Predicted Versus Actual Savings

for a Low-Rise Multifamily 

Retrofit in Boulder, Colorado

L. Arena and J. Williamson

Consortium for Advanced Residential Buildings 

November 2013
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iii

Predicted Versus Actual Savings for a Low-Rise, 

Multifamily Retrofit in Boulder, Colorado

Prepared for:

The National Renewable Energy Laboratory

On behalf of the U.S. Department of Energy’s Building America Program 

Office of Energy Efficiency and Renewable Energy

15013 Denver West Parkway 

Golden, CO 80401 

NREL Contract No. DE-AC36-08GO28308 

Prepared by:

L. Arena and J. Williamson

Steven Winter Associates, Inc.

of the 

Consortium for Advanced Residential Buildings 

61 Washington Street 

Norwalk, CT 06854 

NREL Technical Monitor: Cheryn Metzger

Prepared under Subcontract No. KNDJ-0-40342-03

November 2013 
iv

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v 

Contents

List of Figures ............................................................................................................................................ vi

List of Tables .............................................................................................................................................. vi

Definitions.................................................................................................................................................. vii

Executive Summary................................................................................................................................. viii

1 Introduction and Background ............................................................................................................. 1

1.1 Research Questions ..............................................................................................................3

2 Methodology ......................................................................................................................................... 4

2.1 Evaluation of Post-Retrofit Utility Bills ..............................................................................4

2.2 Comparison of Actual Bills to Modeling Predictions ..........................................................4

2.3 Cost-Benefit Analysis ..........................................................................................................5

2.4 Research/Experimental Method ...........................................................................................5

3 Results................................................................................................................................................... 7

3.1 Spot Audit Findings .............................................................................................................7

3.2 Predicted Versus Actual Energy Use ...................................................................................8

4 Cost Effectiveness Analysis.............................................................................................................. 12

5 Discussion........................................................................................................................................... 17

5.1 Assessing Modeling Assumptions .....................................................................................18

5.1.1 Weather Files .........................................................................................................18

5.1.2 Shared Surfaces ......................................................................................................19

5.1.3 Predicted Electric Loads ........................................................................................20

5.1.4 Occupant Behavior.................................................................................................22

5.1.5 Measuring Infiltration in Attached Dwellings .......................................................23

6 Conclusions ........................................................................................................................................ 24

References................................................................................................................................................. 26

Appendix A. Pre-Retrofit Utility Data ...................................................................................................... 27

Appendix B. Post-Retrofit Utility Data .................................................................................................... 28

Appendix C. Post-Retrofit Audit Summary............................................................................................. 29

Appendix D. Spot Audits: Tenant Modification Assessment ............................................................... 30
vi 

List of Figures

Figure 1. Overhead view of building complex.......................................................................................... 2

Figure 2. Side view of one unit .................................................................................................................. 2

Figure 3. Timeline of project ...................................................................................................................... 3

Figure 4. Gas usage rate (August through November)........................................................................... 8

Figure 5. Electricity usage rate (August through November)................................................................. 8

Figure 6. Total gas usage (August through November).......................................................................... 9

Figure 7. Total electricity usage (August through November)............................................................... 9

Figure 8. BEopt predicted versus actual gas use (August through November) ................................ 10

Figure 9. BEopt predicted versus actual electricity use (August through November)...................... 11

Figure 10. BEopt optimization curve....................................................................................................... 14

Figure 11. Cumulative energy savings ................................................................................................... 16

Figure 12. Individual unit predicted savings.......................................................................................... 17

Figure 13. Building location of analyzed units....................................................................................... 18

Figure 14. Geometry of unit #108 ............................................................................................................ 19

Figure 15. Geometry of unit #208 ............................................................................................................ 19

Figure 16. Electricity usage rate versus OAT for #106.......................................................................... 21

Figure 17. Electric usage rate versus outdoor air temperature for #208 ............................................ 21

Figure 18. Comparison of predicted and actual baseline electrical energy use ................................ 22

Unless otherwise noted, all figures were created by CARB. 

List of Tables

Table 1. Building Specifications Before and After Retrofits................................................................... 2

Table 2. Units Included in Utility Bill Analysis ......................................................................................... 7

Table 3. Options Evaluated in Optimization........................................................................................... 12

Table 4. Modeling Inputs for Economic Analysis .................................................................................. 13

Table 5. Significant Points On Optimization Curve ............................................................................... 15

Table 6. HDD Data ..................................................................................................................................... 19

Unless otherwise noted, all tables were created by CARB. 
vii 

Definitions

ACH50 Air changes per hour at 50 Pascals

AFUE Annual fuel utilization efficiency

BA Building America

BEoptTM Building Energy Optimization Program

CARB Consortium for Advanced Residential Buildings

DOE U.S. Department of Energy

E+ EnergyPlus

FFA Finished floor area

HDD Heating degree day

HVAC Heating, ventilation, and air conditioning

MEL Miscellaneous electric load

NREL National Renewable Energy Laboratory

OAT Outdoor air temperature

SHGC Solar heat gain coefficient 

TMY3 Typical Meteorological Year dataset
viii

Executive Summary

There are approximately 28 million multifamily units in the United States (RECS 2009). In 

addition, about 70% of the U.S. housing stock is more than 30 years old (HUD 2011). This 

represents an enormous opportunity to retrofit older multifamily units that are very similar to the

six Boulder, Colorado units discussed in this report. 

The purpose of this project was to follow up on a Building America (BA) retrofit project to 

determine the actual savings. In addition, this project provided a perfect opportunity to look at 

the use of the Building Energy Optimization Program (BEoptTM) as it applies to multifamily 

retrofits. BEopt is a tool that is used by the BA Program to determine cost optimized packages in 

both new and retrofit construction. It also provides a consistent way to compare one BA project 

from another in various climate regions. 

BEopt was not originally built for multifamily, nor for retrofit projects. However, when Vice 

President Biden announced Recovery through Retrofit in 2009, BA took on the challenge of 

existing homes. BEopt was one component of the BA Program that added retrofits into its 

repertoire and has been continually improved since then. 

Polly et al. (2011) propose a method for improving the accuracy of residential energy analysis 

methods. A key step in this process involves the comparison of predicted versus metered energy 

use and savings. This report will challenge BEopt’s relatively new capabilities and provide 

recommendations for how to improve it in the area of multifamily retrofit. 

In support of these research needs, the Consortium for Advanced Residential Buildings (CARB) 

evaluated the retrofit of a multifamily building in Boulder, Colorado. The updated property is a 

37-unit, two-story apartment complex built in 1950, which underwent renovations in early 2009 

to bring it into compliance with Boulder, Colorado’s SmartRegs ordinance (LBNL 2012). Goals 

of this study were to: 1) evaluate predicted versus actual savings due to the improvements, 2) 

identify areas where the modeling assumptions may need to be changed, and 3) determine 

common changes made by renters that would negatively impact energy savings. In doing so, 

CARB seeks to improve the accuracy of modeling software while assessing retrofit measures to 

specifically determine which are most effective for large multifamily complexes in the cold 

climate region. Other issues that were investigated include the effects of improving building 

efficiency on tenant comfort, the impact on tenant turnover rates, and the potential market 

barriers for this type of community-scale project. 

Savings from the retrofit measures implemented in Boulder, Colorado were calculated on an 

individual apartment basis and for all six apartments together. Total savings for the six units 

showed excellent agreement between predicted and actual values for both gas and electricity use. 

