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

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Technical Report

NREL/TP-7A40-72748

April 2019

Sector-Specific Methodologies for 

Subnational Energy Modeling

Ookie Ma,1 Ricardo Oliveira,2 Evan Rosenlieb,2

and Megan Day2

1 U.S. Department of Energy

2 National Renewable Energy Laboratory
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

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Technical Report

NREL/TP-7A40-72748

April 2019

Sector-Specific Methodologies for 

Subnational Energy Modeling

Ookie Ma,1 Ricardo Oliveira,2 Evan Rosenlieb,2

and Megan Day2

1 U.S. Department of Energy

2 National Renewable Energy Laboratory

Suggested Citation 

Ma, Ookie, Ricardo Oliveira, Evan Rosenlieb, and Megan Day. 2019. Sector-Specific 

Methodologies for Subnational Energy Modeling. Golden, CO: National Renewable 

Energy Laboratory. NREL/TP-7A40-72748. https://www.nrel.gov/docs/fy19osti/72748.pdf.
NOTICE

This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable 

Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding 

provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Strategic Priorities and 

Impact Analysis Office. The views expressed herein do not necessarily represent the views of the DOE or the U.S. 

Government.

This report is available at no cost from the National Renewable 

Energy Laboratory (NREL) at www.nrel.gov/publications.

U.S. Department of Energy (DOE) reports produced after 1991 

and a growing number of pre-1991 documents are available 

free via www.OSTI.gov.

Cover Photos by Dennis Schroeder: (clockwise, left to right) NREL 51934, NREL 45897, NREL 42160, NREL 45891, NREL 48097, 

NREL 46526.

NREL prints on paper that contains recycled content.
iii

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Acknowledgments

The authors thank the following people for their contributions to the development of these 

methodologies and for providing substantive comments that informed the analysis and improved 

the structure and content of the report: Kevin Kurkul and Tom Cyrs of the World Resources 

Institute, Derik Broekhoff of the Stockholm Environment Institute, Galen Maclaurin, Michael 

Gleason, Brian Bush, and Gian Porro. We also gratefully acknowledge funding for this work 

from the Office of Strategic Programs of the U.S. Department of Energy’s Office of Energy 

Efficiency and Renewable Energy under contract number 28759.
iv

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

List of Acronyms

ACS5 5-year American Community Survey

CBECS Commercial Building Energy Consumption Survey

CoStar CoStar Realty Information, Inc.

DOE U.S. Department of Energy

EPA U.S. Environmental Protection Agency

GBS General Building Stock

GHG greenhouse gas

GHGRP Greenhouse Gas Reporting Program

HIFLD Homeland Security Infrastructure Foundation-Level Data

IET Industrial Energy Tool

IPF iterative proportional fitting 

NAICS North American Industrial Classification System

NREL National Renewable Energy Laboratory

PBAplus Principal Building Activity Plus

RII Regional Indicators Initiative

Definitions

Census blocks are the smallest geographic areas used by the Census Bureau and are bounded by 

visible features, such as streets, roads, streams, and railroad tracks. Census blocks nest within all 

other census geographic entities including cities, townships, and counties.

Census tracts are small, statistical subdivisions of a county or county equivalent and generally 

have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people.1

Minor civil divisions are the primary governmental divisions of a county and include townships 

and towns. The minor civil divisions in 12 states, including New England states, New Jersey, 

New York, and Pennsylvania, also serve as general-purpose local governments that can perform 

the same governmental functions as incorporated places.

 1 https://www.census.gov/geo/reference/webatlas/tracts.html
v

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Executive Summary

The National Renewable Energy Laboratory (NREL) and the United States Department of 

Energy (DOE) Office of Energy Efficiency and Renewable Energy Strategic Programs sought to 

address gaps in sub-national energy data. This report describes the methodologies developed to 

estimate the electricity and natural gas consumption and expenditures for the residential, 

commercial, and industrial sectors at sub-state geographies and made available on the State and 

Local Energy Data2 site to enable more strategic energy decisions by a variety of stakeholders.

To estimate energy consumption at the sub-state level, researchers proportionally allocated 

energy sales and revenue values reported by the Energy Information Administration from state 

and utility geographies down to smaller geographies. Within each sector, population distributions 

are developed through the selection of cohorts assumed to have homogeneous energy use 

characteristics. In the residential sector, cohorts are based on locational, occupancy, physical, 

and demographic characteristics of the housing units. In the commercial sector, cohorts are based 

on climate zone, location, and use types of commercial square footage. The industrial sector is 

unique in that large greenhouse gas emitters are required to report at the facility level. These are 

treated individually, but the remaining cohorts are based on the North American Industrial 

Classification System (NAICS) codes and employment size class for business establishments. 

Energy values by cohort come from available statistical samples. These are then rescaled to 

match aggregate values from electric and natural gas utility reported sales and volumes as well as 

business reported fuel expenditures.

These methodologies model only electricity and natural gas consumption and expenditures and 

do not address other forms of building energy consumption and expenditures such as bottled gas 

(generally propane) or heating oil or on-site industrial consumption of coal, coke, or diesel fuel 

for process energy.

Residential sector modeling generally yields more accurate results due to greater data availability 

and uniformity of per customer energy use. The opposite is true of the industrial sector where 

significant disparities in energy consumption by industry and among facilities within industries

cause modeling challenges. In small towns where a single industrial facility can comprise most 

of the sector’s consumption, modeled industrial sector data may be unreliable for planning 

purposes. Overall, a comparison with reported values by sector demonstrates that these sectorspecific modeling methodologies provide reasonable results which can inform subnational-level 

energy decision making where measured, utility-reported data is unavailable. In addition, the 

modeled data provides robust, standardized, and disaggregated national data.

 2 https://apps1.eere.energy.gov/sled/
vi

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table of Contents

Executive Summary.................................................................................................................................... v

Table of Contents....................................................................................................................................... vi

List of Figures ........................................................................................................................................... vii

List of Tables ............................................................................................................................................. vii

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

2 Methodology ......................................................................................................................................... 3

2.1 Energy Estimates for the Residential Sector................................................................................. 3

2.2 Energy Estimates for the Commercial Sector ............................................................................... 5

2.2.1 Calculating Energy Intensity............................................................................................ 6

2.2.2 Data Integration................................................................................................................ 8

2.2.3 Aggregating to Cities ..................................................................................................... 10

2.3 Energy Estimates for the Industrial Sector.................................................................................. 11

2.3.1 Calculating Energy Intensity.......................................................................................... 12

3 Calibration........................................................................................................................................... 16

3.1 Residential sector energy intensity weighting............................................................................. 16

3.2 Commercial sector energy intensity weighting........................................................................... 17

4 Validation of Estimates to Measured Values ................................................................................... 19

4.1 Validation of Residential Modeled Data..................................................................................... 19

4.2 Validation of Commercial and Industrial Modeled Data ............................................................ 21

5 Conclusion .......................................................................................................................................... 26

Appendix A. Derivation of Calibration Equations ........................................................................... 27

Appendix B. Methodology Flowcharts ............................................................................................. 31
vii

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

List of Figures

Figure 1. Census geographies hierarchy ....................................................................................................... 7

Figure 2. Building America climate zones.................................................................................................... 7

Figure 3. Building count distribution between CoStar and GBS.................................................................. 9

Figure 4. Total building square footage distribution between CoStar and GBS......................................... 10

Figure 5. Comparison of state-calibrated residential estimates to community data from Xcel Energy...... 20

Figure 6. Comparison of local-calibrated residential estimates to community data from Xcel Energy. .... 20

Figure 7. Modeled total buildings versus EIA reported commercial customers per state........................... 21

Figure 8. Plot of estimated commercial and industrial retail electricity consumption for municipalities and 

townships in Massachusetts vs. reported values from the Mass Save program..................... 22

Figure 9. The percent error of estimated commercial and industrial electricity consumption vs. reported 

values in the Mass Save dataset ............................................................................................. 23

