NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Restaurant Energy Use 

Benchmarking Guideline 

Roger Hedrick and Vernon Smith

Architectural Energy Corporation

Boulder, Colorado

Kristin Field

National Renewable Energy Laboratory

Golden, Colorado

Subcontract Report

NREL/SR-5500-50547

July 2011
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

1617 Cole Boulevard

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Restaurant Energy Use 

Benchmarking Guideline 

Roger Hedrick and Vernon Smith

Architectural Energy Corporation

Boulder, Colorado

Kristin Field

National Renewable Energy Laboratory

Golden, Colorado

NREL Technical Monitor: Adam Hirsch

Prepared under Subcontract No. LGN-0-40011-01

The Restaurant Retrofit Prioritization Tool is available at:

http://www.nrel.gov/buildings/docs/restaurant_retrofit_prioritization_tool.xlsx 

Subcontract Report

NREL/SR-5500-50547

July 2011
This publication received minimal editorial review at NREL.

NOTICE

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/help/ordermethods.aspx

Cover Photos: (left to right) PIX 16416, PIX 17423, PIX 16560, PIX 17613, PIX 17436, PIX 17721

Printed on paper containing at least 50% wastepaper, including 10% post consumer waste.
iii

Acknowledgments 

The assistance of Michael Deru and Greg Stark at NREL and Don Fisher and David Zabrowski 

at Fisher-Nickel is most gratefully acknowledged. 
iv

Executive Summary

Architectural Energy Corporation (AEC), an energy and environmental research, development, 

and design consulting firm in Boulder, Colorado, prepared this document for the Alliance for 

Sustainable Energy, LLC, the operating entity for the U.S. Department of Energy National 

Renewable Energy Laboratory. The AEC Project Manager is Judie Porter.

Background

A significant operational challenge for food service operators is defining energy use benchmark 

metrics to compare against the performance of individual stores. Without metrics, multiunit 

operators and managers have difficulty identifying which stores in their portfolios require extra 

attention to bring their energy performance in line with expectations. Energy use per unit of 

floor area is highly variable across food service facility types; the single energy use intensity as 

defined for ENERGY STAR® Portfolio Manager would not be adequate to benchmark restaurant 

performance. Also, the variance in food service facility types was significant enough that 

developing metrics at the multiunit operator level would likely be more successful than industrywide metrics.

The size of the floor plate, by itself, is not typically an adequate normalizing factor. Over the 

past 20 years, the floor plate size has changed (often shrinking); the number of meals served at 

each store has simultaneously increased. Other variables, such as number of transactions (meals 

served equivalent), hours of operation, operational practices, and the number and type of 

appliances, have a discernable influence on energy use. The absence or presence of seating in 

conditioned space, location and customer traffic patterns, climate zone, absence or presence of 

automated control systems (time clocks, building energy management systems), facility type 

(stand-alone building, interior space in a larger building, etc.), type of walk-in refrigeration, and 

the amount of outside and parking lot lighting included in the utility bill are also energy use 

factors. 

Development Process

This report presents a method whereby multiunit operators may use their own utility data to 

create suitable metrics for evaluating their operations. It can be used to:

• Provide a high-level view of energy use for all stores. 

• Identify stores with high and low energy use. 

• Track changes in energy use metrics. 

The benchmarking procedure has three major steps. The first two comprise the high-level 

analysis we propose and will often suffice for a broad characterization of the multiunit operator’s 

portfolio. The third step can be added to conduct a more advanced analysis.

1. Collect data (store locations, annual electricity and gas energy use from utility bills, 

transactions, operating hours, floor area, store type, etc.). 

2. Use histograms and scatter plots to prepare statistical summaries by store type. 

3. Prepare multiple regression equations for predicting annual energy use. 
v 

The Benchmarking Guideline consists of this report and an example spreadsheet using Microsoft 

Excel 2003. It shows examples based on data from one multiunit operator. An analyst may use 

it as the starting point to develop a customized analysis. 

Evaluation Approach and Results

The results presented here and in the example spreadsheet are specific to the single dataset 

analyzed and do not suggest metric targets. Transaction data are usually confidential, so the 

example spreadsheet includes only normalized transactions.

Tracking energy use metrics over time will provide visibility into system-wide energy 

performance, identify the top energy users, and enable targeted programs to drive energy use 

toward programmatic energy targets. 

The procedure described here provides a starting point to develop a customized, in-house 

analysis and tracking system that will help multiunit operators understand how stores use energy. 

Customized procedures might include analysis of water use (hot water, irrigation, restrooms) and 

the influence of the number of parking lot lights. In some cases, submetering will have to be 

installed before the parameters can be extended. The procedure could also be extended to 

monthly utility use, particularly if automated data acquisition (through a billing service for 

example) and analyses are implemented. In any case, the procedure should enable multiunit 

operators to better evaluate restaurants in their portfolios and then prioritize investments in 

energy saving actions such as retrofits and operational improvements.
vi

Nomenclature

AEC Architectural Energy Corporation

ASHRAE American Society of Heating, Refrigerating and AirConditioning Engineers 

CBEA Commercial Building Energy Alliance

CDD cooling degree day 

DOE Department of Energy 

HDD heating degree day

HVAC heating, ventilation, and air conditioning

IQ interquartile

NIST National Institute of Standards and Technology 

NREL National Renewable Energy Laboratory 
vii

Contents

Acknowledgments.......................................................................................................................... iii

Executive Summary ....................................................................................................................... iv

Background ................................................................................................................................ iv

Development Process ................................................................................................................. iv

Evaluation Approach and Results ............................................................................................... v

Nomenclature .............................................................................................................................. vi

Figures and Tables ....................................................................................................................... viii

Figures ...................................................................................................................................... viii

Tables ......................................................................................................................................... ix

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

1.1 Factors Influencing Energy Use ....................................................................................... 1

1.2 Limitations of Annual Energy Use Data .......................................................................... 1

2.0 Step-by-Step Discussion of Energy Use Benchmarking Procedure ................................... 3

2.1 High-Level Performance Evaluation ................................................................................ 6

2.1.1 Step 1 ............................................................................................................................ 6

2.1.2 Step 2 ............................................................................................................................ 6

2.1.3 Step 3 ............................................................................................................................ 6

2.1.4 Step 4 ............................................................................................................................ 7

2.1.5 Step 5 ............................................................................................................................ 7

2.1.6 Step 6 .......................................................................................................................... 16

2.2 Advanced Performance Evaluation and Prediction ........................................................ 16

2.2.1 Step 7 .......................................................................................................................... 16

2.2.2 Step 8 .......................................................................................................................... 18

2.2.3 Step 9 .......................................................................................................................... 18

2.2.4 Step 10 ........................................................................................................................ 18

3.0 Summary and Additional Considerations ......................................................................... 22

4.0 References and Bibliography ............................................................................................ 23

Appendix A Histograms of Electricity and Natural Gas Use .................................................. 24

Appendix B Scatter Plots of Electricity and Natural Gas Use versus Normalized 

Transactions ......................................................................................................... 29

Appendix C Discussion of Heating Degree Days and Cooling Degree Days ......................... 34

C.1 Balance Point.................................................................................................................. 34

C.2 Variable Base Degree Days ............................................................................................ 34

C.3 Food Service Facility Balance Points ............................................................................. 34
viii

