A Method for Determining 

Optimal Residential

Energy Efficiency Retrofit 

Packages

B. Polly, M. Gestwick, M. Bianchi, R. Anderson, 

S. Horowitz, C. Christensen, and R. Judkoff

National Renewable Energy Laboratory

April 2011
ii

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A Method for Determining Optimal Residential 

Energy Efficiency Retrofit Packages
 

Prepared for: 

Building America 

Building Technologies Program 

Office of Energy Efficiency and Renewable Energy 

U.S. Department of Energy 

Prepared by: 

the National Renewable Energy Laboratory 

1617 Cole Boulevard 

Golden, CO 80401 

April 2011 

iii 
 

iv 
Acknowledgments 

This work was funded by the U.S. Department of Energy’s (DOE) Building Technologies 

Program (BTP). We wish to thank Joe Hagerman (BTP Research Leader) and David Lee (DOE 

Team Leader, Residential Buildings) for their continued support. We would also like to thank the 

following individuals who gave us feedback on the measures: Michael Blasnik (Blasnik and 

Associates), Bruce Davis (Appalachian State University), Paul Francisco (University of Illinois), 

Maria Mauceri (Advanced Energy), Ken Neuhauser (Building Science Corporation), Bill Rose 

(University of Illinois), and Bill Tesh (Pest Management Systems Inc.). Although their feedback 

led to improvements in measure definitions, it does not constitute or imply an endorsement of the 

material presented in this document. Finally, we would like to thank Dave Roberts (NREL) for 

giving us invaluable feedback on the report and Sean Casey (NREL) for expediting the 

postprocessing of the results. 

v 
Contents
 

Acknowledgments ...................................................................................................................................... v
 

List of Figures .......................................................................................................................................... viii
 

List of Tables .............................................................................................................................................. ix
 

Definitions .................................................................................................................................................... x
 

Nomenclature ............................................................................................................................................. xi
 

Executive Summary .................................................................................................................................. xii
 

Introduction............................................................................................................................. xii
 

Background............................................................................................................................. xii
 

Analysis Method ..................................................................................................................... xii
 

Example Analysis .................................................................................................................. xiv
 

Conclusions............................................................................................................................ xvi
 

Future Work........................................................................................................................... xvi
 

1.0 Introduction........................................................................................................................................... 1
 

2.0 Background........................................................................................................................................... 2
 

3.0 Key Analysis Issues ............................................................................................................................. 4
 

4.0 Retrofit Analysis Method ..................................................................................................................... 6
 

4.1 Modeling Energy Use ..........................................................................................................6
 

4.1.1 Enclosure Assumptions...............................................................................................6
 

4.1.2 Equipment Assumptions .............................................................................................6
 

4.1.3 Determining Annual Energy Uses ..............................................................................7
 

4.2 Modeling Cash Flows ..........................................................................................................8
 

4.2.1 Loan Costs ..................................................................................................................8
 

4.2.2 Replacement Costs ......................................................................................................8
 

4.2.3 Utility Bill Costs .........................................................................................................8
 

4.2.4 Residual Values ..........................................................................................................8
 

4.2.5 Cash Flow Diagrams...................................................................................................9
 

4.3 Metrics .................................................................................................................................9
 

4.3.1 Average Energy Use ...................................................................................................9
 

4.3.2 Equivalent Annual Cost............................................................................................10
 

4.3.3 Minimum Upgrade Reference Scenario ...................................................................10
 

4.4 Optimization ......................................................................................................................13
 

5.0 Example Analysis ............................................................................................................................... 16
 

5.1 Individual Retrofit Measures ..........................................................................................19
 

5.2 Optimization ...................................................................................................................21
 

5.2.1 Example Discussion of Optimization Results for One City .....................................21
 

5.2.2 Summary of Optimization Results ............................................................................30
 

6.0 Conclusions ........................................................................................................................................ 32
 

6.1 Analysis Method Future Work...........................................................................................32
 

6.2 Example Analysis Future Work .........................................................................................33
 

7.0 References .......................................................................................................................................... 35
 

Appendix A. Pre- and Post-Retrofit Performance Assumptions for Example Analysis .................... 41
 

A.1 Enclosure Components .....................................................................................................41
 

A.1.1 Crawl Spaces............................................................................................................41
 

A.1.2 Basements ................................................................................................................43
 

A.1.3 Wall Insulation .........................................................................................................44
 

A.1.4 Whole House Air Sealing ........................................................................................45
 

A.1.5 Attic Insulation and Air Sealing ...............................................................................46
 

vi 
A.1.6 Windows ..................................................................................................................46
 

A.2 HVAC Equipment and Distribution ..................................................................................47
 

A.2.1 Air Conditioning ......................................................................................................47
 

A.2.2 Gas Furnace..............................................................................................................48
 

A.2.3 Air Distribution System ...........................................................................................48
 

A.3 Domestic Water Heating Equipment ................................................................................50
 

A.3.1 Replacement of Standard Gas With ENERGY STAR Gas Water Heater ...............50
 

A.3.2 Replacement of Standard Gas With ENERGY STAR Tankless Water Heater .......50
 

A.4 Appliances and Lighting ...................................................................................................51
 

A.4.1 Lighting....................................................................................................................51
 

A.4.2 Refrigerator ..............................................................................................................52
 

A.4.3 Clothes Washer ........................................................................................................52
 

Appendix B. Sensitivity of Example Analysis Results .......................................................................... 54
 

B.1 Introduction .......................................................................................................................54
 

B.2 General Assumptions ........................................................................................................54
 

B.3 Financial Assumptions ......................................................................................................55
 

B.4 Occupant Assumptions ......................................................................................................59
 

B.5 Building Assumptions .......................................................................................................60
 

vii 
List of Figures 

Figure ES-1. Example annual energy use diagram .............................................................................. xiii
 

Figure ES-2. Example cash flow diagram .............................................................................................. xiii
 

Figure 4-1. Example annual energy use diagram .................................................................................... 7
 

Figure 4-2. Example cash flow diagram .................................................................................................... 9
 

Figure 4-3. Example annual energy uses for MURS (top) and post-retrofit scenario (bottom) ........ 11
 

Figure 4-4. Example cash flows for MURS and post-retrofit scenario ................................................ 12
 

Figure 4-5. Average energy use (AEU) for MURS and post-retrofit scenario ..................................... 12
 

Figure 4-6. Equivalent annual cost (EAC) for MURS and post-retrofit scenario ................................ 13
 

Figure 4-7. Example of optimization results........................................................................................... 15
 

Figure 5-1. Incremental EAC at different levels of energy savings for Houston ................................ 22
 

Figure 5-2. Incremental EAC at different levels of energy savings for Phoenix ................................. 23
 

Figure 5-3. Incremental EAC at different levels of energy savings for Atlanta .................................. 24
 

Figure 5-4. Incremental EAC at different levels of energy savings for San Diego ............................. 25
 

Figure 5-5. Incremental EAC at different levels of energy savings for Seattle ................................... 26
 

Figure 5-6. Incremental EAC at different levels of energy savings for Washington, D.C. ................. 27
 

Figure 5-7. Incremental EAC at different levels of energy savings for Chicago ................................ 28
 

Figure 5-8. Incremental EAC at different levels of energy savings for Minneapolis .......................... 29
 

Figure B-1. Chicago optimization results as a function of nominal discount rate............................. 56
 

Figure B-2. Chicago optimization results as a function of inflation rate ............................................ 56
 

Figure B-3. Chicago optimization results as a function of annual effective loan interest rate ......... 58
 

Figure B-4. Chicago optimization results as a function of nominal fuel escalation rate .................. 58
 

Figure B-5. Chicago optimization results as a function of thermostat set point ............................... 59
 

Figure B-6. Attic insulation/air seal energy savings as a function of pre-retrofit insulation level 

(Chicago) ............................................................................................................................................. 61
 

Figure B-7. Attic insulation/air seal energy savings as a function of post-retrofit R-value 

(Chicago) ............................................................................................................................................. 61
 

viii 
 

 

 

 

List of Tables 

Table ES-1. Assumed Values for Key Financial Parameters ............................................................... xiv
 

Table ES-2. Minimum-Cost and Nearest to Neutral Cost Packages on Least-Cost Curve ................ xv
 

Table 5-1. Assumed Values for Key Financial Parameters ................................................................... 16
 

Table 5-2. Cities, Climate Zones, Foundations, Duct Location, and Energy Tariffs for Each City... 17
 

Table 5-3. Retrofit Measures, Properties, and Costs ............................................................................. 18
 

Table 5-4. Air Conditioner and Furnace Sizes and Costs per Location .............................................. 19
 

Table 5-5. Predicted Average Annual Source Energy Savings (%) for Individual Retrofit
 

Measures ............................................................................................................................................. 20
 

Table 5-6. Predicted Incremental Equivalent Annual Cost (2010$) for Individual Retrofit
 

Measures ............................................................................................................................................. 21
 

Table 5-7. Least-Cost Packages at Different Energy Savings for Houston ........................................ 22
 

Table 5-8. Least-Cost Packages at Different Energy Savings for Phoenix ......................................... 23
 

Table 5-9. Least-Cost Packages at Different Energy Savings for Atlanta ........................................... 24
 

Table 5-10. Least-Cost Packages at Different Energy Savings for San Diego ................................... 25
 

Table 5-11. Least-Cost Packages at Different Energy Savings for Seattle ......................................... 26
 

Table 5-12. Least-Cost Packages at Different Energy Savings for Washington, D.C. ....................... 27
 

Table 5-13. Least-Cost Packages at Different Energy Savings for Chicago ....................................... 28
 

Table 5-14. Least-Cost Packages at Different Energy Savings for Minneapolis ................................ 29
 

Table 5-15. Minimum-Cost and Nearest to Neutral Cost Packages on Least-Cost Curve ................. 30
 

Table 5-16. Order Measures Are Added to Packages on Least-Cost Curve to Minimum Cost
 

Point..................................................................................................................................................... 31
 

Table A-1. Summary of Proposed Basement Wall Measures and Costs ............................................ 44
 

Table A-2. Single-Pane Window Options From the National Residential Efficiency Measures 

Database.............................................................................................................................................. 46
 

Table A-3. Retrofit Window Options ........................................................................................................ 47
 

Table A-4. GSL and CFL Summary by Room ......................................................................................... 51
 

Table A-5. Lighting Retrofit Options ....................................................................................................... 52
 

Table A-6. Lighting Options and Lifetimes ............................................................................................. 52
 

Table B-1. Least-Cost Packages at Different Energy Savings for Chicago (20-year loan) ................ 57
 

ix 
 

 

 

 

 

 

 

 

 

 

Definitions 

AC air conditioner 

ACCA Air Conditioning Contractors of America 

ACH air changes per hour 

ACH50 air changes per hour at a pressure difference of 50 Pa 

AEU average energy use 

AES average energy savings 

AFUE annual fuel utilization efficiency 

AHU air handling unit 

ASHRAE American Society of Heating, Refrigerating and Air-Conditioning 

Engineers 

BSC Building Science Corporation 

BTP U.S. Department of Energy Building Technologies Program 

CEC California Energy Commission 

CEE Consortium for Energy Efficiency 

cfm cubic feet per minute 

CFL compact fluorescent lamp 

CFR Code of Federal Regulations 

DOE U.S. Department of Energy 

EAC equivalent annual cost 

ECM energy conservation measure 

EEM energy efficient mortgage 

EER energy efficiency ratio 

EERE U.S. Department of Energy, Energy Efficiency and Renewable Energy 

EF energy factor 

EIA Energy Information Administration 

EPA U.S. Environmental Protection Agency 

gpm gallons per minute 

GSL general service lamp 

HVAC heating, ventilating, and air conditioning 

IEAC incremental equivalent annual cost 

IECC International Energy Conservation Code 

LBL or LBNL Lawrence Berkeley National Laboratory 

LFL linear fluorescent lamp 

MEF modified energy factor 

MURS minimum upgrade reference scenario 

nACH natural air changes per hour 

NAECA National Appliance Energy Conservation Act 

x 
 

 

 

 

 

 

 

 

NIST National Institute of Science and Technology 

NL normalized leakage 

NREL National Renewable Energy Laboratory 

O.C. on centers 

ORNL Oak Ridge National Laboratory 

polyiso polyisocyanurate 

PW present worth 

R-value thermal resistance, h·ft2·F/Btu [m2

·K/W] 

RECS Residential Energy Consumption Survey 

SEER seasonal energy efficiency ratio 

SHGC solar heat gain coefficient 

SLA specific leakage area 

TM3 Typical Meteorological Year 3 

U-factor or U-value thermal conductance, Btu/ h·ft2·F [W/ m2·K] 

WF water factor 

XPS extruded polystyrene 

Nomenclature

ߙ residual value 

݀

݀

COST

௡

௥

cost of building component 

nominal discount rate 

real discount rate 

i inflation rate (fractional) 

L lifetime, years 

N number of years in analysis period 

R remaining lifetime, years 

TC total cost 

xi 
 

 

Executive Summary 

Introduction 

Businesses, government agencies, consumers, policy makers, and utilities currently have limited 

access to occupant-, building-, and location-specific recommendations for optimal energy retrofit 

packages, as defined by estimated costs and energy savings. This report describes an analysis 

method for determining optimal residential energy efficiency retrofit packages and, as an 

illustrative example, applies the analysis method to a 1960s-era home in eight U.S. cities 

covering a range of International Energy Conservation Code (IECC) climate regions. 

Background 

Since 2003, the U.S. Department of Energy (DOE) has used BEopt, a building energy 

optimization software tool, to determine cost-effective, energy efficient building designs for new 

construction. BEopt evaluates the incremental energy and cost effects of different building 

designs relative to a reference building (e.g., a building that complies with IECC) and provides a 

“least-cost” curve that allows users to determine minimum-cost building designs at various levels 

of energy savings and under various sets of economic assumptions. In 2008, DOE’s National 

Renewable Energy Laboratory (NREL) began research efforts to extend BEopt analysis 

capabilities to existing homes. 

Preliminary efforts at NREL using BEopt for research regarding energy efficiency retrofit 

projects identified several analysis issues specific to existing homes. The issues generally fall in 

three categories: 

• Measures 

• Retrofit and replacement timing 

• Economics. 

To address the analysis issues in these three areas, NREL researchers modified BEopt to model 

home energy retrofits. Researchers added many new features to BEopt, and this report presents 

an analysis method that uses some of those features.1 

Analysis Method 

The heart of the analysis method is determining the annual energy use over the analysis period. 

Annual energy uses are calculated by performing annual building energy simulations, the results 

of which depend on the climate, building characteristics, and occupant behavior. Figure ES-1 

shows an example of annual energy uses over a 30-year analysis period. In this example, not all 

existing equipment below the minimum standard is replaced during the retrofit, so the annual 

energy use changes over the analysis period when that equipment wears out and is replaced with 

more efficient equipment, as defined by the minimum standard. 

1

Although the analysis method was implemented in BEopt, it is described in general terms (independent of a 

particular software program). 

xii 
 

 Annual Energy Use 

1 5 10 15 20 25 

Year 

Figure ES-1. Example annual energy use diagram 

In addition to annual energy uses, cash flows determine optimal retrofit packages. Cash flows 

consist of: 

• Loan payments to cover initial retrofit package costs 

• Replacement costs in the future 

• Annual utility bill costs 

• Residual values at the end of the analysis period. 

Costs, excluding loan payments, are inflated based on the time they are incurred. Figure ES-2 

shows an example cash flow2 in nominal dollars (including the effect of inflation). For this 

example, a retrofit at the beginning of the 30-year analysis period is financed by a 5-year loan. 

Equipment is replaced throughout the analysis period as it wears out. 

1 5 10 15 20 25 

Costs 

Loan Payment 

Replacement Costs 

Residual Values 

Utility Bills 

Time (years) 

Figure ES-2. Example cash flow diagram 

2

 Cash flow diagrams presented in this report assume the following sign convention: expenses are positive and 

receipts are negative. 

xiii 

30 
The analysis method computes two primary metrics for each retrofit scenario: average energy 

use (AEU) and equivalent annual cost (EAC). AEU is the average of the annual energy uses over 

the analysis period (e.g., Figure ES-1), and EAC is the annualized cash flows (e.g., Figure ES-2). 

The EAC and AEU of a retrofit scenario can be compared to an alternative scenario to determine 

the additional cost incurred to achieve a given level of energy savings. This naturally leads to the 

definition of a reference scenario, the baseline against which energy upgrade scenarios are 

compared in terms of cost and energy use. This analysis method uses a minimum upgrade 

reference scenario (MURS) as the baseline. The MURS begins with the existing building at the 

start of the analysis period and assumes all equipment that wears out over the analysis period is 

replaced with the same level of efficiency or the current minimum standard, whichever is more 

efficient. Minimum upgrades are assumed for the reference building so as not to take credit for 

energy efficiency improvements that would have otherwise occurred through natural wear-out 

and replacement. 

A sequential search technique determines optimal retrofit packages for different levels of energy 

savings by calculating the AEU and EAC relative to the MURS for each retrofit scenario 

investigated. These incremental calculations are the average energy savings (AES) and the 

incremental equivalent annual cost (IEAC). The optimization results in a least-cost curve, from 

which the most cost-effective retrofit packages can be identified at various levels of energy 

savings. 