Figure 10. Plot of estimated commercial and industrial retail electricity consumption for select Minnesota 

municipalities vs. reported values from the Minnesota RII program..................................... 24

Figure 11. Plot of estimated commercial and industrial retail natural gas consumption for select 

Minnesota municipalities vs. reported values from the Minnesota RII program................... 25

Figure A1. Example census tracts 1, 2, 3, and 4 and utility service areas A, B, and C .............................. 27

Figure B1. Commercial energy consumption and expenditures methodology summary ........................... 31

Figure B2. Industrial electricity consumption and expenditures methodology summary........................... 32

Figure B3. Industrial natural gas consumption and expenditures methodology summary ......................... 33

List of Tables

Table 1. Cross-Tabulation of ACS5 Data ..................................................................................................... 4

Table 2. Datasets Used for Commercial Sector Modeling............................................................................ 6

Table 3. Modeled Electricity Energy Consumption for Colorado .............................................................. 10

Table 4. Example of Mapping between Cities and Tracts with Weights.................................................... 11

Table 5. Datasets Used for Industrial Sector Modeling .............................................................................. 12
1

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

1 Introduction

Patterns of energy consumption and expenditures vary significantly across the United States and 

among the various subpopulations of households and businesses. However, most energy data are 

available only at the aggregate state or utility service territory level, masking how energy use is 

distributed at finer resolutions. For many important energy-related problems, understanding the 

distributional characteristics is critical for informing potential solutions. As such, estimation 

approaches become necessary for filling informational gaps. 

Several research efforts have estimated disaggregated energy use. Many of these approaches 

cannot be easily generalized because they rely upon specific information not commonly available 

across geographic or sectoral domains. For instance, urban building energy modeling typically 

relies upon the availability of detailed building stock information such as geometry, envelope

and mechanical systems, and use types.3,4 Other efforts, such as Project Vulcan, which models 

county-level energy data, fill some data gaps but leave others. For instance, Parshall et al.

5 have 

translated Vulcan data to energy use data but do not include electricity use since in Vulcan, 

electricity is attributed to the point of generation rather than the point of consumption. Site-level 

data exist for most large electricity generation facilities, either through reporting to the Energy 

Information Administration (EIA)6 or the U.S. Environmental Protection Agency (EPA),7 but 

not for most residential and commercial buildings or all but the largest industrial facilities.

Most estimation techniques rely upon combinations of bottom-up and top-down approaches. 

Bottom-up approaches apply representative values to underlying population distributions. Taking 

a simple example, the energy use intensity of commercial buildings may be assumed to vary by 

use type. These values can then be applied to the prevalence of different use types in different 

locations using building area as the weighing factor. 

Top-down approaches begin with aggregate values and attempt to break down those values into 

progressively smaller geographic units and population cohorts of interest, each higher level 

constraining the values of the associated lower levels. For instance, modeled estimates of 

population growth at the national level are often used to support state-level projections by 

forcing the sum of state-level values to equal the associated national-level values.8

Combined approaches are advantageous because they allow flexibility in re-summation of values 

to reflect different geographies and cohorts and the full leveraging of existing aggregate 

 3 Reinhart, Christoph F., and Carlos Cerezo Davila, 2016. “Urban Building Energy Modeling – A Review of a 

Nascent Field.” Building and Environment. https://www.sciencedirect.com/science/article/pii/S0360132315003248 4 B. Howard, L. Parshall, J. Thompson, S. Hammer, J. Dickinson, and V. Modi, “Spatial Distribution of Urban 

Building Energy Consumption by End Use,” Energy and Buildings.

https://qsel.columbia.edu/assets/uploads/blog/2018/publications/Spatial-Distribution-of-Urban-Building-EnergyConsumption-by-End-Use.pdf 5 Lily Parshall, Kevin Gurney, Stephen A. Hammer, Daniel Mendoza, Yuyu Zhou, Sarath Geethakumar. 2010.

“Modeling Energy Consumption and CO2 Emissions at the Urban Scale: Methodological Challenges and Insights 

from the United States,” Energy Policy.

https://pdfs.semanticscholar.org/3f35/975247c2c263441904ceca205dc5a5999f8a.pdf

6 EIA Form 861. 7 EPA Greenhouse Gas Reporting Program (GHGRP). 8 University of Virginia Weldon Cooper Center, Demographics Research Group. (2019). Virginia Population 

Estimates. Retrieved from https://demographics.coopercenter.org/virginia-population-estimates
2

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

information. One common summation of interest is the city level. A city energy inventory is 

usually conducted as part of a greenhouse gas (GHG) emissions inventory, for instance, since 

energy use drives a significant fraction of emissions.

Developing inventories is a resource-intense activity requiring willingness not only on the part of 

the local government, but also private entities such as electric and natural gas utilities and other 

fuel suppliers, to disclose data. Even in the absence of public support and private cooperation, 

estimated inventories can support improved community energy planning. Furthermore, estimates 

for cities with inventories based on reported data can be used to validate estimation approaches. 

Validation should be undertaken with caution, however. While GHG emissions inventories seek 

to use primarily observational data, in practice inventories rely upon both measured and modeled 

values.

Uses for disaggregated energy information extend beyond city energy planning. For instance, the 

changing nature of electric loads has led to efforts to improve their characterization for electric 

system planning. The distribution of different types of energy consumers and the associated enduses informs systems planners on how electric loads may be impacted by external factors such as 

weather and how electric loads may evolve as new technologies and end-uses penetrate the 

market.

This report describes the methodologies developed to estimate electricity and natural gas 

consumption and expenditures for the residential, commercial, and industrial sectors at sub-state 

geographies. Sector-by-sector descriptions of the bottom-up methodology are provided first. 

Next, the top-down calibration process is discussed. Finally, estimates are validated against 

existing values from city and county-level reported data.
3

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

2 Methodology

Estimates of residential, commercial, and industrial electricity and natural gas use are performed 

similarly, with differences primarily stemming from the different availability of datasets by 

sector. Within each sector, population distributions are developed through the selection of 

cohorts assumed to have homogeneous energy use characteristics. In the residential sector, 

cohorts are based on locational, occupancy, physical, and demographic characteristics of housing 

units. In the commercial sector, cohorts are based on climate zone, location, and use types of 

commercial square footage. The industrial sector is unique in that large GHG emitters report at 

the facility level. These are treated individually, but the remaining cohorts are based on the North 

American Industrial Classification System (NAICS) codes and employment size class for 

business establishments. Energy values by cohort come from available statistical samples. These 

are then rescaled to match aggregate values from electric and natural gas utility reported sales 

and revenues as well as business reporting of fuel expenditures. Final values are calculated at the 

city and county levels. In this work, cities are generally defined as incorporated places. However, 

in 12 states, including New England states, New Jersey, New York, and Pennsylvania, minor 

civil divisions are also included to capture coterminous towns and townships that also serve as 

general-purpose local governments that can perform the same governmental functions as 

incorporated places. In the state of Hawaii, which has only one incorporated place, counties are 

used in lieu of incorporated places. Detailed sector-by-sector methodologies are provided below.

2.1 Energy Estimates for the Residential Sector

Estimates of residential energy consumption are based on cross-tabulations of U.S. Census 

housing data from the 2016 5-year American Community Survey (ACS5).

9 Average energy 

expenditures by different housing unit types are weighted by their housing unit counts to develop 

census tract-level estimates of energy expenditures. Those energy expenditure estimates are then 

rescaled to give energy consumption values, as described later. They are finally aggregated to 

cities and counties based on the census block-level occupied housing unit counts. (Further 

discussion of the spatial apportionment process is provided in Section 2.2.4).

Spatial allocation of different housing unit types relies on the use of an iterative proportional 

fitting (IPF) algorithm.

