Anderson 

2004) (e.g., advertising, price reductions) represent the other typical application of this work. 

One essential difference between these two approaches is our simplifying assumption that market 

adoption approaches 100% over time. Often marketers are trying to determine the total market 

future sales and market penetration whereas we have a-priori defined the total market. 

With that difference in mind we explored three variations to a 100% ultimate penetration Bass 

model in light of the uncertainty of estimating our Bass coefficients. Figure 6 shows the 

maximum excursion from our modeled adoption as follows. The top panel displays the adoption 

assumed correct using a Bass with “p” value of 0.234 and “q” of 0.075 (values approximately 

equal to the average of our database). It presents two realizations of each of the following four 

alternate models: 

•	 Bass model where the “p” values were changed to 0.035 and 0.018 and q is held 

constant. This variation in “p” represents +50%/-30% uncertainty in the coefficient; 

29
 
 

 

 

 

 

 

 

 

 

an uncertainty roughly equal to the standard deviation of “p” in our data set. This 

uncertainty drives a +/-4-year uncertainty in date of 50% market adoption and a +/-

4%-uncertainty in adoption level after 40 years. 

•	 A first extension to the Bass model that allows for a group of early innovators that 

only receive influence from external factors. We vary the size of this population from 

10% to 20% of the population. The “p” parameter is taken as the same for all of the 

population and chosen so the model gives the same 40-year adoption as the correct 

Bass model. 

•	 A second extension of the Bass model that smoothly varies either the “q” parameter 

(qt = q0 × Ft

a) or the “p” parameter (pt = p0 × (1 + Ft)−b). The coefficients a and 

b are chosen so this model gives the same 40 year adoption as the correct Bass model. 

•	 An alternative to the Bass model that determines adoption based on affordability 

(Golder and Tellis 1998), consumer sentiment, and market push. We use an annual 

price-reduction (10%), inflate personal income at 3.3% annually, and fix “consumer 

sentiment” constant. The two realizations apply Gaussian distributions of market 

presence, each with a width of 10 years; one is peaked at 2012 and the other at 2020, 

as indicated. 

In Figure 6, the bottom panel shows the difference of each of the models from the correct Bass 

model, where: 

•	 The red line represents the standard deviation of both realizations of the three models 

that were examined beyond the Bass model (i.e., red, green, and purple in the top 

panel) 

•	 The green line provides the maximum positive deviation of these six models from the 

correct Bass model 

•	 The blue line is the maximum negative deviation of these six models from the correct 

Bass model 

•	 The black dotted lines show the deviation of each of the Bass models (p=0.03) arising 

from uncertainty in the Bass parameters. 

Note that the deviation from the central model arising from the uncertainty in Bass parameters 

brackets deviations from the other models in most years. This suggests to us using an extension 

of, or alternative to, the Bass model provides limited benefits (i.e., second order corrections). To 

test the sensitivity of our conclusions to the adoption parameters we vary these coefficients (both 

systematically and randomly) and note the impact (see section 7). 

30
 
Figure 6. Adoption rate uncertainty arising from variations in adoption model selected . 

31 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

5.4 Limitations 

Despite limitations, the Bass model remains highly relevant because of its ability to fit empirical 

data well and its simplicity of interpretation. A select set of limitations addressed by the model 

include: 

•	 Market size. The Bass model poorly predicts ultimate market penetration. We have 

defined 100% penetration for each measure’s market, thus eliminating the need to 

determine the ultimate market size and eliminating it from our implementation of the 

Bass model. This addresses the concerns of its changing over time and eliminates the 

error introduced by fitting for this parameter. However, this does represent a 

simplifying assumption; if the source material(s) used provide an inaccurate 

characterization of the ultimate market size then that error will be systematically 

introduced to the estimate. 

•	 Competition and interaction with other measures. The Bass model omits the 

impact of future generations of the technology. We first estimate the impact of each 

technology independently and then calculate the impact of future generations by 

including the entire “portfolio” of measures. This provides a first order estimation of 

interactions and competitions but does not include non-linear effects. The procedure 

for this “staging” calculation is described in section 6. 

•	 Market and product attributes. The Bass model does not connect adoption 

coefficients to “common sense” attributes such as cost and auxiliary benefits. We 

attempted to address this limitation with our second alternative of defining “p” and 

“q” coefficients; we anticipate this will be a fruitful area of future work. 

Given the limitations of the Bass model and the scope of work, there are several factors that we 

remain unable to address: 

•	 Stationary assumption. We assumed “p” and “q” do not change over time (other 

than when estimating the impacts of an intervention in a given scenario). As this is 

the simplification of a complicated market into two parameters we would expect these 

two parameters to change over time; data and approaches to do so remain outside the 

project scope. 

•	 Repurchase dynamics. We also make the simplifying assumption that once a 

consumer has adopted the advanced technology they necessarily adopt it at the 

(future) time of replacement. 

•	 Other factor. One can levy other limitations with this approach, including the desire 

to include factors such as multiple-stage adoption, detailed sub-population analysis, 

impact of multiple actors with divergent goals, and inclusion of supply chain or other 

manufacturing and distribution concerns. 

We feel that the above approaches sufficiently analyze the size of the uncertainty and limit 

potential errors within the context of data publically available as of the writing of this report 

(Golder & Tellis 1998). Future work may include improving the quality and volume of data 

available to improve on the parameter estimation and addressing the three limitations that were 

not addressed (as outlined above). 

32
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

5.5 Defining the “Bass-Adoption-Based” Potential 

Following the convention described above and using the superscript “H” to represent the Bassadoption-based scenario we express the energy use, U, in this scenario as: 

UH(y) = Sm H(y) × Ce(y), H(y) × Cm(y) + Se

Where we recall that for each year, y: 

•	 S indicates the equipment stock 

•	 C indicates the energy use per unit stock, and 

•	 The subscripts, m and e represent the measure being analyzed and the existing mix 

of “inefficient” technologies of alternatives, respectively. 

We can now calculate the Bass-adoption-based energy savings potential, P, as: 

PH(y) = [Se

H(y) × Ce(y) + Sm b(y) × Ce(y) + Sm H(y) × Cm(y)] − [Se b (y) × Cm(y)], 

Or equivalently: 

PH(y) = [Se

b(y) − Se b (y) − Sm H(y)] × Ce(y) + [Sm H(y)] × Cm(y) 

The quantities Se

H(y) and SmH(y) are determined using the stock-and-flow calculation described 

in section 4, but now the calculations labeled “A. Competed” and “D. Possibly switched” 

outlined in Figure 3 are treated differently. In the case of the full-adoption-based potential 

calculation (i.e., scenario F described in Section 4.2), the efficient technology represents 100% 

of purchases of families in these categories. In the “Bass-adoption-based” potential the families 

in these categories represent the full market size that can be addressed in each year. The measure 

being analyzed captures the share of this full market in a given year, y, as given by the Bass 

equation (Section 5.1) where the first year of technology availability, y0, is the year following 

completion of R&D efforts (or 2010 if the measure does not require R&D). The Bass 

coefficients specific to this measure and the treatment of both R&D and standards are detailed in 

Section 5.2. In summary, selection of the parameters “p” and “q” vary with time as the nature of 

the implementation of the energy efficiency measure changes such that: 

R&D efforts7: increase the “q” coefficient as discussed above. Also, they accelerate the year of 

market introduction by 0-7 years (National Research Council, 2004). 

Deployment efforts8: increase the “p” as discussed above. 

Standards and codes efforts9: set adoption to 100% in the year they take effect. 

Statistics on the baseline, R&D-aided “q”, deployment-aided “p” and years in which appliance 

standards are applied for the measures in the tool are shown in Table 2. 

7 If a measure is already on the market this lever is not applied.
 8 If a measure is widely adopted this lever is not applied.
 9 Standards and codes levers are only applied to applicable technologies (e.g., it is unlikely one could develop a
 

standard requiring switching from desktop to laptop computers).
 

33
 
 

 

 

 

 

 

 

 

Table 2. Bass Adoption Coefficients from Fit to the Baseline Energy Use for Measures 

in Full Data Set 

p q 

Min: 0.00% 0.00% 

Max: 10.0% 25.0% 

Median 0.5% 0.5% 

Average 1.2% 3.7% 

Standard deviation 2.0% 6.6% 

This energy use scenario provides potential savings that are necessarily less than the fulladoption-based potential calculated above. Also, it is both a more realistic (i.e., accurate) 

characterization of what is most likely to happen after technology introduction but considerably 

more uncertain given the complexity of market dynamics and the limitations in estimating the 

“p” and “q” coefficients. 

To continue the example developed above using R-10 windows Figure 7 adds to Figure 5 the 

light-blue line indicating the energy use for the Bass-adoption-based scenario. In this scenario 

R&D investments accelerate the time of significant market entry by 5 years and increase the “q” 

coefficient from 0.1% in the baseline to 6.0%. Additionally deployment efforts (e.g., ENERGY 

STAR labeling) increase the “p” coefficient from 0.1% in the baseline to 1.5%. Finally buildings 

codes that take effect in 2030 insure that all windows installed in homes meet or exceed R-10. 

Figure 7. Primary energy use 

R: R-10 windows 

Bass-adoption-based energy use scenarios for R-10 windows. 

34
 
This third potential calculation, in addition to the technical and full-adoption potential 

calculations described above, completes the suite of efficiency potentials we have used to 

analyze measures. In aggregate they offer the ability to provide a series of satisfying vantage 

points that can surface technical, economic, or market concerns associated with a particular 

measure. 

35
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6 Developing a Portfolio: Staging Logic 

This section describes the method used for aggregating the many measures analyzed into a 

portfolio of measures to deploy. It motivates the need for an accounting framework (Meier 

1982) to “stage” the measures to avoid counting savings that could be captured by multiple 

measures more than once. It then details the four-step iterative approach to aggregating these 

savings. Finally it provides an interpretation of these portfolios for the three efficiency potential 

scenario calculations discussed previously. 

Having now calculated the energy use and savings of each measure, we can now build a full 

scenario by aggregating the impacts of all measures in a given portfolio. However, this requires 

introducing an accounting framework to avoid “double-counting” savings and attributing any 

savings that would be double-counted in proportion to the measures pursuing that savings. By 

first treating each measure against the “flat landscape” where no other measures are introduced 

(i.e. the “un-staged” technical, full-adoption, and bass-adoption energy potentials defined in 

sections 3, 4, and 5) and then considering their benefits in the context of other efforts we 

provided an additional “lens” through which to view the relative benefits of each measure. 

We refer to this procedure as “staging” the benefits; it requires a four-step iterative process. First 

we stage10 the benefits from lowest to highest based on cost of conserved energy (CCE) 11. 

Second we evaluate the overlap of each measure’s market to those with a lower CCE as detailed 

in section 6.2. Third we determine the nature of the interaction between the measures as detailed 

in section 6.3. Fourth we calculate the “staged savings” by subtracting the savings of all lowercost measures from each measure in light of steps two and three as detailed in section 6.4. 

Finally we iterate this process by re-sorting the list by cost of conserved energy and repeating the 

process until obtaining a stable solution. Each step of this process is detailed below. 

It is important to note that staging by CCE is analogous to the procedure used by utilities and 

regulators to minimize the present value of investments needed to provide energy to consumers. 

As such, the staging by CCE minimized investments. It does not maximize the energy savings at 

a given investment level nor minimize the investment needed to reach a desired savings. These 

later two operations are significantly more complicated. Neither of these alternate optimizations 

admits an algorithmic approach, both require Monte-Carlo simulations such as used for building 

energy modeling (e.g., OptiPlus, BeOpt). 

6.1 Staging by Cost of Conserved Energy 

We begin this process by calculating one of the most critical quantities in the entire analysis— 

the cost of conserved energy. The CCE is analogous to the often used levelized cost of energy or 

levelized cost of electricity (LCOE), which is commonly used in analysis by electric utilities: 

N F(y) ∑y=0 (1 + d)y

LCOE = 

N E(y) ∑y=0 (1 + d)y

10 “Stage” refers to our process of ordering measures from lowest to highest cost of conserved energy. 11 This quantity has also been abbreviated as COCE, LCOCE, or LCCE where the later two abbreviate levelized cost 

of conserved energy. 

36
 
 

 

 

 

 

 

 

 

 

 

 

 

Where: 

d is the discount rate or cost of capital used to value future expenses in current year dollars 

F(y) is the total financial expenditures (i.e., capital and maintenance, but not energy costs) in 

year y; technically these expenditures include capital, financing costs, operations, maintenance, 

fuel, and all costs used to generate the electricity 

E(y) is the energy generated in year y. 

Since energy efficiency represents a counterfactual saved energy measured from a baseline rather 

than energy produced, the cost of conserved energy requires reference to a baseline as described 

in the discussion of scenario calculation above. Specifically for a scenario, X, compared to a 

baseline B we can define: 

N ൫FX(y) − FB(y)൯ ∑y=0 (1 + d)y

CCE = 

N ൫EB(y) − EX(y)൯ ∑y=0 (1 + d)y

Where the superscripts refer to the baseline or scenario being analyzed as described above. We 

calculate this quantity for each measure m as the tool provides all these quantities for each 

measure in both the baseline B and scenario X to be analyzed. 

The CCE represents the lifetime cost of providing a service that otherwise would be provided by 

less efficient equipment in energy units [e.g., $/million BTU (106MMBTU, cents/kilowatt-hour 

(kWh)]. We find this metric to be superior to payback time, net present value of benefits, 

benefit-to-cost ratio, internal rate of return, and other cost metrics because it avoids explicitly 

assuming future energy prices. By instead expressing the cost as an equivalent energy price, it 

facilitates comparison of measures to volatile energy prices, other measures, and local or timedependent effects that can significantly affect the assumed energy prices. It is worth noting that 

this metric can include un-priced externalities such as carbon price12, opportunity value of time, 

and so on if they are used in the payment stream in the above equation. Finally note that it is 

straightforward to approximate the payback time from CCE; if we take the measure lifetime as L 

and the energy price as P the payback time is: 

CCE

T~ × L P

The tool creates a staged potential estimate by ranks from lowest to highest CCE all measures the 

scenario includes. “Ties” in cost are broken by re-ranking all ties from greatest to least potential 

N energy savings in that scenario [i.e., ∑y=0൫EB(y) − EX(y)൯]. Thus, we build our portfolio by 

progressively comparing each measure with all lower CCE measures and reducing the savings to 

12 Although beyond the scope of this work, it is worth noting that carbon benefits could be expressed in the CCE as a 

non-zero financial savings, FB(y), in each year they are captured or added to the energy price to which a measure’s 

CCE is compared (but not both counted as a savings and included in the energy price). 

37
 
 

 

 

 

 

 

 

 

 

avoid “double counting” any savings that are already attributed to lower CCE measures. This 

requires detailed accounting as described below. 

6.1.1 Identifying the Discount Factor to Value Future Costs and Benefits 

Discount factors express how future costs and energy savings are valued in today’s terms as used 

in the equations for LCOE and CCE discussed above. There is no generally accepted answer for 

what the “right” discount rate is, though several concepts and ranges are commonly discussed. 

These include: 

Real and nominal rates: differ on the treatment of inflation. Multiple agencies, such as the 

Bureau of Economic Analysis and the Federal Reserve Board, monitor the price of goods over 

time. The various forms of the Consumer Price Index (CPI) represent one commonly used 

estimate of inflation. A full treatment of inflation is well beyond the scope of this work; it 

suffices to observe the long-run inflation rate in the US is approximately 3.0-3.5%. Thus “real” 

discount rates which are net of inflation will typically be 3-3.5% below nominal rates. 

Specifically to convert from nominal to real discount rates one should divide the nominal rate by 

(1+i), where i is the inflation rate. 

Social costs: typically range from 0-3% real (3%-6% nominal) as estimated by the 

Congressional Budget Office and the Office of Management and Budget. This represents the 

rate of return on a “risk free” investment. 

Investment costs: include greater risk and so, as suggested by the Capital Asset Pricing Model 

(CAPM), are typically higher, in the range of 6-10% real (9%-13% nominal). 

Financing costs: vary greatly given the type of financing. Financing types can include secured 

loans like a home mortgage (historically ranging from a nominal cost of 3% (today’s record low) 

to almost 20% (in the mid-80’s)) or commercial property or small business loan (in a similar 

range, though typically 3 or more percentage points higher). Unsecured loans, such as revolving 

credit lines including credit cards, are typically higher: in the range of 6% to 20% or higher 

depending on credit worthiness 

Behavioral rates: can represent how a consumer makes decisions trading off current and future 

cash rewards and costs. Significant research exists exploring these topics; it suffices for our 

purposes to observe that behavioral discount rates can be 40% or higher. 

For our analysis we chose a social discount rate where we apply the nominal value of 6%. This 

views our efficiency opportunities as a society wide investment, an appropriate lens for public 

sector organizations to use. Sensitivity of our results to this assumption are treated in section 

7.1.2. 

6.2 Evaluating Market Overlap 

We next evaluate the potential market overlap between measures by characterizing the market 

addressed by each measure using a “micro-segment” matrix. This matrix effectively divides all 

building-related energy use into 2,510 segments of energy use (plus 38 markets that are 

uncategorized by the matrix such as ATM machines and data centers), thus averaging ~16 

trillion Btu per “micro-segment” as they divide the ~40,600 trillion Btu of our baseline energy 

use identified in section 2.1 and Table 1. We determine the energy use of each use of each micro38
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

segment by characterizing seven enduse groups with relevant variables as described in detail in 

section 6.3 below. This characterization (i.e., defining segments and calculating their size) was 

driven by the ~300 hundred sources used to generate the measure database though the total 

energy use is taken from NEMS/AEO. 

Each measure is characterized by a “micro-segment” vector, μm, of 2,510 elements each taking 

the value 0-100% to indicate what portion of that micro-segment the measure can address. We 

can perform two important calculations with this construct. First, if we build a vector,Umicro, of 

length 2,510 elements where each element represents the energy use of a micro-segment we 

obtain the total energy use, Um, of the measure’s market by taking the dot product: Um = 

Umicro ∙ μm. Alternatively if we construct the matrix, K, by placing the energy use of each 

micro-segment along the diagonal elements then we can determine the overlap between two 

measures (i.e., the size of the market energy use and stock common to both) by calculating the 

dot product χ1,2 = μ1 ∙ K ∙ μ2, or equivalently ⟨μ1|K|μ2⟩. Examples of both these operations are 

provided at the conclusion of section 6.2.1 

6.2.1 Defining the “Micro-segments” 

Creating our micro-segment vector, μm, or matrix, K, is easily done by considering each of seven 

enduse groups in turn. We can easily list the first six enduse groups by the enduse and the
 

principle variables used to characterize them. By specifying the portion of each variable to 

which the measure applies and performing the necessary multiplication we quickly identify the
 

value of each element of μm and K.
 

Lighting: encompasses each of four sectors categorized by four technologies {incandescent,
 

fluorescent, high intensity discharge, other}, given in TBTUs:
 

Residential {798; 324; 0; 249}
 

Commercial: {124; 3,694; 67; 0}
 

Industrial: { 0; 238; 361; 1}
 

Outdoor: {34, 75, 668, 20}
 

Cooking: requires specification for each of the two major fuels and each of two sectors the 

energy use of the relevant eight most significant devices {microwave, stove, range, boiler, fryer,
 

griddle, steamer, food prep}, given in TBTUs:
 

Residential electric: {169; 426; 0; 0; 0; 0; 0; 100}
 

Residential gas: {0; 237; 0; 0; 0; 0; 0; 0}
 

Commercial electric: {N/A; 28; 3; 2; 12; 8; 16; 22}
 

Commercial gas: {0; 71; 6; 25; 34; 13; 16; 0}
 

Hot water, washing, and drying: combines both the machine energy and energy used to heat
 

water for washing and other uses as follows {dishwasher- machine, clothes washer- machine,
 

clothes dryer, dishwasher- hot water, clothes washer- hot water, non-machine hot water}, given 

in TBTUs:
 

Residential electric: {340; 86; 942; 46; 253; 0; 1;265}
 

Residential gas: {0; 0; 83; 44; 217; 0; 1;213}
 

Residential distillate: {0; 0; 0; 3; 14; 0; 83}
 

39
 
 

 

 

 

 

 

 

Commercial electric: {45; 17; 40; 102; 95; 0; 88} 

Commercial gas: {0; 0; 171; 58; 205; 0; 326} 

Commercial distillate: {0; 0; 0; 0; 6; 0; 11} 

Refrigeration: (requires separate treatment for each of three principle sectors: residential, 

supermarkets, and non-supermarket commercial. Specifying each major device in pairs of 

{existing buildings, new buildings} we have, given in TBTUs: 

Residential:
 

Primary, full size, with ice maker: {725, 265};
 

Primary, full size, no ice maker: { 181, 66};
 

Primary, compact: {4.1, 1.5};
 

Secondary: {13, 5}
 

Top loading freezer {123, 45};
 

Front loading freezer {79, 29}
 

Supermarket:
 

Compressors {241, 231}
 

Display cases {138, 133}
 

Condensers {138, 133}
 

Beverage merchandisers {30, 29}
 

Walk-in refrigerators {26, 25}
 

Walk-in freezers {8, 8}
 

Ice machine {9, 9}
 

Reach-in freezer {6, 6}
 

Reach-in refrigerators {26, 25}
 

Non-supermarket commercial:
 

Beverage merchandisers {725, 265}
 

Ice machine {181, 66}
 

Vending machine {123, 45}
 

Walk-in freezers {79, 29}
 

Reach-in freezer {13, 5}
 

Reach-in refrigerators {4, 1}
 

Television and personal computers: Each sector’s new and existing building use of four
 

technologies Desk-, Lap-, Displays, and TVs is detailed, given in TBTUs:
 

Residential {Desk- {146, 53}, Lap- {103, 38}, Displays {41, 15}, TVs {1053, 385}}
 

Commercial {Desk- {181, 174}, Lap- {8, 8}, Displays {28, 27}, TVs {0, 0}}
 

40
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Ventilation: For each sector we identify the energy use in {existing, new} building pairs for
 

each of three technologies {circulating pumps, constant air ventilation (CAV), and variable-air 

ventilation (VAV)}, given in TBTUs; additionally we note that 70% of commercial ventilation 

energy is used for heating/cooling distribution and 30% for outdoor air delivery while 100% of
 

residential energy is used for heating/cooling distribution:
 

Residential: {circulating pumps: {110,40}, CAV: {305, 112}, VAV: {0,0}}
 

Commercial: {circulating pumps: {68,65}, CAV: {299, 287}, VAV: {655,630}}
 

Space conditioning (heating and cooling): Shown below are the far more complicated space 

conditioning vectors prior to their full cross multiplication but after combining sector and enduse elements. The first line provides the sector and end-use energy use given in TBTUs; the 

following lines present the portion of each of these four quantities in the indicated class of
 

variable:
 

Sector and end-use: {residential heating, residential cooling, commercial heating, commercial 

cooling} = {5161, 2925, 2215, 1977} 

Vintage: {pre-2010, “new”}:
 

Residential heating: {73%, 27%}
 

Residential cooling: {73%, 27%}
 

Commercial heating: {51%; 49%}
 

Commercial cooling: {51%, 49%}
 

Building load: {attic, walls, basement, infiltration, doors, window-conduction, window-radiation, 

internal}:
 

Residential heating: {16%, 25%, 19%, 37%, 5%, 34%, -1%, -35%}
 

Residential cooling: {15%, 10%, -6%, 18%, 0%, 1%, 34%, 29%}
 

Commercial heating: {23%, 39%, 21%, 34%, 0%, 43%, -3%, -58%}
 

Commercial cooling: {1%, 1%, -6%, -4%, 0%, -9%, 40%, 77%}
 

Climate zone: using the five provided through RECS/CBECS surveys:
 

Residential heating: {18%, 33%, 29%, 13%, 7%}
 

Residential cooling: {4%, 14%, 21%, 22%, 40%}
 

Commercial heating: {18%, 33%, 29%, 13%, 7%}
 

Commercial cooling: {4%, 14%, 21%, 22%, 40%}
 

Equipment type: {air-source heat pump, ground-source heat pump, furnace/roof-top unit, boiler/
 

centrifugal chiller, sectional heating/residential-style central air conditioning, electric 

radiator/wall/window air conditioner, other}
 

Residential heating: {5.6%, 1.0%, 59.6%, 20.8%, 0.0%, 10.2%, 2.9%}
 

Residential cooling: {23%, 2%, 0%, 0%, 65%, 9%, 1%}
 

Commercial heating: {6%, 1%, 60%, 21%, 0%, 10%, 3%}
 

Commercial cooling: {2%, 0%, 29%, 25%, 8%, 16%, 21%}
 

41
 
 

 

 

 

 

 

6.2.2 Example Micro-segment Calculations 

Out first example will create the energy use, Um = Umicro ∙ μm, of a contrived market using the 

micro-segment vector, μm. For example, consider a measure that addressed only residential air 

conditioning in new homes servicing heat loads through attics in climate zone 5 employing 

central air conditioning. First note that, for this example, all non-space-conditioning microsegments take the value “0”. Next we create each of the micro-segment elements for space 

conditioning by cross-multiplying the various matrices detailed in section 6.2.1. For our example 

we have: 

Total residential air conditioning: 2925, times 

Portion of cooling in new homes (27%), times 

Portion of cooling attributable to attic loads (15%), times 

Portion of cooling in climate zone 5 (40%), times 

Portion met through central air conditioning (AC) (65%), = 30.8 TBTUs 

This approach has significant limitations if we were to use the micro-segment data directly. In 

this example it is likely that in climate zone 5 (i.e., the hottest and sunniest climate zone) both 

the attic portion (15%) and the portion met through central AC (65%) will likely be more than 

the U.S. average assumed here. However, as we typically use hundreds or thousands of these 

micro-segments when defining a market, there is less of a chance to encounter these errors. With 

sufficient time and data availability (the latter representing the greater constraint), it becomes 

easier to more accurately represent the micro-segment energy use numbers. 

Given this complexity a second example is warranted, in this case we will compute the market 

overlap between two measures: ⟨μ1|Χ|μ2⟩. Consider a measure that addressed commercial gas 

stove, gas ranges, and gas broilers. For this example we would compute the micro-segment 

contributions as follows: 

Note that the space cooling, lighting, hot water, washing, and drying variables are all 0; thus, 

there is no energy use for this measure in any of the corresponding micro-segments used to 

represent those end uses. 

Cooking presents three attributes each with their own variables: Sector {residential, 

commercial}, Fuel {electric, gas, distillate}, and Device {microwave, stove, range, boiler, fryer, 

griddle, steamer, food prep},13 or using initials: {r, c}, {e, g}, {m, s, r, b, f, r, s, p}. We therefore 

represent our measure of commercial gas ranges as: Sector {0,1}, Fuel {0,1}, and Device 

{0,1,1,1,0,0,0,0}. 

We then generate the vector characterizing the market by taking the outer product of these 

values. Using initials this results in 32 non-zero elements of the vector μ1: {rem, res, rer, reb, 

ref, reg, res, ref, rgm, rgs, rgr, rgb, rgf, rgg, rgs, rgf, cem, ces, cer, ceb, cef, ceg, ces, cef, cgm, 

cgs, cgr, cgb, cgf, cgg, cgs, cgf}.The example measure is then: μ1 = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 1, 1, 1, 0, 0, 0, 0}. 

13 Residential food preparation includes toasters, toaster ovens, and coffee makers. 

42
 
 

 

 

 

 

 

 

 

 

 

 

Next we instead consider a (fictitious) measure that only applied to some portion, v, of gas and 

electric ranges built by some manufacturer or before some year, we would have replaced the 

Device vector with {0,0,v,0,0,0,0,0} and obtained: μ2 = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

0, 0, 0, 0, 0, v, 0, 0, 0, 0,0, 0, v, 0, 0, 0, 0, 0}. 

Finally, through the various sources discussed previously, we have estimated the primary energy 

use for each of these micro-segments and can create the on-diagonal elements of the 32x32 submatrix Χ, or equivalently the elements of Umicro, namely, in TBTUs: {169, 426, 0, 0, 0, 0, 0, 

100, 0, 237, 0, 0, 0, 0, 0, 0, 0, 28, 3, 2, 12, 8, 16, 22, 0, 71, 67, 25, 34, 13, 16, 0}. 

As such, the resultant estimate for the market size is 

U1 = Umicro ∙ μ1 = 71+67+25=163 TBTUs 

Taking f=25% for the other market (i.e., gas and electric ranges) would yield a market of: 

U2 = Umicro ∙ μ2 = 25% × (3 + 67) = 17.5 TBTUs. 

Finally, the overlap between these two markets is given by only the last six elements of the 

above, namely: 

χ1,2 = ⟨μ1|Χ|μ2⟩ = {25%, 0, 0, 0, 0, 0} × {67, 25, 34, 13, 16, 0} × {1,1,0,0,0,0} = 

16.75 TBTUs. 

6.2.3 Evaluating Market Overlap 

Recall that in our data gathering we used individual research reports and the National Energy 

Modeling System (NEMS) detailed tables to develop a market size estimate for each measure. 

Now with the micro-segment approach we have a second method to estimate the market size:

Um. This provides both a check to the first estimate of market size as well as the above described 

method to determine market overlap. 

While some measures draw on the same citations for both methods, others represent fully 

independent characterizations and all result from independent excel calculations. We compared 

the estimated market size from each method for each measure to identify errors and estimate 

uncertainty. A statistical analysis on the difference in total energy consumption in 2030 between 

the two methods for all 400 markets reveals: 

Sufficiently accurate: The average and median of the absolute value of the difference between 

the two market analyses are 33 and 10 TBTUs respectively. This is considerably smaller than the 

average and median market sizes of 1,215 and 497 TBTUs respectively. Even more 

compellingly, the average and median of the absolute value of the percent difference are 5.8% 

and 1.7%. 

Few large deviations: Most (86%) of the markets match to within 10%; only 3 of 295 markets 

used in the primary analysis exceed 25% uncertainty (16 of the 400). 

Unbiased results: The average and median deviations of -0.1 and 0.0 TBTUs are well within the 

standard deviation of the deviation (60) and much less than the average and median market sizes 

(1,215 and 497 respectively) indicating that neither method is biased large or small compared to 

the other. 

43
 
 

 

 

 

 

 

 

 

 

 

 

6.3 Determining the Nature of Interaction Between Measures 

Once we have identified the market overlap for two measures we must determine how the 

earlier-staged measure has impacted the market size and savings opportunity of the later staged 

measure. Interactions between measures take one of three forms as labeled in table 3: 

Independent: the measures do not interact. A full treatment of all measures in a building-bybuilding analysis would likely have interaction between all measures. However, for most cases, 

these interactions are much smaller than magnitude of the enduse savings. One simulation study 

(Ugursal & Fung, 1994) suggests that in Canadian homes every MMBTU of site-energy savings 

increases heating load by 0.57 MMBTUs. In U.S. homes we would expect some heating load 

increase less than or equal to this load and a corresponding decrease in air-conditioning. Thus 

the relative impact on primary energy use will be the sum of two offsetting effects that vary by 

climate zone. We neglect all such induced loads categorizing their interactions as independent. 

Market reducing. Some savings measures decrease the energy use a second measure can 

address without impacting the percent savings that second measure provides. The interaction 

between improving a building’s air conditioning efficiency and its envelope (e.g., installing 

highly-insulating windows) demonstrate this. Taking this as the total market overlap between 

measures 1 and 2 (e.g., cooling energy lost by conduction through windows) in energy units as

M1,2 and the savings as a percent of the market addressed for each as p1 and p2, the energy 

savings in the overlap market for each independently would be: P1 = M1,2 × p1, and P2 = M1,2 × 

p2. However, if we stage measure 1 first, the savings from measure 2 are reduced to: 

P2,1 = ൫M1,2 − P1൯ × p2 = M1,2 × (1 − p1) × p2 = P2 − p2 × P1. 

Savings reducing. The final form addresses measures where one measure directly reduced the 

savings of a second typically because the two measures improve the same system component 

(e.g., advancing from a typical R-2 window to R-5 will reduce the benefit calculated by the 

measure advancing from a typical R-2 to R-10). In the latter case, and using the same notation, 

the savings from the second measure is: 

P2,1 = M1,2 × p2 − P1 = M1,2 × (p2 − p1) = P2 − P1. 

We take this quantity to be positive definite; a negative value indicates that measure 1 saves 

more energy than measure 2, therefore, measure 2 should not be deployed. 

The form an interaction between two measures takes is characterized by the “component” the 

measure addresses. The component characterization and comparison matrix is provided in Table 

3. It follows that the energy savings of the measure 1 (i.e., staged first) on the savings of 

measure 2 (i.e., staged second) is reduced by the quantity: P2 − P2,1 = π1,2 × P1, where: π1,2=0, 

p, or 1 for forms 0, 1, and 2, respectively. Table 3 categorizes the interaction between the 28 

different components used to describe measures in the tool, where: 

NA: indicates a comparison that never should occur (i.e., the markets are assumed or 

approximated not to overlap) 

0: specifies components that do not interact: the higher CCE savings remain unaffected by lower 

CCE measures in their market 

1: indicates interactions where the lower CCE measure reduces the market size but not the 

percent savings of the higher CCE measure 

44
 
 

 

2: indicates interactions where the lower CCE measure reduces the market size and savings of 

the higher CCE measure. 

Table 3. Interaction Between Components Describing Measures in Tool 

45
 
 

 

 

 

 

 

 

 

 

 

 

 

 

6.4 Calculating the “Staged” Savings 

Finally, given a set of N measures sorted by increasing CCE, we can calculate the “staged” 

savings πN, of measure N given the saving previously accounted for as: 

N−1

πN = PN − ෍൫fi,N × πi൯ − f1,N × P1

i=2

Note that we use the un-staged savings of the first measure (P1) and the staged savings (πi) of all 

subsequent measures as it holds for the first measure that P1 = π1. This quantity is again taken as 

positive definite indicating that a measure where all possible savings were captured by previous 

measures will offer no additional savings. For example, if a SEER 24 central A/C stages before 

SEER 21 then the staged savings of the SEER 21 unit is 0 since the SEER 24 unit already 

captured all savings available to the SEER 21 unit. Additionally, as we are using a national 

average we take the conservative assumption that the “staged” cost of conserved energy 

increases to: 

PN CCEstaged,N = CCE × 

πN

This equation is accurate once all previous measures have gained 100% market adoption; prior to 

that the actual cost of conserved energy for a measure, m, in an average building will range from 

CCEN to CCEstaged,N depending on which of the less costly measures have been deployed into 

that building. Table 4 shows that for our example including five technologies CCEunstaged = 

$13.61 while CCEstaged = $16.73. 

6.5 Iteration 

Finally we iteratively sort the measures by increasing “staged” cost of conserved energy. We 

continue this resorting process until the order has converged to a stable solution for the scenario 

being analyzed. Note that any scenario’s potential savings can be staged; as discussed in section 

7, staging the maximum-adoption potential is akin to performing a traditional “economic 

potential” analysis whereas staging the bass-adoption potential is useful for representing 

prospective benefits from program activities. 

To conclude the example developed above using R-10 windows, we consider a highly simplified 

staging calculation for home energy use that includes five measures: condensing furnace 

standard, SEER 24 central air-conditioner (a research & development measure), and SEER 21 

central air conditioner (a deployment measure), R-5 window (deployment), and R-10 window 

(R&D). Table 4 shows the staged and un-staged savings and CCE for each measure; it also 

indicates, for each measure, the energy savings captured by all previously staged measures. It is 

instructive to note: 

For each measure, the sum of the staged savings and all previously captured savings is equal to 

the un-staged savings. 

Staging reorders the measures: SEER 21 CAC drops from the 3rd to 5th most cost effective 

because (after R&D efforts) SEER 24 units will be lower cost and capture all their savings. 

If we were to set our cost-effectiveness criteria at an LCOE of $15/MMBTU (typical of today’s 

residential energy prices) four of our measures (i.e., all except R-10 windows) would be cost 

46
 
 

 

 

 

 

 

effective; after staging, however, only two of our measures (SEER 24 CAC and R-5 windows) 

remain cost effective. 

Without staging we would significantly overestimate the total energy savings of these measures 

as 3,410 TBTUs/year in 2030 rather than 1,860 TBTUs/year; this results from eliminating double 

counting in the two CAC and two window measures. 

Table 4. Staged and "Un-staged" Energy Savings 

Unstaged Staged 

Measure N 

Initial 

staging 

Energy 

savings 

CCE 

($/MMBTU) 

Final 

staging 

Energy 

savings 

CCE 

($/MMBTU) 

SEER 24 (R&D) 97 1 816 $3.50 1 816 $3.56 

R-5 windows (Deploy.) 642 2 661 $6.36 2 627 $6.71 

Condensing furnace (Std.) 112 4 303 $13.61 3 246 $16.73 

R-10 windows (R&D) 421 5 817 $29.27 4 171 $144.00 

SEER 21 (Deploy.) 98 3 813 $10.19 5 0 N/A 

Total: 3,410 1,860 

Energy savings captured by prev. measures 

Measure SEER 24 R-5 windows Cond. furnace R-10 windows 

SEER 24 (R&D) N/A N/A N/A N/A 

R-5 windows (Deploy.) 34 N/A N/A N/A 

Condensing furnace (Std.) 0 57 N/A N/A 

R-10 windows (R&D) 3 627 16 N/A 

SEER 21 (Deploy.) 813 0 0 0 

Table 4 shows energy savings (a trillion Btu of maximum-adoption energy savings in 2030) and cost ($/MMBTU, 

primary) for five related home heating- and cooling-related efficiency measures. 