Figures and Tables 

Figures

Figure 2-1 Flow chart summary of high-level performance evaluation ...................................... 4

Figure 2-2 Flow chart summary of advanced performance evaluation and prediction ............... 5

Figure 2-3 Number of stores by annual electricity use .............................................................. 11

Figure 2-4 Number of stores by type by annual electricity use ................................................. 12

Figure 2-5 Example of annual electricity use scatter plot to show outliers ............................... 12

Figure 2-6 Example of annual natural gas use scatter plot to show outliers ............................. 13

Figure 2-7 Example annual electricity use box plot with inner fences ...................................... 14

Figure 2-8 Example annual natural gas use box plot with inner fences .................................... 15

Figure A-1 Histogram of electricity use by store type ............................................................... 24

Figure A-2 Histogram of natural gas use by store type .............................................................. 24

Figure A-3 Histogram of electricity use for store type A (197 stores) ....................................... 25

Figure A-4 Histogram of natural gas use for store type A (197 stores) ..................................... 25

Figure A-5 Histogram of electricity use for store type B (399 stores) ....................................... 26

Figure A-6 Histogram of natural gas use for store type B (399 stores) ...................................... 26

Figure A-7 Histogram of electricity use for store type C (86 stores) ......................................... 27

Figure A-8 Histogram of natural gas use for store type C (86 stores) ........................................ 27

Figure A-9 Histogram of electricity use for store type D (206 stores) ....................................... 28

Figure A-10 Histogram of natural gas use for store type D (206 stores) ..................................... 28

Figure B-1 Scatter plot of electricity use for all stores ............................................................... 29

Figure B-2 Scatter plot of natural gas use for all stores ............................................................. 29

Figure B-3 Scatter plot of electricity use for store type A ......................................................... 30

Figure B-4 Scatter plot of natural gas use for store type A ........................................................ 30

Figure B-5 Scatter plot of electricity use for store type B .......................................................... 31

Figure B-6 Scatter plot of natural gas use for store type B ........................................................ 31

Figure B-7 Scatter plot of electricity use for store type C .......................................................... 32

Figure B-8 Scatter plot of natural gas use for store type C ........................................................ 32

Figure B-9 Scatter plot of electricity use for store type D ......................................................... 33

Figure B-10 Scatter plot of natural gas use for store type D ........................................................ 33
ix

Tables

Table 2-1 Summary of Statistics by Store Type ......................................................................... 9

Table 2-2 Example Annual Kilowatt-Hour Data Range for Use in Histogram Function ........ 11

Table 2-3 Regression Equation Matrix Using Raw Data ......................................................... 19

Table 2-4 Regression Equation Matrix Using Processed Data ................................................ 20

Table 2-5 Regression Equation Matrix With CDDs and HDDs Added ................................... 21
1 

1.0 Introduction

The U.S. Department of Energy National Renewable Energy Laboratory is working with the 

Restaurant Project Team of the Commercial Building Energy Alliance to identify energy savings 

technologies, components, products, and operational strategies. A significant operational 

challenge for food service operators is defining energy use benchmark metrics to compare 

against the performance of individual stores. Without this, multiunit operators and managers 

have difficulty identifying which stores require extra attention to bring their energy performance 

in line with expectations.

Several years ago, a national restaurant subcommittee, working with Fisher-Nickel, Inc., 

attempted to define metrics that could be used for the ENERGY STAR® Portfolio Manager 

Program. The program requires energy use intensity defined as Btu/ft2

, which requires 

combining consumption values for commonly used power sources, such as electricity, natural 

gas, and propane. The committee found that energy use per unit of floor area was highly 

variable across food service facility types because of different facility types, differences in menu, 

number of meals served, cooking and refrigeration appliances, hours of operation, and many 

other factors (unpublished report). The variance was so wide that the subcommittee members 

concluded that the energy use intensity as defined for ENERGY STAR would not be adequate to 

benchmark energy use. In particular, separate metrics need to be used for each energy source. 

They also found that the variance in food service facility types was significant enough that 

developing benchmarks for a given multiunit operator would likely be more successful than the 

national level benchmarks found in ENERGY STAR Portfolio Manager. 

This report presents a method for multiunit operators to use their own utility data to create 

suitable benchmarks for evaluating their operations. It can be used to: 

• Provide a high-level view of energy use for all stores. 

• Identify stores with high and low energy use. 

• Track changes in energy use. 

1.1 Factors Influencing Energy Use

The size of the floor plate, by itself, is not usually adequate as a normalizing factor to fully 

characterize energy consumption. Over the past 20 years, the typical floor plate size has changed 

(often shrinking), and the number of meals served at each store has increased. Hours of 

operation, operational practices, and the number and type of appliances also have a discernable 

influence on energy use. The authors’ experience has shown that the absence or presence of 

seating in conditioned space, location and customer traffic patterns, climate zone, absence or 

presence of automated control systems (time clocks, building energy management systems), 

facility type (stand-alone building, interior space in a larger building, etc.), type of walk-in 

refrigeration, and the amount of outside and parking lot lighting included in the utility bill are 

also factors.

1.2 Limitations of Annual Energy Use Data

Annual energy use is convenient because it has 1/12 the data points of the monthly data from 

which it was derived. But convenience, in this case, hides information that could be used to 

diagnose why the energy use in a particular store is high or low. It also hides data accumulation 
2 

errors. For example, if one month of electricity use were missing for a particular store, it would 

not be apparent, and the resulting lower annual energy use would make that store look more 

energy efficient than its peers. Likewise, apparent high energy use may result from doubling up 

some monthly energy bills. Our scope of work includes analysis using annual energy numbers 

only; however, with careful modifications, one could extend it to include analysis of subannual 

periods. 
3 

2.0 Step-by-Step Discussion of Energy Use Benchmarking Procedure

This procedure consists of several distinct steps: Figure 2-1 shows these steps for the basic, highlevel analysis; Figure 2-2 shows the additional steps necessary to complete the more advanced 

analysis. Greater detail follows the figures.

The procedure is divided into these two analysis levels because the operators tasked with 

identifying restaurants in need of audits, inspections, and retrofits often face tight time 

constraints. In many instances, a quicker, high-level analysis will help the operator identify 

retrofit opportunities; however, some may benefit from the potential for greater accuracy in 

benchmarking and the creation of benchmarking equations that can predict energy consumption 

under varying operational conditions. 
4 

Figure 2-1 Flow chart summary of high-level performance evaluation
5 

Figure 2-2 Flow chart summary of advanced performance evaluation and prediction

In Figure 2-2, it is important to note that the R2 value does not represent the only statistical 

parameter that could be relevant to evaluating the strength of the linear regressions. This statistic 

is emphasized here because it is more widely known and is included in the example spreadsheet. 

Distinguishing which correlation fits the operator’s needs might be a function of the difference in 

R2

 values between two regressions instead of the two absolute values. For example, one 

hypothetical regression might combine the effects of transactions and weekly hours and result in 

an R2

 value of 38%. Another regression might add the effects of heating degree day (HDD) to 

those of transactions and weekly hours and result in an R2

 value of 39%. Including the HDD 

independent variable increased the R2

 value, and therefore the strength of the regression, but 
6 

perhaps not enough to justify the extra effort involved in obtaining the HDD values for multiple 

stores. The analyst might instead choose to focus on regressions based only on transactions and 

weekly hours. Whether or not the analyst feels confident using benchmarking equations with R2

values on the order of 38% is another matter; statisticians prefer R2 values closer to 70%–95%, 

but such correlation strengths may not be achievable without extra submetering or advanced 

energy management system efforts.