10,11 IPF is used sequentially to build increasingly complex crosstabulations. Census tract-level published tables from the ACS512 are used as the marginal totals,

and cross-tabulations of the ACS5 Public Use Microdata Samples for the corresponding Public 

Use Microdata Areas are used as the seeds in the IPF algorithm. The resulting cross-tabulations

(see Table 1) include housing unit tenure, building year of first construction, number of units in 

the building, primary heating fuel type, number of persons, and household income. Finally, 

 9 U.S. Census Bureau American Community Survey 2012-2016 ACS 5-year Estimates 

https://www.census.gov/programs-surveys/acs/data.html 10 IPF is applied in Lovelace, R. (2014). Introducing Spatial Microsimulation with R: A Practical. National Centre 

for Research Methods Working Paper. http://eprints.ncrm.ac.uk/3348/4/spat_microsimulation_R.pdf 11 IPF is also introduced in Pitchard, D. R. & Miller, E. J. (2012). Advances in population synthesis: fitting many 

attributes per agent and fitting to household and person margins simultaneously. Transportation 39(3), 685–704.

http://www.springerlink.com/index/10.1007/s11116-011-9367-4 12 U.S. Census Bureau American Community Survey 2012-2016 ACS 5-year Estimates 

https://www.census.gov/programs-surveys/acs/data.html
4

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

household income and number of persons are collapsed to a single variable using the U.S 

Department of Housing and Urban Development definition of area median income.

Table 1. Cross-Tabulation of ACS5 Data

Variable Categories ACS5 Published 

Table

ACS5 Microdata 

Sample

Tenure Owner-Occupied and Renter-Occupied Included belowa TEN

Building year of 

first construction

2010 and later, 2000-2009, 1980-1999, 1960-1979, 

1940-1959, and 1939 and before

B25036, 

B25127b

YBL

Number of units in 

the building

1 Unit Detached, 1 Unit Attached, 2 Units, 3-4 Units, 

5-9 Units, 10-19 Units, 20-49 Units, 50 and More 

Units, and Mobile and Other Units

B25032, 

B25124b, 

B25127b

BLD

Primary heating 

fuel type

Utility Gas, Bottled Gas, Electricity, Fuel Oil, Wood, 

Coal, Solar, Other, and None

B25117 HFL

Number of 

persons

1-Person, 2-Person, 3-Person, 4-Person, 5-Person, 

6-Person, 7 or More Persons

B25009, 

B25124b

NP

Household income 0-5K, 5-10K, 10-15K, 15-20K, 20-25K, 25-35K, 35K50K, 50-75K, 75-100K, 100-150K, 150K and moreB25118 HINCP

Area median 

income

0-30%, 30-50%, 50-80%, 80-100%, greater than 

100%

not available not available

a Many of the published tables in the ACS5 are broken out by tenure.

b The ACS5 published tables include some cross-tabulations, including building year by number of units and number 

of persons by number of units. These cross-tabulations are incorporated in the IPF sequence to improve agreement 

between housing unit estimates and published Census values.

For each Public Use Microdata Area, corresponding energy expenditure values are calculated for 

each housing unit type in the above cross-tabulation. This requires modification to the microdata 

samples. To improve data accuracy, we removed survey responses for which housing energy 

costs are included in other housing costs, or energy costs across multiple fuels type are 

combined. Census tract-level housing unit counts are used to develop weighted averages.

The ACS5 provides energy expenditures for three categories of fuel types: electricity, gas, and 

other fuels. We assume that electricity is always provided by a utility. However, this assumption 

does not hold true for gas, which includes both utility-delivered natural gas and various forms of 

bottled gas. To make separate estimates of utility and bottled gas, we assume that housing units 

reporting bottled gas as their primary heating fuel type do not have access to utility gas. Those 

gas expenditures are subtracted from the associated census tract-level values to yield estimates of 

only utility gas expenditures.

As demonstrated in Section 4 Validation, resulting values are in good agreement with published 

inventory values. Nevertheless, this approach has several shortcomings aside from the more 

general issues associated with self-reported survey data. First, electricity and gas expenditures 

are taken for only a single month and that month is not reported publicly. Given the strong 

seasonal variation of energy use, extrapolation of annual values from monthly values hinges 

upon the quality of the sampling to cover all months of the year. This cannot be verified with the 

public data. Second, the ACS5 includes only occupied housing units. Unoccupied housing units 

may consume a significant fraction of energy. Third, elimination of responses reporting no 

expenditures associated with fuel use introduces unknown biases into the estimates. Fourth, the 
5

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

availability of piped utility natural gas is unknown and must be inferred from the prevalence of 

utility gas as a heating fuel source.13 Finally, the characteristics of census tracts may differ 

significantly from their larger Public Use Microdata Area (PUMA), leading to poor IPF 

estimation. (For simple comparison, there are roughly 4,500 PUMAs and 73,000 census tracts in 

the U.S.)

2.2 Energy Estimates for the Commercial Sector

The commercial sector methodology starts with commercial customer electricity and natural gas

sales and revenue data reported at the utility level by the EIA.14,15 Utility service territories are

mapped using ABB Velocity Suite data. The EIA-reported utility sales and revenues are 

proportionally allocated to census tracts based on building area by use type and assumed energy 

use intensities. A key missing component for energy estimation is a reliable dataset of all 

commercial buildings in the United States. To address this gap, we combined and normalized 

several datasets to create a comprehensive commercial building stock inventory. (See Appendix 

B for a summary flowchart of the commercial methodology.)

Developing a commercial building inventory included two distinct parts. First, based on the 

number of buildings and total square footage, an initial energy use value is assigned to every 

census tract. This value is calculated by using the Commercial Building Energy Consumption 

Survey (CBECS) reported energy use intensity (the amount of energy used by buildings of 

different use types and size) by the total square footage per building type reported by our 

commercial building inventory dataset. The total of the energy use values calculated in this step 

do not sum to reported utility sales; however, it provides an indication of the relative energy use

across census tracts. EIA totals are then allocated proportionally according to the relative energy 

use across census tracts calculated in the previous step. The datasets used for commercial sector 

modeling are listed in Table 2.

 13 In some cases, the ACS5 incorrectly assigns utility gas heated housing units to areas lacking utility gas 

availability, which can lead to errors in this modeled data. 

14 EIA. 2016. “Annual Electric Power Industry Report (Survey Form No. EIA-861).” U.S. Energy Information 

Administration, Washington, DC, USA. Available online: http://www.eia.gov/electricity. Accessed December 1, 

2018. 15 EIA. 2016. “Annual Report of Natural and Supplemental Gas Supply and Disposition (Survey Form No. EIA176).” U.S. Energy Information Administration, Washington, DC, USA. Available online: 

http://www.eia.gov/naturalgas. Accessed December 1, 2018.
6

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 2. Datasets Used for Commercial Sector Modeling

EIA Form 861 Form 861 is a survey that gathers data on electricity sales, revenue, peak load, and energy 

efficiency.

EIA Form 176 Form 176 provides natural gas sales data at the utility level.

EIA CBECS CBECS is a survey that collects sample information about the U.S. commercial building 

stock, including building activity, physical structure, and energy use.

Federal Emergency 

Management 

Administration’s 

General Building 

Stock (GBS)

The Federal Emergency Management Administration’s natural hazard model, or HAZUS, 

is used to assess the impacts of earthquakes, floods, hurricanes, and tsunamis. HAZUS 

depends on multiple datasets bundled within the model, including the GBS. The GBS 

provides square footage by occupancy and building type, building count by occupancy and 

building type, valuation by occupancy and building type, and general occupancy 

mapping16 at the census block level. The GBS provides information on 33 building types, 

covering residential, commercial, industrial, and government buildings.

CoStar Realty 

Information, Inc. 

(CoStar)

CoStar provides commercial building data for purchase under specific license terms. 

While the GBS is an aggregated dataset at the census block level, CoStar provides 

information at the building level for over 4.5 million commercial buildings in the United 

States. This dataset was aggregated at the census block level to match the GBS.