47
 
 

 

 

 

 

 

 

 

 

 

 

7 Outputs and Their Uses 

The prioritization tool enables multiple calculations and many analyses of interest for 

stakeholders interested in exploring energy efficiency measures in any context. We can create 

portfolios of energy savings for each of the efficiency potential scenarios described above: 

technical, full-adoption-based, and bass-adoption-based. Each of these portfolios then has a 

unique interpretation and implication, and, in turn, reveals: 

Technology limits. Using the technical potential only and considering the contributions of all 

technologies identified will reveal the technical lower-limit for energy use (i.e., maximum 

energy efficiency savings) from known technologies with sufficiently well characterized cost, 

performance, and market-size information. 

Efficiency “potential” studies: We use supply curves that include all measures of interest, 

calculate the staged maximum-adoption potential, sort them by ascending CCE, and chart their 

cost versus their accumulated energy savings. 

Benefits estimation. Selecting specific measures and staging the adoption-based potential shows 

the aggregate programmatic benefits of a potential portfolio. 

Each of these approaches provides a unique and valuable “lens” from which to view the potential 

for energy efficiency. This section provides insight into these outputs by examining the 

technology limits and efficiency potential following an analysis of approximately 450 building 

efficiency measures as detailed in Appendix B. 

7.1 Technology Limits 

The technological minimum energy use indicates the total possible energy savings available to 

U.S. energy users regardless of cost for currently available technologies. We include all 

technologies identified in our database of 450 measures including those on the market today, at 

late stages of development, and at early stages of applied research as detailed in Appendix B. 

Building this unconstrained portfolio (i.e., considering all possible energy saving measures) and 

evaluating the potential in aggregate and for each end use reveals a cumulative 80% energy 

savings detailed in Table 5. Some of the most promising technologies contributing to these 

savings in each enduse include: 

Heating: Heat pump technology is poised to transform the way we heat our homes with 

extensions to cold climates (offering COPs of 2 or higher even at 0 degrees Fahrenheit), 

integration of heating, cooling, and air conditioning, and ground source heat pumps (GSHP) 

which have a current “max-tech” COP of 8 (Hughes 2008). Even accounting for the average 

conversion from end-use to primary energy of 3.1 these offer over 60% reduction in energy use 

compared to 100% efficient fuel combustion. 

Cooling: Liquid desiccant presents near term savings of 40-60% (Eric Kozubal 2011) while 

other technologies including thermo-acoustic and thermo-tunneling offer similar or larger 

savings in the long term (Brown Dirks Fernandez & Stout 2010). 

Lighting: CFLs and lighting controls offer savings of about 35% (Navigant Consulting 2012)in 

the near term with LEDs offering considerably higher cost-effective savings of 85% (i.e., 

increasing Lumens per watt to ~259 (Bardsley Consulting et.al. 2012)) 

48
 
 

 

 

 

 

 

 

 

 

 

Water heating: Solar water heaters offer demand reduction of about 50% (70%-90% in hot 

climates) while electric heat pumps offer 50% (Baxter et.al. 2005) improvement in efficiency 

over today’s electric resistance heaters with advanced electric and gas absorption heaters pushing 

that limit to about 80% in the years to come (Hepbasli & Kalinci 2009). 

Envelop improvements: As detailed in Appendix B multiple roofing, window, wall, and 

basement technologies are available today to reduce unwanted thermal losses and gains of each 

element by 50% or more. 

Cross-cutting: Design, control, operational, and maintenance improvements detailed in 

Appendix B also offer 10-20% savings that cut across multiple enduses. 

Also, for each end use, use our staging procedure to consider the cumulative impact of all 

technologies. If we accumulate all savings regardless of CCE we produce the technical energy 

efficiency savings possible after deploying all measures. It is important to note that this 

“technology limits” analysis provides only a perspective on what energy savings are possible 

with fully unconstrained thinking (i.e., no treatment of cost, markets, or government role in 

driving energy efficiency). It does, however, highlight how far the United States is from 

engineering and thermodynamic limits to its energy use, which suggests that continued 

innovation, research, and development is warranted (American Energy Innovation Council 

2011). It should be further noted that this is likely an underestimate as more energy saving 

technologies will likely emerge over time. An in-depth treatment of technologies by end use is 

the subject of a paper in preparation. 

49
 
 

 

Table 5. Technical Energy Savings Possible in 2030 with R&D and Full Deployment 

Efficiency “Potential” Studies 

By using the full-adoption-based and bass-adoption-based scenario outlined above we can 

generate efficiency supply curves showing available energy savings and the cost of those savings 

in a fashion similar to seminal reports of the past three decades (SERI 1982; Meier Wright 

Rosenfeld & H. 1983; Interlaboratory Working Group on Energy-Efficient and Clean-Energy 

Technologies 2000; Siddiqui 2009; National Academy of Sciences 2009; Choi-Granade 2009). 

50
 
 

 

 

 

 

 

 

 

 

 

 

 

By specifying which measures to include we can develop appropriate goals and a corresponding 

long-term vision for a portfolio of investments. In particular we interpret: 

Ultimate savings: the full-adoption potential represents the ultimate (albeit unrealistic) energy 

savings from the studied measures. Actually capturing this economically attractive potential 

would require a society-wide push (Choi-Granade 2009). 

Captured savings: the Bass-adoption-based potential represents the savings that may be 

captured with existing policy, statutory authority, and investment. Other stakeholders (e.g., 

manufacturers, utilities, state and local governments, and end users) must remain active partners 

to realize these savings (Siddiqui 2009). 

Figure 8 shows one realization of the “ultimate savings” supply curve as determined by 

analyzing the measures accordingly and taking a snapshot of the data in 2030. The blue dotted 

line indicates a CCE of $0.00/MMBTU (i.e., measures below this line justify their investment by 

savings capital and maintenance costs alone), and the green dotted lines indicate the typical 

range of energy costs today. By comparing each measure’s CCE to the cost of energy it is saving 

(i.e., fuel and time-of-day specific pricing) and selecting those with a lower CCE than energy 

price we derive the conventional “economic (energy) potential” which shows a possible annual 

primary energy savings of 55% (23.7 quads) in 2030. Also, by integrating the total present value 

investments of all cost effective measures in this curve and dividing by the total present value 

energy savings of all cost effective investments we obtain an average CCE of $1.30 suggesting 

an average payback time of 2 years. Naturally there are significant uncertainties and variables 

that impact this ultimate savings. We briefly explore below: 

• The impact of technology selection set 

• Variations in discount factor 

• The impact of the cost of energy 

• Uncertainty in measure savings 

• Uncertainty in measure price 

• Impact of “rebound” effects. 

51
 
 

 

 

 

 

 

 

 

 

Figure 8. Energy efficiency supply curve showing annual economic savings in 2030. 

7.1.1 Technology Selection Set- reproducing Related Work 

It is important to note that the ultimate savings potential identified in figure 8, unlike others 

published to date, includes significant expected technology development in addition to measure 

deployment and codes and standards activity. In fact, if we include all emerging technologies 

expected to reach the market within five years and include cost-effective and not-cost effective 

measures the energy savings grows to 72% (30.6 Quads). 

The inclusion of emerging technologies departs from the methodology of many of the historic 

analyses of energy efficiency potential. When we limit the measures the tool analyzes to only 

currently-available, cost-effective measures, as was done most recently in two influential reports 

(Choi-Granade. 2009) (National Academy of Sciences 2009), and use the same discount rate 

(10%) and energy price for cost effectiveness14 as used in those reports we reproduces the ~30% 

energy savings potential identified in those reports. Table 6 summarizes the efficiency potential 

of the five efficiency potential analyses mentioned in this section, namely the technical; 

economic; emerging technology, full-adoption; National Academies approach; and McKinsey 

approach. 

14 National Academies indicates in Table 1.2: “Levelized cost of energy savings is less than the national average 

electricity and natural gas prices" while McKinsey states: "Levelized cost of energy savings is less than the national 

average electricity and natural gas prices." 

52
 
 

 

 

 

 

 

 

Table 6 shows the considerable energy savings available through five analyses. National 

Academies (National Academy of the Sciences; National Academy of Engineering; National 

Research Council 2009) and McKinsey (Choi-Granade 2009) approaches attempt to repeat the 

analysis presented in their respective reports; they reproduce the ~30% cost effective savings 

found therein. Economic extends this analysis to include technologies expected to enter the 

market and be cost effective within five years. “Emerging technologies, full adoption” and “Full 

technical” represents the potentials of emerging technologies and all analyzed technologies 

regardless of cost-effectiveness. 

Table 6. Energy Savings Identified in Five Analyses 

. 

7.1.2 Variations in Discount Factor Significantly Change the Potential 

Discount factors express how future costs and energy savings are valued in today’s terms as used 

in the equations for LCOE and CCE discussed in section 6.1. A brief discussion of the various 

choices for discount rates is presented there. The following figure shows the impact on the 

average cost of conserved energy, economic potential, and full-adoption-based potential as we 

vary the discount factor. One can observe that the difference between the social (3%) and 

behavioral (40%) discount-rate-based potentials of 7,300 TBTUs/year in 2030 goes a long way 

(i.e., accounting for 31% of the potential) to explaining why an efficiency gap exists. Similarly 

the difference between the financing and social discount-rate-based potentials of 5,200 TBTUs 

suggests the portion of this gap (i.e., 22%) that financing mechanisms could address. 

53
 
 

 

 

 

 

 

 

Table 7. Variation of Economic-potential Scenario with Changes in Discount Factor 

In Table 7, economic potential represents energy savings from all currently available and emerging technologies that 

are cost-effective; it uses the full-adoption-based potential. 

7.1.3 Increasing Energy Prices Drive a Corresponding Increase in Potential 

Using our economic potential scenario which gave 53% as the efficiency potential of costeffective technologies including emerging technologies we can vary energy prices of fuels 

independently to understand the sensitivity our findings to energy prices. Table 8 presents our 

findings for ten scenarios that vary energy prices from the AEO 2010 business-as-usual as 

follows: 

-50%: decreases all fuel prices by 50% from the AOE 2010 baseline 

Low gas: drops the forecast natural gas by 50% to $6.70 $/MMBTU in 2030 

High gas: doubles the forecast natural gas price to $26.90 $/MMBTU in 2030 

+50%: increase all fuel prices by 50% from the AEO 2010 baseline 

The resulting efficiency potentials are shown in Table 8 below. 

Table 8. Variation of Economic-potential Scenario with Changes in Energy Price 

54
 
 

 

 

 

 

 

 

7.1.4	 Uncertainty in Savings Can Impact Measure Selection 

Most references consulted are unable to provide robust uncertainty estimates for their 

technology- or national-impact- analysis. Provided these uncertainties are not correlated we 

would anticipate that our conclusion of economic-potential and technical potential are robust to 

errors in savings estimates of individual measures. To test this theory we introduced 20% 

uncorrelated errors to each measures savings estimate by multiplying the percent savings of each 

by a Gaussian-distributed factor with mean 1.0 and standard deviation 0.2, recalculating their 

measure energy savings, and restaging the analysis. Eight such Monte-Carlo realizations 

revealed a 2.4 percentage point standard deviation in the economic potential; that is to say our 1-

sigma confidence interval accounting for savings uncertainty is 52.9%-57.7%. 

7.1.5	 Uncertainty in Measure Cost can Also Impact Measure Selection and Total 

Savings Potential 

We repeated this procedure varying only the measure cost with the same Gaussian-distributed 

factor to reveal a 1-sigma variance of only 0.8 percentage points. Thus we expect a 1-sigma 

confidence interval accounting for cost of 54.5%-56.1%. Assuming these two uncertainties are 

uncorrelated suggests a total uncertainty of our economic potential of 2.5%; using the expected 

value of 55.3% suggests a 98% chance the economic potential exceeds 50%. 

7.1.6	 Rebound Effects Convert Savings to Value 

The engineering efficiency analysis presented herein was primarily developed to provide analytic 

insights in support of strategic planning and budget impact analysis. It is not meant to forecast 

expected future energy use but calculate possible future scenarios to enable decision-making. 

However it is worth noting that in this “market-engineering analysis,” any macro-economic 

effects such as price elasticities, direct rebound, indirect rebound, or other macro-economic 

effects were not included. The potential impacts of these effects include: 

Direct rebound includes increases in service demand “directly” because of improved efficiencies. 

Studies have shown that it varies from 0-40% by end use (Sorrell Dimitropoulos & Sommerville 

2009). Weighting the enduse specific estimates by their share of energy savings provides a range 

of 2%-23% with a central value of 13%. Incorporating these impacts reduces the adoption-based 

potential energy savings from 30% to 23%-28% and increase CCE by 4%-32%. However, note 

that in many of these cases, the services delivered (e.g., Lumen-hours per square foot of 

illuminated space or hours of comfortable in-building environment provided) have increased. 

This represents capture of a net benefit: service demand and therefore quality of life or utility 

have increased. 

Indirect rebound represents increased energy use in the economy as the expenditures saved 

through efficiency measures are spent on other goods and services. Energy is used to create 

these goods and deliver these services; this is often referred to as “imbedded” energy. We 

estimate this as the ratio of total U.S. energy expenditures to total U.S. gross-domestic product; 

this is approximately 8% (Energy Information Agency 2011). One would thus expect indirect 

rebound of 8% (i.e., expenditures formerly 100% energy are now 8% energy). This effect alone 

would reduce the energy savings to 18,490 TBTUs/year and increase CCE to $2.98 however, this 

also represents capture of a net benefit. 

55
 
 

 

 

 

 

 

 

 

 

Price elasticities and macro-economic effects are outside the scope of this analysis; there is no 

rigorous empirical observation (i.e., other than computer simulation) demonstrating the scale of 

these effects. 

It is worth noting that 2010 total energy expenditures of $1,205 Billion included $250 Billion in 

residential and $178 in commercial expenditures (Energy Information Agency 2012). It is 

apparent that significant financial savings can result by capturing these energy savings. Lacking 

a robust estimate of GDP and energy expenditures that account for the savings possible in 2030 

we apply the percentage savings identified in table 2 to 2010 expenditures and provide the 

resultant savings in table 5. Note that this does not account for market dynamics that could 

significantly change the U.S. energy system and expenditures if such massive efficiency 

potential were captured. 

Table 9 shows the direct and indirect rebound effects that are likely to reduce the energy savings 

identified in our analysis. Applying end-use specific direct-rebound effects (Sorrell 

Dimitropoulos & Sommerville 2009) reveals the impact of the 13% average reduction. 

Similarly, assuming financial savings are reinvested in line with current spending patterns and 

attributing the resultant 8% indirect rebound effect further reduces savings. Ultimately, we apply 

the savings net of these rebound effects to 2010 expenditures to reveal the possible annual 

reduction in expenditures assuming no major macro-economic changes. 

Table 9. Direct and Indirect Rebound Effects Likely to Reduce Energy Savings 

56
 
 

 

 

 

 

 

8 Conclusions 

This toolset provides a comprehensive set of calculations needed to determine the prospective 

benefits from energy efficiency measures under the simplifying assumptions detailed above. It 

provides an objective analytic framework, a landscape of measures, and a level playing field 

upon which these measures can compete. The tool enables one to vary input parameters of 

unknown certainty and examine the results through sensitivity analyses. The outputs from the 

analysis can be viewed from various perspectives such as technical limits or efficiency potential. 

Thus a portfolio analysis might use various “lenses” beyond this to examine the potential effect 

of staging or the influence of adoption on market penetration and therefore impact. What results 

is a prioritized list of opportunities worthy of further investigation, a catalog of opportunities that 

are likely of low priority and an extensive dataset with which to compare a broad range of 

opportunities. For DOE, developing a programmatic strategy may require additional 

investigation, such as in-depth market/supply chain analysis and an assessment of the 

appropriateness of DOE’s role in intervening with the development and deployment of a 

particular measure. A future publication will provide a more in-depth presentation of results and 

discussion, highlighting those sectors and technologies that are likely to have the greatest 

influence on U.S. energy efficiency. Essentially, we aspire to keep this tool “evergreen” to easily 

aggregate available opportunities, compare those opportunities on a level playing field, and track 

progress toward a more energy efficient future. 

57
 
 

 

 

 

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Appendix A: Baseline Assumptions 

The Energy Information Agency (EIA) produces the Annual Energy Outlook to provide multiple 

perspectives on the future of energy supply and demand in the United States. It uses the National 

Energy Modeling System (NEMS) to project the production, imports, conversion, consumption, 

and prices of energy through 2030. Extensive documentation is available at 

http://205.254.135.24/oiaf/aeo/overview/ where the following is summarized: 

“The National Energy Modeling System (NEMS) is a computer-based, energy-economy 

modeling system of U.S. through 2030. NEMS projects the production, imports, 

conversion, consumption, and prices of energy, subject to assumptions on 

macroeconomic and financial factors, world energy markets, resource availability and 

costs, behavioral and technological choice criteria, cost and performance characteristics 

of energy technologies, and demographics. NEMS was designed and implemented by the 

Energy Information Administration (EIA) of the U.S. Department of Energy (DOE).” 

We use the detailed output spreadsheets that provide detailed stock and end-use energy 

information disaggregated by end use, fuel type, equipment class (i.e., technology), building 

type, and census division. These spreadsheets provide a consistent and comprehensive basis from 

which to compute the energy use each measure can address (i.e., its “market”). Additional 

resources, both general and specific to each measure, allow us to aggregate or further divide this 

dataset to best represent the market each measure can impact. The general resources used for 

multiple measures include the Commercial Building Energy Consumption Survey (CBECS), 

2003; the Residential Energy Consumption Surveys (RECS) of 2005 and 2009; and the Building 

Energy Data Book (BEDB) of 2009. 

The measure entitled “R: Poor attics to R-40” provides a useful example demonstrating how we 

create a complicated market. This measure applies to energy lost through roofs in all homes built 

prior to the period of analysis (taken as 2010) that have insufficient attic insulation in their attics. 

We compute the stock and energy use in this market as follows: 

We begin with the total stock of homes in the NEMS output files. 

Using the average home lifetime of 80 years provided by RECS, we can determine the number of 

homes each year remaining in the stock (i.e., we reduce the stock in year y by 1/80th of the stock 

in year y-1). 

We also use RECS, 1987 (updated to the current day) to estimate that 64% of these existing 

homes have not had their insulation improved; this provides the total stock and a scaling factor, f. 

Finally, we use the BEDB (2009) to note that 16% of heating and 15% of cooling energy is 

attributed to losses through the roof. As a result, the “market” of energy use for this measure is 

f × (15% × C + 16% × H), where C and H are the total cooling and heating building energy 

use, respectively. 

NEMS provides projected consumption estimates through 2035. We would like to remove “edge 

effects” in calculating the savings and economic benefits and estimate the expected deployment 

of the advanced measure in the baseline. To do this, we use a three-parameter fit as described in 

61
 
 

 

Section 3.3, which extends the data to 2100 and estimates the diffusion of the advanced measure 

in the base case. 

Finally, as noted in section 6.2.3, we do estimate markets twice: once using this method without 

reference to or constraints by any other measures, and a second time using the micro-segment 

approach (as detailed in section 2). In this example our two methods estimate energy lost 

through poorly insulated attics as 595 and 597 TBTUs, respectively; a difference of only two 

TBTUs and agreement of approximately 0.3%. 

62
 
 

 

 

 

 

 

Appendix B: Input Table 

Table 2 provides the essential elements of the “input” table as gathered from the hundreds of 

sources outlined in the bibliography. The fields in this table include: 

Description of the measure: provides the sector (“R:” for residential; “C:” for commercial; 

“O:” for industrial, outdoor, or other; and “A:” for all) and a brief measure description (limited to 

32 characters total). Note that “no FS” indicates fuel switching (e.g., from gas in the base case to 

electricity after deploying the measure) that is not included while “FS only” includes only fuel 

switching. 

Energy savings: expresses the percentage savings over the baseline. The column labeled “SD?” 

indicates if this measure is represented as service “demand reducing“ or as an “equipment 

upgrade” (see discussion in Section 3.2.2). The column labeled “Fuel switch?” indicates if the 

measure changes the service from using one fuel to another (e.g., replacing a gas furnace with a 

ground-source heat pump). 

Price: represents the present value of the price difference per unit between the existing mix of 

“inefficient” technologies and measure being analyzed; the units are provided in the adjacent 

column. 

Units & capacity: indicates the assumed typical equipment size or quantity needed per unit 

stock; also provides the units (e.g., per house, per square foot of building space, or per lamp) 

used in columns “3. Price” and “6. Market size, 2030”. 

Market description: provides a brief description of the market. 

Market size, 2030: indicates the stock in the market in 2030 expressed in units as provided in 

“4. Capacity” above. 

Site use: indicates the site, also known as end use, energy in trillion Btu (TBTUs) per year in 

2030. 

Source use: indicates the source, also known as primary or total, energy in TBTUs per year in 

2030. 

Max adopt: indicates the maximum-adoption potential in source energy in TBTUs/year in 2030. 

CCE: indicates the cost of conserved energy in $/MMBTU, source. This does include cost 

compression as calculated in the economic potential scenario. 

Life: provides the average lifetime, in years, of the existing mix of alternatives in the market the 

measure addresses. 

Also, “Primary sources uses” provide the source or sources from which numbers used to 

calculate savings were directly drawn. “Data used and/or methodology” briefly explains how 

those data were used to represent the measure in our tool. “Other supporting sources” provides a 

list of other sources that informed our treatment of each measure. 

63
 
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

C: Develop automated whole 13% Yes $2.04 per square foot C: HVAC, light­ 103,945 4,728 10,509 266 $15.60 10 No No No No 

building diagnostics ing, and half of 

refrigeration 

C: Optimize whole-building 28% Yes $2.36 per square foot C: All energy use in 35,538 3,247 7,854 2,197 $2.53 10 No No Yes Yes 

controls commercial buildings 

>100,000 ft2 

R: Develop effective full-home 6% Yes $600 per home R: All consumption 141 11,750 23,242 1,041 $3.39 20 No No Yes Yes 

automation systems 

R: Improve billing information 3% Yes $9 per unit R: All consumption 141 11,750 23,242 686 $0.46 1 No No No Yes 

to reduce energy waste and 

accelerate efficient equipment 

deployment 

C: Develop R-10 windows 84% Yes $10.66 per square foot; 

wall: floor space of 

.9, window fill ratio 

of 0.4 

C: Window conductive loss 103,945 732 736 304 $104.88 40 No No Yes Yes 

C: Add aerogel insulaton (i.e., 

R-21 assembly) to walls 

47% Yes $2.37 per square foot; 

wall: floor space of 

.9, window fill ratio 

of 0.4 

C: Heating and cooling from walls 93,550 739 857 255 $29.65 40 No No Yes Yes 

C: Add vacuum insulated panels 

(VIP) to walls 

75% Yes $4.16 per square foot; 

wall: floor space of 

.9, window fill ratio 

of 0.4 

C: Heating and cooling from walls 93,550 739 857 402 $32.99 40 No No Yes Yes 

C: Develop cool roofs with 15% Yes $0.16 per square foot; C: Commercial 49,772 423 1,292 170 $2.30 20 No No Yes Yes 

surface reflectance of 0.75 for 3.7 floors/building cooling in hottest 2 

use in hot climates average climates 

C: Develop dynamic windows 44% Yes FS $4.32 per square foot; C: Commercial solar 103,945 270 823 182 $62.87 40 No No Yes Yes 

for use in existing buildings wall: floor space of heat gain cooling 

(excludes equipment downsiz­ .9, window fill ratio load 

ing benefit) of 0.4 

C: Develop dynamic windows 44% Yes $2.52 per square foot; C: Solar heat gain 50,966 132 404 144 $41.02 40 No No Yes Yes 

for use in new buildings (in­ wall: floor space of cooling in new 

cludes equipment downsizing .9, window fill ratio 

benefit) of 0.4 

C: Develop insulating paints 3% Yes $0.01 per square foot; C: Heating and cool­ 93,550 739 857 18.9 $4.38 10 No No Yes No 

(i.e., add R-0.75 per surface wall: floor space of ing from walls 

painted) .9, window fill ratio 

of 0.4 

C: Further increase use and 11% Yes $0.08 per square foot C: All office HVAC 17,749 284 866 99.6 $0.76 20 No No No No 

performance of dedicated outdoor air systems (with Energy 

recovery) 

C: Implement predictive heat­ 10% Yes $0.25 per square foot; C: Commercial HVAC 103,945 3,192 3,601 340 $3.88 20 No No No No 

ing/cooling algorithms 1 zone per 1,500 

sq. ft. 

C: Improve duct routing prac­ 16% $0 per square foot C: All furnaces and 50,966 875 1,278 142 $0 60 No No No No 

tices in new buildings A/C heating & cooling (i.e., ducts) in 

new buildings 

C: Incorporate vestibules for 54% Yes $0.78 per square foot; C: No vestibule 51,972 306 447 260 $6.49 40 No No No Yes 

building entrances two vestibules per infiltration 

15,000 sq. ft. 

C: Increase duct insulation from 9% Yes $0.32 per square foot C: New buildings, 5,097 137 248 18.6 $6.49 40 No No No Yes 

R-4 to R-8 in new buildings heating & cooling 

uninsulated ducts 

C: Increase rate of retro­ 16% Yes $0.30 per square foot C: Commercial HVAC 103,945 3,192 6,017 904 $3.08 5 Yes Yes No Yes 

commissioning commercial 

buildings 

C: Increase roof insulation (i.e., 42% Yes $0.28 per square foot; C: Heating and cool­ 52,979 226 269 108 $7.90 20 Yes Yes Yes Yes 

R-30) in existing buildings 210,800 ft2

 school ing loads through 

attics, existing 

commercial 

64
Primary sources used Data used and/or methodology Other supporting sources 

TIAX. Energy Impact of Commercial Building Controls and Performance “it could reduce building energy consumption by 5-20%” The average of 5% 

Diagnostics?: Market Characterization, Energy Impact of Building Faults and and 20% was used. Cost estimated to be the same as 1.5 EMS (1.5*$1.36/ft2

) 

Energy Savings Potential. Construction 413 (2005). (see Measure 719). The baseline is assumed to have no EMS. 

TIAX. Energy Impact of Commercial Building Controls and Performance 

Diagnostics?: Market Characterization, Energy Impact of Building Faults and 

Energy Savings Potential. Construction 413 (2005). 

TIAX: “Heating: 10%, Cooling 20%, Ventilation 0%”. “Overall it would appear 

the first cost of an OWBCS associated with an EMCS would be similar to the 

cost of a higher-end Whole Building Diagnostics implementation less the 

cost of additional sensors, i.e., on the order of $100000”. Installed cost of 

an EIS for a 100,000 ft2

 office building was $136,000. Assumes an EMS is 

required ($136,000) plus an additional $100,000 for the OWBCS controls/ 

sensors/software. Assumes 20% savings for EMS alone followed by 10% for 

OWBCS. 

Cost: Williams, Matthews, Breton, and Brady; “Use of a Computer-Based Use average of $1000 today and cost compressed $200 to get $600 Wireless home automation networks: A 

System to Measure and Manage Energy Consumption in the Home” IEEE survey of architectures and technologies 

2006 

Cripps, A., Raw, G. & Ross, D. Energy Demand Research Project: Final Analy- The particular combination of advice and historic feedback on consumption Darby, S. The Effectiveness of Feedback on 

sis Approved by. 179 (2011). that EDF deployed (along with smart meters) reduced electricity consump- Energy Consumption: A Review For Defra 

tion by 2.3% overall3 in the first in-trial year.4 The effect was persistent into of The Literature On Metering, Billing and 

the second in-trial year (4.0% saving). Used: Average of two years at 2.3% Direct Displays. Change 24 (2006). 

and 4% 

6. Apte, J. & Arasteh, D. Window-Related Energy Consumption in the US See tables 4 and 7 for use and savings. Calculated from first principles; costs Arasteh, Dariush; Selkowitz, Steve; Apte, 

Residential and Commercial Building Stock. Buildings 1-38 (Berkeley, CA, from NREL bottom-up VIG build Josh; LaFrance, Marc. Energy Impacts of 

2006). Personal communication: various window experts Today’ s Window Stock 

Savings: first principles increasing R-value by 10 per inch to R-21 assembly 47% savings (increase assembly from R-11 to R-21) at $2.37/sq. ft. 

Cost: Personal communication Symposium participants: $4.39/sq. ft. floor 

space of retrofit. Aspen Aerogel SpaceloftTM: A Revolution in Building and 

Construction Insulation. Aspen Aerogels (2001) 

S.J. Rusek et al, CSI Elevated Temperature Conductivity and Rapid Con- Savings: first principles. Costs: personal communication from symposium 

ductivity Test Method, Quietflex Goodman Global, USA; 10th International participants 

Vacuum Insulation Symposium (9/2011). A. Parekh et al., Incorporation 

of Vacuum Insulation Panels in a Wood Frame Net Zero Energy Home, 

National Resources Canada, Canada; 10th International Vacuum Insulation 

Symposium (9/2011) 

Konopacki, S., Akbari, H., Pomerantz, M., Gabersek, S. & Gartland, L. Cooling Supporting calculations in tool 

Energy Savings of Light-Colored Roofs for Residential and Commercial 

Buildings in 11 U.S. Metropolitan Areas. 117 (1997). 

Lee, E.S., Yazdanian, M. & Selkowitz, S.E. The Energy-Savings Potential of ~95 TBTUs savings at 40% market penetration Arasteh, D.; Selkowitz, S.; Apte, J.; “Zero 

Electrochromic Windows in the US Commercial Buildings Sector. Building Energy Windows”; 2006 ACEEE Summer 

1-42 (2004). Cost: Personal communication - manufacturers and experts Study on Energy Efficiency in Buildings 

Lee, E.S., Yazdanian, M. & Selkowitz, S.E. The Energy-Savings Potential of ~95 TBTUs savings at 40% market penetration Arasteh, D.; Selkowitz, S.; Apte, J.; “Zero 

Electrochromic Windows in the US Commercial Buildings Sector. Building Energy Windows”; 2006 ACEEE Summer 

1-42 (2004). Cost: Personal communication - manufacturers and experts Study on Energy Efficiency in Buildings 

First principles; K value of 0.1 W/m/K, 50 mils paint thickness on 2 surfaces Increase R-value of standard walls by 0.4 at a cost of current paint additives Nansulate claims K=0.017, likely arising from 

spurious testing as this rivals vacuum 

BA Thornton; W Wang; Y Huang; MD Lane; B Liu; “Technical Support Docu- Calculated from data in chapter 5 

ment: 50% Energy Savings for Small Office Buildings” 

Savings: Cho, Energy Conservation and Management 44 (2003); The results show that use of the predictive control strategy could save 

Cost: Nest (http://www.nest.com/inside-and-out/) between 10% and 12% energy during the cold winter months. The energy 

savings are somewhat higher during mild weather conditions. 

Southface Energy Institute, FEMP Technology Alert: Air Distribution System Southface: “The efficiency of air distribution systems has been found to 

Design, 2003 be 60-75% or less; Properly designed and installed duct systems can have 

efficiencies of 80% or more for little or no additional cost.” Savings estimate 

is 16%=1-0.675/0.8 

Elaine Hale, Matthew Leach, Adam Hirsch, and Paul Torcellini, “General Reduce the front door infiltration from 0.253 to 0.158 ACH. The cost... replac­ http://www.hortondoors.com/content. 

Merchandise 50% Energy Savings Technical Support Document”; NREL/ ing two, 8-ft tall sliding doors with a total surface area of 120 ft2

 with four, aspx?cid=1232 

TP-550-46100 September 2009 7-ft tall sliding doors and adding 30 linear feet of interior walls: $5,853 

Cost: NREL retrofit measure database. Savings: First principles of moving LBNL. 2011. Thermal Energy Distribution 

ducts from R-4 to R-8 as per recent code changes Website. ducts.lbl.gov. 

“Building Commissioning: A Golden Opportunity for Reducing Energy Costs Table 3 indicates total primary energy savings in existing buildings of 16% Mills, E., Friedman, H., Powell, T., Bourassa, 

and Greenhouse Gas Emissions”, Mills, E.; Energy Efficiency (2011) 4:145–173 N., Claridge, D., Haasl, T., & Piette, M. A. 

(2004). “The cost-effectiveness of commercial-buildings commissioning: A metaanalysis of energy and non-energy impacts 

in existing buildings and new construction 

in the United States.” Lawrence Berkeley 

National Laboratory Report No.56637 

http://cx.lbl.gov/2004-assessment.html. 

NREL. 2011. Advanced Energy Retrofit Guide for K-12 Schools (Draft). EERE $108,2915 total cost, $59,858 incremental cost for replacing insulating 

Report DOE/GO-102011-3467. Project Lead: Robert Hendron sheathing with R-24 for 210,800 ft2

 school in Chicago, assumes roof is being 

replaced anyway. Energy savings based on increasing R-14 to R-24 EPS. 

65
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

C: Increase roof insulation (i.e., 50% Yes $0.88 per square foot; C: Heating and cool­ 50,966 218 258 129 $25.16 20 No No Yes Yes 

to R-30) in new buildings 45,000 ft2

 grocery ing loads through atstore, 460,000 ft2 tics, new commercial 

office, 50,000 ft2 

retail 

C: Increase use of current­ 5% Yes $0.03 per square foot; C: Commercial 49,772 423 1,292 68.6 $1.07 20 No No No No 

generation cool roofs in hot 

climates 

C: Increase use of efficient 

window attachments 

54% Yes $0.51 

3.7 floors/building 

average 

per square foot; 14 

windows / 3000 

sq. ft. 

cooling in hottest 2 

climates 

C: Windows that 

can benefit from 

attachments 

51,972 501 767 251 $3.45 40 Yes Yes Yes Yes 

C: Install energy recovery ventilators (ERV) in new buildings 26% Yes FS $0.92 per square foot C: All HVAC in new 

buildings 

50,966 1,565 2,950 867 $3.78 20 Yes Yes Yes Yes 

C: Install low-e storm windows 

C: Install low-e window films (in 

all climates) 

C: Paint exterior walls with a 

“cool” paint color 

C: Reduce window area by 20% 

in new buildings 

58% 

59% 

6% 

9% 

Yes 

Yes 

Yes 

Yes 

$0.36 

$0.81 

$0 

-$0.19 

per square foot; 

wall: floor space of 

.9, window fill ratio 

of 0.4 

per square foot; 

wall: floor space of 

.9, window fill ratio 

of 0.4 

per square foot 

per square foot; 

50,000 ft2

 retail 

store 

C: Radiative and 

conductive window 

load - single pane, 

primary energy 

C: Non low-e glass in 

pre-2010 buildings 

C: Commercial 

cooling in hottest 2 

climates 

C: All energy use 

in new commercial 

buildings 

55,091 

19,519 

49,772 

50,966 

694 

43 

423 

999 

1,029 

133 

1,292 

2,415 

658 

104.8 

41.5 

108 

$1.24 

$8.71 

$0 

-$9.60 

40 

40 

10 

20 

No 

No 

No 

No 

No 

No 

No 

No 

Yes 

No 

No 

Yes 

Yes 

No 

Yes 

Yes 

C: Require building commissioning in new buildings 13% Yes $0.30 per square foot C: All HVAC in new 

buildings 

50,966 1,565 2,950 313 $4.77 10 Yes Yes Yes Yes 

C: Seal ducts in existing 

buildings 

20% Yes $0.41 per square foot C: heating & cooling 

in existing buildings 

w/unsealed 

47,681 835 1,514 184 $4.15 40 No No Yes Yes 

C: Use “standard” duct sealing 

in new buildings 

10% Yes $0.33 per square foot C: All furnaces and 

A/C heating & cooling (i.e., ducts) in 

new buildings 

50,966 875 1,650 149 $8.41 40 No No Yes Yes 

C: Use best available duct 

sealing (i.e., beyond codes) in 

existing, large buildings 

16% Yes $0.33 per square foot C: Commercial heating and cooling in 

large existing buildings (>10,000 ft2

) 

42,648 1,045 1,678 230 $3.66 10 Yes Yes Yes Yes 

C: Use best available duct 

sealing (i.e., beyond codes) in 

existing, small buildings 

5% Yes $0.33 per square foot C: Commercial heating and cooling in 

small existing buildings (<10,000 ft2

) 

10,331 247 396 14.8 $12.57 10 No No Yes Yes 

C: Use best available duct sealing (i.e., beyond codes) in new, 

small buildings 

5% Yes $0.33 per square foot C: Commercial heating and cooling in 

small new buildings 

(<10,000 ft2

) 

9,938 237 381 14.1 $26.70 10 No No Yes Yes 

66
Primary sources used Data used and/or methodology Other supporting sources 

Leech, M. et. al.; “Grocery Store 50% Energy Savings Technical Support 

Document”, National Renewable Energy Laboratory (2009). Leech, M. et. 

al.; “Technical Support Document: Strategies for 50% Energy Savings in 

Large Office Buildings”, National Renewable Energy Laboratory (2010). 

Hale E. et. al.; “Technical Support Document: Development of the Advanced 

Energy Design Guide for Medium Box Retail—50% Energy Savings”, National 

Renewable Energy Laboratory (2008) 

Costs and performance drawn from upgrading ASHRAE code roof insulation 

to most cost effective point from indicated source. R-15 to R-30 upgrade, 

$0.88/sq. ft. average cost 

Konopacki, S., Akbari, H., Pomerantz, M., Gabersek, S. & Gartland, L. Cooling Supporting calculations in tool 

Energy Savings of Light-Colored Roofs for Residential and Commercial 

Buildings in 11 U.S. Metropolitan Areas. 117 (1997). 

