The Demand-side Grid (dsgrid) 

Model Documentation

Electrification Futures Study
The Demand-Side Grid (dsgrid) 

Model Documentation

Elaine Hale,

1 Henry Horsey,1 Brandon Johnson,2

Matteo Muratori,

1 Eric Wilson,1 Brennan Borlaug,1

Craig Christensen,

1 Amanda Farthing,1 Dylan Hettinger,1

Andrew Parker,

1 Joseph Robertson,1 Michael Rossol,1

Gord Stephen,

1 Eric Wood,1 and Baskar Vairamohan2

1 National Renewable Energy Laboratory

2 Electric Power Research Institute

Suggested Citation

Hale, Elaine, Henry Horsey, Brandon Johnson, Matteo Muratori, Eric Wilson, et al. 2018. The 

Demand-side Grid (dsgrid) Model Documentation. Golden, CO: National Renewable Energy 

Laboratory. NREL/TP-6A20-71492. https://www.nrel.gov/docs/fy18osti/71492.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 Office of Strategic Programs. The views expressed in the article do not necessarily represent the 

views of the DOE or the U.S. Government.

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Renewable Energy Laboratory (NREL) at 

www.nrel.gov/publications.

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Preface

This report is one in a series of Electrification Futures Study (EFS) publications. The EFS is a 

multi-year research project to explore widespread electrification in the future energy system of 

the United States.

This report documents a new model, the demand-side grid (dsgrid) model, which was developed 

for the EFS and in recognition of a general need for a more detailed understanding of electricity 

load. dsgrid utilizes a suite of bottom-up engineering models across all major economic 

sectors—transportation, residential and commercial buildings, and industry—to develop hourly 

electricity consumption profiles for every county in the contiguous United States (CONUS). The 

consumption profiles are available by subsector and end use as well as in aggregate. This report 

documents a bottom-up modeling assessment of historical (2012) consumption and explains the 

key inputs, methodology, assumptions, and limitations of dsgrid.

The EFS is specifically designed to examine electric technology cost advancement and adoption 

for end uses across all major economic sectors as well as electricity consumption growth and 

load profiles, future power system infrastructure development and operations, and the economic 

and environmental implications of electrification. Because of the expansive scope and the multiyear duration of the study, research findings and supporting data will be published as a series of 

reports, with each report released on its own timeframe. Future research to be presented in future 

planned EFS publications will rely on dsgrid to analyze the hourly electricity consumption under 

scenarios with various levels of electrification. In addition to providing electricity consumption 

data for the planned EFS analysis, dsgrid can be used for other analysis outside the EFS research 

umbrella.

More information and the supporting data associated with this report, links to other reports in 

the EFS study, and information about the broader study are available at www.nrel.gov/efs.
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Acknowledgments

The Electrification Futures Study (EFS) is led by researchers at the National Renewable Energy 

Laboratory (NREL) but relies on significant contributions from a large collaboration of 

researchers from the following research organizations: U.S. Department of Energy (DOE), 

Evolved Energy Research, Electric Power Research Institute, Lawrence Berkeley National 

Laboratory, Northern Arizona University, and Oak Ridge National Laboratory. We would like 

to thank all contributors for useful analysis, data, and input throughout the project.

A technical review committee of senior-level experts provided invaluable input to the overall 

study, with some committee members sharing thoughtful comments on this specific report as 

noted below. Although the committee members offered input throughout the study, the results 

and findings from this analysis and the broader EFS do not necessarily reflect their opinions 

or the opinions of their institutions. The technical review committee is comprised of the 

following individuals:

Doug Arent, Joint Institute for Strategic Energy 

Analysis (committee chair)

Jonathan Hughes, University of Colorado Boulder

Sam Baldwin, DOE Michael Kintner-Meyer, Pacific Northwest 

National Laboratory

Steve Brick, Clean Air Task Force John Larsen, Rhodium Group

Steve Clemmer, Union of Concerned Scientists Bryan Mignone, ExxonMobil Research and 

Engineering Company

Laura Cozzi, International Energy Agency Granger Morgan, Carnegie Mellon University

Francisco de la Chesnaye, Electric Power 

Research Institute

Patrick Riley, GE Global Research

Keith Dennis, National Rural Electric Cooperative 

Association

Laurie ten Hope, California Energy Commission

Carla Frisch, DOE Joan Ogden, University of California Davis

Arnulf Grubler, International Institute for Applied 

Systems Analysis

Susan F. Tierney, Analysis Group, Inc.

Howard Gruenspecht, Massachusetts Institute of 

Technology

For this report, the authors are grateful to the following individuals for their thorough reviews, 

comments, and suggestions: 

• John Agan, Greg Dierkers, Paul Donohoo-Vallett, Robert Fares, Rachael Nealer, 

Kara Podkaminer, Brian Walker, and Jenah Zweig (DOE)

• Erin Boedecker and William Booth (U.S. Energy Information Administration)

• Lester Shen (Center for Energy and Environment)

• Tom Eckman (Northeast Power Coordinating Council, Inc.)

• Joshua Eichman, Jack Mayernik, Colin McMillan, Daniel Steinberg, Doug Arent, and 

Dan Bilello (NREL)
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We are grateful to Trieu Mai (NREL) for his leadership of the EFS and review of this work. 

Primary funding for the EFS is provided by the DOE Office of Energy Efficiency and 

Renewable Energy Office of Strategic Programs. We especially thank Paul Donohoo-Vallett and 

Steve Capanna of DOE for support, leadership, and review of this report and the EFS as a whole. 

We also thank Gian Porro and Dani Salyer (NREL) for their program and project management

support, and Mike Meshek (NREL) for editing support. Any errors and omissions are the sole 

responsibility of the authors. This research was funded under contract number DE-AC36-

08GO28308.
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List of Acronyms

ACS American Community Survey

ADOPT Automotive Deployment Options Projection Tool

AEO Annual Energy Outlook (U.S. EIA)

AMY actual meteorological year

ANSI American National Standards Institute

AWWARF American Water Works Association Research Foundation

BCAP Building Codes Assistance Project

BFG blast furnace gas

BIT bituminous coal

BLQ black liquor

CBECS Commercial Buildings Energy Consumption Survey

CBP County Business Patterns (U.S. Census Bureau)

CEE Center for Energy and Environment

CEMS continuous emission monitoring systems

CHP combined heat and power

CONUS contiguous United States

DFO distillate fuel oil

DG distributed generation

dGen Distributed Generation Market Demand (model)

DHI diffuse horizontal irradiance

DHR diffuse horizontal radiation

DHW domestic hot water

DNI direct normal irradiance

DNR direct normal radiation

DOE U.S. Department of Energy

DOE CHP DB U.S. DOE Combined Heat and Power Installation Database

DPV distributed photovoltaics

EFS Electrification Futures Study

EIA U.S. Energy Information Administration

EPA U.S. Environmental Protection Agency

EPRI Electric Power Research Institute

EPW EnergyPATHWAYS weather (file type)

ERCOT Electric Reliability Council of Texas 

EST Eastern Standard Time

EVI-Pro Electric Vehicle Infrastructure Projection Tool

eVMT electric vehicle miles traveled

FAA Federal Aviation Administration
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FERC Federal Energy Regulatory Commission

GCAM Global Change Assessment Model

GHI global horizontal irradiance

GHR global horizontal radiation

GTM Greentech Media

GW gigawatt

HDV heavy-duty vehicle

HVAC heating, ventilation, and air conditioning

IAC industrial assessment center

IAM integrated assessment model

IEA International Energy Agency

IGATE-E Industrial Geospatial Tool for Energy Evaluation

IPCC Intergovernmental Panel on Climate Change

ISO independent system operator

LBNL Lawrence Berkeley National Laboratory

LDV light-duty vehicle

LFG landfill gas

LIG lignite coal

MDV medium-duty vehicle

MECS Manufacturing Energy Consumption Survey

MG million gallons

MISO Midcontinent Independent System Operator

MRAE mean relative absolute error

MSB biogenic municipal solid waste

NAHB National Association of Home Builders

NAICS North American Industry Classification System

NEEA Northwest Energy Efficiency Alliance

NEMS National Energy Modeling System

NESSIE National Electric System Simulation Integrated Evaluator

NGA National Governors Association

NREL National Renewable Energy Laboratory

NSRDB National Solar Radiation Database

NTD National Transit Database

NYISO New York Independent System Operator

ORNL Oak Ridge National Laboratory

OTR Other

PAT Parametric Analysis Tool

PBA principle building activity
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PEV plug-in electric vehicle

PNNL Pacific Northwest National Laboratory

PV photovoltaics

RBSA Residential Building Stock Assessment

RBSAM Residential Building Stock Assessment Metering

RECS Residential Energy Consumption Survey

RFO residual fuel oil

RTO regional transmission organization

SAE statistically adjusted end-use

SEDS State Energy Data System

SERA Scenario Evaluation, Regionalization and Analysis (model)

SIC Standard Industrial Classification

SUB subbituminous coal

T&D transmission and distribution

TMY typical meteorological year

ULRD Utility Load Research Data

UZA urbanized area

WDS wood/wood waste solids

WECC Western Electricity Coordinating Council
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Executive Summary

Electrical load is the backdrop for all power systems analysis. As a coequal partner in the supplydemand balance that must be maintained on electrical grids at all times and for all timescales, 

load is a major source of variability and uncertainty in grid operations. In the context of our 

evolving energy systems, with increasing shares of variable renewable energy, potential 

electrification of transport and other end uses, and the emerging ability to control energy use at

the kilowatt-scale using grid-level information, obtaining a deeper understanding of electrical 

load is more important now than it has ever been before. Despite this, many studies of future grid 

systems understandably continue to place more emphasis on supply-side resources such as 

generation and transmission, given that they are fewer in number and are better understood in 

terms of cost and performance.

Load forecasting has been at the heart of utility planning for decades, but it is typically done in a 

top-down manner that lacks the granularity in time, geography, end use, and technology that is 

needed to explore the potential impact of technological shifts. Purely econometric methods that

rely on historical load data combined with projections of economic and demographic parameters 

are common. More sophisticated methods include combining econometric techniques with 

simple engineering models of a limited number of key end uses. Nonetheless, there is evidence 

that these methods may be falling short. For example, Carvallo et al. (2017) find systemic 

overestimates of load growth in utility integrated resource plans. These overestimates are 

certainly partially explained by the recession that followed the 2008 financial crisis but are 

persistent enough to suggest other factors (e.g., energy efficiency adoption and performance)

may have been systematically misestimated. This suggests that more complementary, bottom-up 

engineering- or physics-based modeling that incorporates technology and behavioral detail may 

be a valuable addition to the load forecasting process.

Analyses exploring future scenarios of the power sector also typically employ relatively simple

scaling of historical loads. To some extent, this is a matter of necessity; renewable integration 

studies have demonstrated the importance of modeling systems with significant quantities of 

wind or solar generation using time-synchronized load, wind, and solar data. Because it is netload that is most important for system operations, these timeseries must all reflect the same 

weather as it was experienced simultaneously across the region of interest. Thus, historical 

hourly load data are taken from the same year from which the wind and solar data sets are 

derived, and a basic scaling factor is applied. For example, MacDonald et al. (2016) project 

hourly load in 2030 using a single growth rate assumption of 0.7% applied to all hours. Although 

expedient, this methodology is insufficient to capture the impact of significant demand-side 

technological change, such as widespread electrification, which could drastically impact load 

shapes and demand-side flexibility.

The primary purpose of the demand-side grid model (dsgrid) is to fill these gaps by creating 

comprehensive load data sets at a sufficient temporal, geographic, sectoral, and end-use 

resolution to enable detailed analyses of current patterns and future projections of end-use load. 

Furthermore, the dsgrid platform uses a bottom-up methodology that allows highly resolved 

analysis of “what-if” scenarios. dsgrid leverages detailed sectoral energy models to provide 

hourly time series of load by subsector, end use, and county covering a full year (see Figure ES1). Although dsgrid currently emphasizes electricity load data, its component sector models for 
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residential buildings, commercial buildings, and industry provide information on other fuel use, 

including natural gas. The data sets can thus be leveraged to support analysis of numerous 

demand-side technology-driven changes, such as energy efficiency, electrification, and 

operational flexibility (i.e., demand response). The electricity use data are time-synchronized 

with solar and wind data sets so as to be suitable for use in power systems analysis. 

Figure ES-1. dsgrid provides load data for the contiguous United States at high geographic, 

temporal, and sectoral resolution. 

This report documents dsgrid and its initial data set, which covers electricity demand in the 

contiguous United States (CONUS) for the historical year of 2012. We start with a historical year 

to enable model calibration and validation, as well as the development of a mathematical 

description of our model residuals that can be applied to future-year “what-if” scenarios to 

remove known errors between our bottom-up modeling and historical load shapes. This work is 

part of the Electrification Futures Study (EFS),1

 for which the dsgrid team will be developing 

future load snapshots that describe projected year 2050 under baseline and a range of 

electrification assumptions. 

1 www.nrel.gov/efs 
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Within dsgrid’s architecture, each sector’s energy use is modeled with separate methodologies. 

The core sector models, which provide the most detailed level of modeling available in dsgrid, 

cover about 80% of 2012 annual electricity demand (Figure ES-2).

2 An additional 17% is then 

represented with sector-level gap models, which are coarser in both inputs and outputs than the 

fully detailed sector models, but nonetheless leverage some of the core data (e.g., load shapes)

and provide hourly demand timeseries resolved at least to the subsector and state level. The 

remaining 3% is electricity used for unmodeled commercial subsectors.

Figure ES-2. dsgrid models about 80% of 2012 U.S. electricity use in detail.

Brief descriptions of the detailed sector methodologies follow:

• Residential and Commercial Buildings: Building loads are estimated using ResStock 

and ComStock, which use similar statistical methodologies and OpenStudio modeling 

infrastructure to simulate the U.S. single-family detached and commercial building stock

electricity consumption by end-use. These models sample from thousands of probability 

distributions to produce hundreds of thousands of EnergyPlus simulations, which are then 

weighted to represent subsector building stocks at the county level. This detailed 

modeling covers single family homes and commercial buildings mappable to the 16 DOE 

commercial “prototype buildings” (Goel et al. 2014).

• Industrial Manufacturing: Industrial manufacturing loads are modeled with the 

Industrial Geospatial Analysis Tool for Energy Evaluation (IGATE-E), which uses plantlevel databases, the Manufacturing Energy Consumption Survey, and the Electric Power 

Research Institute’s Load Shape Library to construct hourly time series of electricity use 

by manufacturing subsector and end use. Because manufacturing processes vary greatly, 

IGATE-E does not attempt direct simulation of loads but rather compiles data from 

 2 That is, 80% of the site demand is from residential buildings, commercial buildings, industry, and transport. 

This measure excludes such subsectors as public water supply, municipal wastewater treatment, and exterior 

roadway lighting.
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multiple sources and applies statistical techniques to estimate energy consumption down 

to the end-use level. Because IGATE-E only models manufacturing, the additional 

industrial sectors of agriculture, mining, and construction comprise our industrial gap 

model.

• Transportation: Given the focus in this report on constructing a model of historical 

electricity use for the year 2012, and the very low deployment of plug-in electric vehicles 

in that year, we describe the detailed transportation modeling methods that will be used in 

future work to capture EV location and charging; but no detailed sector-level results for 

transportation are presented.3 When this capability is developed for the EFS future model 

year snapshots, on-road plug-in electric vehicle operation will be described using the 

Scenario Evaluation and Regional Analysis (SERA) model and the Electric Vehicle 

Infrastructure Projection Tool (EVI-Pro). SERA describes vehicle infrastructure 

requirements and will be used to disaggregate vehicle adoption to the county level. EVIPro simulates hourly charging profiles based on travel data and charging preference 

assumptions (e.g., residential charging as opposed to reliance on public charging). The 

historical 2012 data set includes a transportation gap model that describes electricity use 

in passenger trains.

While the sector models cover approximately 80% of annual electricity demand, they do not 

provide full coverage of all electricity use in the United States, nor do they account for such load 

modifiers as distributed generation or transmission and distribution (T&D) losses. The model 

therefore incorporates additional data sources and uses them to create gap models, distributed 

generation models, and a model of losses (Figure ES-3). The sectoral gap models describe 

building types not modeled in detail; electricity used for agriculture, mining and construction; 

and electricity used for passenger rail transport. dsgrid additionally contains supplemental gap 

models that represent electricity used for municipal water services and outdoor lighting. 

Distributed generation from solar photovoltaics, combined heat and power, and other distributed 

thermal generators is estimated on hourly, state, and sectoral bases using a variety of data 

sources. The distributed generation data combined with historical electricity sector data are key 

to enabling the calculation of model residuals and related visualizations used to calibrate and 

validate the model. In total, the dsgrid model structure is a confederation of data sets and 

programmatic methods that are available to aggregate, disaggregate, visualize, and perform 

statistical analysis on an overall description of U.S. electricity demand for one model year.

 3 The data visualized in Figure 2 for Transport consists of a small orange sliver topped with a longer blue sliver; the 

blue sliver is labeled “Passenger Rail (95%).” The orange sliver represents 381 GWh of electricity used in the U.S. 

for light-duty vehicles in 2012 that is not modeled in this version of dsgrid.
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Figure ES-3. dsgrid input data architecture

All acronyms are defined in the acronyms list on page vi.

Data Set Summary

The dsgrid historical snapshot for 2012 is a more highly resolved starting point for power sector

studies than has heretofore been available. Except for the transportation gap model, which is at 

the state level, the detailed sectoral models and the sectoral gap models are available at the 

county level by subsector and end use. End use breakdowns are provided for the detailed sector 

models (residential and commercial buildings, and industrial manufacturing) as well as for the 

residential gap model. The detailed building models include hourly data on natural gas and 

district heating and cooling use, in addition to electricity. For all fuels, building energy use is 

reported across nine end uses: fans, pumps, space heating, space cooling, heat rejection

(commercial only), interior lights, exterior lights, water systems, and interior equipment.

Industrial manufacturing electricity use is reported for the 12 U.S. Energy Information 

Administration (EIA) Manufacturing Energy Consumption Survey end uses: conventional boiler 

use; process heating; process cooling and refrigeration; machine drive; electrochemical 

processes; other process use; facility heating, ventilation, and air conditioning (HVAC); facility 

lighting; other facility support; onsite transportation; other nonprocess use; and end use not 

reported.
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The data set is summarized at the annual level for the contiguous United States in Table ES-1. 

The detailed and gap models described above, plus gap models for roadway and parking outdoor 

lighting and for public water supply and wastewater treatment (county-level, based on per-capita 

energy use estimates) form the dsgrid-core components. Together with the hourly residuals,

4

these components, which are shaded green in Table ES-1, provide an hourly estimate of site 

electricity use at the state level. To obtain load profiles that need to be met at the bulk power 

level, one can subtract the contribution from distributed generation and then add in the estimate 

of T&D losses as appropriate depending on model context. These components are shaded blue.

Table ES-1. Summary of Contiguous U.S. Electricity Use in Terawatt-Hours, Top-Down and 

Represented in dsgrid

Component 

Type

Component

Name Residential Commercial Industrial Transport Total

Top-down Hourly loada 3,910 

Derived T&D losses 199

Top-down Annual energyb 1,370 1,350 981 7 3,708

dsgrid Distributed generation 3 31 204 – 237

dsgrid-core Gap models 218 454 184 6 862

dsgrid-core Detailed sector models 1,169 1,107 893 – 3,170 

Derived Total site energyc 1,372 1,381 1,184 7 3,945

Derived Annual sector residualsd -15 -180 107 1 -87

Derived Hourly residualse -126

a FERC Form 714 and independent system operator (ISO) reporting

b U.S. Energy Information Administration (EIA) Form 861

c Total site energy is the top-down annual energy plus distributed generation. This is all the load we are expecting 

to model with the bottom-up detailed sector and gap models.

d The sector level residuals are equal to the total site energy minus the gap and detailed sector model components. 

e The hourly residuals reported in the Total column are the sum of the state-level hourly residuals, which factor in topdown hourly load, T&D losses, distributed generation, and the dsgrid-core model components.

dsgrid model components necessary to represent site-energy use at the hourly level are shaded green.

Components that may be factored in to estimate bulk power system load are shaded blue-grey.

Although the top-down data do not exist to compute hourly residuals resolved by sector, the topdown annual energy data available from U.S. Energy Information Administration (EIA) Form 

861, along with our sectoral estimate of distributed generation, do provide the means to compute 

sectoral residuals on an annual basis. Based on this total site energy estimate, we see from Table 

ES-1 that for the contiguous United States, about 35%, 35%, 30%, and 0.2% of site electricity 

use is attributable to residential, commercial, industrial, and transportation subsectors 

respectively; dsgrid bottom-up estimates capture this energy use within ±1% for the residential 

sector. For commercial, industrial, and transport, dsgrid estimates fall within ± 15%. 

Examining the data at this most aggregated level is helpful, but it belies model discrepancies that 

are apparent when the data are examined at a finer level of temporal or geographic resolution. 

 4 The hourly residuals are computed by comparing the bottom-up load data to top-down bulk power system hourly 

load data and factoring in T&D losses and distributed generation.
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For example, Figure ES-4 depicts the dsgrid bottom-up load data (detailed sector models and gap 

models) and T&D losses alongside the historical hourly load data, and the historical hourly load 

data plus our distributed generation estimates. From this, we can see that our bottom-up 

modeling is able to capture seasonal load shape changes (e.g., the double-peak common in 

winter, and the single afternoon peak common in summer), but it regularly exaggerates the 

differences between weekday and weekend energy use, as well as between daytime and 

nighttime energy use, the latter especially during cooling season (e.g., summer and spring). 

Such discrepancies between the modeled results and the historical data occur because we do not 

artificially constrain our bottom-up models with top-down data. The model residuals that result 

therefore point to aspects of our bottom-up understanding of demand-side energy systems that 

need improvement.

Figure ES-4. Bottom-up sector model and gap model load in gigawatts (GW) compared to bulk-level 

historical hourly load for the entire contiguous United States (CONUS), for four representative weeks

Each week starts on a Sunday, and all data are plotted time-synchronized in Eastern Standard Time.
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An initial effort to understand the information contained in our hourly and sectoral residuals is 

shown in Figure ES-5, which depicts energy-weighted average fit statistics at different levels of 

geographic, temporal, and sectoral aggregation. The calculation details are provided in the body 

of the report, but in all cases, what is shown is 1 minus a measure of relative error (i.e., 1.00 

indicates zero error) averaged over geographic units using the total annual site energy estimate in 

total or by sector as the weight.

Figure ES-5. dsgrid fit statistics for total site energy as a function of geographic, temporal, and 

sectoral resolution

At the most aggregated level, dsgrid bottom-up sectoral and gap models capture the expected site load within 

a relative error of 4%. At the least aggregated levels for which we calculate residuals, the energy-weighted mean 

relative absolute error is about 20% for hourly electricity, and the energy-weighted relative absolute error ranges from 

35% (transportation) to less than 10% (residential) for annual energy use by sector.

The levels of geographic resolution are states (48 plus Washington, D.C.), state groups (24 described in the body 

of the report), census divisions (9), census regions (4), and the CONUS (1).

First, examining the hourly electricity residuals, we see confirmation of what we saw in Table 

ES-1, namely that at the annual level aggregated to all of CONUS, the dsgrid bottom-up models 

captures the site energy represented in the historical hourly load data combined with the dsgrid 

distributed generation models to within a relative error of 4%. However, if we examine the 

model at the most resolved level for which hourly residuals are available (state-level and hourly), 

the level of fit drops almost to 80%. Where is most of the fit lost? Temporally, the first large gap 

occurs when we go from weeks to days; this likely speaks to the weekday-weekend discrepancy 

apparent in Figure ES-4. The daytime-to-nighttime shifts similarly show up in the difference 

between the hour and day curves. Geographically, there is a large difference between census 

divisions and census regions, and a more modest but significant difference between states and 

state groups. For these hourly residuals, which rely on a disaggregation of system load data 

reported by independent system operator (ISO) region or FERC Form 714 planning region, a 
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significant portion of the error introduced by disaggregating from the state group to the state 

level may be attributable to that process (and not to the bottom-up modeling alone).

Examining the sectoral residuals, the relative maturity of the residential modeling is immediately 

apparent, as its level of agreement with the total annual energy use reported by state on EIA 

Form 861 plus our distributed generation estimates is above 92% at all levels of disaggregation 

(down to states). The level of fit for all other sectors is below 85% at the state level, but it 

is greater than 80% for the other two main electricity-consuming sectors: commercial and 

industrial.

Overall, these results show that we can model U.S. electricity load at high resolution, especially 

if we leverage the model residuals computed here to patch the now-revealed discrepancies 

between our modeling and historical data. We also know the likely sources of some of these 

discrepancies. For commercial buildings, we are aware of inaccuracies and uncertainties related 

to count and size of commercial buildings by type, especially for those building types not well 

represented in CoStar. In contrast, industrial manufacturing plant locations are fairly well 

known,

5 but because IGATE-E does not directly model the energy use of industrial processes, 

it relies on the DOE industrial assessment centers (IAC) database to create estimates of energy 

intensity. Relying solely on the IAC Database is problematic because it underrepresents 

industries dominated by very large plants and it may be subject to selection bias. Low data 

availability further necessitates using energy intensity metrics based on number of employees 

rather than other normalization factors that may be better correlated to energy use such as annual 

sales. The temporal discrepancies apparent in the hourly residuals are not all that surprising 

given this was the sector modeling teams’ first time providing hourly modeling results as an end 

product. Demand-side energy research has historically had a much stronger focus on pure energy 

efficiency measured on an annual basis than on the temporal specificity required to understand 

interactions with the grid. Moving forward, we expect to conduct more hourly and subhourly 

calibration at the sectoral, subsectoral, and end-use levels using utility- and facility-scale metered 

data to develop stochastic modeling of operational schedules, control set-points, and occupant 

behavior.

Looking Forward 

dsgrid is a new model designed to provide a solid basis for exploring questions related to future 

electricity load and its relationship to grid operations. Because it is a new model, this 

documentation represents the beginning of an investigation more so than the end of an analysis. 

The model validation documented in this report makes plain the fact that the commercial and 

industrial models in particular need more sectoral-level calibration, and that all the models would 

benefit from additional temporal calibration. To that end, additional calibration of the building 

models to metered data is both ongoing and in various stages of planning as future work. To 

make the model usable in the meantime, we are also developing methods to mathematically 

summarize the model residuals in a manner that can be applied to dsgrid scenarios built by 

modifying the baseline model documented in this report (e.g., to represent load in future years).

 5 IGATE-E relies on the MNI database for this information.
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Subsequent stages of the EFS will include developing dsgrid future-year snapshots of 2050 

electrical load based on the PATHWAYS outputs for the EFS electrification scenarios (Mai et al. 

2018) and evaluating the potential flexibility of those future loads. Under most scenarios, we 

expect significant growth in on-road electric vehicle deployment by 2050. For this reason, 

transportation, while not a significant part of the dsgrid historical load snapshot documented 

here, is anticipatorily included in the methodological documentation.

In addition to the work planned for EFS, the dsgrid data set and modeling framework now exist 

as a resource for investigating questions about U.S. electricity load, from regional differences 

in coincidence with wind and solar generation, to the potential impact of particular energy 

efficiency measures or demand response programs. Some of the underlying data sources are 

actually more granular than the county level, so we also anticipate interest in more-localized,

distribution-level modeling. The data on fuel use other than electricity, especially for the 

residential and commercial building sectors, could also enable more detailed analysis of 

interactions between natural gas, electricity, and water networks.
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Table of Contents

Executive Summary................................................................................................................................... ix

Data Set Summary...............................................................................................................................xiii

Looking Forward................................................................................................................................xvii

1 The Need for a Detailed Model of Electrical Load............................................................................. 1

1.1 Overview of Load Models Described in the Literature................................................................. 4

1.1.1 Energy System and Economy Models.............................................................................. 4

1.1.2 Load Models and Projections for Electricity System Planning........................................ 6

1.1.3 Models of Electricity Load Flexibility ............................................................................. 8

1.1.4 Sectoral Models................................................................................................................ 9

1.2 Scope of dsgrid............................................................................................................................ 14

2 Model Architecture: A Composite Picture Built Up from Sector Models...................................... 16

2.1 Architectural Overview............................................................................................................... 16

2.2 Sector Models.............................................................................................................................. 20

2.2.1 Residential Sector........................................................................................................... 20

2.2.2 Commercial Sector......................................................................................................... 29

2.2.3 Industrial Sector ............................................................................................................. 37

2.2.4 Transportation Sector..................................................................................................... 42

2.2.5 Summary ........................................................................................................................ 47

2.3 Gap and Derived Models............................................................................................................. 52

2.3.1 Non-Sectoral Gap Models.............................................................................................. 52

2.3.2 Distributed Generation Models...................................................................................... 56

2.3.3 Electricity Sector Load Data .......................................................................................... 59

2.3.4 Derived Models.............................................................................................................. 61

3 Historical Year Model Overview........................................................................................................ 67

4 Future Work......................................................................................................................................... 88

4.1 Additional ComStock Calibration............................................................................................... 88

4.2 EFS Scenarios ............................................................................................................................. 88

4.3 Power System Geography ........................................................................................................... 89

References................................................................................................................................................. 90

Appendix A. Residential Sector Model Details.............................................................................. 104

A.1 Data Sources and Methodology Details.................................................................................... 104

A.2 Additional Calibration Details................................................................................................... 105

Appendix B. Commercial Sector Model Details ............................................................................ 107

B.1 ComStock Overview ................................................................................................................. 107

B.2 Commercial Building Characteristics ....................................................................................... 107

B.3 Commercial Building Stock Data Sources................................................................................ 109

B.4 Model Coverage and Gaps........................................................................................................ 112

B.5 Modeling Details....................................................................................................................... 119

Appendix C. Industrial Sector Model Details................................................................................. 120

C.1 IGATE-E: Energy Consumption............................................................................................... 120

C.2 IGATE-E: Load Shapes ............................................................................................................ 123

C.3 Industrial Gap Model ................................................................................................................ 125

Appendix D. Transportation Sector Model Details........................................................................ 126

D.1 The National Transit Database (NTD)...................................................................................... 126

D.2 Methods..................................................................................................................................... 126

D.3 Results ...................................................................................................................................... 128

Appendix E. Model Coverage Analysis Details............................................................................. 132

E.1 Method ...................................................................................................................................... 132

E.2 Results ...................................................................................................................................... 135
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This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Appendix F. Historical Year Model Results, CONUS-Level Description of Gap Models and 

Distributed Generation..................................................................................................................... 141

F.1 Commercial Gaps...................................................................................................................... 141

F.2 Industrial Gaps.......................................................................................................................... 145

F.3 Transportation Gaps.................................................................................................................. 145

F.4 Distributed Generation .............................................................................................................. 147

Appendix G. Historical Year Model Results by Census Division ................................................ 148

G.1 West North Central.................................................................................................................... 149

G.2 East North Central..................................................................................................................... 162

G.3 New England............................................................................................................................. 175

G.4 Mid Atlantic .............................................................................................................................. 188

G.5 South Atlantic............................................................................................................................ 202

G.6 East South Central..................................................................................................................... 217

G.7 West South Central.................................................................................................................... 232

G.8 Mountain ................................................................................................................................... 247

G.9 Pacific ...................................................................................................................................... 263

Appendix H. Weather File Methodology......................................................................................... 278

Appendix I. Combined Heat and Power and Distributed Thermal Model.................................. 280

Appendix J. Raw State-Level System Loss Factors .................................................................... 286
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List of Figures

Figure ES-1. dsgrid provides load data for the contiguous United States at high geographic, temporal, 

and sectoral resolution. ............................................................................................................ x

Figure ES-2. dsgrid models about 80% of 2012 U.S. electricity use in detail............................................. xi

Figure ES-3. dsgrid input data architecture ...............................................................................................xiii

Figure ES-4. Bottom-up sector model and gap model load in gigawatts (GW) compared to bulklevel historical hourly load for the entire contiguous United States (CONUS), for four 

representative weeks.............................................................................................................. xv

Figure ES-5. dsgrid fit statistics for total site energy as a function of geographic, temporal, and 

sectoral resolution ................................................................................................................. xvi

Figure 1. Load models summarized by their geographic, temporal, and sectoral extents and resolution... 15

Figure 2. dsgrid input data architecture ...................................................................................................... 17

Figure 3. ResStock simulations use 216 climate subregions, each represented by a single weather

station..................................................................................................................................... 23

Figure 4. Average of residential load profiles from eight electric utilities available in the Utility

Load Research Data (ULRD)................................................................................................. 26

Figure 5. The ResStock plug load schedule was updated to use the measured plug load profile from 

RBSAM (mid-day valley eliminated). ................................................................................... 26

Figure 6. The ResStock lighting schedule algorithm was updated to eliminate the deep mid-day valley 

and more closely match measured lighting use from RBSAM.............................................. 27

Figure 7. After modification, the ResStock lighting schedule algorithm results in an improved match 

to the RBSAM data for all months of the year....................................................................... 27

Figure 8. Number of IAC assessments by industry and year (as of March 2018) ...................................... 38

Figure 9. Transportation modeling scheme for the road subsector............................................................. 43

Figure 10. EVI-Pro model structure............................................................................................................ 46

Figure 11. dsgrid models about 80% of 2012 U.S. electricity use in detail................................................ 48

Figure 12. dsgrid models about 76% of 2012 U.S. site energy use in detail. ............................................. 49

Figure 13. Diurnal profiles for (a) residential water use (AWWARF 1999) and (b) dsgrid municipal 

water services gap model ....................................................................................................... 54

Figure 14. Sample single-day (a) solar radiation profile and (b) the outdoor lighting profile that results 

from applying the transformation described above................................................................ 56

Figure 15. CEMS CHP plant capacity factor profiles................................................................................. 58

Figure 16. Raw state-level loss factors (a) derived from utility reported losses (EIA Reported) and 

(b) estimated by comparing historical hourly supply and annual metered load data (FERCEIA estimate), ordered by (c) their difference (estimate - reported)...................................... 62

Figure 17. Loss factors for state groupings................................................................................................. 63

Figure 18. Model residuals are calculated using electricity sector data and distributed generation models.