Total gas and electricity savings for the six apartments were predicted to be 61% and 27%, 

respectively. Actual gas and electricity savings were 58% and 30%, respectively. A cost 

optimization using BEopt shows that, for this complex, the measures implemented were a costeffective method of achieving the minimum efficiency levels mandated by the City of Boulder’s 

SmartRegs ordinance. 
1 

1 Introduction and Background

Of the approximately 28 million multifamily housing units in the United States, about 30%, or 

9.7 million, are located in the very cold/cold climates like Boulder’s (RECS 2009). These 

numbers indicate the potential for significant energy savings if improvements can be 

implemented. Performing an in-depth energy analysis to determine the most cost-effective 

improvements is essential to ensure optimal return on investment for property owners. However,

multiple studies confirm that analysis methods tend to overpredict energy use in poorly insulated, 

leaky homes and thus, the savings associated with improving those homes (Polly et al. 2011). 

If actual pre-retrofit energy usage is lower than predicted, as was the case for the units initially 

analyzed, actual savings may also be lower than predicted. Analyzing the accuracy of modeling 

software for these types of buildings is essential to ensure that consultants, property owners, and 

efficiency program administrators are suggesting and making the most efficient and costeffective energy improvements.

Polly et al. (2011) propose a method for improving the accuracy of residential energy analysis 

methods. A key step in that process involves the comparison of predicted versus metered energy 

use and savings. While studies on attached housing have been conducted in the last several years, 

few compare actual bills to predicted. 

In 2011, a research study conducted in the cold climate zone evaluated the retrofit performance 

of a small, stacked, three-family building (Gates and Osser 2011). Findings from that study also 

support Polly’s conclusions: modeling overpredicted the actual energy use in the pre-retrofit case 

more significantly than in the post-retrofit case. However, it does not fully encompass the results 

that are likely to occur in large-scale multifamily complexes. Another study recently conducted 

involved a multifamily retrofit on a medium-scale, 12-unit complex in the mixed-humid climate 

zone (Lyons 2013). That research provides recommendations for the most effective retrofit 

measures for multifamily buildings in that climate zone, but there is no comparison of utility bills 

to modeling predications. 

In a previous research effort under the U.S. Department of Energy’s (DOE) Building America 

(BA) Program, the Consortium for Advanced Residential Buildings (CARB) compared preretrofit utility bills to modeling predictions from REM/Rate for a 37-unit rental property (shown 

in Figure 1 and Figure 2). This property underwent renovations in 2009 to bring it into 

compliance with Boulder, Colorado’s new efficiency requirements for rental properties. Key 

retrofits included insulating and tightening the building envelope, replacing inefficient lights and 

appliances, and replacing the windows and doors. A more detailed list of the pre- and postretrofit building components is provided in Table 1. 
2 

Figure 1. Overhead view of building complex Figure 2. Side view of one unit

Table 1. Building Specifications Before and After Retrofits

Before After

Attic R-9 attic insulation R-50 blown-in cellulose

Walls No insulation R-13 blown-in cellulose 

Windows Single-pane, metal Double-pane, low-e, vinyl

Slab floor No insulation Same

Doors Metal Foam-filled, fiberglass 

Air Leakage* 11.4 ACH50 8.7 ACH50 

Appliances 850 kWh/yr 450 kWh/yr

Fluorescent Lighting 0% 100%

Domestic Hot Water Central 0.59 energy factor Same

Cooling None Same

Heating

60 annual fuel utilization 

efficiency (AFUE), natural 

gas convection, unit 

heaters

Same

*

 Unguarded blower door leakage rates listed are an average of all units monitored. “After” data is an average 

leakage rate from tests done in 2011 retrofit analysis. 

The focus of that research project was to evaluate the effectiveness of the City of Boulder’s 

SmartRegs program—an ordinance that requires rental property owners to bring their rentals up 

to an efficiency level similar to that of the 2004 International Energy Conservation Code by 2018 

(or below a Home Energy Rating System score of about 120). REM/Rate was used in that 

analysis because it was the software that Boulder’s SmartRegs program administrators had used 

to develop their requirements (Arena and Vijaykumar 2012; LBNL 2012). Pre-retrofit 

predictions from that study were found to be significantly higher than actual energy use, 

supporting Polly et al. (2011). The major findings from that study included. 

• The models sometimes underpredicted and sometimes overpredicted electricity 

consumption. 

• The models consistently and significantly overpredicted gas consumption. 
3 

• Spikes in actual electricity consumption indicated the possible use of supplemental space 

conditioning in both the summer and winter. 

Because post-retrofit bills were not available at that time, a continuation of that study was 

proposed that would include additional spot audits on a variety of apartment types, a comparison 

of pre- and post-retrofit utility bills and the corresponding energy savings, and a cost analysis of 

the upgrades. A graphic illustrating the timeline leading up to this project is displayed in Figure 

3. 

Figure 3. Timeline of project

This report provides comparisons of pre- and post-retrofit utility bills for this low-rise, 

multifamily building in climate zone 5B and examines the potential causes of differences 

between actual energy usage and modeled predictions. In doing so, it seeks to improve the 

accuracy of modeling software while assessing retrofit measures to specifically determine which 

are most effective for large multifamily complexes in the cold climate region. Other issues that 

were investigated include the impact of tenant modifications on post-retrofit energy savings, the 

effects of improving building efficiency on tenant comfort and turnover, and the potential market 

barriers for this type of community-scale project. 

1.1 Research Questions

To help consultants, property owners, and program administrators determine the most costeffective methods of improving these buildings, accurate analysis and prediction of the energy 

use of existing buildings are essential. In light of the information presented thus far, it was the 

intent of this research effort to answer the following questions: 

• For this building complex in Boulder, Colorado, what are the actual energy savings 

realized from the improvements compared to the predicted savings? 

• Can discrepancies between modeled bills and actual bills be explained? If so, are changes 

to the accepted modeling assumptions needed? 

• What are the most common changes made by renters that will negatively impact postretrofit energy savings? And what is the extent of that impact?

CARB’s path to answer these research questions involved a variety of tasks, including evaluation 

of utility bills, energy modeling using the Building Energy Optimization Program EnergyPlus 

(BEopt E+) 1.4, and follow-up spot audits on several units. 

2008

•Pre-retrofit 

audits 

(performed by 

others)

2009

•Retrofit 

implementation

2011 - Beginning of CARB's 

Involvement

•Post-retrofit 

audits and preretrofit utility bill 

analysis 

(peformed by 

CARB)

2012 - Proposed 

Continuation

•Post-retrofit 

uitility bill 

analysis and spot 

audits 

(performed by 

CARB)
4 

2 Methodology

2.1 Evaluation of Post-Retrofit Utility Bills

Post-retrofit data were collected from the property owner and compared to pre-retrofit data to 

determine the energy savings from the renovations. Both gas and electricity bills were collected.

With the assistance of the property management company, utility bill releases were distributed to 

all the tenants in the building. Six occupants signed and returned the releases. It should be noted 

that this complex is primarily inhabited by university students, generally resulting in annual 

turnover of apartments. The majority of the tenants had moved in during August 2012, and 

therefore, an entire year of data was not available. Even though pre-retrofit utility bills were 

available for the entire year, data analysis was limited to the time period of August through 

November. The pre- and post-retrofit utility bill data compared for this retrofit analysis are

provided in Appendices A and B. It is important to note that these observations may not apply to 

all multifamily housing because the tenants in this study were university students with possibly 

uncharacteristic energy usage patterns. 

The energy usage was read and reported by the utility company in one-month increments. 

However, full billing cycles were found to range between 29 and 33 days. Furthermore, during 

August, when most tenants were moving in, some billing cycles include as few as 11 nonvacant 

days. CARB had originally intended to perform utility bill analysis for a longer time period, but 

contract timeline restrictions coupled with typical tenant moving schedules limited the size of the 

dataset. In order to minimize differences that developed from utility metering intervals, the data 

were normalized per day. Electrical energy use for each month was analyzed with the metric of 

total kilowatt-hours used/total metered days over the four-month period. Natural gas usage was 

normalized to a similar effect. In order to allow for equal data comparison among unique time 

periods with variant weather conditions, gas usage was evaluated per heating degree day (HDD) 

over the four-month analysis period. 