Savings: first principles calculation from R- and SHGC- impacts of product. Savings: adding R_value of 1.75 and reducing SHGC to 0.35. Price: Comfor- Kotey, N.A., Wright, J.L., Barnaby, C.S., 

Price: Comfortex. Manufacturer & expert interviews tex current prices compressed 1/3rd to represent learning curve. Market: Collins, M.R., “Solar Gain Through Windows 

assumes 50% of windows benefit from attachments with Shading Devices: Simulation versus 

Measurement,” ASHRAE Transactions, Vol. 

115, Pt. 2, (2009) 

Chiras, D. 2009. Energy-recovery Ventilators: Ventilate Your Home with Minimal Energy Loss. Mother Earth News. http://www.motherearthnews.com/ 

Green-Homes/Energy-Recovery-Ventilator.aspx?page=4#ixzz1yRuXsH6b 

U.S. Department of Energy. 2005. Residential Energy Consumption 

Survey. http://205.254.135.7/consumption/residential/data/2005/hc/ 

hcfloorspace_char/pdf/tablehc1.1.2.pdf 

Chiras: “Energy-recovery ventilators are a well-developed technology. Their 

prices range from about $500 to $1,700, not including installation.”. “Most 

energy-recovery ventilation systems on the market today recover about 70 

to 80 percent of the energy in the outgoing air, transferring it to the incoming air.” DOE RECS: Average ft2

 = 2170. Cost estimates for residential ERVs 

are close to $2000 installed based on Chiara article, assuming a high-end 

multi-point system. Average house is 2170 ft2

. Assumes 70% reduction of 

infiltration load, which is 37% of the heating load. 

NREL. 2011. Advanced Energy Retrofit 

Guide for K-12 Schools (Draft). EERE Report 

DOE/GO-102011-3467. Project Lead: Robert 

Hendron 

Kohler, C. Letter report for low-e storm window retrofits, 2006 “A simulation study in 2004 has shown that by using pyrolitic “hard” low-e 

coatings will increase from approximately 15% of the home’s heating energy 

to 18%. The additional cost is...on the order of $100.” Note: 18% of home 

heating/cooling is 58% of window load heating/cooling 

Huang, J. Computer Simulation Analysis of the Energy Impact of Window Tables 12-16 used to generate estimate of population average. Simplified to 

Films In Existing Houses. Selkowitx, S. Personal communication SHGF and used vs. existing baseline. Improving SHGF from 0.74 to 0.30 on 

windows incurring the greatest load 

Tom Petrie, Jerry Atchley, Phil Childs, and André Desjarlais; “Energy Savings “Savings are 4-9% compared to non-IR reflecting walls” Additional support by running HES Pro in 

for Stucco Walls Coated with Cool Colors” ORNL (12/2007) multiple climate zones to verify savings 

Hale: 1000 ft2

 window area in baseline, $47.23/ft2 220. Hale, E., Leach, M., Hirsch, A. & Torcellini, P. General Merchandise 50% . Assumes 20% reduced 

Energy Savings Technical Support Document General Merchandise 50% window area contributes 50% of total energy savings associated with glazEnergy Savings Technical Support Document. 188 (2009). http://www.nrel. ing area, which averages about 8% of whole-building energy use. 

gov/docs/fy08osti/42828.pdf. 

“Building Commissioning: A Golden Opportunity for Reducing Energy Costs Table 3 indicates total primary energy savings in new buildings of 13% Mills, E., Friedman, H., Powell, T., Bourassa, 

and Greenhouse Gas Emissions”, Mills, E.; Energy Efficiency (2011) 4:145–173 N., Claridge, D., Haasl, T., & Piette, M. A. 

(2004). “The cost-effectiveness of commercial-buildings commissioning: A metaanalysis of energy and non-energy impacts 

in existing buildings and new construction 

in the United States.” Lawrence Berkeley 

National Laboratory Report No.56637 

http://cx.lbl.gov/2004-assessment.html. 

Modera, M. Fixing Duct Leaks in Commercial Buildings ASHRAE Journal 

June 2005. TIAX. Energy Impact of Commercial Building Controls and Performance Diagnostics?: Market Characterization, Energy Impact of Building 

Faults and Energy Savings Potential. Construction 413 (2005). 

Modera: 80% heating season duct efficiency (See Figure 2 and measured 

duct efficiency data cited throughout the article. 7.5 cents/ft2

 added for 

diagnostic testing (TIAX 2005). Nominal sealing cost is 33 cents/ft2

 for consistency with Measure 92. Energy savings assumed to be double the savings 

in new homes from Measure 92. 

Modera, M. Fixing Duct Leaks in Commercial Buildings ASHRAE Journal June Modera: 80% heating season duct efficiency (See Figure 2 and measured 13. Goetzler, W., Zogg, R., Burgos, J., Hirai2005. Aeroseal website. http://www.aeroseal.com/problem-we-solve/FAQ­ duct efficiency data cited throughout the article. 50% improvement is wa, H. & Young, J. Energy Savings Potential 

commercial.html assumed over the 80% duct efficiency baseline. Price based on average of and RD & D Opportunities for Commercial 

three Aeroseal case studies and estimated payback of 2.5 years ($0.33/ Building HVAC Systems. 289 (2011). 

ft2

=$247*2.5 years/1879 ft2) 

Modera, M. Fixing Duct Leaks in Commercial Buildings ASHRAE Journal June Modera: “Using the power 2.4 from Franconi et al.,5 a 15% leak translates to Goetzler, W., Zogg, R., Burgos, J., Hiraiwa, 

2005. a 40% increase in fan power.” “Researchers at Lawrence Berkeley Laboratory H. & Young, J. Energy Savings Potential and 

measured duct leakage in six large commercial buildings, three of which RD & D Opportunities for Commercial Buildshowed 5% leakage, while the other three showed 15%, 17% and 25% supply ing HVAC Systems. 289 (2011). 

duct leakage.” Assumes 16% savings based on reduction in leakage from 15% 

to 7.5% and exponent of 2.4. Assumes 50% reduction in leakage rate due to 

aerosol sealing. Assumes same cost as Measure 92. 

Modera, M. Fixing Duct Leaks in Commercial Buildings ASHRAE Journal June Modera: 80% heating season duct efficiency (See Figure 2 and measured 

2005. duct efficiency data cited throughout the article. For light commercial, the 

duct efficiency is about 80% in half the buildings (where the ducts are in a 

ceiling plenum outside the thermal boundary). Calculated 5% heating and 

cooling savings (50% reduction in 20% duct losses in 50% of small buildings). Price same as Measure 92. 

Modera, M. Fixing Duct Leaks in Commercial Buildings ASHRAE Journal Modera: 80% heating season duct efficiency (See Figure 2 and measured 

June 2005. ASHRAE. 2010. Energy Standard for Buildings Except Low-Rise duct efficiency data cited throughout the article. For light commercial, the 

Residential Buildings. ASHRAE 90.1 2010. duct efficiency is about 80% in half the buildings (where the ducts are in 

a ceiling plenum outside the thermal boundary). ASHRAE: “Ductwork and 

all plenums with pressure class ratings shall be constructed to seal class A, 

as required to meet the requirements of Section 6.4.4.2.2...” Price same as 

Measure 92. Aeroseal isn’t necessary to meet code, but is assumed for 5% 

improvement beyond code. Given that the code already addresses the most 

serious leakage (high pressure supply leakage in unconditioned space), savings in the range of 5% is assumed (50% reduction in 10% of leakage outside 

conditioned space in code compliant new buildings). 

67
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

C: Use building wrap to reduce 61% Yes $1.26 per square foot; C: New Heating 50,966 300 482 294 $14.05 80 No No No No 

air infiltration in new buildings wall: floor space of and cooling trough 

.9, window fill ratio infiltration 

of 0.4 

C: Use low pressure drop air 25% $0.04 per square foot; C: Commercial 23,180 451 1,377 347 $0.24 6 No No Yes Yes 

filters in ducted systems represents 6 variable volume 

filter changes over ventilation and air 

lifetime; 1 per 1,500 handlers 

sq. ft. of service 

C: Use R-5 windows 68% Yes $1.83 per square foot C: Window conduc­ 103,945 732 765 249 $20.42 60 No No Yes Yes 

tive loss 

R: Develop R-10 windows 81% Yes $5,687.04 per home; 192 sq. 

ft. glazing 

R: All homes heating 

and Cooling lost 

through windows 

(cond) 

141 1,553 1,721 817 $30.17 30 No No Yes Yes 

R: “Drill & Fill” walls in pre-1970 

homes 

60% Yes $6,500 per home; 30x40 

home, 2 stories, 10’ 

per story 

R: Pre-1970 single 

family homes heating 

and Cooling lost 

through walls 

38.6 199 255 179 $46.66 80 No No Yes Yes 

R: Add aerogel insulation (i.e., 47% Yes $11,414 per home; 2,600 R: All homes heating 141 1,251 386 113 $440.92 40 No No Yes Yes 

R-21 assembly) to walls sq. ft. insulation and Cooling lost 

through walls 

R: Add duct insulation to exist­ 40% Yes $0.88 per square foot R: heating & cooling 51,972 569 1,030 429 $4.04 40 No No No No 

ing buildings currently lacking it in existing buildings 

w/o insulated ducts 

R: Add duct insulation to exist­ 40% Yes $518 per home; 195 ft2 R: Existing homes 60.7 1,661 2,426 941 $1.21 40 Yes Yes No Yes 

ing homes currently lacking it of supply duct and lacking duct 

240 ft2

 return duct insulation 

for 1500 ft2

 home 

R: Add Exterior Insulating Fin­ 68% Yes $13,780 per home; 2,600 R: Pre-2010 homes 74.2 905 1,157 761 $54.39 40 No No Yes No 

ishing Systems (EIFS) to walls sq. ft. insulation heating and Cooling 

of existing buildings (included lost through walls 

infiltration reduction) 

R: Add vacuum insulated panels 75% Yes $20,046 per home; 2,600 R: All homes heating 141 1,251 1,600 740 $118 40 No No Yes Yes 

(VIP) to walls sq. ft. insulation and Cooling lost 

through walls 

R: Added R-6 sheathing to 51% Yes $2,548 per home; 2,600 R: Existing walls and 54.2 1,195 1,528 768 $6.61 40 No No No No 

walls of existing buildings with sq. ft. insulation infiltration; siding 

siding (included infiltration based 

reduction) 

R: Attic radiant barrier (existing 4% $152.71 per home R: Pre-2010 homes 102.3 648 930 28 $19 20 No No No No 

homes) heating and Cooling 

lost through attic 

R: Build new homes using 2x6, 43% Yes $0 per home; 30x40 R: New homes heat­ 39.1 346 443 183 $0 80 Yes Yes Yes No 

24” on-center construction home, 2 stories, 10’ ing and Cooling lost 

per story through walls 

R: Build new homes using 24% Yes -$750 per home R: New homes heat­ 39.1 346 443 91.3 -$19.67 80 Yes Yes Yes Yes 

advanced framing techniques ing and Cooling lost 

through walls 

R: Develop advanced integrated 69% Yes $3,186.97 per home; 30x40 R: Existing homes 42.9 367 527 68.1 $20.75 30 No No Yes Yes 

(i.e., R-75 equivalent) roof home, 2 stories, 10’ heating and Cooling 

systems for existing homes per story lost through attic 

(non-hot climates) 

R: Develop advanced integrated 55% Yes $3,186.97 per home; 30x40 R: Central cooling in 101 330 1,006 73.6 $28.38 40 No No Yes Yes 

(i.e., R-75 equivalent) roof home, 2 stories, 10’ existing single-family 

systems for existing homes in per story homes in hottest two 

hot climates climates 

R: Develop advanced integrated 55% Yes $1,801.33 per home; 30x40 R: Central cooling 12.7 121 368 152 $6.82 20 No No Yes Yes 

(i.e., R-75 equivalent) roof home, 2 stories, 10’ in new single-family 

systems for new homes in hot per story homes in hottest two 

climates climates 

R: Develop dynamic windows 42% Yes $2,304 per square foot; R: Pre-2010 homes 102 213 716 152 $34.25 30 No No Yes Yes 

for use in existing homes (ex­ wall: floor space of heating and Cooling 

cludes equipment downsizing .9, window fill ratio lost through windows 

benefit) of 0.4 (rad) 

68
Primary sources used Data used and/or methodology Other supporting sources 

Elaine Hale, Matthew Leach, Adam Hirsch, and Paul Torcellini, “General The air barrier is assumed to reduce the envelope infiltration from 0.038 to 

Merchandise 50% Energy Savings Technical Support Document”; NREL/ 0.015 ACH... The cost of the air barrier is estimated at $1.40/ft2

 of exterior 

TP-550-46100 September 2009 wall area 

“Air-Filter Life-Cycle Cost”, Seyffer, C.; HPAC Engineering 9/2010 “As much as 40 percent of HVAC-system electricity demand could be “Proper Air Filter Selection Goes a Long 

reduced by applying air-filter life-cycle-cost analysis.” Cost taken from Way”, Matela, D.AFE Journal 10/2010. 

manufacturer website Expert interviews suggest 25% is more 

typical savings 

6. Apte, J. & Arasteh, D. Window-Related Energy Consumption in the US See tables 4 and 7 for use and savings 

Residential and Commercial Building Stock. Buildings 1-38 (Berkeley, CA, 

2006). 

6. Apte, J. & Arasteh, D. Window-Related Energy Consumption in the US See tables 4 and 7 for use and savings. Calculated from first principles; costs Arasteh, Dariush; Selkowitz, Steve; Apte, 

Residential and Commercial Building Stock. Buildings 1-38 (Berkeley, CA, from NREL bottom-up VIG build Josh; LaFrance, Marc. Energy Impacts of 

2006). Today’s Window Stock 

Savings: first principles. Cost: NREL retrofit measure database 

Savings: first principles increasing R-value by 10 per inch to R-21 assembly. 47% savings (increase assembly from R-11 to R-21) at $2.37/sq. ft. 

Cost: Personal communication Symposium participants: $4.39/sq. ft. floor 

space of retrofit. Aspen Aerogel SpaceloftTM: A Revolution in Building and 

Construction Insulation. Aspen Aerogels (2001) 

Same as above
 
 Savings assumed same as residential. Cost assumptions: 2000’ of duct,
 


12”d/3 sq. ft./ft. $2.2/ft2

 of supply duct area. 15000 ft2 typical commercial
 


building.



Hendron, R., and Engebrecht, C. 2010. Building America House Simulation 

Protocols. DOE/GO-102010-3141. http://apps1.eere.energy.gov/buildings/ 

publications/pdfs/building_america/house_simulation_revised.pdf. NREL 

Residential Efficiency Measures Database. http://www.nrel.gov/ap/retrofits/ 

measures.cfm?gId=2&ctId=16. Mills, E.. et al. 2007. “Home Energy Saver: 

Documentation of Calculation Methodology, Input Data, and Infrastructure.” 

Lawrence Berkeley National Laboratory Report No. 51938. http://evanmills. 

lbl.gov/pubs/pdf/home-energy-saver.pdf 

Hendron: Supply duct surface area (ft2

)= 0.27 × FFA, Return duct surface 

area (ft2

)= 0.05 × Nreturns × FFA. NREL: $1.3/ft2 and $1.1/ft2 for supply (R-8) 

and return (R-6) ducts respectively. Mills: efficiency ducts = DF * HSE + 

(1− DF) *CSE. Estimated cost based on insulating 195 ft2

 of supply ducts in 

unconditioned space and (Hendron value for 1500 ft2

 house) and $1.3/ft2 

(NREL Retrofit Measure Database) is $254. Insulating 240 ft2

 of return ducts 

in unconditioned space using $1.1/ft2

 is another $264, for a total of $518. 

Energy savings is derived from HES Pro assumptions for duct distribution 

efficiency using LBNL’s ASHRAE 152 model. 

Straube, J. and Smegal, J.; Building America Special Research Project: High- Costs calculated from manufacturer quotes; savings calculated from first 

R Walls Case Study Analysis; Building Science Corporation 2009 (updated principles 

2011) 

S.J. Rusek et al, CSI Elevated Temperature Conductivity and Rapid Con- Savings: first principles. Costs: personal communication from symposium 

ductivity Test Method, Quietflex Goodman Global, USA; 10th International participants 

Vacuum Insulation Symposium (9/2011). A. Parekh et al., Incorporation 

of Vacuum Insulation Panels in a Wood Frame Net Zero Energy Home, 

National Resources Canada, Canada; 10th International Vacuum Insulation 

Symposium (9/2011) 

Straube, J. and Smegal, J.; Building America Special Research Project: High- Costs calculated from manufacturer quotes; savings calculated from first 

R Walls Case Study Analysis; Building Science Corporation 2009 (updated principles 

2011) 

Miller, W. & Kosny, J. Next-Generation Roofs and Attics for Homes. ACEEE Analyzed savings for ducts and attic loads scaled to national average using 

Summer Study on Energy Efficiency in Buildings 180-195 (2008) existing stock as base 

NAHB Research Center; Southface Energy Institute; Oak Ridge National “Fully implementing advanced framing techniques can result in materials 

Laboratory; National Renewable Energy Laboratory; “Technology Fact cost savings of about $500-$1000 (for a 1,200-2,400-square-foot house, 

Sheet: Advanced Framing” (2000) respectively), labor cost savings of between 3 and 5 percent, and annual 

heating and cooling cost savings of up to 5 percent.” Scaled up 5% wholebuilding savings to share of wall load 

National Association of Home Builders, Southface Energy Institute & Oak “Advanced framing techniques can result in materials cost savings of [$500­ Straube, J. & Smegal, J. Building America 

Ridge National Laboratory, Advanced Wall Framing. U.S. Department of $1000], labor cost savings of [3-5%], and annual heating and cooling cost Special Research Project: High-R Walls 

Energy (2000). savings of up to 5 percent.” Savings includes material cost only; average Case Study Analysis. Building Science 68 

(4%) heating and cooling scaled up from full building to walls portion (Somerville, MA, 2009). 

Miller, W. & Kosny, J. Next-Generation Roofs and Attics for Homes. ACEEE Analyzed savings for ducts and attic loads scaled to national average using 

Summer Study on Energy Efficiency in Buildings 180-195 (2008) existing stock as base 

Miller, W. & Kosny, J. Next-Generation Roofs and Attics for Homes. ACEEE Analyzed savings for ducts and attic loads scaled to national average using 

Summer Study on Energy Efficiency in Buildings 180-195 (2008) new codes as base 

Miller, W. & Kosny, J. Next-Generation Roofs and Attics for Homes. ACEEE Analyzed savings for ducts and attic loads scaled to national average using 

Summer Study on Energy Efficiency in Buildings 180-195 (2008) new codes as base 

Arasteh, D.; Selkowitz, S.; Apte, J.; “Zero Energy Windows”; 2006 ACEEE Table 4; baseline updated to 2010 stock Lee, E.S., Yazdanian, M. & Selkowitz, S.E. The 

Summer Study on Energy Efficiency in Buildings Energy-Savings Potential of Electrochromic 

Windows in the US Commercial Buildings 

Sector. Building 1-42 (2004). Cost: Personal 

communication - manufacturers and experts 

69
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

R: Develop dynamic windows 42% Yes $1,344 per square foot; R: New. homes heat­ 4.1 69.4 233 85.0 $3.63 30 No No Yes Yes 

for use in new homes (includes wall: floor space of ing and Cooling lost 

equipment downsizing benefit) .9, window fill ratio through windows 

of 0.4 (rad) 

R: Develop insulating paints 3% Yes $70 per home; 2,600 R: All homes heating 141 1,251 1,558 32.9 $17.42 10 No No Yes No 

(i.e., add R-0.75 per surface sq. ft. insulation and Cooling lost 

painted) through walls 

R: Develop lower-cost cool 12% Yes $490.75 per home; 1,150 sq. R: All cooling 9.1 403 1,230 124 $1.79 20 No No Yes Yes 

roofs in colors that appeal to ft. roofing in homes in hot 

consumers climates 

R: Improve duct routing prac­ 16% $0 per home R: Forced hot air sys­ 35.0 753 1,100 85.5 $0 80 No No No No 

tices in new homes tems in new homes 

R: Increase duct insulation from 9% Yes $518 per home; 195 ft2 R: New homes lack­ 39.1 1,094 1,981 140 $10.12 40 No No Yes Yes 

R-4 to R-8 in new homes of supply duct and ing duct insulation 

240 ft2

 return duct 

for 1500 ft2

 home 

R: Increase use of efficient 48% Yes $800.80 per home; 14 R: Windows that 70.7 918 1,361 645 $5.69 10 No No Yes Yes 

window attachments windows/home can benefit from 

attachments 

R: Install low-e storm windows 58% Yes $1,500 per home; 10 R: Pre-2010 homes 47.5 563 835 548 $4.80 30 No No Yes Yes 

storms/home with single pane 

glass, conduction 

and radiation 

R: Install low-e window films in 47% Yes $432 per home; 192 sq. R: Pre-2010 homes 40.2 84 256 123 $5.05 30 No No No No 

all climates ft. of windows with non-low-e glass 

R: Install low-e window films in 47% Yes $432 per home; 192 sq. R: All cooling 17.0 51 157 76 $3.48 30 No No No No 

hot climates ft. of windows in homes in hot 

climates 

R: install PCM in attics in exist­ 32% Yes $1,000 per home; 1,000 R: Pre-2010 homes 102 648 930 278 $14.79 40 No No No No 

ing homes sq. ft. insulation heating and Cooling 

lost through attic 

R: install PCM in attics in new 24% Yes $700 per home; 1,000 R: New homes heat­ 27.1 191 275 50.3 $16.97 40 No No No No 

homes sq. ft. insulation ing and Cooling lost 

through attic 

R: install PCM in walls in new 32% Yes $1,300 per home; 2,600 R: New homes heat­ 39.1 346 443 111 $21.39 40 No No No No 

homes sq. ft. insulation ing and Cooling lost 

through walls 

R: Install predictive thermostats 10% Yes $300 per home R: Heating and 160 5,568 8,132 851 $3.89 10 No No Yes Yes 

(e.g., Nest) cooling 

R: Install programmable 4% Yes $90 per home R: All heating and 102 4,028 5,884 143 $3.46 20 Yes Yes No No 

thermostats cooling in existing 

homes 

R: Install radiative barriers in 12% Yes $263.27 per home; 30x40 R: All homes heating 141 893 1,304 111 $12.68 20 No No No No 

attics home, 2 stories, 10’ and Cooling lost 

per story through attic 

R: Insulate crawl spaces to R-17 76% Yes $228 per home; 30x40 R: All homes heating 35.2 163 238 192 $1.98 20 Yes Yes Yes Yes 

home, 2 stories, 10’ and Cooling lost 

per story through crawl space 

foundation 

R: Insulate poorly-insulated 

attics to R-40 

90% Yes $3,640 per home; 30x40 

home, 2 stories, 10’ 

per story 

R: All homes heating 

and Cooling lost 

through poorly 

insulated attic 

33.7 415 595 614 $7.91 40 No No Yes No 

R: Insulate slab foundation 

edges to R-10 in existing homes 

85% Yes $1,437.22 per home; 30x40 

home, 2 stories, 10’ 

per story 

R: Existing homes 

heating and Cooling 

lost through slab 

foundation 

29.6 189 276 109 $8.21 40 No No Yes Yes 

R: Insulate slab foundation 

edges to R-5 in new homes 

70% Yes $715 per home; 30x40 

home, 2 stories, 10’ 

per story 

R: New homes 

heating and Cooling 

lost through slab 

foundation 

10.8 71.2 104 76.5 $5.51 40 No No Yes Yes 

R: Insulate well-insulated attics 

to R-40 

45% Yes $1,768 per home; 30x40 

home, 2 stories, 10’ 

per story 

R: All homes heating 

and Cooling lost 

through moderately 

insulated attics 

49.1 136 195 89.2 $36.12 30 No No No No 

70
Primary sources used Data used and/or methodology Other supporting sources 

Arasteh, D.; Selkowitz, S.; Apte, J.; “Zero Energy Windows”; 2006 ACEEE Table 4; adjusted for new building codes Lee, E.S., Yazdanian, M. & Selkowitz, S.E. The 

Summer Study on Energy Efficiency in Buildings Energy-Savings Potential of Electrochromic 

Windows in the US Commercial Buildings 

Sector. Building 1-42 (2004). Cost: Personal 

communication - manufacturers and experts 

First principles; K value of 0.1 W/m/K, 50 mils paint thickness on 2 surfaces Increase R-value of standard walls by 0.4 at a cost of current paint additives Nansulate claims K=0.017, likely arising from 

spurious testing as this rivals vacuum 

Konopacki, S., Akbari, H., Pomerantz, M., Gabersek, S. & Gartland, L. Cooling Supporting calculations in tool 

Energy Savings of Light-Colored Roofs for Residential and Commercial 

Buildings in 11 U.S. Metropolitan Areas. 117 (1997). 

Southface Energy Institute, FEMP Technology Alert: Air Distribution System Southface: “The efficiency of air distribution systems has been found to 

Design, 2003 be 60-75% or less; Properly designed and installed duct systems can have 

efficiencies of 80% or more for little or no additional cost.” Savings estimate 

is 16%=1-0.675/0.8 

Cost: NREL retrofit measure database. Savings: First principles of moving LBNL. 2011. Thermal Energy Distribution 

ducts from R-4 to R-8 as per recent code changes Website. ducts.lbl.gov. 

Savings: first principles calculation from R- and SHGC- impacts of product. Savings: adding R-value of 1.75 and reducing SHGC to 0.35. Price: Comfortex Kotey, N.A., Wright, J.L., Barnaby, C.S., 

Price: Comfortex. Manufacturer & expert interviews current prices compressed 1/3rd to represent learning curve Collins, M.R., “Solar Gain Through Windows 

with Shading Devices: Simulation versus 

Measurement,” ASHRAE Transactions, Vol. 

115, Pt. 2, (2009) 

Kohler, C Letter report for low-e storm window retrofits, 2006 “A simulation study in 2004 has shown that by using pyrolitic “hard” low-e 

coatings will increase from approximately 15% of the home’s heating energy 

to 18%. The additional cost is...on the order of $100.” Note: 18% of home 

heating/ cooling is 58% of window load heating/cooling 

Huang, J. Computer Simulation Analysis of the Energy Impact of Window Tables 12-16 used to generate estimate of population average. Simplified to 

Films In Existing Houses. Selkowitx, S. Personal communication SHGF and used vs. existing baseline. Improving SHGF from 0.74 to 0.30 on 

windows incurring the greatest load 

Huang, J. Computer Simulation Analysis of the Energy Impact of Window Tables 12-16 used to generate estimate of population average. Simplified to 

Films In Existing Houses. Selkowitx, S. Personal communication SHGF and used vs. existing baseline. Improving SHGF from 0.74 to 0.30 on 

windows incurring the greatest load 

Analysis of the Dynamic Thermal Performance of Fiberous Insulations fitted with PCMs dropped the annual load due to ceiling and duct heat Miller, W.; Kośny, J.; “Next-Generation Roofs 

Containing Phase Change Materials, Jan Kośny, et. al. gains/losses by 23% of that computed for the SR25E75 code compliant roof and Attics for Homes”; 2008 ACEEE Sumand attic assembly. mer Study on Energy Efficiency in Buildings 

Analysis of the Dynamic Thermal Performance of Fiberous Insulations Same as previous adjusted for cost and impacts vs. 2010 building code 

Containing Phase Change Materials, Jan Kośny, et. al. baseline 

Miller, W.; Kośny, J.; “Next-Generation Roofs and Attics for Homes”; 2008 Increasing the air space from 2 to 4 in. (0.051 to 0.102 m) and adding PCM Analysis of the Dynamic Thermal PerforACEEE Summer Study on Energy Efficiency in Buildings caused an additional 10% drop compared with the assembly with a 2-in. air mance of Fiberous Insulations Containing 

space Phase Change Materials Jan Kośny, et. al. 

Savings: Cho, Energy Conservation and Management 44 (2003); The results show that use of the predictive control strategy could save 

Cost: Nest (http://www.nest.com/inside-and-out/) between 10% and 12% energy during the cold winter months. The energy 

savings are somewhat higher during mild weather conditions. 

Energy Information Agency. “Residential Energy Consumption Survey, RECS 2009 analysis of set back behavior and penetration of programmable 

2009”. Department of Energy (2012) thermostats included in spreadsheet 

Medina, M; “On the performance of radiant barriers in combination with dif­ “produced yearly heat-load reductions of approximately 44, 28, and 23%.” Levins, W. P.; Karnitz, M. A.; Knight, D. K.; 

ferent attic insulation levels”; Energy and Buildings 33 (2000) 31-40 Adjusted for heating penalty and average national load as per cool roof “Cooling Energy Measurements of Houses 

calculations. Costs taken from manufacturer’s website (6/11)” with Attics Containing Radiant Barriers.” 

Medina, M; O’Neil, D; Turner, W.; Effect of 

Attic Ventilation on Performance of Radiant 

Barriers”; Transactions of the ASME Vol. 114 

pf 234 11/1992. http://www.reflectixinc.com/ 

basepage.asp?PageIndex=390; 

Savings: First principles (market and baseline drawn from RECs analysis). 

Cost: NREL measure database 

Savings: First principles (market and baseline drawn from RECs analysis). 

Cost: NREL measure database 

“A Builder’s Guide to Residential Foundation Insulation” King, J.; Meyer, G. Supporting calculations in tool take savings calculated from Kansas climate FEMP Technology Fact Sheet: Slab Insulazones then scaled nationally tion", Southface Energy Institute, Oak Ridge 

National Laboratory 

“A Builder’s Guide to Residential Foundation Insulation” King, J.; Meyer, G. Supporting calculations in tool take savings calculated from Kansas climate FEMP Technology Fact Sheet: Slab Insulazones then scaled nationally tion", Southface Energy Institute, Oak Ridge 

National Laboratory 

Savings: First principles (market and baseline drawn from RECs analysis). 

Cost: NREL measure database 

71
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

R: Insulate basements to R-11 73% Yes $1,196 per home; 30x40 R: All homes heating 49.8 230 226 174 $14.48 30 No No Yes No 

home, 2 stories, 10’ and Cooling lost 

per story through basement 

R: Paint exterior walls with a 6% Yes $0 per home R: Central Cooling in 66.6 577 1,761 105 $0 10 No No No No 

“cool” paint color hot climates 

R: poorly ins. attics to R-60 94% $5,460 per home; 30x40 R: All homes heating 33.7 415 595 643 $11.85 30 No No Yes Yes 

home, 2 stories, 10’ and Cooling lost 

per story through poorly 

insulated attic 

R: R-19 basement 83% $1,768 per home; 30x40 R: All homes heating 49.8 230 226 200 $18.73 30 No No No Yes 

home, 2 stories, 10’ and Cooling lost 

per story through basement 

R: Retrofit existing homes to 83% Yes $20,800 per home; 2600 R: Pre-2010 homes 74.2 731 935 817 $70.74 40 No No No Yes 

highly efficient (i.e., R-30) wall square feet wall heating and Cooling 

construction (e.g., EIFS) space lost through walls 

R: Seal air-leaks existing homes 50% Yes $900 per home; 30x40 R: Pre-2010 homes 102 1,350 1,758 943 $5.11 10 Yes Yes Yes Yes 

(50% improvement) home, 2 stories, 10’ heating and Coolper story ing lost through 

infiltration 

R: Seal ducts in existing homes 10% Yes $495 per home R: Existing homes 

with unsealed ducts 

88.8 2,484 4,500 355 $4.11 40 Yes Yes Yes Yes 

R: Use “enhanced” duct sealing 

in new homes 

5% Yes $756.50 per unit R: New homes with 

code-compliant duct 

sealing 

7.9 215 314 12.4 $33.10 40 Yes Yes Yes Yes 

R: Use building wrap to reduce 13% Yes $1,040.56 per home R: New homes heat­ 39.1 515 752 75.7 $33.73 60 No No No No 

air infiltration in new homes ing and Cooling lost 

through infiltration 

R: Use highly insulating (R-7) 75% $90 per unit R: All homes heating 141 233 265 187 $3.69 20 Yes Yes Yes Yes 

doors and Cooling lost 

through doors 

R: Use highly efficient (i.e., 70% Yes $13,000 per home; 2600 R: New homes heat­ 39.1 346 443 279 $114.55 40 No No Yes Yes 

R-30) wall construction in new square feet wall ing and Cooling lost 

homes space through walls 

R: Use insulating shutters on 

windows 

67% Yes $1,480 per home; 15 

windows: $72/ 

window plus one 

day to install 

R: All homes heating 

and Cooling lost 

through windows 

(cond) 

141 1,553 2,269 1,579 $6.33 20 No No No No 

R: Use low pressure drop air 

filters in ducted systems 

25% $60 per home; 

represents 6 

filter changes over 

lifetime 

R: All furnace fan 

energy 

88.1 186 567 198 $2.07 6 No No Yes Yes 

R: Use phase-change materials 32% Yes $4,752 per home; 1,000 R: New homes heat­ 39.1 346 443 111 $78.18 40 No No No No 

(PCM) in walls in new homes sq. ft. insulation ing and Cooling lost 

through walls 

R: Use R-5 windows 63% Yes $973.44 per home R: All homes heating 

and Cooling lost 

through windows 

(cond) 

141 1,553 1,799 662 $6.36 40 No No Yes Yes 

R: Use radiative barriers in 

existing homes (includes ductcooling benefits) 

12% Yes $263.27 per home; 30x40 

home, 2 stories, 10’ 

per story 

R: All cooling 

in homes in hot 

climates 

9.1 403 1,230 137 $1.38 20 No No No No 

R: Use radiative barriers in new 12% Yes $263.27 per home; 30x40 R: All cooling 14.6 224 683 53.2 $4.03 20 No No No No 

homes (includes duct-cooling home, 2 stories, 10’ in homes in hot 

benefits) per story climates 

R: well ins. attics to R-60 64% $3,900 per home; 30x40 R: All homes heating 49.1 136 195 126 $59.24 30 No No Yes Yes 

home, 2 stories, 10’ and Cooling lost 

per story through moderately 

insulated attics 

C: Add stack economizer to 5% Yes $0.76 per square foot; C: Space heating 22,335 214 215 8.43 $90.71 20 No No No No 

boilers 5000 kW boiler, using boiler, existing 

241,000 ft2

 ave. commercial 

hospital 

C: Increase use of most efficient 13% Yes $0.57 per square foot C: Furnaces and 92,302 1,269 1,269 100 $24.62 30 Yes Yes Yes Yes 

(i.e., max-tech) furnaces & boilers 

radiators 

C: Reduce heater oversizing 5% Yes -$0.08 per square foot C: All heating 103,945 1,862 2,183 54.9 -$7.02 30 Yes Yes Yes Yes 

C: Replace large-capacity 20% Yes $0.17 per square foot C: All boiler heating 23,487 421 481 58.7 $2.13 30 Yes Yes No Yes 

boilers with cascaded-multiple 

boilers 

72
Primary sources used Data used and/or methodology Other supporting sources 

Savings: First principles (market and baseline drawn from RECs analysis). R-15 is effective total assembly thermal resistance after adding R-11 

Cost: NREL measure database insulation 

Tom Petrie, Jerry Atchley, Phil Childs, and André Desjarlais; “Energy Savings “Savings are 4-9% compared to non-IR reflecting walls” Additional support by running HES Pro in 

for Stucco Walls Coated with Cool Colors” ORNL (12/2007) multiple climate zones to verify savings 

Savings: first principles increasing R-value. Cost: NREL measure database Increase from R-4 assembly to R-60 

Savings: first principles increasing R-value. Cost: NREL measure database Increase from R-4 assembly to R-23 

Straube, J. and Smegal, J.; Building America Special Research Project: High- Costs calculated from manufacturer quotes; savings calculated from first 

R Walls Case Study Analysis; Building Science Corporation 2009 (updated principles 

2011) 

Savings: first principles (i.e. ,50% target). Cost: NREL retrofit measure database for this performance level 

Aeroseal website. http://www.aeroseal.com/problem-we-solve/FAQ- Aeroseal: $.33/ft2

 based on measure 92. Average house assumption is 1500 

commercial.html ft2

. Assumes twice the energy savings as new homes (Measure 91). 

Hendron, R., and Engebrecht, C. 2010. Building America House Simulation Hendron: 10% supply air leakage, ~6.5% to the outside. Modera: Figure 6 

Protocols. DOE/GO-102010-3141. http://apps1.eere.energy.gov/buildings/ ~80% reduction in leakage for Aeroseal in sections of a large office. 

publications/pdfs/building_america/house_simulation_revised.pdf. Modera, Implication: reduce leakage (80%) from 6.5% to 1.3%. Price is a 15% discount 

M. Fixing Duct Leaks in Commercial Buildings; ASHRAE Journal June 2005 over measure 127 because of efficiencies related to new homes vs. retrofits 

Architectural Energy Company, “Impact of Title 24 Residential Air Leakage Blower door testing indicated that the replacement of two layers of Grade D 

Reduction Credit on Water-Resistive Barriers in California Homes”, 2006 building paper with a spun-bonded polyolefin housewrap reduced the SLA 

by an average of 13% (Wilcox, 2001). 

Savings: First principles (market and baseline drawn from RECs analysis). 