................................................................................................................................................ 65

Figure 19. Bottom-up detailed sectoral model and gap model load compared to bulk-level historical 

hourly load for all of the CONUS in 2012, for four representative weeks ............................ 69

Figure 20. dsgrid hourly residuals plotted along with the bottom-up model data, T&D loss estimates, 

and historical bulk power load data, for the contiguous United States.................................. 70

Figure 21. dsgrid fit statistics for total site energy as a function of geographic, temporal, and sectoral 

resolution................................................................................................................................ 72

Figure 22. Historical and dsgrid load duration curves for the CONUS in 2012......................................... 73

Figure 23. Residential electricity use diurnal patterns by season, modeled for the CONUS in 2012......... 75

Figure 24. Commercial electricity use diurnal patterns by season, as modeled by ComStock 

for the CONUS in 2012 ......................................................................................................... 78

Figure 25. Industrial electricity use diurnal patterns by season, modeled for the CONUS in 2012 ........... 87

Figure 26. Relationship between dsgrid and decadal-scale adoption models............................................. 88
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This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Figure A-1. Comparison of modeled and measured daily kWh load for average single-family homes 

in the ComEd service territory (2012 data; homes without electric space heating)............. 105

Figure A-2. Comparison of modeled and measured daily kWh load for average single-family homes 

in the ComEd service territory (2012 data; homes with electric space heating).................. 106

Figure B-1. Workflow used to characterize the U.S. commercial building stock and model energy use 

with county-level granularity ............................................................................................... 107

Figure B-2. Visualization of probability table heirarchy, sampled using a Latin hypercube sampling.... 108

Figure B-3. The four U.S. census regions and nine census divisions, used by CBECS 2012 .................. 109

Figure B-4. Relationship between major data sources and commercial model coverage......................... 112

Figure C-1. IGATE-E Version 2.0............................................................................................................ 120

Figure C-2. NAICS: 3111 (Animal Food Manufacturing) regression results........................................... 121

Figure C-3. Comparison of IGATE-E estimates to 2014 MECS (optimized) .......................................... 123

Figure C-4. IGATE-E load factor estimates by industry .......................................................................... 124

Figure C-5. NAICS: 3111 (Animal Food Manufacturing) normalized load shape................................... 125

Figure D-6. The Portland, Oregon (red)-Washington (blue) urban area spans two states........................ 128

Figure D-7. 2012 annual electricity demand for rail transport by state .................................................... 129

Figure D-8. Sample electrical transit load profiles for California, New York, and Florida...................... 129

Figure E-1. Proportion of annual electricity use in each sectoral modeling category .............................. 135

Figure E-2. Annual total site energy use gaps in sectoral models ............................................................ 137

Figure F-1. Municipal water gap model diurnal load shapes.................................................................... 143

Figure F-2. Outdoor lighting gap model diurnal load shapes for the CONUS in 2012 ............................ 144

Figure F-3. Transportation gap model diurnal load shapes for the CONUS in 2012 ............................... 146

Figure F-4. Distributed generation model diurnal generation patterns for CONUS in 2012.................... 147

Figure G-1. Bottom-up detailed sectoral and gap model load compared to bulk-level historical hourly 

load plus DG estimates for the West North Central census division in 2012 ...................... 149

Figure G-2. dsgrid hourly residuals shown in context for the West North Central census division......... 150

Figure G-3. Historical and dsgrid load duration curves for the West North Central census division in

2012...................................................................................................................................... 150

Figure G-4. Residential electricity use diurnal patterns by season, modeled for the West North Central 

census division 2012 ............................................................................................................ 152

Figure G-5. Commercial electricity use diurnal patterns by season, modeled for the West North Central 

census division 2012 ............................................................................................................ 155

Figure G-6. Industrial electricity use diurnal patterns by season, modeled for the West North Central 

census division 2012 ............................................................................................................ 160

Figure G-7. Distributed Generation Model Diurnal Generation Patterns, West North Central census 

division 2012........................................................................................................................ 161

Figure G-8. Bottom-up detailed sectoral and gap model load compared to bulk-level historical hourly 

load plus DG estimates for the East North Central census division in 2012........................ 162

Figure G-9. dsgrid hourly residuals shown in context for the East North Central census division. ......... 163

Figure G-10. Historical and dsgrid load duration curves for the East North Central census division 

in 2012 ................................................................................................................................. 163

Figure G-11. Residential electricity use diurnal patterns by season, modeled for the East North Central 

census division 2012 ............................................................................................................ 165

Figure G-12. Commercial electricity use diurnal patterns by season, modeled for the East North Central 

census division 2012 ............................................................................................................ 168

Figure G-13. Industrial electricity use diurnal patterns by season, modeled for the East North Central 

census division 2012 ............................................................................................................ 173

Figure G-14. Distributed Generation Model Diurnal Generation Patterns, East North Central 

census division 2012 ............................................................................................................ 174

Figure G-15. Bottom-up detailed sectoral and gap model load compared to bulk-level historical hourly 

load plus DG estimates for the New England census division in 2012................................ 175
xxiii

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

Figure G-16. dsgrid hourly residuals shown in context for the New England census division. ............... 176

Figure G-17. Historical and dsgrid load duration curves for the New England census division in 

2012...................................................................................................................................... 176

Figure G-18. Residential electricity use diurnal patterns by season, modeled for the New England 

census division 2012 ............................................................................................................ 178

Figure G-19. Commercial electricity use diurnal patterns by season, modeled for the New England 

census division 2012 ............................................................................................................ 181

Figure G-20. Industrial electricity use diurnal patterns by season, modeled for the New England 

census division 2012 ............................................................................................................ 186

Figure G-21. Distributed Generation Model Diurnal Generation Patterns, New England census division 

2012...................................................................................................................................... 187

Figure G-22. Bottom-up detailed sectoral and gap model load compared to bulk-level historical hourly 

load plus DG estimates for the Mid Atlantic census division in 2012................................. 188

Figure G-23. dsgrid hourly residuals shown in context for the Mid Atlantic census division.................. 189

Figure G-24. Historical and dsgrid load duration curves for the Mid Atlantic census division in 2012 .. 189

Figure G-25. Residential electricity use diurnal patterns by season, modeled for the Mid Atlantic 

census division 2012 ............................................................................................................ 191

Figure G-26. Commercial electricity use diurnal patterns by season, modeled for the Mid Atlantic 

census division 2012 ............................................................................................................ 194

Figure G-27. Industrial electricity use diurnal patterns by season, modeled for the Mid Atlantic 

census division 2012 ............................................................................................................ 200

Figure G-28. Distributed Generation Model Diurnal Generation Patterns, Mid Atlantic 

census division 2012 ............................................................................................................ 201

Figure G-29. Bottom-up detailed sectoral and gap model load compared to bulk-level historical 

hourly load plus DG estimates for the South Atlantic census division in 2012................... 202

Figure G-30. dsgrid hourly residuals shown in context for the South Atlantic census division............... 203

Figure G-31. Historical and dsgrid load duration curves for the South Atlantic census division in 

2012...................................................................................................................................... 203

Figure G-32. Residential electricity use diurnal patterns by season, modeled for the South Atlantic 

census division 2012 ............................................................................................................ 205

Figure G-33. Commercial electricity use diurnal patterns by season, modeled for the South Atlantic 

census division 2012 ............................................................................................................ 208

Figure G-34. Industrial electricity use diurnal patterns by season, modeled for the South Atlantic 

census division 2012 ............................................................................................................ 215

Figure G-35. Distributed Generation Model Diurnal Generation Patterns, South Atlantic census

division 2012........................................................................................................................ 216

Figure G-36. Bottom-up detailed sectoral and gap model load compared to bulk-level historical 

hourly load plus DG estimates for the East South Central census division in 2012 ............ 217

Figure G-37. dsgrid hourly residuals shown in context for the East South Central census division. ....... 218

Figure G-38. Historical and dsgrid load duration curves for the East South Central census division 

in 2012 ................................................................................................................................. 218

Figure G-39. Residential electricity use diurnal patterns by season, modeled for the East South 

Central census division 2012 ............................................................................................... 220

Figure G-40. Commercial electricity use diurnal patterns by season, modeled for the East South 

Central census division 2012 ............................................................................................... 223

Figure G-41. Industrial electricity use diurnal patterns by season, modeled for the East South 

Central census division 2012 ............................................................................................... 230

Figure G-42. Distributed Generation Model Diurnal Generation Patterns, East South Central 

census division 2012 ............................................................................................................ 231

Figure G-43. Bottom-up detailed sectoral and gap model load compared to bulk-level historical 

hourly load plus DG estimates for the West South Central census division in 2012........... 232
xxiv

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Figure G-44. dsgrid hourly residuals shown in context for the West South Central census division....... 233

Figure G-45. Historical and dsgrid load duration curves for the West South Central census division 

in 2012 ................................................................................................................................. 233

Figure G-46. Residential electricity use diurnal patterns by season, modeled for the West South 

Central census division 2012 ............................................................................................... 235

Figure G-47. Commercial electricity use diurnal patterns by season, modeled for the West South 

Central census division 2012 ............................................................................................... 238

Figure G-48. Industrial electricity use diurnal patterns by season, modeled for the West South 

Central census division 2012 ............................................................................................... 245

Figure G-49. Distributed Generation Model Diurnal Generation Patterns, West South Central 

census division 2012 ............................................................................................................ 246

Figure G-50. Bottom-up detailed sectoral and gap model load compared to bulk-level historical 

hourly load plus DG estimates for the Mountain census division in 2012........................... 247

Figure G-51. dsgrid hourly residuals shown in context for the Mountain census division....................... 248

Figure G-52. Historical and dsgrid load duration curves for the Mountain census division in 2012 ....... 248

Figure G-53. Residential electricity use diurnal patterns by season, modeled for the Mountain 

census division 2012 ............................................................................................................ 250

Figure G-54. Commercial electricity use diurnal patterns by season, modeled for the Mountain 

census division 2012 ............................................................................................................ 253

Figure G-55. Industrial electricity use diurnal patterns by season, modeled for the Mountain 

census division 2012 ............................................................................................................ 261

Figure G-56. Distributed Generation Model Diurnal Generation Patterns, Mountain census division 

2012...................................................................................................................................... 262

Figure G-57. Bottom-up detailed sectoral and gap model load compared to bulk-level historical 

hourly load plus DG estimates for the Pacific census division in 2012............................... 263

Figure G-58. dsgrid hourly residuals shown in context for the Pacific census division. .......................... 264

Figure G-59. Historical and dsgrid load duration curves for the Pacific census division in 2012............ 264

Figure G-60. Residential electricity use diurnal patterns by season, modeled for the Pacific 

census division 2012 ............................................................................................................ 266

Figure G-61. Commercial electricity use diurnal patterns by season, modeled for the Pacific 

census division 2012 ............................................................................................................ 269

Figure G-62. Industrial electricity use diurnal patterns by season, modeled for the Pacific census

division 2012........................................................................................................................ 276

Figure G-63. Distributed Generation Model Diurnal Generation Patterns, Pacific census division

2012...................................................................................................................................... 277

Figure H-1. Example of alignment of integrated AMY NSRDB solar radiation profiles with TMY....... 278
xxv

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List of Tables

Table ES-1. Summary of Contiguous U.S. Electricity Use in Terawatt-Hours, Top-Down and 

Represented in dsgrid............................................................................................................ xiv

Table 1. Positive and Negative Attributes of the Three Major Building Energy Modeling Approachesa .. 10

Table 2. Building Characteristics, Dependencies, and Data Sources.......................................................... 21

Table 3. Residential Subsectoral Gaps: Size and Proxy Timeseries Description ....................................... 28

Table 4. Residential Scaling Factors: 2010–2014 ACS Occupied Dwelling Units Divided by RECS 

2009 Dwelling Units, by Building Type and Census Division.............................................. 29

Table 5. Building Characteristics, Dependencies, Data Sources, and Number of Variable Values Used 

in the ComStock Modeling Process....................................................................................... 32

Table 7. Manufacturing Plant Characteristics, Modeling Methods, and Data Sources used by

IGATE-E................................................................................................................................ 39

Table 8. End-Use and Load Shape Category Mapping............................................................................... 41

Table 9. Industrial Sector Subsectoral Gaps: Size and Proxy Timeseries Description............................... 42

Table 10. Vehicle Characteristics, Dependencies, and Data Sources Used in the dsgrid Transportation 

Modeling Process................................................................................................................... 44

Table 11. Transportation Sector Subsectoral Gaps: Size and Proxy Timeseries Description..................... 47

Table 12. Geographic, Temporal, and Sectoral Extents, Resolutions, and Biggest Gaps for dsgrid’s 

Constituent Models................................................................................................................ 51

Table 13. Public Water Supply Energy Intensity and Population Served................................................... 53

Table 14. Municipal Wastewater Treatment Energy Intensity and Quantity Treated ................................ 53

Table 15. Estimates of Absolute and Per-Capita Energy Used for Outdoor Lighting ................................ 55

Table 16. Combined Heat and Power and Distributed Thermal Model: Capacity and Behind-the-Meter 

Generation Summary ............................................................................................................. 57

Table 17. Distributed Photovoltaics Capacity and Generation Summary................................................... 59

Table 18. Top-Down Data on U.S. Power System Demand Used in dsgrid to Define System Losses 

and Model Residuals.............................................................................................................. 59

Table 19. Largest Providers of Energy Furnished or Consumed without Charge in 2012 as Reported 

on EIA Form 861 ................................................................................................................... 60

Table 20. Loss Factors Applied at the State Level to Create the System Losses Derived Model .............. 64

Table 21. Summary of Contiguous U.S. Electricity Use in Terawatt-Hours, Top-Down and 

Represented in dsgrid............................................................................................................. 67

Table 22. Residential Subsectors, Summary of Electricity by End Use for the CONUS in 2012 .............. 74

Table 23. Residential Electricity Proportions by Subsector and End Use for the CONUS in 2012 ........... 74

Table 24. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End

Use for the CONUS in 2012 .................................................................................................. 76

Table 25. Commercial Electricity Proportions of Detailed Models by Subsector and End Use 

for the CONUS in 2012 ......................................................................................................... 77

Table 26. Industrial Manufacturing Subsectors, Summary of IGATE-E Electricity Results by End 

Use for the CONUS in 2012 .................................................................................................. 79

Table 27. Industrial Manufacturing Electricity Proportions for Subsectors and End Uses as Modeled 

for the CONUS in 2012 by IGATE-E.................................................................................... 83

Table A-1. List of Data Sources Used to Derive Input Probability Distributions for ResStock............... 104

Table B-1. Mapping of CoStar Building Combo Codes and DOE Prototype Buildings.......................... 113

Table B-2. Mapping of CBECS Detailed Principal Building Activity (PBAPLUS) and DOE 

Prototype Buildings, and Associated Building Counts........................................................ 116

Table B-3. Mapping of CBECS Detailed Principal Building Activity (PBAPLUS) and CoStar 

Combo Codes for those PBAPLUS Categories without a DOE Prototype Match............... 118

Table C-1. Comparison of IGATE-E estimates to 2014 MECS (Initial).................................................. 122

Table C-2. End-Use and Load Shape Category Mapping......................................................................... 124
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This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table D-1. Sample Utilization Matrix ...................................................................................................... 127

Table D-1. Sample of Energy Estimation Matrix ..................................................................................... 130

Table E-1. Subsectors Categorized as Modeled in Detail, Sectoral Gap Model, or Unmodeled.............. 133

Table E-2. Largest Subsectors Represented by Coarse Gap Models, by Total Electricity Use................ 136

Table E-3. Largest Unmodeled Subsectors, by Total Electricity Use....................................................... 136

Table E-4. Largest Subsectors Represented by Coarse Gap Models, by Total Site Energy Use.............. 138

Table E-5. Largest Unmodeled Subsectors, by Total Site Energy Use..................................................... 138

Table E-6. Electrifiable and Non-Electrifiable End Uses by Sector......................................................... 139

Table E-7. End-Use Energy Share by Model Detail and Electrifiability.................................................. 140

Table F-1. Commercial Gap Model Annual Summary for the CONUS in 2012, by Subsector 

and Census Division............................................................................................................. 141

Table F-2. Commercial Gap Model Annual Summary for the CONUS in 2012, Proportions by

Subsector and Census Division............................................................................................ 142

Table F-3. Municipal Water Gap Model Annual Summary for the CONUS in 2012, Electricity Use 

and Proportions by Subsector and Census Division............................................................. 143

Table F-4. Outdoor Lighting Gap Model Annual Summary for the CONUS in 2012, Electricity Use 

and Proportions by Subsector and Census Division............................................................. 144

Table F-5. Industrial Gap Model Annual Summary for the CONUS in 2012, Electricity Use and 

Proportions by Subsector and Census Division ................................................................... 145

Table F-6. Transportation Gap Model for Passenger Rail Electricity, Annual Summary for the 

CONUS in 2012 by Census Division................................................................................... 146

Table F-7. Distributed Generation Model, Annual Summary for the CONUS in 2012 ........................... 147

Table G-1. States Listed by Census Region and Division ........................................................................ 148

Table G-2. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in 

dsgrid, West North Central Census Division ....................................................................... 149

Table G-3. Residential Subsectors, Summary of Electricity by End Use for the West North Central 

Census Division in 2012 ...................................................................................................... 151

Table G-4. Residential Electricity Proportions by Subsector and End Use for the West North Central 

Census Division in 2012 ...................................................................................................... 151

Table G-5. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End 

Use for the West North Central Census Division in 2012 ................................................... 153

Table G-6. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for 

the West North Central Census Division in 2012 ................................................................ 154

Table G-7. Industrial Manufacturing Subsectors, Summary of Model Results for the West North 

Central Census Division in 2012.......................................................................................... 156

Table G-8. Distributed Generation Model, Annual Summary the West North Central Census 

Division in 2012 by Sector and DG Type............................................................................ 161

Table G-9. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in 

dsgrid, East North Central Census Division ........................................................................ 162

Table G-10. Residential Subsectors, Summary of Electricity by End Use for the East North Central 

Census Division in 2012 ...................................................................................................... 164

Table G-11. Residential Electricity Proportions by Subsector and End Use for the East North Central 

Census Division in 2012 ...................................................................................................... 164

Table G-12. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End 

Use for the East North Central Census Division in 2012..................................................... 166

Table G-13. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for 

the East North Central Census Division in 2012.................................................................. 167

Table G-14. Industrial Manufacturing Subsectors, Summary of Model Results for the East North 

Central Census Division in 2012.......................................................................................... 169

Table G-15. Distributed Generation Model, Annual Summary the East North Central Census 

Division in 2012 by Sector and DG Type............................................................................ 174
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This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Table G-16. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented 

in dsgrid, New England Census Division ............................................................................ 175

Table G-17. Residential Subsectors, Summary of Electricity by End Use for the New England 

Census Division in 2012 ...................................................................................................... 177

Table G-18. Residential Electricity Proportions by Subsector and End Use for the New England 

Census Division in 2012 ...................................................................................................... 177

Table G-19. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by 

End Use for the New England Census Division in 2012 ..................................................... 179

Table G-20. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for 

the New England Census Division in 2012.......................................................................... 180

Table G-21. Industrial Manufacturing Subsectors, Summary of Model Results for the New England 

Census Division in 2012 ...................................................................................................... 182

Table G-22. Distributed Generation Model, Annual Summary the New England Census Division 

in 2012 by Sector and DG Type........................................................................................... 187

Table G-23. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented 

in dsgrid, Mid Atlantic Census Division.............................................................................. 188

Table G-24. Residential Subsectors, Summary of Electricity by End Use for the Mid Atlantic 

Census Division in 2012 ...................................................................................................... 190

Table G-25. Residential Electricity Proportions by Subsector and End Use for the Mid Atlantic 

Census Division in 2012 ...................................................................................................... 190

Table G-26. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by 

End Use for the Mid Atlantic Census Division in 2012....................................................... 192

Table G-27. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for 

the Mid Atlantic Census Division in 2012........................................................................... 193

Table G-28. Industrial Manufacturing Subsectors, Summary of Model Results for the Mid Atlantic 

Census Division in 2012 ...................................................................................................... 195

Table G-29. Distributed Generation Model, Annual Summary the Mid Atlantic Census Division 

in 2012 by Sector and DG Type........................................................................................... 201

Table G-30. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in 

dsgrid, South Atlantic Census Division ............................................................................... 202

Table G-31. Residential Subsectors, Summary of Electricity by End Use for the South Atlantic 

Census Division in 2012 ...................................................................................................... 204

Table G-32. Residential Electricity Proportions by Subsector and End Use for the South Atlantic 

Census Division in 2012 ...................................................................................................... 204

Table G-33. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End 

Use for the South Atlantic Census Division in 2012 ........................................................... 206

Table G-34. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for 

the South Atlantic Census Division in 2012 ........................................................................ 207

Table G-35. Industrial Manufacturing Subsectors, Summary of Model Results for the South Atlantic 

Census Division in 2012 ...................................................................................................... 209

Table G-36. Distributed Generation Model, Annual Summary the South Atlantic Census Division 

in 2012 by Sector and DG Type........................................................................................... 216

Table G-37. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in 

dsgrid, East South Central Census Division ........................................................................ 217

Table G-38. Residential Subsectors, Summary of Electricity by End Use for the East South Central 

Census Division in 2012 ...................................................................................................... 219

Table G-39. Residential Electricity Proportions by Subsector and End Use for the East South Central 

Census Division in 2012 ...................................................................................................... 219

Table G-40. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End 

Use for the East South Central Census Division in 2012..................................................... 221
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Table G-41. Commercial Electricity Proportions of Detailed Models by Subsector and End Use 

for the East South Central Census Division in 2012............................................................ 222

Table G-42. Industrial Manufacturing Subsectors, Summary of Model Results for the East South 

Central Census Division in 2012.......................................................................................... 224

Table G-43. Distributed Generation Model, Annual Summary the East South Central Census 

Division in 2012 by Sector and DG Type............................................................................ 231

Table G-44. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in 

dsgrid, West South Central Census Division ....................................................................... 232

Table G-45. Residential Subsectors, Summary of Electricity by End Use for the West South Central 

Census Division in 2012 ...................................................................................................... 234

Table G-46. Residential Electricity Proportions by Subsector and End Use for the West South Central 

Census Division in 2012 ...................................................................................................... 234

Table G-47. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End 

Use for the West South Central Census Division in 2012 ................................................... 236

Table G-48. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for 

the West South Central Census Division in 2012 ................................................................ 237

Table G-49. Industrial Manufacturing Subsectors, Summary of Model Results for the West South 

Central Census Division in 2012.......................................................................................... 239

Table G-50. Distributed Generation Model, Annual Summary the West South Central Census 

Division in 2012 by Sector and DG Type............................................................................ 246

Table G-51. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in 

dsgrid, Mountain Census Division....................................................................................... 247

Table G-52. Residential Subsectors, Summary of Electricity by End Use for the Mountain 

Census Division in 2012 ...................................................................................................... 249

Table G-53. Residential Electricity Proportions by Subsector and End Use for the Mountain 

Census Division in 2012 ...................................................................................................... 249

Table G-54. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End 

Use for the Mountain Census Division in 2012................................................................... 251

Table G-55. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for 

the Mountain Census Division in 2012................................................................................ 252

Table G-56. Industrial Manufacturing Subsectors, Summary of Model Results for the Mountain 

Census Division in 2012 ...................................................................................................... 254

Table G-57. Distributed Generation Model, Annual Summary the Mountain Census Division in 

2012 by Sector and DG Type............................................................................................... 262

Table G-58. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in 

dsgrid, Pacific Census Division ........................................................................................... 263

Table G-59. Residential Subsectors, Summary of Electricity by End Use for the Pacific Census

Division in 2012................................................................................................................... 265

Table G-60. Residential Electricity Proportions by Subsector and End Use for the Pacific census 

division in 2012.................................................................................................................... 265

Table G-61. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by 

End Use for the Pacific Census Division in 2012 ................................................................ 267

Table G-62. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for 

the Pacific Census Division in 2012 .................................................................................... 268

Table G-63. Industrial Manufacturing Subsectors, Summary of Model Results for the Pacific Census 

Division in 2012................................................................................................................... 270

Table G-64. Distributed Generation Model, Annual Summary the Pacific Census Division in 2012 

by Sector and DG Type........................................................................................................ 277

Table H-1. Mapping to EnergyPlus Weather File Fields from NSRDB Fields........................................ 279

Table I-1. Commercial and Industrial Power Plant Capacity and Generation by Prime Mover, per

EIA Forms 860 and 923....................................................................................................... 281
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Table I-2. CHP and Thermal DG Capacity Described in EIA Forms 860 and 923 (EIA) and in 

the DOE Combined Heat and Power Installation Database (CHP DB)a .............................. 281

Table I-3. Mapping Between EIA and CHP DB Prime Mover and Fuel Type Codesa ............................ 282

Table I-4. Results of Matching CHP Plants in the DOE CHP DB with CHP Plant Reports in 2012

EIA Form 860 and Form 923 Data ...................................................................................... 284

Table J-1. Raw State-Level Loss Factors (a) Based on EIA Form 861 Data Alone (Reported), 

(b) Based on Comparing FERC Form 714 and EIA Form 861 Data (Estimated), 

and (c) Their difference (Estimated – Reported) ................................................................. 286
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1 The Need for a Detailed Model of Electrical Load

Electrical load is the backdrop for all power systems analysis. As a coequal partner in the supplydemand balance that must be maintained on electrical grids at all times and for all timescales, 

load is a major source of variability and uncertainty in grid operations. Load forecasting has been 

at the heart of utility planning for decades, but it is typically done in a top-down manner that 

lacks the granularity in time, geography, end use, and technology that is needed to explore the 

potential impact of technological shifts. Purely econometric methods that rely on historical load 

data combined with projections of economic and demographic parameters are common. More 

sophisticated methods include combining econometric techniques with simple engineering 

models of a limited number of key end uses. Nonetheless, there is evidence that these methods 

may be falling short. For example, Carvallo et al. (2017) find systemic overestimates of load 

growth in utility integrated resource plans. These overestimates are certainly partially explained 

by the recession that followed the 2008 financial crisis but are persistent enough to suggest other 

factors (e.g., energy efficiency adoption and performance) may have been systematically 

misestimated. Forecasting future load is only going to become more difficult and more important 

as new demand-side appliances, control technologies, and other distributed energy resources are 

adopted, and as wind and solar generation compose a larger portion of our power supply. This 

suggests that more complementary, bottom-up engineering- or physics-based modeling that 

incorporates technology and behavioral detail may be a valuable addition to the load forecasting 

process.

In the past, U.S. electricity consumption experienced rapid unexpected change coincident with 

important electricity sector developments. For instance, the first wave of air conditioning 

adoption in the 1960s and 1970s coincided with the construction of many new petroleum-fueled 

generators and was swiftly followed by an oil embargo that made the operation of those new 

generators significantly more expensive than expected (U.S. EIA 2016; Bhatnagar and Rahman 

1986; U.S. EIA 2012). Overall, conditions such as these resulted in the traditional growth-based 

treatment of electrical load being insufficient. We thus turned to new approaches, including 

energy efficiency and demand response (Hurley, Peterson, and Whited 2013; Alliance to Save 

Energy 2013).

The pressures on the power system are arguably greater today. Like air conditioning before it, 

electric vehicles are poised to become a new large load for many households and businesses

(McNerney et al. 2016; Banister et al. 2012; Egbue and Long 2012). Heat pumps are also gaining 

market share, primarily in the southern United States, but with a growing potential to compete 

in cold climates as well (Baxter and Groll 2017; Lapsa and Khowailed 2014). At the same time, 

new generation resources such as wind and solar, and complementary resources such as battery 

and other forms of energy storage, are coming online and complicating grid planning and 

operations. In the meantime, aggressive energy efficiency efforts in the form of utility programs, 

state-level goals, and federal appliance standards have proceeded apace. The International 

Energy Agency (IEA) estimates that U.S. energy efficiency investments since 1990 have reduced 

the total final energy consumption required to provide the same services by up to 7.44 
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quadrillion British thermal units (Quads) per year [2,181 terawatt-hours (TWh) per year]6 (IEA 

2015). Utility planners have sometimes underestimated the future impacts of such energy 

efficiency efforts and other causes of low load growth, which has made it more difficult to create 

cost-effective plans for satisfying future load while also integrating wind and solar (Kavalec et 

al. 2016; Carvallo et al. 2017).

Given the impacts of continued adoption of air conditioning and distributed solar photovoltaics, 

the nascent adoption of electric vehicles, and the shift of some economic activity away from 

industry and toward more commercial service work, there is some reason to expect that future 

load profiles may become peakier (NGA 2016). On the other hand, energy efficiency, energy 

storage, and demand response (load scheduling and shifting) technologies, as well as the 

emergence of some forms of computing and other miscellaneous electrical loads as a base load 

may mitigate this trend such that peak load becomes less of a concern (Schwartz et al. 2017; 

Zarakas et al. 2017). Recent data give credence to perhaps both these things happening, with 

very different balances being achieved at a regional level. That is, peak and annual load growth 

have decoupled in many places but not necessarily in the same direction. For instance, the 

northeastern United States has seen peak load growth higher than annual load growth (NGA 

2016), while California has been revising its forecasts in the opposite direction (Kavalec et al. 

2016). In the South, peak and annual growth rates have been similar in recent years (EIA 2014a). 

Because transmission and distribution (T&D) infrastructure are sized to handle peak load (i.e., 

the largest coincident power draw anticipated by system planners or experienced in a given 

year), these distinctions are important to capture with regard to both planning and operations.

Though accurately capturing the split between overall and peak load growth remains important, 

increasing levels of wind and solar deployment are also moving us beyond this paradigm where

annual demand, peak demand, and coincidence factor

7 are sufficient metrics for describing the 

state of current and future electricity loads. Recent renewable integration studies clarify that

future power system operations will focus on balancing net load, that is, load minus all variable 

generation contributions from wind and solar. For systems with large contributions from solar 

photovoltaics (PV), it emerges that the daily minimum net-load point is just as important as the 

daily peak (net-)load point, and that many days will have a peak in the morning in addition to 

one in the afternoon or evening.

8 For systems with large contributions from wind, the most 

challenging times for system operations are even harder to locate, given the seasonal, weekly,

and diurnal variability of the wind resource.

 6 This statistic includes all energy use, not just use in the electricity sector. For 2014, the IEA estimates the U.S. 

“total final consumption” equaled 17,458 TWh and that there would have been an additional 2,181 TWh in the 

absence of all energy efficiency investments since 1990. This compares to EIA’s reporting of 2014 primary energy 

use and electricity sales to the residential, commercial, industry and transportation sectors of 22,138 TWh in total, 

with just 3,764 TWh attributed to electricity sales (which like the total final consumption metric, excludes system 

losses) (U.S. EIA 2017). 7 Coincidence factor is calculated over some set of system sub-components, for instance, substations, feeders, or 

customers, and is the system peak load divided by the sum of the individual component peaks. The coincidence 

factor is 1.0 if all sub-components peak at the same time and otherwise is less than 1.0. 8 Though this is a fairly common pattern in the winter, in areas with high PV penetrations we also see this pattern 

emerge in the summer, when traditionally there has been a single clear peak sometime in the afternoon that is 

approximately coincident with peak air conditioning loads.
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Renewable integration studies have also demonstrated the importance of modeling systems with 

significant quantities of wind or solar generation using time-synchronized load, wind, and solar 

data (Holttinen et al. 2017). Because it is net-load that is most important for system operations, 

these timeseries must all reflect the same weather as it was experienced simultaneously across 

the region of interest. For this reason, renewable integration studies have generally relied on 

historical hourly load data taken from the same year from which the wind and solar data sets are 

derived. For example, the following description from MacDonald et al. (2016, Supplementary 

Information Section 1.3) is typical:

The electric load data was expanded from 2006–2008 to 2030 estimated levels. 

To estimate the 2030 load, quarterly gross domestic product figures from the 

Bureau of Economic Analysis (72) were applied to the expansion (and 

contraction) of the load to 2011, and then a 0.7% per year growth rate (73) was 

applied to 2030. The result is a 14% increase in the demand for each hour. 

However, we know that this methodology is insufficient to capture the impact of significant 

demand-side technological change.

The primary purpose of dsgrid is to create comprehensive electricity load data sets at a sufficient 

temporal, geographic, sectoral, and end-use resolution to enable detailed analyses of current and 

future projections of end-use load. The data sets can be leveraged to support analysis of demandside energy efficiency, electrification, and operational flexibility (i.e., demand response); and 

are time-synchronized with solar and wind data sets so as to be suitable for renewable 

integration studies.