The following sections provide a discussion for each step in the procedure. The results are 

specific to the single dataset analyzed and do not suggest metric targets. Transaction data are 

usually confidential, so the example spreadsheet includes only normalized transactions. 

2.1 High-Level Performance Evaluation

Explanation is provided here for the steps illustrated in Figure 2-1. 

2.1.1 Step 1 

Gather data for electricity (kilowatt-hours and annual peak kilowatts), gas (therms, cubic feet, or 

hundred cubic feet), propane (therms, cubic feet, or hundred cubic feet), other utility bill 

information (such as water and sewer use in gallons or hundred cubic feet), and total cost for 

each utility type summarized annually in a spreadsheet.

2.1.2 Step 2 

Identify the analysis year. Determine annual totals by summing 12 consecutive monthly totals 

and adjusting for the number of included days. An analysis year consists of 365 consecutive 

days, whether or not the data were collected during a leap year. In the event that the 12 

consecutive months amount to slightly more or fewer than 365 days, the total should be adjusted 

by adding or subtracting average daily values symmetrically for the beginning and end months of 

the analysis year (Barley et al. 2005). 

The available data will likely contain errors and anomalies. At this stage, the data may be 

termed raw; after errors or anomalies are removed from the dataset, the dataset will be called 

processed. 

2.1.3 Step 3 

Gather the following information about each store: 

• Transactions (number of meals served equivalent for the same period as the utility bills) 

• Hours of operation 

• Building type (stand-alone, embedded, different brands/menu types, etc.) 

• Floor plate size (note use of net or gross floor area and whether floor area is conditioned) 

• Location (zip code or latitude and longitude) 

• Whether parking lot lighting is included in the utility bill. 

The most important data are the transactions, followed by hours of operation, location (climate), 

and building type. Other factors, such as whether parking lot lighting is included in the electric 

utility bill, also have an observable influence on energy use and help explain the variance in use 

among stores that are otherwise comparable.
7 

2.1.4 Step 4 

Using zip code or latitude/longitude data, add HDDs (Base 50°F) and cooling degree days 

(CDDs) (Base 65°F) for each store in the database. If degree day data using different bases are 

available, these may be used to see if a better regression data fit can be obtained. Appendix C 

includes more information on variable base degree day calculations and why Base 50°F works 

well for HDD and Base 65°F works well for CDD in this dataset. Ensure that there is a 

reasonable match between the days of the month represented by degree days and those 

represented by the utility data. 

This may be a time-consuming one-time task, but it is important because climate influences 

electricity consumption for cooling and natural gas use for heating. The example in the 

Guideline uses “normal degree day data,” which is degree day data based on a historical 

reference period. A more accurate approach would be to use actual CDD and HDD data for the 

actual analysis period, but these data may not be readily available for all locations. Furthermore, 

this step would need to be repeated if the analysis were undertaken each year. Commercial as 

well as governmental sources are available for summarized data. Some datasets are free; others 

are available for a fee. Degree day data can be obtained from the National Climatic Data Center 

(NCDC 2011).

The base temperature (50°F) suggested for the HDD is lower than that typically used for 

residential or commercial office analysis because food service operations have higher internal 

loads. (See Appendix C for further discussion of HDDs and CDDs.) 

2.1.5 Step 5 

Step five involves segregating and processing the data and reviewing basic statistics. For clarity, 

Step 5 consists of five substeps (5a–5e).

2.1.5.1 Step 5a

Segregate data by categorical variables: store type (stand-alone, etc.), whether or not parking lot 

lighting is included in the utility bills, etc. These can be labeled as type “A”, “B”, “C”, and “D.” 

At a minimum, concepts or construction prototypes (differences in menu, food process 

appliances, floor plate area, etc.) should be grouped into separate datasets. The data subsets 

should have at least 50 stores each; fewer than 50 stores do not provide an adequate statistical 

sample (NIST/SEMATECH 2011). Table 2–1 shows differences in energy use and normalized 

transactions for one multiunit operator’s data.

2.1.5.2 Step 5b

Prepare a summary of raw data, including maximum, minimum, mean (average), standard 

deviation, and count. 

This summary will provide some insight into the raw dataset, showing typical energy use and 

providing information about data extremes that may be incorrect data, or data that are not 

appropriate to include in the dataset. 

2.1.5.3 Step 5c

Prepare histograms and scatter plots of each energy type (see example spreadsheet).

The histograms and scatter plots will provide insight into the distribution of the energy data. 

This is important because certain statistical tests, to be applied in later steps, assume a normal 
8 

distribution of the data (a bell shaped or Gaussian curve). Alternative tests may be used if the 

data distribution is skewed to the right or left.

Excel has a Histogram function under the Data Analysis Subtab of the Data Tab. It requires that 

each cell within the range have valid numeric data (no missing data – blank cells, and no alpha 

characters). If data are missing, the record should be copied to another sheet and then the 

original record eliminated from the analysis range. To use the Histogram function, create a 

column with bins of data (kWh/yr in this case), at an interval that will provide a good overview 

of the data spread. Table 2–2 is an example of the data range used to create the charts shown in 

Figure 2-3 and Figure 2-4. Additionally, the example spreadsheet has equations that create the 

counts in the bin. 
9 

Table 2-1 Summary of Statistics by Store Type

Store Type Statistic

Annual 

Electricity Use

(kWh) 

Annual Gas Use 

(Therms) 

NormaIized 

Weekly 

Transactions 

Area 

(ft2) 

Weekly 

Hours CDD65 HDD50

All

Total 297,709,262 9,716,986 344.967 2,899,767 120,659 1,271,779 800,718

Max 603,960 29,776 1.000 8,153 168 3,866 3,552

Min 16,360 426 0.043 1,376 73 121 4 

Mean 335,258 10,943 0.388 3,266 136 1,432 902

Std. Dev 92,936 3,227 0.116 557 19 554 873

Median 341,609 10,820 0.378 3,333 128 1,348 671

Q1 279,484 9,066 0.321 3,000 124 1,158 97

Q3 401,966 12,936 0.453 3,516 143 1,685 1,409

Count 888 888 888 888 888 888 888

Type A

Total 52,201,682 2,218,016 82.296 628,235 26,852 250,046 67,174

Max 406,720 17,248 1.000 4,898 168 3,038 3,552

Min 18,560 756 0.114 1,376 73 121 4 

Mean 264,983 11,259 0.418 3,189 136 1,269 341

Std. Dev 55,060 2,466 0.136 623 21 500 657

Median 266,088 11,244 0.394 3,100 131 1,294 65

Q1 232,089 10,069 0.319 2,759 126 959 15

Q3 301,228 12,747 0.495 3,516 168 1,534 331

Count 197 197 197 197 197 197 197

Type B

Total 148,694,470 3,875,113 143.302 1,293,318 52,513 622,774 508,075

Max 603,960 29,776 0.777 4,320 168 3,866 3,481

Min 16,360 440 0.043 1,650 109 450 28

Mean 372,668 9,712 0.359 3,241 132 1,561 1,273
10

Store Type Statistic

Annual 

Electricity Use

(kWh) 

