Conference Paper

NREL/CP-550-47994

August 2010

Identifying Inefficient 

Single-Family Homes 

With Utility Bill Analysis

Preprint

Sean Casey, Moncef Krarti

University of Colorado, Boulder

Marcus Bianchi, David Roberts

National Renewable Energy Laboratory

Presented at ASME 2010 4th International Conference on 

Energy Sustainability (ES2010)

Phoenix, Arizona

May 17–22, 2010
NOTICE

The submitted manuscript has been offered by an employee of the Alliance for Sustainable Energy, LLC 

(ASE), a contractor of the US Government under Contract No. DE-AC36-08-GO28308. Accordingly, the US 

Government and ASE retain a nonexclusive royalty-free license to publish or reproduce the published form of 

this contribution, or allow others to do so, for US Government purposes.

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any 

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

usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not 

infringe privately owned rights. Reference herein to any specific commercial product, process, or service by 

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

recommendation, or favoring by the United States government or any agency thereof. The views and 

opinions of authors expressed herein do not necessarily state or reflect those of the United States 

government or any agency thereof.

Available electronically at http://www.osti.gov/bridge

Available for a processing fee to U.S. Department of Energy

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information

P.O. Box 62

Oak Ridge, TN 37831-0062

phone: 865.576.8401

fax: 865.576.5728

email: mailto:reports@adonis.osti.gov

Available for sale to the public, in paper, from:

U.S. Department of Commerce

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

phone: 800.553.6847

fax: 703.605.6900

email: orders@ntis.fedworld.gov

online ordering: http://www.ntis.gov/ordering.htm

Printed on paper containing at least 50% wastepaper, including 20% postconsumer waste
 1 

Proceedings of ASME 2010 4th International Conference on Energy Sustainability 

ES2010 

May 17-22, 2010 Phoenix, Arizona, USA 

ES2010-90431 

IDENTIFYING INEFFICIENT SINGLE-FAMILY HOMES 

WITH UTILITY BILL ANALYSIS 

Sean Casey LEED AP, Moncef Krarti, Ph.D., PE, LEED AP 

Department of Civil, Environmental, and Architectural Engineering, University of Colorado at Boulder 

Engineering Center ECOT 441, UCB 428, Boulder, CO 80309 

Marcus Bianchi, Ph.D., David Roberts PE 

Electricity, Resources & Building Systems Integration Center 

 National Renewable Energy Laboratory, 1617 Cole Boulevard, Golden, Colorado, 80401

ABSTRACT 

Differentiating between energy-efficient and inefficient singlefamily homes on a community scale helps identify and 

prioritize candidates for energy-efficiency upgrades. 

Prescreening diagnostic procedures can further retrofit efforts 

by providing efficiency information before a site-visit is 

conducted. We applied the prescreening diagnostic to a 

simulated community of homes in Boulder, Colorado and 

analyzed energy consumption data to identify energy-inefficient 

homes. 

 

A home is defined as efficient if it is compliant with the 

prescriptive measures of the 2009 International Energy 

Conservation Code (IECC-2009) for Boulder, Colorado. 

Previous research indicates a correlation between building 

operational efficiency and the Heating Slope (HS) regression 

parameter resulting from the variable-base degree day method. 

We compared the HS values across a community of houses and 

those of an IECC-2009-compliant home to identify energyinefficient homes on a community-scale. To simulate 

community-wide HS identification, we used DOE-2 energy 

simulation software for defined home archetypes and 

corresponding occupant behavior to artificially generate 567 

sets of monthly natural gas consumption data Home archetypes 

were either compliant or non compliant at three conditioned 

areas; occupant effects were also simulated. Each simulation 

produced twelve months of natural gas use data. We used 

monthly energy consumption datasets to estimate the HS values 

with regression analysis and sorted the homes based on HS 

values. 