Example Analysis 

Researchers implemented the analysis method in BEopt and used it to conduct a retrofit example 

analysis on a 1,280 ft2

, 1960s-era house in eight U.S. cities with climate-appropriate foundation 

types. Although they applied realistic pre-retrofit conditions and retrofit measures in the example 

analysis, the inputs and results are not intended to be representative of the U.S. housing stock as 

a whole. Instead, this example analysis demonstrates how the method can be used to generate 

recommendations for individual retrofit measures and packages of measures specific to a 

building, its occupants, and its location. 

Table ES-1 gives the key assumptions for financial parameters in the example analysis. 

Table ES-1. Assumed Values for Key Financial Parameters 

Financial Parameter Value 

Analysis Period (years) 30 

Inflation Rate (%) 3 

Real Discount Rate (%) 3 

Real Fuel Escalation Rate (%) 0 

Annual Effective Loan Interest Rate (%) 7 

Loan Period (years) 5 

The list that follows contains the types of retrofit measures considered in the example analysis. 

This set of measures is used to illustrate the analysis method—it does not represent all possible 

measures that could be implemented in residential buildings: 

xiv 
 

 

• Air seal and insulate attic floor 

• Close, condition, and insulate crawl space 

• Insulate walls (drill-and-fill) 

• Insulate basement walls 

• Replace the following: 

o AC 

o Clothes washer 

o Furnace 

o Lamps 

o Refrigerator 

o Water heater 

o Windows 

• Seal ducts 

• Seal and insulate ducts. 

• Whole house air seal 

Table ES-2 shows the AES and IEAC for minimum-cost packages and for packages on the leastcost curve that are closest to neutral cost.3

 The AES values for the minimum-cost packages range 

from 18% (San Diego) to 34% (Seattle), and the IEAC values range from –$388 (Washington, 

DC) to –$55 (San Diego). Averaged over the eight cities in this analysis, the minimum-cost 

package achieves 30% AES at an IEAC of –$289. The AES values for the nearest to neutral cost 

packages4 range from 29% (San Diego) to 48% (Seattle and Minneapolis). 

Table ES-2. Minimum-Cost and Nearest to Neutral Cost Packages on Least-Cost Curve 

Location 

Minimum Cost Nearest to Neutral Cost 

AES (%) IEAC (2010$) AES (%) IEAC (2010$) 

Houston, Texas 

Phoenix, Arizona 

Atlanta, Georgia 

San Diego, California 

Seattle, Washington 

Washington, D.C. 

Chicago, Illinois 

Minneapolis, Minnesota 

30 

33 

30 

18 

34 

30 

31 

32 

–185 

–361 

–293 

–55 

–309 

–388 

–351 

–366 

38 

47 

41 

29 

48 

43 

47 

48 

8 

–31 

–56 

14 

–39 

–114 

–86 

–121 

3

 Note the least-cost curve is defined by a set of discrete retrofit packages. There is no guarantee that any of the 

discrete packages delivers an IEAC of exactly $0. 

4

 Targeting the nearest to neutral cost point may be an opportunity to realize significant savings when considered 

before any retrofit. Once a retrofit package is implemented to the minimum cost point, there can be a significant cost 

barrier to get to larger energy savings. 

xv 
 

Conclusions 

The method for analyzing the retrofit of existing houses described in this report uses an 

optimization scheme that considers AEU (determined from building energy simulations) and 

EAC to recommend optimal retrofit packages specific to the building, occupants, and location. 

Energy savings and incremental costs are calculated relative to a MURS, which accounts for 

efficiency upgrades that would occur in the absence of a retrofit because of equipment wear-out 

and replacement with current minimum standards. 

The method was applied, as an illustrative example, to analyze the retrofit of a 1960s-era house 

in the eight U.S. locations. The following were the main conclusions of the example analysis: 

•	 Results were specific to assumptions in the following categories: general (e.g., analysis 

period, MURS), financial (retrofit financing and measure costs), occupant, and building. 

Results should not be interpreted as “average” for U.S. housing stock. 

•	 For the 1,280 ft2

, 1960s-era home and the specific set of retrofit measures considered in 

the analysis, minimum-cost packages varied in average energy savings from 18% in San 

Diego to 34% in Seattle. 

•	 The nearest to neutral cost package gave significant additional savings beyond the 

minimum-cost package. 

•	 Issues such as complexity of the retrofit package, on-site conditions, effect on occupant 

comfort, and so on were not considered, but they could justify certain measures that were 

not included in optimal packages as well as eliminate measures that were. 

Future Work 

Together, the analysis method and example analysis presented in this report are an introduction 

to retrofit optimization; improvements to the analysis method and additional studies considering 

a comprehensive range of building types, locations, and retrofit measures would further 

knowledge of retrofit optimization. 

Some possible improvements to the analysis method include the following: 

•	 Forecasting minimum standards for the MURS 

•	 Modeling equipment performance degradation over the analysis period 

•	 Modeling financing of future replacements 

•	 Modeling heating, ventilating, and air-conditioning (HVAC) equipment down-sizing 

•	 Modeling retrofit effect on occupant behavior 

•	 Optimizing in the future to determine effect of improvements made over time 

•	 Using EIA fuel price projections. 

Some possible improvements to future analyses are as follows: 

•	 Adjusting retrofit costs city by city, similar to energy costs. 

xvi 
 

•	 Adding more house types (e.g., two-story houses). Ideally, the house types analyzed 

should be representative of the locations considered, including assumptions related to fuel 

types. 

•	 Adding retrofit measures, including those for safety and health of the occupants (e.g., 

combustion air for atmospherically vented appliances). 

•	 Investigating different metrics, financial mechanisms, and loan terms to cover a wider 

range of possibilities and perspectives. 

•	 Comparing energy use and savings predictions to measured use and savings from 

laboratory tests, field tests, and pre- or post-retrofit utility bill analysis. 

xvii 
 

1.0 Introduction 

The approximately 130 million homes in the residential sector account for 22% of the energy 

consumed in the United States (EIA 2009). Cost-effective improvements in the energy efficiency 

of the U.S. residential building stock would save energy, lower utility bill costs for homeowners, 

and create jobs. Businesses, government agencies, consumers, policy makers, and utilities 

currently have limited access to occupant-, building- and location-specific recommendations for 

optimal energy retrofit packages, as defined by estimated costs and energy savings. This report 

describes an analysis method for determining optimal residential energy efficiency retrofit 

packages and, as an illustrative example, applies the analysis method to a 1960s-era home in 

eight U.S. cities covering a range of International Energy Conservation Code (IECC) climate 

regions. The example analysis is an introduction to retrofit optimization; more comprehensive 

future analyses will consider a wider range building types, retrofit measures, and locations. 

Section 2 is a background discussion on previous analysis efforts. Section 3 discusses key 

analysis issues for existing homes and contrasts them with those for new homes. Section 4 

describes an analysis method for determining optimal retrofit packages in existing homes. 

Sections 4.1–4.4 describe approaches for modeling energy use, modeling cash flows, calculating 

key metrics, and performing optimizations, respectively. Section 5 presents the example analysis. 

Section 6 presents conclusions regarding the analysis method and example analysis, along with 

descriptions of possible future work. Appendix A defines the retrofit options for the example 

analysis; their associated costs come from the National Residential Efficiency Measures 

Database, version 1.0.0beta (NREL 2010).5

 Estimated energy and cost savings are presented for 

each retrofit measure. Least-cost packages of measures at various levels of energy savings in the 

different climates are presented based on optimization results. Appendix B discusses sensitivity 

of output to financial and performance assumptions. 

5

 Except for the refrigerator and clothes washer costs, which were estimated based on the Preliminary Analytical 

Tools under http://www1.eere.energy.gov/buildings/appliance_standards/residential_products.html, accessed 

between June and August 2010. 

1 
2.0 Background 

Lack of information has long been cited as one reason why consumers do not invest in energy 

conservation measures (ECMs), and the Vice President’s Middle Class Task Force, Council of 

Environmental Quality, recently recognized it as one of the three main barriers to establishing a 

self-sustaining retrofit market (MCTF 2009): “Consumers do not have access to straightforward 

and reliable information on home energy retrofits that they need to make informed decisions.” 

This is true despite decades of work by researchers, engineers, and policy makers evaluating the 

retrofit potential at both local and national scales. 

This document describes and applies emerging analysis capabilities developed at the National 

Renewable Energy Laboratory (NREL) on behalf of the U.S. Department of Energy (DOE) that 

will ultimately improve access to information needed to make informed decisions in a 

sustainable retrofit market. Key outcomes of the ongoing research are: 

•	 Improvement of analysis methods and tools for existing homes 

•	 Identification of cost-effective, safe, and durable retrofit measures and packages of 

measures 

•	 Better understanding of gaps that must be filled to achieve higher energy savings levels. 

Since 2003, DOE has used BEopt, a building energy optimization software tool, to determine 

cost-effective, energy efficient building designs for new construction (Christensen et al. 2006). 

BEopt evaluates the incremental energy and cost effects of different building designs relative to a 

reference building (e.g., a building that complies with IECC) and provides a “least-cost” curve 

that allows users to determine minimum-cost building designs at various levels of energy savings 

and under various sets of economic assumptions. In 2008, NREL began research efforts to 

extend BEopt analysis capabilities to existing homes, making the fundamental changes required 

to perform simulations and optimizations in the context of home energy retrofit. 

The method developed for existing homes expands on previous retrofit analysis efforts. Many 

studies in the past focused on low-income weatherization (e.g., Schweitzer and Eisenberg 2002) 

and utility demand-side management programs (e.g., Fels and Keating 1993). These studies 

prescribed the retrofit measures and did not include optimization. Oak Ridge National 

Laboratory (ORNL) performed a study in the late 1980s to select optimal combinations of 

enclosure and heating equipment retrofits as part of an expanded weatherization program 

(McCold 1987). The ORNL study used a life-cycle cost-benefit metric evaluated over a range of 

retrofit packages for the life of the measures to determine the most cost-effective weatherization 

upgrade. The ORNL study also considered the energy interactions when changing multiple 

components of a house. 

Other studies evaluated broader retrofit potential at the national (e.g., Guler et al. 2001, Koomey 

et al. 1991) or state levels (e.g., Pigg and Nevius 2000) by extrapolating across a housing stock 

using survey data. Savings prediction methods included engineering analyses and statistical 

evaluations using previous programmatic results. Engineering analysis techniques can be 

focused, looking at savings on the component level, or highly integrated, using simulation tools 

and looking at the complex interactions that occur in buildings of all types. Often these studies 

2 
used simple economic metrics to evaluate cost-effectiveness. Simple payback (cost of measure 

divided by cost savings per year), used by Pigg and Nevius (2000), or the energy savings per 

dollar invested, used by Guler et al. (2001), are easy for the general population to understand and 

are sometimes desirable when taking a short-term view, such as evaluating individual ECMs. 

Simple metrics, however, can obscure important time-value-of-money issues and impose a shortterm view of the retrofit, thereby excluding costlier, deep retrofit packages with positive longterm cash flow projections. 

A comprehensive analysis recognizes that homeowners may have other investment options and 

limited access to capital. Investments in their home, where utility bill savings are a primary 

driver, should provide returns at a rate similar to other opportunities. Deep energy retrofits, 

where a house receives a large overhaul to dramatically reduce energy consumption, commonly 

require longer term financing to cover the cost of the project. This adds significant complexity to 

evaluating retrofit cost-effectiveness, because financing mechanisms such as home equity loans 

and energy efficient mortgages naturally lead to a longer term time horizon for the financial 

decision. In addition, homeowners have several retrofit options (e.g., “Should I replace my 

inefficient furnace, add more insulation in the attic, install new windows?”). The analysis method 

presented here was developed with these considerations in mind. 

3 
 

3.0 Key Analysis Issues 

BEopt has identified least-cost paths, technology gaps, and research needs for new homes 

(Anderson and Roberts 2008). Preliminary efforts at NREL using BEopt for similar research 

regarding ECM retrofit projects identified several analysis issues specific to existing homes. The 

following list describes key analysis issues for existing homes related to measures, retrofit and 

replacement timing, and economics, and contrasts them with issues for new home construction. 

•	 Measures 

o	 Design versus Retrofit—A new home is an abstract entity until it is built. Changes 

can be made easily during the design stage, when there are few constraints caused 

by existing conditions. This is not true of existing homes. For example, in new 

homes it is easy to add foam insulation to the exterior of the wall during 

construction to reduce enclosure loads. For existing homes, however, the presence 

of existing exterior finish (e.g., wood siding or brick) can make adding exterior 

foam insulation challenging. 

o	 Costs—Adding or replacing a component in an existing home can have different 

labor and material costs than in a new home. For example, adding foam sheathing 

to exterior wall may require removing old siding and adding new siding, which 

dramatically increases the labor and material costs compared to the same level of 

insulation in new construction. 

o	 Equipment Sizing—For new homes, the heating, ventilating, and air-conditioning 

(HVAC) systems can be right-sized based on the design of the building, so there 

is often an HVAC down-sizing cost benefit to reducing the loads of the building. 

For existing homes, however, down-sizing cost benefits can only be achieved 

when the HVAC system is replaced (which may not coincide with the retrofit 

measures that reduce the load). 

•	 Retrofit and replacement timing 

o	 Equipment Remaining Life—In new construction, all equipment and components 

begin their working lives at the same time—when the house is built. In an existing 

home, each piece of equipment may be at a different point in its lifetime at the 

time of retrofit. Therefore, the cost-effectiveness of upgrading the equipment at 

the time of retrofit must be compared to the cost-effectiveness of waiting until 

wear-out to replace or upgrade the equipment. 

o	 Minimum Standard Equipment—In new construction, equipment meets or 

exceeds the minimum federal standards when the house it built. In an existing 

house, some of the equipment may be below the current minimum federal 

standards6

 at the time of retrofit. Only equipment at least meeting current 

minimum standards is evaluated as replacement options.7 The requirement to 

upgrade to minimally compliant equipment (e.g., seasonal energy efficiency ratio 

6

 From this point forward, minimum federal standards are referred to as "minimum standards,” “minimum
 

equipment,” or “minimum upgrades.”

7

 Assuming only new equipment is available (no used equipment).
 

4 
 

[SEER]13 air conditioner [AC]) at wear-out affects the cost-effectiveness of other 

retrofit measures (e.g., wall insulation). 

o	 Reference Building—The baseline or reference building for new homes is a 

predefined, hypothetical construction (e.g., IECC 2009). This is a necessary point 

of comparison because no as-built frame of reference exists. In retrofit analysis, 

the existing house is often the baseline. This may not be appropriate when 

evaluating the building over a longer term, however—natural upgrades occur as 

old equipment fails and is replaced with higher efficiency units required by 

minimum standards. If the existing house is the baseline in these cases, energy 

savings can be attributed to the retrofit that would have otherwise occurred 

through equipment wear-out and replacement according to minimum standards. 

•	 Economics 

o	 Financing—In BEopt’s new construction analysis, the cost of energy measures is 

included in the mortgage and the interest portion of the mortgage payment is tax 

deductible. For existing homes, retrofit costs are typically financed over a shorter 

period8

 via cash payments or a home equity loan. 

o	 Metrics—For new construction, measures are implemented at the time of 

construction. Cost-effectiveness can therefore be calculated using the first-year 

incremental mortgage and utility bill costs along with the first-year energy 

savings as a surrogate for life-cycle analysis. For existing homes, measures can be 

implemented in different years, causing cash flows and energy savings to change 

over time. The time-dependent nature of the energy costs and savings requires 

life-cycle-based metrics. 

8

 Alternatively, one could consider using longer-term financial products like the energy efficient mortgage (EEM). 

More information can be found at http://www.energystar.gov/index.cfm?c=mortgages.energy_efficient_mortgages. 

5 
 

4.0 Retrofit Analysis Method 

To address the analysis issues identified in Section 3, NREL modified BEopt to model retrofits 

of existing homes. This involved adding many new features to BEopt, and this section describes 

an analysis method that uses some of those features—modeling energy use in Section 4.1, 

modeling cash flows in Section 4.2, key metrics in Section 4.3, and an optimization approach in 

Section 4.4. 

4.1 Modeling Energy Use 

The heart of the analysis method is determining of the annual energy use over the analysis 

period. Annual energy uses are calculated by performing annual building energy simulations, the 

results of which depend on the climate, building characteristics, and occupant behavior. The 

climate is assumed to be the same from year to year over the analysis period. The modeled 

occupant behavior follows the recommendations of the Building America House Simulation 

Protocol (NREL 2010), where occupant behavior is the same from year to year, except for 

lighting.9 Thus, the changes in energy use are primarily caused by changes in building 

characteristics. 

4.1.1 Enclosure Assumptions 

Similar to the analysis approach used in BEopt for new home construction, the analysis method 

considers all enclosure efficiency improvements only at the time of the initial retrofit. In other 

words, this analysis method does not evaluate enclosure improvements performed in the future. 

The analysis focuses on the effects of measures implemented at the beginning of the analysis 

period as opposed to those implemented later. Enclosure improvements at the beginning of the 

analysis period affect the energy use of the building over the entire analysis period. 

This analysis approach evaluates multiple enclosure improvements. Performance characteristics 

for the existing enclosure and all enclosure improvements must be defined to generate the annual 

building energy simulations. In some cases, retrofit options can be linked to other options. For 

example, air-sealing measures can be linked with mechanical ventilation measures to address 

indoor air quality/durability/combustion safety issues. 