ABB/Ventyx 

Energy Velocity 

Suite Utility 

Territories

The proprietary ABB Velocity Suite platform provides utility service territory datasets, 

both for electric and natural gas utilities, in a geographic information system format. These 

data are used to map utilities to other geographical units, such as census tracts and cities.17

2.2.1 Calculating Energy Intensity

The first step in disaggregating energy consumption data from utility service territories to census 

tracts is to estimate the commercial energy intensity of each census tract using energy 

consumption by building use type and size from CBECS. CBECS is organized by building type, 

census region, census division, and Building America climate zone. As demonstrated in Figure 1, 

census regions and divisions are among the largest levels of the census geography hierarchy. 

Buildings may have dramatically different energy use characteristics depending on their location 

within a multi-state census division. To address this problem, Building America climate zones 

were used.

 16 https://www.fema.gov/summary-databases-hazus-multi-hazard 17 ABB/Ventyx Energy Velocity Suite. (2016). Local Distribution Company Territories. Retrieved June 1, 2018.
7

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 1. Census geographies hierarchy18

The U.S. Department of Energy (DOE) developed the Building America climate zones under the 

Building America initiative.19 The climate zones follow regional climate patterns (see Figure 2). 

As such, a single state, census region, or division may contain several climate regimes that affect 

building energy use.

Figure 2. Building America climate zones20

Building America climate zones conform to county administrative boundaries, and since census 

tracts also nest within counties, it is possible to assign climate zones to all census tracts. After 

 18 https://www.census.gov/geo/reference/hierarchy.html 19 https://www.energy.gov/eere/buildings/building-america-bringing-building-innovations-market 20 DOE https://www.energy.gov/eere/buildings/climate-zones
8

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

assignment, energy consumption is divided by square footage to obtain energy use and 

expenditures per square foot and by building use type. The data are then aggregated by climate 

zone, building type, and square footage class, which produces a dataset with energy use values 

per square foot per building use type at the census tract level.

These summed energy intensity values are joined to the building inventory based on building 

type and square footage class. The analysis was conducted using GBS and CoStar building 

inventories separately.

To join the CBECS data with the building inventory, we aligned CoStar building types with the 

Principal Building Activity Plus (PBAplus) building type descriptor system.21 GBS, having 

fewer building types than PBAplus, was mapped to DOE Prototypes,

22 which is a system 

developed by the DOE to support the energy modeling software Energy Plus. In the final step, 

PBAplus building types were mapped to DOE Prototypes.

The post-processed CBECS building energy consumption data was allocated to the census tractlevel building inventory using building type, PBAplus or DOE Prototype, square footage class, 

and climate zone as keys. This created an intermediate dataset with energy use per square foot 

and total square footage per building type at all census tracts, which were summed and 

summarized to obtain total commercial energy use for each census tract. The census tract 

commercial energy consumption and expenditure estimates were then calibrated to state totals 

from EIA Forms 861 and 176 using the approach described in Section 3 Calibration.

2.2.2 Data Integration

This analysis was performed using the different commercial building inventories datasets 

separately. While the original intent was to combine GBS and CoStar data into a single, more 

comprehensive inventory dataset, CoStar has less coverage of rural areas and public buildings 

than GBS. In addition, the GBS displays data projected to 2010 while CoStar covers data 

through May 2018 and each dataset has different building type descriptors, with CoStar 

providing more varied building type categories than GBS.

Thus, combining the datasets proved too complex for the scope of this project. It is important to 

note that this comprehensive, combined commercial building stock dataset could be explored in 

future research to improve the overall method. Instead, a simple process was implemented to 

select the buildings dataset with the higher commercial energy consumption for each census 

tract.

Figures 3 and 4 show the distribution of each dataset. Note how the GBS presents a higher 

distribution in both urban and rural settings while Costar presents a higher square footage total in 

both urban and rural areas, indicating that CoStar more accurately captures building area data,

and the GBS data includes twice as many commercial buildings as Costar in rural areas. As 

CoStar data have a higher total building area and uses building-specific data inputs instead of 

 21 PBAplus is a building activity classification system used by CBECS. See 

https://www.eia.gov/consumption/commercial/building-type-definitions.php 22 https://www.energy.gov/eere/buildings/commercial-reference-buildings
9

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

modeled data, it is assumed that even though CoStar data have fewer urban buildings, it has a 

higher degree of accuracy for each building.

Figure 3. Building count distribution between CoStar and GBS
10

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 4. Total building square footage distribution between CoStar and GBS

Table 3 shows the aggregated totals for electricity consumption for the state of Colorado with the 

sum of total buildings and areas.

Table 3. Modeled Electricity Energy Consumption for Colorado

Building 

Counts 

(CoStar)

Building 

Counts 

(GBS)

Building 

Square 

Footage 

(CoStar)

Building 

Square 

Footage

(GBS)

Total MWh 

Consumption 

(CoStar)

Total MWh 

Consumption 

(GBS)

Rural 7,314 22,190 141,592,708 143,718,864 2,759,376 3,941,318

Urban 45,430 82,861 916,964,757 545,993,028 17,379,394 16,272,724

As shown in Table 3, CoStar’s more detailed data on building area yield a higher total energy 

consumption for urban tracts despite having 54% fewer buildings than the GBS in urban tracts.

2.2.3 Aggregating to Cities

Once tract-level energy estimates are finalized, they are aggregated to cities using a mapping 

process between tracts and cities. This mapping process uses a weighting measure that allows for 

tracts that are not fully contained in a single city to be aggregated accordingly. 

While the residential sector weighting is based on census block-level occupied housing unit 

counts, the commercial sector weighting uses commercial building inventory data, in this case 

GBS, because of the wider coverage, at the census block level. Census blocks are always 

completely contained within a census tract. This means that it is possible to build census tracts

from census blocks. By knowing which census blocks make up a tract, it is then possible to 
11

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

effectively count how many buildings are inside and how many buildings are outside a city, and 

from this generate weights for that census tract. 

Table 4 is an example of the final mapping. In this example, only 50% of the energy computed 

for tract 100002 would be assigned to city 01, and the remaining energy would either be assigned 

to an adjacent city or discarded. Then, after the weight is applied, the consumption values and 

expenditures are summed to obtain the total energy consumption and expenditure for a city.23

Table 4. Example of Mapping between Cities and Tracts with Weights

Tract ID City ID Weight

100001 01 1

100002 01 0.5

2.3 Energy Estimates for the Industrial Sector

The industrial sector methodology uses county-level estimates for 2014 industrial electricity and 

natural gas consumption from the Industrial Energy Tool and a point-level facility inventory to 

estimate the percent of state industrial energy that each city within that state consumes. That 

percentage is then multiplied by 2016 EIA estimates for state total industrial energy consumption 

to scale those city percentage estimates to 2016. The datasets used for industrial sector modeling 

are listed in Table 5.

 23 Jurisdictional boundaries are based on the U.S. Census Bureau 2013 Topologically Integrated Geographic 

Encoding and Referencing (TIGER)/Line Shapefiles: https://www.census.gov/geo/maps-data/data/tiger.html.
12

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 5. Datasets Used for Industrial Sector Modeling

EIA Form 861 Form 861 is a survey that gathers data on electricity sales, revenue, peak load, and energy 

efficiency. Industrial sector estimates use only aggregated state-level Form 861 data.24

EIA Form 176 Form 176 provides natural gas sales data at the utility level. Industrial sector estimates use 

only aggregated state-level Form 176 data.25

Industrial Energy 

Tool (IET) countylevel estimates

NREL developed the IET to be “an open-source, transparent, and flexible tool for 

exploring industrial sector energy and emissions scenarios.” Using a variety of datasets, 

the tool provides industrial energy use estimates disaggregated to the county level and the 

four- to six-digit NAICS subsector (depending on subsector and region).26

Homeland Security 

Infrastructure 

Foundation-Level 

Data 

(HIFLD)/HSIP 

Gold 2012 Facility 

inventory

The HIFLD program was established by the HIFLD subcommittee to “address 

improvements in collection, processing, sharing, and protection of national geospatial 

information across multiple levels of government.”27 As part of the data provided by the 

program, HIFLD obtains data on the locations of businesses across the country from Dunn 

& Bradstreet. While the data are not part of the data that HIFLD makes available to the 

general public, as of 2012 it became available to government entities for planning and 

research purposes (under the name “HSIP Gold”). The subset of the HSIP Gold dataset 

used is a catalog of all manufacturing, mining, and agricultural facilities in the United 

States with coordinates of the location, NAICS code of the sector of the facility, and an 

estimate of the number of employees.