INTRODUCTION 

The U.S. residential sector consumed 11.3 quads (11.9 EJ) in 

2008, 11% of national annual consumption. This figure is 

projected to increase 5% by 2030 [1]. Improvements in 

construction practices and building codes will increase the 

energy-efficiency of new buildings, but energy retrofits can 

effectively reduce energy consumption and carbon dioxide 

emissions. As more attention is paid to building energyefficiency, community-scale conservation programs must 

identify energy-inefficient homes and retrofit opportunities. 

Comprehensive energy audits traditionally fill these roles, but 

readily available information such as energy consumption data, 

conditioned building area, and local daily weather data can be 

used in a pre-screening diagnostic tool to identify energyinefficient homes before a site visit is conducted. Further, the 

prescreening information obtained can aid the analyst infer 

design and operational characteristics. The crux of the analysis 

consists of inversely modeling utility data and inferring 

efficiency characteristics of the homes from the model 

parameters. 

Previous work has addressed the physical significance of model 

parameters resulting from inverse modeling [2-4]. Further, the 

variable base degree-day (VBDD) is a suitable prescreening 

tool to help identify retrofit candidates in commercial buildings 

[5]. Raffio et al. has shown that the proactive use of inverse 

modeling methods can identify retrofit candidates when 

compared across multiple buildings [6]. Our process is similar, 

but uses the VBDD method instead of a change point model, 

compares VBDD parameters to building simulation inputs, 

distinguishes homes that are compliant with 2009 International 

Energy Conservation Code (IECC-2009) from those that are 

noncompliant, and identifies specific design and operational 

characteristics. 
 2 

PRESCREENING PROCESS DEVELOPMENT 

The prescreening methodology is based on heating slope (HS) 

ranking of homes and uses a four-step process to test whether 

energy-inefficient homes can be differentiated from energyefficient homes: 

1. Collect electricity and natural gas consumption data 

for the analysis community. DOE-2, a detailed whole 

building simulation tool, was used to simulate the 

community’s consumption data [7]. 

2. Create VBDD models for each home. 

3. Calculate the HS metrics and rank the area-normalized 

model parameters for each home. 

4. Compare the HS ranking to the simulation inputs by 

checking for IECC-2009 compliance. 

Step 1 –Monthly Consumption 

In the absence of real utility data, the goal is to simulate 567 

“synthetic” utility bills in DOE-2. Each consists of 12 months 

of natural gas consumption. To simulate each home’s annual 

energy consumption, we defined the construction 

characteristics including thermal, operational, and mechanical 

information, as well as occupant effects, to define thermostat 

behavior and miscellaneous loads. Homes were classified into 

three possible size archetypes: 

• A 1058-ft2

 (95-m2) one-story ranch with crawlspace 

• A 2116-ft2 (190-m2

) one-story ranch with conditioned 

basement 

• A 3174-ft2 (285-m2

) two-story home with conditioned 

basement. 

For each archetype, three possible construction conditions were 

defined [8]: 

• Compliant with IECC-2009 for residential buildings in 

Climate Zone 5b (Boulder, Colorado) 

• Moderately below code 

• Substantially below code. 

For each home, three occupant behavior schedules were 

defined: 

• High user 

• Medium user 

• Low user. 

Figure 1 presents a schematic of the combinatorial process that 

produces 567 DOE-2 simulation input files. Each file was 

simulated using TMY3 weather data for Boulder, Colorado, and 

resulted in an annual simulation of hourly energy use. Postprocessing summed hourly energy use to form monthly totals 

 Figure 1: Diagram of energy simulation process 

that represent a typical monthly utility bill. The hourly 

temperatures for the TMY3 weather file were averaged to form 

365 daily average ambient temperatures. The synthetic utility 

bills and the daily average ambient temperatures are used in 

Step 2. 