Cost: NREL measure database 

Straube, J. and Smegal, J.; Building America Special Research Project: High- Costs calculated from manufacturer quotes; savings calculated from first 

R Walls Case Study Analysis; Building Science Corporation 2009 (updated principles 

2011) 

Synertech Systems Corporation, “Windows and Insulating Shutters” (2007). Increases R-value for window assembly to R-13 when closed; assume only 

Wenz, “Baby, it’s warm inside, thanks to insulated shutters”, San Francisco closed at night for 60% heating loss reduction 

Chronicle, 1/19/2008 

“Air-Filter Life-Cycle Cost”, Seyffer, C.; HPAC Engineering 9/2010 “As much as 40 percent of HVAC-system electricity demand could be Proper Air Filter Selection Goes a Long 

reduced by applying air-filter life-cycle-cost analysis.” Cost taken from Way”, Matela, D.AFE Journal 10/2010. 

manufacturer website Expert interviews suggest 25% is more 

typical savings 

Miller, W.; Kośny, J.; “Next-Generation Roofs and Attics for Homes”; 2008 Increasing the air space from 2 to 4 in. (0.051 to 0.102 m) and adding PCM Analysis of the Dynamic Thermal PerforACEEE Summer Study on Energy Efficiency in Buildings caused an additional 10% drop compared with the assembly with a 2-in. air mance of Fiberous Insulations Containing 

space Phase Change Materials Jan Kośny, et. al. 

6. Apte, J. & Arasteh, D. Window-Related Energy Consumption in the US See tables 4 and 7 for use and savings 

Residential and Commercial Building Stock. Buildings 1-38 (Berkeley, CA, 

2006). 

Medina, M; “On the performance of radiant barriers in combination with dif­ produced yearly heat load reductions of approximately 44, 28, and 23% http://www.reflectixinc.com/basepage. 

ferent attic insulation levels”; Energy and Buildings 33 (2000) 31-40 asp?PageIndex=390; Adjusted for heating 

penalty and average national load as power 

cool roof calculations 

Medina, M; “On the performance of radiant barriers in combination with dif­ produced yearly heat load reductions of approximately 44, 28, and 23% http://www.reflectixinc.com/basepage. 

ferent attic insulation levels”; Energy and Buildings 33 (2000) 31-40 asp?PageIndex=390; Adjusted for heating 

penalty and average national load as power 

cool roof calculations 

Savings: first principles increasing R-value Increase from R-23 assembly to R-60 

Cost: NREL measure database 

NREL. 2011. Advanced Energy Retrofit Guide for Healthcare Facilities (Draft). Energy savings from NREL 2011, which estimates a few percent improveProject Lead: Robert Hendron. Kemco case study. Boiler Stack Economizer ment in boiler efficiency. Cost estimated from Kemco: “...annual savings was 

- Provides A 3 1⁄2 Month ROI. (http://www.kemcosystems.com/Case-Studies/ estimated at $32,880 with a 10 month payback.” $27,400 for two boilers 

Food-Stack.html) (5000 kW total), $36.73/kW, 241,000 ft2

 ave. hospital. 

Max tech: Desroches, L.-B. et al. Max Tech and Beyond. 60 (2011) 0.86 AFUE baseline, 0.98 tech limit: 11% savings from AFUE improvement Choi-Granade et. Al.; “Unlocking Energy 

Cost: Manufacturer websites directly Efficiency in the U.S. Economy”; McKinsey & 

Company (2009) 

Djunaedy, E., Van Den Wymelenberg, K., Acker, B., Thimmana, H. (n.d.). 

“Oversizing of HVAC System: Signatures and Penalties. Energy and Buildings.” Energy and Buildings, In Press. Florida Solar Energy Center, “RightSize Heating and Cooling Equipment”, Department of Energy (2002) 

NREL Measure Database (for cost per unit capacity) 

Pg. 6: “estimated an energy savings of 0.2% for every 1% reduction in Choi-Granade et. Al.; “Unlocking Energy 

oversizing.” Pg 1: “One Florida study showed a typical 9 percent increase Efficiency in the U.S. Economy”; McKinsey & 

in annual space cooling electricity usage for units that were oversized by Company (2009) 

50 percent or more;nearly 40 percent of contractors indicated that they 

purposefully over-sized equipment”,assume 50% oversizing on average for 

cooling, 25% for heating 

Manufacturer (Buderus) case studies (http://www.buderus.co.uk/ $7,227 per installation vs. $4,669; typical claims of ~20% savings in British Choi-Granade et. Al.; “Unlocking Energy 

all-about-buderus/case-studies) climates Efficiency in the U.S. Economy”; McKinsey & 

Company (2009) 

73
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

R: Develop cold climate heat 69% Yes $3,000 per home; 3 tons R: Electric heating, 7.8 120 367 214 $5.12 20 No No Yes Yes 

pump (no FS) proportion of cooling 

in coldest 2 climates 

R: Increase use of most efficient 11% Yes $2,160 per unit R: All homes gas 76.4 3,176 3,176 293 $37.95 20 Yes Yes Yes Yes 

(i.e., max-tech) furnaces & heating 

radiators 

R: Install energy & enthalpy 7% Yes $912 per home; 100 cfm R: Heating Total 141 4,077 4,599 224 $33.45 10 No No Yes Yes 

recovery ventilation (i.e., air-to­ air exchange 

air heat exchangers) 

R: Install radiant heated floors 10% Yes $7,000 per home; 1,000 R: Forced hot air sys­ 35.0 753 1,100 92.4 $143.72 80 No No No No 

in new homes square feet tems in new homes 

R: Install radiator reflectors 10% Yes $250 per home; 8 reflec- R: Coils (A/C and 103 977 2,982 235 $6.09 10 No No No Yes 

tors per home HP) - heating and 

cooling 

R: Perform regular maintenance 5% Yes $300 per home R: All homes heating 141 4,598 5,187 261 $39.84 1 Yes Yes Yes Yes 

on heating equipment (including sectional/ 

secondary) 

R: Reduce heater oversizing 5% Yes -$100 per home R: Heating Total 141 4,077 4,599 149 -$5.87 20 Yes Yes Yes Yes 

C: Develop and deploy dual 

(i.e., air and ground) source 

heat pumps (FS only) 

69% Yes FS $1.75 per square foot; 

40 ton total, 10 

ton ground 11,200 

sq. ft. 

C: Non-electric space 

heating, proportion 

of cooling, excluding 

coldest climate 

88,598 2,006 2,810 593 $9.44 30 No No No No 

C: Develop and deploy dual 

(i.e., air and ground) source 

heat pumps (no FS) 

34% Yes $2.50 per square foot; 

40 ton total, 10 ton 

ground assumed 

for 15,000 sq. ft. 

buildings 

C: Electric space 

heating, proportion 

of cooling, excluding 

coldest climate 

5,149 265 795 198 $2.92 30 No No No No 

C: Develop and deploy gas­ -25% Yes FS $9.40 per square foot; 1 C: Cooling plus ASHP 103,945 721 2,200 931 $52.72 20 No No No No 

engine driven heat pumps (FS ton per 280 sq. ft. heating 

only) 

C: Improve auto-fault deduction 

in outside-air dampers 

7% Yes $0.01 per unit; 7.5 tons/ 

RTU, 280 ft2

/ton 

C: All Commercial 

Heating and Cooling, 

RTU 

29,668 192 323 6.3 $1.55 10 No No Yes Yes 

C: Improve auto-fault deduction 

in roof top units (RTUs) 

9% Yes $0.04 per unit; 7.5 tons/ 

RTU, 280 ft2

/ton 

C: All Commercial 

Heating and Cooling, 

RTU 

29,668 192 285 1.8 $3.72 10 No No Yes Yes 

C: Research and develop 

gas-powered (e.g., GAX, gasengine) heat pumps 

39% Yes FS $10.02 per square foot; 

40 tons for 15,000 

sq. ft. 

C: Electric space 

heating, proportion 

of cooling, excluding 

coldest climate 

5,149 265 807 509 $5.43 20 No No No Yes 

C: Retrofit improved controls 30% Yes $1.32 per square foot C: Commercial Heat­ 37,975 506 971 93.0 $10.09 10 No No Yes Yes 

into packaged HVAC units ing/Cooling/Ventilation (HP and RTU), 

existing buildings 

C: Use displacement ventilation 10% Yes -$5 per square foot C: All HVAC in new 50,966 1,565 2,950 96.9 -$154.75 60 No No Yes Yes 

in new buildings buildings 

R: Air-source heat pump ef­ 41% Yes Yes $2,470 per unit R: All non-electric 94.3 4,088 5,194 1,714 $5.95 20 No No No No 

ficiency improvements through heating, proportion 

R&D of cooling 

R: Deploy minisplits heatpumps 44% Yes $1,404 per home; 3 tons R: Minisplit: Electric 14.1 312 951 418 $2.71 20 Yes Yes No Yes 

in new and appropriate existing capacity (3 indoor heating & cooling in 

homes units for mini-split) all new homes + 40% 

of existing homes 

R: Develop and deploy dual 69% Yes FS $2,880 per home; 3 ton R: Non-electric heat­ 53.8 2,424 2,790 311 $16.89 30 No No No No 

(i.e., air and ground) source ing, proportion of 

heat pumps (FS only) cooling not in coldest 

2 climates 

R: Develop and deploy dual 34% Yes $2,880 per home; 3 ton to- R: Electric heating, 35.5 460 1,405 322 $11.02 30 No No No No 

(i.e., air and ground) source tal, 2 ton ground proportion of cooling 

heat pumps (no FS) not in coldest 2 

climates 

74
Primary sources used Data used and/or methodology Other supporting sources 

Sachs et al, 2009, “Emerging Energy-Saving HVAC Technologies and Prac- Sachs represents current units offering 26% savings; Initial tests of DOE 

tices for the Buildings Sector.” Hallowell International; Personal communica­ funded technology show 69% savings. Cost estimated from FOA funding 

tion: Bouza, DOE target 

Max tech: Desroches, L.-B. et al. Max Tech and Beyond. 60 (2011) 0.86 AFUE baseline, 0.98 tech limit: 11% savings from AFUE improvement Choi-Granade et. Al.; "Unlocking Energy 

Cost: Manufacturer websites directly Efficiency in the U.S. Economy"; McKinsey & 

Company (2009) 

Hellevang, K. & Pedersen, C. Air-to-Air Heat Exchangers. North Dakota State Estimated from first principles using 3,000 HDD as U.S. average, 45 CFM as 

University (2009) air handling treated to calculate maximum heating savings 

Watson, Richard D. Advantages of radiant heat. Fine Homebuilding June- Watson: Assumed 1000 sq. ft. heated at $10/ft2

 vs. $3000 installed for 

July 1992. http://www.radiantec.com/why/technical-explanation.php. Robert ducts, “The average 65°F radiant comfort temperature with 59°F day/night 

Hendron, Ed Hancock; Greg Barker; Paul Reeves. 2006. An Evaluation of setback should reduce building heat load by 25% to 35% over convective 

Affordable Prototype Houses at Two Levels of Energy Efficiency. NREL/CP

550-38774. Joseph D’agnese. 2009. Radiant Floor Heating. This Old House about 3% heating energy savings, to achieve equivalent mean radiant 

magazine. http://www.thisoldhouse.com/toh/article/0,,1548320,00.html temperature, plus additional savings for water pump vs air handler fan, 

and for reduced infiltration. 9.5% is the geometric mean of the two studies. 

D’agnese: “Hot-water radiant costs more to install than other types of 

heating systems—from $6 to $15 per square foot depending on the method, 

whether you’re starting from scratch or retrofitting, and where you live.” 

Assumed 1000 sq. ft. heated at $10/ft2

 vs. $3000 installed for ducts 

Novitherm website, calculation, and cost estimation tools Assumes 8 radiators to treat per home, 10% savings in heating 

Choi-Granade et. al. (2009). “Unlocking Energy Efficiency in the U.S. Assumes regular maintenance underway; expert interviews provided cost 

Economy”. McKinsey and Company and savings for incremental effort to maximize efficiency. $300 represents 

present value of $25 annual cost over equipment lifetime 

Djunaedy, E., Van Den Wymelenberg, K., Acker, B., Thimmana, H. (n.d.). Pg. 6: “estimated an energy savings of 0.2% for every 1% reduction in Choi-Granade et. Al.; “Unlocking Energy 

“Oversizing of HVAC System: Signatures and Penalties. Energy and Build­ oversizing.” Pg 1: “One Florida study showed a typical 9 percent increase Efficiency in the U.S. Economy”; McKinsey & 

ings.” Energy and Buildings, In Press. Florida Solar Energy Center, “Right­ in annual space cooling electricity usage for units that were oversized by Company (2009) 

Size Heating and Cooling Equipment”, Department of Energy (2002). NREL 50 percent or more;nearly 40 percent of contractors indicated that they 

Measure Database (for cost per unit capacity) purposefully over-sized equipment”, assume 50% oversizing on average for 

cooling, 25% for heating 

Hadley, D., Collins, T., Parkerk, S., Cornett, G. & Cavedo, F. Energy Savings “At the main U.S. Post Office facility in Valrico, three air- source heat pumps 

from Dual-Source Heat Pump Technology Federal Energy Management were retrofitted with the dual-source technology, reducing daily energy use 

Program E-0220 (2000) by nearly 38%.” Multiple case studies used for average of 34% savings from 

ASHP 

Hadley, D., Collins, T., Parkerk, S., Cornett, G. & Cavedo, F. Energy Savings “At the main U.S. Post Office facility in Valrico, three air- source heat pumps 

from Dual-Source Heat Pump Technology Federal Energy Management were retrofitted with the dual-source technology, reducing daily energy use 

Program E-0220 (2000) by nearly 38%.” Multiple case studies used for average of 34% savings from 

ASHP 

Omar Abdelaziz. Oak Ridge National Laboratory. 2011. Personal communica- Abdelaziz: Savings estimate of 30% source energy savings compared to 

tion. RS Means. 2012. Assemblies Cost Data, 33rd Edition standard electric heat pump, an increase in site energy use of 25%. Cost estimated at twice the cost of an RTU. Baseline cost from RS Means ($9.4/ft2

 for 

twenty single-zone RTUs, 2 tons each, including ductwork and controls) 

TIAX. Energy Impact of Commercial Building Controls and Performance ~$30 per RTU or AHU, 7.5 tons/RTU, 280 ft2

/ton, new equipment only. 20% 

Diagnostics?: Market Characterization, Energy Impact of Building Faults and savings if there’s a damper failure, 35% of dampers are malfunctioning. 

Energy Savings Potential. Construction 413 (2005). 

TIAX. Energy Impact of Commercial Building Controls and Performance Energy savings from TIAX (9% = 0.075 Quads/0.8 Quads). $80 per RTU, 7.5 

Diagnostics: Market Characterization, Energy Impact of Building Faults and tons/RTU, 280 ft2

/ton. Assumes an EMS is present. Maintenance savings and 

Energy Savings Potential. Construction 413 (2005). necessary repairs are neglected. 

Jawahar, C.P. & Saravanan, R. Generator absorber heat exchange based Review indicates COP of 2.0/1.0 demonstrated; commercialization more Goffman, E. The Other Heat Pump. The 

absorption cycle—A review. Renewable and Sustainable Energy Reviews 14, likely at 1.5/0.7. The initial cost is comparable with geothermal COP of 1.5 Environmental Magazine 11-12 (2010) 

2372-2382 (2010) heating, 0.7 cooling 

W Wang, Y Huang, S Katipamula, MR Brambley. 2011. Advanced Controls for Wang: “The results from detailed simulation analysis show significant energy 

Existing Packaged A/C with gas heating. PNNL 20995. http://www.pnnl.gov/ (24% to 35%) and cost savings (38%) from fan, cooling and heating energy 

main/publications/external/technical_reports/PNNL-20955.pdf consumption when packaged units are retrofitted with advanced control 

packages.” The average energy savings (30%) was used. Cost of $1.32/ft2 

based on sum of component measures (129, 141, 101, 104, 722). Only half the 

cost of an EMS was included because of synergies. 

Goetzler, W., Zogg, R., Burgos, J., Hiraiwa, H. & Young, J. Energy Savings Technical Energy-Savings Potential: 0.17 Quads ~10% system energy savings. 

Potential and RD & D Opportunities for Commercial Building HVAC Systems. Costs from Argon Air estimate to install in new building 

(2011). Awbi, Energy Efficient Room Air Distribution ; Renewable Energy (15) 

Hewitt, N.J., Huang, M.J., Anderson, M. & Quinn, M. Advanced air source heat Savings and cost targets reflect Building Technology Program funded 

pumps for UK and European domestic buildings. Applied Thermal Engineer- targets; Hewitt et.al. demonstrate technical feasibility 

ing 31, 3713-3719 (2011) 

Geraghty, K., Baylon, D. & Davis, B. Residential Ductless Mini-Split Heat Geraghty: Table 4 suggests 44% savings for heating; Table 6- total savings 

Pump Retrofit Monitoring. Bonneville Power Administration (2009). of 28%. Price from google search suggests ~30% price premium 

Desroches, L.-B. et al. Max Tech and Beyond, Appendix F. Lawrence Berkeley 

National Laboratory (2011). 

Hadley, D., Collins, T., Parkerk, S., Cornett, G. & Cavedo, F. Energy Savings “At the main U.S. Post Office facility in Valrico, three air- source heat pumps 

from Dual-Source Heat Pump Technology Federal Energy Management were retrofitted with the dual-source technology, reducing daily energy use 

Program E-0220 (2000) by nearly 38%.” Multiple case studies used for average of 34% savings from 

ASHP 

Hadley, D., Collins, T., Parkerk, S., Cornett, G. & Cavedo, F. Energy Savings “At the main U.S. Post Office facility in Valrico, three air- source heat pumps 

from Dual-Source Heat Pump Technology Federal Energy Management were retrofitted with the dual-source technology, reducing daily energy use 

Program E-0220 (2000) by nearly 38%.” Multiple case studies used for average of 34% savings from 

ASHP 

­ systems.” Hendron: Radiant vs forced air in two similar houses indicated 

75
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

R: Develop most efficient (i.e., 41% Yes $1,120 per unit R: All electric heat­ 47.0 577 1,761 310 $3.90 40 No No No Yes 

DOE road map) air-source heat ing, proportion of 

pumps (no FS) cooling 

R: Research and develop 34% Yes $14,400 per home; 3 tons R: All non-electric 94.3 4,088 5,194 2,010 $29.58 20 No No No Yes 

gas-powered (e.g., GAX, gas- heating, proportion 

engine) heat pumps of cooling 

C: Develop air-conditioning 20% Yes $0.09 per square foot; 1 C: Cooling in hot, dry 19,867 145 441 79.7 $1.11 20 No No No Yes 

systems customized for hot-dry ton per 280 sq. ft. climates 

climates 

C: Develop low-lift cooling 40% $0.70 per square foot C: Cooling and ven­ 8,890 123 376 163 $2.76 10 No No No Yes 

tilation in new office 

buildings 

C: Develop magnetic bearings 17% Yes $0 per square foot; C: Commercial Cool­ 41,175 267 813 114 $0 20 No No No No 

for chillers and RTUs 100 ton for chill­ ing (RTU and chiller) 

ers/ 7.5 ton for 100% new and 50% 

RTUs old 

C: Develop more efficient 30% Yes $1.24 per square foot; 1 C: Centrifugal chillers 25,196 163 498 130 $12.70 20 No No Yes Yes 

centrifugal chillers ton per 280 sq. ft. 

C: Develop most efficient (i.e., 44% Yes $0.75 per square foot; C: Commercial cool­ 29,668 192 586 252 $4.60 20 No No Yes Yes 

IEER 20) roof top unit (RTU) 10 tons per 6,000 ing: Rooftop AC 

sq. ft. 

C: Develop thermoelectric sub­ 9% Yes $0 per square foot C: All Commercial 29,668 192 586 43.0 $0 20 No No No No 

cooling for commercial RTUs Cooling, RTU 

C: Develop thermotunneling 

cooling systems 

10% Yes $1.07 per square foot (1 

ton per 280 square 

feet); 5 ton model 

C: Commercial 

Cooling (HP and 

RTU), 100% new and 

50% old 

24,074 156 476 38.1 $29.37 20 No No Yes No 

C: Improve evaporator fan efficiency in all A/C units 74% Yes $0 per square foot; 1 

ton per 280 sq. ft. 

C: Commercial 

evaporator fans 

103,945 38.7 118 88.1 $0 20 No No No No 

C: Improve scroll compressor 28% Yes $0.76 per square foot; C: Commercial cool­ 3,873 25.1 76.5 16.1 $7.49 20 No No Yes Yes 

efficiency 150k sq. ft. building ing: Scroll 

C: Increase efficient of roof-top 38% Yes $0.43 per square foot; C: Commercial cool­ 29,668 192 586 212 $3.22 20 Yes Yes Yes Yes 

units (i.e., current DOE RTU­ 10 RTU’s per 100k ing: Rooftop AC 

challenge specification) sq. ft. 

C: Increase use of ice-storage 5% Yes $1.07 per unit; 20 tons, C: All cooling 103,945 673 2,053 93.0 $59.63 20 No No Yes No 

systems to shift A/C operation 8 hours per 15,000 

to periods with lower ambient sq. ft. 

temperature 

C: Install chilled beams in 20% Yes -$1 per square foot 150 C: New buildings H & 33,993 715 1,348 255 -$8.07 60 No No Yes Yes 

temperate climates BTU/h/sq. ft. C, except coldest two 

climates 

C: R&D to reduce cost of 

adsorption chiller A/C 

40% Yes $0.64 per square foot 

assuming @20-100 

ton 

C: Education, health 

care, lodging, and 

office 

42,405 298 910 240 $3.73 30 No No No Yes 

C: Research magnetic cooling 

for air-conditioning 

17% Yes FS $0 per square foot C: Cooling in humid 

climates 

50,105 378 1,154 194 $0 20 No No No No 

C: Right-size A/C in new 13% Yes -$0.43 per square foot; 1 C: All cooling 103,945 673 2,053 202 -$15.05 20 Yes Yes No Yes 

buildings ton per 280 sq. ft. 

C: Steam-clean A/C cools regu­ 10% Yes $0.20 per square foot C: Cooling plus ASHP 103,945 721 2,200 192 $9.89 5 No No No Yes 

larly for improved performance heating 

C: Upgrade to most efficient 48% Yes $0.36 per square foot; 1 C: Centrifugal chillers 25,196 163 498 239 $2.32 20 No No No Yes 

centrifugal chiller available ton per 280 sq. ft. 

C: Use “smart” refrigerator 9% Yes $0 per square foot C: New Commercial 27,093 175 535 40.7 $0 20 No No No No 

distribution for commercial Cooling, RTU and 

cooling Central Chiller 

C: Use desiccant wheels in hot­ 13% Yes $0.33 per square foot C: Cooling in hot, 30,772 286 873 83.6 $8.96 20 No No No Yes 

humid climates humid climates 

C: Use desiccant-enhanced 82% Yes FS $1.08 per square foot; C: Cooling in dry 53,839 295 899 699 $3.94 20 No No No Yes 

evaporative cooling (e.g., 10 tons per 6,000 climates 

DeVAP) in dry climates sq. ft. 

76
Primary sources used Data used and/or methodology Other supporting sources 

Personal communication: Bouza, DOE. Hewitt, N.J., Huang, M.J., Anderson, Savings and cost targets reflect Building Technology Program funded 

M. & Quinn, M. Advanced air source heat pumps for UK and European do- targets; Hewitt et.al. demonstrate technical feasibility 

mestic buildings. Applied Thermal Engineering 31, 3713-3719 (2011) 

Jawahar, C.P. & Saravanan, R. Generator absorber heat exchange based Review indicates COP of 2.0/1.0 demonstrated; commercialization more Goffman, E. The Other Heat Pump. The 

absorption cycle—A review. Renewable and Sustainable Energy Reviews 14, likely at 1.5/0.7. “The initial cost is comparable with geothermal” COP of 1.5 Environmental Magazine 11-12 (2010) 

2372-2382 (2010) heating, 0.7 cooling 

Desroches, L.-B. et al. Max Tech and Beyond (2011) Pg 22: Such regionally appropriate designs could save 20% to 25% (Used 

lower end of range provided) 

Katipamula, S. Pacific Northwest National Laboratory. “Cost-Effective Large office incremental costs. Energy savings for cooling and ventilation/ 

Integration of Efficient Low-Lift Baseload Cooling Equipment: FY08 Final fans based on Case 1 to Case 8, national average, large office buildings. 

Report”, PNNL-19114 (http://www.pnl.gov/main/publications/external/technical_reports/PNNL-19114.pdf) 

Goetzler, W., Zogg, R., Burgos, J., Hiraiwa, H. & Young, J. Energy Savings Technology is in the research stage. No reliable cost data is available. Energy 

Potential and RD & D Opportunities for Commercial Building HVAC Systems. savings estimate is 17% of whole building energy use. 

289 (2011). 

U.S. Navy; “Evaluation of a Variable-speed Centrifugal Compressor with Average of high capacity Navy (41-67%) and Desroches (26%) numbers 

Magnetic Bearings.” Desroches, L.-B. et al. Max Tech and Beyond (2011) 

Personal communication: Bouza Cost and performance represents funded and supported DOE research 

product 

Goetzler, W., Zogg, R., Burgos, J., Hiraiwa, H. & Young, J. Energy Savings Technology is in the research stage. No reliable cost data is available. Energy 

Potential and RD & D Opportunities for Commercial Building HVAC Systems. savings potential estimated as 0.2 Quads/year out of 2.25 Quads/year (9%). 

289 (2011). 

Goetzler, W., Zogg, R., Burgos, J., Hiraiwa, H. & Young, J. Energy Savings “Applied to relevant annual energy consumption by increasing system efPotential and RD & D Opportunities for Commercial Building HVAC Systems. ficiency by 10%.” Premium of 20% ($1.07/ft2

) based on $9000, 5-ton system, 

289 (2011). 280 ft2

/ton. 

Acul, H. Air Cooled Condensers And Their Effect On Energy. 1-13 (2008) Impacts estimated from table on page 11: Decrease fan use from 4.68 to 1.12 Choi-Granade et. Al.; “Unlocking Energy 

kW/h Efficiency in the U.S. Economy”; McKinsey & 

Company (2009) 

Personal communication: Bouza, DOE 28% savings from DOE Funded research target. Baseline efficiency esti- Manufacturer presentation (http://www. 

mated from multiple references emersoncanada.ca/pages/energy/presentations/Scroll_Compressor_Technology_Optimizing_Efficiency_Feb08.pdf) and 

specifications: Hitachi EU Series DC Inverter 

controlled series, Mitsubishi R407C & 410A. 

DOE; “DOE and Private Sector Partners Introduce a New Money-Saving IEER of 18, Cost and performance estimated from Challenge specifications 

Specification for Commercial Air Conditioners”, 2011. Daikon McQuay Rebel 

RTU Specifications 

Pacific Gas & Electric. “Thermal Energy Storage Strategies for Commercial Citation 1, Table page 12 provides cost and efficiency data. Calmac indicates 

HVAC Systems”. PG&E, 1997. Calmac (Manufacturer) website “Operation at night with 20 degree lower condensing temperatures can 

improve energy efficiency typically by 2 to 8%.” Assume 5% (average of 

208%) savings, cost from 20 tons cooling, 8 hours of capacity/ building at 

$100/ton-hr 

Roth, K., John, D., Zogg, R. & Brodrick, J. Chilled Beam Cooling. ASHRAE “a chiller dedicated for chilled beam cooling has a lower temperature lift and 

Journal 84-86 (2007). Sachs, H..; Lin, W.; Lowenberger, A.; “Emerging operates at a 15% to 20% higher efficiency than for an conventional system.” 

Energy Savings HVAC Technologies and Practices for the Buildings Sector”; Multiple sources provide 20% savings estimate; currently available technolACEEE (2009) ogy provides price estimate at -$1/sq. ft. +/- $3/sq. ft. 

Eco-max technical documentation. Desroches, L.-B. et al. Max Tech and Used low end of manufacturer estimate Choi-Granade et. Al.; “Unlocking Energy 

Beyond (2011) Efficiency in the U.S. Economy”; McKinsey & 

Company (2009) 

Navigant. Energy Savings Potential and RD&D Opportunities for Commercial “Assume 17% savings”. No meaningful cost data is available because the 

Building HVAC Systems, Navigant, September 30, 2011. http://apps1.eere. technology is in the research stage. 

energy.gov/buildings/publications/pdfs/corporate/savings_potential_ 

comm_hvac.pdf 

Djunaedy, E., Van Den Wymelenberg, K., Acker, B., Thimmana, H. (n.d.). 

“Oversizing of HVAC System: Signatures and Penalties. Energy and Buildings.” Energy and Buildings, In Press. Florida Solar Energy Center, “RightSize Heating and Cooling Equipment”, Department of Energy (2002). NREL 

Measure Database (for cost per unit capacity) 

Pg. 6: “estimated an energy savings of 0.2% for every 1% reduction in Florida Solar Energy Center, “Right-Size 

oversizing.” Pg 1: “One Florida study showed a typical 9 percent increase Heating and Cooling Equipment”, Departin annual space cooling electricity usage for units that were oversized by ment of Energy (2002) 

50 percent or more;nearly 40 percent of contractors indicated that they 

purposefully over-sized equipment”, assume 50% oversizing on average for 

cooling, 25% for heating 

Checket-Hanks, B. “Contractor Offers Hope For PTAC IAQ”. The Air Condi- Savings (10%) and cost estimates based on Manufacturer’s website 

tioning, Heating and Refrigeration NEWS, March 10, 2004. Rejuvinair website 

Kistler, P. & Lintner, W. Evaluation of a Variable-speed Centrifugal Compres- Descroches indicated 26% savings available today and references Kistler 

sor with Magnetic Bearings. U.S. Navy (2010). Desroches, L.-B. et al. Max showing the 40-60% is possible with R&D. Cost and performance from Table 

Tech and Beyond. Lawrence Berkeley National Laboratory (2011). 1 and Figure 6 

Goetzler, W., Zogg, R., Burgos, J., Hiraiwa, H. & Young, J. Energy Savings Technology is in the research stage. No reliable cost data is available. Energy 

Potential and RD & D Opportunities for Commercial Building HVAC Systems. savings potential estimated as 0.09 Quads/year out of 0.98 Quads/year 

289 (2011). (9%). 

S.J. Slayzak and J.P. Ryan; Desiccant Dehumidification Wheel Test Guide “energy savings on building cooling systems are estimated to be approxi­

(2000). Desroches, L.-B. et al. Max Tech and Beyond (2011) mately 25%.” Adjusted 25% to 12.5% to incorporate baseline improvement in 

A/C since publication of original source 

Eric Kozubal, Jason Woods, Jay Burch, Aaron Boranian, and Tim Merrigan. Kozubal: $15,200 for standard DX, $20,461 for DeVap (10-ton system). 

Desiccant Enhanced Evaporative Air-Conditioning (DEVap): Evaluation of a Assumes 10 tons per 6,000 sq. ft. for dry climates ($0.88/ft2

). ($0.20/ft2 

New Concept in Ultra Efficient Air Conditioning. http://www.nrel.gov/docs/ added for plumbing to unit) Total source energy 106,268 kWh for baseline 

fy11osti/49722.pdf and 10,506 for DeVAP. Source energy converted to site energy to estimate 

energy savings. 

77
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

C: Use desiccant-enhanced 2% Yes FS $1.17 per square foot; 1 C: Cooling in humid 50,105 378 1,154 628 $4.41 20 No No No Yes 

evaporative cooling (e.g., ton per 280 sq. ft. climates 

DeVAP) in humid climates 

C: Use liquid desiccant air con­ 40% Yes $0.50 per square foot C: Cooling in humid 50,105 378 1,154 462 $2.94 20 No No No Yes 

ditioning in humid climates climates 

C: Use Maisotsenko cycle cool­ 30% Yes $1.79 per square foot; 1 C: Cooling in cold 33,972 113 345 104 $31.39 20 No No No No 

ing (e.g., Coolerado) in cold- ton per 280 sq. ft. and mixed dry 

and mixed-dry climates climates 

C: Use Maisotsenko cycle cool­ 88% Yes $1.79 per square foot C: Cooling in hot, dry 19,867 145 441 385 $5.05 20 No No No No 

ing (e.g., Coolerado) in hot-dry (~24,000 sq. ft.) climates 

climates 

C: Use multiple-small 32% Yes $0.50 per square foot C: Cooling in hot, 30,772 286 873 254 $2.97 20 No No No Yes 

plate (MSP) technology to humid climates 

dehumidify 

C: Use spray-cooled evapora­ 14% Yes $0.53 per square foot; 1 C: Rooftop/ resi style 37,350 242 738 79.2 $14.25 20 No No No No 

tors for packaged A/C ton per 280 sq. ft. A/C’s 

C: Used sprayed mesh to 18% Yes $3.28 per square foot; 1 C: Rooftop/ resi style 37,350 242 738 125 $42.15 20 No No No No 

improve A/C efficiency ton per 280 sq. ft. A/C’s 

R: Add whole house fan to 25% Yes $692 per home; 3 ton R: Central cooling in 42.7 171 522 50.9 $13.70 40 No No No No 

replace A/C use when appropri­ cooling coldest 3 climates, 

ate in existing homes single family existing 

homes 

R: Add whole house fan to 

replace A/C use when appropriate in new homes 

25% Yes $392 per unit R: Central cooling in 

coldest 3 climates, 

new homes 

17.1 55.3 169 37.5 $11.10 40 No No No No 

R: Charge A/C refrigerant to 

improve efficiency (applied in 

all climates) 

5% Yes $128 per unit R: Coils (A/C and 

HP) - heating and 

cooling 

103 977 2,982 104 $5.29 20 No No Yes Yes 

R: Clean A/C coils to improve 

cooling efficiency 

6% Yes $10 per unit R: Coils (A/C and HP) 

- heating and cooling 

103 977 2,982 180 $1.48 1 Yes Yes No Yes 

R: Develop air-conditioning 

systems customized for hot-dry 

climates 

20% Yes $200 per home R: Cooling in hot, dry 

climates 

12.5 119 364 65.8 $1.92 20 No No No Yes 

R: Incorporate awnings in home 

to shade windows 

20% Yes $600 per home; six 

awnings/home 

R: Central Cooling in 

hot climates 

66.6 577 1,761 302 $5.72 20 No No Yes Yes 

R: Incorporate natural ventila­ 46% Yes -$3,000 per home, 3 ton R: Central cooling 8.5 29.2 89.2 41.0 -$32.21 80 Yes Yes Yes Yes 

tion systems into homes in equivalent cooling in new homes in temtemperate climates perate climates 

R: Increase ceiling fan use (in 9% Yes $420 per home with 2.8 R: Heating and 49.3 1,941 2,835 188 $4.55 20 Yes Yes Yes Yes 

lieu of A/C) fans/home cooling in homes w/o 

ceiling fans 

R: Increase efficiency of room 21% Yes $132.60 per unit; 12 kBTU/ R: Room A/C 52.6 89.3 272 47.6 $8.08 20 No No No No 

A/C to 10.7 EER hr for 1,000 sq. ft. 

R: Increase use of central A/C 42% Yes $1,800 per home; 3 ton R: Central A/C 83.9 634 1,933 813 $10.19 20 No No No No 

with 21 SEER cooling 

R: Install night economizers 10% Yes $780 per home R: Single family 64.9 497 1,517 107 $18.98 20 No No Yes Yes 

central cooling 

R: Plant shade trees to provide 12% Yes $900 per home; 3 trees/ R: Central Cooling in 66.6 577 1,761 178 $14.36 20 No No No Yes 

cooling in hot climates home hot climates 

R: R&D to decrease cost of 49% Yes $710 per home; 3 ton R: Central A/C 83.9 634 1,933 816 $3.50 20 No No No Yes 

central A/C with 24 SEER cooling 

R: R&D to decrease cost of 35% Yes $325.10 per unit; 12 kBTU/ R: Room A/C 52.6 89.3 272 94.1 $8.55 20 No No No Yes 

room A/C with 13 EER hr for 1,000 sq. ft. 