EPA Greenhouse 

Gas Reporting 

Program (GHGRP)

The GHGRP tracks the GHG emissions and energy use of large point sources of GHGs. 

This dataset allows for the resolution of exact amounts of natural gas consumption data for 

these large facilities. The dataset does not include electricity consumption data as it only 

tracks fuel sources that emit carbon at the point of consumption.28 As GHGRP facilities 

are also generally included in the HSIP gold dataset, we filtered out facilities from the 

HSIP gold dataset that appear to already be counted by the GHGRP dataset to avoid 

duplicate data. This was accomplished through spatially filtering those HSIP facilities in 

very close proximity to GHGRP facilities with the same NAICS code and manually 

removing those with facility names that appeared to match those of the GHGRP facility.

2.3.1 Calculating Energy Intensity

As the IET leverages many datasets and uses sophisticated methods to provide highly 

disaggregated industrial energy estimates, it serves as a natural starting point for further 

resolving estimates to municipal boundaries. While the IET datasets are estimates, the open 

source nature of the tool provides transparency and enables our final estimates to be traced back 

to publicly reported data. 

 24 EIA. 2016. “Annual Electric Power Industry Report (Survey Form No. EIA-861).” U.S. Energy Information 

Administration, Washington, DC, USA. Available online: http://www.eia.gov/electricity. Accessed December 1, 

2018. 25 EIA. 2016. “Annual Report of Natural and Supplemental Gas Supply and Disposition (Survey Form No. EIA176).” U.S. Energy Information Administration, Washington, DC, USA. Available online: 

http://www.eia.gov/naturalgas. Accessed December 1, 2018. 26 McMillan, Colin and Vinayak Narwade. 2018. The Industry Energy Tool (IET): Documentation. Golden, CO: 

National Renewable Energy Laboratory. NREL/TP-6A20-71990. https://www.nrel.gov/docs/fy19osti/71990.pdf. 27 “Welcome | HIFLD,” accessed December 6, 2018, https://gii.dhs.gov/hifld/. 28 OAR U.S. EPA, “Greenhouse Gas Reporting Program (GHGRP),” Policies and Guidance, U.S. EPA, June 10, 

2014, https://www.epa.gov/ghgreporting.
13

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

To apportion the IET data from counties to cities, it is necessary to use another dataset to 

understand the distribution of industrial activity within counties. Industrial energy consumption 

is far less predictable and uniform than the residential and commercial sectors. Industrial energy 

use within a city or county is often distributed among a small number of facilities, and 

consumption for these facilities can easily range over several orders of magnitude. With the 

possibility of a single or several facilities accounting for most of the industrial energy 

consumption in many counties, it becomes particularly important to pinpoint the exact location 

of industrial facilities. The correct estimation of a small city’s industrial energy consumption 

may hinge on whether a large meat packing plant is correctly identified as being inside city limits 

or across the street in the unincorporated county, for example. 

In addition, per-facility consumption is highly dependent on the subsector. This is demonstrated 

by comparing the EIA national electricity use estimates by manufacturing sector for 2014 with 

the Census Bureau Statistics of US Businesses national counts of facilities by sector for 2014 

(the most recent year for which both are available). For example, it is estimated that 6,891 

apparel manufacturing facilities (NAICS code 315) in the nation used 803 million kilowatt-hours 

for an average use of approximately 0.1165 million kilowatt-hours per facility. The 4,558 

primary metal manufacturing facilities (NAICS code 331) used 138,437 million kilowatt-hours 

of electricity, for an average annual use of approximately 30.37 million kilowatt-hours per 

facility.

29 This shows an average difference of over two orders of magnitude. Thus, it is 

important to estimate both the spatial and subsector distribution of all industrial activity within 

the county.

In a survey of available data, the HSIP Gold dataset met three criteria: comprehensively 

cataloging all industrial facilities in the United States, having point location attribution, and 

categorizing facilities to a subsector. While the 2012 vintage is less than preferable for 2016 

estimates, it is assumed that the turnover in facilities between 2012 and 2016 would, in general, 

not be large enough to overcome the dataset’s advantages. One other weakness of the dataset is 

that it does not inventory construction activities in any way, whereas construction is considered a 

subsector by EIA and is estimated at the county level by the IET. The nature of retail electricity 

and natural gas use for construction is spatially and temporally dynamic. Unlike a factory, the 

point of consumption for a construction company moves throughout the year and between years 

as different projects are started and completed. Therefore, the error in assessing the value for any 

particular year could be particularly high. Additionally, the IET estimates that the construction 

sector only consumes about 5.2% of total industrial energy and the EIA Annual Energy Outlook 

2016 estimates construction energy consumption at 3.6% of total industrial energy. This suggests 

that the construction sector is not likely to be a prime driver of industrial energy use for most 

cities. 

EIA provides state-level industrial energy consumption data, including construction activity. As 

such, it is necessary to express the IET estimates at the county and sector level (hereafter called 

“county-sector estimates”) as proportions of state industrial consumption, including construction. 

The county-sector estimates, including those for the construction sector, were summed to the 

 29 U.S. Census Bureau, “2014 SUSB Annual Data Tables by Establishment Industry,” accessed December 3, 2018, 

https://www.census.gov/data/tables/2014/econ/susb/2014-susb-annual.html; “U.S. Energy Information 

Administration (EIA) - Consumption & Efficiency Data.”
14

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

state level to provide the total electricity and natural gas consumption estimates for the entire 

state. Then, the proportion of each state’s total consumption that was attributed to each countysector was calculated by dividing the county-sector estimate by the state sum. For the purposes 

of this document, this proportion will be known as the “county-sector proportion.”

Since the GHGRP data were used as an input to the IET tool, it was necessary to disaggregate 

the proportion of the IET fuel consumption estimates attributable to GHGRP facilities. This was 

achieved by summing up the natural gas consumption of all GHGRP facilities for each countysector combination to find the total natural gas consumption attributable to GHGRP for each 

county-sector. This number was then divided by the IET county-sector estimates to find the 

proportion of natural gas consumed by GHGRP facilities for each county-sector. To find the 

proportion of the IET county-sector estimates that still need to be resolved to the city level using 

the HSIP dataset, it is only necessary to subtract this proportion from 1. For purposes of this 

document, this proportion will be referred to as the “county-sector HSIP proportion.”

Since the GHGRP facility consumption still had to be attributed to each city, the GHGRP 

facilities were spatially joined with a city boundary dataset to assign a city to each facility. Then, 

the natural gas consumption of all GHGRP facilities for each city-county-sector combination was 

summed. This sum was then divided by the total consumption for each county-sector to express 

the proportion of each county-sector’s energy use attributable to GHGRP facilities in each city in 

each county. The proportion will be referred to as the “city GHGRP proportion.”

To determine the amount of activity within each county-sector that was attributable to each HSIP 

facility, the HSIP facilities were spatially joined with a county boundaries dataset to assign a 

county to each facility. Then, the total employees for each county-sector was summed. It is 

assumed that, within each subsector, the number of employees listed for the facility is a 

reasonable proxy for the size and therefore relative energy requirements of the facility. The 

proportion of the county-sector energy use that was attributable to each facility was then found 

by dividing the employee count for each facility by the county-sector sum. 

To find the proportion of each county-sector energy estimate attributable to each city, the HSIP 

facilities were intersected with a city boundaries dataset to assign a city to each facility. It was 

then possible to sum the county-sector proportions for each facility up to the city level. The 

result of this is the total proportion of the county-sector energy consumption that is estimated to 

be used by HSIP facilities by each city in each county. This will be referred to as the “city HSIP 

proportion.”