The initial effort described here is part of the Electrification Futures Study (EFS);

9 thus, our 

initial focus is on developing time-synchronous electricity data suitable for studying the impact 

of electrified loads on future power systems. This work is proceeding in two stages. What is 

documented here is a mesoscale model of current electrical loads built using a variety of

national-level data sets, bottom-up building energy simulations driven by historical-year weather 

files, and a detailed accounting of electricity use in industrial manufacturing and transportation.

We focus initially on modeling a historical year of electricity use to enable model validation,

calibration, and development of an error model that can be carried forward into our future-year 

data sets.

In the next stage of the EFS, using dsgrid, we will develop future-year snapshots of electrical 

load and evaluate the potential flexibility of those future loads. These snapshots will be based on 

the EFS electrification scenarios for 2050 as represented in the outputs of the 

EnergyPATHWAYS model (Williams et al. 2014). Under most scenarios, we expect significant 

growth in electric vehicle deployment by 2050. For this reason, transportation, while not a 

significant part of the dsgrid historical load snapshot documented here, is anticipatorily included 

in the methodological documentation.

 9 For more information, see https://www.nrel.gov/analysis/electrification-futures.html.
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1.1 Overview of Load Models Described in the Literature

Previous investigations of these kinds of long-term questions about electricity demand,

10

including how electricity demand impacts power system planning and operations in relation to 

various societal factors and goals,

11 have used models intended to capture an entire energy 

system or economy, of which energy demand is a part; load forecasting models used for 

electricity system planning12; models of electricity load flexibility; and sector-specific energy 

models. In this section, the authors describe some of the more prominent efforts in each of these 

categories. In Section 1.2, we summarize them in relation to the aims of dsgrid.

1.1.1 Energy System and Economy Models

The National Energy Modeling System (NEMS)

NEMS13 is a modular energy-economy modeling system used to construct the U.S. Energy 

Information Administration (EIA) Annual Energy Outlook (AEO) and to conduct other U.S. 

energy systems analysis. Four NEMS modules are of primary interest here: Commercial 

Demand, Industrial Demand, Residential Demand, and Transportation Sector Demand. Together, 

they comprise all the NEMS end-use demand modules. The demand modules all accept as inputs 

energy prices and macroeconomic indicators, and they then compute changes in sector demand 

based on assumptions about technology cost, equipment standards, tax and other relevant 

policies. Energy consumption by fuel type is output at the U.S. census division level. NEMS then 

iterates to converge on equilibrium energy prices and quantities (EIA 2009).

The Residential Demand Module and the Commercial Demand Module use the most recent 

Residential Energy Consumption Survey (RECS) or Commercial Building Energy Consumption 

Survey (CBECS) respectively as their base year and then project residential and commercial 

energy use forward several decades. The current end year is 2050. NEMS refers to these as 

structural models, that is, they provide an accounting of energy use by tracking building and 

appliance stock, including both quantity and performance characteristics, which are influenced 

by macroeconomic parameters, energy prices, and technology learning effects. The modules 

output energy use by fuel type and end use. For commercial buildings, there is a distinction 

between major and minor fuels and end uses. The minor fuels and end uses are modeled in less 

detail. Energy efficiency standards, building energy standards, and customer adoption of energy 

efficiency and distributed generation technologies (e.g., PV and combined heat and power or 

CHP) are some of the main factors captured beyond basic macroeconomic trends (EIA 2017d, 

2017e). Residential demand is modeled by tracking housing and appliance stocks (EIA 2017d). 

Commercial building energy use is primarily tracked by floor area per building type, overall 

energy use intensity (in British thermal units per square feet [kBtu/ft2

]), and changes to energy 

 10 Greening, Boyd, and Roop (2007); FERC (2009); Boßmann and Staffell (2015); Alstone et al. (2016b); Shoreh et 

al. (2016); Klingler, Elsland, and Boßmann (2017); Wilson et al. (2017) 11 EIA (2017c); Williams et al. (2014); Mai et al. (2014); Melaina et al. (2016); California Energy Commission Staff 

(2017); Alstone et al. (2017); DOE (2017b) 12 Here, we leave aside purely statistical or econometric models (e.g.., regressions on historical load, population and 

economic indicator data coupled with population and economic forecasts), as they are less able to account for the 

major technological changes we expect to see over the long term. 13 NEMS: National Energy Modeling System, https://www.eia.gov/outlooks/aeo/info_nems_archive.php
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use intensity, based on equipment efficiency and building shell upgrades. Distributed generation 

adoption then reduces the net load seen by the wider energy system (EIA 2017e).

The Industrial Demand Module receives employment data and the value of industrial shipments, 

beyond energy price, as additional inputs from the NEMS Integrating Module. These data are 

used to adjust the demand from 15 manufacturing and 6 non-manufacturing subsectors. Five

of the manufacturing industries are modeled with detailed process flows (i.e., engineering 

representations of individual process steps); two are modeled with detailed end-use accounting; 

the energy demand of the remaining eight is based on a simpler end-use accounting and a motor 

stock model for machine drive electricity use. Manufacturing industries classified as energyintensive are more likely to have a process flow model. All industry, manufacturing and nonmanufacturing is modeled using three end-use components: boilers/steam/cogeneration, 

buildings, and process/assembly. We have so far been characterizing the process/assembly 

portion, which accounts for about 55% of industrial energy use. Building energy use is a small 

(about 4%) component of industrial energy use, and so is not modeled in detail, but it is a simple 

function of employment and industrial output per subsector. Process and building heating is 

supplied from the boilers/steam/cogeneration module, which consumes fuels to produce steam 

and electricity. Existing CHP plants are represented directly, and new ones are built to meet new 

thermal energy requirements, and to consume biomass waste products (e.g., in the pulp and paper 

industry). The non-manufacturing industrial sectors are modeled with unit energy consumption

parameters per ton of throughput or dollar of shipments, as well as interactions with the 

Commercial and Transportation Sector Demand modules. Unit energy consumption is derived 

from a variety of data sources specific to agriculture, mining, and construction. The commercial 

and transportation demand modules supply parameters concerning construction demand, 

commercial building energy intensity, and vehicle fuel efficiencies. Benchmarking for the start 

year is done with a combination of EIA Manufacturing Energy Consumption Survey (MECS) 

and State Energy Data System (SEDS) data (EIA 2014b).

The Transportation Sector Demand module estimates passenger travel demand across several 

transportation modes, and it then uses those estimates along with stock models representing 

several vehicle types and their efficiencies by vintage to output energy demand by fuel type and 

mode. Transportation modes are quite various; they include private and fleet light-duty vehicles; 

aircraft; marine, rail, and truck freight; mass transit; military transportation; and recreational 

boating. Light-duty cars and trucks are captured in a stock model, and vehicle miles are 

estimated at the household and fleet levels. Fifteen alternate fuel vehicle types are modeled 

relative to the baseline of conventional gasoline light-duty vehicles. Aircraft energy use is 

primarily modeled based on global macroeconomic indicators and fleet efficiency. The freight 

transportation modes are modeled together, and they are responsive to the growth in industrial 

output. All other transportation modes are lumped into miscellaneous energy demand 

submodule (EIA 2016c).

Energy-Economic and Integrated Assessment Models (IAMs)

This class of models represents all the physical, economic, and technological systems related to 

the global energy system and its greenhouse gas emissions. The level of modeling detail varies 

by model, but at their core, these are simulations models used to design and compare “what-if”

scenarios regarding human energy systems and their interactions with physical and climate 

systems (e.g., water and land use, and climate change impacts). These models have been used 
6

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extensively to inform national energy strategies and assess long-term options in the context of 

international efforts to mitigate climate change impacts (e.g., scenarios used by Edenhofer et al. 

[2015]). Given its central role in transformation pathways for global energy systems, the electric 

power sector is a major focus of many IAMs. IAMs cover electricity generation to various levels 

of detail (Kriegler et al. 2014), projecting long-term energy demand (in TWh consumed) and 

capacity requirements (in gigawatts [GW] deployed) based on socioeconomic projections, 

technology evolution (e.g., cost and efficiency of supply and demand technologies), and supplydemand balancing factors (e.g., power plants capacity factors). One IAM many U.S. researchers 

are familiar with is the Global Change Assessment Model (GCAM).14

EnergyPATHWAYS

EnergyPATHWAYS is a stock rollover, multi-decadal energy accounting tool designed for 

analysis of energy system transformation.15 It is not a forecasting tool, but it instead simulates 

the whole energy economy at high fidelity given user specified decisions and produces energy 

demand, service demand, system costs, emissions, and the resulting infrastructure build. The tool 

is structured to be reusable for different geographic extents and resolutions, and it has been used 

outside the United States in Europe and Mexico. 

Our primary interest for dsgrid is the U.S. model, early versions of which were developed by 

Williams et al. (2014). In the U.S. model, most demand-side data come from NEMS while 

supply side data comes from a variety of sources, including the National Renewable Energy 

Laboratory (NREL). For the EFS, more detailed input data than what is available from NEMS 

are being used in some cases, which has required extending some model structures. For instance, 

electrifiable industrial end uses are being modeled as stocks instead of as exogenously specified 

energy requirements, state-level population projections from the University of Virginia

Demographics Research Group are being used instead of census population projections, and 

county-level heating and cooling electricity shapes from NREL for the 2010–2012 weather years 

replace a single month-hour average heating and cooling shape for the entire United States.

EnergyPATHWAYS models the electricity system with an annual-hourly dispatch, and it 

includes storage, flexible load, and electric fuel production, which informs costs, emissions, and 

the need for new infrastructure. In addition, the 8,760 hourly load values are assembled bottomup from energy demand projections and subsectoral or technology-specific normalized electricity 

profiles for different geographies. In this way, it captures the changing load-shape patterns from 

electrification, energy efficiency, or growth in service demand.

1.1.2 Load Models and Projections for Electricity System Planning

Renewable Electricity Futures

The Renewable Electricity Futures study (NREL 2012; Hostick et al. 2014) examined a range of 

possibilities for the United States power system in 2050. To understand the impact of electricity 

load, the project team bracketed future load with low- and high-demand cases and adjusted 

sectoral load shapes to account for changes in some building end uses. The High-Demand 

Baseline was based on the 2009 AEO, which required extrapolations for 2030–2050. This was 

 14 “Global Change Assessment Model,” Joint Global Change Research Institute, 

http://www.globalchange.umd.edu/gcam/

15 “EnergyPATHWAYS,” Evolved Energy Research, http://energypathways.readthedocs.io/en/latest/
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done by continuing electricity intensity trends by households for the residential sector, by unit 

floor space for the commercial sector, and per real dollar of shipments for industry. In this case, 

only industrial sector electricity intensity was projected to decline—by 35% from 2010 to 2050. 

The AEO also provided regional load shape information. 

The Low-Demand Baseline modeled more significant changes—primarily energy efficiency 

gains in all sectors—and significant vehicle electrification. To estimate load shapes for buildings, 

overall efficiency projections (in energy use intensity units; e.g., kilowatt-hour [kWh]/ft2-yr) 

were applied to a stock model accounting of new, existing, and retrofitted buildings; the resulting 

overall energy use was then allocated to end uses based on regionalized projections of end-use 

shares per year that accounted for such expectations as there being less efficiency gains for 

miscellaneous electrical loads than for other end uses. These steps were executed in the 

Lawrence Berkeley National Laboratory Building End-Use Loads Forecaster spreadsheet model, 

whose inputs and outputs are NEMS-formatted end-use load shapes, which are hourly and for 

three representative days. Industrial load shapes were primarily adjusted by choosing load factors 

that simultaneously fit Electric Power Research Institute (EPRI) load factor data, overall load 

duration curve data, and electricity sales by sector. Electric vehicle load curves were adopted 

from previous work (Parks, Denholm, and Markel 2007; Markel et al. 2009).

Itron’s Statistically Adjusted End-Use (SAE) Model

The SAE load forecasting approach combines appliance stock models for individual end uses 

with an econometric model that maps end-use energy estimates to overall expectations for utility 

loads (Enterline and Fox 2010). This model is used to construct long-term estimates of energy 

efficiency across 30 end uses, and it has been used in several long-term electricity planning 

studies (WECC 2013; Enterline and Fox 2010; Carvallo et al. 2017). Temporally, it provides 

seasonal and peak adjustments. Sectorally, it provides technical potential energy efficiency 

estimates for existing technologies. Residential and commercial building end uses are modeled 

with an equipment stock approach and square footage trends. Data sources include NEMS 

output, census data on heating shares by state, the Federal Energy Regulatory Commission 

(FERC) demand response potential study (FERC 2009), and additional data on appliance 

saturation and efficiency compiled from state and utility-level studies (Wagner and Barbose 

2012).

Power System Planners

The California Energy Commission has been regularly compiling load forecasts covering one 

decade since the mid-1990s. The projections are a combination of econometric regressions and 

some bottom-up analysis by sector. Kavalec et al. (2016) give low-, mid-, and high-demand 

forecasts for annual electricity use and non-coincident peak demand over 20 forecast zones, with 

detailed analysis of energy efficiency, distributed PV, and energy use by industrial subsector. 

Demand response contributions are also tallied. BC Hydro applies a similar method to conduct 

their long-term load forecasting (BC Hydro 2012). Of course, all electric utilities must perform 

some type of load forecasting; Carvallo et al. (2017) describes the methods used by 11 utilities 

and analyzes their performance in terms of historical forecast errors for annual energy and peak 

demand. Three of the utilities studied used the SAE approach already reviewed, one used a 

bottom-up engineering model, and the rest applied either timeseries or cross-sectional regression. 
8

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Load Shape Models

Boßmann and Staffell (2015) describe two country-level models of future load shapes for 

Europe, focusing on results for Germany and Britain. eLOAD16 works by decomposing current 

load shapes into portions associated with “relevant applications” and the remainder. Thus, end

uses of interest can be modeled differently than the remaining load, to which a basic scaling 

factor may be applied. The end uses of interest are modeled in turn using the FORECAST 

model,

17 which combines a stock accounting approach with stochastic simulation of technology 

adoption decisions. DESSTinEE18 is a spreadsheet simulation model grounded more in 

macroeconomics; it models the demand for energy services as a function of population and 

income. Load shapes are produced from a basic breakdown of current load by end use by 

imposing the projected energy demands and weather (in the form of heating degree days) and 

forming a model of the residuals between historical load data and simulation results for the 

same year.

1.1.3 Models of Electricity Load Flexibility

FERC Demand Response Potential Study

In a study related to load modeling, FERC (2009) estimated the potential percentage drop in peak 

load by customer class and state for three beyond-business-as-usual scenarios, which required 

estimating the breakdown of peak load by sector and end use. Normalized load shapes by 

customer segment in the form of regression models over weather, air conditioning saturation, and 

periodicity over multiple frequencies (season, month, day-of-week, hourly) were estimated from 

utility hourly load data from 21 states. Temperature-dependent and non-temperature-dependent 

loads were distinguished. The only end use modeled in some detail was air conditioning; even 

so, there was no explicit data on the hourly load shape difference between customers with and 

without air conditioning. That difference was inferred by regressing over the utility load data and 

appliance saturation estimates that were available. These data and the methodology were 

adjusted and applied to the Western Interconnection in Satchwell et al. (2013). Overall, this type 

of demand response modeling can be characterized as relying heavily on utility reporting and 

surveys concerning current demand response programs and load shapes overlaid with minimal 

additional information on technology saturation levels.

Demand Response Estimates by Lawrence Berkeley National Laboratory

Work done by Lawrence Berkeley National Laboratory (Olsen et al. 2013) to estimate demand 

response resource by sector and end use is in many ways the predecessor to dsgrid. Their 

methodology for constructing demand response resource curves starts with estimates of hourly 

load by end use, constructed by merging many data sources. Geographically, these estimates 

originally covered only the western United States, but they have since been extended to the 

whole United States at the resolution of utility-state intersection (Hale, Stoll, and Novacheck 

2018). Commercial building loads are estimated using a model of the California commercial 

building stock developed from California Commercial End-use Survey data. The load shapes are 

derived from building energy simulations across California, which obviously do not cover the 

 16 eLOAD: electricity LOad curve ADjustment, http://reflex-project.eu/model-coupling/model-eload/ 17 FORECAST: FORecasting Energy Consumption Analysis and Simulation Tool, “Methodology,” 

http://www.forecast-model.eu/forecast-en/content/methodology.php 18 DESSTinEE: Demand for Energy Services, Supply and Transmission in Europe, https://wiki.openmodinitiative.org/wiki/DESSTinEE. 
9

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full range of U.S. climates. Therefore, they are further adjusted using a linear regression relating 

temperature variability to load variability, in addition to being scaled and offset to match 

predicted monthly energy consumption, when applied to other locations. Residential load by end

use is treated similarly. The underlying data set in that case is a residential end-use forecast from 

the California Energy Commission for 2020. Several other data sources are used to model 

agricultural pumping, data center, municipal lighting, water pumping, refrigerated warehouse, 

and manufacturing loads.

In a series of reports, Lawrence Berkeley National Laboratory extends and applies their demand 

response estimation methodologies to California (Alstone et al. 2016a, 2017). For that work, they 

had access to extensive advanced metering infrastructure and demographic data at the customer 

level from the three major investor-owned utilities in California.19 The data were clustered, the 

load data were disaggregated by end use using first-order engineering models, and projections 

were then made to the study years.

1.1.4 Sectoral Models

Many sector-specific models have been proposed to project electricity consumption at various 

levels of spatial and temporal resolution. This includes is a vast body of literature on modeling 

building electricity use, both for commercial and residential buildings. Transportation has been 

studied quite thoroughly, with different researchers tending to focus on the different subsectors, 

led by light-duty vehicles, road shipment of freight, air travel, and public transportation. Publicly 

available energy analysis of industrial subsectors and end uses has tended to be less 

comprehensive due to the incredibly diverse and business-sensitive nature of industrial activity. 

In this section, we summarize notable efforts in each of these areas. In relation to dsgrid, we are 

interested in (1) whether these models accurately capture energy use over large geographic 

regions, provide sufficient spatial and temporal resolution to be useful in regional or national, 

hourly or finer temporal resolution power system models, and (2) whether they can model future 

energy use based on capturing descriptions of technological change either exogenously or 

endogenously. We generally find many efforts to be lacking in one or more of these areas, each 

of which is necessary to couple sector-level energy models with power system models suitable 

for renewable energy integration studies; but, one can imagine sector-level models being 

extended to serve such a purpose.

1.1.4.1 Buildings Sector

(Swan and Ugursal 2009) review building end-use energy consumption modeling techniques and

categorize the techniques as “top-down,” “bottom-up statistical,” or “bottom-up engineering”

models. These categories, along with positive and negative attributes of each, are described in

Table 1, which was adapted from Swan and Ugursal (2009). In what follows, we focus on the 

two types of bottom-up models: bottom-up statistical and bottom-up engineering, because these 

better align with dsgrid’s needs for temporal resolution, sectoral resolution, and ability to 

describe technological change. Bottom-up statistical models focus on describing the stochastic, 

occupant-driven temporal variation in energy used for individual end uses, and they have mostly 

been restricted to residential buildings based on the availability of, for example, time use surveys 

 19 Demographic data were available for all 11 million utility customers. The hourly load data set covers 

300,000 customers.
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and metered data. In contrast, bottom-up engineering models focus on accurately capturing 

building thermodynamics over typical weather conditions, which is accomplished by simulating 

single-building energy use as an outcome of physical interactions between building equipment, 

building envelope, and weather that are driven by deterministic occupant schedules (Crawley et 

al. 2001).

Table 1. Positive and Negative Attributes of the Three Major Building Energy 

Modeling Approachesa

Top-Down Bottom-Up Statistical Bottom-Up Engineering

Focus Sector-wide annual energy 

use and trends

Stochastic occupant-driven 

temporal variations in energy 

use

Energy use driven by weather, 

building physics, and occupant 

schedules

Positive 

Attributes

Conducts long-term 

forecasting in the absence of 

any discontinuity

Encompasses occupant 

behavior

Models new technologies and 

policy implications

Includes macroeconomic 

and socioeconomic effects

Imputes typical end-use 

energy contribution

Determines each end-use 

energy consumption by type, 

rating, etc.

Requires simple input 

information

Includes macroeconomic 

and socioeconomic effects

Determines end-use qualities 

based on simulation

Encompasses trends 

implicitly

Uses billing data and simple 

survey information

Negative 

Attributes

Relies on historical 

consumption information

Is subject to multicollinearity Assumes occupant behavior and 

unspecified end uses

Provides no explicit 

representation of end uses

Relies on historical 

consumption information

Requires detailed input 

information

Provides coarse analysis Requires large survey 

sample to exploit variety

Is computationally intensive

Does not incorporate 

macroeconomic factors

a Adapted from Swan and Ugursal (2009)

Residential Buildings

Several proposed residential bottom-up models identify the contribution of each end use to the 

aggregate energy consumption profile of the residential sector. Capasso et al. (1994) developed a 

bottom-up statistical model for evaluating the impact of demand side management on residential 

customers, although this analysis did not include weather parameters. A bottom-up statistical 

model of Finnish residential appliances by end use, with application to estimating potential 

demand-side management effects is given by Paatero and Lund (2006). A similar effort for the 

United Kingdom, which was validated using a year’s worth of metered data from 22 homes, is 

described by Richardson et al. (2010). 
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Richardson, Thomson, and Infield (2008) introduce a Markov-chain technique to generate 

synthetic occupancy patterns, based on survey data of people’s time-use in the United Kingdom. 

The stochastic model maps occupant activity to appliance use, creating highly resolved synthetic 

demand data. Widén and Wäckelgård (2010) follow a similar approach to relate residential 

power demand to occupancy profiles in Sweden. Muratori et al. (2013) propose a model, 

validated against metered data, to simulate hourly electricity demand of U.S. households, based 

on a Markovian behavioral model calibrated using the American Time Use Survey. The resulting 

profiles have been used to simulate residential demand response programs (Muratori and Rizzoni 

2016) and the impact of PEV charging (Muratori 2018). Johnson et al. (2014b) predict residential 

power demand based on occupant behavior using a similar Markovian model also driven by the 

American Time Use Survey data. Fischer, Härtl, and Wille-Haussmann (2015) describe detailed 

modeling of residential electricity use for Germany, focusing on non-weather-dependent end 

uses. The resulting hourly power profiles by end use were validated against metered data for 430 

households. Wills, Beausoleil-Morrison, and Ugursal (2017) adapt and validate the Richardson 

model to simulate Canadian residential appliance and lighting demands using 22 high-resolution, 

measured demand profiles from dwellings in Ottawa, Canada. Overall, these models demonstrate 

the ability to capture the time-dependent nature of residential loads by end use using a 

combination of metered data, occupant survey data, and a variety of statistical simulation 

techniques. However, because they do not incorporate engineering models of multiple building 

subsystems interacting with environmental conditions, they can only be used to model limited 

shifts in technology or policy (e.g., changes that are limited to a single end use or are primarily 

driven by a change in occupant schedules).

Early bottom-up engineering modeling efforts involved using 16 multifamily residential building 

prototype models (Ritschard and Huang 1989) and 8 single-family residential prototypes 

(Ritschard, Hanford, and Sezgen 1992) in 16 climates to develop a database of hourly residential 

building loads. Huang, Hanford, and Yang (1999) updated and expanded the Ritschard et al. 

(1992) prototypes to 112 single-family and 63 multifamily residential building prototypes, and 

they used them to determine the contributions of various enclosure components and internal 

gains to residential heating and cooling demand. Building on the earlier work by Ritschard et al. 

(1992) and Huang et al. (1999), Hopkins et al. (2011) increased the number of prototypes by an 

order of magnitude by simulating all 4,382 homes sampled for the 2005 RECS. Many important 

energy-related parameters, such as insulation levels, air tightness, and heating/cooling equipment 

efficiency are not collected by RECS and thus had to be sampled from probability distributions.

ResStock, the latest research effort to model the U.S. single-family housing stock, uses 350,000 

statistically sampled building models, drawing on building characteristics data from the 2009 

RECS, the U.S. Census Bureau’s American Community Survey (ACS) and 10 other data sources 

(Wilson et al. 2016, 2017).

Commercial Buildings

Significant effort has been made to examine energy use in commercial buildings from a bottomup engineering perspective. Some models, such as those developed by Fonseca and Schlueter 

(2015) use simplified, algebraic hourly models to describe end uses in buildings. Others, such as 

Yang, Li, and Augenbroe (2018); Heeren et al. (2013) use quasi steady-state approaches (ISO 

13790:2008: Energy Performance of Buildings – Calculation of Energy Use for Space Heating 

and Cooling 2008, 137), which also avoid differential equation-based physical modeling. These 

simplified models generally require few inputs and run quickly, making them attractive in high-
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level studies of the commercial building stock over long time scales (Yang, Li, and Augenbroe 

2018). Other studies use whole-building energy modeling software that directly simulates zonal 

or finer-scale thermodynamics and is resolved at the hourly or subhourly level (Heiple and Sailor 

2008); B. Griffith et al. 2007; Griffith et al. 2008; Sezgen et al. 1995; Coffey et al. 2015; Dirks et 

al. 2015). These models promise greater accuracy at a cost of longer computational times and 

higher input data requirements.

Another issue in commercial building stock modeling is the development of archetypes that

represent a larger segment in aggregate after weighting factors are applied to the simulation 

results for each model. Reinhart and Davila (2016) use as few as 13 archetypical building models 

to model commercial building energy use in Ireland, whereas 3,168 archetypical models have 

been used in the case of Italy to represent 877,144 commercial buildings. Similarly, U.S. analysis 

is often done using a subset of the 17 DOE “reference building” (Deru et al. 2011) or “prototype 

building” models (Goel et al. 2014), which, depending on the scope of the study in terms of 

building types and climate regions, can result in a few up to 272 archetype buildings (e.g., Hart 

et al. (2015) uses 30, covering six building types and five climate zones). Dirks et al. (2015) use 

approximately 2,000 archetypes to model the eastern half of the United States. As pointed out by

Brogger and Wittchen (2017), a challenge of the archetype approach is ensuring the archetype 

models are actually representative of the stock. To overcome this limitation with archetype 

modeling, a different approach has been taken by Coffey et al. (2015) in which each building 

within the defined stock is modeled using a combination of 3-D building geometry and highlevel information (e.g., floor area and use type). The approach required an expert system that was 

used to create individual whole building energy models, which results in a requirement for 

significant input data that may not be available on a national scale while simultaneous greatly 

expanding the size of the required simulation set.

When evaluating the magnitude of errors in multi-sector commercial analyses, Reinhart and 

Davila (2016) demonstrate that large-scale studies only match the energy consumption of the 

aggregate building stock within 1%–19% of site energy use on an annual basis. while not 

addressing issues of time of use. Akbari et al. (1993); Sezgen et al. (1995) both attempt to 

address this by applying load shapes developed from additional data sources. Though this 

approach may be adequate for predicting future scenarios where the load shape is not expected to 

change significantly, it is inadequate for predicting future scenarios where new technologies are 

expected to change the load shape, which is the case when considering electrification of the 

building stock. The Heiple and Sailor (2008) model is one of the few building stock models that 

has been validated for both annual energy consumption and load shape. In this case, the load 

shape for a tractable geographic extent was validated against top-down models of the city, 

allowing for derivation of measured data for a larger entire region containing that city.

Most of the bottom-up, physics-based models for buildings represent occupant behavior by 

applying schedules that dictate both the presence of occupants (e.g., occupant component of 

thermal loads, and hot water use) and their impact on equipment controls (e.g., lighting system 

status; heating, ventilation, and air conditioning [HVAC] control mode; and HVAC set points). 

Many of these schedules are based on previous research into this topic. Heiple and Sailor (2008)

note that these schedules can noticeably impact the overall results, and they suggest creating 

additional archetype divisions with different operating schedules. Kim and Srebric (2017); Chen, 

Hong, and Luo (2018) examine data sources and methodologies to develop diverse and 
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stochastic schedule sets for university buildings and offices respectively. This research is very 

promising, and it will be an excellent candidate for inclusion in stock-wide modeling, 

particularly as additional classifications of commercial buildings are considered in the literature.

Summary

Ideally, there would be building stock models covering large geographic extents informed by 

both detailed stochastic modeling of occupant behavior (bottom-up statistical models) and 

detailed modeling of building physics interacting with weather, occupant behavior, and 

equipment controls (bottom-up engineering models). However, the current state of the art is that 

these two types of modeling approaches are practiced separately. The occupant-focused 

statistical models (which are mostly restricted to the residential sector) typically have good 

temporal and end-use resolution but a limited ability to model technological change because they 

are stochastic simulations based on historical time-of-use-surveys and metered data. The physicsfocused engineering models (for both residential and commercial buildings) are well suited to 

model the energy impacts of technological change by end use, but they struggle with achieving 

accurate temporal resolution. This shortcoming is primarily driven by the historical emphasis on 

whole-building, annual energy efficiency metrics; a lack of hourly data by end use for 

calibration; and the naturally wide variance in occupant behaviors and control settings that in 

some sense should be averaged out when the primary concern is creating energy efficient designs 

for buildings that may be used by multiple occupants over the course of their designed lifetimes. 

This report documents efforts to begin to calibrate bottom-up engineering building stock models 

(particularly ResStock and the newly minted ComStock) on an hourly or subhourly basis. We 

expect to see more such efforts given the increasingly urgent need to describe both timedependence of energy use and the potential impacts of technological change. 

1.1.4.2 Transportation Sector

Electricity load modeling for the transportation sector, especially as it focuses on the adoption 

and charging profiles of passenger PEVs, has been proposed by many researchers in response to 

the potential for these vehicles to capture significant market share. Though some studies, 

especially those with a focus on climate change mitigation, also examine demand trends in other

transportation subsectors (Garrido and Mahmassani 2000; Kamakate and Schipper 2009; Eom, 

Schipper, and Thompson 2012; Muratori et al. 2017; Winchester et al. 2013; Boeing 2015; 

Paulley et al. 2006), significant electricity consumption is not typically projected for those 

subsectors due to a lack of mature technological options (e.g., electric air or marine transport).

Several vehicle-to-grid studies leverage travel information surveys to predict PEV charging 

demand (Clement-Nyns, Haesen, and Driesen 2010; Denholm and Short 2006; Duvall et al. 

2007; Green, Wang, and Alam 2011). Bashash and Fathy (2012), for example, propose a controloriented model representing the collective charging dynamics of PEVs that uses the U.S.

National Household Travel Survey to predict the number of PEVs connected to the grid at any 

given time. Other studies use behavioral models to predict vehicle use and charging patterns 

(Muratori, Moran, et al. 2013) or queuing theory to estimate the overall charging demand of a 

population of PEVs (Li and Zhang 2012). Ashtari et al. (2012) instrumented 76 PEVs in 

Winnipeg, Canada, to record vehicle driving and parking patterns. One-second charging profiles 

were used to calibrate a stochastic method to predict PEV charging loads and capture the 
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relationship of vehicle departure, arrival, and travel time for different charging scenarios (e.g., 

residential versus workplace and different charging power levels).

In this context, NREL, in partnership with the California Energy Commission, developed the 

Electric Vehicle Infrastructure Projection (EVI-Pro) model20 to simulate spatially and temporally 

resolved demand for PEV charging at residential, workplace, and public destinations based on 

real-world travel data (Wood et al. 2017). EVI-Pro anticipates consumer charging behavior 

while capturing variations with respect to housing type (single-unit versus multiunit dwellings), 

travel period (weekdays versus weekends), and regional differences in travel behavior and 

vehicle adoption. 

1.1.4.3 Industrial Sector

Given the highly complex, heterogeneous, and proprietary nature of the industrial sector, and a 

corresponding lack of comprehensive data sources, detailed models of its energy consumption 

have lagged models of other sectors. In past studies, top-down estimates based on projections 

from the EIA AEO, which is produced annually using NEMS, have been common (Hostick et al. 

2012; Holmes et al. 2014). Though these studies provide a good baseline of aggregate energy 

consumption within the industrial sector, their granularity is typically limited to the census 

division level.

In addition to using AEO data, many studies incorporate data from EIA’s MECS. This national 

survey is conducted every four years and collects information on U.S. manufacturing 

establishments, their energy-related building characteristics, and their energy consumption and 

expenditures (EIA 2017a). Within the MECS, detailed estimates of energy consumption by 

industry type and census region are available at the end-use level. Additionally, the DOE 

Advanced Manufacturing Office has used these data in numerous studies of specific 

manufacturing industries.21

Though these studies provide useful information for the individual industries covered, they 

demonstrate the primary issue in modeling industrial sector energy use, which is that there is no 

“one size fits all” bottom-up modeling approach. Although various industries may share similar 

processes and end uses, their energy consumption may vary greatly depending on what type of 

product is being produced, and no one data source describes process units consistently across all 

industrial manufacturing subsectors. As a result, detailed technology level models are rare, 

limiting the granularity that can be achieved by an industrial sector model.

1.2 Scope of dsgrid

dsgrid is a model of U.S. electricity use with an unprecedented combination of temporal, 

geographic, and sectoral detail and coverage (Figure 1). Previous load models focused on similar 

questions have reasonably focused on (1) seasonal and peak load effects and (2) building end 

uses that are widespread and either (a) large as a proportion of peak load with the potential to be 

controlled (for demand response studies) or (b) with significant energy efficiency potential. 