R: Replace A/C with fan for ex­ 81% -$511 per home; 1 fan plus R: Central cooling in 21.0 58.8 179 180 -$3.06 20 No No No No 

isting homes in cooler climates one room A/C vs. 21 coldest 2 climates, 

kBTU/hr CAC cost existing homes 

R: Replace A/C with fan for new 81% -$1,141 per home; 1 fan plus R: Central cooling in 5.6 22.0 67.3 58.8 -$6.21 20 No No No No 

homes in cooler climates one room A/C vs. 21 coldest 2 climates, 

kBTU/hr CAC cost new homes 

78
Primary sources used Data used and/or methodology Other supporting sources 

Kozubal, E.; Woods, J.; Judkoff, R.; “Development and Analysis of Desiccant 

Enhanced Evaporative Air Conditioner Prototype”; NREL, 2012. Kozubal, E. 

et. al.; “Desiccant Enhanced Evaporative Air-Conditioning (DEVap): Evaluation of a New Concept in Ultra Efficient Air Conditioning”; NREL, 2011 

“Modeling at NREL has shown that the yearly combined source energy for 

the thermal and electrical energy required to operate DEVap is expected to 

be 30%–90% less than state-of-the-art direct expansion cooling (depending 

on whether it is applied in a humid or a dry climate)”, NREL research team 

performed climate and sector specific calculations 

Sachs, H..; Lin, W.; Lowenberger, A.; “Emerging Energy Savings HVAC Tech- Percent savings 66% elec; 85% gas (ACEEE). DuCool observed savings 35%­ Choi-Granade et. Al.; “Unlocking Energy 

nologies and Practices for the Buildings Sector”; ACEEE, 2009. DuCool Case 70% depending on configuration. Most comparable case study to hot-humid Efficiency in the U.S. Economy”; McKinsey & 

Studies, Advantix Company presentations (publicly available, 2011) climate suggests 40% savings. Cost given DuCool payback times Company (2009) 

U.S. Department of Energy. 2007. Coolerado Cooler Helps to Save Cooling DOE: Energy savings of 30% estimated based on Table 1 compared to a 

Energy and Dollars. FEMP Technology Installation Review DOE/GO-102007­ typical EER of 10 for a standard air conditioner. “Installed cost per ton $900– 

2325. http://www1.eere.energy.gov/femp/pdfs/tir_coolerado.pdf. Coolerado $1,100/ton.” NREL: Typical A/C is $3960 for a 3-ton system ($110/kBtu/hr) 

website. http://www.coolerado.com/ 

Coolerado technical manuals, Desroches, L.-B. et al. Max Tech and Beyond. “Utilizing the Maisotsenko cycle, indirect evaporative coolers can provide ap60 (2011) proximately 80% energy savings compared to standard vapor-compression 

air conditioners.”, cost and savings taken from manufacturer’s website and 

documentation 

MSP Technology, “Dehumidification Equipment for Recirculated Air and Savings taken from page 7 of MSP manual; supported by nautica claims 

Dedicated Outside Air Systems (DOAS).” Desroches, L.-B. et al. Max Tech (used manufacturer estimate) 

and Beyond (2011) 

Acul, H. Air Cooled Condensers And Their Effect On Energy. 1-13 (2008) Impacts estimated from table on page 11. Decrease fan use from 4.68 to 1.12 

kW/h 

Acul, H. Air Cooled Condensers And Their Effect On Energy. 1-13 (2008) Impacts estimated from table on page 4, Increase COP from 3.03 to 3.71 Choi-Granade et. Al.; “Unlocking Energy 

Efficiency in the U.S. Economy”; McKinsey & 

Company (2009) 

Southface Energy Institute & Oak Ridge National Laboratory FEMP Technol- Calculations based on information on page 1 of fact sheet, assumes reducogy Fact Sheet: Whole House Fan. (1999) tion of A/C use at night estimated at 25% of total annual load 

Southface Energy Institute & Oak Ridge National Laboratory FEMP Technol- Calculations based on information on page 1 of fact sheet, assumes reducogy Fact Sheet: Whole House Fan. (1999) tion of A/C use at night estimated at 25% of total annual load 

Goswami et. al.; “Effect of refrigerant charge on the performance of air “experimental results show that if a system is undercharged to 90% a 2 per Choi-Granade et. Al.; “Unlocking Energy 

conditioning systems”; Int. J. Energy Res. 2001; 25:741}750 cent increase in the (COP); The maintenance costs included 2 h of labour at Efficiency in the U.S. Economy”; McKinsey & 

$60/h and one pound of refrigerant charge at $8/lb,” assumed typical charge Company (2009) 

level of 85-90% and $150 labor and material cost given expert interviews 

Choi-Granade et. al.; “Unlocking Energy Efficiency in the U.S. Economy”; Savings and cost estimates derived from Exhibits 7, 13 and personal 

McKinsey & Company (2009) communication 

Desroches, L.-B. et al. Max Tech and Beyond (2011) Pg 22: Such regionally appropriate designs could save 20% to 25% (Used 

lower end of range provided) 

First principles: window shading. Performed HES Pro analysis for each of 5 20% average savings, $100/awning cost Choi-Granade et. Al.; “Unlocking Energy 

climate zones. Cost from web-search Efficiency in the U.S. Economy”; McKinsey & 

Company (2009) 

Cardinale, N., Micucci, M. & Ruggiero, F. Analysis of energy saving using Replace A/C with automated ventilation system in temperate climates: no Choi-Granade et. Al.; “Unlocking Energy 

natural ventilation in a traditional Italian building. Energy and Buildings ducting cost or HVAC. Cardinale: Simulations (Italina homes) showed sav- Efficiency in the U.S. Economy”; McKinsey & 

35, (2003). Walker, A (2010). “Natural Ventilation”. Whole Building Design ings of 41%, 46%, and 52%; using average of 46%. New home installed cost Company (2009) 

Guide (http://www.wbdg.org/resources/naturalventilation.php). Price: of $3,000 vs. $6,000 for conventional 

Windowmaster/ Aarhus Engineering College websites 

Aens, E; Turner, S; Zhang, H; Paliaga, G. “Moving Air for Comfort”. ASHRAE Citation 1, Figure 5 indicates 3-6 degree F increase in temperature with air 

Journal, May 2009, pp 8–18. Aynsley, R. “Circulating Fans for Summer and movement admits same comfort; estimates of cooling savings ~3-4%/F. 

Winter Comfort and Indoor Energy Efficiency”. Environment Design Guide, Citation 2 suggests upper heating benefit limit of 10% for homes. Cost from 

Nov. 2007 google search for fans; savings weighted average of 20% cooling, 5% heating 

Savings: First principles. Cost: NREL retrofit measure database 

Savings: first principles. Cost: NREL retrofit measure database 

Itron Inc., Kema, Inc. “California Energy Efficiency Potential Study” Pacific A-10: “The assumed savings for night economizer was 20% of the RASS 

Gas & Electric (2008) household UEC for coastal regions and 10% of the UEC for inland regions.” 

(Used 10% as majority of country is best represented as “inland” in this 

dataset). Price taken from web search 

Energy and Buildings Volume 25, Issue 2, 1997, Pages 139–148; Atmospheric Analysis of examples provided suggests 12 +/- 3% savings from existing 

Environment Volume 32, Issue 1, January 1998, Pages 69–74; Improved esti­ “planting practices” (analysis included in tool). Cost from web-search for 3 

mates of tree-shade effects on residential energy use Energy and Buildings, medium-sized trees installed 

Volume 34, Issue 10, November 2002, Pages 1067-1076 

Savings: first principles. Cost: Navigant Consulting, personal communication 

Savings: First principles. Cost: DOE published target for funded research 

Southface Energy Institute & Oak Ridge National Laboratory FEMP Technol- Calculations based on information on page 1 of fact sheet, Calculated from Choi-Granade et. Al.; “Unlocking Energy 

ogy Fact Sheet: Whole House Fan. (1999) average cost to operate reduction from 11 to 2.1 cents/hour Efficiency in the U.S. Economy”; McKinsey & 

Company (2009) 

Southface Energy Institute & Oak Ridge National Laboratory FEMP Technol- Calculations based on information on page 1 of fact sheet, Calculated from Choi-Granade et. Al.; “Unlocking Energy 

ogy Fact Sheet: Whole House Fan. (1999) average cost to operate reduction from 11 to 2.1 cents/hour Efficiency in the U.S. Economy”; McKinsey & 

Company (2009) 

79
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

R: Right-size A/C in new 13% Yes -$180 per home, 3 tons R: All Cooling, other 134 880 2,684 266 -$6.33 20 Yes Yes No Yes 

buildings of cooling than room A/C 

R: Use desiccant wheels in hot

humid climates 0.3 ACH, 1800 humid climates 

sq. ft. 

R: Use desiccant-enhanced 2% Yes FS $1,770 per home, 3 ton R: Central cooling in 52.7 531 1,621 725 $5.17 20 No No No Yes 

evaporative cooling (e.g., unit humid climates 

DeVAP) in humid climates 

R: Use desiccant-enhanced 82% Yes FS $2,804 per home; 3 ton R: Central cooling in 52.2 338 1,032 656 $8.97 20 No No No Yes 

evaporative cooling (e.g., dry climates 

DeVAP) in dry climates 

R: Use liquid desiccant air con­ 40% Yes $1,100 per home; 3 ton R: Central cooling in 52.7 531 1,621 603 $6.44 20 No No No Yes 

ditioning in humid climates humid climates 

R: Use Maisotsenko cycle cool­ 30% Yes $5,270 per home; 3 ton R: Cooling in cold 27.1 124 380 114 $68.01 20 No No No No 

ing (e.g., Coolerado) in cold- and mixed dry 

and mixed-dry climates climates 

R: Use Maisotsenko cycle cool­ 88% Yes $2,685 per home; 5 ton R: Cooling in hot, dry 12.5 119 364 321 $5.73 20 No No No No 

ing (e.g., Coolerado) in hot-dry climates 

climates 

R: Used sprayed mesh to 18% Yes $273.44 per home; 3 tons R: All Cooling, other 134 880 2,684 363 $4.19 20 No No No No 

improve A/C efficiency of cooling than room A/C 

R: Deploy reverse-cycle chillers 22% Yes $600 per home; family R: Single family 101 2,380 4,747 853 $3.75 20 No No No No 

to capture A/C waste heat for of 4: 3 ton A/C; 70 cooling and water 

water heating gal/day hot H2

O heating 

C: Develop integrated heat 43% Yes FS $0.79 per square foot C: Non-electric space 87,841 2,721 3,823 -438.2 $4.27 20 No No No No 

pumps (i.e., heating, cooling, heating, proportion 

and hot water) (FS only) of cooling and hot 

water 

C: Develop integrated heat 50% Yes $1.05 per square foot C: Electric space 5,709 513 1,307 414 $0.67 20 No No Yes Yes 

pumps (i.e., heating, cooling, heating, proportion 

and hot water) (no FS) of cooling and hot 

water 

C: Reduce cost of ground 81% Yes FS $7.38 per square foot; 1 C: Non-electric space 87,841 2,721 3,823 2,191 $16.38 20 No No Yes Yes 

source heat pumps (FS only) ton per 280 sq. ft. heating, proportion 

of cooling and hot 

water 

C: Reduce cost of ground 65% Yes FS $8.38 per square foot; 1 C: Electric space 5,709 513 1,307 760 $3.82 20 No No Yes Yes 

source heat pumps (no FS) ton per 280 sq. ft. heating, proportion 

of cooling and hot 

water 

R: Develop integrated heat 43% Yes FS $4,265 per home; 3 ton R: All non-electric 83.4 5,576 6,719 -1,208.7 $12.55 20 No No No No 

pumps (i.e., heating, cooling, heating and water 

and hot water) (FS only) heating plus central 

air conditioner 

R: Develop integrated heat 50% Yes $3,864 per home; 3 ton R: All electric heating 47.0 1,112 3,392 1,057.4 $6.71 20 No No No Yes 

pumps (i.e., heating, cooling, & hot water; proporand hot water) (no FS) tion of cooling 

­ 13% Yes $720 per home; assume R: Cooling in hot, 16.8 192 586 75.9 $8.46 20 No No No No 

80
Primary sources used Data used and/or methodology Other supporting sources 

Djunaedy, E., Van Den Wymelenberg, K., Acker, B., Thimmana, H. (n.d.). Pg. 6: “estimated an energy savings of 0.2% for every 1% reduction in oversiz­ Choi-Granade et. Al.; “Unlocking Energy 

“Oversizing of HVAC System: Signatures and Penalties. Energy and Build­ ing.” Pg 1: “One Florida study showed a typical 9 percent increase in annual Efficiency in the U.S. Economy”; McKinsey & 

ings” Energy and Buildings, In Press. Florida Solar Energy Center, “Right-Size space cooling electricity usage for units that were oversized by 50 percent or Company (2009) 

Heating and Cooling Equipment”, Department of Energy (2002). NREL more;nearly 40 percent of contractors indicated that they purposefully overMeasure Database (for cost per unit capacity)” sized equipment”,assume 50% oversizing on average for cooling, 25% for heating 

S.J. Slayzak and J.P. Ryan; Desiccant Dehumidification Wheel Test Guide “energy savings on building cooling systems are estimated to be approxi­

(2000). Desroches, L.-B. et al. Max Tech and Beyond (2011) mately 25%.” Adjusted 25% to 12.5% to incorporate baseline improvement in 

A/C since publication of original source 

Kozubal, E.; Woods, J.; Judkoff, R.; “Development and Analysis of Desiccant “Modeling at NREL has shown that the yearly combined source energy for 

Enhanced Evaporative Air Conditioner Prototype”; NREL, 2012. Kozubal, E. the thermal and electrical energy required to operate DEVap is expected to 

et. al.; “Desiccant Enhanced Evaporative Air-Conditioning (DEVap): Evalua­ be 30%–90% less than state-of-the-art direct expansion cooling (depending 

tion of a New Concept in Ultra Efficient Air Conditioning”; NREL, 2011 on whether it is applied in a humid or a dry climate)”. NREL research team 

performed climate and sector specific calculations 

Eric Kozubal, Jason Woods, Jay Burch, Aaron Boranian, and Tim Merrigan. Kozubal: $7484 for 3-ton DeVap (uninstalled). Plus $2080 for installation. 

Desiccant Enhanced Evaporative Air-Conditioning (DEVap): Evaluation of a Same energy savings as Measure 678. 

New Concept in Ultra Efficient Air Conditioning. http://www.nrel.gov/docs/ 

fy11osti/49722.pdf. NREL Residential Efficiency Measures Database. http:// 

www.nrel.gov/ap/retrofits/measures.cfm?gId=2&ctId=16 

Sachs, H..; Lin, W.; Lowenberger, A.; “Emerging Energy Savings HVAC Tech- Percent savings 66% elec; 85% gas (ACEEE). DuCool observed savings 35%­ Choi-Granade et. Al.; “Unlocking Energy 

nologies and Practices for the Buildings Sector”; ACEEE, 2009. DuCool Case 70% depending on configuration. Most comparable case study to hot-humid Efficiency in the U.S. Economy”; McKinsey & 

Studies, Advantix Company presentations (publicly available, 2011) climate suggests 40% savings. Cost given DuCool payback times Company (2009) 

U.S. Department of Energy. 2007. Coolerado Cooler Helps to Save Cooling DOE: Energy savings of 30% estimated based on Table 1 compared to a 

Energy and Dollars. FEMP Technology Installation Review DOE/GO-102007­ typical EER of 10 for a standard air conditioner. “Installed cost per ton $900– 

2325. http://www1.eere.energy.gov/femp/pdfs/tir_coolerado.pdf. NREL $1,100/ton.” NREL: Typical A/C is $3960 for a 3-ton system ($110/kBtu/hr) 

Residential Efficiency Measures Database. http://www.nrel.gov/ap/retrofits/ 

measures.cfm?gId=2&ctId=16 

Coolerado technical manuals, Desroches, L.-B. et al. Max Tech and Beyond. “Utilizing the Maisotsenko cycle, indirect evaporative coolers can provide ap60 (2011) proximately 80% energy savings compared to standard vapor-compression 

air conditioners.”, cost and savings taken from manufacturer’s website and 

documentation. 

Acul, H. Air Cooled Condensers And Their Effect On Energy. 1-13 (2008) Impacts estimated from table on page 4, Increase COP from 3.03 to 3.71 Choi-Granade et. Al.; “Unlocking Energy 

Efficiency in the U.S. Economy”; McKinsey & 

Company (2009) 

Heller, J. & Cejudo, C. Reverse Cycle Chillers for Multifamily Buildings in the Choi-Granade et. Al.; “Unlocking Energy 

Pacific Northwest: Phase I Final Report. Bonneville Power Administration Efficiency in the U.S. Economy”; McKinsey & 

(2009) Company (2009) 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Table 6-11: Summary of Primary Energy Savings Potential of all Water Heat­ “Murphy,R; Rice, C; Baxter, V; Craddick, W. 

portunities for Commercial Building Appliances. Washington, D.C.: Building ing Technology Options. Percent savings and cost changed to reflect site “Air-Source Integrated Heat Pump for Net 

Technology Program, Department of Energy., http://apps1.eere.energy. energy baseline of non-electric heating -> 43% savings Zero-Energy Houses: Technology Status 

gov/buildings/publications/pdfs/corporate/commercial_appliances_re­ Report”. Oak Ridge National Laboratory 

port_12-09.pdf ORNL/TM-2007/112. Baxter, V.; Murphy, R; 

Rice, K; Craddick, B; “Development of a 

Small Integrated Heat Pump (IHP) for Net 

Zero Energy Homes” 9th International IEA 

Heat Pump Conference (2008)” 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Opportuni- Table 6-11: Summary of Primary Energy Savings Potential of all Water Heatties for Commercial Building Appliances. Washington, D.C.: Building Technol­ ing Technology Options 

ogy Program, Department of Energy., http://apps1.eere.energy.gov/buildings/ 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Liu, X. Assessment of National Benefits from Retrofitting Existing Single- savings of [4.2 quads primary energy], a 45% reduction associated with “Office of the Deputy Under Secretary of 

Family Homes with Ground Source Heat Pump Systems. Oak Ridge National SH–SC–WH in existing U.S. single-family homes. Worked with author to Defense Report to Congress: GroundLaboratory (2010). Hughes, P.J. Geothermal (Ground-Source) Heat Pumps: disaggregate into fuel-switching and non-fuel-switching opportunities. DoD, Source Heat Pumps at Department of 

Market Status, Barriers to Adoption, and Actions to Overcome Barriers. Oak perhaps the largest single customer for GHP retrofit projects, reports that in Defense Facilities. (2007). Hughes, P.J. & 

Ridge National Laboratory (2008) 2006 dollars housing and commercial retrofits cost $4600 and $7000 per Shonder, J.A. The Evaluation of a 4000­

ton respectively Home Geothermal Heat Pump Retrofit at 

Fort Polk , Louisiana: Final Report. Oak 

Ridge National Laboratory (1998)” 

Liu, X. Assessment of National Benefits from Retrofitting Existing Single- savings of [4.2 quads primary energy], a 45% reduction associated with “Office of the Deputy Under Secretary of 

Family Homes with Ground Source Heat Pump Systems. Oak Ridge National SH–SC–WH in existing U.S. single-family homes. Worked with author to Defense Report to Congress: GroundLaboratory (2010). Hughes, P.J. Geothermal (Ground-Source) Heat Pumps: disaggregate into fuel-switching and non-fuel-switching opportunities. DoD, Source Heat Pumps at Department of 

Market Status, Barriers to Adoption, and Actions to Overcome Barriers. Oak perhaps the largest single customer for GHP retrofit projects, reports that in Defense Facilities. (2007). Hughes, P.J. & 

Ridge National Laboratory (2008) 2006 dollars housing and commercial retrofits cost $4600 and $7000 per Shonder, J.A. The Evaluation of a 4000­

ton respectively Home Geothermal Heat Pump Retrofit at 

Fort Polk , Louisiana: Final Report. Oak 

Ridge National Laboratory (1998)” 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Opportuni- Table 6-11: Summary of Primary Energy Savings Potential of all Water Heatties for Commercial Building Appliances. Washington, D.C.: Building Technol­ ing Technology Options. Percent savings and cost changed to reflect site 

ogy Program, Department of Energy., http://apps1.eere.energy.gov/buildings/ energy baseline of non-electric heating -> 43% savings 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Baxter, V.; Murphy, R; Rice, K; Craddick, B; “Development of a Small Inte­ “For the air-source IHP version, the simulation results showed ~46-67% engrated Heat Pump (IHP) for Net Zero Energy Homes” 9th International IEA ergy savings depending upon location. For the ground-source IHP version, 

Heat Pump Conference (2008) the simulation showed over 50% savings in all locations” 

Used 50% as conservative estimate within 46-67% range 

81
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

R: Install ground-source heat­ 81% Yes FS $14,400 per home; 3 ton R: All non-electric 83.4 5,576 6,719 1,881 $19.58 40 No No No Yes 

pumps (GSHP) in single family heating and water 

homes (FS only) heating plus central 

air conditioner 

R: Install ground-source heat­ 65% Yes $14,400 per home; 3 ton R: All electric heating 47.0 1,112 3,392 1,262 $18 40 No No Yes Yes 

pumps (GSHP) in single family 

homes (no FS) 

& hot water; proportion of cooling 

C: Change inside temp 1° F 5% Yes $0 per square foot C: Commercial HVAC 103,945 3,192 4,924 242 $0 1 No No No Yes 

C: Close blinds to reduce solar 

heat gain 

R: Change inside temp 1° F 

16% 

5% 

Yes 

Yes 

$0.14 

$0 

per square foot; 

one unit per 6x6 

window on south 

face 

per home 

C: Windows that 

can benefit from 

attachments 

R: Heating and 

cooling 

51,972 

160 

501 

5,568 

767 

8,290 

105 

417 

$4.11 

$0 

10 

1 

No 

No 

No 

No 

No 

No 

Yes 

Yes 

R: Close blinds to reduce solar 

heat gain 

R: Develop air-source heat 

pumps for cold climates (FS 

only) 

C: Increase efficiency of circulator pumps 

16% 

76% 

60% 

Yes 

Yes 

$440 

$3,000 

$0.05 

per home; 8 south 

facing blinds 

per home; 3 ton 

per unit; Assumed 

1 per 1500 sq. ft. 

R: Windows that 70.7 

can benefit from 

attachments 

R: Non-electric heat­ 37.0 

ing, proportion of 

cooling in coldest 2 

climates 

C: Circulator pumps 32,769 

918 

2,041 

43.7 

1,361 

2,348 

133 

186 

1,781 

80.5 

$9.19 

$3.13 

$1.07 

10 

20 

20 

No 

No 

Yes 

No 

No 

Yes 

No 

No 

Yes 

Yes 

Yes 

Yes 

C: Install demand ventilation 

(e.g., CO2

 sensing) 

20% Yes $0.54 per square foot; 

$800/zone 

C: Commercial HVAC 103,945 3,192 6,017 924 $3.20 10 Yes Yes Yes Yes 

C: Switch ventilation from CAV 25% 

to VAV 

C: Use VAV insulation instead 25% 

of CAV 

R: Switch to variable speed 51% 

motors in furnace fans for home 

ventilation 

$0 

$0 

$296 

per unit 

per unit 

per unit 

C: CAV ventilation 49,917 

C: CAV ventilation 49,917 

R: Furnace Fans Total 88.1 

316 

316 

186 

963 

963 

567 

225 

225 

378 

$0 

$0 

$3.67 

20 

20 

20 

No 

No 

Yes 

No 

No 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

R: Use most efficient circulator pumps for multi-family 

buildings 

C/I/O: Eliminate constant 

electrode heating in rapid start 

ballasts 

60% 

5% 

$75 

$20.30 

per unit 

per unit 

R: Heating systems 

pumps (all radiative 

and heat pumps) 

C/IRapid-Start/ 

Dimming Ballasts/ 

Industrial 

48.2 

223 

49.3 

75.6 

150 

231 

90.9 

0 

$1.91 20 

$7,319.24 5 

Yes 

No 

Yes 

No 

Yes 

No 

Yes 

No 

C/I/O: Improve efficiency of 

HID dimmable ballasts 

C/I/O: Improve high-intensity 

discharge lamps for use in low 

CRI applications 

C/I/O: Increase use of low-CRI 

high-intensity discharge lamps 

C/I/O: Research and develop 

multi-photon phosphors for 

fluorescent lights 

15% 

75% 

63% 

63% 

$125 

$42 

$62.96 

$6 

per ballast 

per lamp 

per lamp 

per lamp 

C/IHIDs and Fluo­ 1,944 

rescents - Dimmable 

Ballasts (Comm/ 

Indust/Outd) 

C/IMV and MH (C,I,O) 89.3 

C/IIncandescent 793 

(Comm), MV and MH 

(Comm/Indust/Outd) 

C/IHIDs and Fluores­ 3,056 

cents (C/I/O) 

1,044 

188 

228 

1,641 

3,184 

573 

694 

5,007 

0 

618 

103 

2,554 

$258.06 

$0.74 

$37.63 

$0.80 

5 

3 

1 

7 

No 

No 

No 

No 

No 

No 

No 

No 

No 

Yes 

Yes 

No 

No 

Yes 

Yes 

No 

C/I: Develop dimmable instant­ 15% Yes $20.30 per ballast C/IFluorescent Dim­ 2,437 682 2,080 155 $22.96 5 No No No No 

start ballasts ming Instant-Start 

Ballasts/Industrial 

C/I: Develop general-use 62% $0.03 per lamp C/IAll commercial 2,889 1,249 3,811 2,181 $0 5 No No No No 

organic light emitting diode Lighting 

(OLED) 

82
Primary sources used Data used and/or methodology Other supporting sources 

Liu, X. Assessment of National Benefits from Retrofitting Existing SingleFamily Homes with Ground Source Heat Pump Systems. Oak Ridge National 

Laboratory (2010). Hughes, P.J. Geothermal (Ground-Source) Heat Pumps: 

Market Status, Barriers to Adoption, and Actions to Overcome Barriers. Oak 

Ridge National Laboratory (2008) 

savings of [4.2 quads primary energy], a 45% reduction associated with 

SH–SC–WH in existing U.S. single-family homes. Worked with author to 

disaggregate into fuel-switching and non-fuel-switching opportunities. DoD, 

perhaps the largest single customer for GHP retrofit projects, reports that in 

2006 dollars housing and commercial retrofits cost $4600 and $7000 per 

ton respectively 

“Office of the Deputy Under Secretary of 

Defense Report to Congress: GroundSource Heat Pumps at Department of 

Defense Facilities. (2007). Hughes, P.J. & 

Shonder, J.A. The Evaluation of a 4000­

Home Geothermal Heat Pump Retrofit at 

Fort Polk , Louisiana: Final Report. Oak 

Ridge National Laboratory (1998)” 

Liu, X. Assessment of National Benefits from Retrofitting Existing SingleFamily Homes with Ground Source Heat Pump Systems. Oak Ridge National 

Laboratory (2010). Hughes, P.J. Geothermal (Ground-Source) Heat Pumps: 

Market Status, Barriers to Adoption, and Actions to Overcome Barriers. Oak 

Ridge National Laboratory (2008) 

savings of [4.2 quads primary energy], a 45% reduction associated with 

SH–SC–WH in existing U.S. single-family homes. Worked with author to 

disaggregate into fuel-switching and non-fuel-switching opportunities. DoD, 

perhaps the largest single customer for GHP retrofit projects, reports that in 

2006 dollars housing and commercial retrofits cost $4600 and $7000 per 

ton respectively 

“Office of the Deputy Under Secretary of 

Defense Report to Congress: GroundSource Heat Pumps at Department of 

Defense Facilities. (2007). Hughes, P.J. & 

Shonder, J.A. The Evaluation of a 4000­

Home Geothermal Heat Pump Retrofit at 

Fort Polk , Louisiana: Final Report. Oak 

Ridge National Laboratory (1998)” 

Berry, C; Swenson, A; “Winter Energy Savings from Lower Thermostat Set- Natural Gas. Households whose main space-heating fuel was natural gas 

tings”, EIA Brief (2000) would have consumed 5 percent less natural gas for space heating and 

would have spent $22 less (for the entire year of 1997) if they had set their 

thermostat 1° F lower 

Cho, S.-hwan, Shin, K.-S. & Zaheer-Uddin, M. The Effect of Slat Angle of Win- Results show that the use of windows with Venetian blinds can reduce the 

dows With Venetian Blinds on Heating and Cooling Loads in South Korea. building heating load by about 5% and cooling loads by as much as 30% 

Energy 20, 1225-1236 (1995) Used conservative weighted average of savings range 

Berry, C; Swenson, A; “Winter Energy Savings from Lower Thermostat Set- Natural Gas. Households whose main space-heating fuel was natural gas 

tings”, EIA Brief (2000) would have consumed 5 percent less natural gas for space heating and 

would have spent $22 less (for the entire year of 1997) if they had set their 

thermostat 1° F lower 

Cho, S.-hwan, Shin, K.-S. & Zaheer-Uddin, M. The Effect of Slat Angle of Win- Results show that the use of windows with Venetian blinds can reduce the 

dows With Venetian Blinds on Heating and Cooling Loads in South Korea. building heating load by about 5% and cooling loads by as much as 30%.” 

Energy 20, 1225-1236 (1995) Used conservative weighted average of savings range 

Sachs et al, 2009, “Emerging Energy-Saving HVAC Technologies and Prac- Energy savings equivalent to no-fuel-switching measure but measured 

tices for the Buildings Sector.” Hallowell International; Personal communica­ against oil and natural gas market 

tion: Bouza, DOE 

Waide, P. & Brunner, C. Energy-Efficiency Policy Opportunities for Electric Page 58: An exemplary study of two optimal pump systems demonstrates Byrne, Jeanne, “Motors Matter”. Home 

Motor-Driven Systems. International Energy Agency (2011). that if all available state-of-the-art efficiency measures of a pump system Energy Magazine Online 1-7 (2000) 

are systematically applied, energy-efficiency savings of 80% to 90% 

Goetzler, W., Zogg, R., Burgos, J., Hiraiwa, H. & Young, J. Energy Savings Page 16: “These savings are equivalent to a 10% reduction in total energy 

Potential and RD & D Opportunities for Commercial Building HVAC Systems. costs.” Page 11: “For a new system to be installed will generally cost $600­

Navigant Consulting (2011) $700 per zone; retrofit at $700-$900/zone.” Used 10% total energy savings 

-> 20% HVAC savings; using average retrofit cost as that is the majority of 

the opportunity 

Directly calculated from NEMS detailed data tables Directly calculated from NEMS detailed data tables 

EPA, (2012) “IAQ Building Education and Assessment Model” 

Murray, M. & Fitzpatrick, S. Residential HVAC Electronically Commutated “ECM motors represent a 51 percent full-load efficiency improvement over Franco, V., Lutz, J., Lekov, A. & Gu, L. 

Motor Retrofit Report Table of Contents. Advanced Energy (2012) permanent split-capacitor (PSC) motors.” “The three ECMs used in this study Furnace Blower Electricity: National and 

cost, on average, $296. The average motor installation cost was approximate- Regional Savings Potential. Florida Solar 

ly $547.50.” Only retrofit is cost effective, so using 51% savings, $296 cost” Energy Center(2008) 

Waide, P. & Brunner, C. Energy-Efficiency Policy Opportunities for Electric Figure 27 and table 23 show savings of 55-63% improvement of existing 

Motor-Driven Systems. International Energy Agency (2011). system with new, optimized pump. Cost from google search 

Navigant Consulting U.S. Lighting Market Characterization Volume II: Energy Page 38: “With the elimination of constant electrode heating, these values 

Efficient Lighting Technology Options. Energy II, 286 (2005). would decline to 37 watts and 38 watts, respectively, correlating to a 5% 

decrease in energy consumption. A 5% decrease in energy consumption 

would result in energy savings of 0.01 quad per year.” and Table 3-16: Ballast 

Price, by Type 

Navigant Consulting U.S. Lighting Market Characterization Volume II: Energy Table 3-45: Technical Potential Energy Savings of HID Dimmable Ballasts 

Efficient Lighting Technology Options. Energy II, 286 (2005). and page 84: “A cost estimates for a 400-watt electronic ballast is $223, approximately $125 more than a 400-watt CWA ballast (Advance Transformer, 

2003).” 

Navigant Consulting U.S. Lighting Market Characterization Volume II: Energy Assumes 10,000 hr life, 10 incandescent as standard cost 

Efficient Lighting Technology Options. Energy II, 286 (2005). 

Navigant Consulting U.S. Lighting Market Characterization Volume II: Energy Table 3-34: Technical Potential Energy Savings of Ceramic Metal Halide Cost data are from google searches of comEfficient Lighting Technology Options. Energy II, 286 (2005). mercially available appliances 

Navigant Consulting U.S. Lighting Market Characterization Volume II: Energy Table 3-17: Technical Potential Energy Savings of Multi-photon Phosphors Cost data are from google searches of comEfficient Lighting Technology Options. Energy II, 286 (2005). mercially available appliances 

Navigant Consulting U.S. Lighting Market Characterization Volume II: Energy Table 3-13: Technical Potential Energy Savings of Dimmable Instant-Start 

Efficient Lighting Technology Options. Energy II, 286 (2005). Ballasts and Table 3-14: Ballast Price, by Type 

Navigant Consulting U.S. Lighting Market Characterization Volume II: Energy Table 3-50: Potential Energy Savings of OLED Technology 

Efficient Lighting Technology Options. Energy II, 286 (2005). 

83
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

C/R: Develop high-CRI, 71% $5.87 per lamp C/RResi and Comm 6,695 296 904 579 $18.94 1 No No No Yes 

low-wattage high-intensity Incandescents 

discharge lamps 

C/R: Implement the recent elec­ 7% Yes $0.13 per ballast; 2 lamps C/RResi and Comm 3,304 1,294 3,949 183 $0.16 20 Yes Yes Yes Yes 

tronic ballast standard in stock per ballast Fluorescents 

C/R: Raise ballast standard to 11% $9 per ballast C/RResi and Comm 3,304 1,294 3,949 186 $10.10 20 No No No Yes 

higher efficiency level Fluorescents 

C: Add skylights for daylighting 8% Yes $1.37 per square foot; C: All fluorescents, 50,966 582 1,777 133 $35.12 20 No No No No 

in new buildings 45,000 ft2

 ave. new commercial 

grocery store 

C: Develop advanced gen­ 72% -$0.77 per lamp C: All commercial 2,889 1,249 3,811 2,674 -$0.04 40 No No Yes Yes 

eral use LED lighting (i.e., DOE Lighting 

roadmap) 

C: Develop low-CRI sulfur lights 35% $436.14 per lamp C: Fluorescents Com­ 1,056 368 1,123 237 $172.40 5 No No No No 

to replace fluorescent mercial/Industrial 

(for sulfur lamps) 

C: Develop low-CRI sulfur lights 30% $414 per lamp C: All HID (C/I/O) 161 353 1,075 95.9 $50.66 2 No No No No 

to replace HID 

C: Increase use of CFLs as 63% -$1.81 per lamp C: Incandescent 703 39.8 121 4.61 -$6.12 1 Yes Yes Yes Yes 

incandescent replacement 

C: Increase use of solid-state 89% -$1,854.86 per sign C: Commercial Signs 25.3 31.0 94.6 103 -$28.42 11 Yes Yes Yes Yes 

lighting for signage 

C: Install hard-wired lighting 13% Yes $0.34 per square foot C: All commercial 103,945 1,249 3,811 431 $7.88 4 No No No No 

occupancy sensors lighting, per square 

foot stock 

C: Install wireless lighting oc­ 13% Yes $0.05 per square foot; C: All commercial 103,945 1,249 3,811 347 $1.02 20 No No Yes Yes 

cupancy sensors for lighting one controller per lighting, per square 

4 luminaires foot stock 

C: Optimize windows for day­ 8% Yes $0.15 per square foot; C: All fluorescents, 50,966 582 1,777 123 $4.64 40 No No Yes Yes 

lighting, new 200,000 ft2

 ave. new commercial 

building 

C: Parking lot LED w/controls 50% $140 per luminaire C: Parking lot lighting 46.7 113 345 0 $5.92 10 No No Yes Yes 

C: Perimeter zone day lighting 40% Yes $184.93 per square foot C: Perimeter zone 481 223 682 185 $16.18 40 No No Yes Yes 

fluorescent 

C: Task lighting 50% $1.96 per square foot C: Task lighting 12,473 143 435 219 $5.52 20 No No No Yes 

C: TDDs and controls, existing 21% Yes $8.50 per square foot; C: All fluorescents, 52,979 605 1,847 378 $67.97 20 No No Yes Yes 

210,800 ft2

 school existing commercial 

C: Troffer specification: 1x4 23% Yes $58 per luminaire C: Lighting: 1x4 38.1 20.9 63.7 12.4 $11.92 10 No No Yes Yes 

without controls improvement troffers 

C: Troffer specification: 2x2 21% Yes $58 per luminaire C: Lighting: 2x2 76.2 41.7 127 21.9 $13.41 10 No No Yes Yes 

without controls improvement troffers 

C: Troffer specification: 2x4 51% Yes $291 per luminaire C: Lighting: 2x4 trof­ 217 119 363 178 $23.27 10 No No Yes Yes 

with controls improvement fers benefitting from 

controls 

84
Primary sources used Data used and/or methodology Other supporting sources 

Navigant Consulting U.S. Lighting Market Characterization Volume II: Energy Table 3-26: Technical Potential Energy Savings of Low-Wattage MH Lamps. Cost data are from google searches of comEfficient Lighting Technology Options. Energy II, 286 (2005). “The GE ceramic MH lamp has an efficacy of 85 lm/W, and would replace a mercially available appliances 

miniature halogen lamp that has an efficacy of 21 lm/W” 

Navigant Consulting & Pacific Northwest National Laboratory Final Rule In depth analysis based on technical data included in tool 

Technical Support Document Energy Conservation Program for Consumer 

Products and Certain Commercial And Industrial Equipment: Fluorescent 

Lamp Ballasts. Department of Energy (2011) 

Universal Lighting Technologies, “Triad High Efficiency System”. Manufac- Difference between Triad and standard electric ballast averaged between 

turer (2011) 0.78 and 0.88 ballast factors 

Leach, M., Hale, E., Hirsch, A. & Tocellini, P. Grocery Store 50 % Energy Sav­ 3% of roof area for 45,000 ft2

 grocery store @ $45.8/ft2. $1.37/ft2 = 

ings Technical Support Document Grocery Store (2009). http://www.nrel. 0.03%*$45.8/ft2

. Energy savings assumes skylights contribute 50% of total 

gov/docs/fy09osti/46101.pdf daylighting energy savings. Average of Miami, Chicago, and Seattle. 

Bardsley Consulting et. al. “Solid-State Lighting Research and Development: Represents cost and performance goals for LED lighting measured against “Navigant Consulting. “2010 U.S. Lighting 

Multi-Year Program Plan”. Department of Energy 2012 today’s installed base Market Characterization”. Department of 

Energy (2005). Navigant Consulting U.S. 