Step 2 – Variable Base Degree-Day Modeling 

Single-family homes can be treated as single-zone buildings, 

where space heating energy use maintains the thermostat setpoint temperature. The heat balance is: 

Qሶ ൌ BLCሶ · ൫Tୱ୮ െ Tୟ൯െQሶ

୧୬୲,ୱ୭୪, (1) 

where: 

Qሶ = Space-conditioning heating Rate, Btu/h (W) 

BLCሶ = Sum of building UA and infiltration, Btu/h-°F 

(W/°C) 

Tୱ୮ = Set-point temperature, °F (°C) 

Tୟ = Ambient temperature, °F (°C) 

Qሶ

୧୬୲,ୱ୭୪ = Rate of internal and solar heat gains, Btu/h (W). 

The BLCሶ is the sum of building enclosure properties, which 

include all UሶA products and infiltration loads (seen in Eq. 2): 

BLCሶ ൌ UሶAୟ୲୲୧ୡ൅UሶA୵ୟ୪୪ୱ൅UሶA୵୧୬ୢ୭୵ୱ൅UሶA୤୭୳୬ୢୟ୲୧୭୬ ൅ mሶ c୮, (2) 
 3 

where U is the thermal transmittance per unit area in units of 

Btu/h-°F-ft2

 (W/°C-m2), A is the heat transfer area in units of ft2 

(m2), and mሶ c୮ is the product of the air infiltration rate and 

specific heat of air. 

It is useful to define an additional temperature, where no 

additional heat is required to maintain the temperature setpoint. This is known as the balance-point temperature, Tb, and 

is: 

Tୠ ൌ Tୱ୮ െ Qሶ

୧୬୲,ୱ୭୪⁄BLCሶ . (3) 

The result is a range of ambient temperatures, below the 

thermostat set-point temperature, where no space-conditioning 

energy is required. Instead, internal and solar gains provide the 

heat necessary to maintain the thermostat set-point temperature. 

Previous work has shown that the thermostat set-point and 

enclosure insulation properties determine the balance-point 

temperature [9]. Thus, the balance-point temperature is a 

combination of specific operational (such as thermostat) and 

specific design (such as insulation) properties. 

As the ambient temperature decreases, the building’s heating 

system provides the necessary make-up energy to balance 

system losses. To model this energy use behavior, the VBDD 

method was developed, featuring an algorithm that calculates a 

best fit balance-point temperature based on regression 

coefficients between energy use and degree-days. Specifically, 

the linear proportionality between monthly heating energy use 

and monthly heating degree-days (HDDs) is suitable for 

residential buildings where the heating load never exceeds the 

capacity of the heating system and consumption data are widely 

available in monthly intervals (see Eq. 4). 

Y ൌ α ൅ β · HDDሺTୠሻ (4) 

where α is the monthly base-load energy use, β is the HS, and 

HDDሺTୠሻ quantifies the heating degree-days calculated to the 

balance-point temperature. In an analysis period of n days, 

heating degree-days are the sum of the positive temperature 

differences for this period: 

HDDሺTୠሻ ൌ ෍ሺTୠ െ Tୟሻା

୬

୧ୀଵ

(5) 

where Ta is the average daily ambient temperature. For 

example, to calculate the degree-days for December, one would 

sum the positive temperature differences for each days. 

Generally, winter months have lower daily average 

temperatures than summer months and thus, have more HDDs. 

Conventionally, degree-days are calculated to a balance-point 

temperature of 65Ԭ (18°C), but in VBDD modeling, an 

iterative process is used to calculate a best-fit balance-point 

temperature. To find the best fit, the VBDD method uses the 

following steps: 

1. Choose an initial balance-point temperature of 40Ԭ

(4°C). 

2. Calculate the degree-days for each of the 12 months. 

3. Regress the monthly energy use with the monthly 

degree-days at the balance-point temperature. 

4. Record the regression R2

. 

5. Repeat the process with the next balance-point 

temperature. 

The upper balance-point boundary is set at 70Ԭ (21°C). After 

completing all regressions, the algorithm will: 

1. Select the optimal balance-point temperature with the 

highest R2

. 