Annual Gas Use 

(Therms) 

NormaIized 

Weekly 

Transactions 

Area 

(ft2) 

Weekly 

Hours CDD65 HDD50

Std. Dev 97,846 3,215 0.104 446 16 533 719

Median 389,560 9,369 0.358 3,333 127 1,501 1,104

Q1 348,280 8,383 0.302 3,230 121 1,240 845

Q3 426,440 11,026 0.416 3,490 132 1,699 1,586

Count 399 399 399 399 399 399 399

Type C

Total 33,943,348 922,638 33.887 303,244 11,777 141,380 132,639

Max 544,280 17,748 0.930 8,153 168 3,231 2,790

Min 99,120 1,870 0.098 1,960 105 845 42

Mean 394,690 10,728 0.394 3,526 137 1,644 1,542

Std. Dev 85,538 2,770 0.109 809 16 636 905

Median 406,739 10,836 0.381 3,490 132 1,378 1,957

Q1 363,120 9,157 0.345 3,333 127 1,252 568

Q3 454,020 12,222 0.443 3,550 141 2,041 2,285

Count 86 86 86 86 86 86 86

Type D

Total 62,869,762 2,701,219 85.482 674,970 29,516 257,579 92,830

Max 466,144 20,149 0.832 5,662 168 2,566 3,020

Min 26,120 426 0.045 2,026 108 121 4 

Mean 305,193 13,113 0.415 3,277 143 1,250 451

Std. Dev 54,063 2,844 0.104 520 19 504 767

Median 304,727 13,294 0.412 3,200 138 1,223 98

Q1 276,883 11,806 0.342 2,882 128 922 27

Q3 336,973 14,663 0.478 3,563 168 1,597 424

Count 206 206 206 206 206 206 206
11

Table 2-2 Example Annual Kilowatt-Hour Data Range for Use in Histogram Function

Range

0 

25,000

50,000

75,000

100,000

125,000

(continue adding 25,000 for 

each subsequent row until...)

625,000

Figure 2-3 shows a histogram for one multiunit operator with about 900 stores in the database. 

There is a dip in the center of the data (at 350,000 kWh/yr) and a small bump at 125,000 kWh/yr. 

These indicate that the distribution represents more than one subdataset, or factor. This operator 

has four brand concepts that influence the overall energy use distribution. 

Figure 2-3 Number of stores by annual electricity use

Figure 2-4 shows that two of the four concepts use more electric energy than the other two. It 

also shows that the bump at 125,000 kWh/yr appears to be associated with the “B” concept, 

which has the most stores using more than 350,000 kWh/yr. This may be a case of missing 

monthly utility bills or a misclassification as part of the “B” set. A full set of histograms for each 

concept is included in Appendix A. 

0

20

40

60

80

100

120

0

25,00050,00075,000

100,000125,000150,000175,000200,000225,000250,000275,000300,000325,000350,000375,000400,000425,000450,000475,000500,000525,000550,000575,000600,000625,000

Number of Stores

Annual kWh
12

Figure 2-4 Number of stores by type by annual electricity use

Scatter plots, such as those in Figure 2-5 and Figure 2-6, show more detail about distribution of 

electricity and gas use versus normalized transactions. Appendix B includes the scatter plots for 

all four store types examined for an example multiunit operator, as well as scatter plots for all of 

the operator’s 900 stores combined. 

Figure 2-5 Example of annual electricity use scatter plot to show outliers

0

20

40

60

80

100

120

0

25,00050,00075,000

100,000125,000150,000175,000200,000225,000250,000275,000300,000325,000350,000375,000400,000425,000450,000475,000500,000525,000550,000575,000600,000625,000

Number of Stores

Annual kWh

A

B

C

D

y = 587325x + 161729

R2 = 0.3919

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

0.0000 0.2000 0.4000 0.6000 0.8000 1.0000

Annual kWh

Normalized Transactions

Store Type B
13

Figure 2-6 Example of annual natural gas use scatter plot to show outliers

2.1.5.4 Step 5d

The National Institute for Standards and Technology (NIST) has an online Engineering Statistics 

Handbook that describes several methods for identifying outliers (NIST/SEMATECH 2011). 

(Excel uses Q1 as the lower quartile and Q3 as the upper quartile, while the NIST Handbook 

refers to the upper quartile as Q2.) The following abstract lays out one of the methods, based on 

the box plot: 

The box plot is a useful graphical display for describing the behavior of the data in the 

middle as well as at the ends of the distributions. The box plot uses the median and the lower 

and upper quartiles (defined as the 25th and 75th percentiles). If the lower quartile is Q1 and 

the upper quartile is Q2, then the difference (Q2 - Q1) is called the interquartile range or IQ. 

A box plot is constructed by drawing a box between the upper and lower quartiles with a 

solid line drawn across the box to locate the median. The following quantities (called fences) 

are needed to identify extreme values in the distribution tails: 

Box plots with fences 

Lower inner fence: Q1 - 1.5*IQ 

Upper inner fence: Q2 + 1.5*IQ 

Lower outer fence: Q1 - 3*IQ 

Upper outer fence: Q2 + 3*IQ 

y = 14979x + 4343.8

R2 = 0.2355

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

0.0000 0.2000 0.4000 0.6000 0.8000 1.0000

Annual Therms

Normalized Transactions

Store Type B
14

Outlier detection criteria

Figure 2-7

: 

A point beyond an inner fence on either side but within the outer fences is considered a mild 

outlier. 

A point beyond an outer fence is considered an extreme outlier. 

 and Figure 2-8 show example box plots for the example multiunit concepts in Figure 

2-4. The light blue bars represent the range of the data (maximum to minimum). The green 

boxes represent the IQ range, which means that 50% of the stores have kWh/yr within that range. 

In this case, the dark green boxes are relatively small compared to the maximum-minimum

range, and they are located above the midpoint of the max-min range. This suggests that 

investigating the energy use of stores below the lower inner fence (below the bottom red dashed

line) may identify data anomalies that could be either corrected or eliminated from the dataset. If 

so, the changes would reduce the standard deviation and likely improve the regression model 

predictive accuracy. 

The energy use of stores above the upper inner fence is likely higher than required. These stores 

are candidates for review. The first action item is to ensure that no double monthly entries were 

made. The second is an energy audit, including review of operational practices, equipment in 

use, hours of operation, and set points. For example, exterior lighting, including building 

lighting and parking lot lights, may be left on longer than needed. 

Excel does not have a box plot template, although the stock charts have a similar format. Figure 

2-7 and Figure 2-8 are custom charts that will be provided in the example spreadsheet with 

instructions.

Figure 2-7 Example annual electricity use box plot with inner fences

0

100

200

300

400

500

600

700

A

197 Stores

B

398 Stores

C

86 Stores

D

206 Stores

kWh/yr

Thousands

Store Type

Inner Quartile Range

Max-Min

Upper Inner Fence

Lower Inner Fence
15

Figure 2-8 Example annual natural gas use box plot with inner fences

2.1.5.5 Step 5e

Use the summaries prepared in Steps 5b, 5c, and 5d to look for data anomalies, including partial 

year data, missing data, and billing errors.