2.2 Comparison of Actual Bills to Modeling Predictions 

Energy modeling was performed using BEopt E+ v1.4 to assess savings that resulted from each 

retrofit measure. Because BEopt cannot model a multifamily building as a single building with

multiple apartments, separate models were created for each of the six units that had both pre-and 

post-retrofit utility bills available. CARB also investigated differences in predicted and actual 

energy use, and differences between actual weather data and the model weather data. 

Errors in this analysis may have been introduced by the nonstandard use of BEopt as a 

multifamily modeling tool, the fact that this was a self-selected group of participants, and by the 

fact that all the apartments were occupied by different tenants in the pre- and post-retrofit 

periods. The influence of different tenants on energy usage behavior will have a significant 

impact on the actual savings. The potential impact of this difference was considered when 

assessing the savings; however, it was not directly accounted for in the modeling. Additionally, 

not all gas consumption is attributed to heating—a small portion is also used for cooking needs. 

This was accounted for in the energy model. 

The modeling predictions were then compared to actual savings as indicated by utility bill 

differences between pre-and post-retrofit periods. In order to minimize the variation caused by 

tenant move-in dates and weather conditions, electricity usage was normalized as the average 
5 

kWh/day and gas usage as therms/HDD. A summary of the complete data analysis method is 

provided below: 

1. Normalized usage rates were determined for gas (therms/HDD) and electricity (kWh/day) 

for each unit in the pre- and post-retrofit case. Predicted usage rates were developed 

through BEopt energy simulations, while actual utility usage rates were calculated 

directly with tenant utility bills.

a. Electricity usage was normalized by summing the kilowatt-hours over the 

active August through November usage period and dividing by the total 

number of days in that period (this varied depending on move-in date). 

b. Gas usage was normalized by summing the therms used over the four-month 

period and dividing by the total HDDs during that time. This was done to 

eliminate the effects of different weather conditions between the energy model 

and the actual period for which the bills were available. 

2. The electricity and gas values obtained in step 1 above were multiplied by 121 days and 

1,314 HDDs respectively (a typical August through November time period) to obtain 

each unit’s total normalized gas and electrical energy usage. 

The normalized predicted and actual energy usage was aggregated to obtain the total 

consumption for all six units. Total usage was determined for each of the four corresponding 

categories: 2008 utility bills, 2012 utility bills, BEopt results pre-retrofit, and BEopt results postretrofit. 

2.3 Cost-Benefit Analysis 

The cost effectiveness of this community-scale project was determined by quantifying the costs 

of the renovation, the cost savings from the utility bills, and the impact of financial incentives.

The cost-benefit analysis was performed to assess the economic feasibility of each retrofit 

measure while identifying the improvements that yielded the highest savings. A simulated cost 

analysis was performed over a 30-year period with BEopt to forecast the average utility bills and 

upgrade and maintenance payments that the building owner and tenants would incur. 

Additionally, BEopt was used to develop a retrofit optimization curve that provides insight into 

the upgrade combinations that can be implemented for the lowest cost and greatest savings. 

Lastly, the report discusses various federal, state, and other incentives that impact the economic 

feasibility of these improvements. 

2.4 Research/Experimental Method

In addition to modeling and utility bill analysis, spot audits were conducted on several units to 

evaluate the condition of the improvements several years after completion. The goals of the 

audits were to determine the longevity of the retrofit measures, to assess if any had been changed 

or degraded over time, and to investigate if the occupants had made any changes that would 

impact the expected energy savings. The audits included: 
6 

• Envelope air leakage tests

• Showerhead and faucet water flow measurements

• Lighting and appliance surveys

• Verification of attic insulation levels

• Visual inspection of the condition of the units 

• Identification of any supplemental heating or cooling equipment. 

CARB had intended to supplement the audits with occupant interviews to determine comfort 

levels and satisfaction with the renovated property; however, after speaking to the property 

management company, it was decided that these would hold little value, primarily due to two 

factors: (1) most of the tenants whose apartments were audited had been there only a few 

months; and (2) most tenants stayed for only a year due to the fact that they were university 

students. While vacancies in Boulder are extremely low, turnover is very high in most rentals, 

especially near the university. 
7 

3 Results

Post-retrofit utility bills were obtained for six of the units in the apartment complex and

represented a wide array of apartment styles. Table 2 provides a basic description of each of the 

post-retrofit units where utility bills were analyzed. A summary of post retrofit characteristics for 

all buildings audited is shown in Appendix C. 

Table 2. Units Included in Utility Bill Analysis

Unit # Type Floor Area 

(ft2) 

# Attached 

Walls

# 

Bedrooms 

106 First-floor middle unit 

conditioned space above 433 2 1 

108 First-floor middle unit 

conditioned space above 573 2 2 

121 First-floor end apartment 

no apartment above 433 1 1 

208 Second-floor middle unit 

conditioned space below 647 2 2 

211 Second-floor end unit

conditioned space below 490 1 1 

215 Second-floor middle unit

conditioned space below 490 2 1 

3.1 Spot Audit Findings

Spot audits were conducted on 9 of the 37 apartments to evaluate the condition of the 

improvements several years after completion. A summary of spot audit findings is shown in 

Appendix D. The goals of the audits were to determine the longevity of the retrofit measures, to 

assess if any had been changed or degraded over time, and to investigate if the occupants had 

made any changes that would impact the expected energy savings.

Several of the apartments had incandescent bulbs in the light fixtures, but not in every fixture. 

The largest percentage of incandescent to fluorescent noted was approximately 50%. Three of 

the nine units inspected had window air conditioners, but none had more than one. No 

supplemental heaters were found in any unit despite the fact that the heating systems are 

convection, point-source heaters located in the living room. All faucets that were inspected still 

tested to low flow specifications, thus none had been switched back by the tenants. 

As part of a qualitative assessment, CARB suspects that there is no correlation between the few 

changes made and the variations in predicted versus actual energy use in the apartments. For 

units that had a window air conditioner installed or a large number of incandescent bulbs, there 

was no consistent evidence to indicate substantial energy increases. Further research and larger 

datasets are needed to fully confirm these findings.
8 

3.2 Predicted Versus Actual Energy Use

Energy modeling was performed with the National Renewable Energy Laboratory’s (NREL) 

hourly energy simulation software, BEopt E+ 1.4. This modeling was completed for each of the 

units that had both pre and post-retrofit utility bills available. Consequently, 12 models were 

created: a pre-retrofit and a post-retrofit case for each of the six apartments. All of the predicted 

modeling results were then compared to the actual energy usage that was obtained from the 

tenants’ utility bills (Figure 4 and Figure 5) for both the pre- and post-retrofit cases. Again, all 

results are normalized, and describe site energy use from August through November only. 

Figure 4. Gas usage rate (August through November) 

As seen in Figure 4, actual natural gas savings and predicted savings vary significantly between 

units. The models did not consistently over- or underpredict gas use for the six units when 

analyzed individually. The only trend displayed is a consistent decrease in actual gas usage 

between pre- and post-retrofit periods for every unit, though the magnitude of that savings varies 

significantly. 

Figure 5. Electricity usage rate (August through November) 

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

#106 #108 #121 #208 #211 #215

therms/HDD

Unit #

2008 Utility Bills

Beopt Pre retrofit

2012 Utility Bills

Beopt Post retrofit

0

2

4

6

8

10

12

14

16

#106 #108 #121 #208 #211 #215

kWh/day

Unit #

2008 Utility Bills

Beopt Pre retrofit

2012 Utility Bills

Beopt Post retrofit
9 

Alternatively, Figure 5 demonstrates the average rate (kWh/day) at which electricity was 

consumed. This comparison of actual and predicted electricity savings yielded an unexpected 

result. For all three first floor units, the utility bills indicated that electricity use increased slightly

from the pre- to post-retrofit case, while all three second-floor units saw a significant decrease in 

electricity usage. The reasons for this are unclear at this time, but could be due to differences in 

consumption between the 2008 and 2012 occupants. Unlike gas use, the energy model 

consistently overpredicted electricity use in all but 2 of the 12 models. 