4.1.2 Equipment Assumptions 

Equipment replacement is one of the reasons the analysis approaches used for new construction 

cannot be used directly for retrofit analysis. The equipment in existing homes may be below the 

current minimum standard, and may have remaining useful life. For this analysis method, when 

equipment wears out in the future, it is replaced with either the same level of efficiency or the 

minimum standard efficiency, whichever is more efficient. In other words, this analysis method 

does not evaluate equipment efficiency upgrades in the future beyond those required by 

minimum standards. Similar to enclosure measures, the analysis concentrates on the effect of 

retrofit packages implemented at the beginning of analysis period as opposed to retrofit packages 

implemented later. Even though future upgrades beyond the minimum standard are not 

evaluated, the annual energy use can change over time as equipment below the minimum 

standard wears out and is replaced with the current minimum standard. 

9

 Ten percent of the energy savings from compact fluorescent lamps (CFLs) is taken back by assuming longer hours 

of operation. 

6 
Multiple types of equipment can be evaluated with various levels of efficiency considered for 

each equipment type. Performance inputs for the existing equipment and any possible 

replacements must be defined. In the present analysis method, equipment efficiency is assumed 

to be constant over the life of the equipment. Section 6.1 identifies modeling degradation of 

equipment and enclosure technology as an area of future work. 

HVAC equipment sizing is relevant in this analysis approach—it requires information about the 

existing HVAC equipment size and whether equipment installed at the time of retrofit and at 

wear-out is properly sized. For this analysis method, simulations determine the HVAC 

equipment size and all replacement equipment is the same size as the existing equipment. These 

are simplifications. In the field, various approaches are used for equipment sizing, from rules of 

thumb (e.g. tons/square foot) to detailed calculations (such as the Air Conditioning Contractors 

of America (ACCA) Manual J [ACCA 2006]). A particular home may have over- or undersized 

HVAC equipment (over-sizing is more common) before or after a retrofit. Additionally, when 

HVAC equipment is replaced at the same time or after other improvements to the enclosure, 

reduced heating and cooling loads may allow for equipment downsizing, which can save energy 

and reduce the replacement cost. More research will help determine how consistently and 

properly HVAC equipment is resized on replacement. 

4.1.3 Determining Annual Energy Uses 

Based on the enclosure and equipment analysis approaches described, annual building energy 

simulations determine the annual energy uses over the analysis period. Figure 4-1shows an 

example of the annual energy uses over a 30-year analysis period. In this example, not all 

existing equipment below the minimum standard is replaced during the retrofit, so the annual 

energy use changes over the analysis period when that equipment wears out and is replaced with 

more efficient equipment, as defined by the minimum standard (replacements decrease the 

annual energy use at years 5 and 15 in this example). Annual Energy Use 

1 5 10 15 20 25 

Year 

Figure 4-1. Example annual energy use diagram 

7 

30 
 

 

4.2 Modeling Cash Flows 

In addition to annual energy uses, cash flows determine optimal retrofit packages. Cash flows 

consist of loan payments to cover initial retrofit package costs, replacement costs in the future, 

annual utility bill costs, and residual values at the end of the analysis period. Costs, excluding 

loan payments, are inflated based on the time they occur10 in the analysis period using 

ܥܱܵܶ௬௘௔௥ୀ௞ ൌ ܥ ܱܶܵ௜௡௜ ௧௜௔

where i is the inflation rate, ܥܱܵܶ௜௡௜௟ ௧௜௔

ܥܱܵܶ௬௘௔௥ୀ௞ is the cost at the end of year k. Under this approach, the inflation rate is assumed to 

௞ ሺ1 ൅ ݅ሻ

 is the cost at the beginning of the analysis period, and 

௟ , (4-1)
 

be the same every year. 

4.2.1 Loan Costs 

Energy efficiency upgrades for new homes are typically bundled into the mortgage, because 

improvements are made at the time of construction. For retrofits of existing homes, homeowners 

commonly use shorter-term financing such as home equity loans and, in some cases, pay for 

energy efficiency retrofits using cash. In this analysis method, the terms of the financing are 

flexible. Any loan period less than or equal to the analysis period can be considered. The loan 

finances all measures made at the time of retrofit (time = 0), assuming all other replacements in 

the future are paid for in cash. The yearly loan payments for principal and interest depend on the 

assumed annual effective interest rate and loan period. 

The cost of the retrofit package represents the sum of the labor and material costs of the retrofit. 

Costs for the retrofit depend greatly on the existing conditions of the home, especially if 

problems exist related to moisture, mold, indoor air quality, insects, asbestos, fire hazards, the 

building structure, and so on. No single set of costs applies to all possible retrofit measures for 

all types of existing homes. Judgment should be made as to the existing conditions of the home 

and the specific material and labor requirements for each retrofit measure. 

4.2.2 Replacement Costs 

As described in Section 4.1, enclosure components are not replaced beyond the beginning of the 

analysis period, while all equipment is replaced with minimum standard efficiency equipment or 

the same equipment, whichever is more efficient. Equipment replacement costs are included in 

the cash flow analysis and are adjusted based on the assumed inflation between the beginning of 

the analysis period and the time of replacement. 

4.2.3 Utility Bill Costs 

Utility bill costs are estimated from the annual energy uses, assuming energy rates 

(dollars/therm, dollars/kilowatt-hour) inflate with time according to Equation 4-1. 

4.2.4 Residual Values 

All components that wear out during the analysis period are assumed to have no residual value 

(e.g., salvage value, resell value, or scrap value). The fact that multiple technologies are modeled 

with different useful lives, however, inevitably leads to components with remaining life at the 

end of the analysis period. As recommended in NIST Handbook 135: Life-Cycle Cost Manual 

10 An end-of-year convention is used: all costs incurred during a specific year are modeled as incurring at the end of 

that specific year. 

8 
 

 

prorating its initial cost (α): 

for the Federal Energy Management Program (Fuller and Petersen, 1995), the “value in place” of 

a component with remaining life at the end of the analysis period is calculated by linearly 

, 

ோ

௅

ߙ ൌ ܶா௡ௗ

where ܥܱܵܶா௡ௗ is the inflated cost at the end of the analysis period, R is the component 

(4-2) ܥܱܵ

remaining life (years), and L is the component lifetime (years). The residual value for each 

component with remaining life at the end of the analysis period is included as a receipt in the 

cash flow at the end of the analysis period. These receipts represent the value added to the 

building when components are expected to function beyond the end of the analysis period. 

Accounting for residual values prevents technologies from being unfairly favored or penalized 

depending on how their service lives line up with the end of the analysis period. 

4.2.5 Cash Flow Diagrams 

Figure 4-2 shows an example cash flow11 in nominal dollars (including the effect of inflation). 

For this example, a retrofit at the beginning of the 30-year analysis period is financed by a 5-year 

loan. Replacements occur throughout the analysis period, the smallest of which correspond to 

CFL replacements; the large replacement costs in years 16 and 19 correspond to major 

equipment replacements. All replacements are paid for in cash at the time of the replacement. In 

this example, energy rates increase over time because of the assumed inflation rate, which causes 

the increase in utility bills seen in the figure. Costs 

Loan Payment 

Replacement Costs 

Residual Values 

Utility Bills 

1 5 10 15 20 25 

Time (years) 

Figure 4-2. Example cash flow diagram 

4.3 Metrics 

This analysis method computes two primary metrics for each scenario: average energy use 

(AEU) and equivalent annual cost (EAC). 

4.3.1 Average Energy Use 

AEU is the average of the annual energy uses over the analysis period: 

11 Cash flow diagrams presented in this report assume the following sign convention: expenses are positive and 

receipts are negative. 

9 
 

 

ೖసಿ

, (4-3) ா௎ೖ

ே

∑ೖసభ ܧܣܷ ൌ

where EUk is the energy use in year k and N is the number of years in the analysis period. 

4.3.2 Equivalent Annual Cost 

EAC is calculated by annualizing the cash flows (e.g., Figure 4-2). The most straightforward way 

to annualize complex cash flows is to determine the present worth (PW) of the cash flow by 

converting the total cost (TC) for each year to the value at the beginning of the analysis period:

െܶܥ௬௘௔௥ୀ௞ሺ1 ൅ ݀௡ሻ

and 

ே

k 

଴

௞ୀ

௞ୀ

 is the total cost in year 

ܹܲ ൌ ∑

where ܶܥ௬௘௔௥ୀ௞

ି௞ (4-4)
 

݀

, 

௡ is the nominal discount rate. The EAC is then

݀௥ determined by annualizing the PW using the real discount rate, : 12

ሺെܹܲ ݀௥

1 െ 1

݀௥

ሻ ܥܣܧ ൌ

൬

,

൰ ሺ1 ൅ ሻே

(4-5)
 

which gives the annual equivalent cost for the complex, time-series cash flow. The nominal and 

real discount rates are related as

ሻ. (4-6) ሻሺ1 ൅ ݀௥ ሺ1൅݀௡ሻ ൅1ሺൌ ݅

4.3.3 Minimum Upgrade Reference Scenario 

The EAC and AEU of a retrofit scenario can be compared to an alternative scenario to determine 

the additional cost incurred to achieve a given level of energy savings. This naturally leads to the 

definition of a reference scenario, the baseline against which energy upgrade scenarios are 

compared in terms of cost and energy use. This analysis method uses a minimum upgrade 

reference scenario (MURS) as the baseline. The MURS begins with the existing building at the 

start of the analysis period and assumes all equipment that wears out over the analysis period is 

replaced with the same level of efficiency or the current minimum standard, whichever is more 

efficient. Minimum upgrades are assumed for the reference building so as not to take credit for 

energy efficiency improvements that would have otherwise occurred through natural wear-out 

and replacement. In this sense, the MURS is the minimum that a homeowner could do to their 

house over the analysis period assuming that standards in the future will require at least the 

current level of efficiency. 

Figures 4-3 and 4-4 show example annual energy use and cash flow diagrams, respectively, for 

both the MURS and a post-retrofit scenario. As seen in Figure 4-3, the annual energy use of the 

MURS decreases at two points in the analysis period when the existing equipment below the 

minimum standard wears out and is replaced with the more efficient minimum standard 

equipment. For the post-retrofit scenario, equipment and enclosure measures decrease the annual 

energy uses relative to the MURS. All existing equipment below the minimum standard is 

replaced at the beginning of the analysis period, so the annual energy use does not change from 

12 To obtain the equivalent annual cost in terms of dollars at the beginning of the analysis period. 

10 
year to year. Unlike the MURS, the post-retrofit scenario has loan costs related to financing the 

retrofit. Both the post-retrofit scenario and the MURS have costs related to replacements, utility 

bills, and residual values, though they are different as a result of the retrofit and replacement 

schedule. Annual Energy Use Annual Energy Use (Post-Retrofit Scenario) (MURS) 

1 5 10 15 20 25
 

Year 

1 5 10 15 20 25
 

Year 

Figure 4-3. Example annual energy uses for MURS (top) and post-retrofit scenario (bottom) 

11 

30 

30 
Loan Payment 

Replacement Costs 

Residual Values 

Utility Bills 

Costs (MURS) Costs (Post-Retrofit Scenario) 

1 5 10 15 20 25 

Time (years) 

1 5 10 15 20 25 

Time (years) 

Figure 4-4. Example cash flows for MURS and post-retrofit scenario 

Figures 4-5 and 4-6 show the AEU and EAC for both the example MURS and the example postretrofit scenario. As seen in Figure 4-5, significant average energy savings (AES, difference in 

AEU) is achieved through the retrofit. Figure 4-6 shows that although the post-retrofit scenario 

incurs loan costs, its EAC is less than that of the MURS because of decreased utility bill costs. Average Energy Use 

MURS Post-Retrofit 

Figure 4-5. Average energy use (AEU) for MURS and post-retrofit scenario 

12 
Equivalent Annual Cost

Loan Payment 

Replacement Costs 

Residual Values 

Utility Bills 

MURS Post-Retrofit 

Figure 4-6. Equivalent annual cost (EAC) for MURS and post-retrofit scenario 

4.4 Optimization 

The metric for the optimization algorithm used in BEopt to determine optimal new construction 

building designs was modified based on the AEU and EAC. Christensen et al. (2006) give a 

detailed description of the sequential search algorithm—the following is a general description of 

the optimization: 

A. Optimization starts with the reference scenario. Annual building energy simulations are 

performed to determine annual energy uses and cash flows for that scenario, from which 

AEU and EAC are computed. 

B. For each iteration, the optimization individually investigates each possible measure (e.g. 

R-30, R-40, R-50) within a category (e.g., attic insulation) as a unique scenario. Each 

category/measure combination represents a different scenario for which simulations are 

performed, annual energy uses and cash flows are determined, and AEU and EAC are 

calculated. 

C. The objective functions J(AEU) and K(EAC,AEU) for the optimization are: 

ܬሺܧܣܷሻ , ൌ ܧܣܷ௘௥௣௩௜௢௨௦ െ ܧܣܷ௖௨௥௥௘௡௧ (4-7)
 

ሻܷܧܣ ܥܣܧሺܭ

ܥܣܧ௘௥௣௩௜௢௨௦ െ ܧ ܣܥ௖ ௨௥௥௘௡௧ ൌ ܧܣܷ௘௥௣௩௜௢௨௦ െ ܧܣܷ௖௨௥௥௘௡௧

, , (4-8)
 

where “previous” refers to the optimal point for the last iteration and “current” refers to 

the scenario under consideration. For the first iteration, the previous values are those for 

the reference scenario. Each category/measure scenario is then evaluated against the 

previous scenario. The following logic is used to progress through the optimization: 

13 
1.	 If J(AEU) for all scenarios investigated in the iteration is negative, then the 

optimization is complete (maximum savings achieved). When one or more 

scenarios are positive, proceed to step 2. 

2.	 For those category/measure scenarios where J(AEU) is positive, select the 

scenario with maximum value of K(EAC, AEU) as the “optimal” scenario for the 

iteration. This is the scenario with the largest equivalent annual cost savings per 

unit of energy savings. 

3.	 Repeat steps 1 and 2 using the optimal scenario from the last iteration as the 

previous scenario in Equations 4-7 and 4-8. Evaluate the objective functions for 

all scenarios in this iteration and all scenarios in the previous iterations. Select the 

next optimal point and repeat step 3 until the optimization is complete. 

The optimization process uses the sequential search technique to determine optimal retrofit 

packages. One benefit of this technique is that ECMs are analyzed together, which accounts for 

their energy and cost interactions over the analysis period. For example, window replacements 

are simulated both in the context of the existing HVAC equipment as well as minimum HVAC 

equipment upgrades in the future. Because scenarios are evaluated from all previous iterations, 

the optimization can “look back” and determine if it should divest in one technology and reinvest 

that money in a different technology. For example, as more enclosure measures are included in 

the retrofit, the optimal HVAC equipment efficiency may step down because the reduced loads 

make it increasingly difficult to recover the cost premium of more efficient equipment. 

Figure 4-7 shows example optimization results. The vertical axis is the incremental equivalent 

annual cost (IEAC, 2010 dollars) relative to the MURS. The horizontal axis is the AES of the 

retrofit scenario relative to the MURS. The starting point on the optimization curve (point A) is 

simply the MURS scenario. Thus, there is zero IEAC and AES. The red points are example 

scenarios from the first iteration of an optimization. Point B is selected as the optimal point for 

this iteration, and then the blue points are investigated in the second iteration. When selecting the 

optimal point in the second iteration, all previous points (red and blue) are considered. Point C is 

selected in the second iteration, and the yellow points are investigated for the third iteration. 

Considering all previous points again when selecting the optimal point for the third iteration (red, 

blue, and yellow), point D is chosen. This process continues (represented as grey points) until no 

further energy savings can be achieved. The line connecting the optimal points is the least-cost 

curve, which can be plotted and compared to least-cost curves for other optimizations. 

14 
 

Incremental Equivalent Annual Cost ($) 0 

Point A 

Point B 

Point C Point D 

Average Energy Savings (%) 

Figure 4-7. Example of optimization results 

Although the results of an optimization can be displayed using a curve, the least-cost curve is 

made up of discrete points representing discrete retrofit packages. Also, significant uncertainty 

can exist in the cost and energy savings for each scenario, so in reality points near the least-cost 

curve may be equally acceptable solutions. In the end, selecting optimal retrofit packages 

involves judgment considering uncertainty in the results as well as other intangible differences 

such as difficulty in implementing each retrofit package because of on-site circumstances and the 

effects of each retrofit scenario on occupant comfort, occupant health, safety, building durability, 

and so on. 

15 
 

 

5.0 Example Analysis 

The analysis method described in Section 4 was implemented in BEopt and used to conduct a 

retrofit example analysis on a 1960s-era house in eight different U.S. cities. This section presents 

the results for the eight cities, which span most IECC climate zones. Although the example 

analysis applied realistic pre-retrofit conditions and retrofit measures (refer to Appendix A for 

details), inputs and results do not represent the U.S. housing stock as a whole. This example 

analysis demonstrates how the analysis method can be used to generate recommendations for 

individual retrofit measures and packages of measures specific to a building, its occupants, and 

its location. Appendix B discusses the sensitivity of example analysis results to the general, 

financial, occupant, and building assumptions. 

As described in Section 4.3, the analysis determines EAC for each retrofit scenario, considering 

the costs over the analysis period related to the initial loan (used to finance the retrofit), future 

replacements, utility bills, and residual values (negative costs). Table 5-1 lists the assumed 

values of key financial parameters. 