Several standard statistical tests can be used to characterize outlier data points. The data may 

show unusually low energy use values because a month or several months are missing. 

Unusually high values may be due to double counting or a store that has unusually high 

transactions or longer operating hours than others. This is where normalizing values against 

transactions, operating hours, and other independent variables can provide guidance about 

whether to keep the outlier in the data or remove it as being unrepresentative. 

Decide whether to remove data anomalies.

An extreme outlier is likely to be a data error and should be rejected. A mild outlier may be due 

to unusual conditions, but is nonetheless likely to be valid and should be retained. Some mild 

outliers in utility bill data will be due to missing months of data. In any event, extreme and mild 

outliers should be investigated to determine what caused the underlying high or low energy use. 

Stores with partial annual utility or transaction data (one or more months missing) should not be 

included in the analysis. 

Figure 2-7 and Figure 2-8 depict outliers in data for an example multiunit operator. Figure 2-7

shows that every store type has lower mild outliers and that Type A and Type C have higher mild 

outliers. The lower outliers are likely cases of missing monthly data that should be investigated. 

Figure 2-8 shows lower mild outliers for each store type, but Type C has one or more very high 

gas users compared to the other stores.

0

5

10

15

20

25

30

A

197 Stores

B

398 Stores

C

86 Stores

D

206 Stores

Annual Therms

Thousands

Store Type

Inner Quartile Range

Max-Min

Upper Inner Fence

Lower Inner Fence
16

2.1.6 Step 6 

After completing Steps 1 through 5, the operator has enough information to do a high-level data 

evaluation. The work to this point answers the following fundamental questions: 

• Are there significant differences in store type that should be evaluated separately (types A, B, 

C, and D in the example data)? Recall that each dataset should include at least 20 stores for 

valid statistical parameters.

• What is the performance range? The maximum and minimum statistics show the range. 

• How much energy does the average store consume? The mean statistic will show the average 

consumption. The median is also a useful statistic, as it represents the 50th percentile in the 

distribution. If the mean and median values are close, the distribution does not likely exhibit 

much skew. 

• What do the distributions of energy consumption look like? Is there a lot of scatter, or are 

data points clustered tightly together? If there is skew, is it toward high or low consumption? 

Do there appear to be many or few outliers? The histograms and scatter plots help identify 

these characteristics. Box plots are also helpful in identifying outliers. 

• How many stores exhibit higher-than-expected consumption, but not so high that a data 

anomaly is suspected? These are retrofit candidates and should be evaluated more closely by 

site visits, evaluation of finer-grained data (through an energy information system or submetering, or both), energy audits, or some combination of all three. Higher-than-expected, 

but not suspicious, consumption numbers lie above the third quartile and below the upper 

inner fence in a box plot. The example spreadsheet identifies these values with a yellow 

highlight on the “Complete Inputs” worksheet. 

If the operator does not wish to perform the advanced performance evaluation (see Section 2.2), 

the final action should be to investigate stores identified as potential retrofit candidates. If 

several data anomalies appear, they should be corrected and the previous steps redone. 

2.2 Advanced Performance Evaluation and Prediction

Explanation is provided here for the steps illustrated in Figure 2-2. 

2.2.1 Step 7 

Step 7 involves finding the best correlation factors for the linear regression equations that will 

predict performance. For clarity, Step 7 consists of three substeps (7a–7c).

2.2.1.1 Step 7a

Identify potentially significant independent variables. 

The regressions performed in this analysis will use electric or natural gas consumption as 

dependent variables and factors affecting consumption as independent variables. The 

independent variables used in the example analysis are:

• Normalized transactions

• Weekly hours of operation 

• Floor area

• HDD

• CDD. 
17 

The list of independent variables to consider is unique to an operator. The five variables 

mentioned provide a helpful starting point. 

2.2.1.2 Step 7b 

For each subgroup (Store Type A, Store Type B, etc.), use the Excel LINEST function to 

develop a regression model. This model provides factors to be applied to continuous variables to 

compute a reference point for energy consumption. For example, if the significant independent 

variables are floor area, HDD50, weekly operating hours, and annual transactions, the annual 

reference energy will be calculated using an equation such as: 

 (2-1) ࢇࢋ࢘࡭࢘࢕࢕࢒ࡲ ൈ ࢋ ൅ ૙૞ࡰࡰࡴ ൈ ࢊ ൅ ࢙࢛࢘࢕ࡴࢍ࢔࢏࢚ࢇ࢘ࢋ࢖ࡻ࢟࢒࢑ࢋࢋࢃ ൈ ࢉ ൅ ࢙࢔࢕࢏࢚ࢉࢇ࢙࢔ࢇ࢘ࢀ ൈ ࢈ ൅ ࢇ ൌ ࢎࢃ࢑

Where: 

a = constant 

b = transactions slope 

c = weekly operating hours slope 

d = HDD50 slope 

e = floor area slope 

The LINEST function requires the independent variables (in this case, transactions, weekly 

operating hours, floor area, and HDDs) to be in one contiguous data range within a spreadsheet 

tab. (See the “Regression Analysis” tab of the example spreadsheet for more details.) 

2.2.1.3 Step 7c 

Prepare tables or plots of predicted versus actual energy consumption to evaluate the 

performance of the regression model. 

Table 2-3 depicts first pass results for an example multiunit operator. The first pass at creating a 

regression equation did not remove outliers, but six records with no electricity data were 

removed. Table 2-3 shows the results of the regression analysis (the R2

 is quite low and varies 

considerably by store type and mix of independent variables). 

Table 2-4 is based on the same data as Table 2-3, except that five records with no gas use were 

removed, as were two records for Store Type B, each with reported annual electricity use in 

excess of 2 million kWh/yr. Note the improvement in the R2

 for Store Type B electricity use; 

however, each had gas use, and removing those records slightly decreased the R2

 for Store Type 

B gas use. 

Overall, Table 2-4 shows that the data fit using normalized transactions, weekly hours, and floor 

area provides the best fit compared to combinations of two variables or a single variable. 

Normalized transactions have the largest influence on the data fit for electricity. No single 

independent variable has a similar influence on the data fit for gas. 

Table 2-5 is based on the analysis in Table 2-4 with the addition of CDDs and HDDs. The R2

values are improved, but generally the selected variables appear to account for 60% or less of the 

actual energy use by store type. The R2

 values would likely improve with further investigation 

and removal of outliers. Closer matching of dependent to independent variables, such as using 

only sub-metered HVAC energy as the dependent variable and weather parameters (HDD and 

CDD) as the independent variables, should also improve the R2

 correlations. In general, the 

resulting equations based on the LINEST function will not provide useful results unless the R2

 is 
18

greater than 70%-80%. Commercial statistical software packages are available, usually for a fee, 

and can provide additional parameters to help determine the quality of a linear regression.

2.2.2 Step 8

Use the combination of independent variables with the best correlation factors to create 

benchmarking equations. These will contain a constant term and one slope term (rate of change 

of dependent variable with respect to independent variable) per independent variable. The 

equations will take the form of Equation (2-1). 

The example spreadsheet shows the R2 statistic to guide the analyst toward the best correlation. 

A higher R2 value indicates a better correlation. Statistics such as P-factors and F-test could also 

be used to help determine the strength of these linear regressions. Statistical software is 

commercially available, usually for a fee.