This gives all the information necessary to calculate each city’s proportion of the total state 

energy consumption attributable to each city-county-sector combination (see Appendix B for 

summary flowcharts of the industrial methodology). For electricity, the calculation is:

County-Sector-City Proportion = County-Sector Proportion * city HSIP Proportion

For natural gas, the calculation is:

County-Sector-City Proportion = County-Sector Proportion * ((County-Sector HSIP 

Proportion * city HSIP Proportion) + city GHGRP proportion)
15

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

The resultant data can be summed by city to express the proportion of total state energy use

attributable to each city. It is important to note that these proportions in most cases do not sum to 

one due to industrial activity that took place outside of cities. The proportion is then multiplied 

by the 2016 State energy consumption and expenditures (as given by EIA form 861 for 

electricity or EIA form 176 for natural gas) to give industrial sector electricity and natural gas 

consumption by city for 2016. Note that care must be taken to properly account for unit 

conversions. EIA form 861 reports electricity sales in gigawatt-hours and revenue in millions of 

dollars. EIA form 176 reports natural gas sales in million cubic feet (MMcf) and prices in dollars 

per thousand cubic feet (Mcf), such that revenues must be calculated as:

Revenue = Sales in MMcf * Dollars per Mcf * 1,000

The city consumption numbers for both electricity and natural gas must be multiplied by 1,000 to 

be in the units reported in our final dataset of megawatt-hours and thousand cubic feet. The 

electricity revenue numbers also must be multiplied by 1,000 to be in the final units reported of 

thousands of dollars. 

Potential shortcomings in this method of estimating industrial energy include the use of 

employee size as a proxy for energy use in industrial facilities. Employee size is used in both the 

IET tool county estimates and the assignment of county energy consumption to cities via the 

HSIP facility data. While the accuracy of this proxy is improved in both cases by using economic 

subsectors in addition to employee size, there is uncertainty as to the accuracy of this approach. 

In addition, the HSIP facility datasets do not include employee size estimates for the mining 

subsector. Another shortcoming is that the industrial facility catalogue provided by HSIP is not 

exhaustive and has a vintage several years prior to the estimates produced for 2016. Errors may

also occur when a single facility dominates industrial energy consumption in small cities.
16

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

3 Calibration

Modeling energy consumption and expenditures at the census tract level represents a bottom up 

approach. Estimates can be improved through fully leveraging existing aggregate information as 

well as the data available at different geographies and cohorts used to build the bottom up 

approach. To combine the bottom up and top-down approach and create a more robust model, we 

rescale census-tract estimates using energy intensity weighting and sum to state totals reported in

EIA Forms 861 and 176.

This rescaling or calibration process of allocating total state energy consumption to each census 

tract uses two key assumptions. For electricity, it was assumed that all tracts are served by a 

utility company. This assumption is supported by the high electrification rate found in the United 

States. For natural gas, it was assumed that not all tracts are served by utility gas, as other fuel 

types such as bottled gas, coal, or liquid fuel may serve as substitutes for utility gas.

To inform the subsequent steps, electric and natural gas utility territories were mapped to census 

tracts. This mapping was conducted using the Ventyx utility territories spatial data, which 

provides boundary information for electric and natural gas companies.30 A simple intersection 

was conducted between the territories and the census tracts. Utility IDs were also normalized to 

facilitate merging the spatial representation of the territories and the data provided by EIA.

Mapping utility service territories to census tracts sought to achieve two goals. First, it was 

important to know which tracts should be excluded from the natural gas allocation process. 

Second, to rescale bottom up estimates using energy intensity weighting, each tract was assigned 

the energy sales rate (dollars per megawatt-hour for electricity and dollars per million cubic feet 

for natural gas) of the utility that serves it, which was obtained from EIA’s data files. For tracts 

that had no electric utility initially assigned to it, as in states with unbundled utility service, the 

state average per-unit commodity and delivery service values price was used. For tracts where 

several utilities were present, the average of the prices was used.

Census-level consumption and expenditure estimates were geographically rescaled to match 

utility sales (electricity) or volumes (natural gas) and revenues. Different weighting approaches 

were used for the residential and commercial sectors.

3.1 Residential sector energy intensity weighting

Rescaling cannot be accomplished exactly for the residential sector because the number of utility 

customers does not correspond to the number of housing units and utility territories often 

overlap. Therefore, the calibration process first assigns utility customers by utility to census tract

using the IPF algorithm. The geospatial overlay of Ventyx utility boundaries with census tracts 

yields the binary matrix UU of 1’s and 0’s, with columns corresponding to utilities and rows to

census tracts. Column marginal sums are utility customers and row marginal sums are total 

housing units, buildings, and establishments. The resulting matrix CC is the approximate 

assignment. Note that the IPF does not converge as the column and row marginal sums total to 

 30 ABB/Ventyx Energy Velocity Suite. (2016). Local Distribution Company Territories. Retrieved June 1, 2018.
17

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

different values. However, each iteration preserves the corresponding marginal sum, in this case, 

chosen to be utility customers to match the EIA data.

Per customer consumption and expenditures are assumed to be similar among customers within a 

single census tract but served by different utilities. Based on this assumption, per customer 

consumption ll and per customer expenditures ll′ should also be similar whether taken from the 

utility-reported values, with SSrrrrrr as a diagonal matrix of utility-reported sales and volumes per 

customer,

ll = �UU ∙ �CC ∙ SSrrrrrr�

TT

� ∙ 11

or estimated from the customer perspective, with RR eeee as a diagonal matrix of estimated 

consumption per customer,

ll′ = [UU ∙ (CCTT ∙ RR eeee)] ∙ 11.

Matrix multiplication by the column vector of ones 11 yields a row-wise sum. The estimated 

values are then rescaled by the ratio of these quantities to give the following calibrated result

RRcccc = RR eeee ∙ (ll ⊘ ll′)

where the operator ⊘ represents element-wise division. Derivation of these equations is 

provided in Appendix A. The diagonal elements of RRcccc are the calibrated census tract-level 

estimated consumption per customer.

The calibration approach described in Appendix A leads to improved agreement with available 

measured inventory data. However, there are several instances where this approach can lead to 

poor agreement: 

• The equivalence of ll and ll′ holds under the assumption that census tracts are relatively 

homogeneous in per-customer energy consumption. However, the calibration process is 

biased toward larger utilities in instances where two or more utilities share a single 

census tract and those utilities have very different per customer energy consumption 

values. 

• There are data gaps and inaccuracies in the mapping of utilities to census tracts, leading 

to incorrect assignment of customers to utilities and customers to census tracts. 

• The IPF approach may poorly reflect the true geographic distribution of utility customers. 

• Utility service territories may be large, encompassing many cities and counties. For 

instance, in a severe case, most of the state of Rhode Island is served by a single 

distribution utility. The calibration process merely reflects the state averages and provides 

poor results for areas served by the state’s two much smaller municipal utilities. 

3.2 Commercial sector energy intensity weighting

Differences in the number of buildings provided by the CoStar and GBS commercial building 

inventories and the number of customers reported on EIA’s Forms 861 and 176 are the primary 

cause of misalignment between state totals and census tract-level estimates. Nonetheless, the 
18

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

CBECS-derived energy consumption data can be converted into weights that inform the relative 

energy uses among tracts, which are the metrics used to estimate the energy intensity of each 

tract.

Once the spatial disaggregation was complete, the energy consumption and expenditure 

calculations were finalized. Energy consumption values were obtained by distributing state totals 

to all relevant census tracts using the energy intensity weights obtained from CBECS.

Expenditures are equal to consumption multiplied by the sales rate ($/energy unit).
19

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

4 Validation of Estimates to Measured Values

4.1 Validation of Residential Modeled Data

There are several publicly available sources of data for validation purposes. Xcel Energy, an 

investor-owned utility that operates in several western and midwestern states, reports 

community-level electricity and natural gas sales and revenue data. Also, many cities in 

Minnesota report consumption data through the Regional Indicators Initiative (RII). Further, 

Massachusetts reports community-level electricity data through Mass Save.31 Finally, many local 

governments have undertaken GHG emissions inventories. As part of an intermediary step, those 

inventories often report electricity and natural gas consumption by sector.