Total gas and electricity usage values for all six apartments are shown in Figure 6 and Figure 7. 

Adding the values for all six apartments produces closer agreement between utility bills and 

modeling as compared to evaluating them individually. Overall, gas use was slightly 

underpredicted for the six apartments and electricity use was significantly overpredicted. 

However, gas and electricity savings between pre- and post-retrofit conditions show good 

agreement between the predicted and actual values.

Figure 6. Total gas usage

(August through November) 

Figure 7. Total electricity usage

(August through November)

Over a typical August through November period of 1,314 HDDs, gas usage rates translate to a 

total actual gas usage of 392 therms in the pre-retrofit case and 164 therms in the post-retrofit 

case. Alternatively, BEopt modeling predicted a total pre-retrofit usage of 349 therms and a postretrofit usage of 137 therms. At an estimated $8/month gas service charge per unit and a rate of 

$0.7901/therm, the actual gas savings would result in a savings of $372 over the course of four 

months. In comparison, BEopt predicted savings of 212 therms, which would amount to a total 

monetary savings of $360. Total actual gas use in the retrofitted apartments was 58.2% lower 

than the pre-retrofit cases. In comparison, the energy model predicted total savings of 60.7% 

compared to the pre-retrofit case, showing very good agreement with the actual savings. 

Agreement between predicted and actual total electricity savings was also good. Over a typical 

August through November period of 121 days, electricity usage rates translated to a total actual 

electricity usage of 5,968 kWh in the pre-retrofit case and 4,199 kWh in the post-retrofit case. 

Alternatively, BEopt modeling predicted a total pre-retrofit usage of 8,142 kWh and a total postretrofit usage of 5,942 kWh. At an estimated $8/month electricity service charge per unit and a 

rate of $0.1013/kWh, the actual 1,769 kWh savings would result in a total monetary savings of 

392

349

164

137

0

50

100

150

200

250

300

350

400

450

Therms

Total usage of six units

2008 Utility

Bills

Beopt Pre

retrofit

2012 Utility

Bills

Beopt Post

retrofit

5,968

8,142

4,199

5,942

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

kWh

Total usage of six units

2008 Utility

Bills

Beopt Pre

retrofit

2012 Utility

Bills

Beopt Post

retrofit
10 

$371 over the course of four months. The total BEopt predicted electricity savings of 2,200 kWh 

amounts to a monetary savings of $415 over this same four-month period. Overall, the total 

electricity usage decreased by 29.6% for the six units. In comparison, the model predicted a total 

savings of 27.0%, once again, showing excellent agreement with the actual savings. 

Agreement between actual and predicted savings quantifies only a portion of the energy model’s 

accuracy. It is also important to compare the magnitude of each of the two energy usage values. 

The relationship between predicted and actual energy use values has been plotted in Figure 8. 

The line “y = x” signifies the region where modeling predictions match utility bills (line of 

perfect fit). Thus, the closer the ratio is to this line, the more accurate the modeling prediction. 

Figure 8. BEopt predicted versus actual gas use (August through November) 

As seen in Figure 8, the pre-retrofit gas utility usage points appear to be equally scattered both 

above and below the line of perfect fit (with three overpredictions and three underpredictions). In 

fact, the total sum of all six pre-retrofit predictions was only 11.0% less than the total actual 

usage. Alternatively, the post-retrofit bills are distributed with a majority lying close to or 

slightly above the line of perfect fit. However, the sum of post-retrofit predictions is 16.2% less 

than total actual energy usage. Even though the energy use in the majority of the post-retrofit

units is overpredicted, the total sum of the predictions is still less than the total actual use. This 

indicates that the two underpredicted values carry a greater combined error than the four 

overpredicted values.
11 

A similar plot describing the relationship between the model’s predicted electricity use and the 

actual electricity use is provided in Figure 9. As seen from the plot, both the pre- and postretrofit cases are predominantly distributed above the line of perfect fit. Hence, the model 

overpredicts electricity consumption in both cases. The total sum of pre-retrofit predictions was 

36.4% greater than total actual energy usage while total post-retrofit predictions were 41.5% 

greater. 

Figure 9. BEopt predicted versus actual electricity use (August through November) 

0

2

4

6

8

10

12

14

16

0 2 4 6 8 10 12 14 16

BEopt Predicted Electric Use [kWh/day]

Actual Electric Use [kWh/day]

Line of Perfect

Fit

PRE Predicted

vs Actual Use

POST Predicted

vs Actual Use

106

121

211

215

108

208

121

108 208

211 215 106
12 

4 Cost Effectiveness Analysis

The primary renovations made to this community-scale project entailed increasing the attic and 

wall insulation, installing high efficiency windows and doors, replacing the appliances with 

energy-efficient models, and installing efficient lighting. Modeling predicts that improvements 

will result in source energy savings of 26%–41%. 

Using BEopt E+ 1.4, a cost optimization was performed on the retrofit package implemented to 

determine how close it came to the lowest-cost optimization curve. Ideally, the package selected 

would fall on the lowest-cost curve, indicating that the package was a cost-effective method of 

achieving the predicted energy savings. Table 3 lists the pre- and post-retrofit building 

specifications (first and last items listed for each component) along with any other efficiency 

levels analyzed in the optimization. Combinations of the options shown were run for a secondfloor middle unit (#211).

Table 3. Options Evaluated in Optimization 

Component Option

Attic Insulation

Ceiling, R-9, cellulose, blown-in, vented

Ceiling, R-38, cellulose, blown-in, vented 

Ceiling, R-50, cellulose, blown-in, vented 

Wall Insulation Uninsulated, 2 × 4, 16 in. o.c.

R-13, loose-fill, cellulose, no void 2 × 4 16 in. o.c.

Windows

Single-pane, clear, metal

Double-pane, low-e, nonmetal, air, low solar heat 

gain coefficient (SHGC)

Lighting 0% fluorescent hardwired and plugin

100% fluorescent hardwired and plugin 

Infiltration 13.5 ACH50

10.1 ACH50

Furnace Unit heater (AFUE = 60%)

Unit heater (AFUE = 80%)

HVAC retrofits were not implemented for several reasons. The units were heated with individual 

unit gas heaters without blowers. To retrofit each of the 37 apartments with new heaters would 

require costly electrical and venting upgrades as well as substantial cosmetic repairs, all of which 

made this improvement cost prohibitive. Domestic hot water was left as is because the water 

heaters were not considered to be at the end of their useful lives, and the improvement over the 

existing efficiency levels would also have been cost prohibitive. Apartments were not previously 

equipped with window air conditioners by the property management company. No changes to 

this condition were made during the renovation. 

For a majority of the retrofit measures, costs were referenced from NREL’s material cost 

database (BEopt default costs). However, for some improvements, costs were estimated based on 

prior experience with specific retrofit measures. For instance, infiltration improvements were 

given no additional cost because no “air sealing task” was physically performed for these 
13 

apartments. Air sealing benefits are a byproduct of attic insulation, wall insulation, and properly 

installed windows. Additionally, an upgrade of the furnaces to AFUE 80% unit heaters was 

valued at $2,500. This increased cost includes additional installation costs for wiring and venting 

that would be required to comply with the current building code and interior cosmetic work that 

would be required after the replacement. 

For the financial analysis, the economic values listed in Table 4 were used as modeling inputs. 