2.2.3 Step 9

Use the benchmarking equations created in Section 1.1.1 to calculate the expected performance 

of each store. Insert the values of the known operational characteristics (transactions and weekly 

hours), for example, for each store into the benchmarking equations to predict electricity and 

natural gas consumption for each store. 

2.2.4 Step 10

Compare the expected performance (see Section 2.2.3) to the actual performance reported in 

utility or metered data, and note any significant differences. Examining differences by 

percentage will be more helpful than looking at absolute differences in kilowatt-hours or therms.

Stores with percent changes less than zero use less energy than predicted by their operational 

characteristics. Conversely, stores with positive percent changes use more energy than expected. 

Among such stores, slight variations may indicate noise in the data or inaccuracy in the 

prediction. Changes greater than 5%, however, should be noted; changes greater than 10% may 

indicate a candidate for a site visit or energy audit. Any changes greater than 25% should be 

considered a data error (see Section 2.1.5.5). 
19

Table 2-3 Regression Equation Matrix Using Raw Data 

(excluding six records with no electricity data and including 45 records with no gas data)

Independent Variables

Store 

Type

Electricity Natural Gas

Constant (yintercept) 

Floor 

Area 

Slope

Transactions 

Slope

Weekly 

Hours 

Slope R2

Constant (yintercept) 

Floor 

Area 

Slope

Transactions 

Slope

Weekly 

Hours 

Slope R2

Transactions, Weekly 

Hours, Floor Area

A –18,229 18.2 15.5 1,155.7 0.57 810 0.4 0.4 54.4 0.38

B 71,873 –6.2 70.4 518.3 0.26 –4,741 0.0 1.4 71.1 0.38

C 383,019 –19.5 48.8 –862.9 0.29 –4,408 0.9 0.3 78.2 0.44

D 32,013 21.3 25.9 642.0 0.45 3,719 0.9 0.7 24.2 0.15

Transactions, Floor Area

A 109,989 13.7 25.7 0 0.45 6,840 0.2 0.9 0 0.25

B 124,687 –3.9 72.5 0 0.26 2,504 0.3 1.7 0 0.28

C 287,514 –22.3 45.7 0 0.27 4,243 1.1 0.6 0 0.26

D 111,276 17.8 31.5 0 0.42 6,704 0.8 0.9 0 0.13

Transactions 

A 151,358 0 26.2 0 0.43 7,305 0 0.9 0 0.25

B 111,498 0 72.7 0 0.26 3,477 0 1.7 0 0.28

C 243,464 0 37.2 0 0.24 6,439 0 1.1 0 0.18

D 166,122 0 32.3 0 0.39 9,158 0 0.9 0 0.12

Weekly Hours, Floor Area

A –48,365 22.8 0 1,759.3 0.47 –13 0.5 0 70.8 0.34

B 227,089 –28.4 0 1,858.3 0.04 –1,629 –0.5 0 98.0 0.20

C 377,643 12.6 0 –200.1 0.01 –4,446 1.1 0 82.9 0.43

D 2,961 29.5 0 1,426.8 0.27 2,973 1.1 0 44.3 0.11

Weekly Hours

A 29,639 0 0 1,719.6 0.41 1,666 0 0 70.0 0.33

B 144,668 0 0 1,784.0 0.03 –3,011 0 0 96.7 0.20

C 396,596 0 0 –13.9 0.00 –2,815 0 0 98.9 0.34

D 110,877 0 0 1,347.6 0.20 7,139 0 0 41.3 0.07

Floor Area

A 202,767 19.2 0 0 0.04 10,099 0.3 0 0 0.01

B 450,797 –22.0 0 0 0.00 10,163 –0.1 0 0 0.00

C 354,445 11.4 0 0 0.01 5,164 1.6 0 0 0.21

D 223,710 24.5 0 0 0.05 9,832 1.0 0 0 0.03

Transactions, Weekly 

Hours 

A 45,724 0 17.0 1,065.1 0.53 2,095 0 0.5 52.5 0.37

B 53,453 0 70.7 495.9 0.26 –4,833 0 1.4 71.0 0.38

C 359,699 0 42.0 –992.0 0.27 –3,384 0 0.6 83.8 0.40

D 109,767 0 27.8 527.0 0.41 7,109 0 0.7 19.2 0.13
20

Table 2-4 Regression Equation Matrix Using Processed Data 

(excluding five records with no gas data and two records with extremely high electricity data)

Independent Variables

Store 

Type

Electricity Natural Gas

Constant (yintercept) 

Floor 

Area 

Slope

Transactions 

Slope

Weekly 

Hours 

Slope R2

Constant (yintercept) 

Floor 

Area 

Slope

Transactions 

Slope

Weekly 

Hours 

Slope R2

Transactions, Weekly 

Hours, Floor Area

A –19,262 19.0 142,059 1,205.7 0.58 744 0.4 3,237 57.5 0.39

B 180,031 16.6 606,116 –595.9 0.39 –3,400 0.1 12,485 62.3 0.33

C 383,019 –19.5 503,967 –862.9 0.29 –4,408 0.9 3,587 78.2 0.44

D 38,137 21.5 235,902 690.0 0.43 4,096 0.9 4,953 27.1 0.13

Transactions, Floor Area

A 113,795 14.2 253,542 0 0.44 7,093 0.2 8,556 0 0.23

B 119,427 13.9 580,567 0 0.38 2,939 0.4 15,157 0 0.24

C 287,514 –22.3 471,909 0 0.27 4,243 1.1 6,491 0 0.26

D 122,565 17.8 299,890 0 0.38 7,414 0.8 7,468 0 0.10

Transactions 

A 156,417 0 259,884 0 0.41 7,650 0 8,639 0 0.23

B 166,748 0 574,570 0 0.38 4,344 0 14,979 0 0.24

C 243,464 0 383,790 0 0.24 6,439 0 10,884 0 0.18

D 177,243 0 308,343 0 0.35 9,857 0 7,845 0 0.08

Weekly Hours, Floor Area

A –45,420 23.1 0 1,737.4 0.50 148 0.5 0 69.6 0.37

B 316,101 –1.0 0 461.9 0.01 -597 -0.2 0 84.1 0.18

C 377,643 12.6 0 –200.1 0.01 –4,446 1.1 0 82.9 0.43

D 16,503 28.4 0 1,365.4 0.27 3,642 1.1 0 41.3 0.10

Weekly Hours

A 33,610 0 0 1,697.5 0.43 1,886 0 0 68.8 0.35

B 313,126 0 0 459.1 0.01 –1,256 0 0 83.5 0.18

C 396,596 0 0 –13.9 0.00 –2,815 0 0 98.9 0.34

D 120,705 0 0 1,287.6 0.20 7,621 0 0 38.3 0.06

Floor Area

A 202,573 19.6 0 0 0.05 10,089 0.4 0 0 0.01

B 371,681 0.6 0 0 0.00 9,524 0.1 0 0 0.00

C 354,445 11.4 0 0 0.01 5,164 1.6 0 0 0.21

D 228,168 23.5 0 0 0.05 10,044 0.9 0 0 0.03

Transactions, Weekly 

Hours 

A 47,258 0 159,300 1,109.1 0.53 2,196 0 3,614 55.4 0.38

B 229,374 0 596,510 –535.8 0.39 –2,998 0 12,407 62.8 0.33

C 359,699 0 433,541 –992.0 0.27 –3,384 0 6,680 83.8 0.40

D 116,531 0 256,619 573.5 0.38 7,526 0 5,859 22.0 0.10
21

Table 2-5 Regression Equation Matrix With CDDs and HDDs Added 

Independent 

Variables

Store 

Type

Electricity Natural Gas

Constant (yintercept)