Table 5-1. Assumed Values for Key Financial Parameters 

Financial Parameter Value 

Analysis Period (years) 30 

Inflation Rate (%) 3 

Real Discount Rate (%) 3 

Real Fuel Escalation Rate (%) 0 

Annual Effective Loan Interest Rate (%) 7 

Loan Period (years) 5 

Houses in each city are single-level, 1,280 ft2 ranches and are identical except for a climate 

appropriate foundation, the location of the ductwork, shingle solar reflectance, and exterior 

finish. Table 5-2 lists the IECC climate zone,13 foundation type, duct location, and energy 

tariffs14 for each city considered (ordered according to IECC climate zone). 

13 EnergyPlus weather files (.epw), obtained from processing typical meteorological year (.TM3) weather data, were 

used to drive the simulations: http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/. 

14 Gas: http://tonto.eia.doe.gov/dnav/ng/ng_pri_sum_a_EPG0_PRS_DMcf_a.htm (used 2008 year, 10.29 

therms/Mcf conversion factor). 

Electricity: http://www.eia.doe.gov/cneaf/electricity/esr/table5.html. 

16 
 

 

 

Table 5-2. Cities, Climate Zones, Foundations, Duct Location, and Energy Tariffs for Each City 

Citya IECC 

Climate 

Zone 

Foundation Type Location of Ducts Electricity 

($/kWh) 

Gas 

($/therm) 

Houston, Texas 

Phoenix, Arizona 

Atlanta, Georgia 

San Diego, California 

Seattle, Washington 

Washington, D.C. 

Chicago, Illinois 

Minneapolis, Minnesota 

2 

2 

3 

3 

4 

4 

5 

6 

Slab 

Slab 

Vented Crawl Space 

Slab 

Vented Crawl Space 

Unconditioned 

Basement 

Unconditioned 

Basement 

Unconditioned 

Basement 

Attic 

Attic 

Vented Crawl Space 

Attic 

Vented Crawl Space 

Unconditioned 

Basement 

Unconditioned 

Basement 

Unconditioned 

Basement 

0.1304 

0.1027 

0.0993 

0.1381 

0.0754 

0.1304b

0.1107 

0.0974

1.34 

1.71 

1.80 

1.24 

1.27 

1.34b 

1.17 

1.10 

a Cities are listed in order of IECC Climate Zone. 

b Standard values in BEopt do not reflect current published values because of incomplete data availability at the 

time of entry. 

Table 5-3 gives a high-level description of the retrofit measures with associated costs15 and 

relevant pre-retrofit conditions. Enclosure measures are first, followed by air distribution, HVAC 

equipment, water heating, appliances, and lighting. The MURS is the pre-retrofit building 

modeled over the analysis period, except that the SEER 10 AC is replaced with SEER 13 at 

wear-out and the old refrigerator (739 kWh/yr) is replaced with a standard refrigerator (514 

kWh/yr, $585) at wear-out. Existing equipment in the home is assumed to have half of its useful 

lifetime remaining. For example, the assumed useful lifetime of refrigerators is 19 years, so the 

existing refrigerator in the pre-retrofit house has 9.5 years of useful lifetime remaining. Again, 

Appendix A gives more details about the pre-retrofit home (including assumed lifetimes of 

equipment) and retrofit measures. 

15 Costs were taken from the National Residential Efficiency Measures Database (NREL 2010). The current version 

of the database can be found at http://www.nrel.gov/ap/retrofits/index.cfm. 

17 
 

 

Table 5-3. Retrofit Measures, Properties, and Costs 

Measure Pre-Retrofit Post-Retrofit Measure Cost 

(2010$) 

Air Seal (~55% Improvement, Including 

Mechanical Ventilation) 0.0009 SLA 0.0004 SLA + Mech. Vent.a $2,880 

Insulate and Air Seal Attic Floor (IECC 

Climate Zones 1-4) R-11, 0.0009 SLA R-38, 0.0007 SLA $2,075 

Insulate and Air Seal Attic Floor (IECC 

Climate Zones 5-8) R-19, 0.0009 SLA R-49, 0.0007 SLA $2,215 

Drill-and-Fill Wall Uninsulated R-13 cavity $2,545 

Close, Condition and Insulate Crawl 

Space 

Vented, nACH = 2.0 

Uninsulated 

Sealed, nACH = 0.35, 

Wall = R-13 $3,840 

Insulate Basement Wall, Basic 

(unfinished) Uninsulated Wall 1⁄2 Wall, 1-in. polyiso, R-6 $700 

Insulate Basement Wall, Advanced 

(finished) Uninsulated Wall Full Wall, 1-in. XPS w/ gypsum, R-6.4 $2,765 

Replace Windows Single-Paneb Standard (IECC 2009) or 

ENERGY STAR® 

$25/ft2 

$33/ft2 

Seal Ducts R-1, 15% Leakage R-1, 8% Leakage $890 

Seal and Insulate Ducts R-1, 15% Leakage R-6, 8% Leakage $1,800 

Replace AC SEER 10 SEER 13 or SEER 16 See Table 5-4 

Replace Furnace AFUE 80 AFUE 92.5 See Table 5-4 

Replace Tank Water Heater With 

ENERGYSTAR Tankless Water Heater Tank, EF = 0.59 Tankless, EF = 0.82c $1,825 

Replace Tank Water Heater with 

ENERGY STAR Tank Water Heater Tank, EF = 0.59 Tank, EF = 0.67 $1,500 

Replace Refrigerator Old, 739 kWh/yr Standard (514 kWh/yr) or 

ENERGY STAR (411 kWh/yr) 

$585 

$674 

Replace Clothes Washer MEF = 1.41, WF = 8.6 MEF = 2.47, WF = 3.9 $648 

Replace Lamps 

(%Incandescent:%CFL:%LFL) 66:21:13 43:44:13 or 32:55:13 CFL = $0.17/W 

a Exhaust-only mechanical ventilation according to ASHRAE 62.2-2007 (ASHRAE 2007), including infiltration 

credit (see Appendix A for details). 

b The ratio of the total window area to the exterior wall area of the living space is 17.8% both pre- and post-retrofit, 

which corresponds to 205 ft2 of window area. 

c

 Tankless water heater energy factor is derated by 8% in BEopt analysis according to California’s Title 24-2008 

(CEC 2010). 

Table 5-4 details the heating and cooling equipment sizes and costs. Note that, consistent with 

the climate, the AC size in San Diego is the smallest (2 tons)—cities in warmer climates have 5 

ton units.16 The furnace is the smallest in San Diego (30 kBtu/h) and the largest in Minneapolis 

16 AC sizes were capped at 5 tons because this is the largest available single unit in the market. 

18 
(80 kBtu/h). The costs increase linearly with the size of the equipment, as defined by the 

National Residential Efficiency Measures Database (NREL 2010). 

Table 5-4. Air Conditioner and Furnace Sizes and Costs per Location 

City AC Size 

(tons) 

AC Retrofit ($) 

(AC MURS) ($) 

Furnace 

Size 

(kBtu/h) 

Furnace Retrofit ($) 

(Furnace MURS) ($) 

Houston, Texas 

Phoenix, Arizona 

Atlanta, Georgia 

San Diego, California 

Seattle, Washington 

Washington, D.C. 

Chicago, Illinois 

Minneapolis, Minnesota 

5 

5 

5 

2 

4 

5 

4 

5 

8,820 

(7,560) 

8,820 

(7,560) 

8,820 

(7,560) 

3,530 

(3,025) 

7,060 

(6,050) 

8,820 

(7,560) 

7,060 

(6,050) 

8,820 

(7,560) 

50 

40 

60 

30 

50 

60 

70 

80 

1,650 

(800) 

1,320 

(640) 

1,980 

(960) 

990 

(480) 

1,650 

(800) 

1,980 

(960) 

2,310 

(1,120) 

2,640 

(1,280) 

5.1 Individual Retrofit Measures 

Table 5-5 lists the predicted average annual source energy savings (%) for individual retrofit 

measures applied to the example analysis house in the different cities. Note that the energy 

savings listed are valid only when a single measure is implemented in the house; if more than 

one measure is implemented, the calculated savings are not necessarily additive because there 

can be interactions between measures (e.g., installing a more efficient furnace reduces the 

amount of energy that can be saved by enclosure improvements such as efficient windows). 

Some of the important results are as follows: 

•	 Drill-and-fill insulation in the walls saves the most energy in most locations. The 

exceptions are sealing and insulating ducts in Houston and closing the crawl space in 

Seattle. 

•	 Because space conditioning in San Diego is not very significant, the house there saves 

less energy from enclosure measures than in the other locations. For the same reason, 

replacing appliances, lighting, and water heating saves more energy in San Diego (as a 

percentage). 

•	 Because of the substantial cooling load in Phoenix, the house there benefits significantly 

from replacing the AC with a higher efficiency unit. 

19 
•	 With its high heating loads, Minneapolis benefits most from drill-and-fill, window 

replacement, attic insulation, and upgrading to a more efficient furnace. 

•	 Sealing and insulating ducts delivers significant energy savings in each city. 

Table 5-5. Predicted Average Annual Source Energy Savings (%) for Individual Retrofit Measures 

Table 5-6 shows the predicted IEAC (2010 dollars) for the individual retrofit measures 

implemented in each location. A negative IEAC indicates that the retrofit scenario has a lower 

life-cycle cost than the MURS. There are many observations worth making about this table: 

•	 Drill-and-fill insulation and lighting upgrades reduce EAC in every location considered. 

•	 An upgraded furnace reduces EAC in the colder climates, but not in San Diego, Phoenix, 

and Houston. 

•	 Adding 4 feet of basement insulation, adding 8 feet of basement insulation, and closing 

the crawl space reduce EAC in each applicable location. 

•	 An AC upgrade is not cost-effective in any location considered. Note that the AC 

upgrade, as with all equipment replacements in this example analysis, must justify 

replacing the existing equipment, which has half of its useful life remaining. 

•	 Duct sealing and insulation reduces EAC everywhere but San Diego. 

•	 As previously mentioned, measures can deliver other benefits beyond energy savings, 

and a higher associated cost could be justified by these benefits. 

20 

Retrofit Measure Houston Phoenix Atlanta San Diego Seattle DC Chicago Minneapolis

Drill and Fill 8.8 13.5 8.9 9.1 10.0 9.8 10.5 10.8

Attic Ins./Attic Seal 6.5 6.0 8.2 5.3 9.2 9.1 7.5 7.9

4 ft Basement Ins. — — — — — 3.1 3.9 4.2

8 ft Basement Ins. — — — — — 5.1 6.5 7.0

Close Crawl Space — — 7.9 — 12.0 — — —

Standard Window 8.2 9.2 6.5 3.4 6.3 7.5 8.0 8.4

ENERGY STAR Window 8.3 9.4 7.3 3.9 8.7 8.1 9.6 10.1

Max Air Seal 1.7 1.3 2.3 0.5 3.0 3.6 5.1 5.9

Seal Ducts 3.8 4.0 2.3 1.7 2.2 2.1 2.1 2.3

Seal and Ins. Ducts 9.3 11.4 5.0 6.3 6.1 4.8 5.4 5.4

Tank Water Heater (gas, EF = 0.67) 1.5 1.2 1.5 2.2 1.4 1.4 1.2 1.1

Tankless Water Heater (gas, EF = 0.82) 3.0 2.4 2.9 4.3 2.6 2.6 2.2 2.0

Lighting (44% CFL) 0.9 0.7 0.7 1.0 0.5 0.6 0.5 0.4

Lighting (55% CFL) 1.3 1.1 1.0 1.6 0.7 0.9 0.7 0.6

SEER 13 AC 2.7 3.9 1.8 1.4 0.4 1.3 0.7 0.6

SEER 16 AC 5.8 9.7 4.8 3.2 1.3 3.6 1.9 1.5

92.5 AFUE Furnace 2.6 1.3 4.5 2.5 6.9 5.7 7.3 7.8

Standard Refrigerator 0.5 0.4 0.4 0.5 0.2 0.3 0.2 0.2

ENERGY STAR Refrigerator 1.1 0.9 0.9 1.3 0.5 0.7 0.5 0.5

ENERGY STAR Clothes Washer 1.8 1.4 1.8 2.6 1.7 1.7 1.5 1.4
Table 5-6. Predicted Incremental Equivalent Annual Cost (2010$) for Individual Retrofit Measures 

5.2 Optimization 

The optimization approach described in Section 4.4 determined least-cost packages of measures 

at different levels of energy savings for each location. Figures 5-1 through 5-8 and Tables 5-7 

through 5-14 show a series of least-cost curves and tables with the packages determined from 

the optimization for each location, respectively. The Houston results are discussed in Section 

5.2.1 as an example of what information can be obtained from the optimization results. Section 

5.2.2 includes a high-level summary of all results. 

5.2.1 Example Discussion of Optimization Results for One City 

Table 5-7 shows the incremental EAC and AES values for the retrofit packages considered in 

Houston. Each point on the plot in Figure 5-1 represents a different retrofit package. The dark 

line is the least-cost curve, which connects the optimal packages determined from the 

optimization. Many packages are close to the least-cost curve. Because of uncertainty in the EAC 

and AES values calculated for each package, some of these packages near the least-cost curve 

may in reality deliver equivalent or better results than those on the least-cost curve. The 

minimum-cost package, represented by the lowest point in the graph, reduces the EAC by about 

$185 and represents an average source energy savings of 30%. 

The packages that define the least-cost curve in Figure 5-1 can be seen in Table 5-7. AES values 

are listed next to optimal packages in Figure 5-1 (1, 1, 11, 18, 19, etc.) appear in the left column 

of Table 5-7 in increasing value of AES. The first point on the least-cost curve delivering energy 

savings is obtained by upgrading the lighting to 44% CFL. Because this is a single measure, it is 

also shown in Tables 5-5 and 5-6. Although many other measures in those tables deliver higher 

savings individually, they are not selected as the first point because they deliver less decrease in 

EAC per unit of energy savings (have a more positive slope) than the 44% CFL measure. The 

next package on the least-cost curve is 55% CFL lighting (instead of 44% CFL). After that, the 

next package is sealing and insulating the ducts, leading to a combined AES of 11%. For the rest 

of the optimization, each measure that is added to the previous package to obtain another point 

21 

Retrofit Measure Houston Phoenix Atlanta San Diego Seattle DC Chicago Minneapolis

Drill and Fill –70 –198 –122 –16 –120 –155 –171 –181

Attic Ins./Attic Seal –23 –33 –104 37 –99 –136 –82 –95

4 ft Basement Ins. — — — — — –54 –68 –77

8 ft Basement Ins. — — — — — –7 –31 –45

Close Crawl Space — — –23 — –83 — — —

Standard Window 100 82 132 215 131 81 58 47

ENERGY STAR Window 183 165 195 294 158 145 100 85

Max Air Seal 116 119 78 145 75 44 13 –8

Seal Ducts –33 –39 –9 22 –2 –6 –8 –13

Seal and Ins. Ducts –92 –153 –32 8 –45 –33 –48 –50

Tank Water Heater (gas, EF = 0.67) 61 54 45 62 58 50 60 62

Tankless Water Heater (gas, EF = 0.82) –6 –21 –34 –2 –8 –24 –5 0

Lighting (44% CFL) –21 –17 –13 –19 –8 –12 –14 –13

Lighting (55% CFL) –30 –24 –18 –28 –11 –17 –20 –18

SEER 13 AC 118 90 153 163 147 162 131 174

SEER 16 AC 132 55 186 213 202 202 165 230

92.5 AFUE Furnace 37 30 –30 21 –78 –63 –71 –71

Standard Refrigerator 7 10 13 9 17 14 12 14

ENERGY STAR Refrigerator 0 7 13 3 22 14 12 14

ENERGY STAR Clothes Washer –7 1 –5 –8 1 –5 –7 –4
 

on the least-cost curve remains part of the next package as the energy savings increases, except 

for the window upgrade (one can install either standard or ENERGY STAR windows, but not 

both). The minimum cost package (30% AES) has drill-and-fill wall insulation, attic 

insulation/air seal, duct seal/insulation, tankless water heater, 55% CFL lighting, ENERGY 

STAR refrigerator, and ENERGY STAR clothes washer. 