The BEopt default loan rate of 7.0% was used for this analysis, as it was close to true rates being 

offered at the time the improvements were made (2008–2009). BEopt’s Colorado state utility 

rate average was used for both pre- and post-retrofit cases. These rates were found to be within 

an acceptable range of what was actually charged by the local utility company. 

Table 4. Modeling Inputs for Economic Analysis

Economic Variables Modeling Inputs

Project Analysis Period 30 years

Inflation Rate 1.6%

Discount Rate (Real) 3.0%

Loan Period 30 years

Loan Interest Rate 7.0%

Electricity Rate $0.1013/kWh + $8.00 monthly charge

Natural Gas Rate $0.7901/therm + $8.00 monthly charge

Fuel Escalation Rate 0.0%

The optimization curve that resulted from this analysis is provided in Figure 10. Each point on 

this graph represents the annualized energy-related costs and source energy savings associated 

with the selected material options. The lowest cost option at various savings is connected to form 

the minimum cost optimization line. 
14 

Figure 10. BEopt optimization curve

Along the lowest cost optimization curve, there are three points of significance: (1) the lowest 

cost case; (2) the selected post-retrofit design case; and (3) the highest savings option. Table 5

displays a summary of the building components associated with each case. Case #2 was chosen 

as the retrofit package for this complex because it was the most cost-effective combination of 

improvements that would result in compliance with the SmartRegs ordinance. Additionally, this 

design case lies at the point which provides the greatest savings that can be achieved from the 

selected construction options without increasing annualized energy related costs above the preretrofit costs.

0

200

400

600

800

1,000

1,200

0 5 10 15 20 25 30 35 40 45

Annualized Energy Related Costs [$/yr]

Avg. Source Energy Savings [%/yr]

Optimization Points

1 Least Cost Line

2

3
15 

Table 5. Significant Points On Optimization Curve

Point 1 2 3 

Classification Minimum cost Selected design case 

for second-floor unit Maximum % savings

Water Loads Low-flow showers 

and sinks

Low-flow showers 

and sinks

Low-flow showers 

and sinks

Wood Stud 

R-13 loose-fill 

cellulose no void 

2 × 4 16 in. o.c.

R-13 loose-fill 

cellulose no void 

2 × 4 16 in. o.c.

R-13 loose-fill 

cellulose no void 

2 × 4 16 in. o.c.

Unfinished Attic Ceiling R-38 cellulose 

blown-in vented 

Ceiling R-50 cellulose 

blown-in vented

Ceiling R-50 cellulose 

blown-in vented

Window Type Single-pane clear

metal

Double-pane low-e 

nonmetal air low 

SHGC 

Double-pane low-e 

nonmetal air low 

SHGC 

Infiltration

Reduced by 25%

(from 13.5 ACH50 to 

10.1 ACH50)

Reduced by 25% 

(from 13.5 ACH50 to 

10.1 ACH50)

Reduced by 25% 

(from 13.5 ACH50 to 

10.1 ACH50)

Refrigerator Inefficient old 

refrigerator

Electrolux ENERGY 

STAR® efficient 

refrigerator

Electrolux ENERGY 

STAR efficient 

refrigerator

Lighting 100% fluorescent 

hardwired and plugin 

100% fluorescent 

hardwired and plugin 

100% fluorescent 

hardwired and plugin 

Furnace Unit heater

(AFUE = 60%)

Unit heater 

(AFUE = 60%)

Unit heater 

(AFUE = 80%)

Energy Savings 27.6% 37.6% 39.3% 

Annualized 

Energy-Related 

Costs ($/yr)

$685 $773 $939 

It is important to note that the cost analysis performed in this section does not account for the 

federal, state, and local funding that has been put in place to help finance the cost of the retrofit 

improvements. Rental property owners have access to retrofit incentives that involve insulation, 

appliance, lighting, water conservation, energy auditing, financing options, and more. As a 

result, these financial incentives may also have a significant impact on a rental property’s

decision on which retrofit measures are economically desirable. 

Each individual upgrade was evaluated to determine the corresponding energy savings. Figure 11

is a modified version of a Pareto diagram, which describes how each of the building 

improvements influenced the overall predicted source energy savings of 37.6% (analysis 

performed on unit #215). The plot provides an end-use breakdown of each cumulative measure. 

Upgrades are ordered from left to right in decreasing order of savings. 
16 

Figure 11. Cumulative energy savings

As seen in Figure 11, adding attic floor insulation results in the greatest savings for these units. 

This diagram also reveals that improvements to the building envelope (added insulation, window 

upgrades, and the infiltration reductions that result) account for ~60% of the total savings. As 

expected, these building envelope improvement measures are most effective in reducing gas 

usage. Lighting and refrigerator placements also provide significant contributions to the total 

savings. These two electricity-based measures are responsible for ~30% of the total savings. The 

last portion of the savings, ~10% of the total, comes from benefits of low-flow sinks and 

showerheads. This improvement saves both gas needed to provide domestic hot water while also 

preserving water itself. (Note: recent versions of BEopt software no longer have inputs for 

modeling savings from low-flow fixtures.) 

87

79

73

69 64

60

57 54

9%

16%

21%

26%

31%

34%

38%

0%

5%

10%

15%

20%

25%

30%

35%

40%

0

10

20

30

40

50

60

70

80

90

100

Pre-retrofit Attic

insulation

Wall

insulation

CFL's ENERGY STAR

® Refrig.

Low-e

windows

Low flow

showers/sinks

Infiltration

reduction

Avg. Source Energy Use [MMBtu/yr]

Avg. Source Energy Savings [%/yr]

Misc. (E) Lg. Appl. (E) Lights (E) HVAC Fans/Pump (E)

Heating (G) Hot Water (G) Lg. Appl. (G) Savings
17 

5 Discussion

Since post-retrofit bills were available for only four months, the building improvements were 

compared for the four-month period of August through November only. Fortunately, this time 

period encompasses both heating and cooling conditions for the region. However, extrapolating 

the four-month, post-retrofit utility usage out to an entire year would have stretched the limits of 

its accuracy. Year-long extrapolation, on a per-day or HDD basis, would involve making several 

assumptions that would have significantly skewed the accuracy of the analysis.

There are a variety of elements that influence the model’s projected savings and energy use of 

each unit. Primary factors include number of exposed walls, number of shared walls, finished 

floor area (FFA), number of bedrooms, and presence of vented attic space. A categorized 

breakdown of each unit’s pre- and post-retrofit energy uses, as well as the projected savings, 

based on simulation, has been provided in Figure 12. Total source energy savings between preand post-retrofit cases (show at the top of the chart) ranged from 27% to 41%. 

Figure 12. Individual unit predicted savings 

Units that are located on the second floor were projected to experience significantly higher 

savings than first-floor units. This difference in savings is primarily due to the R-50 cellulose 

insulation upgrade performed in all second-floor units. On average, second-floor units saw 

projected savings 10.5 percentage points higher than first-floor units. 

Another important difference between the first- and second-floor units stems from the impact 

that an uninsulated slab-on-grade foundation has on overall heating use. As seen in Figure 12, 

the energy usage predictions of pre-retrofit units #108 and #208 are very similar. However, it 

82

61

107

75

91

63

107

64

96

57

87

54

27% 30% 31% 41% 41% 38%

0

20

40

60

80

100

120

 Pre-106

Post-106

Pre-108

Post-108

Pre-121

Post-121

Pre-208

Post-208

Pre-211

Post-211

Pre-215

Post-215

Average Source Energy Savings [MMBtu/yr]

Misc. (E) Lg. Appl. (E) Lights (E) Heating (G) Hot Water (G) Lg. Appl. (G) HVAC Fans/Pump (E)
18 

would intuitively seem that the second floor units should use significantly more heating energy 

during the pre-retrofit case because of their poorly insulated attics. (Note: the pre-retrofit firstfloor ceilings are modeled as adiabatic surfaces while second-floor ceilings are modeled with R9 ceiling insulation). This indicates that the model predicts that a poorly insulated foundation 

slab in cold-climate regions can have a comparably negative impact to a poorly insulated roof. 