 20 EVI-Pro: Electric Vehicle Infrastructure Projection Tool, https://www.afdc.energy.gov/evi-pro-lite 21 The so-called Bandwidth Studies are listed at “Energy Analysis by Sector,” DOE, 

https://energy.gov/eere/amo/energy-analysis-sector. 
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A recent effort to develop more end-use and geographic specificity (Olsen et al. 2013) is

leveraged here, but what the report describes is fundamentally different in scope. 

dsgrid uses large-scale building energy modeling and gridded meteorological data to accurately 

reflect regional differences in equipment types as well as weather to enable realistic 

multidimensional “what-if” analyses. It also provides hourly subsectoral detail on industrial 

manufacturing loads and captures other important non-building loads. Projections of future load

will include detailed modeling of PEVs, both passenger and commercial vehicles, coupled with 

multiple charging strategy and scheduling flexibility assumptions.

Figure 1. Load models summarized by their geographic, temporal, and sectoral extents 

and resolution

dsgrid provides an unprecedentedly detailed picture of current and, when coupled with EnergyPATHWAYS, 

future continental United States (CONUS) electricity load

In the remainder of this report, the authors describe dsgrid: its modeling approach and initial 

baseline results for one historical year. Future work will use outputs from the 

EnergyPATHWAYS model to develop detailed descriptions of future load scenarios.

The dsgrid data sets and model are a timely effort to understand the time-varying nature of 

electrical demand and flexibility at an unprecedented level of detail that will be used to explore 

future scenarios of the U.S. electric sector.
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2 Model Architecture: A Composite Picture Built Up 

from Sector Models

So that it can provide the necessary sector level details and compute residuals against historical 

electricity-sector data, dsgrid was not developed as a single model but rather as a confederation 

of models and data sets bound by processes for comparing and harmonizing data across several 

levels of geographic, temporal, and sectoral resolution. 

In this section, the authors summarize the model as a whole and its constituent parts. We first 

provide an overall architectural view (Section 2.1). This is followed by brief descriptions of the 

sector models, which are provided in two “flavors”: (1) detailed, bottom-up models whose data 

are available at hourly resolution by subsector, end use, and county, and (2) gap models that rely 

on a mixture of top-down and bottom-up data sources and are typically coarser than the detailed 

models in at least one dimension (Section 2.2). After summarizing the sector-level modeling as a 

whole (Section 2.2.5), we describe two additional gap models, models of distributed generation, 

and derived data sets describing system losses and model residuals (Section 2.3).

2.1 Architectural Overview

The heart of the dsgrid model is the bottom-up detailed sector modeling described briefly here 

and in more detail in Section 2.2 and Appendices A, B, and C. However, to create a 

comprehensive picture of U.S. electrical load, the model must also account for other aspects of 

system load, such as loads not captured in detail by the sector models, distributed generation, and 

T&D losses; and should also be calibrated and validated against historical data. To accomplish 

these goals the detailed bottom-up sector models are complemented by coarser gap models, 

distributed generation models, and several historical electric-sector data sets within an analytic 

framework that allows comparison and computation across different levels of geographic, 

temporal, and sectoral resolution (Figure 2, next page).

The detailed bottom-up energy modeling for each sector is conducted with separate 

methodologies, following the overall philosophy of leveraging and supporting the energy 

modeling work conducted by research groups focused on particular end-use sectors, rather than 

attempting to recreate or repurpose such work from a pure power systems point of view. The 

scope and methods used for each sector model are thus products of prior work done by each 

sector modeling team combined with modifications necessary to meet the temporal and 

geographic resolution required by dsgrid. Brief descriptions of the methodologies follow:

• Residential and Commercial Buildings (NREL): Building loads are estimated using 

ResStock and ComStock, which use similar statistical methodologies and OpenStudio 

modeling infrastructure to simulate U.S. single-family detached and commercial building 

stock electricity consumption by end-use. These models sample from thousands of 

probability distributions to produce hundreds of thousands of EnergyPlus simulations, 

which are then weighted to represent subsector building stocks at the county level. This 

detailed modeling covers single family homes and commercial buildings mappable to the 

16 DOE commercial “prototype buildings” (Goel et al. 2014).

• Industrial Manufacturing (EPRI/ORNL): Industrial manufacturing loads are modeled 

with the Industrial Geospatial Analysis Tool for Energy Evaluation (IGATE-E), which 
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uses plant-level databases, the Manufacturing Energy Consumption Survey, and the

Electric Power Research Institute’s Load Shape Library to construct hourly time series of 

electricity use by manufacturing subsector and end use. Because manufacturing processes 

vary greatly, IGATE-E does not attempt direct simulation of loads but rather compiles 

data from multiple sources and applies statistical techniques to estimate energy 

consumption down to the end-use level. Because IGATE-E only models manufacturing, 

the additional industrial sectors of agriculture, mining, and construction comprise the

industrial gap model.

Figure 2. dsgrid input data architecture

CHP = combined head and power

DPV = distributed solar photovoltaics

EIA = U.S. Energy Information Administration

EVI-Pro = Electric Vehicle Infrastructure 

Projection Tool

FERC = Federal Energy Regulatory Commission

IGATE-E = Industrial Geospatial Tool for Energy 

Evaluation

ISO= Independent System Operator

SERA = Scenario Evaluation, Regionalization and 

Analysis model

• Transportation (NREL): Given the focus in this report on constructing a model of 

historical electricity use we describe the detailed transportation modeling methods that 

will be used in future work to capture EV location and charging; but no detailed sector-
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level results for transportation are presented. When this capability is developed for the 

EFS future model year snapshots, on-road plug-in electric vehicle operation will be 

described using the Scenario Evaluation and Regional Analysis (SERA) model and the 

Electric Vehicle Infrastructure Projection Tool (EVI-Pro). SERA describes vehicle 

infrastructure requirements and will be used to disaggregate vehicle adoption to the 

county level. EVI-Pro simulates hourly charging profiles based on travel data and 

charging preference assumptions (e.g., residential charging as opposed to reliance on 

public charging). The historical data set includes a transportation gap model that 

describes electricity use in passenger trains.

For the initial historical snapshot described here, the year being modeled by dsgrid is 2012. This 

choice is based on data availability, including meteorological data that impacts some of the 

sector models and ultimately needs to be time synchronized with the renewable generation data 

to be used in the electricity supply modeling portion of EFS. The current baseline years for the 

sector models are 

• Residential Buildings: 2009 RECS and 2010–2014 ACS

• Commercial Buildings: 2012 CBECS

• Industry: 2014 MECS

• Transportation: 2012 National Transit Database

The sector models that are not primarily calibrated to 2012-specific data sets are scaled to 

represent the historical 2012 model year. For example, for the residential sector, the 2010–2014 

ACS provides dwelling unit counts by census tract, but when calibrating against 2009 RECS 

energy consumption data, the 2009 RECS dwelling unit counts are scaled up to match the 

occupied dwelling unit counts from the 2010–2014 ACS for each building type and census 

division. Industry scaling from 2014 back to 2012 is done using the EIA SEDS data, which show 

about a 1% increase in industrial retail electricity use over that time (EIA 2017b). 

Moving up from the bottom of Figure 2, the dsgrid gap models capture (1) residential, 

commercial, industrial, and transport subsectors that are not handled in our detailed sectoral 

modeling and (2) municipal/utility end uses such as municipal lighting, and water pumping and 

treatment. The magnitude of most of these gaps is first estimated using data products from the 

EIA and other federal agencies, namely, the EIA Residential Energy Consumption Survey 

(RECS), the Commercial Building Energy Consumption Survey (CBECS), the AEO, the 

National Transit Database (NTD), and the County Business Patterns data (EIA 2013d, 2016a, 

2015a; FTA 2017; U.S. Census Bureau 2018). We then form our gap model by assigning a proxy 

timeseries to each of the gap magnitudes identified in those data sets. For some subsectors we 

also geographically downscale our estimates using additional data sources available from the 

bottom-up models. The processes and specific assumptions made for each gap are described in 

Section 2.2 (sectoral gap models) and in Section 2.3.1 (municipal end uses). The temporal 

resolutions of the gap energy magnitude data are uniformly annual. The geographic and sectoral 

resolutions vary. Some of the key data are only available at the census division level or for the 

nation as a whole, but in all cases, we can geographically downscale at least to the state, and 

sometimes to the county.
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To approximate total site-level grid-delivered electricity use by sector and state, the authors

estimate hourly distributed generation from customer-sited PV, combined heat-and-power 

(CHP), and distributed thermal plants, and subtract this from the sum of the detailed and gap

bottom-up load models. The distributed generation models are distinguished by sector 

(residential, commercial, or industrial) to facilitate annual by-sector, by-state comparisons to 

historical by-sector electricity demand. The hourly profiles, in addition to facilitating the creation 

of grid-delivered electricity profiles, allow for hourly by-state comparisons to historical total 

electricity demand. The main data sources for our distributed PV (DPV) model are Perea et al. 

(2017) and Sigrin et al. (2016). CHP and distributed thermal capacity, generation, and behindthe-meter fractions are estimated using EIA Forms 860 and 923 (EIA 2013a, 2018), and the DOE 

CHP Database (ICF Inc. and DOE 2016). CHP and distributed thermal hourly profiles are 

generated from continuous emission monitoring systems (CEMS) data (US EPA 2016; EPA 

2013).

To calibrate and validate the initial dsgrid data set for historical year 2012, we leverage two 

historical electricity-sector data sets. Shown at the top of Figure 2, with no sectoral resolution 

and coarse geographic resolution, we have a historical data set of hourly electrical load that is 

comprised of independent system operator (ISO) data, and FERC Form 714 filings (FERC 2016; 

SPP 2016; pjm 2016; MISO 2016; ISO New England 2016; NYISO 2016). The native resolution 

of that data is at the utility, balancing authority, or ISO region level. For this version of dsgrid we 

use a version of the data set that has first been disaggregated down to individual transmission 

nodes, and then re-aggregated up to the state level. In future versions we plan to enable analysis 

at the county or utility level by fully leveraging the nodal disaggregations, but it was not 

tractable to achieve a reliable geographic matching at that level of detail within the scope of this 

initial project. This load data represents the system operator perspective, that is, it is equal to 

generation plus imports minus exports, and is thus the total amount of generation plus net 

imports that the system had to obtain to meet its load and cover all T&D losses.

Annual retail sales data are available from EIA Form 861 (EIA 2013b), where they are reported 

by sector (i.e., residential, commercial, industrial, and transport22) for every utility by state. EIA 

Form 861 also reports the amount of electricity furnished or consumed by respondent without 

charge, which we add to the commercial retail sales. These data—electricity consumption by 

sector and state—represent the total amount of electricity sold or furnished to utility customers, 

and thus excludes system losses. These data are more resolved sectorally and geographically than 

are the top-down hourly load data, but they are temporally coarser. EIA Form 861 also reports 

losses by utility and state, and this is the main data source for our loss model.

In Section 2.3, in addition to further describing the gap and distributed generation models, the 

authors describe how we estimate system losses and compute model residuals. Hourly system 

losses are estimated by combining the historical hourly load, historical annual sales, and 

historical system losses data. Model residuals are estimated by combining the historical data sets, 

 22 In the coming decades, we expect significant amounts of electricity used to charge EVs to show up in EIA filings 

as residential, commercial or industrial, rather than as transportation, electricity use based on behind-the-meter 

charging at residences and commercial or industrial sites.
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the hourly system losses model, and the dsgrid components—detailed bottom-up sector models, 

gap models, and distributed generation models.

2.2 Sector Models

The core of dsgrid leverages previous work by four energy modeling teams, each of which brings

multiple large-scale data sets together to form a holistic picture of an individual sector: 

residential buildings, commercial buildings, industrial manufacturing, and transportation. The 

four modeling teams have deep institutional experience estimating the energy use of their various 

sectors, although usually on an annual or seasonal, rather than hourly basis. By expanding the 

temporal, and in some cases, geographic resolution of these energy-use estimates, harmonizing 

assumptions, and undertaking a collaborative calibration effort, dsgrid creates a highly resolved

picture of United States electricity use.

2.2.1 Residential Sector

ResStock is a bottom-up simulation methodology for modeling residential building stocks with a 

high degree of granularity. In development by NREL since 2013 (Wilson et al. 2016, 2017), 

ResStock characterizes the energy use of U.S. single-family detached housing23 using a 

hierarchical structure of conditional probability tables and detailed energy simulations of 

hundreds of thousands of representative buildings.

2.2.1.1 Input Data Sets

The conditional probability distributions are synthesized from data queried, translated, 

aggregated, and extrapolated from 11 sources, including U.S. census data, RECS, builder 

surveys, and other data from field studies. These data are supplemented by engineering estimates 

where data are lacking. Details of the housing stock characterization data model can be found in 

Wilson et al. (2017). A summary of the input data sources is shown in Table 2. The data sources 

are described in detail in Appendix A.

 23 ResStock is currently limited to single-family detached housing. Capabilities for low-rise multifamily and mobile 

homes are under development.
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Table 2. Building Characteristics, Dependencies, and Data Sources

Dependencies Data Sources

Characteristics

LocationVintageHeating FuelUsage LevelDaytime UseFloor AreaNumber of StoriesFound. Type2009 RECS (EIA 2012) NAHBIECC 2009 RBSA (NEEA 2012)Ritschard et al. 1992ACSLabs et al. 1988Chan et al. 2012Wenzel et al. 1997Lucas and Cole 2009Eng. Exp. & CalibrationGeographic ResolutionNumber of Options

Meta Location TMY3 216

Vintage  • C 7

Heating fuel   • C 6

Usage level • U.S. 3

Daytime use U.S. 2

Geometry Floor area   • R 6

Number of stories     • R 3

Foundation type  • • 48 5

Attached garage    • R 2

Orientation U.S. 4

Envelope Window type   • • • R 5

Wall insulation   • • R 8

Attic insulation   • • R 7

Foundation insulation   • • R 5

Air leakage      • • R 12

Equipment Heating system type    • • R 6

Heating system efficiency   • • • R 10

Cooling system type   • R 7

Cooling system efficiency   • • • R 7

Duct insulation, tightness    • • • U.S. 5

DHW system type    • R 5

DHW system efficiency • • U.S. 3

Cooking type    • R 10

Clothes dryer type    • R 10
22

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Dependencies Data Sources

Characteristics

LocationVintageHeating FuelUsage LevelDaytime UseFloor AreaNumber of StoriesFound. Type2009 RECS (EIA 2012) NAHBIECC 2009 RBSA (NEEA 2012)Ritschard et al. 1992ACSLabs et al. 1988Chan et al. 2012Wenzel et al. 1997Lucas and Cole 2009Eng. Exp. & CalibrationGeographic ResolutionNumber of Options

Occupancy Heating, cooling set points   • TMY3 3

Cooking usage   • U.S. 3

Clothes dryer usage   • U.S. 3

Lighting, appliances, MELs   • • U.S. 3

 = direct dependency  = indirect dependency italics = archetype parameters MELs = miscellaneous electric loads

C = Census Tract R = Regional (custom) TMY3 = 216 typical meteorological year subregions NAHB = National Association of Home Builders 

ResStock statistically represents housing stock characteristics with 6,000 conditional probability distributions derived from 11 data sources. This table provides 

information on how each parameter’s probability distributions depend on other archetype parameters, as well as each parameter’s data sources, geographic 

resolution, and number of options (bins).
23

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ResStock also depends on geospatial weather data. It uses 216 climate subregions as the core 

geographic resolution for its building energy simulations (see Figure 3). The 216 subregions are 

clusters of approximately 84,000 National Solar Radiation Data Base 10-km2 grid cells that are 

grouped based on proximity, elevation, and data similarity, using a method described in Lopez 

(2011). For this work, 216 EnergyPlus weather (EPW) files, one for each climate subregion’s 

representative location, was assembled using National Solar Radiation Database (NSRDB) data 

from 2012 as described in Appendix H.

To achieve consistency with the commercial, industrial, and transportation sector components of 

dsgrid, ResStock results are mapped from the 216 TMY3 locations to the 3,107 counties in the 

CONUS. This mapping is done using census-tract-level household data from the 2012 ACS 

combined with a geospatial filter that excludes non-residential land. The filter is derived from 

a 200-m residential land mask computed from LandScan Nighttime and Daytime Gridded 

Population data (ORNL 2011) and Homeland Security Infrastructure Program facility location 

data (HSIP 2012). This mapping uses iterative proportional fitting to mesh data from the ACS 

Public Use Microdata and the American Housing Survey to ultimately account for the 

distribution of building types, vintage, heating fuel, cooling type, floor area, and household 

income within census tracts. Aggregated timeseries results for each county are standardized to 

Eastern Standard Time (EST) to synchronize electrical load profiles across all U.S. time zones.

Figure 3. ResStock simulations use 216 climate subregions, each represented by a single 

weather station.
24

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2.2.1.2 Methodology

The data sets described above are used to create a hierarchy of thousands of conditional 

probability tables that collectively define more than 100 building components. Statistical 

sampling based on a modified Latin hypercube sampling approach is used to select 

representative homes from the parameter space defined by the housing stock data model (Wilson 

et al. 2016). Detailed subhourly building energy simulations modeling a year’s worth of 

operations are assembled using OpenStudio and run in EnergyPlus (Roth, Goldwasser, and 

Parker 2016; Crawley et al. 2000). For this project, the energy consumption of the single-family 

housing stock of the CONUS is represented by 350,000 building/location models run on 

Peregrine, NREL’s high-performance computer, or distributed cloud computing. 

Convergence testing of simulation results sliced various ways led us to select 350,000 as the 

number of building/location models (combinations of building characteristics and climate 

locations) to represent the current U.S. housing stock. Weighting factors are used to scale results 

up from 350,000 to the 73.2 million regularly occupied single-family detached homes included 

in the analysis, based on the 2010–2014 ACS (Manson et al. 2017; U.S. Census Bureau 2017). 

2.2.1.3 Output Data

Each EnergyPlus simulation produces timeseries results of whole-building electricity and fuel 

use, as well as electricity and fuel use by end use. ResStock simulations typically use ten-minute 

timesteps for zone calculations with smaller timesteps used for HVAC system calculation on an 

as-needed basis. Timeseries results can be reported with ten-minute resolution but are aggregated 

to hourly resolution for dsgrid. The end uses reported for dsgrid ResStock analysis are based on 

standard EnergyPlus reportable meters, and the dsgrid bottom-up residential data set reports 

hourly timeseries of: 

• Fans: electricity (kWh)

• Pumps: electricity (kWh)

• Heating: electricity (kWh)

• Cooling: electricity (kWh)

• Interior lights: electricity (kWh)

• Exterior lights: electricity (kWh)

• Water systems: electricity (kWh)

• Interior equipment: electricity (kWh)

• Heating: gas (kBtu)

• Water systems: gas (kBtu)

• Interior equipment: gas (kBtu)

• Water systems: water (gal)
25

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Customizable OpenStudio scripts can be applied to report consumption for more detailed end

uses, such as plug loads, cooking, and clothes drying. Additional reports related to building 

performance (e.g., relative humidity and zone temperatures) can also be generated. 

The output timeseries are provided at the county level and are time-synchronous with 2012 

meteorological data, based on the geospatial disaggregations and weather data transformations 

described above. 

2.2.1.4 Calibration

ResStock, and building energy modeling in general, have previously focused on predicted annual 

energy savings results for applications related to energy efficiency in buildings. ResStock is 

currently calibrated by comparing modeled energy use by fuel type to the corresponding energy 

use metrics given in the 2009 RECS (EIA 2013d) for slices of the housing stock, such as region, 

vintage, and space heating fuel type. Iterative changes to model inputs were made to bring 

modeled consumption into better agreement with the reference consumption. The use of 

ResStock for producing electric load profiles under various scenarios is being validated as part of 

this effort. Models for bottom-up load modeling can generally be categorized as models that 

generate either (1) aggregated load profiles or (2) agent load profiles for individual buildings or 

appliances. Individual building agent load profiles reflect the stochastic behavior of event-based 

load spikes (e.g., toaster on for five minutes), which when summed over hundreds or thousands 

of buildings, approach the smoothness of aggregated load profiles needed for bulk power system 

analysis. Initially, dsgrid and ResStock focus on the former category of aggregated load profiles, 

though the latter category of agent load profiles is of interest for other applications.24

In 2016, NREL completed initial validation of the non-weather dependent end use load profiles 

(e.g., appliances, plug loads) generated by ResStock. In 2017, the validation effort focused on the 

weather-dependent end uses (e.g., space heating, space cooling, and domestic water heating). 

Sources of measured electric load profiles being used for validating ResStock include:

• Utility Load Research Data (ULRD): Electric utility companies collect load profile 

data from a sample of their customers for various internal and external uses. NREL has 

obtained a collection of ULRD for one or more years from about 30 U.S. electric utilities. 

The hourly load profiles are typically aggregated by customer class; residential customers 

that use electricity for space heating are sometimes split into a separate “space heat” 

class. Commonwealth Edison (ComEd) is one utility that splits ULRD into single-family 

and multifamily classes as well. Thus, the ComEd ULRD is particularly useful for 

validating ResStock single-family load profiles. Figure 4 summarizes some of the 

residential data in this data set, which is clearly summer peaking.

 24 Though ResStock models individual buildings, it cannot yet model event-based load spikes for most end uses.
26

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Figure 4. Average of residential load profiles from eight electric utilities available in the 

Utility Load Research Data (ULRD)

• End Use Submetering Studies: These studies measure the load profiles of individual 

end uses (e.g., heating, cooling, water heating, appliances, and miscellaneous plug loads) 

within individual buildings. The data collection is usually expensive and intrusive to 

building occupants, so submetering studies are uncommon and typically limited in terms 

of number of homes, geographic scope, and representativeness. The Residential Building 

Stock Assessment Metering (RBSAM) study, which recorded data at 15-minute intervals 

for over 150 end uses in about 100 single-family homes in the Pacific Northwest (Larson 

et al. 2014), is the primary submetering study used for ResStock validation. Notable 

changes made to ResStock inputs based on comparing ResStock outputs to the RBSAM 

data set are shown in Figure 5, Figure 6, and Figure 7. Additional plots showing 

preliminary calibration of weather-dependent end uses are included in Appendix A.

Figure 5. The ResStock plug load schedule was updated to use the measured plug load profile

from RBSAM (mid-day valley eliminated).
27

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Figure 6. The ResStock lighting schedule algorithm was updated to eliminate the deep mid-day 

valley and more closely match measured lighting use from RBSAM. 

The algorithm is a piecewise function with six components that are functions of building latitude, season, and 

sunrise/sunset times.

Figure 7. After modification, the ResStock lighting schedule algorithm results in an improved 

match to the RBSAM data for all months of the year.
28

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Other model improvements include addressing unrealistic coincidence in occupant behavior 

regarding exhaust ventilation and hot water use events. For exhaust ventilation (e.g., bathroom, 

kitchen, and clothes dryer exhaust), the model was changed from simulating every home having 

exhaust ventilation events at the same time to using a probability distribution to assign timing of 

the exhaust events. This had the effect of spreading spot ventilation out over the day realistically 

for the sample of homes modeled in ResStock. For hot water use, a change was made to use 

1,615 repeating weeks for the draw profile instead of a set of 50 annual draw profiles. This led 

to significantly more diversity in hot water draw patterns across homes, and it reduced the 

occurrence of unrealistically coincident water heater electricity demand.

2.2.1.5 Subsector Gap Model

When ResStock was initially developed, NREL’s residential modeling algorithms were focused 

on single-family detached housing, based on a history of developing energy modeling and 

optimization tools for research involving large, production homebuilders in the U.S. Department 

of Energy (DOE) Building America Program (Christensen et al. 2006; Wilson et al. 2014). 

Consequently, ResStock currently does not represent multifamily housing, including duplexes, 

and low-, mid-, and high-rise residential buildings.25 Mobile homes are also not currently 

represented. Larger multifamily buildings, in particular, mid- and high-rise residential buildings, 

are represented as part of the commercial building stock (Deru et al. 2011). To facilitate 

comparison to EIA Form 861 residential data (EIA 2013c), the dsgrid detailed residential sector 

model is ultimately comprised of the ComStock mid- and high-rise apartment building

subsectors sitting alongside the ResStock results in a single data file.

The remaining residential subsectors form the residential gap model (Table 3). The energy use 

for these gap subsectors is approximated by scaling the RECS 2009 energy use by the number 

of dwelling units reported in the 2010–2014 ACS divided by the number of dwelling units in 

RECS 2009. These factors are computed by subsector for the 27 reportable domains in RECS 

2009 (large states and groups of states) (Table 4). The energy use is then distributed to counties 

based on the 2010–2014 ACS occupied-only housing unit counts, again, on a residence-type 

basis. 

Table 3. Residential Subsectoral Gaps: Size and Proxy Timeseries Description

Est. Portion of CONUS 

Residential Electricity 

Use (%)

Proxy Timeseries

Subsector Description

Geographic 

Resolution

End-Use 

Resolution

Mobile homes 7.2 Single-family 

detached (1,000 ft2

size bin)

State Same as 

ResStock

Low-rise apartments 5.0 Mid-rise apartments 

(ComStock)

State Same as 

ComStock

Single-family attached 

(duplexes and 

townhomes)

4.7 Single-family 

detached

(1,000 ft2 size bin)

State Same as 

ResStock

 25 ResStock multifamily capabilities are under development as of 2018.
29

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The proxy timeseries for the residential gap model are also summarized in Table 3. As can be 

seen in the table, low-rise apartments use the mid-rise apartment load timeseries, broken down 

by end use, from ComStock; and the other two gap subsectors, single-family attached and mobile 

homes, use timeseries from ResStock, not the full county-level data, but the portion describing 

homes smaller than 1,500 ft2. In all cases, for each building type and county, if a county-level 

proxy timeseries is available, it is used. If no proxy timeseries is available at the county level, the 

state-level aggregate is used. In all cases, the proxy timeseries are scaled to match the electricity 

use (megawatt hours [MWh]) per building type and county estimated based on RECS 2009 

(energy use) and the 2010–2014 ACS (energy use scaled to 2012 using Table 4, and downscaled 

to counties based on relative building counts).

Table 4. Residential Scaling Factors: 2010–2014 ACS Occupied Dwelling Units Divided by RECS 

2009 Dwelling Units, by Building Type and Census Division

Census Division

Single Family 

Detached

Single Family 

Attached

Apartments in 2

to 4 Unit 

Buildings

Apartments in 

Five5 or More 

Unit Buildings Mobile Homes

East North Central 100% 109% 98% 100% 108%

East South Central 96% 84% 135% 106% 116%

Middle Atlantic 99% 105% 103% 106% 93%

Mountain 100% 85% 160% 138% 89%

New England 105% 92% 100% 97% 134%

Pacific 106% 112% 96% 99% 94%

South Atlantic 102% 133% 103% 110% 83%

West North Central 102% 101% 116% 113% 72%

West South Central 107% 58% 89% 103% 121%

2.2.2 Commercial Sector

ComStock is a bottom-up, physics-based model developed for this project by NREL to capture 

energy-related characteristics and model energy use of the U.S. commercial building stock. The 

ComStock methodology builds on the framework established by ResStock (Wilson et al. 2017)

but differs in several ways guided by the unique physical and operational characteristics of, as 

well as the data available for, commercial buildings. Primary differences include how the 

geospatial distribution of buildings is calculated and how empirical data are translated into 

building energy model inputs. ComStock is described here in brief. Detailed information on the 

data, process, and assumptions used to develop ComStock are available in Appendix B. 

The ComStock model is comprised of five parts: 

• A database of commercial building characteristics 

• Conditional probability tables synthesized from these data
30

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• A sampling methodology implemented in the Parametric Analysis Tool26

• Model articulation using the OpenStudio software development kit27 to create statistically 

representative models; the programmatic re-creation and customization of DOE prototype 

buildings28 within this framework29 is a key building block.

• The EnergyPlus building energy simulation engine.

30

ComStock results consist of scaled, statistically representative building load profiles for the 

CONUS commercial building stock with county-level granularity. The modeling process 

is modular and highly flexible, such that alternate input data sets can easily be substituted and 

various OpenStudio scripts (termed “measures”) can be inserted into the workflow. Thus, 

ComStock can be used to explore alternate geographies or years, as well as energy efficiency or 

demand response measures that model alternate design or operational decisions.

2.2.2.1 Input Data Sets

We initiate the ComStock workflow with a hierarchical structure of conditional probability tables 

for nine major determinants of commercial building energy use: building location (i.e., county), 

building type, floor-to-ceiling height, building energy code, primary HVAC system, number of 

floors, total floor area, and vintage. We synthesize conditional probability tables with binned 

values for each of these building characteristics (with the exception of energy code) from 

national data sets, namely CBECS (EIA 2016a) and CoStar, which is a building-level 

commercial real estate inventory from which we derive aggregates at the census block level 

(CoStar 2017). The resulting probability tables distill critical building characteristics at 

appropriate geo-spatial extents, for instance indicating that about 74% of primary schools in the 

United States are one story and 15% have two stories.

 26 For information about the OpenStudio Parametric Analysis Tool, see www.openstudio.net and the Parametric 

Analysis Tool 2.1.0 (PAT) Interface Guide at http://nrel.github.io/OpenStudio-userdocumentation/reference/parametric_analysis_tool_2/. 27 For information about the software development kit, see https://www.openstudio.net/developers. 28 For information about DOE prototype models, see the U.S. Department of Energy Building Energy Codes 

Program, “Commercial Prototype Building Models,” https://www.energycodes.gov/development/commercial/

prototype_models, last updated April 14, 2016. 29 For information about the OpenStudio-Standards Gem framework, see https://github.com/NREL/openstudiostandards. 30 For information about EnergyPlus, see energyplus.net.
31

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Each building is assumed to meet the minimum energy efficiency requirements of either 

ASHRAE Standard 90.1 (ASHRAE 2016) or the DOE “reference buildings” pre-2004 

assumptions (Deru et al. 2011). We assume building codes are commensurate with building 

vintage (e.g., if a building was constructed in 1994, we assume it adheres to the 1980–2004 

ASHRAE 90.1 standards). In future work, we will derive state- and county-specific code 

compliance from the Building Codes Assistance Project database and more-granular code 

adoption data derived from municipality and county data-sources (BCAP 2017). We may also 

use the last retrofit year field in the CoStar data to capture the proportion of older buildings that 

should now be considered compliant with newer building codes based on having been deeply 

renovated. Additional building characteristics are derived primarily from building type and code 

set using defaults specified in the DOE prototype buildings (Goel et al. 2014) and the NREL 

Sector Model (B. Griffith et al. 2007), and are subsequently maintained by NREL in the 

OpenStudio suite of modeling tools.

31 The major building characteristics captured by ComStock, 

their interdependencies, and data sources are summarized in Table 5.

 31 See Footnote 29.
32 

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Table 5. Building Characteristics, Dependencies, Data Sources, and Number of Variable Values 

Used in the ComStock Modeling Process

Dependencies Data Sources

Characteristics

DivisionLocation (county)CoStar Building TypeDOE Prototype BuildingVintageNumber of FloorsBuilding ShapeCBECS (EIA 2012)RECS (EIA 2009)CoStar (2017)DOE Prototype BuildingsNREL Sector ModelNREL NSRDB# of Options (bins) if table exists

Meta Division 9

Location 

(county) X X N/Ab

CoStar building 

type

X X 102

DOE prototype 

building X X 17

Vintage X X X X 10

Energy code X X 6

Space type 

breakdowna X

Weather data X X

Geometry Rotationc X 8

Number of 

floors X X X 16

Area X X X 10

Floor-to-ceiling 

height X X 46

Building shape X 12

Aspect ratiod X X 6

Window-to-wall 

ratioa X X

Envelope Construction 

typea X X

Wall propertiesa X X

Windows 

propertiesa X X

Internal 

Loads Peoplea X X X

Lightsa X X

Plug loadsa X X X
33 

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Dependencies Data Sources

Characteristics

DivisionLocation (county)CoStar Building TypeDOE Prototype BuildingVintageNumber of FloorsBuilding ShapeCBECS (EIA 2012)RECS (EIA 2009)CoStar (2017)DOE Prototype BuildingsNREL Sector ModelNREL NSRDB# of Options (bins) if table exists

Elevatorsa X X X

Kitchen 

equipmenta X X X

Service 

Water 

Heating

Showers, sinks, 

etc.a X X

Laundrya X X

Schedules Operation 

schedulesa X X

HVAC HVAC system 

typee X X 52

HVAC controlsa X X X

HVAC 

efficienciesa X X X

italics = archetype parameters, which are building characteristics whose values influence the conditional probability 

tables of other building characteristics

a For energy simulations, values of these parameters are determined using EnergyPlus/OpenStudio defaults based 

on the dependencies shown

(i.e., no probability tables are associated with these characteristics).

b The number of counties is dependent on the analysis area.

c Rotation is defined as 8 orientation bins offset by 45 degrees, with a uniform probability distribution.

d Aspect is defined as 6 bins between about 0.5 and 6.5. Each shape uses only some of the bins.

e We infer HVAC system type based on the main heating and cooling types recorded in (EIA 2016a), using lookup 

logic from (Griffith et al. 2008).