1 

1 

11 

18 19 26 29 30 

37 

38 

41 

41 

-500 

-400 

-300 

-200 

-100 

0 

100 

200 

300 

400 

500 

600 

700 

Incremental Equivalent Annual Cost (2010 $) 

0 10 20 30 40 50 60 

Average Source Energy Savings (%) 

Figure 5-1. Incremental EAC at different levels of energy savings for Houston 

Table 5-7. Least-Cost Packages at Different Energy Savings for Houston 

22 

AES        

(%)

IEAC        

(2010 $)

Drill and FillAttic Ins./Attic Seal4 ft Basement Ins.8 ft Basement Ins.Close Crawl SpaceStandard WindowENERGY STAR Window

Max Air Seal

Seal DuctsSeal and Ins. Ducts

Tank Water Heater (gas, EF = 0.67)

Tankless Water Heater (gas, EF = 0.82)

Lighting (44% CFL)

Lighting (55% CFL)

SEER 13 ACSEER 16 AC92.5 AFUE Furnace

Standard RefrigeratorENERGY STAR Refrigerator

ENERGY STAR Clothes Washer

1 –21 X

1 –30 X

11 –122 X X

18 –159 X X X

19 –165 X X X X

26 –178 X X X X X

29 –184 X X X X X X

30 –185 X X X X X X X

37 –59 X X X X X X X X

38 8 XX X X X X X XX

41 204 XX X X X X XX XX

41 288 XX X X X X XX XX
 

1 1 

15 

23 

26 

31 33 34 

42 

46 

47 

47 

-500 

-400 

-300 

-200 

-100 

0 

100 

200 

300 

400 

500 

600 

700 

Incremental Equivalent Annual Cost (2010 $) 

0 10 20 30 40 50 60 

Average Source Energy Savings (%) 

Figure 5-2. Incremental EAC at different levels of energy savings for Phoenix 

Table 5-8. Least-Cost Packages at Different Energy Savings for Phoenix 

23 

AES        

(%)

IEAC        

(2010 $)

Drill and FillAttic Ins./Attic Seal4 ft Basement Ins.8 ft Basement Ins.Close Crawl SpaceStandard WindowENERGY STAR Window

Max Air Seal

Seal DuctsSeal and Ins. Ducts

Tank Water Heater (gas, EF = 0.67)

Tankless Water Heater (gas, EF = 0.82)

Lighting (44% CFL)

Lighting (55% CFL)

SEER 13 ACSEER 16 AC92.5 AFUE Furnace

Standard RefrigeratorENERGY STAR Refrigerator

ENERGY STAR Clothes Washer

1 –17 X

1 –24 X

15 –222 X X

23 –316 X X X

26 –337 X X X X

31 –361 X X X X X

33 –361 X X X X X X

34 –356 X X X X X X X

42 –254 X X X X X X X X

46 –88 XX X X X X X XX

47 –31 XX X X X X XX XX

47 51 XX X X X X XX XX
 

1 1 

10 

18 

21 23 27 30 31 

33 

38 

40 

41 

43 

44 

44 

-500 

-400 

-300 

-200 

-100 

0 

100 

200 

300 

400 

500 

600 

700 

Incremental Equivalent Annual Cost (2010 $) 

0 10 20 30 40 50 60 

Average Source Energy Savings (%) 

Figure 5-3. Incremental EAC at different levels of energy savings for Atlanta 

Table 5-9. Least-Cost Packages at Different Energy Savings for Atlanta 

24 

AES        

(%)

IEAC        

(2010 $)

Drill and FillAttic Ins./Attic Seal4 ft Basement Ins.8 ft Basement Ins.Close Crawl Space

Standard WindowENERGY STAR Window

Max Air Seal

Seal DuctsSeal and Ins. Ducts

Tank Water Heater (gas, EF = 0.67)

Tankless Water Heater (gas, EF = 0.82)

Lighting (44% CFL)

Lighting (55% CFL)

SEER 13 ACSEER 16 AC92.5 AFUE Furnace

Standard RefrigeratorENERGY STAR Refrigerator

ENERGY STAR Clothes Washer

1 –13 X

1 –18 X

10 –140 X X

18 –244 X X X

21 –279 X X X X

23 –284 X X X X X

27 –292 X X X X X X

30 –293 X X X X X X

31 –281 X X X X X X X

33 –242 X X X X X X X X

38 –160 X X X X X X X X

40 –113 X X X X X X X X X

41 –56 X X X X X X X X X X

43 169 XX XX X X X XX XX

44 238 XX X X X X X XX XX

44 325 XX X XX X X X XX XX
 

1 2 4 13 18 19 24 

29 

30 

33 

34 

35 

-500 

-400 

-300 

-200 

-100 

0 

100 

200 

300 

400 

500 

600 

700 

Incremental Equivalent Annual Cost (2010 $) 

0 10 20 30 40 50 60 

Average Source Energy Savings (%) 

Figure 5-4. Incremental EAC at different levels of energy savings for San Diego 

Table 5-10. Least-Cost Packages at Different Energy Savings for San Diego 

25 

AES        

(%)

IEAC        

(2010 $)

Drill and FillAttic Ins./Attic Seal4 ft Basement Ins.8 ft Basement Ins.Close Crawl SpaceStandard WindowENERGY STAR Window

Max Air Seal

Seal DuctsSeal and Ins. Ducts

Tank Water Heater (gas, EF = 0.67)

Tankless Water Heater (gas, EF = 0.82)

Lighting (44% CFL)

Lighting (55% CFL)

SEER 13 ACSEER 16 AC92.5 AFUE Furnace

Standard RefrigeratorENERGY STAR Refrigerator

ENERGY STAR Clothes Washer

1 –19 X

2 –28 X

4 –36 X X

13 –52 X X X

18 –55 X X X X

19 –51 X X X X X

24 –30 X X X X X X

29 14 X X X X X X X

30 55 X X X X X X X X

33 275 X X X X X X X X X

34 521 XX X X X X XX XX

35 603 XX X X X X XX XX
0 1 

11 

20 

31 34 35 39 

45 

46 

47 

48 

49 

49 

-500 

-400 

-300 

-200 

-100 

0 

100 

200 

300 

400 

500 

600 

700 

Incremental Equivalent Annual Cost (2010 $) 

0 10 20 30 40 50 60 

Average Source Energy Savings (%) 

Figure 5-5. Incremental EAC at different levels of energy savings for Seattle 

Table 5-11. Least-Cost Packages at Different Energy Savings for Seattle 

26 

AES        

(%)

IEAC        

(2010 $)

Drill and FillAttic Ins./Attic Seal4 ft Basement Ins.8 ft Basement Ins.Close Crawl Space

Standard WindowENERGY STAR Window

Max Air Seal

Seal DuctsSeal and Ins. Ducts

Tank Water Heater (gas, EF = 0.67)

Tankless Water Heater (gas, EF = 0.82)

Lighting (44% CFL)

Lighting (55% CFL)

SEER 13 ACSEER 16 AC92.5 AFUE Furnace

Standard RefrigeratorENERGY STAR Refrigerator

ENERGY STAR Clothes Washer

0 –8 X

1 –11 X

11 –130 X X

20 –231 X X X

31 –300 X X X X

34 –309 X X X X X

35 –308 X X X X X X

39 –303 X X X X X X X

45 –164 X X X X X X X X

46 –122 X X X X X X X X

47 –102 X X X X X X X X X

48 –39 X X X X X X X X X X

49 46 XX X XX X X X X XX

49 254 XX X XX X X X XX XX
 

1 1 

4 

14 

23 

25 27 30 33 

40 41 

42 

43 

45 

-500 

-400 

-300 

-200 

-100 

0 

100 

200 

300 

400 

500 

600 

700 

Incremental Equivalent Annual Cost (2010 $) 

0 10 20 30 40 50 60 

Average Source Energy Savings (%) 

Figure 5-6. Incremental EAC at different levels of energy savings for Washington, D.C. 

Table 5-12. Least-Cost Packages at Different Energy Savings for Washington, D.C. 

27 

AES        

(%)

IEAC        

(2010 $)

Drill and FillAttic Ins./Attic Seal4 ft Basement Ins.8 ft Basement Ins.Close Crawl SpaceStandard WindowENERGY STAR Window

Max Air Seal

Seal DuctsSeal and Ins. Ducts

Tank Water Heater (gas, EF = 0.67)

Tankless Water Heater (gas, EF = 0.82)

Lighting (44% CFL)

Lighting (55% CFL)

SEER 13 ACSEER 16 AC92.5 AFUE Furnace

Standard RefrigeratorENERGY STAR Refrigerator

ENERGY STAR Clothes Washer

1 –12 X

1 –17 X

4 –72 X X

14 –225 X X X

23 –358 X X X X

25 –382 X X X X X

27 –388 X X X X X X

30 –388 X X X X X X X

33 –364 X X X X X X X X

40 –268 X X X X X X X X X

41 –255 X X X X X X X X X X

42 –186 X X X X X X X X X X

43 –114 XX X X X X X X X XX

45 185 XX X XX X X X XX XX
 

0 1 

5 

15 

22 24 26 31 34 

41 42 

44 

45 

47 

48 

-500 

-400 

-300 

-200 

-100 

0 

100 

200 

300 

400 

500 

600 

700 

Incremental Equivalent Annual Cost (2010 $) 

0 10 20 30 40 50 60 

Average Source Energy Savings (%) 

Figure 5-7. Incremental EAC at different levels of energy savings for Chicago 

Table 5-13. Least-Cost Packages at Different Energy Savings for Chicago 

28 

AES        

(%)

IEAC        

(2010 $)

Drill and FillAttic Ins./Attic Seal4 ft Basement Ins.8 ft Basement Ins.Close Crawl SpaceStandard WindowENERGY STAR Window

Max Air Seal

Seal DuctsSeal and Ins. Ducts

Tank Water Heater (gas, EF = 0.67)

Tankless Water Heater (gas, EF = 0.82)

Lighting (44% CFL)

Lighting (55% CFL)

SEER 13 ACSEER 16 AC92.5 AFUE Furnace

Standard RefrigeratorENERGY STAR Refrigerator

ENERGY STAR Clothes Washer

0 –14 X

1 –20 X

5 –88 X X

15 –254 X X X

22 –333 X X X X

24 –340 X X X X X

26 –346 X X X X X X

31 –351 X X X X X X X

34 –341 X X X X X X X X

41 –267 X X X X X X X X X

42 –257 X X X X X X X X X X

44 –201 X X X X X X X X X XX

45 –138 X X X X X X X X X XX

47 –86 XX X XX X X X X XX

48 98 XX X XX X X X XX XX
 

0 1 

5 

15 

23 24 30 32 35 

42 

44 

45 

47 

48 

49 

-500 

-400 

-300 

-200 

-100 

0 

100 

200 

300 

400 

500 

600 

700 

Incremental Equivalent Annual Cost (2010 $) 

0 10 20 30 40 50 60 

Average Source Energy Savings (%) 

Figure 5-8. Incremental EAC at different levels of energy savings for Minneapolis 

Table 5-14. Least-Cost Packages at Different Energy Savings for Minneapolis 

29 

AES        

(%)

IEAC        

(2010 $)

Drill and FillAttic Ins./Attic Seal4 ft Basement Ins.8 ft Basement Ins.Close Crawl SpaceStandard WindowENERGY STAR Window

Max Air Seal

Seal DuctsSeal and Ins. Ducts

Tank Water Heater (gas, EF = 0.67)

Tankless Water Heater (gas, EF = 0.82)

Lighting (44% CFL)

Lighting (55% CFL)

SEER 13 ACSEER 16 AC92.5 AFUE Furnace

Standard RefrigeratorENERGY STAR Refrigerator

ENERGY STAR Clothes Washer

0 –13 X

1 –18 X

5 –95 X X

15 –268 X X X

23 –361 X X X X

24 –364 X X X X X

30 –366 X X X XXX

32 –366 X X X X X X X

35 –353 X X X X X X X X

42 –284 X X X X X X X X X

44 –240 X X X XXXXX X X

45 –226 X X X X X X X X X XX

47 –168 X X X X X X X X X XX

48 –121 X X X X X X X X X XX

49 125 XX X XX X X X XX XX
 

 

 

5.2.2 Summary of Optimization Results 

To summarize the results obtained from the optimization for the eight cities, Table 5-15 shows 

the AES and incremental equivalent annual cost (IEAC) for minimum-cost packages and for 

packages on the least-cost curve that are closest to neutral cost.17 The AES values for the 

minimum-cost packages range from 18% (San Diego) to 34% (Seattle) and the IEAC values 

range from –$388 (Washington, DC) to –$55 (San Diego). Averaged over the cities considered 

in this analysis, the minimum cost package achieves 30% AES at an IEAC of –$289. The AES 

values for the nearest to neutral cost packages18 range from 29% (San Diego) to 48% (Seattle and 

Minneapolis). 

Table 5-15. Minimum-Cost and Nearest to Neutral Cost Packages on Least-Cost Curve 

Location 

Minimum Cost Nearest to Neutral Cost 

AES (%) IEAC (2010$) AES (%) IEAC (2010$) 

Houston, Texas 

Phoenix, Arizona 

Atlanta, Georgia 

San Diego, California 

Seattle, Washington 

Washington, D.C. 

Chicago, Illinois 

Minneapolis, Minnesota 

30 

33 

30 

18 

34 

30 

31 

32 

–185 

–361 

–293 

–55 

–309 

–388 

–351 

–366 

38 

47 

41 

29 

48 

43 

47 

48 

8 

–31 

–56 

14 

–39 

–114 

–86 

–121 

Table 5-16 shows how measures are incorporated sequentially in the packages on the least-cost 

curve with increasing levels of average energy savings. Some observations are as follows: 

•	 CFL lighting is always the first measure to be adopted. 

•	 CFL lighting, drill-and-fill wall insulation, tankless water heater, and 4-ft basement 

insulation/close crawl space (where appropriate) are in the minimum-cost package for 

each city. 

•	 Attic sealing and insulation is in every minimum-cost package except in San Diego, 

where space conditioning loads are smaller. 

•	 Furnace upgrade is selected in Washington D.C., Chicago, and Minneapolis for the 

minimum cost packages. 

•	 Window replacement, AC replacement, 8-ft basement insulation (where appropriate), 

tank water heater upgrade, duct sealing (no added insulation), and maximum air sealing 

are not included in any minimum cost package. 

17 Note the least-cost curve is defined by a set of discrete retrofit packages. There is no guarantee any of the discrete 

packages delivers an IEAC of exactly $0. 

18 Targeting the nearest to neutral cost point may be an opportunity to accomplish significant savings when 

considered before any retrofit is performed. Once a retrofit package is implemented to the minimum cost point, there 

can be a significant cost barrier to get to larger energy savings. 

30 
Table 5-16. Order Measures Are Added to Packages on Least-Cost Curve to Minimum Cost Po

Packages to Minimum Cost Point 

Location 1 2 3 4 5 6 7 

Houston, Texas 

Lighting 

(44% CFL) 

Lighting 

(55% CFL) 

Seal and 

Insulate Ducts Drill-and-Fill ENERGY STAR Clothes Washer Attic Sealing and Insulation Tankless W

Heate

Phoenix, Arizona 

Lighting 

(44% CFL) 

Lighting 

(55% CFL) Drill-and-Fill Seal and Insulate Ducts Tankless Water Heater Attic Sealing and Insulation ENERGY S

Clothes W

Atlanta, Georgia 

Lighting 

(44% CFL) 

Lighting 

(55% CFL) Drill-and-Fill Attic Sealing and Insulation Tankless Water Heater ENERGY STAR Clothes Washer Seal and In

Ducts 

San Diego, California 

Lighting 

(44% CFL) 

Lighting 

(55% CFL) 

ENERGY STAR 

Clothes Washer Drill-and-Fill Tankless Water Heater 

Seattle, Washington 

Lighting 

(44% CFL) 

Lighting 

(55% CFL) Drill-and-Fill Attic Sealing and Insulation Close Crawl Space Tankless Water Heater 

Washington, D.C. 

Lighting 

(44% CFL) 

Lighting 

(55% CFL) 

4-ft Basement 

Insulation Drill-and-Fill Attic Sealing and Insulation Tankless Water Heater ENERGY 

Clothes W

Chicago, Illinois 

Lighting 

(44% CFL) 

Lighting 

(55% CFL) 

4-ft Basement 

Insulation Drill-and-Fill Attic Sealing and Insulation ENERGY STAR Clothes Washer Tankless W

Heate

Minneapolis, Minnesota 

Lighting 

(44% CFL) 

Lighting 

(55% CFL) 

4-ft Basement 

Insulation Drill-and-Fill Attic Sealing and Insulation ENERGY STAR Clothes Washer 92.5 AF

Furnac

aThe seal and insulate ducts measure added in the previous package on the least-cost curve is not included in the minimum cost package 

the crawl space brings the ducts inside the thermal enclosure and therefore reduces the benefit of duct sealing and insulation. 

31 
6.0 Conclusions 

This report describes a method for analyzing potential energy efficiency retrofits of existing 

houses. The method uses an optimization scheme that considers average energy use (determined 

from building energy simulations) and equivalent annual cost to recommend optimal retrofit 

packages specific to the building, occupants, and location. Energy savings and incremental costs 

are calculated relative to a MURS, which accounts for efficiency upgrades that would have been 

made in the absence of a retrofit because of equipment wear-out and replacement with current 

minimum standards. 

In the illustrative example, the method was applied to analyze the retrofit of a 1960s-era house in 

eight U.S. locations. Retrofit measures were defined considering safety and durability, in 

addition to cost-effectiveness. Financial considerations were developed to be consistent with the 

specific needs of retrofitting existing houses; measure costs were determined using the National 

Residential Efficiency Database (NREL 2010). A least-cost curve was presented for each 

location, from which optimal retrofit packages were highlighted at various levels of energy 

savings, including the minimum cost package and the package nearest to neutral cost. 

The main conclusions of the example analysis presented in Section 5 follow: 

•	 Results were specific to assumptions in the following categories (as described in 

Appendix B): general (analysis period, MURS, etc.), financial (retrofit financing and 

measure costs), occupant, and building. Results should not be interpreted as “average” for 

U.S. housing stock. 

•	 For the 1,280 ft2, 1960s-era home and the specific set of retrofit measures considered in 

the analysis, minimum cost packages varied in average energy savings from 18% in San 

Diego to 34% in Seattle. 

•	 The nearest to neutral cost package provided significant additional savings beyond the 

minimum-cost package. 

Issues such as complexity of the retrofit package, on-site conditions, and effect on occupant 

comfort were not considered and may justify certain measures that were not included in optimal 

packages, as well as eliminate measures that were. Together, the analysis method and example 

analysis presented in this report are an introduction to retrofit optimization; improvements to the 

analysis method and additional studies considering a comprehensive range of building types, 

locations, and retrofit measures are needed. Future efforts related to the analysis method and 

example analysis are presented in Sections 6.1 and 6.2, respectively. 

6.1 Analysis Method Future Work 

Despite the advances made to address the issues outlined in Section 4, several additional issues 

and areas of future improvement were identified while developing and implementing the analysis 

method. 