2. Report the regression parameters. 

3. Continue the analysis on the next 12-month data set. 

We performed VBDD regressions on all datasets; the results are 

used in Step 3. Figure 2 shows analysis results from three 

example VBDD regressions. Each is performed on 12 months 

of simulated natural gas consumption versus monthly HDDs to 

the individual best-fit balance-point temperature. The listed 

balance-point temperatures illustrate differing values from 

home to home. The regressions show the amount of heating 

energy needed per month to maintain the thermostat set-point 

and those with smaller slopes use less heating energy and 

indicate efficient homes. Favorable R2

 values were found for all 

567 regressions: 97% were at least to 0.98. 

Figure 2: VBDD linear regressions of natural gas consumption and heating 

degree-days for three simulations with different best-fit balance-point 

temperatures 

0

100

200

300

400

500

600

0 200 400 600 800 1000 1200

Monthly Natural Gas Use [therms]

Monthly Heating Degree-Days [0F-day]

High Slope Med Slope Low Slope

Tb= 62°F 64°F 61°F 
 4 

Step 3 – Heating Slope Metric 

The regression parameter β from the VBDD models in Step 2 

was used as the HS metric. To test accuracies of different 

ranking metrics, we calculated two additional comparative 

metrics for each simulation. The first, fuel use (FU), is the 

annual sum of natural gas use. The second, fuel use per heating 

degree-days (FU/HDD65°F), is the FU metric divided by the 

number of HDDs based on TMY3 weather data, which are 

calculated for a balance-point temperature of 65°F, (5781°Fday/3194°C-day for Boulder, Colorado) [10]. This is a 

conventional weather-normalized metric for comparing energy 

use between homes. FU/HDD65°F is: 

FU/HDD 65Ԭ ൌ FU

∑౤ ሺ଺ହԬିT౗ሻశ ౟సభ

. (5) 

Step 4 – Test: Identifying IECC 2009-Compliant Homes 

The final step shows the accuracy of the HS ranking in 

identifying energy-efficient homes in a Boulder, CO 

community. Before ranking the homes, we separated them into 

the three archetypes and identified them as either compliant or 

incompliant with IECC-2009, based on simulation inputs. For 

each archetype, a HS threshold was defined that serves as a cutoff score for code compliance: any score above the threshold is 

labeled incompliant and vice-versa. The HS threshold for each 

archetype was defined as the highest HS value for a compliant 

home and any simulation with a HS equal or less than the 

threshold is assumed to be compliant. The experiment was 

conducted over three home archetypes, so each has a unique HS 

threshold (see Table 1). 

Table 1: Summary of HS threshold for all archetypes 

For instance, if a one-story ranch with crawlspace archetype 

simulation resulted in an HS of 450 Btu/h-°F (230 W/°C), 

according to Table 1, this exceeds the HS threshold and the 

house would be classified as incompliant. The primary 

assumption is that a home’s energy efficiency is related to its 

enclosure and mechanical energy efficiencies, which are 

directly related to the amount of space-conditioning energy 

consumption per household. The goal for HS ranking is to give 

a relative indication of the home’s energy efficiency. The 

metrics FU and FU/HDD65°F, along with threshold values, 

were calculated for each simulation along with threshold 

values. The results for ranking by each metric are applied to all 

567 simulations. Figure 3 shows those for the one-story ranch 

with crawlspace archetype. The black bars represent 

incompliant, inefficient homes and the gray bars represent 

compliant, efficient homes. The x-axis shows the 189 

archetype simulations, the y-axis shows the ranking metric, and 

Figure 3: Comparative results of HS, FU, and FU/HDD (650F) ranking for 

1-story ranch with crawlspace archetype 

the threshold for each metric is indicated as a red arrow. Table 

2 lists the threshold values for each ranking metric. 