ComStock, along with ResStock, utilizes actual meteorological year (AMY) weather files 

derived from the NSRDB as an input into the EnergyPlus simulation engine (Appendix H). 

However, ComStock uses individual weather files for each county in the CONUS. Each weather 

file location was chosen to be the closest NSRDB grid cell to the LandScan grid cell with the 

highest daytime population in the county (ORNL 2011). 

2.2.2.2 Methodology

The conditional probability tables created from ground-truth data sets are stored in tab-separatedvalues file format, and the Parametric Analysis Tool is used to sample these tables hierarchically. 
34

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We apply the same Latin hypercube stratified sampling methodology as ResStock (Wilson et al. 

2016), selecting values for building characteristics based on the relative probabilities (i.e.,

frequencies) of those values, as described in CBECS 2012 and CoStar data. The resulting sets 

of building characteristics are translated into inputs for energy models in OpenStudio and 

EnergyPlus format using the Parametric Analysis Tool and OpenStudio measures (Roth, 

Goldwasser, and Parker 2016). This process is completed for 350,000 building simulations on 

internal server systems and cloud-computing infrastructure. Weighting factors are applied to 

scale results from the simulated 350,000 buildings to the total number of commercial buildings 

in the CONUS as estimated by CBECS 2012 (EIA 2016a).

2.2.2.3 Output Data

ComStock output data are similar to those of ResStock. The reported end uses are based on 

standard EnergyPlus reportable meters:

• Fans: electricity (kWh)

• Pumps: electricity (kWh)

• Space heating: electricity (kWh)

• Space heating: gas (kBtu)

• Space cooling: electricity (kWh)

• Interior lights: electricity (kWh)

• Exterior lights: electricity (kWh)

• Water systems: electricity (kWh)

• Water systems: gas (kBtu)

• Interior equipment: electricity (kWh)

• Interior equipment: gas (kBtu)

• Space cooling: district cooling (kBtu)

• Space heating: district heating (kBtu)

• Heat rejection: electricity (kWh)

When space heating or cooling is provided by district systems, the quantity of energy reported is 

the amount of thermal heating or cooling energy delivered to the building. To capture the load 

this represents in terms of electricity and natural gas, we assume conversion factors of 0.58 kWh 

electricity/ton cooling-hour for district cooling and 1.25 kBtu natural gas/kBtu heating for 

district heating (Xcel Energy 2016).

Based on the reported metadata of each simulation, the results are categorized on a county-bycounty basis. The data are reported by commercial subsector and end use by summing across all 

applicable data points within each county to achieve aggregated load timeseries. These values, 

along with a scaling factor of 9.034, which is a result of simulating approximately one seventh 
35

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of all commercial buildings in each county and a simulation failure rate of approximately 15%, is

persisted and transferred to dsgrid.

32

2.2.2.4 Calibration

The modeling techniques used in the creation of the ComStock building energy models are based 

on a significant body of peer-reviewed work completed by NREL, the Pacific Northwest 

National Laboratory, and Lawrence Berkeley National Laboratory over the last decade (Deru et 

al. 2011). The inputs have been reviewed by numerous experts, including ASHRAE committee 

members who design these buildings professionally. These models are under a constant state of 

update and review through the OpenStudio Standards Gem, an open-source project whose

continual testing and development is supported by several ongoing projects.

As ComStock is still in early stages of development, additional calibration steps are in progress. 

Two primary categories of error are currently being examined in the ComStock model. The first 

class of error stems from the uncertainty regarding the number of commercial buildings in the

United States, the square footage of said buildings, and their primary use. The second class is 

due to uncertainty in the modeling assumptions being applied and potential bugs in their 

implementation. All work in the immediate future is focused on finding and addressing these 

error sources to (1) improve our estimate of the total stock being modeled in terms of number 

and size of buildings by type and (2) improve our estimates of the energy use of these buildings 

at different timescales (e.g., annual, hourly, and subhourly).

Data sources (e.g., CBECS and CoStar) disagree on such basic data as the number, size and type 

of commercial buildings. Some of these differences arise from different definitions of what 

constitutes a commercial building. Determining the validity of these data sets relative to one 

another, however, requires a set of ground-truth observations. The ComStock team is proceeding 

by obtaining such observations from various public and private utility data sets. Doing so allows 

for verification of the number of buildings of various rate classes, albeit for limited sets of 

geographies. Additional data sets for building types that are not bought and sold frequently 

(e.g., federal buildings and schools) are also being incorporated to further supplement CBECS 

and CoStar.

The modeling assumptions built into the OpenStudio Standards Gem reflect the best information 

available from existing sources. Many of the inputs are taken from building energy performance 

codes such as ANSI-ASHRAE 90.1, which governs what properties buildings must have based 

on their age, type, and location to minimally comply with energy efficiency codes. Other inputs 

are taken from the ASHRAE Advanced Energy Design Guides, which are a series of 

publications written by experts on designing particular building types. The approach to modeling 

many of the complex building controls and equipment was taken from the DOE prototype 

buildings, which have been developed by NREL, the Pacific Northwest National Laboratory, and 

Lawrence Berkeley National Laboratory over the last decade (Deru et al. 2011; Goel et al. 2014). 

Additionally, because of the sheer number of inputs required for each building energy model, 

 32 Factoring the simulation failure rate into the overall scaling factor represents an implicit assumption that 

simulation failures are evenly distributed across geographies, building types, etc. The authors do not expect that 

assumption to be fully correct, but did also not find any clear, easily correctable trends in the specific simulations 

that failed. This is an area we will look at closely and correct in future work.
36

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some inputs and modeling assumptions were developed using professional engineering expertise 

developed by the ComStock team over many years of modeling buildings for a diverse array of 

research projects and field studies.

2.2.2.5 Subsector Gap Model

The DOE “reference buildings” and “prototype buildings” represent 17 commercial building 

types that are then replicated across climate zones and vintage to create hundreds to thousands of 

archetypes (Deru et al. 2011; Goel et al. 2014).

33 These models34 serve as a baseline standard 

from which each simulated building is articulated in EnergyPlus. The building types documented 

in the CBECS 2012 and CoStar data sets must therefore be mapped to these DOE prototype 

buildings for modeling purposes. To avoid producing inaccurate energy models, building types 

that did not have a reasonable prototype equivalent were excluded from ComStock, and they are 

instead included in the gap model. See Appendix B for the full mapping from CBECS and 

CoStar building types to DOE prototype buildings, along with associated building counts used 

for this analysis.

The CBECS building types included in the commercial building gap model are listed along with 

their annual energy use in Table 6. These building types are those that are not mappable to the 

DOE prototype buildings but are mappable to CoStar building types (Table C-3 and surrounding 

text). This latter mapping is then used to downscale the electricity use reported in CBECS to 

counties based on the CoStar building counts. The proxy timeseries for these gaps are the 

aggregated ComStock timeseries, summed over subsectors and end use. If a county-level profile 

is available, it is used; if not, a state-level profile is applied. Unlike with the residential gap 

model, we do not expect the end-use breakdowns to hold when applied to these various building 

types35 and so do not supply them.

 33 The DOE “reference buildings” span 16 building types: Large Office, Medium Office, and Small Office; 

Warehouse; Stand-alone Retail and Strip Mall; Primary School and Secondary School; Supermarket; Quick Service 

Restaurant and Full Service Restaurant; Hospital and Outpatient Health Care; Small Hotel, Large Hotel; and Midrise 

Apartment. The DOE “prototype buildings” do not include a Supermarket model, but they add a High-Rise 

Apartment. Thus, the two sets of archetypical building models together represent 17 commercial building types. 34 ComStock builds off of the OpenStudio Standards gem, which is a programmatic implementation of the DOE 

prototype buildings. At the time of this writing, 16 building types: all of those listed above except for Supermarket; 

are available. Because of deficiencies in the CoStar data, we are also not able to distinguish between Mid-Rise and 

High-Rise Apartments, nor between Primary and Secondary Schools (school buildings are generally underrepresented in CoStar) at this time. As such, this version of dsgrid provides results for 14 commercial building subsectors: Large Office, Medium Office, Small Office, Warehouse, Stand-alone Retail, Strip Mall, Primary School, 

Quick Service Restaurant, Full Service Restaurant, Hospital, Outpatient Health Care, Small Hotel, Large Hotel, and 

Midrise Apartment. Future planned work to add Supermarkets to the prototype buildings, and to supplement CoStar 

with data sources that better represent public-sector building counts, should remedy these shortcomings. 35 Commercial buildings are much more heterogenous than residential buildings, both in design and use. For 

example, the end use breakdowns between grocery stores, vehicle repair shops, and libraries look very different; 

and, the temporal distribution of load will be very different between offices, libraries, and religious buildings. The 

inability to match a CBECS or CoStar building type to a prototype building essentially signals that we do not yet 

have an energy model that properly captures one or both of these aspects. 
37

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Table 6. Commercial Sector Subsectoral Gaps: Size and Proxy Timeseries Description

Est. Portion 

of CONUS 

Commercial

Electricity 

Use (%)

Proxy Timeseries

Subsector Description

Geographic 

Resolution

End-Use 

Resolution

Grocery store/food market 2.7 ComStock aggregation County None

Recreation 2.0 ComStock aggregation County None

Religious worship 1.9 ComStock aggregation County None

Entertainment/culture 1.6 ComStock aggregation County None

Laboratory 1.5 ComStock aggregation County None

Convenience store 1.2 ComStock aggregation County None

Vehicle service/

repair shop 1.2 ComStock aggregation County None

Convenience store with 

gas station 1.0 ComStock aggregation County None

Library 0.9 ComStock aggregation County None

Vehicle storage/ 

maintenance 0.7 ComStock aggregation County None

Other public order and 

safety 0.6 ComStock aggregation County None

Vehicle dealership/ 

showroom 0.6 ComStock aggregation County None

Fire station/police station 0.5 ComStock aggregation County None

2.2.3 Industrial Sector

The Industrial Geospatial Analysis Tool for Energy Evaluation (IGATE-E) is a model that 

utilizes multiple data sources and statistical approaches to estimate the energy consumption of 

manufacturing plants across the United States. Originally developed by Oak Ridge National 

Laboratory in 2012, the tool has been used to investigate the potential for demand response and 

CHP at the plant levels (Alkadi et al. 2013; Bhandari et al. 2018).

IGATE-E can be differentiated from the other sector models used by dsgrid in that it does not 

attempt direct simulation of loads, because of the highly complex and specialized nature of 

manufacturing processes. For example, process heating, which represents one of the largest 

opportunities for electrification within the manufacturing subsector, is comprised of more than 

10 types of heating operations36 and 20 distinct system types37 used to carry out these operations

(LBNL et al. 2015). Furthermore, the applicability of each of these operational categories and 

 36 LBNL et al. (2015) provides basic descriptions of agglomeration and sintering, calcining, curing, drying, fluid 

heating, forming, high-temperature heating and melting, low-temperature heating and melting, heat treating, 

incineration/thermal oxidation, metals reheating, smelting, and other heating processes.

37 LBNL et al. (2015) categorizes process heating systems as fuel-based, electric-based, steam-based, or other; 

where other is comprised of systems such as heat recovery, heat exchange, and fluid heating. That report lists 

14 kinds of fuel-based furnaces and 11 electricity-based process heating technologies.
38

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systems varies greatly from one manufacturing industry to another. Because of the impracticality 

of modeling so many different use cases, IGATE-E instead compiles data from multiple sources 

and applies statistical techniques to estimate energy consumption down to the plant and end-use 

levels. Because IGATE-E only models manufacturing, the additional industrial subsectors of 

agriculture, mining, and construction comprise the industrial gap model.

2.2.3.1 Input Data Sets

Within IGATE-E, several data sets are used to estimate energy consumption for the 

manufacturing subsector. The primary data sets are the DOE Industrial Assessment Centers 

(IAC) Database and the Manufacturers’ News, Inc. (MNI) EZ Select database. The IAC 

Database is a public data source that contains information on more than 18,000 plant-level 

assessments (DOE 2017a). The assessments date to 1981 and primarily focus on small- and 

medium-sized plants.

38 Although the IAC Database contains a large amount of data, its 

relevancy may be limited in industries where fewer recent assessments have been completed and 

where large manufacturing plants play a major role. Furthermore, because plants must choose to 

undergo an assessment, a self-selection bias may be present, with a recent analysis of IAC 

participants indicating participants were less energy efficient than their peers (Dalzell, Boyd, and 

Reiter 2017). Though a detailed evaluation of the statistical validity of the IAC Database was not 

conducted, Figure 8 summarizes how many assessments are available by both industry and 

assessment year. For its use in this analysis, IGATE-E ignores assessments that do not provide 

information on a plant’s NAICS code. As a result of this requirement, IGATE-E only uses 

assessment data from 2002 onward, limiting problems that could have arisen from using 

older data.

Figure 8. Number of IAC assessments by industry and year (as of March 2018)

For assessments occurring before 2002, results are available by SIC code only.

NAICS = North American Industry Classification System

 38 Eligibility requirements to qualify for an IAC Assessment include energy bills between $100,000-/yr and 

2,500,000/yr.
39

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Additional data sources have been explored to supplement this database; however, the 

proprietary/competitive nature of plant-level information has made it difficult to find additional 

data beyond what is currently available from the IAC Database.

The MNI database is a commercial data set that provides industry, location, and employment 

information for approximately 294,000 manufacturing sites across the United States, which 

provides approximately complete coverage of manufacturing establishments, see Footnote 44. 

Other sources were considered for this purpose, such as the Dun & Bradstreet Hoovers database, 

but the MNI was found to be the most cost-effective based on the needs of the project. Together, 

the MNI and IAC data sets are used to develop plant-level energy and demand estimates.

To refine IGATE-E’s initial energy consumption estimates, data from the 2014 MECS are used. 

Finally, end-use level consumption information by industry (i.e., three-digit NAICS) from the 

MECS, along with data from EPRI’s Load Shape Library,39 is used to construct disaggregated 

8,760 load shapes. Table 7 summarizes the manufacturing plant characteristics, modeling 

methods, and data sources used by IGATE-E.

Table 7. Manufacturing Plant Characteristics, Modeling Methods,

and Data Sources used by IGATE-E

Methods Data Sources

Resolution Characteristics

Regression AnalysisEnergy Analysis Energy Analysis Load FactorAli Load Shape AnalysisIAC DatabaseMNI EZ Select Dtb 2014 EIA MECSEPRI Load Shape Lib

Plant Level Location (State/County/Zip Code) X X

Industry Code (NAICS/SIC) X X X X X

Energy Consumption (kWh or MMBtu/yr) X X X

Electricity Demand (kW/month) X X

Number of Employees X X X X

Industry Level Industry Code (NAICS/SIC) X X X

Energy Consumption (kWh or MMBtu/yr) X X

End-Use Energy Consumption (kWh/yr) X X

Sectoral Level Load Shapes by End-Use X X

 39 The EPRI Load Shape Library provides representative industrial load shapes by end-use and region. Daily load 

shapes are provided for various scenarios (i.e., weekday versus weekend and peak season vs. off-peak season). Load 

shapes are derived from simulations using the EPRI NESSIE (National Electric System Simulation Integrated 

Evaluator) model platform. The inputs to NESSIE are derived from data estimated by the EIA’s National Energy 

Modeling System (NEMS) as well as from data collected by EPRI through its laboratory testing and research.
40

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2.2.3.2 Methodology

To estimate electricity consumption within the manufacturing subsector, IGATE-E performs 

regression analyses of energy consumption versus number of employees using data from the IAC 

Database. Regression results are developed for individual industries based on their North 

American Industry Classification System (NAICS)40 code; this system is used to classify 

business establishments according to their primary economic activity, specifically the type of 

product being produced. Within the model, a linear regression analysis is conducted by industry 

for every three- and four-digit NAICS code.41

Working within this framework, the basic premise of IGATE-E is that manufacturing facilities 

producing similar products (as categorized by their NAICS code) will utilize similar processes 

that ultimately have similar energy intensities (i.e., energy usage per product produced). 

Currently, number of employees is used as a proxy for the product being produced.42 Using these 

industry-specific regression results, aggregate energy usage is estimated for each plant using 

employment information from the MNI database. Finally, usage estimates are adjusted by 

industry (three-digit NAICS) and census region to better align with the MECS.

Hourly annual load shapes are created by combining industry-level load factor estimates with 

diurnal load shapes provided by sector and end use from the EPRI Load Shape Library. Load 

factors are estimated from the IAC Database data by conducting a regression analysis of peak 

electricity demand versus annual electricity use and then estimating an average load factor for 

each industry based on the slope of these regressions. Plant-level peak electricity demand falls 

out by combining load factor with annual energy consumption estimates. The EPRI Load Shape 

Library provides daily load shapes by sector and end use for various scenarios (e.g., weekday 

versus weekend). To disaggregate energy consumption at the end-use level, data from the 2014 

MECS (by three-digit NAICS) is applied. Next, categories included in the EPRI Load Shape 

Library are matched to the most appropriate MECS end-use category (Table 8). Finally, 

disaggregated load shapes are constructed for each industry based on end-use consumption 

estimates from the 2014 MECS and load shapes from the EPRI Load Shape Library. The 

resulting load shapes are either “stretched” or “flattened” on an industry-by-industry basis to 

match the load factor estimates derived previously. Within IGATE-E, load shapes are applied at 

the individual plant-level based on a plant’s peak electricity demand.

 40 For more information, see https://www.census.gov/eos/www/naics/. 41 Within IGATE-E, a minimum of five IAC assessments per regression is enforced. While most four-digit 

industries have enough data to meet this requirement, those that do not utilize three-digit regressions. 42 IGATE-E originally utilized annual sales data for this purpose; however, inconsistencies in quality and the 

sporadic availability of this data limited its value. Although nominally annual sales should correlate better to energy 

usage than number of employees, the latter data field is more consistently provided in IGATE-E’s data sets, and it 

has thus proven to be a better proxy in practice.
41

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Table 8. End-Use and Load Shape Category Mapping

2014 MECS

End-Use Category

EPRI Load Shape

Library Category

Conventional Boiler Use Other

Process Heating Process Heating

Process Cooling and Refrigeration Other

Machine Drive Machine Drives

Electro-Chemical Processes Other

Other Process Use Other

Facility HVAC HVAC

Facility Lighting Lighting

Other Facility Support Other

Onsite Transportation Other

Other Nonprocess Use Other

End Use Not Reported Other

2.2.3.3 Output Data

For use in dsgrid, plant-level peak demand estimates are compiled by county and NAICS code. 

These peak demand values are then mapped to the corresponding normalized load shape. Load 

shapes are available on a per-four-digit NAICS code and time zone basis (conforming to the 

dsgrid convention of 2012 as experienced in EST, with end-of-hour data points, and accounting 

both for time zones and for varying daylight saving time policies). Within the load shapes, 

electricity consumption detail is provided for the following end-use categories: conventional 

boiler use, process heating, process cooling and refrigeration, machine drives, electrochemical 

processes, other process use, facility HVAC, facility lighting, other facility support, onsite 

transportation, and other nonprocess use. These categories match those reported in the MECS.43

2.2.3.4 Calibration

Several differences emerge when IGATE-E’s estimates are compared to the 2014 MECS. 

Industries where consumption is significantly underestimated include:

• 322: Paper

• 324: Petroleum and Coal Products

• 325: Chemicals

• 331: Primary Metals.

In reviewing the MECS, these industries were found to have the highest energy intensities, 

suggesting the lack of regression data for large manufacturing plants may be limiting IGATE-E’s 

 43 In addition to electricity consumption, IGATE-E also estimates annual natural gas consumption at the individual

plant-level. Currently, however, no effort has been made to disaggregate natural gas usage by end-use category.
42

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accuracy in these cases. For industries where consumption is significantly overestimated, 

discrepancies in the number of establishments and employees considered by MECS compared to 

IGATE-E may be the primary issue.44

While additional research is being conducted to understand these differences, IGATE-E’s initial 

estimates are adjusted by industry and census region to match the MECS. This is accomplished 

by scaling individual plant estimates so that aggregate consumption estimates match those from 

the 2014 MECS. The primary drawback to this is that errors in the MECS are ultimately 

reproduced by IGATE-E. Future efforts will focus on further developing IGATE-E’s 

optimization approach to avoid “overadjusting” initial estimates to match the MECS.

2.2.3.5 Subsector Gap Model

Within the industrial sector, IGATE-E’s methodology has specifically been developed for the 

manufacturing subsector. Subsectors not covered by IGATE-E include agriculture, forestry, 

fishing and hunting; mining, quarrying, and oil and gas extraction; and construction. In dsgrid, 

these non-manufacturing subsectors are represented in the industrial gap model using annual 

electricity estimates from the AEO and generalized load shapes from the EPRI Load Shape 

Library. To develop county level results for these subsectors, AEO national estimates are first 

disaggregated to the state level based on employment data from the U.S. Census Bureau’s 

Statistics of U.S. Businesses and then at the county level based on number of establishments data 

from the U.S. Census Bureau’s County Business Patterns (CBP).

Table 9. Industrial Sector Subsectoral Gaps: Size and Proxy Timeseries Description

Proxy Timeseries

Subsector

Est. Portion of 

CONUS 

Industrial 

Electricity 

Use (%) Description

Geographic 

Resolution

End-Use 

Resolution

Mining, quarrying, and oil 

and gas extraction 8.8

IGATE-E generic 

industrial load shape County None

Construction 6.4

IGATE-E generic 

industrial load shape County None

Agriculture, forestry, fishing 

and hunting 3.7

IGATE-E generic 

industrial load shape County None

2.2.4 Transportation Sector

For dsgrid, we are primarily interested in electrification of road transportation, namely electricity 

use for passenger and commercial plug-in hybrid electric vehicles and battery electric vehicles. 

To model this electricity use, we rely on three NREL models, as summarized in Figure 9:

 44 For example, while the MNI database and the U.S. Census 2015 Statistics of U.S. Businesses 

(https://www.census.gov/programs-surveys/susb.html) largely agree on the number of manufacturing 

establishments, reporting 294,427 and 292,825 respectively; the 2014 MECS estimates 175,107 establishments.
43 

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• Automotive Deployment Options Projection Tool (ADOPT): used to inform vehicle 

adoption and vehicle attributes (e.g., range and fuel economy)

• Scenario Evaluation and Regional Analysis (SERA): used to provide spatially 

distribute regional vehicle adoption 

• Electric Vehicle Infrastructure Projection Tool (EVI-Pro): used to generate 

hourly charging profiles based on travel data and charging preference assumptions 

(e.g., residential charging as opposed to reliance on public charging).

Core bottom-up transportation modeling in dsgrid focuses on electrification of on-road 

transportation via PEVs (including plug-in hybrids), for both light-duty and medium- and heavyduty vehicles. All other forms of transportation (e.g., rail, air, marine, and off-road vehicles) are 

considered in the gap model. Moreover, we do not consider alternative electrification strategies 

such as battery swapping or dynamic charging technologies (e.g., embedded roadway or catenary 

charging) that may be especially appropriate for heavy-duty on-road vehicles (Navidi, Cao, and 

Krein 2016; Cordoba Ledesma 2015). Note that although off-road vehicles are generally 

considered a transportation gap in dsgrid, energy consumed by off-road vehicles used in

construction, agriculture, or mining would be categorized as an industrial, rather than a 

transportation gap, in line with energy consumption statistics (EIA 2017c).

Figure 9. Transportation modeling scheme for the road subsector

LDV = light-duty vehicle, MDV = medium-duty vehicle, HDV = heavy-duty vehicle

The electricity consumption in road transportation in 2012, however, was very limited, with 

roughly 70,000 plug-in electric passenger vehicles on the road concentrated in a few urban areas 

(IHS Markit 2017). This number represents about 0.4 TWh of electricity, or less than 0.05% of 

U.S. electricity use (EIA 2015a). Therefore, it is not included in the 2012 historical dsgrid 

snapshot that is the focus of this documentation, but we do here document the methodology that 

will be used to model PEV charging load profiles and flexibility for future scenario snapshots 

that include higher PEV market shares. We also describe our transportation gap model, which 

consists of a first-order spatial and temporal disaggregation of transit rail electricity use, which 

accounted for approximately 6.6 TWh in 2012 (EIA 2015a).

• Overall	

electrification	

ambition

• LDV/MDV/HDV	

role

Scenario	

Definition

• Based	on	cost,	

performance,	

and	projected	

improvement

Vehicle	

Adoption	

• Based	on	

spatial	

distribution	 of	

early	adopters

Regionalization	

of	Vehicle	

Deployment

• Based	on	travel	

surveys,	 vehicle	

characteristics,	

and	charging	

assumptions

Real-World	

Hourly	Power	

Profiles
44

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In the remainder of this section, we document how PEV electricity use will be estimated in the 

EFS future load snapshots using SERA and EVI-Pro, given (1) an exogenously specified fleet of 

electric vehicles (informed by ADOPT runs for passenger PEVs) and (2) how rail transit 

electricity use has been disaggregated in the gap model. Given electrification scenarios designed 

as part of the EFS project, which define overall (national) adoption of electrified technologies in 

the transportation sector (Mai et al. 2018), SERA will be used to disaggregate to the county level 

and EVI-Pro will be used to develop hourly charging profiles. Charging flexibility and 

willingness to delay charging will be assessed in a scenario framework based on bounding 

conditions and expert judgement. The potential to use that flexibility to provide grid services and 

participate in demand response will not be considered here, but it will be assessed using 

operational grid models in future work.

2.2.4.1 Input Data Sets

To describe electricity use in the on-road transportation sector, we rely on vehicle registration

data from IHS Automotive, vehicle attributes from the ADOPT model, consumer attitude and 

preference for alternative fuel vehicles included in the SERA model, and numerous travel 

surveys and charging behavior assumptions included in the EVI-Pro model. Table 10

summarizes the dependencies and data sources used to model plug-in vehicle electricity 

use in dsgrid.

Table 10. Vehicle Characteristics, Dependencies, and Data Sources Used in the 

dsgrid Transportation Modeling Process

Dependencies Data Sources

Category Characteristics

Overall electrificationEFS scopeConsumer preferenceTechnology developmentInfrastructure availabilityCharging behaviorEIA AEOEFS assumptionsIHS AutomotiveADOPT (sources therein)SERA (sources therein)EVI-Pro (sources therein)

Subsectors Transportation electricity use x x

Passenger PEVs x x x x x x x x

Commercial PEVs x x x x

Others x x x x

Passenger Vehicles Overall PEV adoption x x x x x x x x

Spatial disaggregation x x x x

PEV characteristics x x x

PEV use patterns x x x x

Charging profiles (hourly) x x
45

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Dependencies Data Sources

Commercial 

Vehicles

Overall PEV adoption x x x x x

Spatial disaggregation x x

Electrification strategy x x x

Vehicle attributes x x

Vehicle use patterns x x x

Charging profiles (hourly) x x x

2.2.4.2 Methodology

Electricity use for passenger light-duty vehicles is modeled using a suite of mature tools 

developed by an NREL transportation team. These tools project vehicle adoption in the lightduty sector and alternative fuel infrastructure build-outs, and they model the energy use of 

individual passenger and commercial vehicles, including charging profiles for PEVs. The version 

of dsgrid documented here focuses on electrification of road transport, including passenger 

vehicles as well as medium- and heavy-duty commercial vehicles. Three different models are 

combined to arrive at hourly, county-level electric vehicle charging profiles. Detailed 

transportation models are used to project electrification of road transportation (which makes up

approximately 79% of the approximately 26 quads of 2012 transportation energy consumption 

and is the most likely sector to be impacted by electrification). Other subsectors (rail, marine 

shipping, and aviation) are not be considered in detail. 

The ADOPT tool will be used to inform passenger vehicle adoption scenarios over time based 

on characteristics of the existing vehicle fleet, technical and cost targets, and policy assumptions 

(Brooker et al. 2015). ADOPT will also project attributes and characteristics of future vehicles 

(e.g., battery capacity and vehicle range, and fuel economy) ADOPT provides yearly estimates 

of sales and vehicle stock at the county level that are further geospatially resolved using the 

SERA model (Bush et al. 2017). SERA has a highly geographically resolved understanding of 

transportation demands— down to the 0.5-km scale—and it will properly regionalize the 

ADOPT projections as needed and provide regional estimates of the resulting annual electricity 

demand. SERA will also be used to estimate potential for electrification for medium and heavyduty commercial vehicles based on scenarios informed by relevant literature and current truck 

traffic volumes resolved in time and space. Medium-duty battery electric vehicles and buses will 

be modeled based on relevant literature in a scenario approach (i.e., educated assumptions about 

electrification of the existing fleet). In the heavy-duty road sector—although battery-powered 

trucks are technically conceivable—the range and the fuel energy density (both in terms of 

weight and volume) requirements make the deployment of PEVs harder (even though some 

private companies have been working to develop such products). Therefore, truck electrification 

will be modeled based on instantaneous charging, either wireless charging or charging via 

electrified road corridors (catenary charging). These charging profiles will be estimated for key 

corridors using heavy-duty traffic information and spatially disaggregated using the SERA 

model. 
46

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Finally, given a projected adoption of passenger and medium-duty PEVs for a given year, EVIPro will be used to estimate hourly charging profiles (Wood et al. 2017). EVI-Pro considers 

different recharging infrastructure scenarios, informed by the SERA model, including 

preferential use of residential, workplace, and public charging stations. EVI-Pro uses real-world 

travel data to simulate spatially and temporally resolved demand for PEV charging at homes, 

workplaces, and public destinations. It anticipates consumer charging behavior while capturing 

variations with respect to housing type (single versus multiunit dwellings), travel period 

(weekdays versus weekends), and regional differences in travel behavior and vehicle adoption.

Figure 10 illustrates the main modeling steps in EVI-Pro.

Figure 10. EVI-Pro model structure

Source: Wood et al. (2017)

eVMT = electric vehicle miles traveled 

A fundamental assumption in EVI-Pro is that consumers prefer charging scenarios that enable 

them to complete all their current travels (which are driven in gasoline vehicles) and to maximize 

the miles driven on electricity (for plug-in hybrid electric vehicles). To define which charging 

scenarios consumers will elect, individual travel days from available travel surveys are simulated 

in the model. Each travel day is simulated multiple times for each potential combination of 

charging behavior (e.g., L1-Home, L2-Home, and L1-Home plus L1-Work) and the lowest-cost 

option if then selected, considering different levels of consumer preference for alternative 

charging solutions. 

Past works using ADOPT, SERA, and EVI-Pro focused primarily on light-duty vehicles. For the 

future load snapshots, the team plans to construct a fairly detailed model of medium- and heavyduty electric vehicles, based on the EFS focus on on-road transport electrification. To estimate 
47

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the adoption of such vehicles the team will leverage internal expertise and coordinate with the 

scenario development group to produce realistic projections.

2.2.4.3 Output Data

ADOPT, SERA, and EVI-Pro are mature models that have been used extensively to answer a 

variety of questions related to transportation sector transformation and potential implications in 

terms of refueling infrastructure deployment and impact on the electric grid. The EFS, however, 

will push the boundaries of the three models by integrating them to generate spatially and 

temporally highly-resolved profiles for a set of different electrification scenarios. For the future 

scenario load snapshots, the models will be combined to produce hourly baseline charging and 

charging flexibility metrics (e.g., charging “schedulability” profiles) by subsector and scenario 

(specified by a set of charging preferences) for each county in the CONUS.

2.2.4.4 Calibration

SERA and EVI-Pro, which will be used to project future electricity demand for road 

transportation, have been calibrated using current vehicle use and statistics based on available 

regional and National travel surveys (Bush et al. 2017; Wood et al. 2017).

2.2.4.5 Subsector Gap Model

dsgrid models on-road transportation at a high level of detail. For the purposes of describing 

dsgrid, all other transportation subsectors are “gaps.” For this version of dsgrid, we focus on 

the amount of electricity used for transportation in 2012, a total of about 7.0 TWh. Most of 

the electricity used in transportation in 2012 was used by trains: 6.6 TWh partitioned among 

intercity rail, transit rail, and commuter rail, per EIA (2015a). This energy use is disaggregated in 

the transportation gap model to produce hourly profiles using a first-order disaggregation method 

based on NTD data (FTA 2017). In particular, total electricity consumption for rail is 

disaggregated spatially based on annual electric energy expenditure for propulsion (in kWh), 

as reported by NTD for 67 companies covering 45 U.S. urban areas (FTA 2017). To resolve 

this demand hourly, because no detailed modeling is available, we leverage data on hours of 

operation for different days of the week and number of rail cars operating in each day, which are 

also available in the NTD. Table 11 summarizes this gap model. Additional details are provided 

in Appendix D.

Table 11. Transportation Sector Subsectoral Gaps: Size and Proxy Timeseries Description

Proxy Timeseries

Subsector

Est. National 

Electricity 

Use (TWh) Description

Geographic 

Resolution

End-Use 

Resolution

Passenger rail 6.6

Constructed based on 

hours of operation and 

number of rail cars 

operating by day type State None

2.2.5 Summary

The purpose of dsgrid is to estimate potential future load shapes, especially as they might be 

impacted by energy efficiency, electrification, and demand response (i.e., set-point and 
48

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operational modifications made in support of grid operations). Given this, an initial gap analysis 

of our planned model was completed up front to assess how much electricity use—and how 

much of all potentially electrifiable site energy use—is covered by the bottom-up sector models, 

as these proportions give a first indication of how well we should expect to be able to model 

major shifts in future electricity load. We have also revisited the gap analysis to accurately 

reflect what in the end is covered in our detailed and gap sector models, as well as what energy 

use remains unmodeled by dsgrid.