Lighting Market Characterization Volume II: 

Energy Efficient Lighting Technology Options. Department of Energy (2005).” 

Navigant Consulting U.S. Lighting Market Characterization Volume II: Energy Table 3-48: Technical Potential Energy Savings of Molecular Discharge Cost data are from google searches of comEfficient Lighting Technology Options. Energy II, 286 (2005). Lamps mercially available appliances 

Navigant Consulting U.S. Lighting Market Characterization Volume II: Energy Table 3-48: Technical Potential Energy Savings of Molecular Discharge Cost data are from google searches of comEfficient Lighting Technology Options. Energy II, 286 (2005). Lamps mercially available appliances 

ENERGY STAR (n.d.). Savings Calculator for ENERGY STAR Qualified Light- Cost per unit comes from the savings calculator ENERGY STAR (n.d.). Light Bulbs for Coning. Retrieved from: http://www.energystar.gov/ia/business/bulk_purchas­ sumers. Retrieved from: http://www.energying/bpsavings_calc/LightingCalculator.xlsx?94b4-cb27&94b4-cb27 star.gov/index.cfm?fuseaction=find_a_product.showProductGroup&pgw_code=LB 

Navigant Consulting U.S. Lighting Market Characterization Volume II: Energy Table 6-6: Summary Table for Utilization: Fixtures, “Monochromatic LEDs 

Efficient Lighting Technology Options. Energy II, 286 (2005). exhibit 80-90% energy savings over neon in signage fixtures. 

Leach, M., Hale, E., Hirsch, A. & Tocellini, P. Grocery Store 50 % Energy Energy savings assumed the same as Measure 206. Cost of $0.36/ft2

 based Brambley, M. et al. Advanced sensors and 

Savings Technical Support Document Grocery Store 50% Energy Savings the Grocery Store TSD. controls for building applications: Market 

Technical Support Document. 174 (2009). assessment and potential R&D pathways. Pacific Northwest National Laboratory (2005). 

Navigant Consulting U.S. Lighting Market Characterization Volume II: Energy 

Efficient Lighting Technology Options. Energy II, 286 (2005). 

NREL. 2011. Advanced Energy Retrofit Guide for K-12 Schools (Draft). EERE 

Report DOE/GO-102011-3467. Project Lead: Robert Hendron 

Energy savings based on the average of the K-12 schools AERG (11%) and 

the Navigant Lighting Study (15%). From Navigant, the incremental cost of 

$24/controller translates to about $0.014/ft2

 (assuming 126 controllers per 

210,000 ft2

 based on the K-12 AERG). The K-12 AERG suggests a cost of 

about $0.08/ft2

. The difference was split and $0.05/ft2 was used. 

Brambley, M. et al. Advanced sensors and 

controls for building applications: Market 

assessment and potential R&D pathways. Pacific Northwest National Laboratory (2005). 

Hendron, R. Senior Engineer - National Renewable Energy Laboratory Commercial Buildings Group. Personal communications. 

Department of Energy, “CBEA High-Efficiency Troffer Lighting Specification” “Navigant Consulting Energy Savings 

(2012). Myer, personal communication Estimates of Light Emitting Diodes in Niche 

Lighting Applications. 116 (2008). Navigant 

Consulting U.S. Lighting Market Characterization Volume I: National Lighting Inventory and Energy Consumption Estimate. 

Renewable Energy I, 120 (2002).” 

Brambley, M. et al. Advanced sensors and controls for building applications: PNNL page 2.23: “A report by the EPA (2001) suggests that daylit offices 

Market assessment and potential R&D pathways. Energy 156 (2005).at can achieve up to 35%-40% savings, and that other daylit spaces (class­

<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.111.8772&rep rooms, grocery stores, and retail outlets) can achieve 40%-60% savings.” 

=rep1&type=pdf> and used $1.60 per sq-ft for 80 billion square feet 

California Lighting Technology Center. (n.d.). Integrated Office Lighting Sys­ “Energy savings: 50% annual energy savings; over 15 years, one system can Heschong Mahone Group, Inc. (2009). 

tem (IOLS). Retrieved from Demonstration: http://cltc.ucdavis.edu/content/ yield savings of 7,500 kWh” Small Office (“Encon” Building) Site Report. 

view/673/359/ Pacific Gas and Electric Company. 

NREL. 2011. Advanced Energy Retrofit Guide for K-12 Schools (Draft). EERE $1.79 million for TDDS, controls, and dimming ballasts. Analysis based on a 

Report DOE/GO-102011-3467. Project Lead: Robert Hendron typical 210,800 ft2

 high school. 

Department of Energy, “CBEA High-Efficiency Troffer Lighting Specification” 1’x 4’ : 1.6 TWh (site) – 23% savings against incumbent technology “Navigant Consulting Energy Savings 

(2012). Myer, personal communication Estimates of Light Emitting Diodes in Niche 

Lighting Applications. 116 (2008). Navigant 

Consulting U.S. Lighting Market Characterization Volume I: National Lighting Inventory and Energy Consumption Estimate. 

Renewable Energy I, 120 (2002).” 

Department of Energy, “CBEA High-Efficiency Troffer Lighting Specification” 2’ x 2’ : 2.6 TWh (site) – 21% savings against incumbent technology “Navigant Consulting Energy Savings 

(2012). Myer, personal communication Estimates of Light Emitting Diodes in Niche 

Lighting Applications. 116 (2008). Navigant 

Consulting U.S. Lighting Market Characterization Volume I: National Lighting Inventory and Energy Consumption Estimate. 

Renewable Energy I, 120 (2002).” 

Department of Energy, “CBEA High-Efficiency Troffer Lighting Specification” Including controls in "1-to-1" replacement measure increases savings to 51% “Navigant Consulting Energy Savings 

(2012). Myer, personal communication and cost to $291/luminaire; applicable to approximately 50% of fixtures Estimates of Light Emitting Diodes in Niche 

Lighting Applications. 116 (2008). Navigant 

Consulting U.S. Lighting Market Characterization Volume I: National Lighting Inventory and Energy Consumption Estimate. 

Renewable Energy I, 120 (2002).” 

85
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

C: Troffer specification: 2x4 35% Yes $116 per luminaire C: Lighting: 2x4 362 198 605 191 $14.64 10 No No Yes Yes 

without controls improvement troffers 

C: Use spectrally enhanced 17% Yes $1.95 per lamp C: fluorescent 2,406 1,188 3,624 539 $0.77 5 No No No Yes 

lighting 

O: Install off-grid solar-powered 100% $80.03 per luminaire O: All lighting, 120 256 782 835 $1.85 2 No No No No 

outdoor lighting Outdoor 

R/C/I/O: Develop LED lighting 79% $39.29 per lamp R/CR/C/I/O Reflector 785 62.7 191 91.3 $16.83 10 No No No Yes 

to replace incandescent PAR lamps 

R: Develop advanced gen­ 90% -$23.10 per home; 30 R: Lighting, all (by 141 441 1,346 1,208 -$0.11 68 Yes Yes Yes Yes 

eral use LED lighting (i.e., DOE lamps replaced/ home) 

roadmap) home 

R: Increase use of CFLs 63% -$1.81 per lamp R: Resi incandescents 5,992 257 783 115 -$8.33 1 Yes Yes Yes Yes 

R: Replace incandescent deco­ 70% $15.49 per unit R: Decorative Light 195 34.8 106 123 $2.22 3 Yes Yes No Yes 

rative strings with LEDs Strings 

R: Use occupancy sensor for 15% Yes $5.43 per sensor; one R: Lighting, all (by 141 441 1,346 165 $0.19 20 No No Yes Yes 

lighting control in homes unit per 8 bulbs home) 

C/R: Develop advanced display 20% $0.80 per unit C/RComputers and 297 507 1,548 329 $0.08 4 No No Yes Yes 

power management systems TVs for power management and Resi 

C/R: Develop and use inter­ 10% Yes $0.10 per unit C/RAudio/video 357 129 395 26.1 $0.11 7 No No Yes Yes 

device power control for audiovisual equipment power control, Resi 

C/R: Develop cholesteric liquidcrystal displays (LCDs) for 

monitors and TVs 

50% $150 per unit C/RMonitors Resi + 

Comm 

474 36.1 110 55.7 $130.20 4 No No No Yes 

C/R: Increase use of network 

presence proxy for PC power 

management 

30% Yes $0 per unit C/RResi and Comm 

Standby Power 

89.5 285 868 262 $0 4 No No Yes Yes 

C/R: Switch from desktop to 

notebook PCs 

80% $100 per unit C/RResi and Comm 

Desktop PCs 

44.2 182 555 305 $2.25 4 No No No Yes 

C/R: Use more efficient (i.e., 

level of proposed standards) 

small motors in miscellaneous 

devices 

21% $41 per unit C/RSmall motors 

(covered in recent 

rulemaking) 

29.2 192 586 109 $0.69 12 Yes Yes Yes Yes 

C/R: Use most efficient (i.e., 

max-tech) small motors in 

miscellaneous devices 

33% $159 per unit C/RSmall motors 

(not in other analyzed products) 

108 619 1,890 599 $2.67 5 No No Yes Yes 

C/R: Deploy energy efficient ethernet protocols (i.e., 

802.3az) 

5% Yes $0.03 per unit C/REnergy efficient 

ethernet and Resi 

239 379 1,158 31.9 $0.02 4 Yes Yes Yes Yes 

C/R: Develop an LCD monitor 

standard 

40% $75 per unit C/RMonitors Resi + 

Comm 

474 36.1 110 56.5 $38.47 8 No No Yes Yes 

C/R: Increase use of desktop 

computer power management 

44% Yes $0 per unit C/RAll desktop PCs 44.2 182 555 161 $0 4 No No Yes Yes 

C/R: Increase use of laptop 

computer power management 

28% Yes $0 per unit C/RResi and Comm 

Notebook PCs 

420 48.8 149 35.2 $0 4 Yes Yes Yes Yes 

C/R: Increase use of most efficient (i.e., max-tech) desktop 

computers 

80% $392.63 per unit C/RResi and Comm 

Desktop PCs 

44.2 182 555 508 $6.38 4 Yes Yes Yes Yes 

C: Controllable power outlets, 

new 

19% Yes $1.75 per square foot; 

50,000 ft2

 retail 

store 

C: All plug loads in 

new commercial 

buildings 

50,966 999 3,047 514 $12.96 20 No No No No 

C: Dry distribution transformers, High 

69% $0 per unit C: Dry Distribution 

Transformers 

0.86 123 375 170 $0 32 No No Yes Yes 

86
Primary sources used Data used and/or methodology Other supporting sources 

Department of Energy, “CBEA High-Efficiency Troffer Lighting Specification” 2’ x 4’ : 16.4 TWh (site) – 35% savings against incumbent technology “Navigant Consulting Energy Savings 

(2012). Myer, personal communication Estimates of Light Emitting Diodes in Niche 

Lighting Applications. 116 (2008). Navigant 

Consulting U.S. Lighting Market Characterization Volume I: National Lighting Inventory and Energy Consumption Estimate. 

Renewable Energy I, 120 (2002).” 

Navigant Consulting U.S. Lighting Market Characterization Volume II: Energy Table 5-3: Price of Lamps, Spectrally Enhanced vs. Non-Spectrally Enhanced 

Efficient Lighting Technology Options. Energy II, 286 (2005). 

Navigant Consulting U.S. Lighting Market Characterization Volume II: Energy Page 193: “Off-grid luminaires would affect energy consumption in the Cost data are from google searches of comEfficient Lighting Technology Options. Energy II, 286 (2005). outdoor stationary sector, essentially taking it 100% off grid, reducing en­ mercially available appliances 

ergy used from 0.6 quad (NCI, 2002) to 0 quad (technical potential energy 

savings, primary energy).” 

Navigant Consulting. “Energy Savings Estimates of Light Emitting Diodes in Savings: Page 14, table 2.4. Cost: Page 59: “LED MR16 and PAR30 lamps 

Niche Lighting Applications”. Department of Energy (2011). cost nearly eight times more than the conventional halogen lamps they 

replaced”, supported by google search 

Bardsley Consulting et. al. “Solid-State Lighting Research and Development: Represents cost and performance goals for LED lighting measured against “Navigant Consulting. “2010 U.S. Lighting 

Multi-Year Program Plan”. Department of Energy (2012). today’s installed base Market Characterization”. Department of 

Energy (2005). Navigant Consulting U.S. 

Lighting Market Characterization Volume II: 

Energy Efficient Lighting Technology Options. Department of Energy (2005).” 

ENERGY STAR (n.d.). Savings Calculator for ENERGY STAR Qualified Light- Cost per unit comes from the savings calculator 

ing. Retrieved from: http://www.energystar.gov/ia/business/bulk_purchasing/bpsavings_calc/LightingCalculator.xlsx?94b4-cb27&94b4-cb27 

ENERGY STAR (n.d.).Decorative Light Strings for Consumers. Retrieved “ENERGY STAR Qualified Decorative Light Strings consume 70% less energy Cost data are from google searches of comfrom: http://www.energystar.gov/index.cfm?fuseaction=find_a_product. than conventional incandescent lights strands.” mercially available appliances 

showProductGroup&pgw_code=DS 

Navigant Consulting U.S. Lighting Market Characterization Volume II: Energy Page 108: “Typical LED drive electronics operate at efficiencies in the range Cost data are from google searches of comEfficient Lighting Technology Options. Energy II, 286 (2005). of 75% to 85%. For comparison, the most efficient ballasts for fluorescent mercially available appliances 

lamps operate at a little better than 90% efficiency, with single digit losses 

(Lightfair, 2003).” 

Personal communication: Nordman, LBNL (12/2010) 20% savings, very low costs relative to savings, usually several orders of 

magnitude difference (using benefit/cost ratio of 100) 

Personal communication: Nordman, LBNL (12/2010) 10% savings, very low costs relative to savings, usually several orders of 

magnitude difference (using benefit/cost ratio of 100) 

Roth, K.W., Larocque, G.R., Kleinman, J. & Doe, B.C. Energy Consumption by Page 4-13, Table 4-9 Cost data are from google searches of comOffice and Telecommunications Equipment in Commercial Buildings Volume mercially available appliances 

II?: Energy Savings Potential. Engineering II, 201 (2004). 

Personal communication with Nordman on Dec. 15, 2010 30% savings, very low costs relative to savings, usually several orders of 

magnitude difference (using benefit/cost ratio of 100) 

Roth, K.W., Mckenney, K., Ponoum, R. & Paetsch, C. Residential Miscellaneous Page 4-33: Specifically, a desktop PC plus a monitor has a UEC about five Cost data are from google searches of comElectric Loads: Energy Consumption Characterization and Savings Potential. times greater than a notebook PC. mercially available appliances 

Energy 197 (2007). 

Department of Energy, Small Electric Motors Final Rule Technical Support Efficiency level 5 from chapter 5 of 2010 TSD (and MTAB pg. 44 Fig. 2) 

Document (2010). Desroches, L.-B. et al. Max Tech and Beyond (2011) 

Department of Energy, Small Electric Motors Final Rule Technical Support Highest efficiency level in 2010 TSD (and MTAB pg. 44 Fig. 2) represents 

Document (2010). Desroches, L.-B. et al. Max Tech and Beyond (2011) increasing small motor efficiency from 62% (ave. installed base” to best 

available (92%) 

Personal communication with Nordman on Dec. 15, 2010 5% savings, very low costs relative to savings, usually several orders of 

magnitude difference (using benefit/cost ratio of 100) 

Roth, K.W., Mckenney, K., Ponoum, R. & Paetsch, C. Residential Miscellaneous Page 4-46: On average, a 17-inch LCD monitors draws about 40% less power Cost data are from google searches of comElectric Loads: Energy Consumption Characterization and Savings Potential. than CRT monitors in active mode....” mercially available appliances 

Energy 197 (2007). 

Roth, K.W., Mckenney, K., Ponoum, R. & Paetsch, C. Residential Miscellaneous Page 4-31, last sentence. Cost data are from google searches of comElectric Loads: Energy Consumption Characterization and Savings Potential. mercially available appliances 

Energy 197 (2007). 

Roth, K.W., Mckenney, K., Ponoum, R. & Paetsch, C. Residential Miscellaneous Page 4-31, last sentence. Cost data are from google searches of comElectric Loads: Energy Consumption Characterization and Savings Potential. mercially available appliances 

Energy 197 (2007). 

Roth, K.W., Mckenney, K., Ponoum, R. & Paetsch, C. Residential Miscellaneous Table 4-20: Personal Computers Summary Cost data are from google searches of comElectric Loads: Energy Consumption Characterization and Savings Potential. mercially available appliances 

Energy 197 (2007). 

E.T. Hale, D.L. Macumber, N.L. Long, B.T. Griffith, K.S. Benne, S.D. Pless, and Energy savings and cost from Hale et al. $87,500 for a 50,000 ft2

 retail store. 

P.A. Torcellini. 2008. Technical Support Document: Development of the Ad- Assumes reduction from 40% to 15% MELs operation during unoccupied 

vanced Energy Design Guide for Medium Box Retail—50% Energy Savings. hours 

(http://www.nrel.gov/docs/fy08osti/42828.pdf) 

TSD supporting spreadsheets: http://www1.eere.energy.gov/buildings/appli- This data came from a complex calculation/manipulation of the TSD supance_standards/docs/dt_nopr_tools_nia_ria.zip porting spreadsheets that is included in the tool 

87
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

C: Improve data center energy 70% -$14k per data center C: Data Centers 0.02 551 1,682 1,688 -$13.10 4 Yes Yes Yes Yes 

management (e.g., CADE) 

C: Increase fume hood 50% -$500 per unit C: Fume Hoods 0.99 67.5 206 104 -$0.32 10 Yes Yes Yes Yes 

efficiency 

C: Reduce standby power of 

miscellaneous equipment to 1W 

67% $0 per unit C: Commercial 

Standby Power 

163 137 418 305 $0 5 Yes Yes Yes Yes 

C: Use more efficient (i.e., 

proposed standard) liquid 

distribution transformers 

10% $0 per unit C: Liquid Distribution 

Transformers 

2.1 199 607 63.6 $0 32 No No No No 

C: Use most efficient (i.e., 

max-tech) liquid distribution 

transformers 

50% $0 per unit C: Liquid Distribution 

Transformers 

2.1 199 607 202 $0 32 No No Yes Yes 

C: Use most efficient fitness 

equipment 

50% $4,500 per unit C: Fitness equipment 1.1 5.40 16.5 9.47 $38.73 7 No No Yes No 

C: Use regenerative technology 31% $10,000 per unit C: Regenerative 0.12 1.97 6.01 1.38 $40.51 25 No No Yes Yes 

for geared elevators geared elevators in 

mid-rise commercial 

buildings 

R: Decrease set-top box power 25% $0.50 per unit R: Set-top boxes, 118 87.2 266 77.7 $0.07 5 Yes Yes Yes Yes 

use Resi 

R: Develop an active-power 50% $10 per unit R: TV Total 530 471 1,438 761 $0.47 10 Yes Yes Yes Yes 

draw TV standard 

R: Develop OLED monitors 75% $140.63 per unit R: Resi TVs and 742 541 1,651 1,210 $6.90 8 No No No Yes 

Resi and Comm PC 

monitors 

R: Incorporate power manage­ 72% $0 per unit R: Video game 98.0 18.6 56.7 59.0 $0 5 Yes Yes Yes Yes 

ment into video game consoles consoles 

R: Increase insulation on coffee 26% $41 per unit R: Coffee Maker Total 85.1 17.5 53.2 15.0 $28.23 3 No No Yes Yes 

maker carafes 

R: Reduce standby power of 67% $0 per unit R: Residential 182 152 465 339 $0 5 Yes Yes Yes Yes 

miscellaneous equipment to 1W standby power 

R: Reducing spa standby 6% Yes $0 per unit R: Spas Total 5.1 40.7 124 6.13 $0 15 No No Yes No 

temperature (2-4°F) 

R: Replace remaining CRT 40% $150 per unit R: Resi TVs and 742 541 1,651 667 $12.42 8 No No No Yes 

monitors with LCD monitors Resi and Comm PC 

monitors 

R: Use more efficient motors in 63% $86 per unit R: Ceiling Fan Total 306 72.8 222 131 $12.42 13 No No Yes Yes 

ceiling fans 

R: Use most efficient (i.e., 15% $0 per unit; 70 pint/ R: Residential 17.5 229 700 79.3 $0 12 Yes Yes Yes Yes 

ENERGY STAR) packaged day capacity Dehumidifier 

dehumidifiers 

R: Use most efficient hydraulic 21% $10,000 per unit R: Efficient Hydraulic 0.62 10.5 32.0 5.77 $43.75 25 No No Yes Yes 

elevator elevators in low-rise 

residential buildings 

R: Use most efficient vacuum 50% $0 per unit R: Vacuums 176 24.9 76.1 38.6 $0 7 Yes Yes Yes Yes 

cleaner 

R: Use regenerative technology 30% $9,000 per unit R: Regenerative 0.04 0.66 2 0.51 $27.79 25 No No Yes Yes 

for geared elevators elevators in high-rise 

residential buildings 

R: Use techniques from 63% $79 per unit R: Pool Pumps 10.3 53.0 162 102 $0.56 10 Yes Yes Yes Yes 

California pool pump standard 

nationally 

C: Add doors to supermarket 30% Yes $160 per unit C: All Reach-Ins 2.5 46.5 142 41.2 $0.60 10 No No Yes Yes 

display cases 

C: Increase ice machine wall 3% Yes $77 per unit C: Ice Machines 1.4 37.0 113 2.52 $2.77 9 No No No No

insulation 

C: Optimize air jets for reduced 20% Yes $160 per unit C: All Reach-Ins 2.5 46.5 142 26.7 $1.04 10 Yes Yes Yes Yes 

energy use in open display 

cases 

88
Primary sources used Data used and/or methodology Other supporting sources 

Zogg, R. et al. Energy Savings Potential and RD & D Opportunities for Com- Combination of virtualization, power management, and CADE-related 

mercial Building Appliances Department of Energy (2009). utilization management measures: 70% with a resultant decrease in IT 

Kaplan, J.M., Forrest, W. & Kindler, N. Revolutionizing Data Center Energy infrastructure and maintenance personnel cost 

Efficiency. McKinsey & Company (2008) 

Mckenney, K., Guernsey, M., Ponoum, R. & Rosenfeld, J. Commercial Miscel- Page 5-45: Table 9 Cost data are from google searches of comlaneous Electric Loads: Energy Consumption Characterization and Savings mercially available appliances 

Potential in 2008 by Building Type. Energy 244 (2010). 

Choi-Granade, C. e. (2009). Unlocking Energy Efficiency in the U.S. Page 49: “A standby standard could reduce standby consumption by 

Economy. McKinsey & Company. roughly two-thirds, yielding 90 - 110 TWh in savings.” 

TSD supporting spreadsheets: http://www1.eere.energy.gov/buildings/appli- This data came from a complex calculation/manipulation of the TSD supance_standards/docs/dt_nopr_tools_nia_ria.zip porting spreadsheets that is included in the tool 

TSD supporting spreadsheets: http://www1.eere.energy.gov/buildings/appli- This data came from a complex calculation/manipulation of the TSD supance_standards/docs/dt_nopr_tools_nia_ria.zip porting spreadsheets that is included in the tool 

Mckenney, K., Guernsey, M., Ponoum, R. & Rosenfeld, J. Commercial Miscel- Page 5-42, Table 8. Cost data are from google searches of comlaneous Electric Loads: Energy Consumption Characterization and Savings mercially available appliances 

Potential in 2008 by Building Type. Energy 244 (2010). 

E Source. (n.d.). Elevators. Retrieved from Business Energy Advisor: Table 2: Elevator payback period 

http://www.esource.com/escrc/0013000000DP22YAAT/BEA1/PA/ 

PA_ElevatorsEscalators/PA-57 

Personal communication: Nordman, LBNL (12/2010) 25% savings, very low costs relative to savings, usually several orders of 

magnitude difference (using benefit/cost ratio of 100) 

Roth, K.W., Mckenney, K., Ponoum, R. & Paetsch, C. Residential Miscellaneous Page 4-100, last sentence and Table 4-96 Cost data are from google searches of comElectric Loads: Energy Consumption Characterization and Savings Potential. mercially available appliances 

Energy 197 (2007). 

Roth, K.W., Mckenney, K., Ponoum, R. & Paetsch, C. Residential Miscellaneous Page 4-48, Table 4-43 and page 4-47, “Organic light-emitting diodes Cost data are from google searches of comElectric Loads: Energy Consumption Characterization and Savings Potential. (OLED)-based monitors potentially could offer energy savings of 50% over mercially available appliances 

Energy 197 (2007). LCD monitors” 

Desroches, L.B., & Garbesi, K. (2011). Max Tech and Beyond. Lawrence Berke- Page 20, Table 3 

ley National Laboratory. 

Roth, K.W., Mckenney, K., Ponoum, R. & Paetsch, C. Residential Miscellaneous Page 4-23, Table 4-16: Coffee Makers Summary Cost data are from google searches of comElectric Loads: Energy Consumption Characterization and Savings Potential. mercially available appliances 

Energy 197 (2007). 

Choi-Granade, C. e. (2009). Unlocking Energy Efficiency in the U.S. Page 49: “A standby standard could reduce standby consumption by 

Economy. McKinsey & Company. roughly two-thirds, yielding 90 - 110 TWh in savings.” 

Roth, K.W., Mckenney, K., Ponoum, R. & Paetsch, C. Residential Miscellaneous Page 5-24: “the ADL model indicates reducing the set point by 2 to 4 F can 

Electric Loads: Energy Consumption Characterization and Savings Potential. reduce the idle power draw by approximately three to eight percent (ADL 

Energy 197 (2007). 2004)” 

Roth, K.W., Mckenney, K., Ponoum, R. & Paetsch, C. Residential Miscellaneous Page 4-46: On average, a 17-inch LCD monitors draws about 40% less power Cost data are from google searches of comElectric Loads: Energy Consumption Characterization and Savings Potential. than CRT monitors in active mode....” mercially available appliances 

Energy 197 (2007). 

Environmental Protection Agency, “ENERGY STAR Ceiling Fan Product List”, Represents second fastest of six speeds from one of the most efficient 

2012 manufacturers using a DC motor at 280 CFM/W 

Installed base estimated at typical 100 CFM/W 

63% reduction possible at $86 premium 

ENERGY STAR. (n.d.). Dehumidifiers for Consumers. Retrieved from “Dehumidifiers that have earned the ENERGY STAR are 15% more efficient 

Products: http://www.energystar.gov/index.cfm?fuseaction=find_a_product. than non-qualified models.” and cost is from the savings calculator 

showProductGroup&pgw_code=DE 

E Source. (n.d.). Elevators. Retrieved from Business Energy Advisor: Table 2: Elevator payback period 

http://www.esource.com/escrc/0013000000DP22YAAT/BEA1/PA/ 

PA_ElevatorsEscalators/PA-57 

Roth, K.W., Mckenney, K., Ponoum, R. & Paetsch, C. Residential Miscellaneous Page 5-32: “If the high end of the Captive Air-Flow Cleaner tested power 

Electric Loads: Energy Consumption Characterization and Savings Potential. draw is used as the average power draw of vacuum cleaners, the active 

Energy 197 (2007). mode power draw and, hence, UEC of vacuum cleaners would decrease by 

almost 50% relative to the current estimate” 

E Source. (n.d.). Elevators. Retrieved from Business Energy Advisor: Table 2: Elevator payback period 

http://www.esource.com/escrc/0013000000DP22YAAT/BEA1/PA/ 

PA_ElevatorsEscalators/PA-57 

Pacific Gas & Electric, Sempra Energy; “Codes and Standards Enhancement Table 23 (savings), Table 25 (cost, but designs 2 to 1 only), using 63% sav- Roth, K.W., Mckenney, K., Ponoum, R. & 

Initiative: Draft Report Residential Swimming Pools”, (2007) ings, $79 extra cost Paetsch, C. Residential Miscellaneous Electric Loads: Energy Consumption Characterization and Savings Potential. Tiax (2007) 

Garry, M; “New Tests Support Doors on Cases”, Supermarket News Multiple examples cited indicating (+/-) small change in sales, ~30% energy 

10/4/2010. Garry, M; “Fresh & Easy Installs Case Doors in 35 Stores” Super- savings, ~3-5 year paybacks quoted. Using 30% savings and ~ 4 year paymarket News 9/20/2011 back and no statistically significant proof of sales impact 

Department of Energy, “Energy Conservation 

Commercial Refrigeration Equipment (http://apps1.eere.energy.gov/buildDOE/Navigant (2009), Energy Savings Potential and R&D Opportunities for Table 5-24, page 152, line 1, “Thicker Insulation” Program: Energy Conservation Standards for 

ings/publications/pdfs/corporate/commercial_refrig_report_10-09.pdf) Residential Refrigerators, Refrigerator-Freezers, and Freezers” Federal Register (9/2010) 

and supporting documentation 

Amin, Mazyar; Dabiri, Dana; Navaz, Homayun K., “Comprehensive study Used geometric mean of min and maximum values in table 2; assumed 

on the effects of fluid dynamics of air curtain and geometry, on infiltration similar cost to adding doors as labor should dominate retrofit cost 

rate of open refrigerated cavities”; Applied Thermal Engineering Volume 31, 

Issues 14–15, October 2011, Pages 3055–3065 

89
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

C: Reduce evaporator thermal 5% $25 per unit C: Ice Machines 1.4 37.0 113 14.0 $0.17 8 No No No No 

cycling in ice machine 

C: Reduced meltage during 4% Yes $110 per unit C: Ice Machines 1.4 37.0 113 3.43 $3.06 8 No No No No 

harvest in ice machine 

C: Retrofit lighting in refriger­ 74% $975 per unit C: WI Coolers, BM, 6.7 210 641 421 $0.90 13 No No No Yes 

ated displays to LEDs VM, Display Cases 

C: Use brushless DC motors in 9% $63 per unit C: Compressors-DC 8.2 155 472 173 $0.19 11 No No No Yes 

compressors 

C: Use brushless DC motors in 

refrigerator-freezer evaporator 

fans 

17% $98 per unit C: Evaporator Fans 9.9 346 1,054 249 $0.23 12 No No No Yes 

C: Use central fiber-optic 

lighting system in supermarket 

refrigeration systems 

25% $1,607.14 per unit C: WI Coolers, BM, 

VM, Display Cases 

6.7 210 641 176 $3.60 13 No No No No 

C: Use more efficient (i.e., cur­ 15% $140 per unit; 706 lbs/ C: Ice Machines 1.4 37.0 113 24.8 $0.48 10 Yes Yes Yes Yes 

rently available) ice machines day 

C: Use most efficient (i.e., maxtech) ice machines 23% $334 per unit C: Ice Machines 1.4 37.0 113 35.3 $0.88 9 Yes Yes Yes Yes 

C: Use most efficient (i.e., maxtech) supermarket refrigeration Supermarkets 

systems 

28% $228,536 per unit C: Rfrg System, 0.03 286 873 264 $1.67 12 Yes Yes Yes Yes 

C: Use most efficient (i.e., max­ 33% $296 per unit C: Vending Machines 3.5 43.5 133 82.3 $0.73 12 Yes Yes Yes Yes 

tech) vending machine 

C: Use variable-speed compres­ 20% $86 per unit C: Compressors-DC 8.2 155 472 224 $0.19 10 No No No Yes 

sors for refrigeration 

R: Use LED lighting in 1% Yes $7.99 per unit; 25 R: Standard Size 153 410 1,250 3.99 $13.22 15 No No No No 

refrigerators Lumens Refrigerators 

R: Add fans to upright freezers 8% Yes $1.45 per unit R: Upright Freezers 20.0 35.3 108 8.63 $0.19 15 No No Yes No 

R: Decrease refrigerator size 6% Yes -$90 per unit R: Standard Size 153 410 1,250 43.6 -$16.88 15 No No No No 

Refrigerators 

R: Develop thermo-accoustic 15% $1,549.75 per unit R: All Refrigerators 229 502 1,532 94.4 $147.66 15 No No No No 

refrigerators and Freezers 

R: Develop thermo-tunneling 63% $1,549.75 per unit R: All Refrigerators 229 502 1,532 638 $21.42 15 No No No Yes 

refrigerators and Freezers 

R: Eliminate icemaker parasitic 14% Yes -$110 per unit R: Primary Refrigera­ 108 325 991 100 -$7.14 15 No No Yes Yes 

energy use (i.e., hot-wire used tors with Icemakers 

for cube separation) 

R: Implement refridge standard 25% $1.45 per unit R: All Refrigerators 229 502 1,532 313 $0.06 15 Yes Yes Yes Yes 

(i.e., final rule 9/2011) and Freezers 

R: Increase freezer insulation 2% Yes $28.44 per unit; 25 ft R: Freezers and Com­ 75.9 92.3 282 2.29 $59.53 15 No No Yes No 3 

pact Refrigerators 

R: Increase use of adap­ 4% Yes $8 per unit R: All Refrigerators 229 502 1,532 19.3 $4.39 15 No No Yes No 

tive defrost in home and Freezers 

refrigerator-freezers 

90
Primary sources used Data used and/or methodology Other supporting sources 

DOE/Navigant (2009), Energy Savings Potential and R&D Opportunities for Table 5-24, page 152, line 6, “Reduced Evaporator Thermal Cycling Department of Energy, “Energy Conservation 

Commercial Refrigeration Equipment (http://apps1.eere.energy.gov/build- Program: Energy Conservation Standards for 

ings/publications/pdfs/corporate/commercial_refrig_report_10-09.pdf) Residential Refrigerators, Refrigerator-Freezers, and Freezers” Federal Register (9/2010) 

and supporting documentation 

DOE/Navigant (2009), Energy Savings Potential and R&D Opportunities for Table 5-24, page 152, line 5, “Reduced Meltage During Harvest” Department of Energy, “Energy Conservation 

Commercial Refrigeration Equipment (http://apps1.eere.energy.gov/build- Program: Energy Conservation Standards for 

ings/publications/pdfs/corporate/commercial_refrig_report_10-09.pdf) Residential Refrigerators, Refrigerator-Freezers, and Freezers” Federal Register (9/2010) 

and supporting documentation 

DOE/Navigant (2009), Energy Savings Potential and R&D Opportunities for Page 125: “An estimated potential energy savings of 74% could be achieved 

Commercial Refrigeration Equipment (http://apps1.eere.energy.gov/build­ through the use of LED lighting.” 

ings/publications/pdfs/corporate/commercial_refrig_report_10-09.pdf) 

DOE/Navigant (2009), Energy Savings Potential and R&D Opportunities for Average of energy savings and installed cost premium of high efficiency Department of Energy, “Energy Conservation 

Commercial Refrigeration Equipment (http://apps1.eere.energy.gov/build­ compressors from tables 5-17, 5-19, 5-20, 5-22, 5-26, 5-27 Program: Energy Conservation Standards for 

ings/publications/pdfs/corporate/commercial_refrig_report_10-09.pdf) Residential Refrigerators, Refrigerator-Freezers, and Freezers” Federal Register (9/2010) 

and supporting documentation 

Navigant Consulting Energy Savings Potential and R&D Opportunities for Tables 5-8, 13, 14, 17, 19, 20, 22, 26, 27 

Commercial Refrigeration Final Report. Department of Energy (2009) Averaged cost and savings over equipment in indicated tables 

DOE/Navigant (2009), Energy Savings Potential and R&D Opportunities for Table 6-1, page 157 for energy savings. For cost assumes $30,000 measure Department of Energy, “Energy Conservation 

Commercial Refrigeration Equipment (http://apps1.eere.energy.gov/build­ cost for 14 cases rather than $7,500 for 14 cases of lighting. Resultant per- Program: Energy Conservation Standards for 

ings/publications/pdfs/corporate/commercial_refrig_report_10-09.pdf) case incremental cose: ($30,000-$7,500)=$1607 Residential Refrigerators, Refrigerator-Freezers, and Freezers” Federal Register (9/2010) 

and supporting documentation 

Consortium for Energy Efficiency (2007), “Commercial Ice Makers” (http:// The average annual energy use of a 500 lb./day air-cooled ice-maker is ENERGY STAR, “Commercial Ice 

www.cee1.org/resrc/facts/com-ice-fx.pdf) 5,000 kWh with a potential increase in efficiency of 15 percent (less than a Machines”, http://www.energystar.gov/ 

two-year payback). index.cfm?fuseaction=find_a_product. 

showProductGroup&pgw_code=CIM 

DOE/Navigant (2009), Energy Savings Potential and R&D Opportunities for Table 5-24, line 8, page 152 Department of Energy, “Energy Conservation 

Commercial Refrigeration Equipment (http://apps1.eere.energy.gov/build- Program: Energy Conservation Standards for 

ings/publications/pdfs/corporate/commercial_refrig_report_10-09.pdf) Residential Refrigerators, Refrigerator-Freezers, and Freezers” Federal Register (9/2010) 

and supporting documentation 

DOE/Navigant (2009), Energy Savings Potential and R&D Opportunities for Cost is sum of compressor racks, condensers, sprmkt walk-ins, and 60x 

Commercial Refrigeration Equipment (http://apps1.eere.energy.gov/build­ display cases, all max tech options; savings are reduced to account for load 

ings/publications/pdfs/corporate/commercial_refrig_report_10-09.pdf) reduction (derived from Table 5-12) 

DOE/Navigant (2009), Energy Savings Potential and R&D Opportunities for Table 5-27, Page 154, see “max tech” 