Table 2: Summary of ranking threshold values for 1-story ranch with 

crawlspace archetype 

As seen in the one-story ranch with crawlspace archetype, both 

the FU and FU/HDD65°F rankings classify several codecompliant homes as incompliant. The same homes, however, 

were correctly identified by the HS ranking. Both had high 

base-load and miscellaneous gas use, which were disaggregated 

by the VBDD method, but not by the FU and FU/HDD65°F 

rankings. This highlights the strength of the VBDD method: it 

Archetype HS 

[Btu/h-°F] 

HS 

[W/°C] 

1-story with 

Crawlspace 365 186 

1-story with Basement 480 245 

2-story with Basement 1002 512 

Metric Type Threshold Value 

HS 365 [Btu/h-°F] 

FU 704 [Therms/year] 

FU/HDD(65°F) 12 [kBtu/°F-day] 

° 
 5 

disaggregates of weather-independent energy use from spaceconditioning energy use. 

Discussion 

The HS ranking identifies inefficient homes more accurately 

than do the FU and FU/HDD 65°F rankings. Table 3 shows a 

10% difference between ranking accuracies and the success rate 

of HS ranking in identifying IECC-2009compliant homes is 

favorable. One explanation for more favorable results using the 

HS ranking is that the VBDD modeling parameters contribute 

more information to the identification process. The VBDD 

method separates base loads from weather-dependent energy 

uses, which the other rankings cannot disaggregate. In practice, 

weather normalizing total FU to ambient temperatures helps 

correct for colder and warmer conditions compared across 

years, but does not add disaggregation to the analysis. An 

advantage to the VBDD method is that energy use can be 

attributed to different end uses, such as space-conditioning and 

base load. This enables heating performance to be 

characterized individually and compared across the community. 

An important issue is the assignment of a threshold value, as it 

is the primary identification criterion. In simulated 

communities, threshold values can be calculated from the 

known simulation inputs. In a “real world” application, the 

characteristics of the home are not well known, unless an 

extensive technical audit has been performed. 

Table 3: Summary of all ranking metric results 

Inferring Design Properties 

The HS of a VBDD regression is related to the BLC and the 

furnace efficiency: 

HS ൌ BLC ηfurnace ⁄ . (6) 

The BLC consists of the building enclosure properties 

including all building UA (attic, walls, windows, and 

foundation) and infiltration loads, mሶ c୮. The furnace efficiency, 

η୤୳୰୬ୟୡୣ, is the Annual Fuel Utilization Efficiency. Figure 4 

shows a linear proportionality of the average HS metric from 

the VBDD regressions for each given simulation BLC. Each 

property is a simulation variable defined in Step 1 of the HS 

ranking process (See Figure 1). 

 Figure 4: Comparison of HS and calculated simulation BLC 

Figure 5: Average balance-point temperatures compared to occupant 

archetypes 

Inferring Operational Properties 

We inferred building characteristics from the VBDD models. 

Figure 5 shows that the balance-point temperature correlates 

with occupant behavior. On average, the higher heating 

thermostat set-points result in higher balance-point values for 

all home archetypes. This shows one example of an operational 

characteristic that can be inferred from the VBDD regression 

parameters, namely, high thermostat set-points. As mentioned 

by Raffio et al. [6], regressions with high balance-point 

temperatures are candidates for programmable thermostats. 

The monthly base load, which is the VBDD parameter α, can 

also be inferred. High monthly base loads indicate hot tubs, 

energy-inefficient hot water heaters, and other large energyconsuming appliances. Figure 6 compares the different 

monthly base-load gas uses across the simulations and shows 

that homes with high base-load energy uses can be identified. 

y = 1.1667x - 33.097

R2 = 0.9791

0

750

1500

2250

0 750 1500 2250

HS [Btu/h-°F]

Simulation BLC [Btu/h-°F]

y=x Linear (HS vs. Simulated BLC)

15

16

17

18

19

60

61

62

63

64

65

66

67

1-Story Ranch 

with Crawlspace

1-Story Ranch 

with Basement

2-Story with 

Basement

Average Balance Temperature [°C]

Average Balance Temperature [°F]