The analysis uses subsectoral data available in 2009 RECS, 2012 CBECS, 2014 MECS and the 

2012 historical data available in the AEO 2015 (EIA 2013d, 2016a, 2017a, 2015a).

45 We 

surveyed the sector modeling teams to determine which subsectors are fully described, which are 

included in our sectoral gap models, and which are not included in either of these categories. The 

proportion of U.S. electricity use and total site energy use modeled in detail, coarsely, or not at 

all was then estimated by tagging subsectors in the national-level data sets. Details about the 

model coverage analysis methods and results are available in Appendix E.

For the baseline dsgrid snapshot described here, perhaps the most important metrics are those 

associated with 2012 electricity use. As shown in Figure 11, dsgrid models about 80% of current 

U.S. electricity use at full subsector and end-use resolution. Looking across all electricity use, 

one can see the largest gaps are in commercial and residential buildings. Industrial energy use 

that is not covered by IGATE-E includes mining, construction, and agriculture. Transportation 

electricity use is currently very small and consists mostly of passenger rail, which is not modeled 

in detail by dsgrid but is assigned an hourly timeseries of electricity use by state. The actual 

sectoral gaps are listed in detail above, in Table 3, Table 6, Table 9, and Table 11, and in the 

surrounding text.

Figure 11. dsgrid models about 80% of 2012 U.S. electricity use in detail.

 45 It is generally the case that coverage in our sector models is mostly determined by sub-sector rather than by 

geography or end use.
49

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Looking forward to questions of end-use electrification and operational flexibility—and 

recognizing that our sector models model all energy use, not just electricity—we extend our 

analysis of model coverage to compare total energy use by each fully modeled subsector against 

the total energy use of the sector. This comparison is done on a site energy basis, based on 

dsgrid’s focus on loads as they are experienced from the utility customer perspective. The 

results of that breakdown are shown in Figure 12.

Figure 12. dsgrid models about 76% of 2012 U.S. site energy use in detail.

When examining site energy use, the large roles of transportation and industry in our energy 

economy quickly become clear, as does the large potential for electrification. With a coverage by 

this metric of 76%, dsgrid appears to be well-positioned to tackle questions about what potential 

roles end-use electrification could play in our energy future. While electricity made up only 23% 

of site energy use in 2012, 76% of the non-electric remainder was used for nominally 

electrifiable end uses, including electrifiable transport (comprising 46% of non-electric 

energy consumption) and space and water heating (17%) (Appendix E).

The significant gaps in coverage from a site energy perspective are approximately the same as 

those from the electricity-only perspective for commercial and residential buildings, as well as 

industry. For transport, fuel use other than electricity opens additional subsectors, such that the 

most significant gap in our transportation modeling when all fuels are considered is no longer 

rail, as it was when we just looked at electricity. Considering all fuels, the biggest transportation 

subsectors we do not model in detail are air, shipping and freight, natural gas pipelines, and 

military transport.
50

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The electricity and energy coverages provided by the core sector models, plus a description of 

their geographic, temporal, and sectoral extents and resolution, are summarized in Table 12.

Each sector model was further developed for this project, compared to where they were at the 

start in order to support the needs of power system modelers in general, and the EFS specifically. 

Of the four sectors, residential and transportation started at fairly mature levels of model 

development. The residential team thus focused on validating the ResStock hourly profiles by 

end use, and geographically downscaling the results to counties by leveraging the ACS and other 

geographic information system data layers. The transportation team focused on developing 

supplemental models of rail electricity use, and electrification of medium- and heavy-duty 

vehicles, the latter in anticipation of later stages of the EFS. Before this project, IGATE-E had 

been used to estimate demand response and CHP potential for the industrial manufacturing 

sector. Work for the EFS included further validation of annual energy use estimates, and 

improved methods for constructing hourly timeseries of electricity load. ComStock did not exist 

before this work, but it was possible to build the model up in a timely manner by leveraging the 

DOE prototype buildings and the methodology pioneered by the ResStock team.
51

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Table 12. Geographic, Temporal, and Sectoral Extents, Resolutions, and Biggest Gaps for dsgrid’s Constituent Models 

Buildings Industry Transport

ResStock ComStock IGATE-E SERA EVI-PRO

Geographic Extent Contiguous United States Contiguous United States United States Contiguous United 

States 

Contiguous United 

States

Resolution County County ZIP Code 0.5-km grid County (flexible)

Temporal Extent Annual Annual Annual Decadal Daily

Resolution Subhourly Subhourly Hourly Annual Subhourly

Sectoral Extent Single-family detached Sixteen DOE commercial 

prototype buildings

Manufacturing Passenger and 

commercial vehicle

stocks

PEVs

Resolution 6,000 conditional 

probability distributions 

adjust 80 model inputs.

3,400 conditional 

probability distributions 

adjust nine model inputs.

86 four-digit NAICS 

codes

Flexible based on 

inputs

Residential, 

workplace, and 

public recharging

Subsectoral 

Gaps

Single family attached, 

2–4 unit apartments, 

mobile homes

Grocery store, recreation, 

religious worship, 

entertainment/culture, 

laboratory, convenience 

store, vehicle 

service/repair;

Other building types are 

not modeled.

Agriculture and forestry, 

mining and other 

extractive industries, 

construction

Electricity for transport by rail; other fuel used 

for air, natural gas pipelines, military transport, 

shipping, rail is not modeled.

End-Use Resolution Fans, pumps, heating, 

cooling, interior lights, 

exterior lights, water 

systems, interior 

equipment

Fans, pumps, heating, 

cooling, interior lights, 

exterior lights, water 

systems, interior 

equipment, district 

heating, district cooling, 

heat rejection

Boilers, process heating, 

process cooling, machine 

drives, electrochemical 

processes, facility HVAC, 

facility lighting, facility 

other, non-process uses

EV charging

2012 

Electricity 

Coverage

1,310 TWh

83% detailed model, 

17% gap model

1,429 TWh

73% detailed model, 

16% gap model,

10% unmodeled

1,331 TWh

86% detailed model, 

14% gap model

7 TWh

5% detailed model,

95% gap model

2012 Site 

Energy 

Coverage

10 Quads

83% detailed model, 

17% gap model

8 Quads

74% detailed model,

17% gap model,

9% unmodeled

15 Quads

67% detailed model, 

33% gap model

26 Quads

79.4% detailed model,

0.1% gap model,

20.5% unmodeled
52

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2.3 Gap and Derived Models

To develop a complete picture of U.S. electricity use, dsgrid includes gap models for subsectors 

that are nominally classified as commercial but are unrelated to commercial buildings, and 

distributed generation models. Derived components describing power system losses and model 

residuals are constructed by leveraging top-down data sources that describe load from the 

electricity sector point-of-view and by comparing them to the other components of dsgrid.

2.3.1 Non-Sectoral Gap Models

Several types of energy use fall outside easy categorization. There are municipal uses of energy, 

such as water distribution and treatment, wastewater treatment, and outdoor lighting. There are 

also commercial enterprises such as amusement parks and ski resorts that in a sense could be 

categorized as “commercial buildings” but defy traditional building energy modeling. The EIA, 

at least on Form 861, designates all these subsectors as commercial (EIA 2013c). Here we limit 

our attention to the three municipal subsectors mentioned above: water supply, wastewater

treatment, and outdoor lighting.

2.3.1.1 Municipal Water Services

Municipalities and other water utilities provide two main services: public water supply and 

wastewater treatment. Public water supply involves providing, treating, and distributing water 

to buildings and other locations such as parks and campgrounds. Energy is required to move the 

water, typically through pipelines, and to treat it to bring its quality up to drinking water level. 

After use, some water is in turn collected by wastewater utilities, which treat it to make it 

suitable for returning to the environment. This is also an energy-intensive process.

Pabi et al. (2013) provide a detailed description of the public water supply and wastewater 

treatment processes used in the United States and develop a national-level estimate of electricity 

used for those purposes in 2011; they find that these processes were responsible for about 1.8% 

of all U.S. electricity use. Our gap model uses the Pabi et al. (2013) numbers, first broken down 

on a per capita basis and then built back up for our geographic scope in 2012.46

For public water supply, the decomposition was accomplished by computing an average energy 

intensity in kilowatt-hours per million gallons supplied (kWh/MG) as shown in Table 13, and 

combining this with the estimate that each public water system user uses 171 gallons per day47

(Pabi et al. 2013). Because the total U.S. population in 2011 was about 313 million,

48 the per 

capita use of public water is about 166 gallons per day and thus the annual energy use for public 

water supply is estimated to be 125 kWh per year per person for most locations in the CONUS.

For eight counties in Southern California, we apply an additional 7,610 kWh/MG of pumping 

energy (Navigant Consulting, Inc. 2006), bringing the annual energy use for those counties up to 

586 kWh per year per person. The counties this estimate applies to are Ventura, Los Angeles, 

 46 The estimates in Pabi et al. (2013) are for the entire U.S. The scope of this report is the continental U.S. (excludes 

territories, Alaska, and Hawaii).

47 This number was originally derived from USGS data on public water withdrawals and EPA data on population 

served by public water systems, both reporting for the year 2005. 48 This is the number cited in Pabi et al. (2013). Updated estimates are higher—315.3 million for the states and 

Puerto Rico in 2011. We use the number cited in the source paper since it is likely more in line with the information 

that was used to estimate the population served data in Table 14.
53

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Orange, Riverside, San Diego, San Bernardino, Imperial, and Inyo, which were selected based 

on an examination of the description of California’s hydrologic regions found in California 

Department of Water Resources (2009). 

Table 13. Public Water Supply Energy Intensity and Population Served

Source Energy Intensity (kWh/MG) Population Served

Surface 1,600 205,181,000 

Groundwater 2,100 89,225,000 

Desalination 12,000 9,416,000 

Population-weighted average: 2,069 Total: 303,822,000 

Summary of data from Pabi et al. (2013)

The estimate of annual electricity used per capita for wastewater is computed similarly, except 

that the unit of service in this case is millions of gallons of water treated per day, rather than 

population served. Average energy intensity is computed as in Table 14, from which we also find 

that 35,845 MG were treated in 2011. After combining that estimate of amount of water treated 

per day with the U.S. population in 2011, we estimate that 114 gallons of water are treated per 

capita per day, which translates to an average energy use of 96 kWh per year per person.

Table 14. Municipal Wastewater Treatment Energy Intensity and Quantity Treated

Treatment Type Energy Intensity (kWh/MG) Water Treated (MG/day)

Less than 

secondary 750 422 

Secondary 2,080 13,142 

Greater than 

secondary 2,690 16,776 

No discharge 2,960 1,815 

Pumping reuse 

water 1,280 3,500 

Partial 830 190 

Population-weighted average: 2,310 Total: 35,845

Summary of data from Pabi et al. (2013)

To create hourly profiles by county, we multiply the energy per person per year by the U.S. 

Census Bureau population-by-county estimates for 2012 (Ruggles et al. 2017); we then use daily 

residential water use profiles from AWWARF (1999) (Figure 13a) as a proxy for when energy is 

needed for conveyance and treatment. We assume that the energy needed for the public water 

supply roughly follows the “total” profile and that the energy needed for wastewater treatment 

roughly follows the “indoor” profile, because all the water being used must be supplied, but 

water used outdoors does not generally flow back to the wastewater treatment plant. To create 

the daily profiles actually used in dsgrid (Figure 13b), we additionally layer on information 

pulled from wastewater treatment load profiles reported by Thompson et al. (2008). In Figure 1 

in that report, we first see that even though the water use profiles in AWWARF (1999) are for 
54

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residences only and the wastewater treatment plant presumably serves establishments other than 

households, the residential water use pattern of one peak in the morning and another in the 

evening are still apparent in the load profiles for an entire wastewater treatment plant. This does 

not cause us to make any adjustments to the profiles, but it does give us some confidence that the 

residential water use shapes are a reasonable, if far from ideal, starting point. Second, we notice 

that the wastewater treatment plant has a large baseload; that is, its load is typically between 

1,500 kW and 2,200 kW. By pulling the minimum and maximum load for each day shown, we 

further estimate an average ratio of daily minimum to daily maximum load of 0.75, and we 

impose this on our public water supply and the wastewater treatment load shapes in going from 

Figure 13a to Figure 13b. Finally, based on comparing the timing of the two peaks in Figure 13a, 

to the timing of the two peaks in the Thompson et al. (2008) Figure 1 curves, we also impose a 

time shift of two hours on the wastewater treatment load curve. That is, we assume it takes two 

hours from when water is used for the wastewater treatment plant to see that water use as an 

increase in its electricity usage. 

Figure 13. Diurnal profiles for (a) residential water use (AWWARF 1999) and (b) dsgrid municipal 

water services gap model

2.3.1.2 Outdoor Lighting

Buccitelli et al. (2017) provide national-level estimates of all lighting energy use in the United 

States, first broken down into the gross categories of residential, commercial, industrial, and 

outdoor, and then providing significant granularity for types of fixtures and bulb technologies 

used per subsector and room-type (residential), per sub-subsector (commercial and industrial) 

and per application type (outdoor). Because the dsgrid sectoral models already estimate their 

own lighting use, the interest here is in the estimates for outdoor lighting energy use. Buccitelli et 

al. (2017) provide descriptions and energy use estimates for nine outdoor lighting applications: 

airfields, billboards, commercial and industrial building exteriors, communication towers, 

parking, railways, roadways, sports fields, and traffic signals. The total energy use Buccitelli et 

al. (2017) attribute to these outdoor lighting applications in 2015 is 202 TWh; they estimate that 

only three contribute more than 1 TWh to the total, namely parking (103 TWh), roadways (63 

TWh), and commercial and industrial exterior lighting (33 TWh). Commercial and industrial 

exterior lighting is represented in ComStock and IGATE-E. Our outdoor lighting gap model 

represents the parking and roadway lighting components based on these and additional data in 

Buccitelli et al. (2017) combined with per-capita normalization and solar data that indicate when 

these types of outdoor lights would be expected to turn on and off.
55

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In addition to presenting 2015 estimates, Buccitelli et al. (2017) update earlier (Ashe et al. 2012)

estimates of 2010 lighting energy use based on revised methodology. We therefore normalize 

these estimates for 2010 and 2015 by the national population in those years and then interpolate 

to obtain 2012 per-capita estimates of electricity use for outdoor lighting for parking and 

roadways (Table 15). Ultimately, electricity consumed for these applications is specified by 

county using the same population by county estimates used in the municipal water services gap 

model (Ruggles et al. 2017).

Table 15. Estimates of Absolute and Per-Capita Energy Used for Outdoor Lighting

Energy Estimate Year Unitsa Parking Roadway

2010 TWh/yr 113 66

kWh/person-year 365 213 

2015 TWh/yr 103 63

kWh/person-year 321 196 

2012 kWh/person-year 349 207 

a Values in TWh/yr are from Buccitelli et al. (2017). Those estimates are normalized to kWh/person-year with U.S. 

population estimates of 309,348,193 for 2010, and 320,896,618 for 2015. We interpolate by year and scale by the 

fact that 2012 is a leap year to obtain the normalized value for 2012.

Hourly load shapes are determined by inverting county-level, per-unit solar radiation profiles 

constructed from NSRDB data initially accessed for the distributed PV model (Section 2.3.3). 

To smooth out the hourly profiles, and recognizing that the on and off times of outdoor lighting 

are not fully synchronous, we first compute a moving-window average of solar radiation for each 

hour in the day (using standard time convention throughout the year), and we then invert the 

profile using the function:

ff = �

0 xx > tt

1

2 �cos �

ππ

tt ∙ xx� + 1� 0 ≤ xx ≤ tt

where f is the outdoor lighting fraction, x is the solar radiation fraction, and t is a threshold value 

representing that a certain fraction of solar radiation (compared to the annual maximum) implies 

that all outdoor lighting (at least for parking lots and roadways) should be off. For this version of 

dsgrid, profiles are generated using a moving window average that for every data point takes the 

mean of the solar radiation for that day and time along with the solar radiation at the same hour 

of the day for the previous 30 days and the coming 30 days; and we take the threshold on 

fractional solar radiation to be 0.05 in the above equation. The resulting transformation is 

illustrated in Figure 14.
56

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Figure 14. Sample single-day (a) solar radiation profile and (b) the outdoor lighting profile that 

results for the same day from applying the transformation described above

The sample is Washington, D.C. on March 15, 2012.

2.3.2 Distributed Generation Models

Because dsgrid is primarily a bottom-up model of site energy use, distributed generation 

estimates must be layered on to estimate the net load that is ultimately served by load-serving 

entities/distribution utilities. In the CONUS in 2012, approximately 78,319 MW of CHP and 

other fuel-based distributed generation capacity and 1,475 MW of DPV generation were 

operating and serving a variety of residential, commercial, and industrial loads. Together,

these two technologies comprise our distributed generation model.

2.3.2.1 Combined Heat and Power

The DOE Combined Heat and Power (CHP) Installation Database provides information on 

CHP plants that are large and small (1 kW to 1,633,000 kW); are of different types (e.g., steam 

turbine, fuel cell, reciprocating engine fueled by coal, oil, natural gas, wood, and waste heat);

provide different services (e.g., for manufacturing, universities, hospitals, agriculture, and 

utilities); and operate all over the United States. However, though this data source provides 

capacity and location information in addition to the characteristics already described, it does not 

provide any indication of typical operation schedules or annual generation quantities (ICF Inc. 

and DOE 2016).

In contrast, using both EIA Form 860 and EIA Form 923, it is possible to obtain estimates of 

capacity, generation, and direct use (portion of generated electricity used behind the meter) by 

plant, but only for those that provide reports (generally plants 1 MW and larger). That is, these 

EIA data do not comprehensively account for all CHP plants, but for those that are represented, 

a good deal of operational data is provided (EIA 2013a, 2018).

Hourly profiles of plant operation are even harder to come by. Operational profiles by operating 

unit are available from the U.S. Environmental Protection Agency (EPA) for a subset of the 

plants that report on EIA Form 860. These data comes from continuous emission monitoring 

systems (CEMS) (U.S. EPA 2016; EPA 2013), which are generally required for thermal plants 

with nameplate capacity greater than 25 MW (EPA 2009). To assemble an hourly model of how 

CHP and other behind-the-meter thermal generation operated in 2012, all these data sources were 
57

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brought together to estimate capacity, capacity factor (annual generation expressed as a fraction 

of nameplate capacity running for an entire year), and behind-the-meter fraction for all relevant, 

known plants by sector and by state. Hourly profiles were assigned based on selecting a profile 

from the CEMS data, adjusting it to match the capacity factor, and then shifting it to fit the 

modeled plant’s time zone.

As explained in detail in Appendix I, our CHP model accepts the DOE CHP Installation 

Database as the ground-truth regarding CHP capacity, adds to it some behind-the-meter thermal 

(but not CHP) capacity from the EIA data sources, and then proceeds to fill in operational details 

via a matching process that accounts for such characteristics as sector, NAICS code, plant prime 

mover, plant size, and plant location. A similar (but necessarily abbreviated) matching process 

is applied to select a starting-point CEMS profile, which is then adjusted to match the required 

capacity factor. The profile is scaled by the behind-the-meter fraction identified from the EIA 

data. In the end, these profiles are aggregated up to the sector level by state, differentiated by 

CHP and not-CHP. A capacity and generation summary for select states and all of the CONUS is 

provided in Table 16. The starting-point CEMS profiles are shown for select weeks in Figure 15.

Table 16. Combined Heat and Power and Distributed Thermal Model: Capacity and

Behind-the-Meter Generation Summary

Capacity (GW) Behind-the-Meter

Generation (TWh)

State Ind. Com. Res. Total Ind. Com. Res. Total

TX 17.3 0.4 0.000 17.6 38.1 1.5 0.000 39.6 

CA 6.8 1.9 0.002 8.7 23.3 5.3 0.005 28.6 

LA 7.2 0.0 — 7.2 15.5 0.2 — 15.7 

NY 3.4 2.1 0.106 5.6 14.6 6.4 0.316 21.3 

MI 3.0 0.3 — 3.3 6.6 0.6 — 7.2 

AL 3.2 0.0 — 3.2 8.9 0.0 — 8.9 

FL 2.8 0.4 — 3.2 9.5 1.5 — 11.1 

NJ 2.8 0.3 0.006 3.2 12.3 1.1 0.019 13.4 

PA 2.3 0.5 0.005 2.8 6.3 1.5 0.014 7.8 

IN 2.4 0.1 — 2.5 4.1 0.3 — 4.3 

All 

others 20.1 4.0 0.007 24.1 62.7 10.5 0.020 73.2

Total 71.2 10.2 0.125 81.5 201.8 29.0 0.375 231.2

Res. = residential, Com. = commercial, and Ind. = industrial
58

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Figure 15. CEMS CHP plant capacity factor profiles

2.3.2.2 Distributed Photovoltaics (DPV)

County-level distributed PV capacity was determined by using the dGen model (Sigrin et al. 

2016) to geographically disaggregate and then segment by panel tilt and orientation, state-level 

historical estimates of distributed PV capacity compiled by GTM Research and the Solar Energy 

Industries Association (Perea et al. 2017). The county-level capacity totals were then allocated 

proportionally by population density across NSRDB geographic grid cells (Wilcox 2012), and 

the System Advisor Model (NREL 2014) was used to generate profiles based on solar panel tilt 

and orientation, along with the solar radiation timeseries available for each NSRDB site.

Table 17 summarizes the capacity and annual generation captured by this model of 2012 DPV 

in the CONUS.
59

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Table 17. Distributed Photovoltaics Capacity and Generation Summary

Capacity (MW) Generation (GWh)

State Res. Com. Ind. Total Res. Com. Ind. Total

CA 875 425 425 1,724 1,347 663 664 2,674 

NJ 153 334 334 821 190 421 421 1,032 

AZ 173 79 79 331 289 133 132 553 

MA 34 75 75 184 41 92 92 225 

PA 41 67 67 175 49 83 82 214 

CO 78 45 45 169 120 68 69 257 

NY 62 33 33 127 78 40 39 157 

MD 31 26 26 84 40 35 34 109 

All 

others 178 203 203 585 232 276 281 789

Total 1,624 1,287 1,287 4,199 2,386 1,810 1,814 6,010

2.3.3 Electricity Sector Load Data

To calibrate and validate the 2012 dsgrid data set, we leverage two categories of load data that 

cover the entire electricity sector from the supply side: historical hourly load data such as those 

available from the FERC Form 714 reports (FERC 2016) and the utility data reported on EIA 

Form 861 (EIA 2013a). The data used are summarized in Table 18.

Table 18. Top-Down Data on U.S. Power System Demand Used in dsgrid to Define System Losses 

and Model Residuals

Source Fields

Temporal 

Resolution

Geographic 

Extent

Geographic 

Resolution

FERC 714 and 

ISO Reportinga

Planning area electricity 

demand 

Hourly CONUS FERC Planning 

Region/ISO 

Subregion

EIA Form 861b

Retail Sales

Retail sales by sector Annual U.S. Utility-state 

intersection

EIA Form 861b

Operational Data

Energy furnished or consumed 

without charge, energy losses

Annual U.S. Utility-state 

intersection

a FERC (2016); SPP (2016); pjm (2016); MISO (2016); ISO New England (2016); NYISO (2016)

b EIA (2013a)
60

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Hourly load data for 2012 were derived from FERC Form 714 and from the ISOs cited in Table 

18. For consistency, the source data were converted to hour-ending (if not already reported as 

such) and shifted to EST where necessary. To determine the load at the state level, the source 

data were first disaggregated to the transmission nodes represented in the Multiregional 

Modeling Working Group 2015–2026 Summer Peak load flow case in the Eastern 

Interconnection49 and the Western Electricity Coordinating Council (WECC) Transmission 

Expansion Planning Policy Committee 2024 Common Case in the Western Interconnection

(Brinkman et al. 2016). Next, the load was aggregated to the state level based on the location of 

each node derived above. Load for Texas was determined by aggregating all the nodes 

mentioned above that are located in Texas along with the historical hourly load provided by the 

Electric Reliability Council of Texas (ERCOT 2017).

Data from two EIA Form 861 tables are used. The total amount of electricity provided by loadserving entities in 2012 is estimated by taking the retail sales by sector (residential, commercial, 

industrial, and transportation) from the Retail Sales table, and adding to it the energy listed in the 

Operational Data table as “furnished without charge” or “consumed by respondent without 

charge.” Per EIA (2013b), the residential sector is defined as any customers whose energy is 

primarily consumed for residential end uses (i.e., “space heating, water heating, air conditioning, 

lighting, refrigeration, cooking, and clothes drying”); “manufacturing, construction, mining, 

agriculture (irrigation), fishing, and forestry establishments” are considered to comprise the 

industrial sector; and transportation is described as “railroads and railways.” Everything else is 

defined as falling in the commercial sector, which therefore includes commercial buildings but 

also “public street and highway lighting, municipalities, divisions or agencies of State and 

Federal Governments under special contracts or agreements, and other utility departments, as 

defined by the pertinent regulatory agency and/or electric utility.” Because of this latter 

description, the energy listed as “furnished without charge” or “consumed by respondent without 

charge” is ultimately added to the commercial retail sales. As shown in Table 19, more than half 

this energy is furnished by just six of the 2,190 reporting utilities, and at least 8.8 TWh appears 

to be associated with California water projects. 

Table 19. Largest Providers of Energy Furnished or Consumed without Charge in 2012 

as Reported on EIA Form 861

Furnished without Charge Consumed by Respondent without Charge

Utility Name State

Quantity 

(TWh)

Proportion 

of Total (%) Utility Name State

Quantity 

(TWh)

Proportion 

of Total (%)

New York State 

Electric and Gas 

Corporation

NY 3.7 32% California Dept. of 

Water Resources CA 7.4 42%

Western Area Power 

Administration CO 2.9 25%

Metropolitan Water 

District of Southern

California

CA 1.4 8%

PUD No 2 of Grant 

County WA 1.5 13% Bonneville Power 

Administration OR 1.2 7%

Remaining 2,187 reporters 3.6 30% Remaining 2,187 reporters 7.5 43%

Total 11.7 100% Total 17.5 100%

 49 Data provided by Energy Visuals, Inc., http://www.energyvisuals.com/
61

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We also use the EIA Form 861 Operational Data on Total Energy Losses to inform our model 

of system losses. 

All these historical data are used at the state level. Although the EIA Form 861 data are natively 

provided at a finer resolution (the utility-state intersection level), that level of resolution does not 

map cleanly to counties. Furthermore, some of the historical hourly load data are provided at a 

resolution coarser than single states. For these reasons, we find it most straightforward to use 

these historical data at the state level or coarser and leave it as future work to refine our 

mappings between political and electrical geography.

2.3.4 Derived Models

The historical electricity sector data are used to construct two derived components of dsgrid. 

There is an hourly, state-level model of power system losses (T&D), and we compute two sets of 

residuals: annual by sector by state and all electricity load hourly by state.

2.3.4.1 System Losses

T&D power system losses are the difference between the amount of electricity generated by 

utilities and independent generators and the amount of electricity consumed by end users as 

measured by on-site electricity meters. Estimates of these losses are created and included in the 

dsgrid historical load data set (1) to provide a way to estimate system-level load based on the 

dsgrid site-level demand data, and (2) to facilitate the calculation of hourly model residuals.

The historical electricity sector data provides two estimates of system losses. There is direct 

reporting of losses in the EIA Form 861 Operational data. We can also estimate system losses by 

subtracting the sum of the EIA Form 861 annual energy by sector (sales plus energy furnished or 

consumed without charge) from the hourly historical load data. The latter estimate falls out 

because the FERC Form 714 and ISO reporting represents generation plus imports minus exports 

for each reporting power system, and therefore includes all the system losses; whereas the sales 

and electricity furnished statistics represent site (i.e., metered) electricity load, which does not 

include losses.
62

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Figure 16. Raw state-level loss factors (a) derived from utility reported losses (EIA Reported) and 

(b) estimated by comparing historical hourly supply and annual metered load data (FERC-EIA 

estimate), ordered by (c) their difference (estimate - reported)

To develop our model of system losses, we compare these two loss estimates, both expressed as 

a loss factor (%) relative to the EIA annual energy reported by state.

50 This exercise reveals large 

discrepancies for several states, some for which the reported EIA annual energy is greater than 

the aggregated hourly load data (resulting in negative load factor estimates) and others for which 

the reported EIA annual energy is much smaller than the aggregated hourly load data (resulting 

in loss factors greater than 15% or even 20%) (Figure 17). Based on the provenance of the hourly 

load data, it is not too surprising that the disaggregation to states is inaccurate. Perhaps more 

surprising are the very low loss estimates we obtain for some states using EIA data alone (e.g., 

West Virginia and Utah’s estimates are less than 1%, see Table J-1 in Appendix J.) 

 50 Per Section 2.3.3, this consists of annual sales reported by sector plus electricity furnished or consumed without 

charge.
63

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Figure 17. Loss factors for state groupings

The state groupings were developed to mitigate the greatest discrepancies seen in the raw state-level data.

The loss factors used in the dsgrid system losses model are the values shown in this figure for EIA Reported.

Given this finding, and our general knowledge of electrical system topology (e.g., the extents of 

the Eastern, Western, and ERCOT interconnections, and the footprints of ISOs/RTOs51), we 

developed groupings of states that mitigate the worst of these discrepancies. The resulting 

reported and estimated loss factors are shown in Figure 17 and reported in Table 20.The dsgrid 

system losses model applies the loss factors estimated using only EIA data (losses, annual sales, 

and energy furnished or consumed without charge) for the state groupings; that is, each state in 

a particular group is assigned the corresponding loss factor highlighted in Table 20. These loss 

factors generally fall into a reasonable range (2.5%–9.5%), and 12 of 24 are within one 

percentage point of the estimate obtained by comparing historical hourly system load and 

historical annual metered load data. The discrepancies that remain between the two loss factor 

estimates point to two areas of future work: the development of better mappings between 

electrical and political geography, and the development of a better/more resolved understanding 

of system losses.52

 51 RTO = regional transmission organization 52 Some jurisdictions, e.g., ERCOT (http://www.ercot.com/mktinfo/metering/dlfmethodology, 

http://www.ercot.com/content/wcm/key_documents_lists/143946/2018_Transmission_Loss_Factors_Final.xlsx) 

collect detailed information on T&D losses. From these data we see that there are real regional differences in loss 

factors, and differences depending on line loadings and weather. The dsgrid system losses model relies on national 

level data sets, which are subject to reporting and geographical mapping error, for expediency. Untangling the 

reporting and mapping errors from true regional differences, and disaggregating transmission from distribution 

losses would be appropriate areas of future work.
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Table 20. Loss Factors Applied at the State Level to Create the System Losses Derived Model 

State Groups

EIA Annual 

Site Energy 

(TWh)

EIA Annual 

Losses 

(TWh)

Reported 

Loss Factor 

(%)

FERC 

Hourly Load 

(TWh)

FERC – EIA 

Loss 

Estimate 

(TWh)

Estimated 

Loss Factor 

(%)

Estimated -

Reported 

(% pt)

CO, MT, UT, WY 118.1 5.5 4.6 116.2 -1.9 -1.6 -6.2

AL, FL, GA, MS 487.8 30.3 6.2 496.0 8.2 1.7 -4.5

CA, NV 304.3 13.9 4.6 309.7 5.5 1.8 -2.8

ID, OR, WA 166.1 12.2 7.3 175.7 9.6 5.8 -1.5

MI 105.0 5.6 5.4 109.4 4.3 4.1 -1.2

OH 153.0 13.1 8.5 164.5 11.5 7.5 -1.0

KS, NE, SD 83.3 5.6 6.7 88.5 5.2 6.2 -0.5

KY, WV 120.0 3.7 3.0 123.1 3.2 2.6 -0.4

VA 108.0 3.5 3.3 111.1 3.1 2.9 -0.4

LA 84.9 4.0 4.7 88.8 3.9 4.6 -0.1

PA 144.9 7.7 5.3 152.6 7.7 5.3 0.0

NC 128.4 9.4 7.3 138.0 9.6 7.5 0.1

ME, NH, VT 28.1 1.0 3.6 29.1 1.1 3.8 0.2

OK 59.9 5.6 9.4 65.8 5.8 9.8 0.4

DE_NJ 86.7 3.9 4.5 91.1 4.4 5.1 0.6

SC 78.1 3.0 3.8 81.6 3.6 4.6 0.8

TX 366.2 19.4 5.3 388.8 22.6 6.2 0.9

DC, MD 73.2 3.6 4.9 77.9 4.7 6.4 1.5

CT, MA, RI 93.0 2.4 2.6 96.9 3.9 4.2 1.6

IA, IL, IN, WI 364.2 11.0 3.0 381.2 17.0 4.7 1.7

AZ, NM 98.4 6.1 6.2 106.4 8.1 8.2 2.1

NY 147.2 5.8 3.9 163.1 15.9 10.8 6.9

MN, ND 83.0 6.1 7.3 94.8 11.8 14.2 6.9

AR, MO, TN 226.1 16.2 7.2 259.2 33.0 14.6 7.4

Data used in the derived model are highlighted.
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Finally, the hourly system losses are estimated based on a rearrangement of the expression

System Load(tt) = Metered Electricity ∙(1 + Loss Factor) ,

into

Metered Electricity(tt) = System Load(tt)

1 + Loss Factor.