The effect of insulating the slab to theoretical adiabatic conditions was examined in BEopt. The 

results indicate that unit #108, with an adiabatic slab, would use ~38% less space heating energy 

than with its current uninsulated case. The prediction for such a large reduction in energy use is

partly due to the fact that the slab perimeter is a large percentage of the total exposed surface 

area in the model even though the area itself is not very large. Even so, in reality it seems highly 

unlikely that insulating the slab would have a similar effect to that of insulating an R-9 vented 

attic to R-50. 

Unit location (interior versus exterior) also had a significant influence on savings levels. An 

aerial view of the complex with each unit’s approximate location is provided in Figure 13. Units 

located on the end of the complex, as opposed to those located between two others, often saw 

higher savings due to the greater percentage of exposed wall area.

Figure 13. Building location of analyzed units

5.1 Assessing Modeling Assumptions

Energy modeling can be a useful tool for forecasting savings that accompany a building retrofit. 

However, the accuracy of modeling is directly contingent upon user input and software 

capabilities. There are limitations to all energy modeling software that can influence the accuracy 

of their predictions. The following sections discuss modeling assumptions and limitations of the 

results generated with BEopt E+ 1.4. 

5.1.1 Weather Files

It is important to consider the differences that exist between the input weather data used by 

modeling software and the actual weather conditions experienced during the time of utility bill 
19 

collection. The modeling software used in this analysis utilizes weather characteristics from the 

TMY3 database. This database includes weather files for Boulder, Colorado based on typical 

conditions between 1976 and 2005. Consequently, this database represents the weather values for 

a typical year only and will not be fully applicable to the analysis of a year when extreme 

weather patterns existed. In order to minimize this effect, gas use data were normalized per 

HDD. 

The differences between the TMY3 weather data used in modeling and the actual conditions are 

summarized in Table 6. Compared to the typical year, the heating seasons for both 2008 and 

2012 were milder than the TMY3 data indicate.

Table 6. HDD Data

Weather Conditions

(August Through November)

HDD65

(°F-day)

Percent Difference From Typical

(%)

TMY3 (30-Year Typical) 1,314 –

Pre-Retrofit Bills (2008) 1,221 –7.1%

Post-Retrofit Bills (2012) 1,148 –12.6%

5.1.2 Shared Surfaces

Some of the discrepancies between actual energy use and modeled results may be explained by 

the limitations of the software and assumptions that were made when modeling the building 

geometry of each unit. For instance, modeling only one floor of a multiple-story building (when 

units are located on either the first floor or second floor) presented a unique challenge that 

stretched the capabilities of BEopt. Since the software does not directly have an option to model 

this feature, shared floors and ceilings were modeled as near-adiabatic surfaces by increasing the 

R-values to extremely high levels and reducing the framing factors drastically. The ceiling of a 

first-floor unit (with another unit above) was modeled as a high resistance, near-adiabatic 

surface. Alternatively, the floor of a second-story unit was modeled as a crawlspace with nearadiabatic qualities. Figure 14 and Figure 15 show examples of these occurrences in units #108 

and #208. Even though these units were attached, with #208 being the second floor over #108, 

they needed to be modeled as two separate buildings in order to run a BEopt energy simulation. 

Figure 14. Geometry of unit #108 Figure 15. Geometry of unit #208
20 

Since the temperature difference between the two floors was not exactly zero, the intermediate 

surface between them was not truly adiabatic. It is likely that a significant amount of heat from 

the first-floor units was transferred to the second-floor units. This provides a probable 

explanation as to why the model overpredicted energy use on all three second-floor apartments. 

5.1.3 Predicted Electric Loads

Besides making predictions for major appliances, lighting, and heating, ventilation, and air 

conditioning, energy modeling software estimates miscellaneous electric loads (MELs). These 

electric loads include plug-in electronics such as televisions, computers, and microwaves. There 

are various methods for estimating this electricity use, however, energy modeling typically 

makes predictions that are heavily influenced by FFA and number of bedrooms (#BR) in the 

home. For instance, the MEL estimate for a gas/electric household in BEopt is based on the BA 

House Simulation Protocol developed by NREL and is calculated with the linear equation form 

that is indicated in Equation 1 (Hendron and Engebrecht 2010). 

MEL MEL ( ) ( ) BR # FFA. Total = Baseline + C1 + C2 . (1) 

C1 is a coefficient in the units of MEL kWh/#BR, and C2 is a coefficient in the units of MEL 

kWh/FFA. This equation assumes that all units use at least a baseline MEL of 1,595 kWh/yr 

(barring unit size or occupancy levels and based on 100% of benchmark use).1 To put this into 

perspective, the actual 2008 total annual pre-retrofit electricity use of the one-bedroom units in 

this study averaged only 2,462 kWh, while the BEopt model predicted an average MEL of 2,038 

kWh. Hence, even before major appliances and lights have been factored into the modeling 

estimates, the model already accounted for 83% of the actual pre-retrofit energy usage. It is 

important to note that this equation was developed to model single-family homes. As a result, 

using it to estimate electricity usage in smaller multifamily apartments may be inappropriate. 

Further research needs to be carried out in order to develop an appropriate MEL equation that 

can be used to accurately describe energy usage in small multifamily units.

In order to quantify the baseline electrical energy use, actual and predicted pre-retrofit electricity

usage for two units was plotted against outdoor air temperature (OAT). Total baseline electricity

use is referred to as the building’s electricity use that is independent of heating or cooling 

consumption. Figure 16 demonstrates this relationship for unit #106. As seen from modeling 

predictions, baseline energy appears to decrease as OAT increases. This trend is a factor of 

increased lighting needed during winter months rather than electrical energy needed for 

supplemental heat. From actual use, it appears that this tenant used a supplemental electric 

resistance heater during cold OATs. As a result, these occurrences were excluded from the 

baseline calculation. 

 

1 Energy usage of a benchmark home is structured after a typical 2009 International Energy Conservation Code-built 

home
21 

Figure 16. Electricity usage rate versus OAT for #106

Alternatively, Figure 17 shows electricity usage rates for unit #208. The large electricity usage 

spike that occurs during hot temperatures (> 75o

F) is likely a result of the tenant using a window 

air conditioning unit. These occurrences were also excluded from the baseline energy estimates. 

As opposed to unit #106, the model was able to accurately predict baseline energy use for this 

unit. The increased accuracy may be dependent on the fact that this is one of the larger units in 

the study (~650 ft2 and 2 ); therefore more directly correlating with the electrical usage trends of 

the benchmark home. 

Figure 17. Electric usage rate versus outdoor air temperature for #208

After removing supplemental electric heating and cooling use with the filtering method shown 

above, the average kWh/day baseline electrical use was determined for each unit. These rates 

ranged from 2.9 to 13.2 kWh/day. There is a large variation between the units, indicating the 

significant impact that tenant behavior has on energy usage. This rate was then used to estimate 

annual baseline usage and was compared to BEopt’s predictions. The agreement between actual 

0

2

4

6

8

10

12

14

0 10 20 30 40 50 60 70 80 90

kWh/day

Average Outdoor Air Temperature [oF]

#106: Actual

Use

#106: Beopt

Predicted

0

5

10

15

20

25

30

0 10 20 30 40 50 60 70 80 90

kWh/day

Average Outdoor Air Temperature [oF]

#208: Actual

Use

#208: Beopt

Predicted

Speculated 

electric 

resistance 

heater use

Baseline 

energy use

Air conditioner use Baseline 

energy use
22 

and predicted baseline use is shown in Figure 18. As seen from this graph, a majority of the 

baseline use is overpredicted. On average, BEopt total baseline energy use overpredicted

electricity usage by 49% for these small attached units. Further disaggregation of the baseline 

energy use was not possible with just utility bill data. 