High User Medium User Low User

Ranking Metric 

Number of 

Homes 

Identified 

Number of 

Homes 

Missed 

Overall 

Identification 

Accuracy 

HS Ranking 567 0 100% 

FU Ranking 513 54 90% 

FU/HDD 650F Ranking 511 56 90% 
 6 

Figure 6: Ranking of α parameter values for VBDD models 

Figure 7: VBDD regression results for three example single-family homes 

from Boulder, CO dataset 

Utility Bill Applications 

The VBDD method can be applied to empirical utility data and 

shows similar results. It was applied to a collection of actual 

electricity and natural gas utility bills for several single-family 

homes in Boulder, Colorado, using local daily weather data and 

monthly natural gas consumption. Figure 7 shows VBDD 

regressions for three example homes and the results illustrate 

the VBDD method’s applicability to actual utility data. 

SUMMARY, CONCLUSIONS AND FUTURE WORK 

Summary 

This study demonstrates a four-step prescreening diagnostic 

process in which a Heating Slope (HS) ranking is applied to a 

simulated community of homes. A whole-building detailed 

energy simulation tool was used to simulate 12 months of 

natural gas use for 567 homes for various home and occupant 

archetypes. Each simulation was labeled as compliant or 

incompliant with IECC-2009, given the home archetype and 

energy simulation inputs. VBDD models were estimated using 

TMY3 weather data and the simulated natural gas consumption. 

HS values were calculated from the VBDD modeling, along 

with comparative ranking metrics. Finally, the HS rankings 

were compared with the pre-ranking IECC-2009 compliance 

label to test the accuracy of using HS values to identify 

incompliant, energy inefficient homes. Additional 

characteristics were investigated including base-load energy use 

and thermostat set-points. 

Conclusions 

This study concluded that: 

• The HS ranking identified 100% of the inefficient 

homes, compared to 90% for FU and FU/HDD 65°F 

rankings. 

• High base-load natural gas users were identified. 

• Homes with high thermostat set-points were identified. 

• The HS ranking is an effective identification 

procedure for inefficient homes, as well as other 

design and operational characteristics. 

Further Studies 

Further investigations are needed to demonstrate the HS 

ranking of empirical utility data and to evaluate the 

effectiveness of identifying inefficient homes when energyefficiencies and operational characteristics are uncertain or 

unknown. Two difficulties are commonly encountered when 

analyzing empirical utility data: 

• While the occupant behavior is known in this study, it 

is often highly uncertain with empirical data. 

• Incomplete, missing, or erroneous monthly data may 

impact the ranking results. While this has a large 

effect on the annual FU, the HS ranking can still be 

applied with missing data because it relies on 

regressions, rather than sums. 

Future studies will include analysis on the effects of missing 

months and statistical outliers of consumption data. The HS 

ranking has been demonstrated on a heating-dominated climate 

and only on natural gas consumption. Further studies should be 

conducted in different weather conditions, including coolingdominated climates. 

ACKNOWLEDGMENTS 

The authors wish to thank Collin Tomb and Jim Logan of Jim 

Logan Architects, Dr. Paul Norton, Scott Horowitz, and Ben 

Polly of the National Renewable Energy Laboratory, and Dr. 

Andy Bardwell of Bardwell Consulting for their support and 

guidance. Financial support from NREL to conduct this 

preliminary study is gratefully acknowledged. 

0

100

200

300

400

0 200 400 600 800 1000 1200

Natural Gas Use [Therms/Bill]

Heating Degree-Days [°F-day] 

Home 1 Home 2 Home 3
 7 

REFERENCES 

[1] Energy Information Administration, 2009. Annual Energy 

Outlook 2010 Early Release with Projections to 2035, 

DOE/EIA-0383. 

[2] Fels, M., 1986. “PRISM: An Introduction”, Energy and 

Buildings, Vol. 9, pp. 5-18. 

[3] Kissock, K. Haberl J. and Claridge, D, 2003. “Inverse 

Modeling Toolkit (1050RP): Numerical Algorithms”, ASHRAE 

Transactions, Vol. 109. Part 2. 