One significant difficulty with validation is that the number of reported utility customers may 

differ significantly from the number of occupied housing units. There are several reasons why 

this occurs. Most commonly, there are discrepancies between the community boundaries used by 

the reporting entity and the community boundaries used by the U.S. Census. There are also

difficulties with estimating the number of utility customers. While we can assume that most 

occupied housing units are served by an electric utility, there may be many unoccupied housing 

units that also are served by an electric utility, and multi-family units may share a single meter. 

Furthermore, many occupied housing units are not served by a natural gas utility, and multifamily units may share a single meter or common heating unit. Our assignment of natural gas 

customers to census tracts is approximate. Comparisons of state- and local-calibrated estimates 

to community data from Xcel Energy (electricity and natural gas) and Mass Save (electricity 

only) are shown in Figures 5 and 6, where each community-wide consumption data point 

reported is charted according to its difference with modeled data. State-level calibration rescales 

energy consumption estimates to match state-wide totals (see Figure 5). However, localcalibrated estimates (see Figure 6) rescale energy consumption based on utility-level totals (as 

described in Section 3.1 and Appendix A).

 31 “Mass Save® | Energy Assessments | Equipment Rebates | Incentives,” accessed July 3, 2018,

http://www.masssavedata.com/Public/GeographicSavings?view=U
20

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 5. Comparison of state-calibrated residential estimates to community data from Xcel 

Energy and Mass Save.

Figure 6. Comparison of local-calibrated residential estimates to community data from Xcel 

Energy and Mass Save.

This comparison illustrates that the majority of residential energy consumption estimates based 

on the methodology described herein are within +/- 10% of the 515 reported electricity and 76 

reported natural gas city and county inventory values provided by Xcel Energy and Mass Save.
21

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

There are several outliers with large discrepancies. Many of those communities are very small, 

with less than 1000 occupied housing units, where potential errors in the Census survey data or 

differences in geographic boundary definitions between utilities and Census could generate large 

differences on a percentage basis. To add, the Mass Save data does not include customer counts, 

preventing basic checks against this issue.

4.2 Validation of Commercial and Industrial Modeled Data

High-quality validation datasets for the industrial and commercial sectors are scarce, a key 

reason this work was undertaken. Xcel Energy’s Community Energy Reports32 provide

consumption and expenditure data for 200 cities located within the utility’s service territory, 

which covers parts of six states. However, Xcel Energy only reports disaggregated commercial 

and industrial consumption in its Community Energy Reports when certain rules about the 

number of facilities are met to protect anonymity. In addition, the classification of a company as 

commercial or industrial is done according to a voluntary survey. When the survey is not 

submitted, the company is simply classified as commercial. This means the commercial energy 

consumption is likely overestimated while industrial energy consumption is underestimated. 

With no information on survey response rates, there is no way to estimate the extent to which 

this is the case. 

Definitions of customer types can also conflict with definitions of building activity types. 

Datasets such as data from EIA forms 861 and 176 and Xcel Community Energy Reports report 

the total number of customers, which reflects the total number of connections and not the total 

number of buildings serviced. The total number of customers is usually lower than the total 

number of buildings, as evident in Figure 7.

Figure 7. Modeled total buildings versus EIA reported commercial customers per state

 32 https://www.xcelenergy.com/working_with_us/municipalities/community_energy_reports
22

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

It is difficult to assess how much this difference between buildings and customers impacts the 

final energy estimates. Since the tract energy intensity was calculated based only on the total 

square footage of the building, one can argue that this difference may not have a strong impact. 

However, it is important to note that the current approach does not take into consideration the 

difference between occupied and unoccupied commercial buildings. By only using the building 

square footage and use type, the method assumes all buildings are occupied and therefore are 

using energy at the average rate for buildings of the same class.

One high-quality dataset is municipal electricity use for industrial and commercial sectors 

gathered by the Mass Save program led by the State of Massachusetts.33 The Mass Save program 

collects data on energy use for every municipality in the state, except for some that are censored 

for certain anonymity reasons. Still, the dataset gives data on combined commercial and 

industrial retail electricity use for 299 cities. While there are still certain discrepancies between 

what is reported and what our estimates include (for instance, the Mass Save data likely include

electricity use by the construction sector whereas our estimates do not include this), the dataset 

presents one of the best and largest sources for validation available for commercial and industrial 

electricity consumption.

Our aggregated estimates for commercial and industrial electricity use vs. those reported by 

Mass Save are shown in Figure 8. Due to the skewed distribution of electricity use, both 

estimates are log transformed. 

Figure 8. Plot of estimated commercial and industrial retail electricity consumption for 

municipalities and townships in Massachusetts vs. reported values from the Mass Save program

 33 “Mass Save® | Energy Assessments | Equipment Rebates | Incentives,” accessed December 6, 2018, 

https://www.masssave.com/.
23

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

A simple linear regression of the log-transformed estimated values predicting the logtransformed reported values gives an intercept of -0.69 and a slope of 1.05, meaning that there is 

very little bias in the estimates, and an R-squared value of 0.87, implying that most of the 

variance in the estimated values is due to the actual underlying difference in electricity 

consumption. There are certain outlier values where the estimates are several times larger or a 

small fraction of the reported values; however, these outliers seem to be more common for cities 

with small reported values as opposed to large reported values. This makes sense as facilities 

datasets used for the estimates have more accurate coverage within larger cities than small towns

where a single facility can dominate energy consumption. 

From the perspective of a local planner, it matters less how closely the dataset tends to reflect

energy use and instead how much the estimate for a single city can be relied on to track the city’s

actual energy use. From this perspective, it may be more constructive to understand the 

distribution of the percent errors between the estimates and reported values. Such a histogram, 

with bins like the bins used for the residential validation, is shown in Figure 9. 

Figure 9. The percent error of estimated commercial and industrial electricity consumption vs. 

reported values in the Mass Save dataset

While the estimates can predict the reported values with low bias, the average percent error is 

very high compared to the residential data. While 52.8% of the cities have an estimate value 

within 30% of the reported values, 30.8% of the values have an error greater than 50%. This is 

not due to a bimodal distribution but rather to a preponderance of small towns with reported 

consumption values of under 10,000 MWh, for which the model reliably tends to overestimate 

consumption. Therefore, while the estimates may represent an advancement in the ability to 

estimate these values at such a scale, they also underscore the need for better data on commercial 

and industrial energy use and facilities to be collected both at the local and national level. While 
24

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

our estimated values seem to represent an unbiased and relatively low noise representation of the 

true consumption at an aggregate level, they may be unacceptably inaccurate to inform local 

planning decisions, particularly for smaller towns. 

Because this validation exercise only represents electricity consumption from one state, we 

thought it necessary to seek at least one other validation dataset. The other validation dataset 

used is from the Minnesota RII program. Like Mass Save, the program reports combined 

commercial and industrial consumption, in this case for both electricity and natural gas. Data for 

only 46 cities were available for 2016, much smaller than the Mass Save dataset of 299 cities,

but still enough for approximate validation.

A simple linear regression of the estimated electricity consumption values and reported values 

gives a relatively biased fit, with an intersect value of 2.2, a slope value of 0.82, and an RSquared valued of 0.72. However, a single outlier data point was identified that had an outsized 

effect on these coefficients. With the outlier removed, the results improved significantly to an 

intercept value of 0.42, a slope value of 0.97 and an R-Squared value of 0.84.

A simple linear regression of the estimated natural gas consumption values and reported values 

also gives a relatively biased fit, with an intersect value of 3.8, a slope value of 0.7, and an Rsquared valued of 0.7. However, the same point that was an outlier for the electricity values also 

was a very influential outlier for the natural gas values. With the outlier removed, the results 

improved significantly to an intercept value of 1.05, a slope value of 0.9 and an R-squared value 

of 0.85. Figures 10 and 11 show scatterplots of the log-transformed values for electricity and 

natural gas estimates vs. the reported values without the outlier removed.