This yields an estimate of hourly losses equal to:

Losses(tt) = System Load(tt) − Metered Electricity(tt)

= System Load(tt) ∙ 

Loss Factor

(1 + Loss Factor)

,

which we compute by state using the historical hourly system load and the loss factors 

highlighted in Table 20.

2.3.4.2 Residuals

The distributed generation models described in Section 2.3.2, the historical electricity-sector data 

described in Section 2.3.3, and the system losses model just described enable us to calculate 

model residuals for the initial dsgrid data set for historical year 2012. As depicted in Figure 18, 

two sets of residuals are calculated—one for each historical electric-sector data set. The left side 

of Figure 18 shows the process of comparing the sum of the EIA Form 861 annual load data and 

the distributed generation models to the dsgrid bottom-up models (detailed and gap) to compute 

annual residuals by state and sector. Similarly, the right side of Figure 18 outlines the process for 

computing hourly, all-electricity residuals. 

Figure 18. Model residuals are calculated using electricity sector data and distributed 

generation models.
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Mathematically, computing hourly residuals for each state starts with the following relationship

for all electricity demand at a given time t:

System Load(tt) − Losses(tt) = Detailed Load(tt) + Gap Load(tt) −

Distributed Generation(tt) + εεtt,

where system load is the total amount of generation plus net imports that must be provided by 

the system, the losses model is as defined in the above section, the other time varying terms are 

dsgrid model components, and εεtt accounts for all site-load residuals currently missed by our 

model. We therefore calculate hourly residuals as:

εεtt = System Load(tt) − Losses(tt) − Detailed Load(tt) − Gap Load(tt)

+ Distributed Generation(tt).

The mathematical expression for the annual sectoral residuals is similar. For those we start from 

the expression:

Annual Retail Sales(ss)

= Detailed Load(ss) + Gap Load(ss) − Distributed Generation(ss) + εεss,

where s represents the sector: residential, commercial, industrial, or transportation. The sector 

residuals are thus:

εεss = Annual Retail Sales(ss) − Detailed Load(ss) − Gap Load(ss) + Distributed Generation(ss).

Both types of residuals are calculated at the state level and analyzed in what follows.
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3 Historical Year Model Overview

The data included in this version of dsgrid are summarized in Table 21. All the components 

described in Figure 18 are included; namely, the historical hourly load derived from ISO and 

FERC Form 714 reporting is in the first row. Together with the third row, which is EIA Form 

861 data on retail sales (plus energy furnished without charge, which has been added to the 

commercial column), this is the ground truth to which we compare the core dsgrid data. The core 

dsgrid data consist of the bottom-up sector models plus the gap models, which, when they are 

summed together, represent an estimate of site electricity use. These data are supplemented with 

models of distributed generation, and with T&D losses, to enable the calculation of residuals and 

the creation of modeling-relevant aggregated load profiles.

Table 21. Summary of Contiguous U.S. Electricity Use in Terawatt-Hours, Top-Down and 

Represented in dsgrid

Component 

Type

Component

Name Residential Commercial Industrial Transport Total

Top-down Hourly loada 3,910 

Derived T&D losses 199

Top-down Annual energyb 1,370 1,350 981 7 3,708

dsgrid Distributed generation 3 31 204 – 237

dsgrid-core Gap models 218 454 184 6 862

dsgrid-core Detailed sector models 1,169 1,107 893 – 3,170 

Derived Total site energyc 1,372 1,381 1,184 7 3,945

Derived Annual sector residualsd -15 -180 107 1 -87

Derived Hourly residualse -126

a FERC Form 714 and independent system operator (ISO) reporting

b U.S. Energy Information Administration (EIA) Form 861

c Total site energy is the top-down annual energy plus distributed generation. This is all the load we are expecting 

to model with the bottom-up detailed sector and gap models.

d The sector level residuals are equal to the total site energy minus the gap and detailed sector model components. 

e The hourly residuals reported in the Total column are the sum of the state-level hourly residuals, which factor in topdown hourly load, T&D losses, distributed generation, and the dsgrid-core model components.

dsgrid model components necessary to represent site-energy use at the hourly level are shaded green.

Components that may be factored in to estimate bulk power system load are shaded blue-grey.

From this summary, we see that the order of magnitude of electricity use is similar for the 

residential, commercial, and industrial sectors, at around 1,000 TWh each. Electricity used for 

transportation in 2012 was about two orders of magnitude less, rounding up to 10 TWh. The 

relative maturity of the dsgrid sector models is also apparent: at this highest level of aggregation,

our residential modeling is within about 1% of site energy use, whereas commercial and 

industrial estimates are within 15%. Examining the hourly—rather than the sectoral—residuals, 

when all the hourly residuals are summed up directly, allowing positive and negative errors to 

cancel out, dsgrid is overall overestimating site electricity use by about 3%. 

The calculation of sectoral and hourly residuals depends on our assumptions about distributed 

generation, for which there is no definitive source of ground truth. However, modeling 
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distributed CHP, other distributed thermal generators, and distributed PV (see Section 2.3.2 and 

Appendix I) yields estimates of 0.2%, 2%, and 17% of sectoral electricity coming from behindthe-meter generation for the residential, commercial, and industrial sectors respectively.

We gain further insight into the model, including temporally correlated errors, by exploring the 

hourly timeseries data. For example, the modeled load per sector (detailed sectoral and gap) and 

T&D losses are plotted alongside the historical hourly load and the historical hourly load plus the 

modeled distributed generation in Figure 19 (next page) for four weeks, one selected from each 

season, for all of the contiguous United States. Overall, this shows that dsgrid is capturing 

seasonal load shapes—winter and autumn days display the double-peak pattern, whereas load in 

spring and summer demonstrates only one peak per day—but regularly exaggerates the 

differences between weekday and weekend energy use, as well as between daytime and 

nighttime energy use, the latter especially during cooling season (e.g., summer and spring).

The differences between dsgrid data and the historical load data are more clearly visible in 

Figure 20, which is structured similarly to Figure 19, but with the data grouped to explicitly plot 

the portion of load met by distributed generation and the hourly residuals. When the hourly 

residuals are positive they represent missing load, we refer to them as “underage,” and they are 

plotted between the modeled load and system loss components. Negative residuals represent 

overestimates of load; these are referred to as “overage” and are plotted above the historical 

hourly load plus DG line. In addition to explicitly showing the residuals that are implicit in 

Figure 19, these plots verify the correctness of our residuals calculations and demonstrate how 

the component categories of the dsgrid model fit together. Regional versions of Figure 19 and 

Figure 20 are available in Appendix G.
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Figure 19. Bottom-up detailed sectoral model and gap model load compared to bulk-level 

historical hourly load for all of the CONUS in 2012, for four representative weeks
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Figure 20. dsgrid hourly residuals plotted along with the bottom-up model data, T&D loss 

estimates, and historical bulk power load data, for the contiguous United States

To get a clearer understanding of model fit, we analyze the hourly and sectoral residuals 

computed at different geographic, temporal, and sectoral resolutions. To do this, the dsgrid 

model is first aggregated to different temporal and geographic resolutions. Temporally, we 

summarize the size of the all-electricity hourly residuals at the following levels of aggregation, 

with all aggregations based on EST:

• Hour

• Day

• Week

• Season (based on solar solstices and equinoxes)

• Year.
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Geographically, we summarize both types of residuals at these levels of aggregation: 

• State (48 in the contiguous United States)

• State groups (24 from the loss model component, Table 20)

• Census division (9 in total, see Appendix G)

• Census region (4 in total, see Appendix G)

• CONUS.

To create summary fit statistics, we rely on relative absolute error metrics, suitably averaged

over the relevant dimensions. For the hourly residuals, there is no sectoral dimension and we 

begin by calculating mean relative absolute error (MRAE) over the temporal dimension, 

calculating one MRAE for each geographical unit g:

MRAEgg =

∑ �εεtt,gg�

EEtt,gg

tt

∑tt 1 , EEtt,gg = System Load(tt, gg) − Losses(tt, gg) + Distributed Generation(tt, gg)

This statistic is then inverted into a measure of fit:

(MRAE Fit)gg = 1 − MRAEgg,

and averaged over the geographical units using the annual site energy as the weighting factor:

MRAE Fit = ∑gg (MRAE Fit)gg ∙ ∑tt EEtt,gg

∑gg,tt EEtt,gg

.

For the sectoral residuals, there is no temporal dimension. For the individual sectors, we 

therefore have a basic relative absolute error (RAE) statistic for each geographical unit g:

RAEss,gg = �εεss,gg�

EEss,gg

, EEss,gg = Annual Electricity(ss, gg) + Distributed Generation(ss, gg).

and if we convert to a fit metric and perform an energy-weighted average we get:

(RAE Fit)ss,gg = 1 − RAEss,gg,

(RAE Fit)ss = ∑gg (RAE Fit)ss,gg ∙ EEss,gg

∑gg EEss,gg

= ∑gg�EEss,gg − �εεss,gg��

∑gg EEss,gg

.

To calculate overall fit across all sectors, the final formula is leveraged to compute:

(RAE Fit)tttttttttt = ∑gg�∑ss EEss,gg − �∑ss εεss,gg��

∑gg,ss EEss,gg

.
72

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The resulting fit statistics are plotted in Figure 21.

Figure 21. dsgrid fit statistics for total site energy as a function of geographic, temporal, 

and sectoral resolution

First examining the hourly electricity residuals, we see visual confirmation of what we saw 

in Table 21, namely that in total (annual values for the contiguous United States), the dsgrid 

bottom-up models capture the site energy represented in the historical hourly load data combined 

with the dsgrid distributed generation models to within a relative error of 4%. However, if we 

examine the model at the most resolved level for which hourly residuals are available (state-level 

and hourly), the level of fit drops almost to 80%. Where is most of the fit lost? Temporally, the 

first large gap occurs when we go from weeks to days; this likely speaks to the weekdayweekend discrepancy apparent in Figure 19 and Figure 20. The daytime-to-nighttime shifts 

similarly show up in the difference between the hour and day curves. The load duration curves53

in Figure 22, which represent the hourly CONUS data, are another confirmation of these trends.

Specifically, consistent with its provenance as a confederation of sectoral energy models 

historically focused on annual energy use metrics, dsgrid is capturing the total amount of load 

reasonably well, but it contains significantly more load variability than the historical data.

Geographically, there is a large difference between census divisions and census regions, and a 

more modest, but significant difference between states and state groups. For these hourly 

residuals, which rely on a disaggregation of system load data reported by ISO region or FERC 

Form 714 planning region, a significant portion of the error introduced by disaggregating from 

the State Group to State level may be attributable to that process (and not to the bottom-up 

modeling alone).

 53 Load duration curves are constructed by sorting timeseries load data in descending order and plotting the result.
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Examining the sectoral residuals, the relative maturity of the residential modeling is immediately 

apparent, as its level of agreement with the total annual energy use reported by state on EIA 

Form 861 plus our distributed generation estimates is above 92% at all levels of disaggregation 

(down to states). The level of fit for all other sectors is below 85% at the state level, but it 

is greater than 80% for the other two main electricity-consuming sectors: commercial 

and industrial. 

Figure 22. Historical and dsgrid load duration curves for the CONUS in 2012

The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of the

detailed sector and gap models.

The detailed sector models provide information on both subsector and end-use breakdowns down 

to the county, hourly level. The annual CONUS summary for the residential detailed (i.e., 

ResStock and midrise apartments from ComStock) and gap models is in Table 22 in absolute 

terms (GWh). Table 23 reports the relative proportions of electricity by end use per subsector, 

and of electricity use by subsector for the entire residential sector. The diurnal load shapes for 

the sector, broken down by end use, and averaged over each season and weekday/weekend 

combination are shown in Figure 23.
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Table 22. Residential Subsectors, Summary of Electricity by End Use for the CONUS in 2012

Subsector

Interior 

Equipment 

(GWh)

Space 

Cooling 

(GWh)

Interior 

Lights 

(GWh)

Space 

Heating 

(GWh)

Fans 

(GWh)

Water 

Systems 

(GWh)

Exterior 

Lights 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Single Family Detached 430,941 223,229 138,204 131,556 101,198 95,846 28,036 1,342 — 1,150,352 

Mobile Home 36,646 18,185 7,707 7,690 7,229 9,211 1,254 47 — 87,970 

Apartment in Building 

2 to 4 Units 22,346 8,805 9,275 4,324 9,158 — 11,158 780 279 66,125 

Single Family Attached 28,485 11,454 6,075 5,306 5,065 6,326 990 97 — 63,797 

Midrise Apartment 

Building a 6,174 2,503 2,720 1,236 2,641 — 3,247 250 87 18,858 

Total 524,593 264,176 163,980 150,113 125,290 111,383 44,685 2,516 366 1,387,102 

a The current version of ComStock does not model water heating. See Footnote 54 for more information.

Table 23. Residential Electricity Proportions by Subsector and End Use for the CONUS in 2012

Subsector

Interior 

Equipment 

(%)

Space 

Cooling 

(%)

Interior 

Lights 

(%)

Space 

Heating 

(%)

Fans 

(%)

Water 

Systems 

(%)

Exterior 

Lights 

(%)

Pumps 

(%)

Heat 

Rejection 

(%) Total (%)

Single Family Detached 37.5 19.4 12.0 11.4 8.8 8.3 2.4 0.1 — 82.9 

Mobile Home 41.7 20.7 8.8 8.7 8.2 10.5 1.4 0.1 — 6.3 

Apartment in Building 

2 to 4 Units 33.8 13.3 14.0 6.5 13.8 — 16.9 1.2 0.4 4.8 

Single Family Attached 44.6 18.0 9.5 8.3 7.9 9.9 1.6 0.2 — 4.6 

Midrise Apartment 

Building a 32.7 13.3 14.4 6.6 14.0 — 17.2 1.3 0.5 1.4 

Total 37.8 19.0 11.8 10.8 9.0 8.0 3.2 0.2 0.0 100.0 

a The current version of ComStock does not model water heating. See Footnote 54 for more information.
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These results show that on the continental scale, ResStock and ComStock (for midrise apartment 

buildings) find interior equipment to be the largest group of loads in residences, with a relatively 

flat profile. The next largest end uses (space cooling, interior lighting, and space heating) show 

stronger seasonal and diurnal patterns. Fan, exterior lighting and pumping energy is more 

prominent in multifamily than single-family buildings. As shown in Appendix G, the relative 

importance of these end uses varies considerably across regions; however, interior equipment is 

the largest end use across all the census divisions, comprising 33% (West South Central) to 48% 

(New England) of residential energy use.

Figure 23. Residential electricity use diurnal patterns by season, modeled for the CONUS in 2012
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End-use information is only provided for the commercial building subsectors that are modeled 

in detail.54 The annual summaries for the CONUS are provided in Table 24 and Table 25 on 

an absolute basis and a proportional basis respectively. These data demonstrate considerable 

differences in the relative importance of end uses depending on the commercial building type 

that is being modeled. For example, although interior equipment is the largest end use for large 

offices, the largest subsector in the stock model, it is not the largest end use for the sector as a 

whole. That distinction instead falls to interior lighting, largely based on its importance in retail 

buildings, both standalone buildings and strip malls. Also, while significant work is still to 

be done to calibrate ComStock, especially regarding capturing building types that appear 

underrepresented in the CoStar data (e.g., schools, government offices, and other public 

buildings), the stock model does demonstrate the relative importance of office and retail 

buildings, as compared to building types such as hotels and restaurants, when accounting 

for U.S. electricity use.

Table 24. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End Use 

for the CONUS in 2012

Subsector

Interior 

Lights 

(GWh)

Interior 

Equipment 

(GWh)

Fans 

(GWh)

Space 

Cooling 

(GWh)

Exterior 

Lights 

(GWh)

Space 

Heating 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Large Office 79,566 92,373 61,201 53,542 16,165 1,342 7,537 2,822 314,548 

Strip Mall 170,722 24,812 38,248 22,080 34,871 7,583 131 54 298,501 

Standalone Retail 

Store 72,456 22,961 22,286 14,389 11,016 7,126 57 23 150,314 

Medium Office 19,490 22,237 14,368 11,388 6,934 741 789 275 76,222 

Small Office 15,961 21,861 10,893 8,286 6,235 1,424 80 50 64,791 

Warehouse 20,906 8,321 7,058 1,984 10,120 6,280 49 26 54,742 

Full Service 

Restaurant 9,906 22,215 5,592 4,634 2,767 914 56 20 46,105 

Large Hotel 9,188 14,671 8,412 7,994 2,309 499 463 164 43,700 

Hospital 6,702 8,875 4,455 4,686 513 8 775 359 26,373 

Primary School 4,759 3,796 2,241 1,871 612 421 77 37 13,813 

Outpatient 

Treatment Facility 3,726 5,503 1,504 987 1,334 — 170 76 13,299 

Small Hotel 1,075 1,508 147 112 716 87 14 5 3,664 

Quick Service 

Restaurant 88 521 174 126 52 4 1 1 966 

Total 414,544 249,654 176,579 132,077 93,644 26,430 10,198 3,912 1,107,037 

 54 In addition, information on electricity used for water heating is not provided in the current version of ComStock. 

According to the 2012 CBECS, only 4% of energy used for water heating in commercial buildings comes from 

electricity, that is, about 6 TWh of the total 148 TWh (507 trillion BTU) for the entire United States. This end use 

will be modeled in future versions of ComStock.
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Table 25. Commercial Electricity Proportions of Detailed Models by Subsector and End Use 

for the CONUS in 2012

Subsector

Interior 

Lights (%)

Interior 

Equipment 

(%) Fans (%)

Space 

Cooling 

(%)

Exterior 

Lights 

(%)

Space 

Heating 

(%)

Pumps 

(%)

Heat 

Rejection 

(%) Total (%)

Large Office 25.3 29.4 19.5 17.0 5.1 0.4 2.4 0.9 28.4 

Strip Mall 57.2 8.3 12.8 7.4 11.7 2.5 0.0 0.0 27.0 

Standalone Retail 

Store

48.2 15.3 14.8 9.6 7.3 4.7 0.0 0.0 13.6 

Medium Office 25.6 29.2 18.9 14.9 9.1 1.0 1.0 0.4 6.9 

Small Office 24.6 33.7 16.8 12.8 9.6 2.2 0.1 0.1 5.9 

Warehouse 38.2 15.2 12.9 3.6 18.5 11.5 0.1 0.0 4.9 

Full Service 

Restaurant

21.5 48.2 12.1 10.1 6.0 2.0 0.1 0.0 4.2 

Large Hotel 21.0 33.6 19.2 18.3 5.3 1.1 1.1 0.4 3.9 

Hospital 25.4 33.7 16.9 17.8 1.9 0.0 2.9 1.4 2.4 

Primary School 34.5 27.5 16.2 13.5 4.4 3.0 0.6 0.3 1.2 

Outpatient 

Treatment Facility

28.0 41.4 11.3 7.4 10.0 — 1.3 0.6 1.2 

Small Hotel 29.3 41.2 4.0 3.1 19.5 2.4 0.4 0.2 0.3 

Quick Service 

Restaurant

9.1 53.9 18.0 13.0 5.3 0.4 0.1 0.1 0.1 

Total 37.4 22.6 16.0 11.9 8.5 2.4 0.9 0.4 100.0 

The seasonal and diurnal patterns of the detailed commercial building models’ electricity use are 

shown in Figure 24. Large commercial buildings are generally more isolated from environmental 

conditions, and they use less daylight than residential buildings, such that we see a more 

prominent role for interior lighting and fans, and relatively less heating and cooling as compared 

to Figure 23. As modeled, some commercial building types also use significant quantities of 

electricity for exterior lighting. After further model calibration, we may find this result to be an 

overstatement given the Buccitelli et al. (2017) estimate of 33 TWh for the exterior lighting of 

commercial and industrial buildings altogether as compared to our estimate of 93 TWh for 

commercial buildings alone. Based on the overall dsgrid validation results and the end-use 

shapes shown here, commercial building lighting, space cooling, and interior equipment 

schedules are strong candidates for additional sectoral calibration, both diurnally and by 

weekday/weekend.

Industrial manufacturing electricity use, as modeled by IGATE-E, is summarized in Table 26

and Table 27 (following pages). This sector is even more diffuse than the commercial sector. One 

subsector alone (Iron and Steel Mills and Ferroalloy Manufacturing) accounts for more than 10% 

of electricity use. Four additional industries (Petroleum and Coal Products Manufacturing; Basic 

Chemical Manufacturing; Pulp, Paper, and Paperboard Mills; Plastics Product Manufacturing) use 

more than 5% of sectoral electricity. Together, these five industries comprise about 37% of total 

electricity use; the remaining 63% is spread out over an additional 81 categories.

The largest electrical end use, according to our model, is machine drive, which accounts for 449 

TWh of the total of 893 TWh. However, some manufacturing industries’ electricity use is 

dominated by process heating, for example, Iron and Steel Mills and Ferroalloy Manufacturing, 
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Foundries, and Nonferrous Metal (except Aluminum) Production and Processing. Semiconductor 

and Other Electronic Component Manufacturing stands out for facility HVAC being as important 

an electrical end use as machine drive. The diurnal patterns modeled by IGATE-E, by end use 

and taken in total across all of manufacturing, are shown in Figure 25. Based on dsgrid’s 

underprediction of weekend energy use, the weekday/weekend scheduling shown here is another 

potential source of error that the dsgrid team will investigate moving forward, in tandem with 

similar calibrations for residential and commercial buildings.

dsgrid also provides load shapes and limited sectoral and end-use information for its gap models

(commercial, industrial, transportation, municipal water, and outdoor lighting) and distributed 

generation models. CONUS-level summary data for these components are presented in 

Appendix F. Some analogous information is also presented for these components at the census 

division level in Appendix G.

Figure 24. Commercial electricity use diurnal patterns by season, as modeled by ComStock

for the CONUS in 2012
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Table 26. Industrial Manufacturing Subsectors, Summary of IGATE-E Electricity Results by End Use for the CONUS in 2012

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Iron and Steel Mills and Ferroalloy Manufacturing 25,740 33,045 5,981 21,982 1,422 4,199 1,489 1,148 450 1,883 157 264 97,760 

Petroleum and Coal Products Manufacturing 54,092 2,264 2,538 971 2,824 1,690 476 656 498 750 7 69 66,835 

Basic Chemical Manufacturing 33,489 2,156 3,733 9,943 5,639 2,406 900 705 1,245 803 149 148 61,316 

Pulp, Paper, and Paperboard Mills 45,452 2,361 2,639 493 1,005 2,306 2,028 645 2,770 301 122 60,122 

Plastics Product Manufacturing 25,843 7,733 4,880 — 3,862 4,562 — 1,099 241 — 207 — 48,426 

Pharmaceutical and Medicine Manufacturing 21,874 1,412 2,463 6,586 3,735 1,585 596 467 824 532 99 98 40,272 

Converted Paper Product Manufacturing 29,841 1,553 1,745 327 667 1,524 1,345 428 1,837 199 81 — 39,546 

Animal Slaughtering and Processing 9,820 973 1,909 51 5,963 1,680 412 456 699 504 211 95 22,773 

Pesticide, Fertilizer, and Other Agricultural 

Chemical Manufacturing 11,556 749 1,320 3,549 2,013 848 321 252 444 287 53 53 21,445 

Resin, Synthetic Rubber, and Artificial Synthetic Fibers 

and Filaments Manufacturing 11,503 741 1,284 3,420 1,940 827 310 243 428 276 51 51 21,073 

Other Chemical Product and Preparation Manufacturing 9,734 627 1,089 2,904 1,647 701 263 206 364 235 44 43 17,856 

Motor Vehicle Parts Manufacturing 6,810 1,935 3,307 114 889 2,214 867 697 147 276 248 211 17,716 

Other Nonmetallic Mineral Product Manufacturing 8,279 3,929 1,003 274 601 767 334 237 138 — 59 25 15,645 

Other Wood Product Manufacturing 11,436 971 1,154 45 110 922 166 244 274 167 38 52 15,580 

Semiconductor and other Electronic 

Component Manufacturing 3,472 1,806 3,648 342 1,736 1,390 1,107 738 132 327 39 290 15,027 

Printing and Related Support Activities 7,800 601 2,454 164 1,078 1,224 207 406 122 — 158 — 14,213 

Foundries 3,574 4,577 823 3,012 195 578 204 157 62 258 21 36 13,497 

Nonferrous Metal (except Aluminum) Production 

and Processing 3,496 4,447 785 2,846 184 553 193 149 58 244 20 34 13,010 

Soap, Cleaning Compound, and Toilet 

Preparation Manufacturing 6,995 453 797 2,140 1,214 512 194 152 268 173 32 32 12,961 

Other Fabricated Metal Product Manufacturing 5,707 1,954 2,029 505 422 1,278 470 437 — — 89 26 12,917 

Architectural and Structural Metals Manufacturing 5,263 1,806 1,888 471 394 1,188 439 408 — — 83 25 11,965 

Dairy Product Manufacturing 5,081 502 973 26 3,020 857 209 231 354 255 107 48 11,663 

Aerospace Product and Parts Manufacturing 4,147 1,178 2,011 69 540 1,347 527 424 89 168 151 128 10,779 
80

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Fruit and Vegetable Preserving and Specialty 

Food Manufacturing 4,150 411 807 21 2,522 710 174 193 296 213 89 40 9,628 

Beverage Manufacturing 3,322 393 1,088 19 1,815 860 229 305 148 705 188 40 9,113 

Glass and Glass Product Manufacturing 4,633 2,192 555 151 332 425 184 131 76 — 32 14 8,725 

Cement and Concrete Product Manufacturing 4,593 2,183 560 153 337 428 187 133 77 — 33 14 8,698 

Navigational, Measuring, Electromedical, and Control 

Instruments Manufacturing 1,965 1,026 2,097 198 1,004 797 641 427 76 189 23 168 8,610 

Paint, Coating, and Adhesive Manufacturing 4,518 292 514 1,379 782 330 125 98 173 111 21 20 8,363 

Motor Vehicle Manufacturing 3,130 893 1,545 54 418 1,032 407 328 69 130 117 99 8,221 

Bakeries and Tortilla Manufacturing 3,532 350 682 18 2,127 601 147 163 249 180 75 34 8,158 

Other Food Manufacturing 3,424 339 659 17 2,050 581 142 157 240 173 72 33 7,888 

Rubber Product Manufacturing 4,021 1,204 762 — 604 712 — 172 38 — 32 — 7,544 

Alumina and Aluminum Production and Processing 1,998 2,545 451 1,638 106 318 111 86 34 140 12 20 7,457 

Coating, Engraving, Heat Treating, and Allied Activities 3,229 1,111 1,171 294 245 736 274 254 — — 52 15 7,381 

Steel Product Manufacturing from Purchased Steel 1,848 2,353 416 1,510 98 293 102 79 31 129 11 18 6,888 

Other Electrical Equipment and Component 

Manufacturing 1,775 1,456 1,048 492 406 621 231 143 29 — 44 — 6,245 

Fiber, Yarn, and Thread Mills 3,698 530 920 0 228 452 112 92 127 — 54 — 6,214 

Grain and Oilseed Milling 2,643 261 509 13 1,586 449 110 121 186 134 56 25 6,094 

Veneer, Plywood, and Engineered Wood Product 

Manufacturing 4,461 377 442 17 42 354 63 93 104 63 14 20 6,051 

Animal Food Manufacturing 2,555 254 501 13 1,570 440 108 120 184 133 55 25 5,960 

Other General Purpose Machinery Manufacturing 2,480 575 1,215 60 241 829 138 219 57 — 60 — 5,873 

Other Miscellaneous Manufacturing 1,932 681 1,421 41 420 819 111 286 94 — 34 14 5,854 

Forging and Stamping 2,408 825 860 214 179 541 200 186 — — 38 11 5,462 

Communications Equipment Manufacturing 967 506 1,039 98 499 395 318 212 38 94 11 83 4,260 

Fabric Mills 2,387 343 602 0 150 295 73 61 84 — 35 — 4,030 

Household and Institutional Furniture and Kitchen 

Cabinet Manufacturing 1,962 146 806 15 66 667 — 134 48 — 44 19 3,905 

Engine, Turbine, and Power Transmission 

Equipment Manufacturing 1,500 346 725 36 143 495 82 130 34 — 35 — 3,527 
81

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Industrial Machinery Manufacturing 1,471 339 710 35 140 485 80 128 33 — 35 — 3,457 

Agriculture, Construction, and Mining 

Machinery Manufacturing 1,454 335 699 35 138 478 79 125 33 — 34 — 3,411 

Motor Vehicle Body and Trailer Manufacturing 1,279 364 625 22 168 418 164 132 28 52 47 40 3,339 

Electrical Equipment Manufacturing 922 760 556 263 217 328 123 77 16 — 23 — 3,286 

Metalworking Machinery Manufacturing 1,347 312 657 33 130 448 75 118 31 — 32 — 3,183 

Medical Equipment and Supplies Manufacturing 992 349 722 21 212 417 56 145 48 — 17 7 2,986 

Commercial and Service Industry 

Machinery Manufacturing 1,225 283 592 29 117 405 67 106 28 — 29 — 2,880 

Sugar and Confectionery Product Manufacturing 1,238 123 241 6 755 212 52 58 89 64 27 12 2,877 

Computer and Peripheral Equipment Manufacturing 640 337 703 67 340 266 217 145 26 64 8 57 2,869 

Lime and Gypsum Product Manufacturing 1,375 653 167 46 100 128 56 40 23 — 10 4 2,601 

Textile and Fabric Finishing and Fabric Coating Mills 1,461 211 370 0 93 182 45 37 52 — 22 — 2,473 

Ship and Boat Building 899 258 453 16 123 302 120 97 20 38 35 29 2,392 

Other Textile Product Mills 1,214 161 299 7 152 244 10 56 113 — 57 — 2,313 

Machine Shops; Turned Product; and Screw, Nut, 

and Bolt Manufacturing 968 333 350 88 73 220 82 76 — — 15 5 2,209 

Clay Product and Refractory Manufacturing 1,113 530 137 38 82 104 46 32 19 — 8 3 2,112 

Spring and Wire Product Manufacturing 913 314 331 83 69 208 77 72 — — 15 4 2,088 

Other Transportation Equipment Manufacturing 781 222 382 13 103 255 100 81 17 32 29 24 2,039 

Ventilation, Heating, Air-Conditioning, and Commercial 

Refrigeration Equipment Manufacturing 622 144 305 15 61 208 35 55 14 — 15 — 1,475 

Household Appliance Manufacturing 410 336 241 113 93 143 53 33 7 — 10 — 1,437 

Manufacturing and Reproducing Magnetic and 

Optical Media 294 153 310 29 148 118 94 63 11 28 3 25 1,275 

Railroad Rolling Stock Manufacturing 453 130 225 8 61 150 60 48 10 19 17 14 1,196 

Seafood Product Preparation and Packaging 511 51 100 3 315 88 22 24 37 27 11 5 1,194 

Boiler, Tank, and Shipping Container Manufacturing 478 163 166 41 34 105 38 35 — — 7 2 1,069 

Office Furniture (including Fixtures) Manufacturing 501 37 208 4 17 172 — 35 12 — 11 5 1,003 

Audio and Video Equipment Manufacturing 205 107 216 20 103 82 66 44 8 19 2 17 888 
82

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Tobacco Manufacturing 320 38 103 2 171 82 22 29 14 66 18 4 867 

Electric Lighting Equipment Manufacturing 233 191 137 64 53 81 30 19 4 — 6 — 817 

Cut and Sew Apparel Manufacturing 319 39 229 — 14 135 — 27 17 — 6 — 786 

Sawmills and Wood Preservation 488 42 49 2 5 39 7 10 12 7 2 2 666 

Cutlery and Handtool Manufacturing 237 81 85 21 18 54 20 18 — — 4 1 540 

Textile Furnishings Mills 225 30 56 1 29 46 2 11 21 — 11 — 431 

Other Furniture Related Product Manufacturing 136 10 55 1 4 45 — 9 3 — 3 1 268 

Other Leather and Allied Product Manufacturing 132 17 21 1 8 23 1 5 1 — 1 — 209 

Footwear Manufacturing 48 6 8 0 3 8 1 2 1 — 0 — 76 

Leather and Hide Tanning and Finishing 27 3 4 0 2 5 0 1 0 — 0 — 43 

Hardware Manufacturing 14 5 5 1 1 3 1 1 — — 0 0 31 

Apparel Accessories and Other Apparel Manufacturing 6 1 4 — 0 2 — 0 0 — 0 — 14 

Apparel Knitting Mills 1 0 1 — 0 0 — 0 0 — 0 — 2 

Total 449,086 106,835 82,372 67,815 63,218 56,031 19,226 16,901 14,683 10,449 4,030 2,702 893,346 
83

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Table 27. Industrial Manufacturing Electricity Proportions for Subsectors and End Uses as Modeled for the CONUS in 2012 by IGATE-E

Subsector

Machine Drive (%)Process Heating (%)Facility HVAC (%)Electro Chemical Processes (%)Process Cooling And Refrigeration (%)Facility Lighting (%)Other Process Use (%)Other Facility Support (%)Conventional Boiler Use (%)End Use Not Reported (%)Onsite Transportation (%)Other Nonprocess Use (%)Total (%)