•	 Financing replacements in the future—The current analysis method assumes that all 

future replacements are paid for in cash. In reality, financing may be used for larger 

replacements such as heating and cooling systems. 

32 
 

•	 Fuel price projection—The current analysis method assumes that fuel prices increase 

with inflation. Price projection (e.g., EIA19 forecasting) could be included in future 

studies. 

•	 HVAC equipment size—When HVAC equipment is replaced after or at the same time as 

other efficiency measures, it may be possible to downsize the equipment. In the current 

analysis method, equipment is not resized. 

•	 Impact of retrofits on occupant behavior—Occupants may “take back” energy savings 

expected from a retrofit by changing their behavior to achieve more comfort. Or they 

may save additional energy because retrofits to the enclosure increase radiant 

temperatures and reduce drafts, allowing occupants to maintain comfort at more efficient 

thermostat settings. Future analysis methods could include validated models for the effect 

of retrofits on occupant behavior. 

•	 Optimization in the future—The current analysis method makes recommendations only 

for a retrofit package implemented at the beginning of the analysis period, assuming all 

later replacements meet the current minimum standard or the existing efficiency, 

whichever is more efficient. Optimization could also be performed for retrofits after the 

beginning of the analysis period, such that the optimal package is a retrofit plan for the 

analysis period. An example optimal package could be drill-and-fill now, add foam 

sheathing at time of siding replacement, and upgrade to SEER 18 AC at time of SEER 10 

AC wear-out. 

•	 Performance degradation—The performance of building components can degrade with 

time. For example, without proper maintenance, an AC will become less efficient with 

time owing to problems such as loss of charge and fouling of the heat exchangers. This 

could affect both the energy savings of retrofit packages (e.g., degradation of new 

equipment over time) and the energy use of the existing building (e.g., already degraded 

equipment). More information is needed about the performance degradation of enclosure 

and equipment components to include this effect in the analysis. 

•	 Standards forecasting—The current analysis method replaces all equipment below the 

current minimum standard with current minimum standard at the time of wear-out. Taken 

a step further, all equipment could be replaced with at least the future minimum standard, 

where forecasting is required to estimate the future standards. 

6.2 Example Analysis Future Work 

In general, the results in the example analysis presented in Section 5 reflect a long-term view of 

costs and energy savings—although the loan period was 5 years, replacement costs and utility 

bill cost savings over the 30-year analysis period were considered. Some homeowners may 

require a shorter time horizon, for example, when they plan to move and do not expect to recover 

the value of the retrofit at time of sale, or they are primarily concerned with year-to-year cash 

flow during the loan period and cannot afford to invest in retrofit measures for 5 years that are 

justified by energy savings over 30 years, or both. In these scenarios, the analysis period should 

be shortened to ensure the investment is justified by the energy savings that occur before the 

homeowner moves or the homeowner experiences a positive cash flow during the loan period 

(energy savings over loan period justifies loan payments). Alternatively, a longer term loan 

19 See http://www.eia.doe.gov/oiaf/forecasting.html. 

33 
 

approaching the length of the analysis period could produce positive cash flow for the 

homeowner immediately. Future analyses should consider a variety of homeowner perspectives 

and financing mechanisms. 

A more accurate characterization of the U.S. housing stock (sufficient for building energy 

modeling) is needed to build a statistically representative set of house descriptions in each 

climate region. This will involve developing representative house descriptions from identified 

existing datasets or from characterization data collected in the future. Applying the analysis 

method described in this report to these representative models would allow for extrapolating 

individual results to average potential energy savings for the U.S. housing stock, and generate 

climate and building-type specific recommendations for optimal individual retrofit measures and 

packages of measures. Other possible improvements for future studies include the following: 

•	 Adjust retrofit costs city by city, similar to energy costs. 

•	 Add more house types (e.g., two-story houses). Ideally, the house types analyzed should 

be representative of the locations considered, including assumptions related to fuel types. 

•	 Add retrofit measures, including those for safety and health of the occupants (e.g., 

combustion air for the atmospherically vented appliances). 

•	 Investigate different metrics, financial mechanisms, and loan terms to cover a wider 

range of possibilities. 

Finally, efforts continue at NREL to assess and improve the accuracy of analysis tools by 

developing new and improved models and validating software predictions against measured data. 

Energy use and savings predictions in this and future studies should be compared to measured 

use and savings from laboratory tests, field tests, and pre- and post-retrofit utility bill analysis. 

34 
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36 
———. (2007b). Lighting Technologies: A Guide to Energy-Efficient Illumination. U.S. 

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———. (2010c). Methodology for Estimated Energy Savings from Cost-Effective Air Sealing 

and Insulating. U.S. Environmental Protection Agency. 

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Accessed June 15, 2010. 

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Protection Agency. 

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June 15, 2010. 

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of Ducts: Comparisons to Measured Results.” Energy and Buildings (28:3); pp. 287–297. 

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Accessed June 23, 2010. 

37 
Guler, B.; Fung, A.S.; Aydinalp-Koksal, M. (2001). “Impact of Energy Efficiency Upgrade 

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ion_revised.pdf. 

IBACOS. (2003). Best Practices: Window and Installation Flashing Guidelines. IBACOS. 

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CA: Lawrence Berkeley National Laboratory. 

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38 
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39 
 

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40 
Appendix A. Pre- and Post-Retrofit Performance Assumptions for 

Example Analysis 

This appendix describes the technical retrofit measure definitions and the assumptions used in 

the example analysis. The relevant cost data were taken from the National Residential Efficiency 

Measures Database, v1.0.0beta (NREL 2010). As described in Section 5, the example analysis of 

the 1280 ft2, 1960s-era house illustrates the analysis method presented in Section 4. Accordingly, 

the pre-retrofit assumptions are not meant to be “average” for the U.S. housing stock, and the 

post-retrofit assumptions may not represent the only viable retrofit options for the example 

house. 

A.1 Enclosure Components 

A.1.1 Crawl Spaces

Pre-Retrofit Performance Assumptions—Crawl spaces are often designed as a vented assembly 

with an earthen floor and may contain mechanical services for the house (e.g., furnace, 

ductwork, plumbing). Moisture management is a critical consideration when retrofitting crawl 

spaces. 

Seattle and Atlanta were modeled with uninsulated, vented crawl spaces. An important modeling 

input in BEopt for vented crawl spaces is the air changes per hour (ACH) within the space. Open 

crawl space natural ventilation rates can vary widely from house to house, depending on the 

characteristics of the building, the vents, the surrounding buildings and vegetation, the weather, 

and so on. Some ACH values from the literature are: 

• 0.67 ACH (ASHRAE 2001) 

• 0.4–1.7–2.5 ACH (Kurnitski 2000) 

• 2–8 ACH (Trethowen 1994) 

• 3.5–5 ACH (Karagiozis 2005) 

• 1 ACH (Lstiburek 2008). 

The BEopt default value of 2 ACH used in this analysis falls within the range of values presented 

in the literature, but more research is needed to determine average natural infiltration levels or to 

develop more detailed algorithms for predicting crawl space natural ventilation rates on at the 

hourly or sub-hourly timescale. 

The characteristics of the pre-retrofit crawl space are: 

• Naturally vented, BEopt default 2 ACH 

• Ducts in unconditioned crawl space (duct leakage into space) 

• Uninsulated crawl space ceiling and walls 

• No vapor barrier on crawl space floor. 

41 
 

Post-Retrofit Performance Assumptions—Building code generally requires crawl space 

ventilation (natural or mechanical) unless it has all of the following characteristics (Lstiburek 

2004): 

•	 A vapor barrier installed on the crawl space floor 

•	 Insulation installed on the exterior crawl space walls 

•	 Conditioned crawl space. 

Although the intent of crawl space ventilation requirements in building code is primarily to 

control moisture, building scientists have demonstrated that in certain climates (such as hot 

humid areas in the Southeast), natural ventilation can actually increase the moisture in the crawl 

space (Lstiburek 2004, Davis et al. 2005). An alternative approach is closing and conditioning 

the crawl space according to the general requirements stated previously. Specific requirements 

for closing crawl spaces depend on local building code. Example specifications can be found in 

the literature (Dastur et al. 2005, Lstiburek 2004). 

For the purposes of this energy analysis, the natural ventilation rate of the closed crawl space is 

assumed to be 0.35 ACH. 

The retrofit option included in this study is based on a previous field study (Davis et al. 2005). 

The following list contains the major characteristics of the retrofit; smaller details may be 

included when determining cost (such as adding a crawl space drain). 

•	 Install vapor barrier on crawl space floor 

•	 Install vapor barrier on crawl space exterior walls 

•	 Air seal where appropriate 

•	 Install 2 inches of R-13 foil-faced polyisocyanurate (polyiso) foam on exterior crawl 

space walls, leaving a 3-inch gap between crawl space floor (to prevent wicking) and a 3­

inch gap between the top sill (for termite inspection) 

•	 Add supply duct that provides conditioned air to space when heating, ventilating, and airconditioning (HVAC) system runs. 

For the retrofit described, it is assumed no problems exist in the crawl space related to 

combustion safety, radon, mold, rot, insects, flooding, and drainage. Additionally, it is assumed 

that the conditioned air from the HVAC system adequately removes moisture from the crawl 

space (no additional dehumidification is needed). The insulation is modeled in BEopt as covering 

the entire crawl space wall, so the insulation R-value is derated to R-11.4 to approximate the 

effect of the 3-inch gaps in insulation. Because of modeling constraints in DOE2.220, the supply 

duct airflow into the crawl space is not modeled explicitly. The amount of conditioned air 

entering the crawl space through the supply duct (~30 cfm) would, however, be small compared 

to the duct leakage into the crawl space (e.g., 10% supply side leakage with a 2,000-cfm HVAC 

20 DOE2.2 is a detailed building energy simulation program and was the primary simulation engine underlying 

BEopt analysis for this report. For more information on the DOE2 software please visit http://www.doe2.com/. 

42 
 

system flow rate corresponds to 200 cfm of conditioned air entering the crawl space when the air 

handler is running). 

Other crawl space retrofits such as insulating the underside of the floor joists with rigid 

insulation were not considered in this analysis. This type of retrofit may necessitate moving the 

mechanical equipment into the attic, which in turn may require closing and conditioning the attic 

space. Future analyses may include this combination of options. 

Lifetime—The assumed lifetime for this measure is greater than the 30-year analysis period. 

Some maintenance of the vapor barrier may be required, depending on how frequently people 

enter the space. 

A.1.2 Basements 

Pre-Retrofit Performance Assumptions—An uninsulated, unconditioned basement is assumed for 

the pre-retrofit case in Chicago, Minneapolis, and Washington, DC. BEopt models basements 

with zero exposed above-grade wall sections. This results in an ACH of zero. The unconditioned 

basement also has zero cavity insulation between the floor joists above the basement. 

Post-Retrofit Performance Assumptions—It may not be appropriate to insulate all basement walls 

because of moisture concerns. The current analysis assumes that the basement does not have a 

history of moisture issues. If evidence or knowledge of such problems exists, a perimeter drain 

may be needed (BSC 2007a). The high cost21 associated with removing sections of the slab floor, 

installing the drainage system, and repouring concrete mean an undertaking of this magnitude 

would not necessarily be motivated solely by the energy saving potential. This scenario is not 

considered in the current analysis. 

There are a number of recommended retrofit options for insulating existing basement walls 

where there are no known moisture problems, as shown in Table A-1 (BSC 2007a, Yost and 

Lstiburek 2002). The options involve using either fire-rated polyiso or extruded polystyrene 

(XPS). Other insulation systems used in basement applications, such as “blankets” with 

impermeable surfaces, have proven to be problematic (Aldrich and Zuluaga 2006, Yost and 

Lstiburek 2002) and are not recommended. XPS must be protected with at least a 15-minute 

thermal barrier (e.g., 0.5-in. gypsum board) (BSC 2007a). This essentially means the insulation 

retrofit turns an unfinished basement into a finished basement, thereby adding a significant 

benefit to the project beyond the energy savings alone. Secondary benefits occur with many 

other retrofit measures as well, though often in less direct ways. For example, improvements to 

the above-grade enclosure may be economically justified on the energy savings associated with 

them but the homeowner enjoys the added benefit of improved comfort. These benefits are not 

accounted for in the current analysis because they are difficult to monetize. 

The current analysis only includes the 0.5-in. wall polyiso and 1–in. XPS with furring strip 

options. The half-wall polyiso option does not result in a finished basement and can be 

considered an energy-only measure. Because the 1–in. XPS with furring strips option does not 

21 Yost and Lstiburek (2002) note: “The cost of accomplishing [moisture flow and airflow control] can be difficult 

and frequently expensive. In many older homes and some newer homes basements cannot be insulated safely and 

inexpensively. The cost of properly insulating a basement while controlling moisture should be compared with the 

cost of constructing additional quality living space above grade.” 

43 
 

result in wall cavities, running services (e.g., electrical) through the wall is not possible and 

therefore the result is a less-finished option than the framed options. 

Table A-1. Summary of Proposed Basement Wall Measures and Costs 

Measure Effective (Nominal) 

R-valuea 

Estimated Total Cost 

(evaluated for our test 

house)b 

Half wall (4-ft) application of 1-in. fire-rated polyiso 

1–in. XPS fastened w/furring strips covered with 

gypsum sheathing (rough finish, no paint) 

2–in. XPS fastened w/furring strips covered with 

gypsum sheathing 

1–in. XPS plus frame wall with gypsum sheathing 

2–in. XPS plus frame wall with gypsum sheathing 

1–in. XPS plus frame wall, gypsum sheathing, R-13 

fiber glass batts 

2–in. XPS plus frame wall, gypsum sheathing, R-13 

fiber glass batts 

R-6 (R-6) 

R-6.4 (R-5) 

R-11.4 (R-10) 

R-6.7 (R-5) 

R-11.7 (R-10) 

R-16.6 (R-18) 

R-21.6 (R-23) 

$700 

$2,760 

$3,490 

$3,600 

$4,330 

$4,730 

$5,450 

a The effective R-value was calculated using the simple parallel paths method and published material properties 

(ASHRAE 2005). A 16% framing factor was used for furred assemblies (1 × 3 in. furring strips, 16–in. on centers 

[O.C.]), and an 18% framing factor was used for stud assemblies (2 × 4 in., 16–in. O.C., including top and bottom 

plates). 

b Costs taken from RSMeans (RSMeans, 2010). Gypsum considered taped and finished but not painted. 

Lifetime—The assumed lifetime for this measure is greater than the 30-year analysis period. 

A.1.3 Wall Insulation 

Pre-Retrofit Performance Assumptions—It is assumed that the example analysis house is 2×4 

stick-built with no wall insulation. Joint Appendix IV of Title 24 (CEC 2005) indicates a U-value 

of 0.356 Btu/ h·ft2·°F for a 16 in. O.C. wall with no cavity insulation. This leads to an overall Rvalue of the wall, including the film coefficients of 2.8 Btu/ h·ft2·F. The pre-retrofit wall in 

BEopt has an R-value of 2.2 excluding gypsum board and film coefficients. Including the 

expected R-values of drywall (~R-0.5) and the film coefficients yields a total assembly R-value 

close to the CEC Title 24 definition of an empty cavity wall. 

Post-Retrofit Performance Assumptions—The nominal levels of dense pack cellulose cavity 

insulation are assumed to be the maximum attainable (R-13) for an empty 2×4 cavity (3.5 in. 

deep) (Greenfiber 2010). Our analysis is conservative in that it does not take into account the airsealing benefits of dense pack insulation (Lstiburek 2010b). 

Lifetime—The assumed lifetime for this measure is greater than the 30-year analysis period. 

44 
 

A.1.4 Whole House Air Sealing

Pre-Retrofit Performance Assumptions—The Lawrence Berkeley National Laboratory (LBNL) 

has done extensive work quantifying air leakage in U.S. homes, e.g., Chan et al. (2005), Sherman 

and Dickerhoff (1998), and Sherman and McWilliams (2007). These data (~100,000 houses) 

were used by McWilliams and Jung (2006) in the development of a statistical model to estimate 

the normalized leakage of a home using the following variables: climate zone, age, floor area, 

number of stories, whether or not the home has been previously improved for energy efficiency, 

whether or not the home is considered “low income,” and foundation type. The output of this 

model, normalized leakage (NL), can be used in the calculation of BEopt inputs (i.e., either 

whole-house SLA [specific leakage area] or natural ACH [nACH]). For example, using a 1,280 

ft2 home built in 1960, located in a cold climate with an unconditioned basement and assuming 

the home has not been previously air-sealed nor is considered low income, the total SLA is 

calculated to be 0.0010 (ACH50 = 21.6, nACH = 0.92). The SLA increases in dry climates 

(0.0012) and decreases for humid climates (0.0007). The assumed total SLA for the retrofit 

analyses done to date for all climates, 0.0009 (18.6 ACH50), appears to be within the range 

estimated by the McWilliams and Jung model. 

Other, more general references (Tiller and Creech 2006) show the typical infiltration rates for 

existing homes to be between 10 and 50 ACH50. The assumed value for the retrofit analysis is 

within this general range. 

Post-Retrofit Performance Assumptions—The analysis includes two measures that affect the 

infiltration rate of the house: an attic and air seal (20% reduction) and an aggressive air seal with 

exhaust-only mechanical ventilation (55% reduction with 75% of ASHRAE 62.2 or 32 cfm22). 