Figure 10. Plot of estimated commercial and industrial retail electricity consumption for select 

Minnesota municipalities vs. reported values from the Minnesota RII program
25

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 11. Plot of estimated commercial and industrial retail natural gas consumption for select 

Minnesota municipalities vs. reported values from the Minnesota RII program

Taken together, these plots mostly reinforce the story that the Mass Save dataset shows. While 

significant outliers occur, once they are removed, the validation shows the estimates are unbiased 

predictors of reported values. The natural gas validation shows there may be more of a bias with 

natural gas data than with electricity, with a slight bias towards underestimation. Since the 

Minnesota RII dataset represents a sample with some self-selection bias (unlike the Mass Save

dataset, which is an almost comprehensive dataset for the entire state), some bias may exist due 

to the types of towns that report to RII in addition to the small sample size. If the RII tends to 

have data from smaller towns, which the Mass Save data suggest we do a poorer job of 

estimating, this could give the appearance of bias in our estimates that does not exist in the entire 

dataset.
26

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

5 Conclusion

The method of proportionally allocating energy use and expenditure values reported by EIA 

from utility geographies down to smaller geographic levels is shown to be a valid approach 

through comparing the resulting estimates with reported values at the city-level. This method 

provides scalability, enabling users to obtain energy estimates for a single county or city or for 

the whole nation.

The quality of the estimates is directly related to the quality of the input data. This is particularly 

true for the quality of the commercial building inventory data used to generate the energy 

intensities where a more comprehensive dataset with a wider range of building types and more 

extensive spatial coverage yields better results. Of the three sectors, greater existing data 

availability and uniformity of building-level energy use in the residential sector enable more 

accurate modeling. The opposite is true of the industrial sector where significant disparities exist 

in energy consumption by industry and among facilities within industries, making estimation

challenging. In aggregate, validation demonstrates that estimated consumption provides an 

unbiased, relatively low noise representation of true consumption. However, increased 

inaccuracy in individual, small cities and towns, particularly for industrial estimates, makes 

modeled industrial data less reliable for planning purposes in these jurisdictions.

Overall, validation demonstrates that these sector-specific methodologies provide reasonable 

results that can inform city-level energy decision making where measured, utility-reported data 

are unavailable. The methodologies result in a robust, standardized national dataset of city-level 

energy consumption and expenditures available on the State and Local Energy Data site.

34

One area for potential improvement in future versions is the validation step. The scarcity of 

standardized city-level data makes it challenging to validate the results at a national scale. 

Validation with city-level reported data should be conducted to the extent possible, with the goal 

of identifying patterns of overprediction and underprediction. Such patterns can inform which 

input dataset and metrics to revise and improve.

 34 https://apps1.eere.energy.gov/sled/
27

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix A. Derivation of Calibration Equations

The calibration approach seeks to identify groupings of census tracts within states that share a set 

of utilities. Then, the average per customer consumption over those census tracts should equal 

the corresponding average per-customer sales by the associated utilities. This assumes that states 

are separable in the sense that there are smaller than state-level groupings. 

Start with a matrix CC, which maps utility customers (columns) to census tracts (rows), and a 

matrix UU, which is a binary representation of CC:

UUiiii = �

1 if CCiiii ≠ 0

0 if CCiiii = 0 Eq. A.1

Then, the following quantity provides the total number of utility customers shared by each 

census tract

cc = [UU ∙ CCTT] ∙ 11 Eq. A.2

where matrix multiplication by the column vector of ones 11 yields a row-wise sum. For example, 

suppose there are four census tracts labeled 1, 2, 3, and 4 and three utilities labeled A, B, and C, 

as illustrated in Figure A1.

Figure A1. Example census tracts 1, 2, 3, and 4 and utility service areas A, B, and C

Further suppose this corresponds with the following mapping:

CC = �

400 0

200 50

0

0

0 300

0 0

0

600

�

1 2

3 4

A A

B C

B
28

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

which assigns 400 customers of utility A to census tract 1, 50 customers of utility B to census 

tract 2, and so forth. Then, Eq. A.2 gives:

cc = �

600

950

350

600

�

which shows that that census tract 1 shares 200 utilities customers in census tract 2 to give a total 

of 600 customers, census tract 2 shares 400 customers in census tract 1 and 300 customers in 

census tract 2 to give a total of 950 customers, and so forth. 

The average per customer sales for each grouping may be calculated accordingly:

= ��UU ∙ �CC ∙ SSrrrrrr�

TT

� ∙ 11� ⊘ {[UU ∙ CCTT] ∙ 11} Eq. A.3

where the operator ⊘ represent element-wise division. Here, we have simply inserted SSrrrrrr as a 

diagonal matrix of utility reported sales and volumes per customer to produce a customerweighted average.

This same quantity may be calculated based on consumption per customer at the county level. In 

this case, the numerator is revised to read:

rr = {[UU ∙ (CCTT ∙ RR eeee)] ∙ 11} ⊘ {[UU ∙ CCTT] ∙ 11} Eq. A.4

where RR eeee is a diagonal matrix of estimated consumption per customer.

Under the assumption that utility customers of different utilities but within a single census tract 

have the same average per-customer consumption, the equality rr = should hold. Then, we can 

try to enforce the equality accordingly:

RRcccc = RR eeee ∙ ( ⊘ rr) Eq. A.5

where the diagonal elements of RRcccc are the calibrated census tract-level estimated consumption 

per customer. This equation is equivalent to equation Y if we simply set the numerator of as ll

and the number of rr as ll′.

To complete the simple example, if we assume:

SSrrrrrr = �

14 0 0

0 10 0

0 0 16

�

and
29

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

RR eeee = �

2300 0

0 2100

0 0

0 0

0 0

0 0

1700 0

0 2700

�

then

= �

14

12.5

10

16

�

and

rr = �

2233

2058

1757

2700

�

The calibrated per-customer consumption values are then:

RRcccc = �

2300 0

0 2100

0 0

0 0

0 0

0 0

1700 0

0 2700

� ∙ ��

14

12.5

10

16

� ⊘ �

2233

2058

1757

2700

��

= �

14.4 0

0 12.8

0 0

0 0

0 0

0 0

9.7 0

0 16

�

We can check this numerical result by re-calculating the utility values:

SSeeeeee = {CC ∙ [11 ∙ RRcccc ]} ⊘ {11 ∙ CC} = [13.9 10.1 16] Eq. A.6

showing good agreement with the diagonal values in SSrrrrrr.

In this example, we have provided numerical values of similar magnitudes to the values used in 

the residential calibration. A value of 14 corresponds to 14 MWh/year, which is within the 

normal range of annual household electricity consumption. A value of 2,300 corresponds to 

$2,300/year in electricity expenditures, also within the normal range. Residential estimates come 

from the 2016 ACS5, which samples expenditures and not consumption. However, this analysis 

assumes that expenditures are roughly proportional to consumption.

Note that the choice of RRcccc is not unique, and in general, there are many possible values that 

provide agreement with the utility-reported values. However, this approach has the benefit of 

making a choice that retains some sense of the original guess RR eeee, as most choices of RRcccc are 
30

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

not physically plausible. One could further refine this approach by taking the calibrated value as 

the seed value in a numerical minimization, such that the utility values are matched exactly. 

Minimizing the difference between the values in Eq. A.6 and the diagonal elements of Eq.A.5 

leads to the following result. Differences are less than 2%. 

RRffff = �

14.6 0

0 12.8

0 0

0 0

0 0

0 0

9.5 0

0 16

�

Improving agreement with utility values is desirable; however, in practice, performing this 

minimization step is challenging. Any errors or unusually divergent values in CC and SSrrrrrr can 

lead to RRffff values that deviate significantly from the original RR eeee guesses. Thus, in this report, 

we omit this second step and limit the calibration to Eq. A.5.
31

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix B. Methodology Flowcharts

Figure B1. Commercial energy consumption and expenditures methodology summary
32

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure B2. Industrial electricity consumption and expenditures methodology summary
33

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure B3. Industrial natural gas consumption and expenditures methodology summary