Figure 18. Comparison of predicted and actual baseline electrical energy use

While MEL energy use is often hard to predict, because it is highly dependent on the individual 

homeowner, past research has shown that MELs are expected to consume 29% of total electric 

load and the percentage is expected to grow to 36% by 2020 (Roth et al. 2008). However, 

predicted MELs for these units account for more than 50% of the predicted total baseline 

electricity consumption. Based on this fact and the results of this study, further analysis into the 

appropriateness of the BA house simulation protocol assumptions for MELs for multifamily 

apartments seems warranted.

5.1.4 Occupant Behavior

One of the most important influences on a building’s energy use is the tenant’s behavior. 

Unfortunately, this is often one of the most difficult factors to predict. Energy simulation 

software makes assumptions on tenant-influenced factors such as indoor temperature set points 

and occupancy schedules. Any changes to these assumptions by the occupants will significantly 

skew the results. For instance, indoor set point was assumed to be 71o

F for all units in this 

analysis. If some units were kept at a lower set point (which is likely), the model would have 

overpredicted natural gas usage. During the spot audits, it was noted that several thermostats 

were set lower than 71°F. They were not programmable thermostats though, and therefore, it 

could not be determined what their normal settings were. Only one occupant was available the 

day of the audit to answer these types of questions, and indicated that the thermostat was kept at 

68°F most of the time. This tenant also indicated that, even while his heat wasn’t working at the 

1,066

2,152

2,825

4,826

2,845 2,812

3,881

4,362

3,881

4,476

3,953 3,992

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

#106 #108 #121 #208 #211 #215

Annual kWh

Actual Usage:

Baseline (E)

Beopt Model:

Lights (E)

Beopt Model:

Lg. Appl. (E)

Beopt Model:

Misc. (E)
23 

time of the audit, the apartment never really got very cold in the winter. He did have a room air 

conditioner in his apartment, but indicated that he rarely used it. As a matter of record, several of 

the students had their thermostats completely turned off the day of the audits despite the OAT 

being only 30°F.

Overall, because this housing complex is primarily inhabited by students who are extremely 

transient, the energy improvements have not resulted in reduced occupancy turnover. The 

management company indicated that, while it has no problem renting the units, the ultimate goal 

of many of the students is to eventually find housing in a more desirable location in one of 

Boulder’s more popular areas. Evaluating the effects of energy improvements on occupant 

turnover would be better answered in a multifamily building where turnover is more typical. The 

fact that there were different tenants during the pre- and post-retrofit case introduces an 

additional source of error to the study’s results. 

5.1.5 Measuring Infiltration in Attached Dwellings

Some of the differences between the energy model and actual energy usage may have evolved 

from the way that infiltration was measured and modeled. For this analysis, unguarded blower 

door tests were performed during the pre- and post-retrofit cases. As a result, air leakage values 

were collected and modeled under the assumption that all measured air leakage was through 

surfaces exposed to the outdoors. However, for units with shared walls, some of the leakage 

was most likely between the conditioned spaces and would not directly attribute to heating 

energy use. 

In a previous study, CARB analyzed the relationship between guarded and solo blower door 

testing. The intent of this study was to investigate an approach for developing an algorithm that 

helped indicate the corresponding guarded blower door leakage based on a solo leakage value 

and a collection of variables (unit location, exposed wall area, common wall area, etc.). This 

study suggested that for retrofit work, if total leakage is assumed to be all to the outside, the 

energy benefits of air sealing can be significantly over-predicted. (Faakye et al. 2013). 

In this study, the total gas savings were overpredicted by 2.5 percentage points. This 

overprediction may have stemmed from an overprediction in infiltration due to the testing 

method chosen. For instance, results from the unguarded blower door measurements show that 

infiltration was reduced from 17.1 ACH50 to 12.8 ACH50 (a net reduction of 4.3 ACH50). The 

algorithm developed by CARB (still a work in progress) suggests that the actual leakage to the 

exterior may have only been 70% of the leakage measured during the unguarded test. This would 

mean that the true reduction in air leakage due to the retrofits may have been closer to a 

reduction from 11.97 ACH50 to 8.96 ACH50 resulting in a net reduction of only 3 ACH50. 
24 

6 Conclusions

Given the age and number of existing multifamily housing units in the United States, there is the 

potential for substantial reductions in building energy use if improvements are made. The first 

step to achieving this reduction is the accurate modeling and prediction of the buildings’ existing 

consumption. If accurate models are not available, designers, property owners, and 

administrators of efficiency programs will not have the ability to make the most cost-effective, 

impactful recommendations and decisions. This project attempted to provide insight into the 

predicted versus actual energy use of a 37-unit, multifamily apartment complex in climate zone 

5B. Utility bill analysis, spot audits, and modeling have led to the following conclusions. 

Q. For this building complex in Boulder, Colorado, what are the actual energy savings 

realized from the improvements compared to the predicted savings?

A. Savings from the retrofit measures were calculated on an individual apartment basis and for 

all six apartments together. Total savings for the six units showed excellent agreement between 

predicted and actual values for both gas and electricity use. Total gas and electricity savings for 

the six apartments were predicted to be 61% and 27%, respectively. Actual gas and electricity

savings were 58% and 30%, respectively. 

Q. Can discrepancies between modeled bills and actual bills be explained? If so, are 

changes to the accepted modeling assumptions needed?

A. Differences between predicted and actual savings for the individual apartments varied more 

widely than differences between the totals of all six apartments. Actual natural gas savings and 

predicted savings vary significantly between units. The model did not consistently over- or 

underpredict gas use for the six units when analyzed individually. The only consistent trend 

displayed is a consistent decrease in actual gas usage between pre-and post-retrofit periods for 

every unit, though the magnitude of that savings varies significantly. 

No discernible patterns in the predictions for gas use were found. There were both under- and 

overpredictions for the apartments and the magnitude of those predictions varied widely as well.

Discrepancies between predicted and actual bills may have arisen through a variety of sources

and were discussed in detail in the previous section. The most significant factor may be that all 

of these units were occupied by two different tenants during the two periods analyzed—2008 

and 2012. Considering that not one thermostat was set to 71°F during the spot audits, this room 

temperature assumption may need to be reevaluated. This activity is currently underway 

by NREL.

Unlike gas use, the energy model consistently overpredicted electricity use in all but one of the 

12 models. Researchers anticipate that the BA house simulation protocol assumptions for 

baseline electricity usage (lighting, appliances, and MELs) may be too high for small apartments. 

The appropriateness of these BA assumptions should be examined for multifamily housing units. 
25 

Q. What are the most common changes made by renters that will negatively impact postretrofit energy savings; e.g., replacing compact fluorescent lamps with incandescent light, 

installing window air conditioners? And what is the extent of that impact?

Several of the apartments had incandescent bulbs in the light fixtures, but not in every fixture. 