[4] Reddy T.A., Deng S. and Claridge D.E., 1999. 

Development of An Inverse Method to Estimate Overall 

Building and Ventilation Parameters of Large Commercial 

Buildings, ASME Journal of Solar Energy Engineering, 

121(1):40-46. 

[5] Reynolds C., Komor P. and Fels M.F., 1990. “Using 

Monthly Billing Data to Find Energy Efficiency Opportunities 

in Small Commercial Buildings”, Proceedings of the ACEEE 

1990 Summer Study on Energy Efficiency in Buildings, 

Washington D.C., Volume 10, pp. 10.221—10.232. 

[6] Raffio, R., Isambert, O., Mertz, G., Schreier, C., Kissock, 

K., 2007. “Targeting Residential Energy Assistance”, 

Proceedings of the ASME 2007 Conference on Energy 

Sustainability, ES2007-36080. 

[7] Winkelmann, F.C.,B.E. Birdsall, W.F. Buhl, K.L. Ellington, 

A.E. Erdem, J.J. Hirsch, and S. Gates., 1993. “DOE-2 

Supplement Version 2.1E”, LBL-34947, November 1993, 

Lawrence Berkeley National Laboratory. Springfield, Virginia, 

National Technical Information Service. 

[8] International Code Council, 2009. “International Energy 

Conservation Code – 2009 Edition”. 

[9] Huang, J., Ritschard, R.L., Bull, J.C., Chang, L., 1987. 

“Climatic indicators for estimating residential heating and 

cooling loads”. ASHRAE 93, 72–111. 

[10] Wilcox, S. and W. Marion, 2008. User's Manual for TMY3 

Data Sets, NREL/TP-581-43156. April 2008. Golden, 

Colorado: National Renewable Energy Laboratory. 
F1147-E(10/2008)

REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, 

gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this 

collection of information, including suggestions for reducing the burden, to Department of Defense, Executive Services and Communications Directorate (0704-0188). Respondents 

should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a 

currently valid OMB control number.

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION.

1. REPORT DATE (DD-MM-YYYY)

August 2010

2. REPORT TYPE

Conference Paper

3. DATES COVERED (From - To)

4. TITLE AND SUBTITLE

Identifying Inefficient Single-Family Homes With Utility Bill Analysis: 

Preprint

5a. CONTRACT NUMBER

DE-AC36-08-GO28308

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

S. Casey, M. Krarti, M. Bianchi, and D. Roberts

5d. PROJECT NUMBER

NREL/CP-550-47994

5e. TASK NUMBER

ARRB.1100

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory

1617 Cole Blvd.

Golden, CO 80401-3393

8. PERFORMING ORGANIZATION

REPORT NUMBER

NREL/CP-550-47994

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)

NREL

11. SPONSORING/MONITORING

AGENCY REPORT NUMBER

12. DISTRIBUTION AVAILABILITY STATEMENT

National Technical Information Service

U.S. Department of Commerce

5285 Port Royal Road

Springfield, VA 22161

13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

Differentiating between energy-efficient and inefficient single-family homes on a community scale helps identify and 

prioritize candidates for energy-efficiency upgrades. Prescreening diagnostic procedures can further retrofit efforts 

by providing efficiency information before a site-visit is conducted. We applied the prescreening diagnostic to a 

simulated community of homes in Boulder, Colorado and analyzed energy consumption data to identify energyinefficient homes.

15. SUBJECT TERMS

energy-efficient; prescreening diagnostic; building america

16. SECURITY CLASSIFICATION OF: 17. LIMITATION 

OF ABSTRACT

UL

18. NUMBER 

OF PAGES

19a. NAME OF RESPONSIBLE PERSON

a. REPORT

Unclassified

b. ABSTRACT

Unclassified

c. THIS PAGE

Unclassified 19b. TELEPHONE NUMBER (Include area code)

Standard Form 298 (Rev. 8/98)

Prescribed by ANSI Std. Z39.18