Floor 

Area 

Slope

Transactions 

Slope

Weekly 

Hours 

Slope

CDD 

Slope

HDD 

Slope R2 Constant (yintercept)

Floor 

Area 

Slope

Transactions 

Slope

Weekly 

Hours 

Slope

CDD 

Slope

HDD 

Slope R2

Transactions, 

Weekly Hours, 

Floor Area, 

HDD, CDD

A –37,425 12.0 1.2 1,210 18.8 148,333 0.59 826 0.0 1.8 49.7 0.1 6,544 0.59

B 195,377 –10.6 –22.5 –381 18.0 597,142 0.41 –6,507 2.0 3.2 32.3 –0.1 14,011 0.56

C 517,680 –66.7 –59.1 –329 –11.5 414,951 0.34 –4,772 0.8 1.8 55.5 0.4 6,306 0.59

D 906 24.3 –0.2 757 22.9 217,807 0.48 2,226 –0.1 1.5 31.2 0.6 9,712 0.25
22

3.0 Summary and Additional Considerations

Tracking energy use metrics over time will provide visibility into system-wide energy 

performance, identify the top energy users, and enable targeted programs to drive energy use 

toward the typical metrics.

The procedure described here provides a starting point to develop a customized in-house analysis 

and tracking system that will help multiunit operators understand how stores use energy. 

Customized procedures might include analysis of water use (hot water, irrigation, restrooms, 

etc.) and the influence of the number of parking lot lights. In some cases, submetering will have 

to be installed before the parameters can be extended. The procedure could also be extended to 

monthly, weekly, or even daily utility use, if automated data acquisition (through a billing 

service, for example) and analyses are implemented. Looking at utility subdaily data is not 

recommended, as hourly scatter can be large enough to obscure broader seasonal trends. Such

data could provide useful information, however, for investigating component performance or 

behavioral contributions. Any extension of the analysis to subannual periods would require 

careful modifications to the example spreadsheet.

As new stores are built, or periodic renovations are undertaken, performance metrics will also 

change, and hopefully energy use will decrease over time. But certain actions that increase 

revenue (adding new equipment to support new menu items or extending operations from 18 to 

24 hours per day, for example) may also increase energy use. If the transactions also increase, 

the revenue metrics may improve even though total energy use increases.

Another factor influencing year-to-year energy use is weather. Actual HDDs and CDDs are 

preferred, but manually obtaining these values for the whole data system may be time consuming 

and costly. Third-party services available via the Internet can help automate this process. 

Data for an example multiunit operator are used in this Guideline and the accompanying 

spreadsheet to illustrate the performance evaluation process. The example results underscore the 

challenges that restaurant operators face when trying to benchmark performance. Even when 

data from only one multiunit operator are analyzed, with four concepts (A, B, C, and D) analyzed 

separately to increase correlation strength, the highest correlation strength found in the example 

corresponds to an R2 value less than 60%. Obtaining meaningful cross-sectional benchmarking 

correlations across the entire restaurant sector would present even greater challenges.

In spite of such challenges, the process in this Guideline and accompanying spreadsheet provides 

a step-by-step starting resource that restaurant owners, energy managers, and operators can use 

to investigate their stores and refine performance metrics. This should enable them to better 

evaluate restaurants in their portfolios and then prioritize investments in energy-saving actions 

such as retrofits and operational improvements. 
23

4.0 References and Bibliography

ASHRAE. (2009). 2009 ASHRAE Handbook of Fundamentals. Atlanta, GA: American 

Society of Heating, Refrigerating and Air-Conditioning Engineers. 

Barley, C.D.; Deru, M.; Pless, S.; Torcellini, P. (2005). Procedure for Measuring and 

Reporting Commercial Building Energy Performance. Golden, CO: National Renewable 

Energy Laboratory: NREL/TP-550-38601. 

Claar, C.; Mazzucchi, R.; Heidell, J. (1985). The Project on Restaurant Energy Performance 

(PREP) – End-Use Monitoring and Analysis. Richland, WA: Pacific Northwest National 

Laboratory, Report No. PNL-5462. 

www.nrel.gov/docs/fy06osti/38601.pdf. 

National Climatic Data Center, NCDC. (2011). “U.S. Climatic Normals: Monthly Station 

Normals.” http://cdo.ncdc.noaa.gov/cgibin/climatenormals/climatenormals.pl?directive=prod_select2&prodtype=CLIM81&subrnum. 

Accessed April 1, 2011. 

National Institute of Standards and Technology, NIST; SEMATECH. (2011). e-Handbook of 

Statistical Methods. www.itl.nist.gov/div898/handbook. Accessed March 30, 2011. 

Smith, V.; Fisher, D. (2001). “Estimating Food Service Loads and Profiles.” ASHRAE 

Transactions 2001, 107(2). Atlanta, GA: American Society of Heating, Refrigerating and AirConditioning Engineers. 

Smith, V.; Young, R.; Spata, A.; Fisher, D. (1999). “Virtual vs. Real: Modeling the Energy 

Performance of a Quick Service Restaurant.” ASHRAE Transactions 1999, 105. Atlanta, GA: 

American Society of Heating, Refrigerating and Air-Conditioning Engineers. 

Zhang, J.; Schrock, D.; Fisher, D.; Livchak, A.; Zabrowski, D.; Lane, M.; Athalye, R.; Liu, B. 

(2010). Technical Support Document: 50% Energy Savings for Quick-Service Restaurants. 

Richland, WA: Pacific Northwest National Laboratory, PNNL-19809. 
24

Appendix A Histograms of Electricity and Natural Gas Use

This appendix includes histograms of electricity and natural gas use by store type. Figure A-1

and Figure A-2 show the entire dataset; Figure A-3 through Figure A-10 show separate 

histograms for each store type. 

Figure A-1 Histogram of electricity use by store type

Figure A-2 Histogram of natural gas use by store type

0

20

40

60

80

100

120

0

25,00050,00075,000

100,000125,000150,000175,000200,000225,000250,000275,000300,000325,000350,000375,000400,000425,000450,000475,000500,000525,000550,000575,000600,000625,000

Number of Stores

Annual kWh

A

B

C

D

0

20

40

60

80

100

120

140

160

0

1,0002,0003,0004,0005,0006,0007,0008,0009,000

10,00011,00012,00013,00014,00015,00016,00017,00018,00019,00020,00021,00022,00023,00024,00025,00026,00027,00028,00029,00030,000

Number of Stores

Annual Therms

A

B

C

D
25

Figure A-3 Histogram of electricity use for store type A (197 stores)

Figure A-4 Histogram of natural gas use for store type A (197 stores)

0

5

10

15

20

25

30

35

40

45

0

25,00050,00075,000

100,000125,000150,000175,000200,000225,000250,000275,000300,000325,000350,000375,000400,000425,000450,000475,000500,000525,000550,000575,000600,000625,000