Retrofit data for air sealing reveal a wide range of likely outcomes. Sherman and Dickerhoff 

(1998) found that air sealing retrofits resulted in an average reduction in leakage of 25%. 

Improvements to a “typical” home of approximately 45% (calculated from the nACH values 

referenced) are cited by ENERGY STAR (EPA 2010c). This analysis assumes a comprehensive 

whole house air sealing retrofit beyond what is “typical.” 

Air sealing retrofits considered in this analysis do not result in an outdoor air ventilation rate 

below the acceptable level as specified in the most recent version of ASHRAE Standard 62.2 

(ASHRAE 2007).23 For a three-bedroom, 1,280 ft2

 house, ASHRAE 62.2-2007 specifies a 

continuous outdoor air flow rate of 42.8 cfm. The natural pre-retrofit infiltration rate is 131 cfm. 

A 55% reduction of the pre-retrofit natural rate results in a post-retrofit natural rate of 59 cfm. As 

per 62.2-2007, “When excess infiltration has been measured....the rates in Section 4.1 may be 

decreased by half of the excess of the rate calculated....that is above the default rate”. Therefore, 

if the excess rate is 59 – 42.8 = 16.2 cfm, one half is 8.1 cfm resulting in a ventilation rate 

requirement as per 62.2-2007 of 42.8 – 8.1 = 34.7 cfm. The use of 75% of 62.2-2007, or 32 cfm 

(display variable from BEopt), is appropriate for approximating energy usage due to exhaustonly mechanical ventilation. 

Lifetime—The assumed lifetime for this measure is greater than the 30-year analysis period. 

22 The exhaust-only mechanical ventilation option in BEopt assumes a fan energy of 0.3 W/cfm (e.g. 10 W for 32
 

cfm). 

23 An updated version is scheduled to be released September 2010 (ASHRAE 2010).
 

45 
A.1.5 Attic Insulation and Air Sealing

Pre-Retrofit Performance Assumptions—Initial levels of insulation (R-19 in Chicago and 

Minneapolis, R-11 in all others) are assumed because adding insulation to attic is an easy retrofit 

that took place in many houses that were built in the 1960s. 

Post-Retrofit Performance Assumptions—Final levels of insulation are taken from the U.S. 

Environmental Protection Agency’s (EPA’s) “Recommended insulation levels for retrofitting 

existing wood-framed buildings” (EPA 2010b). Chicago and Minneapolis are retrofitted to R-49 

and all other cities are retrofitted to R-38. Blown cellulose is the assumed material, although 

other insulation products such as fiberglass insulation are equally acceptable options. 

Before performing an attic insulation retrofit, air-sealing the attic floor is recommended to 

prevent air leakage from the conditioned zone of the house. Failure to do so could result in 

health, safety, and durability issues. A number of references are available to guide and inform 

this work, including: 

•	 The EPA’s “A Do-It-Yourself Guide to Sealing and Insulating with ENERGY STAR” 

(EPA 2007a) 

•	 Building Science Corporation’s document on attic air sealing, including details: “Guide 

to Attic Air Sealing” (Lstiburek 2010a) 

•	 Building America’s “Best Practices Series” on air sealing (Baechler and Love 2010). 

The analysis assumes a 20% whole-house infiltration reduction from air sealing the attic floor. 

Lifetime—The assumed lifetime for this measure is greater than the 30-year analysis period. 

A.1.6 Windows 

Pre-Retrofit Performance Assumptions—The Residential Energy Consumption Survey (RECS) 

includes specific questions characterizing the type of windows installed in the survey homes. 

According to the RECS microdata (EIA 2009), 60% of 1960s-era homes have single-pane 

windows. In this particular vintage, slightly less than 50% have had a full window replacement 

during the life of the house at the time of the survey. These data show that a reasonable fraction 

of 1960s-era homes have single pane windows today, as assumed in the example analysis. The 

properties of this window type were derived from the National Residential Efficiency Measures 

Database (v1.0.0beta, NREL 2010) and represent the single-pane, clear, wood frame assembly 

(shown in bold in Table A-2). 

Table A-2. Single-Pane Window Options From the National Residential Efficiency Measures 

Database
 

Window Type U-Factor SHGC 

Single-Pane, Clear, Aluminum Frame 

Single-Pane, Tinted, Aluminum Frame 

Single-Pane, Clear, Vinyl Frame 

Single-Pane, Clear, Wood Frame 

Single-Pane, Tinted, Vinyl Frame 

Single-Pane, Tinted, Wood Frame 

1.27 

1.27 

0.89 

0.89 

0.89 

0.89 

0.75 

0.64 

0.64 

0.64 

0.54 

0.54 

46 
 

 

 

 

Post-Retrofit Performance Assumptions—Our analysis assumes full window replacement, though 

other retrofit options exist (EfficientWindows 2010). Windows should be installed to carefully 

manage bulk water entry into and air leakage across the enclosure. Details for managing these 

risks can be found in several Building America publications (BSC 2007b, 2009, IBACOS 2003). 

Full window replacement will likely reduce the air leakage associated with the fenestration 

components of the enclosure. The air leakage associated with windows and doors, as a 

percentage of total air leakage, is typically between 6% and 22% (ASHRAE 2005). Because of a 

lack of data quantifying air leakage reductions associated with window replacement, the current 

analysis does not account for these effects. 

The retrofit window options (see Table A-3) are in part based on the 2009 IECC (IECC 2009) 

and ENERGY STAR (EPA 2010a). Modifications were made to reflect the current market 

conditions and to ensure consistency with the Building America House Simulation Protocol 

(Hendron and Engebrecht 2010). 

Table A-3. Retrofit Window Options 

Standard Houston Phoenix Atlanta San Diego DC Seattle Chicago Minneapolis 

IECC Climate Zone 2 2 3 3 4 4 5 6 

IECC U-value 0.40 0.40 0.40 0.40 0.35 0.35 0.35 0.35 

IECC SHGC 0.30 0.30 0.30 0.30 0.35 0.35 0.35 0.35 

ENERGY STAR U-value 0.40 0.40 0.35 0.35 0.32 0.30 0.30 0.30 

ENERGY STAR SHGC 0.27 0.27 0.30 0.30 0.40 0.50 0.50 0.50 

Lifetime—The assumed lifetime for this measure is greater than the 30-year analysis period. 

A.2 HVAC Equipment and Distribution 

A.2.1 Air Conditioning

Pre-Retrofit Performance Assumptions—As of 2007, 51.8% of U.S. homes had central AC 

systems, and 12.8% had heat pumps (DOE 2010). In the same year, the following numbers of 

units were sold (DOE 2010): 

• Split system central AC = 3.58 million 

• Single package central AC = 0.34 million 

• Split heat pump = 1.57 million 

• Single package heat pump = 0.20 million. 

Because of their high saturation level and sales, split system central AC systems were considered 

in this analysis for the pre-retrofit building and the space cooling retrofit options. 

As of January 23, 2006, the national minimum standard for split system central AC is SEER 13 

(CFR 2002; DOE 2010). The pre-retrofit AC used in our analysis is the BEopt standard SEER 10 

option. The minimum replacement AC is the BEopt standard SEER 13 option. 

47 
Post-Retrofit Performance Assumptions—Federal tax credits (as of June 2010) for central AC 

systems require a minimum SEER 16 (EER 13) (CEE 2009, DOE 2010). The highest tier 

efficiency level for split system central AC under the CEE Residential Central Air Conditioner 

and Heat Pump Initiative is also SEER 16 (EER 13) ((DOE 2010, EPA 2010d). The BEopt 

SEER 16 (EER 11.61) standard option is considered as a potential retrofit in our analysis. 

Lifetime—A previous literature review of AC lifetimes shows (DOE 2010): 

• Appliance Magazine: 11 year average for residential central AC 

• Air-Conditioning, Heating and Refrigeration Institute: 12–15 years 

• DOE (2010) study: 19 years. 

The BEopt default lifetime of 18 years was used in this analysis. 

A.2.2 Gas Furnace 

Pre-Retrofit Performance Assumptions—Indoor gas furnaces are the most common type of 

residential space heating equipment (DOE 2006). The National Appliance Energy Conservation 

Act (NAECA) was passed in 1987 and established a minimum annual fuel utilization efficiency 

(AFUE) of 78% for gas furnaces effective January 1, 1992 (DOE 2006, LBNL 2010). The AFUE 

of 78% remains the national minimum standard. According to the Department of Energy (DOE 

2006), “Most of the [indoor] gas furnaces on the market have an efficiency of 80 percent 

AFUE.” 

An 80% AFUE gas furnace is assumed for the pre-retrofit building. The BEopt 80% AFUE 

standard option is selected for the pre-retrofit furnace in our analysis. 

Post-Retrofit Performance Assumptions—Condensing gas furnaces accounted for approximately 

one-third of sales as of 2006 and ranged in efficiency from about 90% to 94% AFUE (DOE 

2006). The analysis considers a 92.5% AFUE condensing gas furnace retrofit option. The BEopt 

92.5% AFUE standard option is selected for the post-retrofit furnace in our analysis. 

Lifetime—The average lifetime used by the U.S. Department of Energy (DOE 2006) for an 

indoor gas furnace is 20 years. The BEopt default furnace lifetime of 20 years was used in the 

analysis. 

A.2.3 Air Distribution System

Pre-Retrofit Performance Assumptions—Many studies have been done to quantify duct leakage 

in U.S. homes. These tests do not always use the same methods or metrics (e.g., cubic feet per 

minute or percent fan flow) and have limited sample sizes. Additionally, some tests do not 

distinguish between supply and return leakage fractions. BEopt requires supply and return 

leakage, as well as supply and return leakage at the air handler unit (AHU), as a percentage of 

total fan flow. 

ASHRAE Standard 152 was introduced in 2004 and includes two methods for measuring duct 

leakage in the field (Modera 2005). 

48 
Francisco et al. (1998) observed 20% supply (± 4% stdev) and 20% return (±17% stdev) leakage 

to the outside pre-retrofit (n = 6, Pacific Northwest). This was reduced to ~7% supply (±4% 

stdev) and 6% return (±5% stdev) leakage to the outside through retrofit measures (~70% 

reduction). This study used a combination of blower door, duct leakage, and register flow 

measurements in its calculations. 

Jump et al. (1996) measured duct leakage (n = 24 houses in Sacramento) using a flow subtraction 

technique (leakage = total AHU flow – Σ register flows). Average supply leakage pre- and postretrofit was 18% and 8% (±8/8% stdev). Average return leakage pre- and post-retrofit was 17% 

and 13% (±10/9% stdev) (~40% reduction). 

Karins et al. (1997) investigated duct leakage (n = 25 units in nine multifamily buildings in New 

York) using the flow subtraction technique and duct leakage measurements (supply side only). 

Pre-retrofit supply leakage ranged from 14% to 35% using the flow subtraction technique and 

18% to 35% using the duct leakage test. This was reduced to 0-6% post retrofit using the flow 

subtraction and 14% to 30% using the duct leakage test. Pre-retrofit return leakage was large at 

56% to 83% and was reduced to 11% to 51%. This study shows the possible variability in results 

when using or comparing different testing techniques. 

Yuill and Musser (1997) studied retrofit effectiveness for duct leakage using whole house and 

duct pressurization at different levels such that the results could be used for curve fitting (n = 5, 

Pennsylvania). The authors note that there were variable pressures in the duct system that could 

have “significantly impacted” the results of their test (p. 265). The results are not presented in 

such a way to identify leakage as a percentage of system flow. The average post-retrofit 

reduction in leakage was 16% (ignoring one outlier point, entire sample range is 5%–93% 

leakage reduction). 

Erinjeri et al. (2007) performed a comprehensive statistical review of duct leakage measurements 

taken using three different test methods (variations on blower door methods to evaluate leakage 

to outside) in homes in Louisiana (n = 39, 32, or 35, depending on test and comparison). Results 

are presented in absolute terms (i.e., cubic feet per minute of leakage) with no further 

information about a particular system that would allow for the comparison of the test results to 

BEopt inputs. The authors note that, however, assuming a system size of 3 tons (with 400 

cfm/ton), the average total duct leakage in Louisiana homes is 29%. 

Walker et al. (1996) used the flow subtraction technique to measure duct leakage (pre- and postretrofit) in two apartment buildings in New York where the retrofit was performed on visible 

duct sections in the basement only. Large, obvious leaks (e.g., disconnected ducts) were repaired 

before the retrofit. Average supply leakage pre-retrofit was 34% and was improved to 22%. 

Average return leakage pre-retrofit was 82% and improved to 57%. 

Walker et al. (2009) measured leakage at the AHU cabinet in a laboratory setting. The results 

indicated a leakage rate of 5% of total system flow. 

Siegel et al. (2002) describe a useful comparison of duct leakage in new construction using the 

test procedure defined in ASHRAE Standard 152. They found an average supply side leakage to 

49 
the exterior of 6% and an average return side leakage to the outside of 5% (total supply and 

return leakages were 9% and 8%, respectively). 

The BEopt inputs used in the analysis are 10% supply leakage and 5% return leakage (inclusive 

of cabinet leakage). The literature has a large range of results, and assuming 15% total duct 

leakage is toward the conservative end of the range. 

Francisco and Palmiter (1998) and Jump et al. (1996) have the most descriptive results for single 

family houses. Their results show supply leakage greater than return leakage. This observation is 

consistent with the BEopt inputs used in this analysis. 

Post-Retrofit Performance Assumptions—Post-retrofit duct leakage assumes a 50% reduction to 

4.4% and 3.6% supply and return leakage, respectively. A ~50% reduction in duct leakage is 

possible (see Francisco and Palmiter, 1998) but could be toward the upper range of what is 

commonly achieved in the field (see Jump et al. 1996; Yuill and Musser 1997; Walker et al 

1996). 

Lifetime—The assumed lifetime for this measure is greater than the 30-year analysis period. 

A.3 Domestic Water Heating Equipment 

A.3.1 Replacement of Standard Gas With ENERGY STAR Gas Water Heater 

Pre-Retrofit Performance Assumptions—The BEopt standard efficiency water heater (energy 

factor [EF] = 0.59, 40-gal tank) was chosen for the pre-retrofit house and corresponds to the 

minimum NAECA efficiency level (DOE 2009). The assumed cost for replacing this water 

heater with the same model was $930. 

Post-Retrofit Performance Assumptions—The standard gas storage water heater is replaced with 

a higher efficiency gas storage water heater of the same volume meeting ENERGY STAR 

requirements effective September 1, 2010 (EPA 2010e). The ENERGY STAR performance 

requirements are EF ≥ 0.67, first hour rating (FHR) ≥ 67 gal/h. BEopt inputs for this water heater 

were developed using the method presented in Burch and Erickson (2004). 

Lifetime—The 11-year default BEopt lifetime was used for storage tank water heaters. 

A.3.2 Replacement of Standard Gas With ENERGY STAR Tankless Water Heater 

Pre-Retrofit Performance Assumptions—The BEopt standard efficiency water heater (EF = 0.59, 

40 gal) was chosen for the pre-retrofit house and corresponds to the minimum NAECA 

efficiency level (DOE 2009). The assumed cost for replacing this water heater with the same 

model was $930. 

Post-Retrofit Performance Assumptions—The standard gas storage water heater is replaced with 

a gas tankless water heater meeting ENERGY STAR requirements effective September 1, 2010 

(EPA 2010e). The ENERGY STAR performance requirements are EF ≥ 0.82, gallons per minute 

(gpm) ≥ 2.5 over a 77°F temperature rise. The nominal EF is derated in BEopt according to CEC 

(2010). 

Lifetime—The 20-year default BEopt lifetime was used, which agrees with DOE (2009). 

50 
 

 

 

 

 

 

 

A.4 Appliances and Lighting 

A.4.1 Lighting

Pre-Retrofit Performance Assumptions—The pre-retrofit lighting inputs are consistent with the 

Building America House Simulation Protocol (Hendron and Engebrecht 2010). Specifically, it is 

assumed that 21% of the lighting in the house is compact fluorescent lamps (CFLs) and 13% 

linear fluorescent lamps (LFLs). All other lighting is incandescent. 

Post-Retrofit Performance Assumptions—The EERE US Lighting Market Characterization 

(Navigant 2002) references a study from Grays Harbor, Washington, where a detailed inventory 

of light fixtures indicated that ~50% of all sockets could receive CFLs (Jennings et al. 1996). 

Using the data from Appendix D of Navigant (2002) and noting that CFLs typically replace 

general service lamps (GSLs) only, the potential for CFLs as a percentage of installed lamps was 

calculated to be approximately 80% (Table A-4). This level of penetration may not, however, be 

feasible (e.g., because of dimming requirements), nor is it necessarily cost-effective to install 

CFLs in parts of the house with low hours of use. Additional data from Navigant (2002) report 

the average hours of use in each room. These data were combined with Table A-4 data for 

interior, conditioned spaces to determine retrofit package options; i.e., when considering interior, 

high-use spaces, what fraction of installed capacity makes sense for CFL installations? The 

results are shown in Table A-5. Package A includes the top five most lit rooms by annual hours 

of use (Navigant 2002): kitchen, utility room, living room, dining room, and family room. 

Package B adds to Package A the sixth most lit room by hours, the bathroom, and Package C 

adds the seventh most lit room by hours, the hall. Packages B and C are used in this analysis. 