Commercial Refrigeration Equipment (http://apps1.eere.energy.gov/buildings/publications/pdfs/corporate/commercial_refrig_report_10-09.pdf) 

Department of Energy, “Energy Conservation Program: Energy Conservation 

Standards for Residential Refrigerators, Refrigerator-Freezers, and Freezers” 

Federal Register (9/2010) and supporting documentation 

Arthur D. Little reported savings of approximately 25 % compared to singlespeed motor systems in 1999; Tecumseh Products Company demonstrated 

that energy savings of 15%. An average of the estimates of this cost increase 

provided by the manufacturers weighted by manufacturer market share is 

near $56; an additional $30 for addition of an electronic control system 

Using average of two studies: 20% at $86 cost 

LEDLight.com, “Standard Appliance LED Light Bulb”, http://www.ledlight. Plastic assembly so it is more durable than a regular light bulb and it only 

com/standard-appliance-led-light.aspx consumes 1/10 the power so it is more energy efficient” and current price is 

listed at $7.99 each. 90% reduction applied to refrigerator lighting load: ~1% 

Department of Energy, “Energy Conservation Program: Energy Conservation All data taken from TSD and supporting engineering and national impact 

Standards for Residential Refrigerators, Refrigerator-Freezers, and Freezers” estimates 

Federal Register (9/2010) and supporting documentation 

Department of Energy, “Energy Conservation Program: Energy Conservation Represents 1 step down in fridge size for equipment classes 3 & 5 (as per 

Standards for Residential Refrigerators, Refrigerator-Freezers, and Freezers” equations 5.4.12). Change in cost captured from Kenmore ENERGY STAR 21 

Federal Register (9/2010) and supporting documentation vs. 18.2 sq. ft. top-mounted freezer price difference 6/12 

Navigant Consulting Energy Savings Potential and R&D Opportunities for Performance average of table 6-1, cost 

Commercial Refrigeration Final Report. Department of Energy (2009) 

Brown, D., Dirks, J., Fernandez, N., & Stout, T. (2010). The Prospects of “The theoretical performance of thermotunneling devices operating near 

Alternatives to Vapor Compression Technology for Space Cooling and Food room temperature has been estimated by GE, Borealis, and Tempronics to 

Refrigeration Applications. Pacific Northwest National Laboratory be in the range of 50% to 80% of Carnot (COP ~3-5).” Represents max-tech 

improvements to refrigerator but compressor (COP 2.2) replaced with 

technology limit for themotunneling (COP 3.0)” 

Meier, A. Martinex, M; “Energy Use of Icemaking in Domestic Refrigerators”, “The refrigerators’ gross electricity use increased about IO%, or 100 kWh/ 

LBNL-39183 (1996) yr, due to operation of the automatic icemaker...The net energy difference, 

i.e., after subtracting the energy needed to make ice manually, is roughly 55 

kWh/yr.” 55 kWh/year corresponds to ~ 9.5% of today’s typical refrigerator 

of 585 kWh/year; current Kenmore icemaker kit priced at $110 (6/12) 

Department of Energy, “Energy Conservation Program: Energy Conservation All data taken from TSD and supporting engineering and national impact 

Standards for Residential Refrigerators, Refrigerator-Freezers, and Freezers” estimates 

Federal Register (9/2010) and supporting documentation 

Department of Energy, “Energy Conservation Program: Energy Conservation Table 5-A.3.10, 11, 13, 14 

Standards for Residential Refrigerators, Refrigerator-Freezers, and Freezers” Averaged data to get 10% savings improvement, $37 cost 

Federal Register (9/2010) and supporting documentation 

Department of Energy, “Energy Conservation Program: Energy Conservation “energy consumption can be reduced by three to four percent with adaptive 

Standards for Residential Refrigerators, Refrigerator-Freezers, and Freezers” defrost.” “DOE used an incremental cost of $8 in the energy analysis for 

Federal Register (9/2010) and supporting documentation adaptive defrost ($0 if electronics onboard).” Used $8 controller cost, average of 3-4% savings range provided” 

91
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

R: Minimize cold loss upon 16% Yes $0 per unit R: Refrigerator Total 178 412 1,256 215 $0 1 No No Yes No 

refrigerator door opening 

R: Perform routine dusting of 3% Yes $10 per unit R: All Refrigerators 229 502 1,532 33.7 $3.72 15 Yes Yes Yes Yes 

refrigerator heat exchanger and Freezers 

coils 

R: Remove replaced refrigera­ 38% Yes $79.53 per unit R: Refrigerators as 42.7 5.78 17.6 6.99 $28.58 15 No No No No 

tors (rather than using as back- Second Units 

up refrigerator) 

R: Retire pre-2010 fridges on 63% Yes $81 per unit R: Refrigerators 0.89 2.68 8.16 -146.9 $1.22 100 No No No No 

accelerated schedule Eligible for Early 

Retirement 

R: Use linear compressor in 

refrigerator-freezers 

15% $50 per unit; net of 

space savings 

value 

R: All Refrigerators 

and Freezers 

229 502 1,532 154 $4.44 15 No No No No 

R: Use more efficient (i.e., currently available) compressors 14% $14.02 per unit R: All Refrigerators 

and Freezers 

229 502 1,532 140 $1.43 15 No No No No 

R: Use most efficient (i.e., max

tech) refrigerators 

C: Use most efficient high­ 30% $3,500 per unit C: Conveyor High 0.20 44.3 96.0 29.0 $1.20 20 No No Yes Yes 

temperature conveyor-style Temp Dishwashers 

dish washer 

C: Use most efficient high­ 33% $2,100 per unit C: Door Type High 0.03 5.60 11.1 3.71 $1.02 15 No No Yes Yes 

temperature door-style dish Temp DW 

washer 

C: Use most efficient low­ 30% $3,500 per unit C: Conveyor Low 0.20 29.1 47.5 14.3 $2.44 20 No No Yes Yes 

temperature conveyor-style Temp Dishwashers 

dish washer 

C: Use most efficient low­ 36% $2,000 per unit C: Door Type Low 0.08 10.9 16.4 5.97 $1.41 15 No No Yes Yes 

temperature door-style dish Temp DW 

washer 

C: Develop condensing single 14% Yes $896.53 per unit C: Single-Load Coin 0.07 4.74 7.30 1.02 $3.59 11 No No Yes Yes 

load clothes dryers Op Dryers 

C: Develop condensing tumble 14% Yes $217.77 per unit C: Tumble Dryers 2.7 58.5 70.5 9.82 $3.60 11 No No Yes Yes 

clothes dryers 

C: Develop dryer exhaust heat 45% Yes $200 per unit C: Single-Load Coin 0.07 4.74 7.30 3.42 $0.98 1 No No No Yes 

recovery for clothes dryers Op Dryers 

C: Develop heat pump clothes 50% $0 per unit C: Single-Load Coin 0.07 4.74 4.74 2.32 $0 11 No No Yes Yes 

dryers Op Dryers 

C: Develop microwave clothes 25% $295 per unit C: Single-Load Coin 0.07 4.74 7.30 1.60 $0.80 11 No No No Yes 

dryers Op Dryers 

C: Develop nylon bead clothes 94% -$6,000 per unit C: Comm. Washers 6.2 469 526 495 -$4.21 15 No No Yes Yes 

washers and Dryers 

C: Increase motor efficiency in 10% Yes $0 per unit C: Machine Energy 0.57 0.39 1.18 0.10 $0 7 No No Yes Yes 

single-load clothes washer for Single Load Coin 

Op Washers 

C: Recycling waste-water in 53% Yes -$20,803.75 per unit; 20 GPM C: Hot Water for 0.17 237 237 128 -$1.53 15 No No No Yes 

commercial washers (multi-load over 4 machines Both Tunnel Washers 

and tunnel) and Multi-Load 

Washers 

C: Switch from multi-load to 82% -$121,250 per unit C: Hot Water for 0.17 233 257 238 -$4.49 15 No No No Yes 

tunnel Multi-Load Washers 

C: Use advanced agitation ap­ 15% Yes $0 per unit C: Hot Water for Top 0.46 3.27 4.16 0.62 $0 7 No No No Yes 

proaches in clothes washer Loading Coin Op 

Washers 

C: Use advanced ozone systems 89% $7,500 per unit C: Hot Water for 0.17 233 297 266 $0.26 15 No No Yes Yes 

for multi-load clothes washers Multi-Load Washers 

C: Use auto sprayer shutoff in 50% Yes $4,000 per unit C: Conveyor Type 0.40 73.4 147 73.7 $1.09 20 Yes Yes Yes Yes 

dishwashers Dishwashers 

C: Use automatic fill control in 15% Yes $20 per unit C: Hot Water for Top 0.46 3.27 4.16 0.62 $1.13 7 No No No Yes 

clothes washers Loading Coin Op 

Washers 

­ 55% $116.21 per unit R: Refrigerator Total 178 412 1,256 669 $1.71 15 Yes Yes Yes Yes 

92
 

Primary sources used Data used and/or methodology Other supporting sources 

Saidur, R., Masjuki, H. H., Choudhry, I.A. (2000). “Role of Ambient Tem- Annual energy use reduced in test from 2,290 kWh/year to 1,930 kWh/year Department of Energy, “Energy Conservation 

perature, Door Opening, Thermostat Setting Position and Their Combined representing a 16% savings Program: Energy Conservation Standards for 

Effect on Refrigerator-Freezer Energy Consumption.” Energy Conservation Residential Refrigerators, Refrigerator-Freezand Management, 43(6), 845-854. (http://www.sciencedirect.com/science/ ers, and Freezers” Federal Register (9/2010) 

article/pii/S0196890401000693) and supporting documentation 

Meier, A. Martinex, M; “Energy Use of Icemaking in Domestic Refrigerators”, “None of these studies obtained more than a 6% reduction in energy use.” 

LBNL-39183 (1996) Used average of 0% and 6% as min/max savings estimated; $10 for brush 

cost from websearch (5/11) 

Department of Energy, “New Opportunities Multiply Savings: Refrigerator By 2005 22% of homes had a second refrigerator; 10% of new purchases 

Market Report, 2009”, 2009 keep their old refrigerators as second units. 

Supporting calculations in tool 

Department of Energy, “New Opportunities Multiply Savings: Refrigerator 730 kWh of savings off a base of 1165 kWh (i.e., 63%) 

Market Report, 2009”, 2009 $134 of economic costs of early retirement 

Department of Energy, “Energy Conservation Program: Energy Conservation EER 7.5 from table 3.3.10; cost estimated form web search for existing prodStandards for Residential Refrigerators, Refrigerator-Freezers, and Freezers” ucts with value added for internal refrigerator space 

Federal Register (9/2010) and supporting documentation 

Department of Energy, “Energy Conservation Program: Energy Conservation Tables 5-A.3.2 (and .4, .6) (and also p.5-46) 

Standards for Residential Refrigerators, Refrigerator-Freezers, and Freezers” Averaged maximum compressor improvements over all refrigerator types 

Federal Register (9/2010) and supporting documentation 

Department of Energy, “Energy Conservation Program: Energy Conservation All measure tables drawn from engineering and national impact analysis 

Standards for Residential Refrigerators, Refrigerator-Freezers, and Freezers” from TSD and supporting material 

Federal Register (9/2010) and supporting documentation 

 Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 4-22, page 104, see “Conveyor HT 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

 Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 4-22, page 104, see “Door-Type HT” 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

 Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 4-22, page 104, see “Conveyor LT” 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

 Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 4-22, page 104, see “Door-Type LT” 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

 Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-36, page 272, see “inlet air preheat, condensing mode” Cost assumes 5-year payback based on 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ annual savings of $172.51 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

 Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-37, page 273, see “inlet air preheat, condensing mode” Cost assumes 5-year payback based on 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ annual savings of $41.73 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Navigant Consulting; “Energy Savings Potential and RD&D Opportunities for Tables 8-32 & 8-36 

Commercial Building Appliances” (2009) 

 Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-36, page 272, see “heat pump”. Cost estimated from average of 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ prices in table 8-33, converted to US dollars. 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

 Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-36, page 272, see “microwave” 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

“Misra, A., Pimphalkhute, H., Murthi, S. & Kamdar, T. Pricing strategy: Water­ “Savings are estimated to be approximately 15%, associated with machine 

less Washing Machine (2010). Xeros website. Desroches, L.-B. et al. Max Tech energy (shorter cycle times). The small amount of cold water used per cycle 

and Beyond (2011)” results in 100% savings in water heating energy, and 100% savings in clothes 

dryer energy.” Used Weighted average of savings for full cycle 

Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-23, page 257, see “increased motor efficiency” 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Savings: Navigant Consulting; “Energy Savings Potential and RD&D Op- Table 8-24 & 8-25 

portunities for Commercial Building Appliances” (2009). Cost: RMC Water 

and Environment; Integrated Water Resources Program Report, Appendix 

H (12/2007) 

Goetzler, W., Zogg, R., Burgos, J., Hiraiwa, H. & Young, J. Energy Savings Savings from Goetzler: Tables 8-24, 8-25 

Potential and RD & D Opportunities for Commercial Building HVAC Systems. Cost from New York Times payback calculation 

Navigant Consulting (2011). Garbarine, R. Commercial Property / New Jersey ; 82% savings, $120,000 savings per establishment 

In Piscataway, a Central Laundry for Region Hiltons. New York Times 5 (1999) 

Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-23, page 257, see “advanced agitation” 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-25, page 258, see “advanced ozone system” “An Introduction to the Use of Ozone in the 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ Laundry Industry”, http://www.cwtozone. 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf com/uploads/SalesDocs/Markets/Laundry/ 

EcoTex/Sales%20Sheets-individual/Intro%20 

to%20EcoTex%20RR%20032808.pdf 

Hobart Cle Warewasher Opti-Rinse, http://www.hobartcorp.com/products/ “Exclusive Opti-RinseTM technology for 50% less energy and water usage 

warewashing/conveyor-type/cle-warewasher/ than comparable models”

 Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-23, page 257, see “automatic fill control” Control is available in residential units; apfor Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ proximate cost based on replacement of wapublications/pdfs/corporate/commercial_appliances_report_12-09.pdf ter level control sensor for *residential* unit 

93
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

C: Use direct-drive motors in 

single load clothes washers 

60% $132 per unit C: Hot Water for Top 

Loading Coin Op 

Washers 

0.46 3.27 4.16 2.45 $1.90 7 No No No No 

C: Use improved cycle termination in clothes dyers 15% Yes $21 per unit C: Single-Load Coin 

Op Dryers 

0.07 4.74 7.30 1.09 $0.08 11 No No Yes Yes 

C: Use improved drum design in 20% Yes $768.45 per unit C: Single-Load Coin 0.07 4.74 7.30 1.46 $2.64 7 No No Yes Yes 

clothes washer Op Dryers 

C: Use improved water extrac­ 25% Yes $960.57 per unit C: Single-Load Coin 0.07 4.74 7.30 1.82 $2.64 7 No No Yes Yes 

tion in clothes washer Op Dryers 

C: Use low-temperature 

detergent in multi-load clothes 

washers 

20% Yes $996.86 per unit C: Hot Water for 

Multi-Load Washers 

0.17 233 297 63.3 $0.65 1 No No No Yes 

C: Use low-temperature 

detergent in single load clothes 

washers 

20% Yes $94.60 per unit C: Hot Water for 

Single Load Coin Op 

Washers 

0.57 3.50 4.46 0.95 $13.94 1 No No No Yes 

C: Use more efficient (i.e., cur­ 60% $1,200,000 per unit C: Tunnel Washers 0 3.41 3.41 2.05 $9.93 11 No No Yes Yes 

rently available) tunnel washers 

C: Use petroleum-based dry 58% $52,500 per unit C: Dry Cleaning 0.05 49.3 53.7 36.8 $4.23 15 Yes Yes Yes Yes 

cleaning 

C: Use spray-rinse in top­ 50% $182.06 per unit C: Hot Water for 0.57 3.50 3.50 1.73 $4.47 7 No No No Yes 

loading washers Single Load Coin Op 

Washers 

C: Use soil sensors in clothes 10% Yes $100 per unit; 3.8 ft3 C: Hot Water for 0.57 3.50 4.46 0.44 $9.85 7 No No Yes Yes 

washers capacity Single Load Coin Op 

Washers 

R: Advanced washer-dryer pair 40% Yes $1,800 per pair R: Clothes Washing, 120 620 1,064 426 $34.11 10 No No Yes Yes 

Drying, and Water 

Heating 

R: Develop condensing clothes 14% Yes $73.24 per unit R: Gas dryer 24.4 82.6 216 30.3 $3.46 12 No No Yes No 

dryers 

R: Develop dryer exhaust heat 45% Yes $200 per unit R: Dryer Total 120 391 1,019 583 $10 1 No No Yes Yes 

recovery for clothes dryers 

R: Develop heat pump clothes 50% $731 per unit R: Electric dryer 95.2 309 942 416 $11.45 12 No No Yes Yes 

dryers (no FS) 

R: Develop microwave clothes 25% $295 per unit R: Electric dryer 95.2 309 808 129 $15.47 12 No No No No 

dryers 

R: Develop modulating gas 15% $31.86 per unit R: Dryer Total 120 391 391 23.5 $10.08 12 No No Yes Yes 

clothes dryers 

R: Develop nylon-bead clothes 83% $558.25 per unit R: Clothes Washing, 120 620 1,348 998 $3.68 12 No No No Yes 

washers Drying, and Water 

Heating 

R: Develop zeolithic 39% $486 per unit R: Dishwash Total 98.5 111 340 114 $26.73 13 No No Yes Yes 

dishwashers 

R: Eliminate heated dry in 7% Yes $0 per unit R: Dishwash Total 98.5 111 340 24.6 $0 1 Yes Yes Yes Yes 

dishwashers 

R: hang-dry laundry 100% Yes $0 per unit R: Dryer Total 120 391 1,019 1,040 $0 12 No No No No 

R: Use high-speed water extrac­ 25% Yes $79.01 per unit R: Dryer Total 120 391 1,025 222 $2.58 12 Yes Yes Yes Yes 

tor in clothes washers 

R: Use improved cycle termina­ 15% Yes FS $21 per unit R: Dryer Total 120 391 1,025 159 $0.99 12 Yes Yes Yes Yes 

tion in clothes dyers 

R: Use improved hydraulic ef­ 25% Yes $0 per unit R: Dishwash Total 98.5 111 340 69.5 $0 12 Yes Yes Yes Yes 

ficiency in dishwasher 

R: Use low-temperature deter­ 20% Yes $10.80 per unit R: Hot Water for 120 200 326 73.7 $4.31 1 No No No Yes 

gent in clothes washers Clothes Washing 

R: Use low-temperature deter­ 20% Yes $10.80 per unit R: Hot Water for 98.5 127 196 28.9 $10.04 1 No No No Yes 

gent in dish washers Dishwashing 

R: Use more efficient (i.e., 43% $0 per unit R: Front Loading 18.1 15.1 46.0 21.5 $0 11 Yes Yes Yes Yes 

currently available) front load Washers 

clothes washer 

94
Primary sources used Data used and/or methodology Other supporting sources

 Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-23, page 257, see “direct drive motor” 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

 Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-36, page 272, see “improved cycle termination” Cost estimated from part currently in use in 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ residential units. 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

 Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-23, page 257, see “improved drum design” Cost assumes 3-year payback from annual 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ savings of $246 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

 Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-23, page 257, see “improved water extraction” Cost assumes 3-year payback based on 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ annual savings of $308.06 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Navigant Consulting; “Energy Savings Potential and RD&D Opportunities for Table 8-24 & 8-25 Chemical and Engineering News, Vol. 85 

Commercial Building Appliances” (2009) #5, Jan. 2007 

Navigant Consulting; “Energy Savings Potential and RD&D Opportunities for Table 8-24 & 8-25 Chemical and Engineering News, Vol. 85 

Commercial Building Appliances” (2009) #5, Jan. 2007 

Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-27, page 259, see “advanced tunnel washer” 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Figure 8-33, page 281. Energy savings is percent difference between PCI and EPA, “Case Study: Liquid Carbon Dioxide 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ (CO2

) Surfactant System For Garment Care”, 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Petroleum 

http://www.epa.gov/dfe/pubs/garment/ 

lcds/micell.htm 

Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-23, page 257, see “spray rinse technology” Cost assumes 5-year payback based on 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ annual savings of $34.81 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-23, page 257, see “adaptive control system” 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Personal communication: Bouza, DOE (1/2012) 

Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-36, page 272, see “inlet air preheat, condensing mode” Cost assumes 5-year payback based on 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ annual savings of $14.29 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Navigant Consulting; “Energy Savings Potential and RD&D Opportunities for Tables 8-32 & 8-36 

Commercial Building Appliances” (2009) 

Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-36, page 272, see “heat pump”. Cost estimated from average of 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ prices in table 8-33, converted to US dollars. 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-36, page 272, see “microwave” 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-36, page 272, see “modulating gas burner” Cost assumes 5-year payback of savings of 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ $6.09 per year 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Desroches, L. et al. (2011). MaxTech and Beyond, Maximizing Appliance and Page 19, Table 3 

Equipment Efficiency by Design, http://efficiency.lbl.gov/drupal.files/ees/ 

MTAB%20Final%20LBNL%20Report.pdf 

Siemens product description (available in the UK) 
 Siemens: 0.83 kWh/cycle. AHAM blog: in 1991 the average dishwasher con­


sumed 2.67 kilowatts (kWh) per cycle and by 2010 it was only 1.37 kWh/ cycle 


ACEEE, “Dishwashing”, http://www.aceee.org/consumer/dishwashing “An electric heating element is generally used to dry dishes at the end of the 

final rinse cycle, consuming about 7% of dishwasher energy use.” 

First principles: 100% savings at zero marginal cost (assuming same time 

invest to hang & fold as dry & fold) 

Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-23, page 257, see “improved water extraction” Cost assumes 3-year payback based on 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ annual savings of $25.69 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-36, page 272, see “improved cycle termination” Cost is approx. price of replacement temfor Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ perature sensor 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Dries, John, (2008), “Improving Dishwasher Efficiency”, http://www.ap­ “Assuming that the upper and lower spray-arms operate the same amount 

pliancemagazine.com/editorial.php?article=1978 of time, the average power consumption of this wash system went from 208 

to 155 W, a reduction in overall power consumption of 25%, while improving 

the hydraulic washing power of the dishwasher—all without any increase to 

product cost or complexity.” 

Navigant Consulting; “Energy Savings Potential and RD&D Opportunities for Table 8-24 & 8-25 Chemical and Engineering News, Vol. 85 

Commercial Building Appliances” (2009) #5, Jan. 2007 

Navigant Consulting; “Energy Savings Potential and RD&D Opportunities for Table 8-24 & 8-25 Chemical and Engineering News, Vol. 85 

Commercial Building Appliances” (2009) #5, Jan. 2007 

Energy Efficient and Smart Appliance Agreement of 2010, AHAM power Savings listed in agreement 

point 

95
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

R: Use more efficient (i.e., 37% $0 per unit R: Top Loading 102 13.2 40.4 0 $0 11 Yes Yes Yes Yes 

currently available) top load Washers 

clothes washer 

R: Use more efficient (i.e., cur­ 5% $0 per unit R: DV Total 95.2 309 942 0 $0 12 No No Yes Yes 

rently available) vented electric 

clothes dryer 

R: Use more efficient (i.e., 5% $0 per unit R: DS_WASH 24.4 82.6 82.6 0.22 $0 12 No No Yes Yes 

currently available) vented gas 

clothes dryer 

R: Use soil sensors in dishwashers 25% Yes $30 per unit R: Dishwash Total 98.5 111 340 73.8 $2.62 11 Yes Yes No Yes 

R: Use soil sensors in clothes 10% Yes $100 per unit; 3.8 ft3 R: Hot Water for 120 200 326 13.5 $37.71 11 No No Yes Yes 

washers capacity Clothes Washing 

C: Adapt current generation (EF 64% $1,075 per unit; 120 C: Small WH Needs 1.3 57.7 176 74.0 $0.82 30 No No Yes Yes 

2.2) heat-pump water heaters gallons (Electric) 

(HPWH) to commercial uses 

(FS only) 

C: Adapt current generation (EF 50% $3,000 per unit C: All Comm Elec 1.7 46.6 142 56.6 $5.63 16 No No Yes Yes 

2.2) heat-pump water heaters Storage WH 

(HPWH) to commercial uses 

(no FS) 

C: Advance air-source heat­ 83% FS $5,698 per unit C: All Comm Gas 1.9 294 294 138 $5.55 10 No No No No 

pump water-heater (ASHPWH) Storage WH 

to its technology limit (FS only) 

C: Advance air-source heat­ 79% $7,505 per unit C: All Comm Elec 1.7 46.6 142 101 $10.07 10 No No No Yes 

pump water-heater (ASHPWH) Storage WH 

to its technology limit (no FS) 

C: Advance gas-driven HPWH 45% FS $32,781.95 per unit C: All Comm Elec 1.7 46.6 142 107 $40.63 10 No No No No 

to its tech limit (FS only) Storage WH 

C: Advance gas-driven HPWH 64% $30,974.95 per unit C: All Comm Gas 1.9 294 294 185 $22.19 10 No No No Yes 

to its tech limit (no FS) Storage WH 

C: Advance Solar-assisted 88% FS $31,477 per unit C: All Comm Gas 1.9 294 294 181 $29.78 10 No No No Yes 

air-source heat-pump water- Storage WH 

heaters to their technology limit 

(FS only) 

C: Advance Solar-assisted 81% $33,284 per unit C: All Comm Elec 1.7 46.6 142 106 $51.67 10 No No No Yes 

air-source heat-pump water- Storage WH 

heaters to their technology limit 

(no FS) 

C: Develop advanced HPWH 72% $28,031 per unit C: All Comm Elec 1.7 46.6 142 47.3 $34.85 30 No No Yes Yes 

(COP 5.1, EF 4.4) (no FS) Storage WH 

C: Develop gas absorption 40% $13,500 per unit C: All Comm Gas 1.9 294 294 115 $15.05 11 No No No Yes 

heat-pump water heaters Storage WH 

C: Employ drain-water waste­ 30% Yes $8,000 per unit C: All Comm Storage 3.8 358 456 125 $19.20 12 No No Yes Yes 

heat recovery WH 

C: Recover waste heat from air 11% Yes $3,320 per unit C: Water Heating in 3.1 295 375 23.4 $25.35 16 No No No No 

conditioning for water heating Hottest Two Climates 

C: Reduce installed cost of solar 61% Yes $51,805 per unit; 1000 C: All Comm Elec 1.7 46.6 142 83.1 $68.80 10 No No No Yes 

water heating systems-indirect square feet Storage WH 

active, elec. BU (no FS) absorber 

C: Reduce installed cost of solar 61% Yes FS $51,805 per unit; 1000 C: All Comm Elec 1.7 46.6 142 122 $46.17 12 No No No No 

water heating systems-indirect square feet Storage WH 

active, gas BU (FS only) absorber 

C: Reduce installed cost of solar 61% Yes FS $16,059.55 per unit; 310 C: All Comm Gas 1.9 294 294 165 $11.78 12 No No Yes Yes 

water heating systems-indirect square feet Storage WH 

active, gas BU (no FS) absorber 

C: Use condensing gas water 22% $3,285 per unit C: All Comm Gas 1.9 294 294 87.2 $5.78 11 No Yes Yes Yes 

heaters Storage WH 

C: Use heat pump water heaters 45% $3,500 per unit C: Commercial 0.42 119 216 92.5 $0.96 15 No No No Yes 

in commercial kitchens (i.e., for Kitchens 

hot water and cooling) 

96
Primary sources used Data used and/or methodology Other supporting sources 

Energy Efficient and Smart Appliance Agreement of 2010, AHAM power Savings listed in agreement 

point 

Energy Efficient and Smart Appliance Agreement of 2010, AHAM power Savings listed in agreement 

point 

Energy Efficient and Smart Appliance Agreement of 2010, AHAM power Savings listed in agreement 

point 

ACEEE, “Dishwashing”, http://www.aceee.org/consumer/dishwashing 

Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 8-23, page 257, see “adaptive control system” 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

U.S. Department of Energy, Office of Energy Efficiency and Renewable DOE 2011: Used 120 gal HPWH at 2.27 EF. $1022 used for price of baseline 

Energy (EERE). (2011, March 30). Residential Heating Products Final Rule electric, and $2097 used for price of best 120 gallon HPWH, based on scalAnalytical Tools. Retrieved from Appliance & Equipment Standards: http:// ing 50-gallon. Assumed 1.5 water heaters per building. 

www1.eere.energy.gov/buildings/appliance_standards/residential/heating_products_fr_spreadsheets.html 

Goetzler, W., Zogg, R., Burgos, J., Hiraiwa, H. & Young, J. Energy Savings Cost taken from google shopping search, average of 12 venders (installed); Navigant Consulting Research and DevelopPotential and RD & D Opportunities for Commercial Building HVAC Systems. scaled to commercial capacity; EF 2.38 ment Roadmap for Water Heating. DepartNavigant Consulting (2011). Tables 6-11, 6-24 50% savings, $2,700 incremental to tank cost ment of Energy (2011). 

Zhang, J., Wang, R.Z. & Wu, J.Y. System optimization and experimental re- Table 3 yields seasonally averaged COP of ~4.8, Cost from 80%/20% ratio of Xu, G., Zhang, X. & Deng, S. A simulation 

search on air source heat pump water heater. Applied Thermal Engineering investment cost/energy cost study on the operating performance of a 

27, 1029-1035 (2007). solar–air source heat pump water heater. 

Applied Thermal Engineering 26, 1257-1265 

(2006). 

Zhang, J., Wang, R.Z. & Wu, J.Y. System optimization and experimental re- Table 3 yields seasonally averaged COP of ~4.8, Cost from 80%/20% ratio of Xu, G., Zhang, X. & Deng, S. A simulation 

search on air source heat pump water heater. Applied Thermal Engineering investment cost/energy cost study on the operating performance of a 

27, 1029-1035 (2007). solar–air source heat pump water heater. 

Applied Thermal Engineering 26, 1257-1265 

(2006). 

Zhang et. al. “Analysis on the heating performance of a gas engine driven air Figure 5 shows the PER: “The primary energy ratio (PER) is defined as the Hepbasli, A. & Kalinci, Y. A review of heat 

to water heat pump based on a steady-state model”, Energy Conversion and heat gained divided by the input energy.” Cost taken as 64% “investment” pump water heating systems. Renewable 

Management 46 (2005) 1714–1730 cost, 36% operating at 6 MMBTU/year + $50 service biannually. and Sustainable Energy Reviews 13, 1211­

Implies: $2,880 cost improvement from COP 0.59 to 1.67 1229 (2009). 

Zhang et. al. “Analysis on the heating performance of a gas engine driven air Figure 5 shows the PER: “The primary energy ratio (PER) is defined as the Hepbasli, A. & Kalinci, Y. A review of heat 

to water heat pump based on a steady-state model”, Energy Conversion and heat gained divided by the input energy.” Cost taken as 64% “investment” pump water heating systems. Renewable 

Management 46 (2005) 1714–1730 cost, 36% operating at 6 MMBTU/year + $50 service biannually. and Sustainable Energy Reviews 13, 1211­

Implies: $2,880 cost improvement from COP 0.59 to 1.67 1229 (2009). 

Li. Et.al, “Experimental performance analysis on a direct-expansion solar- The seasonal average value of the COP and the collector efficiency was Guoying, X; Ziaosong, Z; Lei, Y; Shiming, 

assisted heat pump water heater”; Applied Thermal Engineering 27 (2007) measured as 5.25 and 1.08, respectively. Used COP provided for perfor- D.; "Performance of a solar-air source heat 

2858–2868 mance; estimated cost using capital/operating cost target provided pump system for water heating on different 

weather conditions" IEEE (2009) 

Li. Et.al, “Experimental performance analysis on a direct-expansion solar- The seasonal average value of the COP and the collector efficiency was Guoying, X; Ziaosong, Z; Lei, Y; Shiming, 

assisted heat pump water heater”; Applied Thermal Engineering 27 (2007) measured as 5.25 and 1.08, respectively. D.; "Performance of a solar-air source heat 

2858–2868 Used COP provided for performance; estimated cost using capital/operating pump system for water heating on different 

cost target provided weather conditions" IEEE (2009) 

r744.com, “Eco Cute Update: new line-up with 5.1 COP by Panasonic, up­ “achieve a COP of 5.1”; The current retail price of this Eco Cute is 814,653 Yen Navigant Consulting Research and Developgrades by Hitachi and domestic statistics in Japan” (2011) (~€7,940.12) ment Roadmap for Water Heating. DepartZogg, Robert, et al. “CO2

 Heat pump water heaters.” ASHRAE Journal (Nov, ment of Energy (2011). 

2007) 

Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Table 6-11, page 183, see “heat pump water heater” and Table 6-21, page 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ 209. See “heat pump water heater” for cost and use midpoint of range given 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Goetzler, W., Zogg, R., Burgos, J., Hiraiwa, H. & Young, J. Energy Savings Tables 6-11, 6-20, 30% savings at a cost of $300-$500/4 gpm, assume ~15­ GFX case studies 

Potential and RD & D Opportunities for Commercial Building HVAC Systems. 20 drains per water heater to capture benefits 

Navigant Consulting (2011) 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Energy savings and cost estimates from Navigant Tables 6-11 and 6-19, 

portunities for Commercial Building Appliances. Washington, D.C.: Building respectively. 

Technology Program, Department of Energy. http://apps1.eere.energy. 

gov/buildings/publications/pdfs/corporate/commercial_appliances_report_12-09.pdf 

Itron, Inc.; “California Center for Sustainable Energy Solar Water Heating Thorough analysis of multiple sources; represents aggregate savings net of Hudon, Burch "Solar Water Heating RoadPilot Program” (3/2011). Federal Energy Management Program, “Solar Water parasitic loads. Costs represent program targets informed by roadmap (in map" (in preparation) 

Heating with Low-Cost Plastic Systems” (1/2012) preparation) 

Itron, Inc.; “California Center for Sustainable Energy Solar Water Heating Thorough analysis of multiple sources; represents aggregate savings net of Hudon, Burch "Solar Water Heating RoadPilot Program” (3/2011). Federal Energy Management Program, “Solar Water parasitic loads. Costs represent program targets informed by roadmap (in map" (in preparation) 

Heating with Low-Cost Plastic Systems” (1/2012) preparation) 

Itron, Inc.; “California Center for Sustainable Energy Solar Water Heating Thorough analysis of multiple sources; represents aggregate savings net of Hudon, Burch "Solar Water Heating RoadPilot Program” (3/2011). Federal Energy Management Program, “Solar Water parasitic loads. Costs represent program targets informed by roadmap (in map" (in preparation) 

Heating with Low-Cost Plastic Systems” (1/2012) preparation) 

Navigant/DOE (2009): Energy Savings Potential and RD&D Opportunities Energy savings comes from average of Tables 6-11 and 6-21. Cost comes 

for Commercial Building Appliances, http://apps1.eere.energy.gov/buildings/ from Table 6-21: max cost of $8,300 minus $5015 of standard commercial 

publications/pdfs/corporate/commercial_appliances_report_12-09.pdf gas storage water heater 

Washington State University (2005), “Energy Efficiency Fact Sheet: Com- Estimated new unit cost is $5,000, $3,500 

mercial Water Heaters” more than a traditional water heater 

97
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

C: Use most efficient (i.e., maxtech) tankless gas water heats 

for buildings with small water 

heating loads 

15% $1,895 per unit C: Small WH Needs 0.66 219 279 61.8 $1.55 12 No No No Yes 

C: Use residential heat pump 71% FS $1,090.58 per unit C: All Comm Gas 1.9 294 294 136 $1.02 11 No No No No 

water heaters for buildings Storage WH 

with larger hot water demand 

(FS only) 

C: Use residential heat pump 74% FS -$425 per unit; 120 C: Small WH Needs 1.4 156 156 41.6 -$0.93 11 No No No No 

water heaters for buildings gallons (Gas) 

with low hot water demand 

(FS only) 

C: Wrap water heaters with 40% Yes $20 per unit C: Commercial Water 3.8 10.7 13.7 5.40 $0.75 12 No No No Yes 

insulation blankets Heater Standby 

Losses 

R: Advance air-source heatpump water-heater (ASHPWH) 

to its technology limit (FS only) 

86% FS $9,331.67 per unit R: Gas water heating 72.7 1,399 1,399 728 $69.38 10 No No No No 

R: Advance air-source heatpump water-heater (ASHPWH) heating 

to its technology limit (no FS) 

79% $9,858.67 per unit R: Electric water 63.2 535 1,631 1,173 $67.47 10 No No No Yes 

R: Advance gas-driven heatpump water-hears to their capacity heating 

technology limit (FS only) 

45% FS $2,328 per unit; 50 gallon R: Electric water 63.2 535 1,631 1,203 $9.81 10 No No No No 

R: Advance gas-driven heatpump water-hears to their 

technology limit (no FS) 

64% $1,801 

capacity 

per unit; 50 gallon R: Gas water heating 72.7 1,399 1,399 813 $13.40 10 No No No Yes 

R: Advance Solar-assisted 88% 

air-source heat-pump waterheaters to their technology limit 

(FS only) 

R: Advance Solar-assisted 81% 

air-source heat-pump waterheaters to their technology limit 

(no FS) 

R: Develop advanced HPWH 74% 

(COP 5.1, EF 4.4) (no FS) 

FS $3,303 

$3,830 

$9,948 

per unit 

per unit 

per unit 

R: Gas water heating 

R: Electric water 

heating 

R: Electric water 

heating 

72.7 

63.2 

63.2 

1,399 

535 

535 

1,399 

1,631 

1,631 

761 

1,189 

1,094 

$33 

$24.09 

$38.68 

10 

10 

13 

No 

No 

No 

No 

No 

No 

No 

No 

Yes 

Yes 

Yes 

Yes 

R: Develop reverse-cycles chillers for commercial buildings 63% $0.94 per square foot R: Waterheating with 

tanks 

103,945 692 881 484 $10.45 20 No No No No 

R: Develop reverse-cycles chillers for multi family homes 63% $677 per unit; 20 tons 

per building 

R: Multi family hot 

water 

32.0 328 527 291 $4.82 15 No No No No 

R: Develop gas-powered heat 

pump water heaters (e.g., absorption, gas-engine) (no FS) 

61% $1,436 per unit R: Gas water heating 72.7 1,399 1,399 707 $11.22 11 No No No Yes 

R: Increase insulation in oilfueled water heaters 15% Yes $53 per unit R: LPG and Distillate 

hot water 

4.4 88.9 89.8 11.7 $1.30 12 No No Yes No 

R: Install drain-water heat­ 35% Yes $1,165 per unit R: HW in Bathrooms 141 907 1,399 426 $23.35 12 No No No Yes 

recovery in bathrooms 

R: Install drain-water heat­ 35% Yes $1,265 per unit R: Hot Water for 141 414 638 208 $62.55 12 No No Yes Yes 

recovery in clothes washers Laundry 

98
Primary sources used Data used and/or methodology Other supporting sources 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Opportunities for Commercial Building Appliances. Washington, D.C.: Building Technology Program, Department of Energy. http://apps1.eere.energy.gov/buildings/publications/pdfs/corporate/commercial_appliances_report_12-09. 

pdf. U.S. Department of Energy, Office of Energy Efficiency and Renewable 

Energy (EERE). (2011, March 30). Residential Heating Products Final Rule 

Analytical Tools. Retrieved from Appliance & Equipment Standards: http:// 

www1.eere.energy.gov/buildings/appliance_standards/residential/heating_products_fr_spreadsheets.html 

Energy savings from Navigant. Table 6-21. Cost from DOE 2011: Large (>55 

gal) storage. Does not include gas access and plumbing costs. 