The largest percentage of incandescent to fluorescent noted was approximately 50%. Three of 

the nine units inspected had window air conditioners in the apartments, but none had more than 

one. No supplemental heaters were found in any unit despite the fact that the heating systems are 

convection, point-source heaters located in the living room. Very few post-retrofit changes were 

discovered. 
26 

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Energy Laboratory. Accessed January 19, 2013. www.nrel.gov/docs/fy11osti/50865.pdf

RECS. (2009). Table HC10.15 Average Square Footage of Multifamily Homes, By Housing 

Characteristics, 2009. Residential Energy Consumption Survey. Assessed January 6, 2013. 

www.eia.gov/consumption/residential/data/2009/ 

Roth, K.t; Mckenney, K.; Paetsch, C., Ponoum, P.; TIAX LLC. (2008). “U.S. Miscellaneous 

Electric Loads Electricity Consumption.” ACEEE Summer Study on Energy Efficiency in 

Buildings. Accessed January 15, 2012. 

www.aceee.org/files/proceedings/2008/data/papers/9_62.pdf
27 

Appendix A. Pre-Retrofit Utility Data

Unit Usage month Read date Days Therms HDD Gas bill Read date Days KWh KWh bill Therm/HDD KWh/day

Nov 12/4/2008 31 32 713 $ 35.71 12/3/2008 30 90 16.05 $ 

Oct 11/3/2008 31 31 363 $ 34.22 11/3/2008 31 90 16.09 $ 

Sep 10/3/2008 29 17 102 $ 22.61 10/3/2008 29 88 17.58 $ 

Aug 9/4/2008 31 10 43 $ 22.62 9/4/2008 31 106 19.87 $ 

Nov 12/4/2008 31 4 713 $ 14.86 12/3/2008 30 196 $ 26.06

Oct 11/3/2008 31 5 363 $ 15.49 11/3/2008 31 166 $ 23.24

Sep 10/3/2008 29 4 102 $ 14.41 10/3/2008 29 142 $ 23.71

Aug 9/4/2008 31 5 43 $ 17.40 9/4/2008 31 217 $ 32.69

Nov 12/4/2008 31 37 713 $ 39.46 12/3/2008 30 300 35.91 $ 

Oct 11/3/2008 31 24 363 $ 29.17 11/3/2008 31 255 31.61 $ 

Sep 10/3/2008 29 10 102 $ 18.16 10/3/2008 29 230 33.68 $ 

Aug 9/4/2008 31 2 43 $ 13.94 9/4/2008 31 245 35.93 $ 

Nov 12/4/2008 31 42 713 $ 43.16 12/3/2008 30 369 42.40 $ 

Oct 11/3/2008 31 31 363 $ 34.21 11/3/2008 31 374 42.85 $ 

Sep 10/3/2008 29 7 102 $ 16.26 10/3/2008 29 388 51.59 $ 

Aug 9/4/2008 31 5 43 $ 16.98 9/4/2008 31 581 74.78 $ 

Nov 12/4/2008 31 36 713 $ 38.71 12/3/2008 30 178 24.39 $ 

Oct 11/3/2008 31 18 363 $ 24.85 11/3/2008 31 224 28.70 $ 

Sep 10/3/2008 29 4 102 $ 14.37 10/3/2008 29 260 37.08 $ 

Aug 9/4/2008 31 0 43 $ 11.88 9/4/2008 31 517 67.36 $ 

Nov 12/4/2008 31 18 713 $ 25.29 12/3/2008 30 264 32.46 $ 

Oct 11/3/2008 31 14 363 $ 21.95 11/3/2008 31 218 28.15 $ 

Sep 10/3/2008 29 7 102 $ 19.86 10/3/2008 29 257 51.67 $ 

Aug 9/4/2008 9 0 15 * 9/4/2008 9 40 $0

0.05 9.74

#106

#108

#121

#208

8.51

14.15

#211

0.07

0.07

0.06

#215 0.03 7.87

3.09

0.01 5.96

* Bill was added to following month
28 

Appendix B. Post-Retrofit Utility Data

Unit Usage month Read date Days Therms HDD Gas bill Read date Days KWh KWh bill Therm/HDD KWh/day

Nov 12/4/2012 33 4 614 15.15 $ 12/3/2012 33 138 23.13 $ 

Oct 11/1/2012 29 3 455 14.57 $ 10/31/2012 29 136 22.93 $ 

Sep 10/3/2012 29 3 79 30.96 $ 10/2/2012 29 137 59.58 $ 

Aug 9/4/2012 32 8 0 * 9/3/2012 29 152 *

Nov 12/4/2012 33 0 614 12.81 $ 12/3/2012 33 137 23.03 $ 

Oct 11/1/2012 29 0 455 12.80 $ 10/31/2012 29 138 (8.03) $ 

Sep 10/3/2012 29 1 79 13.08 $ 10/2/2012 29 250 48.66 $ 

Aug 9/4/2012 32 0 0 $ 12.63 9/3/2012 29 257 49.27 $ 

Nov 12/4/2012 33 16 614 20.73 $ 12/3/2012 33 253 35.38 $ 

Oct 11/1/2012 29 14 455 19.61 $ 10/31/2012 29 242 34.21 $ 

Sep 10/3/2012 29 12 79 16.78 $ 10/2/2012 29 286 37.29 $ 

Aug 9/4/2012 32 13 0 $ 17.06 9/3/2012 29 328 41.40 $ 

Nov 12/4/2012 33 4 614 15.15 $ 12/3/2012 33 213 31.14 $ 

Oct 11/1/2012 29 5 455 15.74 $ 10/31/2012 29 179 27.48 $ 

Sep 10/3/2012 29 3 79 13.97 $ 10/2/2012 29 285 37.20 $ 

Aug 9/4/2012 22 4 0 $ 11.01 9/3/2012 21 209 35.21 $ 

Nov 12/4/2012 33 18 614 23.42 $ 12/3/2012 33 60 14.82 $ 

Oct 11/1/2012 29 10 455 18.69 $ 10/31/2012 29 64 15.23 $ 

Sep 10/3/2012 29 8 79 16.23 $ 10/2/2012 29 77 16.18 $ 

Aug 9/4/2012 24 6 0 $ 12.71 9/3/2012 23 74 22.18 $ 

Nov 12/4/2012 33 4 614 15.15 $ 12/3/2012 33 103 19.36 $ 

Oct 11/1/2012 29 3 455 14.57 $ 10/31/2012 29 111 20.25 $ 

Sep 10/3/2012 29 3 79 13.97 $ 10/2/2012 29 133 21.84 $ 

Aug 9/4/2012 12 1 0 $ 5.49 9/3/2012 11 54 16.83 $ 

7.91

0.04 2.41

0.01 3.93

4.69

0.001 6.52

0.05 9.24

0.016

0.01

#215

#106

#108

#121

#208

#211

* Bill was added to following month
29 

Appendix C. Post-Retrofit Audit Summary

Unit # Type 

Utility 

Bill 

Data?

Floor 

Area 

(ft2)

# 

Attached 

Walls

# 

Bedrooms 

103 First-floor end apartment – no 

apartment above No 433.3 1 1 

106 First-floor middle unit – 

conditioned space above Yes 433.3 2 1 

108 First-floor middle unit – 

conditioned space above Yes 573.0 2 2 

117 First-floor middle unit – 

conditioned space above No 433.3 2 1 

119 First-floor end unit – no 

apartment above No 729.5 1 3 

121 First-floor end apartment – no 

apartment above Yes 433.3 1 1 

208 Second-floor middle unit – 

conditioned space below Yes 647.3 2 2 

211 Second-floor end unit Yes 490.4 1 1 

215 Second-floor middle unit Yes 490.4 2 1 
30 

Appendix D. Spot Audits: Tenant Modification Assessment

Unit #

Lighting Space 

Conditioning 

Modifications 

Faucet 

Flow Rate

(GPM) 

ACH 50 Compact 

Fluorescent 

Lamps

Incandescent

103 8 0 N/A N/A 10.6

106 N/A N/A N/A - 12.8

108 13 4 N/A <1.5 10.3

117 5 0 A/C in living 

room

<1.5 N/A

119 9 8 A/C in bedroom <1.5 8.4

121 5 1 A/C in bedroom N/A 15.8

208 5 3 Fans in rooms <1.5 8.8

211 11 0 none <1.5 10.1

215 5 2 none <1.2 12.8
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