Table A-4. GSL and CFL Summary by Room 

Area Number of Lamps 

(total) Number of GSLs Number of CFLs 

Bathroom 6.88 6.14 0.04 

Bedroom 9.94 9.40 0.09 

Closet 0.77 0.72 0.01 

Dining Room 1.23 1.16 0.01 

Family Room 2.38 1.79 0.04 

Garage 4.23 1.87 0.03 

Hall 5.12 4.82 0.03 

Kitchen 5.11 2.68 0.14 

Living Room 5.97 5.38 0.22 

Office 1.16 0.86 0.03 

Other 2.05 1.00 0.01 

Outdoor 4.06 2.50 0.05 

Utility Room 1.81 1.36 0.08 

Total 50.71 39.69 0.78 

% of Total 78% 2% 

Source: Adapted from Navigant (2002). 

51 
 

 

Table A-5. Lighting Retrofit Options 

Package Number of Lamps 

(total) Number of GSLs Percentage of GSLs 

A 

B 

C 

16.5 

23.38 

28.50 

12.38 

18.52 

23.34 

29% 

44% 

55% 

Source: Adapted from Navigant (2002). 

Lifetime—Lamps are aggregated in BEopt and a common, effective lifetime is calculated. 

Individual lamp lifetimes came from ENERGY STAR (EPA 2007b). Table A-6 shows the 

packages and their associated lifetimes. 

Table A-6. Lighting Options and Lifetimes 

Timing Lamp Distribution 

(%CFL:%LFL:%Incandescent) 

Effective Lamp Lifetimes 

(years) 

Pre-Retrofit 

Post-Retrofit

Post-Retrofit 

21:13:66 

44:13:43 

55:13:32 

3.98 

6.21 

7.57 

A.4.2 Refrigerator

Pre-Retrofit Performance Assumptions—The “Below Standard, Large (20 ft3)” refrigerator in the 

National Residential Efficiency Measures Database (v1.0.0beta, NREL 2010) was assumed for 

the pre-retrofit building and has a rated annual energy consumption of 739 kWh/yr. 

Post-Retrofit Performance Assumptions—Two refrigerator options were evaluated for the 

retrofit: 

1.	 Standard option: The “Standard, Large (20 ft3

)” refrigerator in the National Residential 

Efficiency Measures Database was evaluated and has a rated annual energy consumption 

of 514 kWh/yr. 

2.	 ENERGY STAR: The “ENERGY STAR, Large (20 ft3

)” refrigerator in the National 

Residential Efficiency Measures Database was evaluated and has a rated annual energy 

consumption of 411 kWh/yr. 

Lifetime—The BEopt default lifetime of 19 years was used in the analysis. 

A.4.3 Clothes Washer 

According to the ENERGY STAR website (EPA 2010f), as of January 1, 2011, the minimum 

federal standard for clothes washers is modified energy factor (MEF) ≥ 1.26 and water factor 

(WF) ≤ 9.5, and the ENERGY STAR requirements are MEF ≥ 2.0 and WF ≤ 6.0. 

52 
 

Pre-Retrofit Performance Assumptions—A 3.5 ft3 top-loading clothes washer with MEF = 1.41 

and WF = 8.6 was assumed for the pre-retrofit building. The assumed cost of replacing this 

clothes washer with the same model is $500. 

Post-Retrofit Performance Assumptions—A 3.7 ft3

, MEF = 2.47, WF = 3.9, top-loading clothes 

washer was evaluated as an ENERGY STAR retrofit option. 

Lifetime—A lifetime of 13 years was assumed in the analysis. 

53 
Appendix B. Sensitivity of Example Analysis Results 

B.1 Introduction 

The example analysis results (Section 5) for the individual retrofit measures and packages of 

retrofit measures are sensitive to: 

• General assumptions 

o Analysis approach (hourly simulations, life-cycle cost) 

o Length of analysis period 

o Minimum upgrade reference scenario (MURS) 

• Financial assumptions 

o Discount rate 

o Loan interest rate and length 

o Fuel prices and fuel escalation rate 

o Retrofit and replacement costs 

o Residual values 

• Occupant assumptions 

o Same occupants year to year 

o Behavior of occupants 

• Building assumptions 

o Basic building features (geometry, foundation type, fuel type, etc.) 

o Pre-retrofit enclosure and equipment characteristics 

o Retrofit enclosure and equipment characteristics. 

Results should be interpreted with the understanding that alternative assumptions could produce 

different values for incremental equivalent annual cost (IEAC) and average energy savings 

(AES), as well as lead to different optimal packages of retrofit measures. This appendix 

discusses sensitivity to key assumptions and gives some examples. 

B.2 General Assumptions 

As discussed in Section 5, NREL researchers developed an analysis method to determine optimal 

retrofit packages and estimate energy savings specific to the building, occupants, and location. 

This approach uses annual building energy simulations to determine energy savings over the 

analysis period relative to a MURS. The equivalent annual cost of the retrofit is calculated 

relative to the MURS considering retrofit costs, replacement costs, utility bill costs, and residual 

values using a life-cycle cost analysis approach. The example analysis results are therefore 

sensitive to the building energy simulation program used to perform the simulations (BEopt with 

DOE-2.2 engine), the approach used to perform the economic analysis, the length of the analysis 

period, and the assumptions related to the MURS. 

A development version of BEopt with the DOE-2.2 simulation engine was used to complete the 

annual building energy simulations. The results depend on the specific algorithms and default 

assumptions associated with the DOE-2.2 engine, as well as many “background” inputs and 

assumptions in BEopt that are not part of the DOE-2.2 engine, but also not part of the userselected inputs. A version of BEopt using EnergyPlus as the simulation engine has been 

developed, though further testing is needed before the example analysis simulations can be run 

54 
 

 

using the EnergyPlus version of BEopt. Eventually, optimizations should be performed using 

both the DOE-2.2 and EnergyPlus versions of BEopt to investigate the sensitivity or results to 

the underlying simulation engine and other background inputs and assumptions. 

A life-cycle-cost analysis determined the financial implications of different retrofit scenarios. 

Inherent in this analysis approach is the assumption of a time horizon (analysis period) over 

which cash flows and annual energy uses are calculated. Therefore, the choice of a life-cyclecost analysis and the 30 year time horizon affects the results presented in Section 5. Alternative 

methods such as simple payback based on first costs and first-year energy savings may lead to 

different prioritizations of individual measures and different recommendations for retrofit 

packages. 

The MURS is the baseline for calculating incremental EAC and average energy savings. Thus, 

all results presented in Section 5 are sensitive to the MURS assumptions. Other analysis 

approaches may calculate energy savings relative to the pre-retrofit building and assume that 

level of energy savings extends over a number of years. Using the MURS as the baseline ensures 

that no credit is taken for energy savings that would have otherwise occurred through equipment 

wear-out and replacement, as required by the current minimum standards. Forecasting standards 

into the future and including those forecasts in the MURS would tend to reduce the average 

energy savings predictions because the reference scenario would have a more efficient building, 

on average. 

B.3 Financial Assumptions 

We made several financial assumptions to complete the example analysis. Assumptions related 

to the discount rate, inflation, loan interest rate/length, fuel prices/escalation, retrofit costs, 

replacement costs, and residual values all affect the EAC (used as a metric in the optimization) 

and therefore affect the results for both individual measures and packages of measures. 

A 6.1% nominal discount rate was used in Section 5 to convert the time-series cash flows into 

present worth. Figure B-1 shows the Chicago optimization results24 for nominal discount rates of 

3%, 5%, and 10%. As seen in Figure B-1, the AES at minimum cost decreases as the discount 

rate increases.25 This is in part because energy efficiency upgrades are competing with more 

lucrative alternative investment options at higher rates of return. 

24 The sensitivity analysis in this appendix was performed with preliminary input assumptions that are slightly 

different from the ones used in the main body of the document. 

25Plots in this appendix are taken directly from the BEopt output screen, where “Annualized Energy Related Costs” 

on the y-axis are the annualized utility bills costs of the MURS plus the IEAC (in current dollars). 

55 
Nominal Discount Rate (%) 

5 

3 

10 

Figure B-1. Chicago optimization results as a function of nominal discount rate 

The inflation rate affects the future costs of utility bills and replacements, as well as the residual 

values, which are based on the inflated cost of the component or equipment at the end of the 

analysis period. A 3% inflation rate was assumed for the example analysis. Figure B-2 shows 

Chicago optimization results for inflation rates of 0%, 3%, and 5%. The annualized energy 

related costs at 0% AES (y-intercept) are the annualized utility bill costs of the MURS. Both 

annualized utility bills of the MURS (y-intercept) and the AES at minimum cost increase as the 

inflation rate increases. 

Inflation Rate (%) 

3 

0 

5 

Figure B-2. Chicago optimization results as a function of inflation rate 

56 
A 5-year loan having an annual effective interest rate of 7% was assumed for the example 

analysis. Table B-1 shows the optimization results for Chicago assuming a 20-year loan with the 

annual effective interest rate of 7%. Compared to the results for the 5-year loan (Table 5-13), the 

packages on the least-cost curve and their associated AES are identical. The only difference is 

that the IEAC values for packages in the 20-year scenario are greater than those in the 5-year 

loan period. 

Table B-1. Least-Cost Packages at Different Energy Savings for Chicago (20-year loan) 

The loan interest rate affects the cost of financing the initial retrofit, with higher values 

increasing the IEAC of individual measures and packages of measures. Figure B-3 shows the 

Chicago optimization results for loan interest rates of 3%, 5%, 7%, 10%, and 20%. The results 

shown in Figure B-3 indicate that the AES and minimum-cost increases as the interest rate of the 

loan decreases. 

For the example analysis, the nominal fuel escalation rate was identical to the inflation rate at 

3%. The fuel escalation rate affects the price of energy (dollars per therm, dollars per kilowatthour) over the analysis period. Figure B-4 shows Chicago optimization results for nominal fuel 

escalation rates of 3%, 5%, 7.5%, and 10%. The annualized utility bill costs of the MURS (yintercept) increase as the fuel escalation rate increases. As the fuel escalation rate increases, 

however, the AES at minimum cost increases and the incremental EAC at minimum cost 

decreases. In other words, the retrofit scenarios become more cost-effective the higher the fuel 

escalation rate because future utility bill cost savings are larger and help justify more investment 

in energy efficiency now. 

57 

AES        

(%)

IEAC        

(2010 $)

Drill and FillAttic Ins./Attic Seal4 ft Basement Ins.8 ft Basement Ins.Close Crawl SpaceStandard WindowENERGY STAR Window

Max Air Seal

Seal DuctsSeal and Ins. Ducts

Tank Water Heater (gas, EF = 0.67)

Tankless Water Heater (gas, EF = 0.82)

Lighting (44% CFL)

Lighting (55% CFL)

SEER 13 ACSEER 16 AC92.5 AFUE Furnace

Standard RefrigeratorENERGY STAR Refrigerator

ENERGY STAR Clothes Washer

0 –14 X

1 –20 X

5 –86 X X

15 –247 X X X

22 –321 X X X X

24 –326 X X X X X

26 –326 X X X X X X

31 –326 X X X X X X X

34 –311 X X X X X X X X

41 –224 X X X X X X X X X

42 –212 X X X X X X X X X X

44 –152 X X X X X X X X X XX

45 –83 XX X X X X X X X XX

47 –27 XX X XX X X X X XX

48 173 XX X XX X X X XX XX
Loan Interest Rate (%) 

7 

3 

5 

10 

20 

Figure B-3. Chicago optimization results as a function of annual effective loan interest rate 

Nominal Fuel 

3 

5 

7.5 

10 

Escalation Rate (%) 

Figure B-4. Chicago optimization results as a function of nominal fuel escalation rate 

The most obvious financial assumptions in the example analysis of Section 5 are the costs of 

retrofits measures and replacements. Costs for enclosure and equipment upgrades/replacements 

were taken from the National Residential Efficiency Measures Database (v1.0.0beta, NREL 

2010). Costs for particular measures are typical and national, and therefore they do not vary city 

by city in the analysis. The specific cost of a retrofit can vary greatly depending on the location, 

existing conditions in the building, the availability of contractors, rebates, and so on. It is also 

important to note that the measures database is a dynamic entity and that the quality of the cost 

58 
 

data improves over time as new information becomes available and as markets adapt to new 

technologies and deployment efforts. 

Financial assumptions related to residual values also affect the results of the example analysis. 

Because the analysis period is 30 years, the real discount rate is 3%, and the inflation rate is 3%, 

the effect of the residual values is diminished compared to analyses taking place over shorter 

time horizons or with smaller real discount rates. 

B.4 Occupant Assumptions 

Occupant behavior for the example analysis was the same every year and was based on the 

standard assumptions outlined in the Building America House Simulation Protocol (Hendron and 

Engebrecht 2010). In reality, occupant behavior could change from year to year depending on 

specific events (e.g., kids leaving for college) or could change gradually over time (e.g., less use 

of basement with aging of occupants). Occupant behavior is a very significant driving force for 

energy use: space conditioning loads are highly dependent on thermostat operation, and other 

end uses such as domestic hot water, appliances, lighting, and miscellaneous electric loads are 

known to vary greatly depending on occupant behavior. Figure B-5 shows Chicago optimization 

results for occupants having high energy use thermostat set points, “Benchmark” energy use set 

points, and low energy use set points. As expected, the annualized utility bill costs of the MURS 

(y-intercept) is highest for the high energy use set point case and lowest for the low energy use 

set point case. Higher AES and lower IEAC are achievable at minimum cost for the high energy 

set point case than the lower energy set point case. These results demonstrate the significant 

effect that occupant behavior has on the energy and cost savings achievable through energy 

retrofit. 

Benchmark 

Low Energy Use 

High Energy Use 

Figure B-5. Chicago optimization results as a function of thermostat set point 

Another key assumption in the example analysis is that occupant behavior does not change as a 

result of the retrofit.26 Occupants can, however, “take back” energy savings in exchange for more 

26 The only exception for the example analysis is lighting; 10% of the energy savings resulting from using CFLs is 

taken back by assuming longer hours of operation. 

59 
comfort. For example, occupants in a leaky, poorly insulated home may set their thermostat to 

65°F to keep monthly gas bills below $250. Retrofitting the building may lead to gas bills per 

month of $180 for a 65°F thermostat set point, but the occupants may choose to raise the set 

point to 68°F to increase comfort at the expense of reduced energy savings. Alternatively, more 

energy may be saved than expected because retrofits to the enclosure increase radiant 

temperatures and reduce drafts, allowing occupants to maintain comfort at more efficient 

thermostat settings. More research is needed to quantify the impact of energy efficiency 

improvements on occupant behavior. 

B.5 Building Assumptions 

Researchers made several assumptions in the example analysis regarding the building 

characteristics before and after the retrofit, as well as the building characteristics for the MURS. 

These characteristics are needed for the annual building energy simulations and for estimating 

the costs of each the retrofit and future replacements. 

Basic features of the building, such as the geometry, fuel types, age, etc. were fixed from city-tocity. Other features such as the foundation type and exterior finish were varied depending on 

city. As described in Section 5, building assumptions (pre-retrofit and retrofit) are meant to be 

realistic for the 1960s-era house, but will not necessarily yield “typical” or “national average” 

results for average energy savings, EAC, and optimal retrofit packages. 

To demonstrate the sensitivity of results to building assumptions, consider the attic 

insulation/air-seal retrofit: for Chicago R-19 insulation was assumed for the pre-retrofit building 

and R-30 was added as the retrofit. Figure B-6 shows the average energy savings of the attic 

insulation/air seal retrofit in Chicago as a function of the pre-retrofit insulation level (assuming 

the post-retrofit R-value is always R-49). At a pre-retrofit insulation level of R-49, the only 

energy savings is gained from air sealing the attic floor. As the pre-retrofit insulation level 

decreases, the AES increases gradually until reaching about R-20, at which point AES values 

begin to increase dramatically. In general, the higher the pre-insulation R-value the less sensitive 

AES predictions are to that input. Figure B-6 demonstrates the idea that small errors in the 

characterization of pre-retrofit R-values (when very little insulation exists) can lead to large 

errors in AES predictions because of the high sensitivity. 

The example analysis results are also sensitive to the description of the retrofit: continuing with 

the previous example, other attic post-retrofit insulation levels could be investigated and would 

yield different average energy savings and equivalent annual cost results. Figure B-7 shows the 

attic insulation/air seal energy savings for Chicago as a function of the post-retrofit R-value 

(assuming the pre-retrofit R-value is always R-19). As seen in Figure B-7, at a post-retrofit Rvalue of R-19 (no insulation added), the energy savings is gained from air sealing the attic floor. 

As the post-retrofit R-value increases, the AES increases quickly and then begins to level off at 

higher post-retrofit R-values. Figure B-7 demonstrates the idea of “diminishing returns”: the 

amount of additional energy savings per additional unit of insulation decreases as higher overall 

insulation levels are attained. 

60 
 Average Annual Source Energy Savings (%) 

35 

30 

25 

20 

15 

10 

5 

0 

0 10 20 30 40 50 

Pre-Retrofit R-Value 

Figure B-6. Attic insulation/air seal energy savings as a function of pre-retrofit insulation level 

(Chicago) 

9 

Average Annual Source Energy Savings

(%) 

8 

7 

6 

5 

4 

3 

2 

1 

0 

0 10 20 30 40 50 60 70 

Post Retrofit R-Value 

Figure B-7. Attic insulation/air seal energy savings as a function of post-retrofit R-value (Chicago) 

Figures B-6 and B-7 showed the sensitivity of average energy savings predictions to building 

assumptions. Although not shown here, optimization results are also sensitive to building 

assumptions related to the pre-retrofit building, the retrofit, and the MURS. 

61 
DOE/GO-102011-3261 ▪ April 2011

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