Goetzler, W., Zogg, R., Burgos, J., Hiraiwa, H. & Young, J. Energy Savings Cost taken from google shopping search, average of 12 venders (installed); Navigant Consulting Research and DevelopPotential and RD & D Opportunities for Commercial Building HVAC Systems. scaled to commercial capacity, EF 2.38 ment Roadmap for Water Heating. DepartNavigant Consulting (2011). Tables 6-11, 6-24 Implies: 70% savings, $2,700 incremental to tank cost ment of Energy (2011). 

U.S. Department of Energy, Office of Energy Efficiency and Renewable Assumed two large water heaters for baseline; energy savings based on 

Energy (EERE). (2011). Water Heater Final Rule Analytical Tools. Retrieved residential EF correct for small commercial 

from Appliance & Equipment Standards: http://www1.eere.energy.gov/ 

buildings/appliance 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Opportunities for Commercial Building Appliances. Washington, D.C.: Building Technology Program, Department of Energy. http://apps1.eere.energy.gov/buildings/publications/pdfs/corporate/commercial_appliances_report_12-09. 

pdf. U.S. Department of Energy, Office of Energy Efficiency and Renewable 

Energy (EERE). (2011, March 30). Residential Heating Products Final Rule 

Analytical Tools. Retrieved from Appliance & Equipment Standards: http:// 

www1.eere.energy.gov/buildings/appliance_standards/residential/heating_products_fr_spreadsheets.html 

Energy savings of 40% from Tables 6-11 in the Navigant reference. Cost from 

DOE 2011. 

Zhang, J., Wang, R.Z. & Wu, J.Y. System optimization and experimental re- Table 3 yields seasonally averaged COP of ~4.8, Cost from 80%/20% ratio of Xu, G., Zhang, X. & Deng, S. A simulation 

search on air source heat pump water heater. Applied Thermal Engineering investment cost/energy cost study on the operating performance of a 

27, 1029-1035 (2007). solar–air source heat pump water heater. 

Applied Thermal Engineering 26, 1257-1265 

(2006). 

Zhang, J., Wang, R.Z. & Wu, J.Y. System optimization and experimental re- Table 3 yields seasonally averaged COP of ~4.8, Cost from 80%/20% ratio of Xu, G., Zhang, X. & Deng, S. A simulation 

search on air source heat pump water heater. Applied Thermal Engineering investment cost/energy cost study on the operating performance of a 

27, 1029-1035 (2007). solar–air source heat pump water heater. 

Applied Thermal Engineering 26, 1257-1265 

(2006). 

Zhang et. al. “Analysis on the heating performance of a gas engine driven air Figure 5 shows the PER: “The primary energy ratio (PER) is defined as the Hepbasli, A. & Kalinci, Y. A review of heat 

to water heat pump based on a steady-state model”, Energy Conversion and heat gained divided by the input energy.” Cost taken as 64% “investment” pump water heating systems. Renewable 

Management 46 (2005) 1714–1730 cost, 36% operating at 6 MMBTU/year + $50 service biannually. and Sustainable Energy Reviews 13, 1211­

Implies: $2,880 cost improvement from COP 0.59 to 1.67 1229 (2009). 

Zhang et. al. “Analysis on the heating performance of a gas engine driven air Figure 5 shows the PER: “The primary energy ratio (PER) is defined as the Hepbasli, A. & Kalinci, Y. A review of heat 

to water heat pump based on a steady-state model”, Energy Conversion and heat gained divided by the input energy.” Cost taken as 64% “investment” pump water heating systems. Renewable 

Management 46 (2005) 1714–1730 cost, 36% operating at 6 MMBTU/year + $50 service biannually. and Sustainable Energy Reviews 13, 1211­

Implies: $2,880 cost improvement from COP 0.59 to 1.67 1229 (2009). 

Li. Et.al, “Experimental performance analysis on a direct-expansion solar- The seasonal average value of the COP and the collector efficiency was Guoying, X; Ziaosong, Z; Lei, Y; Shiming, 

assisted heat pump water heater”; Applied Thermal Engineering 27 (2007) measured as 5.25 and 1.08, respectively.” Used COP provided for perfor- D.; "Performance of a solar-air source heat 

2858–2868 mance; estimated cost using capital/operating cost target provided pump system for water heating on different 

weather conditions" IEEE (2009) 

Li. Et.al, “Experimental performance analysis on a direct-expansion solar- The seasonal average value of the COP and the collector efficiency was Guoying, X; Ziaosong, Z; Lei, Y; Shiming, 

assisted heat pump water heater”; Applied Thermal Engineering 27 (2007) measured as 5.25 and 1.08, respectively.” Used COP provided for perfor- D.; "Performance of a solar-air source heat 

2858–2868 mance; estimated cost using capital/operating cost target provided pump system for water heating on different 

weather conditions" IEEE (2009) 

r744.com, “Eco Cute Update: new line-up with 5.1 COP by Panasonic, up­ “achieve a COP of 5.1”; The current retail price of this Eco Cute is 814,653 Yen Navigant Consulting Research and Developgrades by Hitachi and domestic statistics in Japan” (2011). Zogg, Robert, et (~€7,940.12). EF 3.56 (converted from COP 5.1); using provided cost assum­ ment Roadmap for Water Heating. Departal. “CO2

 Heat pump water heaters.” ASHRAE Journal (Nov, 2007) ing 1-to-1 drop in for resi unit ment of Energy (2011). 

Heller, J. & Cejudo, C. Reverse Cycle Chillers for Multifamily Buildings in the Used same percent savings and cost as multi-family measure 

Pacific Northwest : Phase I Final Report. Bonneville Power Administration 

(2009) 

Heller, J. & Cejudo, C. Reverse Cycle Chillers for Multifamily Buildings in the The savings from this alternative design are about 1,700 kWh/yr per unit 

Pacific Northwest : Phase I Final Report. Bonneville Power Administration with an expected measure life of 15 years. This represents a simple payback 

(2009) of about 4.4 years at a retail electricity cost of $0.09/kWh. 

Reducing kWh/unit from 2,700 to 1,000 with indicated payback 

Navigant Consulting Research and Development Roadmap for Water Heat- Performance target from Roadmap; Robur price list for air-water gas 

ing. Department of Energy (2011). absorption heat pump (40 kW) indicates 11,480 euros ($14,586) (www. 

lhprotrade.com/getcatfile.php?w=18). Scaling linearly with capacity gives: a 

4.5 kW unit cost of $1641. Adding median installation cost of a condensing 

boiler ($874) gives a total cost: $2515. 51% savings (0.59 -> 1.2 EF) 

U.S. Department of Energy, Office of Energy Efficiency and Renewable Cost and energy savings from DOE 2011, Level 0 to Level 5. EF improved 

Energy (EERE). (2011, March 30). Residential Heating Products Final Rule from 0.53 to 0.62 (15% savings). 

Analytical Tools. Retrieved from Appliance & Equipment Standards: http:// 

www1.eere.energy.gov/buildings/appliance_standards/residential/heating_products_fr_spreadsheets.html 

Home Depot Canada. Accessed January 2012. http://www.homedepot. Cost and energy savings are same as Measure 551. 

ca/catalog/drain-water-heat-recovery/173006. CADDET. 1997. Residential 

waste water heat-recovery. System: GFX. CADDET Newsletter. http://www. 

gfxtechnology.com/CADDET.pdf. 

Home Depot Canada. Accessed January 2012. http://www.homedepot. Cost and energy savings are same as Measure 551, plus $100 for storage 

ca/catalog/drain-water-heat-recovery/173006. CADDET. 1997. Residential capability 

waste water heat-recovery. System: GFX. CADDET Newsletter. http://www. 

gfxtechnology.com/CADDET.pdf. 

99
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

R: Install drain-water heat­ 35% Yes $500 per unit R: Hot Water for 141 571 880 266 $16 12 No No Yes Yes 

recovery in kitchens Kitchens 

R: Install night time timers on 3% Yes $40 per unit R: Hot Water Total 141 2,027 3,127 80.5 $3.84 12 No No No No 

water heaters 

R: Lower water heater thermo­ 4% Yes $0 per unit; decrease R: Hot Water Total 141 2,027 3,127 94.1 $0 12 No No Yes No 

stat setting by 15 degrees 

R: Reduce installed cost of solar 61% Yes $2,900 per home; 48.3 sq. R: Electric water 63.2 535 1,631 920 $12.58 13 No No Yes Yes 

water heating systems-elec. BU ft. absorber heating 

- indirect active (no FS) 

R: Reduce installed cost of solar 45% Yes $2,030 per home; 48.3 sq. R: Electric water 63.2 535 1,631 644 $13.92 12 No No No No 

water heating systems-elec. BU ft. absorber heating 

- indirect passive (no FS) 

R: Reduce installed cost of solar 61% Yes FS $2,900 per home; 48.3 sq. R: Electric water 63.2 535 1,631 1,307 $8.59 15 No No No No 

water heating systems-gas BU ­ ft. absorber heating 

indirect active (FS only) 

R: Reduce installed cost of solar 61% Yes FS $2,900 per home; 48.3 sq. R: Gas water heating 72.7 1,399 1,399 773 $17.66 11 No No Yes Yes 

water heating systems-gas BU ­ ft. absorber 

indirect active (no FS) 

R: Reduce installed cost of solar 44% Yes FS $2,030 per home; 48.3 sq. R: Electric water 63.2 535 1,631 1,186 $6.77 15 No No No No 

water heating systems-gas BU ­ ft. absorber heating 

indirect passive (FS only) 

R: Reduce installed cost of solar 44% Yes FS $2,030 per home; 48.3 sq. R: Gas water heating 72.7 1,399 1,399 510 $16.03 16 No No No No 

water heating systems-gas BU ­ ft. absorber 

indirect passive (no FS) 

R: Use condensing gas water 23% $814 per unit R: Gas water heating 72.7 1,399 1,399 192 $19.80 11 No No Yes Yes 

heaters 

R: Use current generation (EF 70% FS $892 per unit R: Gas water heating 72.7 1,399 1,399 77.0 $37.05 30 No No No No 

2.2) HPWH (FS only) 

R: Use current generation (EF 55% $892 per unit R: Electric water 63.2 535 1,631 974 $3.94 13 No Yes Yes Yes 

2.2) HPWH (no FS) heating 

R: Use drain-water heat recov­ 35% Yes $1,165 per unit R: Hot Water for 141 509 785 255 $46.79 12 No No Yes Yes 

ery in showers Showers 

R: Use electric ignition in gas 8% $444 per unit R: Gas water heaters 63.0 1,212 1,212 11.7 $65.98 11 No No Yes No 

water heaters with pilot lights 

R: Use electric, faucet-based 23% $175 per unit R: Hot Water for 141 1,695 2,540 134 $6.83 12 No No No Yes 

tankless water heaters Showers and Faucets 

R: Use gas-driven heat pump 23% FS $1,963 per unit R: Electric storage 13.0 111 338 131 $12.49 12 No No No No 

water heaters (FS only) water heaters with 

gas availability 

R: Use low-flow faucets 71% Yes -$409.15 per unit R: High flow water 84.4 570 879 773 -$1.85 15 No No Yes Yes 

heating for showers/ 

faucets 

R: Use most efficient available 22% $160 per unit R: LPG and Distillate 4.4 88.9 89.8 0.0 $6.55 12 No No Yes No 

oil water heater hot water 

R: Use most efficient tankless 0% Yes FS $2,332 per unit; 199,000 R: Electric storage 13.0 111 338 203 $9.44 12 No No No Yes 

gas water heater (FS only) BTU/hr capacity water heaters with 

gas availability 

100
Primary sources used Data used and/or methodology Other supporting sources 

Home Depot Canada. Accessed January 2012. http://www.homedepot. Cost and energy savings are same as Measure 551, plus $100 for storage 

ca/catalog/drain-water-heat-recovery/173006. CADDET. 1997. Residential capability 

waste water heat-recovery. System: GFX. CADDET Newsletter. http://www. 

gfxtechnology.com/CADDET.pdf. 

Water heater energy use—and how to save on it” (http://michaelbluejay. “A timer for an old (pre-1998) electric heater costs around $40 and saves 

com/electricity/waterheaters.html) about 25kWh/mo. for a family of two using 40 gallons a day with the heater 

off four to six hours a day, but only 14kWh/mo. for a family of four using 80 

gallons a day.” 

http://www.aceee.org/consumer/water-heating All households down 15-deg F; Each 10-deg F reduction in thermostat setting 

saves 3-5% in WH energy costs. Using average of range (4%) at zero cost 

Itron, Inc.; “California Center for Sustainable Energy Solar Water Heating Thorough analysis of multiple sources; represents aggregate savings net of Hudon, Burch "Solar Water Heating RoadPilot Program” (3/2011). Federal Energy Management Program, “Solar Water parasitic loads. Costs represent program targets informed by roadmap (in map" (in preparation) 

Heating with Low-Cost Plastic Systems” (1/2012) preparation) 

Itron, Inc.; “California Center for Sustainable Energy Solar Water Heating Thorough analysis of multiple sources; represents aggregate savings net of Hudon, Burch "Solar Water Heating RoadPilot Program” (3/2011). Federal Energy Management Program, “Solar Water parasitic loads. Costs represent program targets informed by roadmap (in map" (in preparation) 

Heating with Low-Cost Plastic Systems” (1/2012) preparation) 

Itron, Inc.; “California Center for Sustainable Energy Solar Water Heating Thorough analysis of multiple sources; represents aggregate savings net of Hudon, Burch "Solar Water Heating RoadPilot Program” (3/2011). Federal Energy Management Program, “Solar Water parasitic loads. Costs represent program targets informed by roadmap (in map" (in preparation) 

Heating with Low-Cost Plastic Systems” (1/2012) preparation 

Itron, Inc.; “California Center for Sustainable Energy Solar Water Heating Thorough analysis of multiple sources; represents aggregate savings net of Hudon, Burch "Solar Water Heating RoadPilot Program” (3/2011). Federal Energy Management Program, “Solar Water parasitic loads. Costs represent program targets informed by roadmap (in map" (in preparation) 

Heating with Low-Cost Plastic Systems” (1/2012) preparation) 

Itron, Inc.; “California Center for Sustainable Energy Solar Water Heating Thorough analysis of multiple sources; represents aggregate savings net of Hudon, Burch "Solar Water Heating RoadPilot Program” (3/2011). Federal Energy Management Program, “Solar Water parasitic loads. Costs represent program targets informed by roadmap (in map" (in preparation) 

Heating with Low-Cost Plastic Systems” (1/2012) preparation) 

Itron, Inc.; “California Center for Sustainable Energy Solar Water Heating Thorough analysis of multiple sources; represents aggregate savings net of Hudon, Burch "Solar Water Heating RoadPilot Program” (3/2011). Federal Energy Management Program, “Solar Water parasitic loads. Costs represent program targets informed by roadmap (in map" (in preparation) 

Heating with Low-Cost Plastic Systems” (1/2012) preparation) 

Water Resources Engineering, I. Water Conservation Market Penetration Tables 4-7 and 4-9 represents installed faucet aerator base; used to scale 

Study. East Bay Municipal Utility District (2002) nationally; fixture cost ($90 for 5 per home)provided by web search and is 

net of water savings valued at $8/1000 gallons 

Fitzpatrick, S, Murray, M; “GE Heat Pump Water Heater Report”, Advanced Cost taken from google shopping search, average of 12 venders (installed), Navigant Consulting Research and DevelopEnergy (2011) EF 2.38. EF 3.56 (converted from COP 5.1); using provided cost assuming ment Roadmap for Water Heating. Depart1-to-1 drop in for resi unit ment of Energy (2011). 

Fitzpatrick, S, Murray, M; “GE Heat Pump Water Heater Report”, Advanced Cost taken from google shopping search, average of 12 venders (installed) Navigant Consulting Research and DevelopEnergy (2011) 62% savings, (EF 0.89 -> 2.38), cost $1438 (average installed). EF 2.38 ment Roadmap for Water Heating. Department of Energy (2011). 

Home Depot Canada. Accessed January 2012. http://www.homedepot. Home Depot Canada: Purchase costs of $500-$1600 for Power Pipe ($480­

ca/catalog/drain-water-heat-recovery/173006. CADDET. 1997. Residential $1550 US $). $150 installation assumed. Estimated cost is $1015 equipment + 

waste water heat-recovery. System: GFX. CADDET Newsletter. http://www. $150 installation ($1165 total), for existing buildings. CADDET: Improvement 

gfxtechnology.com/CADDET.pdf. in EF from 0.84 to 1.29 (35% energy savings). 

U.S. Department of Energy, Office of Energy Efficiency and Renewable DOE 2011 for efficiency improvement and cost. Savings based on moving EF 

Energy (EERE). (2011, March 30). Residential Heating Products Final Rule from 0.59 to 0.64. 

Analytical Tools. Retrieved from Appliance & Equipment Standards: http:// 

www1.eere.energy.gov/buildings/appliance_standards/residential/heating_products_fr_spreadsheets.html 

Web search of available products. U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE). (2011, March 30). Residential 

Heating Products Final Rule Analytical Tools. Retrieved from Appliance 

& Equipment Standards: http://www1.eere.energy.gov/buildings/appliance_standards/residential/heating_products_fr_spreadsheets.html. Robert 

Hendron, Jay Burch, Marc Hoeschele, Leo Rainer. “Potential for Energy 

Savings Through Residential Hot Water Distribution System Improvements”. 

ASME 2009 3rd International Conference on Energy Sustainability collocated with the Heat Transfer and InterPACK09 Conferences (ES2009). July 

19–23, 2009, San Francisco, California, USA 

Cost for Bosch 1000P NG (2-3.5 gpm) based on web search of prices; $376­

461 for each of three 3.5 gal unit ($418 each on average). Incremental cost 

calculated relative to standard gas ($1079 from DOE 2011): $176=3*418-1079. 

Does not include gas access and plumbing costs. Wasted hot water (23%) 

calculated based on Hendron 2009. Possible savings due to lower hot water 

set points or better efficiency were neglected. 

Navigant Consulting Research and Development Roadmap for Water Heat- Performance target from Roadmap; Robur price list for air-water gas 

ing. Department of Energy (2011). absorption heat pump (40 kW) indicates 11,480 euros ($14,586) (www. 

lhprotrade.com/getcatfile.php?w=18). Scaling linearly with capacity gives: a 

4.5 kW unit cost of $1641. Adding median installation cost of a condensing 

boiler ($874) gives a total cost: $2515. 51% savings (0.59 -> 1.2 EF) 

Water Resources Engineering, I. Water Conservation Market Penetration Tables 4-7 and 4-9 represents installed faucet aerator base (used to scale McMordie-Stoughton, K.L., Elliott, D., Parker, 

Study. East Bay Municipal Utility District (2002) nationally); fixture cost ($90 for 5 per home)provided by web search G., Solana, A. & Sullivan, G. Update of 

(3/2012) net of water savings valued at $8/1000 gallons. Results in $409 Market Assessment for Capturing Water 

cost savings per home, 71% reduction moving to 2 gpm faucets Conservation Opportunities in the Federal 

Sector. Pacific North West National Laboratory (2005). 

U.S. Department of Energy, Office of Energy Efficiency and Renewable Efficiency and cost from DOE 2011, Efficiency Level 7. Baseline efficiency 

Energy (EERE). (2011, March 30). Residential Heating Products Final Rule and cost is for standard electric storage. Does not include gas access and 

Analytical Tools. Retrieved from Appliance & Equipment Standards: http:// plumbing costs. 

www1.eere.energy.gov/buildings/appliance_standards/residential/heating_products_fr_spreadsheets.html 

U.S. Department of Energy, Office of Energy Efficiency and Renewable Efficiency and cost from DOE 2011, Efficiency Level 7. Baseline efficiency 

Energy (EERE). (2011, March 30). Residential Heating Products Final Rule and cost is for standard electric storage. Does not include gas access and 

Analytical Tools. Retrieved from Appliance & Equipment Standards: http:// plumbing costs. 

www1.eere.energy.gov/buildings/appliance_standards/residential/heating_products_fr_spreadsheets.html 

101
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

R: Use most efficient tankless 33% Yes $1,805 per unit; 199,000 R: Gas water heating 72.7 1,399 1,399 448 $22.28 11 No No No Yes 

gas water heater (no FS) BTU/hr capacity 

R: Water heater off when away 1% Yes $0 per home R: Water heater tank 139 239 730 6.72 $0 1 No No Yes Yes 

losses 

R: Wrap water heaters with 50% Yes $20 per unit R: Water heater tank 139 239 369 183 $1.07 12 Yes Yes Yes Yes 

insulation blankets losses 

C: Add heat transfer fins to 5% Yes $0 per unit C: Natural Gas 0.37 12.9 12.9 0.51 $0 10 No No Yes Yes 

stock pots Griddles 

C: Develop electric ignition for 4% $100 per unit C: Natural Gas All 4.1 224 224 1.91 $9.15 10 No No No No 

commercial gas cooking Cooking 

C: Develop heat pipe griddles 4% $0 per unit C: Electric and Gas 1 15.5 47.4 0 $0 4 No No Yes Yes 

Griddles, Comm 

C: Improve insulation on fryers 7% Yes $0 per unit C: Electric and Gas 1.5 37.3 98.6 2.66 $0 12 No No Yes No 

Fryers, Comm 

C: Improve insulation on 7% Yes $0 per unit C: Electric and Gas 0.73 15.5 41.0 1.25 $0 10 No No Yes No 

griddles Griddles 

C: Improvements in food prepa­ 2% $0 per unit C: Food Preparation 3.7 7.09 21.6 0 $0 12 No No Yes Yes 

ration equipment motors Appliances 

C: Incorporate infrared burners 37% $1,750 per unit C: Natural Gas 0.24 25.2 25.2 11.5 $2.87 10 Yes Yes Yes Yes 

into broilers Broilers 

C: Incorporate infrared burners 30% $750 per unit C: Natural Gas Fryers 0.86 33.5 33.5 12.3 $4.14 12 No No Yes Yes 

into fryers 

C: Incorporate infrared burners 30% $1,000 per unit C: Natural Gas Ovens 1.3 70.1 70.1 25.5 $4.27 10 No No Yes Yes 

into gas ovens 

C: Incorporate infrared burners 39% $1,700 per unit C: Natural Gas 0.99 66.2 66.2 31.9 $4.07 10 Yes Yes Yes Yes 

into gas ranges Ranges 

C: Incorporate power burners 31% Yes $500 per unit C: Natural Gas Fryers 0.86 33.5 33.5 9.70 $3.62 12 Yes Yes Yes Yes 

into gas fryers 

C: Incorporate power burners 34% $3,000 per unit C: Natural Gas 0.99 66.2 66.2 25.0 $8.01 10 No No Yes Yes 

into gas ranges Ranges 

C: Reduce broiler idle energy 26% Yes $0 per unit C: Natural Gas 0.24 25.2 25.2 6.13 $0 10 No No Yes Yes 

Broilers 

C: Use more efficient (i.e., cur­ 9.0% $1,696 per unit C: Electric Broilers 0.02 0.50 1.52 0 $202.15 10 No No Yes Yes 

rently available) electric broiler 

C: Use more efficient (i.e., cur­ 6% $275 per unit C: Electric Fryers 0.62 3.80 11.6 0 $234.16 12 No No Yes Yes 

rently available) electric fryer 

C: Use more efficient (i.e., cur­ 11% $800 per unit C: Electric Griddles 0.37 2.58 7.86 0 $181.60 10 No No Yes Yes 

rently available) electric griddle 

C: Use more efficient (i.e., cur­ 39% $3,825 per unit C: Electric Ovens 1.1 9.23 28.2 5.57 $49.46 10 No No Yes Yes 

rently available) electric oven 

C: Use more efficient (i.e., cur­ 24% $3,400 per unit C: Electric Ranges 0.10 1.12 3.42 0 $86.61 10 No No Yes Yes 

rently available) electric range 

C: Use more efficient (i.e., 57% $0 per unit C: Electric Steamers 0.53 5.26 16.0 7.01 $0 10 Yes Yes Yes No 

currently available) electric 

steamer 

C: Use more efficient (i.e., cur­ 12.5% $1,223 per unit C: Natural Gas 0.24 25.2 25.2 3.44 $8.01 10 No No Yes Yes 

rently available) gas broiler Broilers 

C: Use more efficient (i.e., cur­ 31% $1,219 per unit C: Natural Gas Fryers 0.86 33.5 33.5 12.8 $6.46 12 No No Yes Yes 

rently available) gas fryer 

102
Primary sources used Data used and/or methodology Other supporting sources 

U.S. Department of Energy, Office of Energy Efficiency and Renewable Efficiency and cost from DOE 2011, Efficiency Level 7. Baseline efficiency and 

Energy (EERE). (2011, March 30). Residential Heating Products Final Rule cost is weighted average of standard gas storage/tankless. Does not include 

Analytical Tools. Retrieved from Appliance & Equipment Standards: http:// gas access and plumbing costs. 

www1.eere.energy.gov/buildings/appliance_standards/residential/heating_products_fr_spreadsheets.html 

Calculated from first principles Tank with 40 sq. ft. surface area, typical R-30, dT=60F http://www.aceee.org/consumer/ 

water-heating 

U.S. Department of Energy, Office of Energy Efficiency and Renewable Cost from DOE 2011. Insulating blankets designed to reduce tank losses by 

Energy (EERE). (2011, March 30). Residential Heating Products Final Rule roughly 50%. 

Analytical Tools. Retrieved from Appliance & Equipment Standards: http:// 

www1.eere.energy.gov/buildings/appliance_standards/residential/heating_products_fr_spreadsheets.html 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 56: Table 2-48 

portunities for Commercial Building Appliances. Washington, D.C.: Building 

Technology Program, Department of Energy. 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 53, Table 2-46 and Page 54, Table 2-47 

portunities for Commercial Building Appliances. Washington, D.C.: Building 

Technology Program, Department of Energy. 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 377, Table A-20 

portunities for Commercial Building Appliances. Washington, D.C.: Building 

Technology Program, Department of Energy. 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 51, Table 2-42 Cost data are from google searches of comportunities for Commercial Building Appliances. Washington, D.C.: Building mercially available appliances 

Technology Program, Department of Energy. 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 51, Table 2-42 

portunities for Commercial Building Appliances. Washington, D.C.: Building 

Technology Program, Department of Energy. 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 78, Table 3-9 

portunities for Commercial Building Appliances. Washington, D.C.: Building 

Technology Program, Department of Energy. 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 48, Table 2-33 and Page 49, Table 2-35 

portunities for Commercial Building Appliances. Washington, D.C.: Building 

Technology Program, Department of Energy. 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 48, Table 2-33 and Page 49, Table 2-35 

portunities for Commercial Building Appliances. Washington, D.C.: Building 

Technology Program, Department of Energy. 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 48, Table 2-33 and Page 49, Table 2-35 

portunities for Commercial Building Appliances. Washington, D.C.: Building 

Technology Program, Department of Energy. 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 48, Table 2-33 and Page 49, Table 2-35 

portunities for Commercial Building Appliances. Washington, D.C.: Building 

Technology Program, Department of Energy. 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 49, Table 2-36 and Page 50, Table 2-38 

portunities for Commercial Building Appliances. Washington, D.C.: Building 

Technology Program, Department of Energy. 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 49, Table 2-36 and Page 50, Table 2-38 

portunities for Commercial Building Appliances. Washington, D.C.: Building 

Technology Program, Department of Energy. 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 56, Table 2-48 

portunities for Commercial Building Appliances. Washington, D.C.: Building 

Technology Program, Department of Energy. 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 45, Table 2-31 Cost data are from google searches of comportunities for Commercial Building Appliances. Washington, D.C.: Building mercially available appliances 

Technology Program, Department of Energy. 

ENERGY STAR (n.d.). Savings Calculator for ENERGY STAR Qualified Com- Using indicated spreadsheet Enter “1” for gas oven: “results summary” tab 

mercial Kitchen Equipment. Retrieved from: http://www.energystar.gov/ shows energy savings and cost 

ia/business/bulk_purchasing/bpsavings_calc/commercial_kitchen_equipment_calculator.xls?ba38-b08e 

ENERGY STAR (n.d.). Savings Calculator for ENERGY STAR Qualified Com- Enter “1” for electric Griddle and tab over to the “results summary” tab for 

mercial Kitchen Equipment. Retrieved from: http://www.energystar.gov/ the energy savings and cost 

ia/business/bulk_purchasing/bpsavings_calc/commercial_kitchen_equipment_calculator.xls?ba38-b08e 

ENERGY STAR (n.d.). Savings Calculator for ENERGY STAR Qualified Com- Enter “1” for electric oven and tab over to the “results summary” tab for the 

mercial Kitchen Equipment. Retrieved from: http://www.energystar.gov/ energy savings and cost 

ia/business/bulk_purchasing/bpsavings_calc/commercial_kitchen_equipment_calculator.xls?ba38-b08e 

Brown, R., Borgeson, S., Koomey, J. & Biermayer, P. U.S. Building-Sector Page 8, Table 5. Cost is $7,400 - $4,000 = $3,400 

Energy Efficiency Potential. Environmental Protection 33 (2008). 

Brown, R., Borgeson, S., Koomey, J. & Biermayer, P. U.S. Building-Sector Page 8, Table 5 

Energy Efficiency Potential. Environmental Protection 33 (2008). 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 45, Table 2-31 Cost data are from google searches of comportunities for Commercial Building Appliances. Washington, D.C.: Building mercially available appliances 

Technology Program, Department of Energy. 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 376, Table A-17 and A-18 Cost data are from google searches of comportunities for Commercial Building Appliances. Washington, D.C.: Building mercially available appliances 

Technology Program, Department of Energy. 

103
 -

1. Description of the measure 2. Energy savings 3. Price 

4. Units & 

capacity 

5. Market 

description 

6. Market 

size, 2030 

7. Site 

use 

8. Source 

use 

9. Max 

adopt 10. CCE 

11. 

Life 12. Used in scenario? 

Percent SD? Fuel 

switch? 

McK NAS Emerg Tech 

C: Use more efficient (i.e., cur­ 12% $800 per unit C: Natural Gas 0.37 12.9 12.9 1.59 $16.98 10 No No Yes Yes 

rently available) gas griddle Griddles 

C: Use more efficient (i.e., cur­ 29% $0 per unit C: Natural Gas Ovens 1.3 70.1 70.1 103.8 $31.39 10 Yes Yes Yes Yes 

rently available) gas oven 

C: Use more efficient (i.e., cur­ 25% $1,830 per unit C: Natural Gas 0.99 66.2 66.2 699.2 $3.94 10 No No Yes Yes 

rently available) gas range Ranges 

C: Use more efficient (i.e., cur­ 43% $0 per unit C: Natural Gas 0.26 16.4 16.4 656.40 $8.97 10 Yes Yes Yes No 

rently available) gas steamer Steamers 

C: Use most efficient (i.e., max

tech) electric steamer 

­

­

73% $2,500 per unit C: Electric Steamers 0.53 5.26 16.0 931.4 $52.72 10 Yes Yes Yes Yes 

C: Use most efficient (i.e., max

tech) gas steamer Steamers 

C: Use pulse combustion burn­ 31% $760 per unit C: Gas Fryers and 1.2 46.5 46.5 13.4 $4.84 11 No No Yes Yes 

ers in gas fryers & griddles Griddles 

R: Energy-consumption indica­ 12% Yes $35 per unit R: Cooking (including 267 458 784 81.6 $7.46 10 No No Yes Yes 

tors on cooking devices microwaves) 

R: Incorporate all microwave 20% $55 per unit R: Microwave Total 127 55.3 169 35.5 $12.62 10 Yes Yes Yes Yes 

improvements (i.e., “max-tech”) 

R: Microwave use instead of 12.5% Yes FS $0 per unit R: Cooking - mi­ 135 113 199 -83.4 $0 10 No No No No 

oven/stove crowave to replace 

oven/stove 

R: Replace electric with induc­ 20% $550 per unit R: Electric cooktops 90.6 140 426 84.0 $39.25 10 No No Yes No 

tion cook top 

R: Replace gas with induction 70% FS $800 per unit R: Gas cooktops 44.8 237 237 169 $13.26 10 No No Yes No 

cook top 

73% $3,700 per unit C: Natural Gas 0.26 16.4 16.4 15.0 $4.28 10 Yes Yes Yes Yes 

104
Primary sources used Data used and/or methodology Other supporting sources 

ENERGY STAR (n.d.). Savings Calculator for ENERGY STAR Qualified Com- Enter “1” for Gas Griddle and tab over to the “results summary” tab for the 

mercial Kitchen Equipment. Retrieved from: http://www.energystar.gov/ energy savings and cost 

ia/business/bulk_purchasing/bpsavings_calc/commercial_kitchen_equipment_calculator.xls?ba38-b08e 

ENERGY STAR (n.d.). Savings Calculator for ENERGY STAR Qualified Com- Using indicated spreadsheet Enter “1” for gas oven: “results summary” tab 

mercial Kitchen Equipment. Retrieved from: http://www.energystar.gov/ shows energy savings and cost 

ia/business/bulk_purchasing/bpsavings_calc/commercial_kitchen_equipment_calculator.xls?ba38-b08e 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 51: “Insulation at major heat loss locations in cooking appliances can Cost data are from google searches of comportunities for Commercial Building Appliances. Washington, D.C.: Building reduce standby heat loses by 25% in both electric and gas powered models” mercially available appliances 

Technology Program, Department of Energy. 

Brown, R., Borgeson, S., Koomey, J. & Biermayer, P. U.S. Building-Sector Page 11, Table 6 

Energy Efficiency Potential. Environmental Protection 33 (2008). 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 56: Table 2-48 and Page 56: Table 2-49 

portunities for Commercial Building Appliances. Washington, D.C.: Building 

Technology Program, Department of Energy. 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 56: Table 2-48 and Page 56: Table 2-49 

portunities for Commercial Building Appliances. Washington, D.C.: Building 

Technology Program, Department of Energy. 

Navigant Consulting. (2009). Energy Savings Potential and RD&D Op- Page 377, Table A-18 and Page 51, Table 2-41 

portunities for Commercial Building Appliances. Washington, D.C.: Building 

Technology Program, Department of Energy. 

Wood and Newborough, Energy and Buildings 35 (2003) “The average reduction for households employing an ECI was 15%”, but 

deeper reading of paper suggest 12% savings once outliers are eliminated 

Roth, K.W., Mckenney, K., Ponoum, R. & Paetsch, C. Residential Miscellaneous Table 4-32: Theoretical Energy Savings Scenarios for Microwave Ovens 

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Energy 197 (2007). 

Pimentel et al, Environment Development and Sustainability 11 (2009) Section 2.3.6: “Using a microwave to cook 2,500 kcal of food could reduce
 


the energy needed to prepare this food in an oven by 50%.”,
 


Calculated by multiplying 50% energy savings x 25% of oven cooking time
 


replaced with microwave = 12.5%
 


Electrolux. (n.d.). 30” Induction Cooktop. Retrieved from http://www.electro­ “Cooking with induction is...20% more efficient than electric.” and MSRP of 

luxappliances.com/kitchen-appliances/cooktops/induction/ew30ic60is induction stove is $2,000 

Electrolux. (n.d.). 30” Induction Cooktop. Retrieved from http://www.electro­ “Cooking with induction is 70% more efficient than gas...” and MSRP of 

luxappliances.com/kitchen-appliances/cooktops/induction/ew30ic60is induction stove is $2,000 

105
 

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