Number of Stores

Annual kWh

0

5

10

15

20

25

30

35

40

45

50

0

1,0002,000

3,0004,0005,0006,0007,0008,0009,000

10,00011,00012,00013,00014,00015,00016,00017,00018,00019,00020,00021,00022,00023,00024,00025,000

Number of Stores

Annual Therms
26

Figure A-5 Histogram of electricity use for store type B (399 stores)

Figure A-6 Histogram of natural gas use for store type B (399 stores)

0

10

20

30

40

50

60

70

80

0

25,00050,00075,000

100,000

125,000150,000175,000200,000225,000250,000275,000300,000325,000350,000375,000400,000425,000450,000475,000500,000525,000550,000575,000600,000625,000

Number of Stores

Annual kWh

0

20

40

60

80

100

120

0

1,0002,0003,0004,0005,0006,0007,0008,0009,000

10,00011,00012,00013,00014,00015,00016,00017,00018,00019,00020,00021,00022,00023,00024,00025,00026,00027,00028,00029,00030,000

Number of Stores

Annual Therms
27

Figure A-7 Histogram of electricity use for store type C (86 stores)

Figure A-8 Histogram of natural gas use for store type C (86 stores)

0

2

4

6

8

10

12

14

16

0

25,00050,00075,000

100,000125,000150,000175,000200,000225,000250,000275,000300,000325,000350,000375,000400,000425,000450,000475,000500,000525,000550,000575,000600,000625,000

Number of Stores

Annual kWh by Store

0

2

4

6

8

10

12

14

16

18

0

1,0002,0003,0004,0005,0006,0007,0008,0009,000

10,00011,00012,00013,00014,00015,00016,00017,00018,00019,00020,00021,00022,00023,00024,00025,00026,00027,00028,00029,00030,000

Number of Stores

Annual Therms
28

Figure A-9 Histogram of electricity use for store type D (206 stores)

Figure A-10 Histogram of natural gas use for store type D (206 stores)

0

5

10

15

20

25

30

35

40

45

50

0

25,00050,00075,000

100,000125,000150,000175,000200,000225,000250,000275,000300,000325,000350,000375,000400,000425,000450,000475,000500,000525,000550,000575,000600,000625,000

Number of Stores

Annual kWh by Store

0

5

10

15

20

25

30

35

40

45

0

1,0002,0003,0004,0005,0006,0007,0008,0009,000

10,00011,00012,00013,00014,00015,00016,00017,00018,00019,00020,00021,00022,00023,00024,00025,00026,00027,00028,00029,00030,000

Number of Stores

Annual Therms
29

Appendix B Scatter Plots of Electricity and Natural Gas Use 

versus Normalized Transactions

This appendix includes scatter plots of electricity and natural gas use data. Figure B-1 and 

Figure B-2 show the entire dataset; Figure B-3 through Figure B-10 show separate plots for each 

store type.

Figure B-1 Scatter plot of electricity use for all stores

Figure B-2 Scatter plot of natural gas use for all stores

y = 302461x + 217759

R2 = 0.1419

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

0.0 0.2 0.4 0.6 0.8 1.0

Annual kWh

Normalized Transactions

y = 12987x + 5897.5

R2 = 0.217

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

0.0 0.2 0.4 0.6 0.8 1.0

Annual Therms

Normalized Transactions
30

Figure B-3 Scatter plot of electricity use for store type A

Figure B-4 Scatter plot of natural gas use for store type A

y = 259884x + 156417

R2 = 0.4105

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

0.0 0.2 0.4 0.6 0.8 1.0

Annual kWh

Normalized Transactions

y = 8639.2x + 7650

R2 = 0.2262

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

0.0 0.2 0.4 0.6 0.8 1.0

Annual Therms

Normalized Transactions
31

Figure B-5 Scatter plot of electricity use for store type B

Figure B-6 Scatter plot of natural gas use for store type B 

y = 587325x + 161729

R2 = 0.3919

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

0.0 0.2 0.4 0.6 0.8 1.0

Annual kWh

Normalized Transactions

y = 14979x + 4343.8

R2 = 0.2355

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

0.0 0.2 0.4 0.6 0.8 1.0

Annual Therms

Normalized Transactions
32

Figure B-7 Scatter plot of electricity use for store type C

Figure B-8 Scatter plot of natural gas use for store type C

y = 383790x + 243464

R2 = 0.239

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

0.0 0.2 0.4 0.6 0.8 1.0

Annual kWh

Normalized Transactions

y = 10884x + 6439.5

R2 = 0.1834

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

0.0 0.2 0.4 0.6 0.8 1.0

Annual Therms

Normalized Transactions
33

Figure B-9 Scatter plot of electricity use for store type D

Figure B-10 Scatter plot of natural gas use for store type D

y = 308343x + 177243

R2 = 0.3549

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

500,000

0.0 0.2 0.4 0.6 0.8 1.0

Annual kWh

Normalized Transactions

y = 7845.4x + 9857.2

R2 = 0.083

0

5,000

10,000

15,000

20,000

25,000

0.0 0.2 0.4 0.6 0.8 1.0

Annual Therms

Normalized Transactions
34

Appendix C Discussion of Heating Degree Days and Cooling 

Degree Days

This appendix presents the concepts of building balance points and degree days in detail. 

C.1 Balance Point

Heating and cooling requirements vary for each building depending on multiple variables, 

including the amount of glazing, level of insulation, occupancy and process load outdoor air 

ventilation, wind conditions, thermostat set points, amount and control of natural ventilation, 

door openings and closings, and internally generated heat. In general there is an average outdoor 

temperature at which a building requires no mechanical heating or cooling. This is called the 

balance point. In reality, the balance point changes constantly, based on how external weather 

and internal heat-generating loads change throughout the day. Thus, it is typically a temperature 

range, but for convenience in estimating required annual heating or cooling energy, a single 

temperature is selected. The heating balance point may differ from the cooling balance point.

C.2 Variable Base Degree Days

A degree day unit represents the difference between the actual average daily outdoor temperature 

compared to a selected base temperature (the balance point temperature). For example, if the 

average outdoor temperature for a day is 35°F and heating is required for a particular building 

when the outdoor temperature is below 60°F, the HDDs for that day total 25. CDDs are 

similarly calculated: if the average daily temperature is 75°F and the building requires cooling 

when the average outdoor temperature exceeds 65°F, the total CDDs for that day are 10.

C.3 Food Service Facility Balance Points

Zones in a building, especially in a food service facility, may have different balance points. The 

energy used by food service appliances is typically 30%–50% of total building energy (Claar et 

al. 1985; Smith et al. 1999; Smith and Fisher 2001; Zhang et al. 2010). Some of this energy 

leaves the building through kitchen exhaust, exfiltration through drive-through windows, and 

with take-out orders. Heat gain from appliances typically causes kitchens to be warm to hot; 

thus, they may have longer cooling hours and seasons than the dining room, which may have 

high heat gains from lighting, customers, solar gains through glazing, soup and salad bars, and 

drink machines. Thermostat set points for the kitchen and dining areas may be different as well. 

These differences in loads on the mechanical heating and cooling systems result in different 

balance points for these zones. There are likely to be many hours when the dining room requires 

heating and the kitchen requires cooling simultaneously.

Field experience and measured data indicate that kitchen balance points are 40°–55°F; dining 

rooms are 55°–65°F.