Iron and Steel Mills and Ferroalloy Manufacturing 26.3 33.8 6.1 22.5 1.5 4.3 1.5 1.2 0.5 1.9 0.2 0.3 10.9 

Petroleum and Coal Products Manufacturing 80.9 3.4 3.8 1.5 4.2 2.5 0.7 1.0 0.7 1.1 0.0 0.1 7.5 

Basic Chemical Manufacturing 54.6 3.5 6.1 16.2 9.2 3.9 1.5 1.2 2.0 1.3 0.2 0.2 6.9 

Pulp, Paper, and Paperboard Mills 75.6 3.9 4.4 0.8 1.7 3.8 3.4 1.1 4.6 0.5 0.2 — 6.7 

Plastics Product Manufacturing 53.4 16.0 10.1 — 8.0 9.4 — 2.3 0.5 — 0.4 — 5.4 

Pharmaceutical and Medicine Manufacturing 54.3 3.5 6.1 16.4 9.3 3.9 1.5 1.2 2.0 1.3 0.2 0.2 4.5 

Converted Paper Product Manufacturing 75.5 3.9 4.4 0.8 1.7 3.9 3.4 1.1 4.6 0.5 0.2 — 4.4 

Animal Slaughtering and Processing 43.1 4.3 8.4 0.2 26.2 7.4 1.8 2.0 3.1 2.2 0.9 0.4 2.5 

Pesticide, Fertilizer, and Other Agricultural 

Chemical Manufacturing 53.9 3.5 6.2 16.6 9.4 4.0 1.5 1.2 2.1 1.3 0.2 0.2 2.4 

Resin, Synthetic Rubber, and Artificial Synthetic Fibers 

and Filaments Manufacturing 54.6 3.5 6.1 16.2 9.2 3.9 1.5 1.2 2.0 1.3 0.2 0.2 2.4 

Other Chemical Product and Preparation Manufacturing 54.5 3.5 6.1 16.3 9.2 3.9 1.5 1.2 2.0 1.3 0.2 0.2 2.0 

Motor Vehicle Parts Manufacturing 38.4 10.9 18.7 0.6 5.0 12.5 4.9 3.9 0.8 1.6 1.4 1.2 2.0 

Other Nonmetallic Mineral Product Manufacturing 52.9 25.1 6.4 1.8 3.8 4.9 2.1 1.5 0.9 — 0.4 0.2 1.8 

Other Wood Product Manufacturing 73.4 6.2 7.4 0.3 0.7 5.9 1.1 1.6 1.8 1.1 0.2 0.3 1.7 

Semiconductor and Other Electronic 

Component Manufacturing 23.1 12.0 24.3 2.3 11.6 9.2 7.4 4.9 0.9 2.2 0.3 1.9 1.7 

Printing and Related Support Activities 54.9 4.2 17.3 1.2 7.6 8.6 1.5 2.9 0.9 — 1.1 — 1.6 

Foundries 26.5 33.9 6.1 22.3 1.4 4.3 1.5 1.2 0.5 1.9 0.2 0.3 1.5 

Nonferrous Metal (except Aluminum) Production 

and Processing 26.9 34.2 6.0 21.9 1.4 4.3 1.5 1.1 0.4 1.9 0.2 0.3 1.5 

Soap, Cleaning Compound, and Toilet Preparation 

Manufacturing 54.0 3.5 6.1 16.5 9.4 4.0 1.5 1.2 2.1 1.3 0.2 0.2 1.5 

Other Fabricated Metal Product Manufacturing 44.2 15.1 15.7 3.9 3.3 9.9 3.6 3.4 — — 0.7 0.2 1.4 

Architectural and Structural Metals Manufacturing 44.0 15.1 15.8 3.9 3.3 9.9 3.7 3.4 — — 0.7 0.2 1.3 

Dairy Product Manufacturing 43.6 4.3 8.3 0.2 25.9 7.4 1.8 2.0 3.0 2.2 0.9 0.4 1.3 

Aerospace Product and Parts Manufacturing 38.5 10.9 18.7 0.6 5.0 12.5 4.9 3.9 0.8 1.6 1.4 1.2 1.2 

Fruit and Vegetable Preserving and Specialty Food 

Manufacturing 43.1 4.3 8.4 0.2 26.2 7.4 1.8 2.0 3.1 2.2 0.9 0.4 1.1 

Beverage Manufacturing 36.5 4.3 11.9 0.2 19.9 9.4 2.5 3.3 1.6 7.7 2.1 0.4 1.0 
84

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

Subsector

Machine Drive (%)Process Heating (%)Facility HVAC (%)Electro Chemical Processes (%)Process Cooling And Refrigeration (%)Facility Lighting (%)Other Process Use (%)Other Facility Support (%)Conventional Boiler Use (%)End Use Not Reported (%)Onsite Transportation (%)Other Nonprocess Use (%)Total (%)

Glass and Glass Product Manufacturing 53.1 25.1 6.4 1.7 3.8 4.9 2.1 1.5 0.9 — 0.4 0.2 1.0 

Cement and Concrete Product Manufacturing 52.8 25.1 6.4 1.8 3.9 4.9 2.1 1.5 0.9 — 0.4 0.2 1.0 

Navigational, Measuring, Electromedical, and Control 

Instruments Manufacturing 22.8 11.9 24.4 2.3 11.7 9.3 7.4 5.0 0.9 2.2 0.3 1.9 1.0 

Paint, Coating, and Adhesive Manufacturing 54.0 3.5 6.1 16.5 9.4 3.9 1.5 1.2 2.1 1.3 0.2 0.2 0.9 

Motor Vehicle Manufacturing 38.1 10.9 18.8 0.7 5.1 12.6 5.0 4.0 0.8 1.6 1.4 1.2 0.9 

Bakeries and Tortilla Manufacturing 43.3 4.3 8.4 0.2 26.1 7.4 1.8 2.0 3.1 2.2 0.9 0.4 0.9 

Other Food Manufacturing 43.4 4.3 8.4 0.2 26.0 7.4 1.8 2.0 3.0 2.2 0.9 0.4 0.9 

Rubber Product Manufacturing 53.3 16.0 10.1 — 8.0 9.4 — 2.3 0.5 — 0.4 — 0.8 

Alumina and Aluminum Production and Processing 26.8 34.1 6.0 22.0 1.4 4.3 1.5 1.1 0.4 1.9 0.2 0.3 0.8 

Coating, Engraving, Heat Treating, and Allied Activities 43.7 15.1 15.9 4.0 3.3 10.0 3.7 3.4 — — 0.7 0.2 0.8 

Steel Product Manufacturing from Purchased Steel 26.8 34.2 6.0 21.9 1.4 4.3 1.5 1.1 0.4 1.9 0.2 0.3 0.8 

Other Electrical Equipment and Component 

Manufacturing 28.4 23.3 16.8 7.9 6.5 9.9 3.7 2.3 0.5 — 0.7 — 0.7 

Fiber, Yarn, and Thread Mills 59.5 8.5 14.8 0.0 3.7 7.3 1.8 1.5 2.0 — 0.9 — 0.7 

Grain and Oilseed Milling 43.4 4.3 8.4 0.2 26.0 7.4 1.8 2.0 3.1 2.2 0.9 0.4 0.7 

Veneer, Plywood, and Engineered Wood Product 

Manufacturing 73.7 6.2 7.3 0.3 0.7 5.9 1.0 1.5 1.7 1.0 0.2 0.3 0.7 

Animal Food Manufacturing 42.9 4.3 8.4 0.2 26.3 7.4 1.8 2.0 3.1 2.2 0.9 0.4 0.7 

Other General Purpose Machinery Manufacturing 42.2 9.8 20.7 1.0 4.1 14.1 2.4 3.7 1.0 — 1.0 — 0.7 

Other Miscellaneous Manufacturing 33.0 11.6 24.3 0.7 7.2 14.0 1.9 4.9 1.6 — 0.6 0.2 0.7 

Forging and Stamping 44.1 15.1 15.7 3.9 3.3 9.9 3.7 3.4 — — 0.7 0.2 0.6 

Communications Equipment Manufacturing 22.7 11.9 24.4 2.3 11.7 9.3 7.5 5.0 0.9 2.2 0.3 2.0 0.5 

Fabric Mills 59.2 8.5 14.9 0.0 3.7 7.3 1.8 1.5 2.1 — 0.9 — 0.5 

Household and Institutional Furniture and Kitchen 

Cabinet Manufacturing 50.2 3.7 20.6 0.4 1.7 17.1 — 3.4 1.2 — 1.1 0.5 0.4 

Engine, Turbine, and Power Transmission Equipment

Manufacturing 42.5 9.8 20.6 1.0 4.1 14.0 2.3 3.7 1.0 — 1.0 — 0.4 

Industrial Machinery Manufacturing 42.5 9.8 20.5 1.0 4.1 14.0 2.3 3.7 1.0 — 1.0 — 0.4 

Agriculture, Construction, and Mining Machinery 

Manufacturing 42.6 9.8 20.5 1.0 4.0 14.0 2.3 3.7 1.0 — 1.0 — 0.4 

Motor Vehicle Body and Trailer Manufacturing 38.3 10.9 18.7 0.6 5.0 12.5 4.9 4.0 0.8 1.6 1.4 1.2 0.4 

Electrical Equipment Manufacturing 28.1 23.1 16.9 8.0 6.6 10.0 3.8 2.3 0.5 — 0.7 — 0.4 
85

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

Subsector

Machine Drive (%)Process Heating (%)Facility HVAC (%)Electro Chemical Processes (%)Process Cooling And Refrigeration (%)Facility Lighting (%)Other Process Use (%)Other Facility Support (%)Conventional Boiler Use (%)End Use Not Reported (%)Onsite Transportation (%)Other Nonprocess Use (%)Total (%)

Metalworking Machinery Manufacturing 42.3 9.8 20.6 1.0 4.1 14.1 2.3 3.7 1.0 — 1.0 — 0.4 

Medical Equipment and Supplies Manufacturing 33.2 11.7 24.2 0.7 7.1 14.0 1.9 4.9 1.6 — 0.6 0.2 0.3 

Commercial and Service Industry Machinery 

Manufacturing 42.5 9.8 20.6 1.0 4.1 14.0 2.3 3.7 1.0 — 1.0 — 0.3 

Sugar and Confectionery Product Manufacturing 43.0 4.3 8.4 0.2 26.2 7.4 1.8 2.0 3.1 2.2 0.9 0.4 0.3 

Computer and Peripheral Equipment Manufacturing 22.3 11.7 24.5 2.3 11.9 9.3 7.6 5.0 0.9 2.2 0.3 2.0 0.3 

Lime and Gypsum Product Manufacturing 52.9 25.1 6.4 1.8 3.9 4.9 2.1 1.5 0.9 — 0.4 0.2 0.3 

Textile and Fabric Finishing and Fabric Coating Mills 59.1 8.5 15.0 0.0 3.7 7.3 1.8 1.5 2.1 — 0.9 — 0.3 

Ship and Boat Building 37.6 10.8 18.9 0.7 5.2 12.6 5.0 4.0 0.9 1.6 1.4 1.2 0.3 

Other Textile Product Mills 52.5 7.0 12.9 0.3 6.6 10.5 0.4 2.4 4.9 — 2.5 — 0.3 

Machine Shops; Turned Product; and Screw, Nut, 

and Bolt Manufacturing 43.8 15.1 15.8 4.0 3.3 10.0 3.7 3.4 — — 0.7 0.2 0.2 

Clay Product and Refractory Manufacturing 52.7 25.1 6.5 1.8 3.9 4.9 2.2 1.5 0.9 — 0.4 0.2 0.2 

Spring and Wire Product Manufacturing 43.7 15.1 15.9 4.0 3.3 10.0 3.7 3.4 — — 0.7 0.2 0.2 

Other Transportation Equipment Manufacturing 38.3 10.9 18.7 0.6 5.0 12.5 4.9 4.0 0.8 1.6 1.4 1.2 0.2 

Ventilation, Heating, Air-Conditioning, and Commercial 

Refrigeration Equipment Manufacturing 42.2 9.8 20.7 1.0 4.1 14.1 2.4 3.7 1.0 — 1.0 — 0.2 

Household Appliance Manufacturing 28.5 23.4 16.7 7.8 6.5 9.9 3.7 2.3 0.5 — 0.7 — 0.2 

Manufacturing and Reproducing Magnetic and 

Optical Media 23.0 12.0 24.3 2.3 11.6 9.3 7.4 4.9 0.9 2.2 0.3 1.9 0.1 

Railroad Rolling Stock Manufacturing 37.9 10.8 18.8 0.7 5.1 12.6 5.0 4.0 0.8 1.6 1.4 1.2 0.1 

Seafood Product Preparation and Packaging 42.8 4.3 8.4 0.2 26.4 7.4 1.8 2.0 3.1 2.2 0.9 0.4 0.1 

Boiler, Tank, and Shipping Container Manufacturing 44.7 15.2 15.5 3.8 3.2 9.8 3.6 3.3 — — 0.7 0.2 0.1 

Office Furniture (including Fixtures) Manufacturing 49.9 3.7 20.8 0.4 1.7 17.2 — 3.5 1.2 — 1.1 0.5 0.1 

Audio and Video Equipment Manufacturing 23.0 12.0 24.3 2.3 11.6 9.3 7.4 4.9 0.9 2.2 0.3 1.9 0.1 

Tobacco Manufacturing 37.0 4.4 11.9 0.2 19.7 9.4 2.5 3.3 1.6 7.6 2.0 0.4 0.1 

Electric Lighting Equipment Manufacturing 28.6 23.4 16.7 7.8 6.5 9.9 3.7 2.3 0.5 — 0.7 — 0.1 

Cut and Sew Apparel Manufacturing 40.6 5.0 29.1 — 1.8 17.1 — 3.4 2.2 — 0.8 — 0.1 

Sawmills and Wood Preservation 73.4 6.2 7.4 0.3 0.7 5.9 1.1 1.6 1.8 1.1 0.2 0.3 0.1 

Cutlery and Handtool Manufacturing 43.9 15.1 15.8 4.0 3.3 9.9 3.7 3.4 — — 0.7 0.2 0.1 

Textile Furnishings Mills 52.2 6.9 13.0 0.3 6.6 10.6 0.4 2.5 4.9 — 2.5 — 0.0 

Other Furniture Related Product Manufacturing 50.8 3.8 20.4 0.4 1.7 16.9 — 3.3 1.2 — 1.1 0.5 0.0 
86

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

Subsector

Machine Drive (%)Process Heating (%)Facility HVAC (%)Electro Chemical Processes (%)Process Cooling And Refrigeration (%)Facility Lighting (%)Other Process Use (%)Other Facility Support (%)Conventional Boiler Use (%)End Use Not Reported (%)Onsite Transportation (%)Other Nonprocess Use (%)Total (%)

Other Leather and Allied Product Manufacturing 63.0 7.9 10.2 0.3 3.7 10.8 0.7 2.3 0.7 — 0.3 — 0.0 

Footwear Manufacturing 63.0 7.9 10.2 0.3 3.7 10.8 0.7 2.3 0.7 — 0.3 — 0.0 

Leather and Hide Tanning and Finishing 63.0 7.9 10.2 0.3 3.7 10.8 0.7 2.3 0.7 — 0.3 — 0.0 

Hardware Manufacturing 43.6 15.0 15.9 4.0 3.3 10.0 3.7 3.5 — — 0.7 0.2 0.0 

Apparel Accessories and Other Apparel Manufacturing 39.9 5.0 29.5 — 1.8 17.3 — 3.5 2.2 — 0.8 — 0.0 

Apparel Knitting Mills 39.8 5.0 29.5 — 1.8 17.3 — 3.5 2.2 — 0.8 — 0.0 

Total 50.3 12.0 9.2 7.6 7.1 6.3 2.2 1.9 1.6 1.2 0.5 0.3 100.0 
87

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Figure 25. Industrial electricity use diurnal patterns by season, modeled for the CONUS in 2012
88

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4 Future Work

dsgrid is a new model designed to provide a solid basis for exploring questions about future 

electricity load and its relationship to grid operations. Because dsgrid is a new model, this 

documentation represents the beginning of an investigation rather than the end of an analysis. 

Three areas of work are planned for the near future.

4.1 Additional ComStock Calibration

The ComStock model, although built from relatively mature prototype building energy models,

is still under development and has not yet been fully calibrated against a sufficiently large body 

of hourly (or finer) commercial building metered data. In the near future, the team plans to 

conduct several rounds of comparing ComStock to metered data sets, making model adjustments 

accordingly, and again measuring level of agreement. Of most interest is comparing the

ComStock load profile results to aggregated commercial sector utility consumption data. 

Consolidated Edison in New York, for example, releases historical 8,760 load data for portions 

of their service territory, and Commonwealth Edison releases significant customer data that 

has been anonymized but provides rate classification, which allows for the determination of 

commercial class customers. Calibration to these detailed energy data will provide valuable 

insights into ComStock’s temporal accuracy, from which we expect to derive scheduling

adjustments. 

4.2 EFS Scenarios

In parallel with the dsgrid development efforts described here, the EFS team has been developing 

a set of electrification scenarios using the EnergyPATHWAYS model (Mai et al. 2018). As part 

of that process, some sector models described here provided inputs to EnergyPATHWAYS,

so that the initial conditions of these two modeling efforts have been somewhat harmonized, 

as depicted on the left side of Figure 26. The next step of these efforts is shown on the right side 

of Figure 26, where EnergyPATHWAYS outputs in the form of descriptions of future building, 

industrial, and transportation stocks are translated and fed into the various components of dsgrid. 

Each component will then be run using these adjusted inputs to create annual snapshots of future 

electricity demand under multiple scenarios.

Figure 26. Relationship between dsgrid and decadal-scale adoption models
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In anticipation of taking this step, the dsgrid and EnergyPATHWAYS teams have coordinated 

on (1) use of the LandScan population data sets for both current conditions and (2) a 2030 

county-level projection. Population is a key determiner of the magnitude and geographic

dispersion of energy use both now and as we might project it into the future. Thus, standardizing 

on finely resolved population data sets will help ensure between-model compatibility and 

realistic downscaling. Also, the dsgrid team has met with the EnergyPATHWAYS team to start 

evaluating what kinds of mappings will need to be performed to turn their outputs into 

dsgrid inputs. 

In addition to translating EnergyPATHWAYS outputs into a form that is usable by the dsgrid 

sector models, we will also need to develop appropriate methods for projecting our gap models

and residuals into the future, possibly adjusting them based on some EnergyPATHWAYS 

outputs as well. We look forward to implementing and executing this capability to construct 

the EFS electrification scenario snapshots in 2018–2019.

4.3 Power System Geography

Finally, the team will be mapping dsgrid data to the geography of U.S. power systems. Some 

state-level regulatory and reporting requirements notwithstanding, electric utilities and 

aggregations thereof are not generally aligned with political boundaries. Many electric utilities 

have service territories in more than one state; many geographic regions are served by multiple 

utilities; and the largest wholesale power markets (e.g., PJM and MISO) cover many states. The 

latter are typically broken down into regions, but even those are quite coarse and cross state 

boundaries. Relevant to dsgrid, they often cover areas with significantly different weather. 

For the purposes of this report, we have focused on describing load at the state level or 

aggregates thereof, because this generally allows for high-level calibration and regional 

description; however, to apply this and future data sets to power system models, we will need 

to disaggregate and reaggregate the data based on the logic of electric utilities and transmission 

nodes. The FERC hourly load data used in this work was derived from disaggregations of 

regional load time series to transmission nodes that were created to support several ongoing 

renewable integration studies being conducted by NREL. We plan to retrace this logic and 

the geographic and electrical entities associated with it to assign dsgrid load timeseries to 

transmission nodes for the operational modeling portion of EFS.
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Appendix A. Residential Sector Model Details

A.1 Data Sources and Methodology Details

Detailed documentation of the ResStock methodology can be found in Wilson et al. (2017). 

For reference, a summary of the major input data sources is shown in Table A-1.

Table A-1. List of Data Sources Used to Derive Input Probability Distributions for ResStock

Full Reference

Number of 

Probability 

Distributions

“Residential Energy Consumption Survey (RECS), 2009 RECS Survey Data,” U.S. Energy 

Information Administration, accessed in 2012, 

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

2,792

Used engineering experience or calibration because of a lack of data 1,235

Wanyu R. Chan, Jeffrey Joh, and Max H. Sherman, Air Leakage of US Homes: Regression 

Analysis and Improvements from Retrofit (Technical Report LBNL-5966E) (Berkeley, CA: 

Lawrence Berkeley National Laboratory, 2012, Eqn. 2 and Table 1).

1,050

Thomas P. Wenzel, Jonathan G. Koomey, Gregory J. Rosenquist, Maria C. Sanchez, and 

James W. Hanford, Energy Data Sourcebook for the U.S. Residential Sector (Technical Report 

LBNL-40297) (Berkeley, CA: Lawrence Berkeley National Laboratory, 1997).

760

American Community Survey: Five-Year Summary File,” U.S. Census Bureau, 2012 (from 

National Historical Geographic Information System, Minnesota Population Center, 2015). 432

“New Construction Builder Practice Survey Data,” National Association of Home Builders, 1982, 

1987 152

Used default values from Eric Wilson, Cheryn Engebrecht Metzger, Scott Horowitz, and Robert 

Hendron, 2014 Building America House Simulation Protocols (Technical Report NREL/TP-5500-

60988) (Golden, CO: National Renewable Energy Laboratory, 2014), 

http://energy.gov/eere/buildings/downloads/building-america-2014-house-simulation-protocols.

96

“New Construction Overview,” Home Innovation Research Labs 1999, 2007 (New Housing 

Characteristics; Insulation; Sheathing—Wall), 

http://www.homeinnovation.com/trends_and_reports/data/new_construction.

56

“Residential Building Stock Assessment: Single-Family Characteristics and Energy Use,” 

Northwest Energy Efficiency Alliance, 2012. 52

Kenneth Labs, John Carmody, Raymond Sterling, Lester Shen, Yu Joe Huang, and Danny 

Parker, Buildings Foundation Design Handbook (Technical Report ORNL/Sub/86-72143/l) (Oak 

Ridge, TN: 1988).

48

International Code Council, 2009 International Energy Conservation Code (Washington, D.C.: 

2009). 40

Ronald L. Ritschard, James W. Hanford, and A. Osman Sezgen, Single Family Heating and 

Cooling Requirements: Assumptions, Methods, and Summary Results (Technical Report LBL30377) (Berkeley, CA: Lawrence Berkeley National Laboratory, 1992).36

R.G. Lucas and P.C. Cole, Impacts of the 2009 IECC for Residential Buildings at State Level

(Technical Report PNNL-18545) (Richland, WA: Pacific Northwest National Laboratory, 2009) 35

“Building America Field Data Repository,” National Renewable Energy Laboratory, 2015. 7

a The table lists the data sources used to develop the statistical model of housing stock characteristics used for 

this analysis.
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A.2 Additional Calibration Details

Figure A-1 and Figure A-2 illustrate preliminary progress on calibrating weather-dependent enduse load profiles (space heating and air conditioning), using 2012 Utility Load Research Data

(ULRD) from ComEd. This initial calibration used simple multipliers on the heating and cooling 

end uses; ongoing work involves determining the most appropriate parameter changes to achieve 

a similar result (e.g., thermostat setpoints, seasonal usage patterns, and number of homes using 

air conditioning). 

Figure A-1. Comparison of modeled and measured daily kWh load for average single-family 

homes in the ComEd service territory (2012 data; homes without electric space heating)

Uncalibrated

Calibrated

2012

w/o Electric Heat
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Figure A-2. Comparison of modeled and measured daily kWh load for average single-family 

homes in the ComEd service territory (2012 data; homes with electric space heating)

Uncalibrated

Calibrated

2012

w/ Electric Heat
107

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Appendix B. Commercial Sector Model Details

B.1 ComStock Overview

The commercial building sector stock model (ComStock) is a highly granular, bottom-up model 

that uses multiple data sources, statistical sampling methods, and advanced building energy 

simulations to estimate annual subhourly energy consumption of commercial buildings across 

the United States. The methodology used to develop ComStock closely mirrors that of the 

residential building stock model described by (Wilson et al. 2016).

The ComStock modeling process consists of four constituent parts: a database of real 

commercial building characteristics, conditional probability tables synthesized from these data, 

the Parametric Analysis Tool (PAT) used to translate probability tables to simulation inputs, and 

building energy simulations created using OpenStudio and simulated with EnergyPlus. The 

following sections build on the ComStock overview provided in Section 2.2.2, and describe in 

greater detail the data, modeling approaches, and assumptions used to develop ComStock. A 

flowchart of the ComStock modeling process is depicted in Figure B-1.

Figure B-1. Workflow used to characterize the U.S. commercial building stock and model energy 

use with county-level granularity

B.2 Commercial Building Characteristics

The scope of the ComStock model encompasses commercial buildings that can be mapped to 

the 16 DOE prototype buildings: full service restaurant, hospital, large hotel, midrise apartment, 

high-rise apartment, outpatient, primary school, quick service restaurant, retail, secondary 

school, small hotel, strip mall, warehouse, small office, medium office, and large office (Goel et 
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al. 2014). In addition to Goel et al. (2014), Deru et al. (2011) provides additional background on 

the initial reference building models that were subsequently developed into the prototype 

buildings used for codes and standards analysis. Commercial buildings whose principle activities 

do not fall into one of the prototype building categories are currently not included in the core 

portion of ComStock.

55 They are instead captured by the dsgrid commercial gaps model.

Every aspect of a commercial building ultimately influences its energy consumption—often in 

complex and interrelated ways—thus necessitating robust modeling engines seeded with accurate 

building characteristics. The primary building characteristics needed to accurately model 

commercial building energy consumption were determined based on previous modeling 

experience, available data, and engineering judgement. Many building characteristics (e.g., 

number of floors, HVAC system type, and total area) depend on other influential variables (e.g., 

location, building type, and vintage). The building characteristics, dependencies, and data 

sources used to establish the ComStock database are shown in Table 5. The hierarchical 

dependency of the probability tables used to determine building simulation inputs are illustrated 

in Figure B-2. These dependencies are somewhat arbitrary (e.g., the number of floors in a 

building may be codependent on building location and building type). Further statistical analysis 

is needed to verify the optimal inheritance structure of these dependencies and the effects of 

these assumptions. For building characteristics listed in Table 5 that are not included in the 

Figure B-2 dependencies flowchart, parameters were assigned values based on established 

EnergyPlus and OpenStudio defaults.

Figure B-2. Visualization of probability table heirarchy, sampled using a Latin hypercube sampling 

 55 Supermarkets are a building type present in the original reference buildings, but they dropped from the prototype 

building set. They are currently in the process of being added back in as full-fledged prototype buildings, at which 

point they will also be incorporated into ComStock.
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B.3 Commercial Building Stock Data Sources

Simulating end-use electrification in U.S. commercial buildings requires accurate high-resolution 

data on the type, size, location, and characteristics of existing commercial buildings. For these 

characteristics, we use a combination of the data contained within the U.S. Energy Information 

Administration (EIA 2016a) Commercial Buildings Energy Consumption Survey (CBECS) and 

CoStar (2017), a commercial real estate inventory with building point data.

2012 Commercial Buildings Energy Consumption Survey (CBECS)

The 2012 CBECS is a national building survey conducted by the EIA. The most recent CBECS 

began data collection in April 2013 for reference year 2012. The resulting data include surveyed 

information on energy-related building characteristics and energy usage details for buildings 

over 1,000 square feet in which at least 50% of the floorspace is used for commercial purposes. 

CBECS public-use microdata (EIA 2016a) consist of anonymized building details for 6,720 

surveyed buildings. The smallest level of geographic resolution for these data is the census 

division—a relatively large geographic area covering multiple states and climate regions (Figure 

B-3). Each individual survey response also includes a sample weight, which represents the 

number of buildings in the general commercial building population that the observation 

represents. 

Figure B-3. The four U.S. census regions and nine census divisions, used by CBECS 2012

The buildings surveyed for CBECS were selected from a sampling frame of commercial 

buildings developed by EIA. This sampling frame consists of two parts: an area frame and a 

list frame. The area frame, which accounts for approximately 80% of the total frame, was first 

created in 2003 and was constructed using multi-stage area probability sampling. To create the

frame, trained field staff walked or drove through selected areas (often the size of census tracts 

or groups of census tracts) and recorded information about every commercial building. The list 

frame, which accounts for about 20% of the total frame, consists of five administrative lists of 

government buildings, schools, hospitals, airports, and other large buildings, namely: 
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• U.S. federal buildings that are at least 200,000 square feet from Federal Real Property 

Council files obtained from the General Services Administration

• Four-year colleges and universities estimated to have at least 1,000,000 square feet of 

floorspace from the Integrated Postsecondary Education Data System

• Hospitals with at least 200,000 square feet of floorspace purchased from the IMS 

Healthcare Market Index 

• Airports from the Federal Aviation Administration (FAA) Forms 5010, Airport Master 

Record file, and FAA enplanements data file

• Other large buildings over 200,000 square feet (e.g., hotels and offices) from the 

Common Premises Location file from Dun & Bradstreet.

Both the area and list frames were updated for the 2012 CBECS. The final sampling frame was 

stratified by building size and type; the number of sampled buildings within each subgroup was 

calculated to minimize the variance of the estimated energy consumption. Finally, the buildings 

to be sampled were chosen using a probability proportionate to size selection procedure. 

For the selected buildings, the CBECS was administered in two phases. During the first stage, a 

trained interviewer administered a set questionnaire to the building respondent using a computerassisted recorded interview tool, either in-person or by telephone. If the building respondent 

could not provide sufficient data, or the provided data were deemed erroneous using EIA 

regression models, a second phase of data collection was used. In this Follow-up Energy 

Suppliers Survey, EIA contacted the individual energy supplier for the building to obtain usage 

and expenditure data from the supplier’s records. 

During the subsequent data processing stage, items with nonresponses were treated with a hotdeck imputation technique, meaning the response for that missing item was randomly chosen 

from a completed response of another similar building. Additional steps were taken to adhere to 

confidentiality requirements by removing potentially identifying information from the public-use 

microdata file. This included masking specific square footage details and binning numeric 

responses for building characteristics such as floor-to-ceiling height and year of construction. 

For a full list of the confidentiality and masking procedures used by the EIA, refer to the User’s 

Guide to the 2012 CBECS Public Use Microdata File (EIA 2016b). For more information on the 

methodology used to produce the CBECS data, visit the CBECS website.

56

CoStar Realty Database 

CoStar Group, Inc. is a commercial real estate information and marketing company. As one of its

primary product offerings, CoStar maintains a full inventory of building point data (aggregated 

to the census block level for the purpose of this analysis) with information on building area and 

market subtype (CoStar 2017). In the CONUS, CoStar reports data for a total of 2,860,201 

commercial buildings of 94 property subtypes, which is just over half the number of buildings 

estimated by CBECS.

 56 https://www.eia.gov/consumption/commercial/
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According to CoStar, a team of research associates maintains the properties in CoStar’s database 

through canvassing (500,000 properties nationwide annually); phone calls (10,000 calls daily 

to brokers, owners, developers, and real estate professionals); photographs (1 million taken 

annually); and data exchange (5.1 million changes per day) (CoStar Group, Inc. 2017).

Because the CoStar database is proprietary, additional documentation on CoStar sampling 

methodologies and data collection methods is difficult to ascertain. Public information on CoStar 

property data can be found on their website.57 

Data Source Limitations

Due to the vast quantity and variation in U.S. commercial buildings, it is unsurprising that an 

accurate, high-resolution database of building counts, sizes, and types is difficult to create and 

maintain. The two nation-wide databases identified and described in this report—CBECS and 

CoStar—each entail limitations and uncertainties whose potential impacts were considered in the 

development of the ComStock database. 

CBECS Limitations

With regard to building characteristics, the primary limitations of the available CBECS data 

are its coarse geographic resolution, limited number of sampled buildings, and inaccuracy of 

respondent reports. Because the highest available resolution of the CBECS data is the census 

division level, conducting analyses at the desired geospatial resolution for dsgrid (county) is 

infeasible without additional data manipulation. 

The low number of surveyed buildings (6,720 total) in the 2012 CBECS provides only a limited 

measure of the expected variability and kurtosis in the commercial building stock. However, 

CBECS does provide relative standard error calculations for each estimate in the CBECS 

summary tables. CBECS uses the jackknife method for estimating these standard errors, 

whereby replicate weights are repeatedly used to estimate the statistic of interest and calculate 

the differences between these estimates and the full-sample estimate (EIA 2016a). Using the 

provided relative standard errors, calculations derived from CBECS data can be evaluated to 

determine the precision of the survey estimate relative to the true population value. 

Another weakness of the CBECS method is the dependence on the knowledge and accuracy of 

the building respondents when questioned by EIA’s trained surveyors. As analyzed in an EIA 

report that retrospectively evaluated the efficacy of the 2012 CBECS (EIA 2015b), square 

footage estimates and Principle Building Activity (PBA) designations collected during the 

building survey portion of CBECS were limited by the respondents’ knowledge. 

CoStar Limitations

Inadequate information regarding error metrics, survey forms, and geospatial coverage represents

a fundamental limitation of CoStar data. Certain fields, such as square footage are more likely to 

be accurate, as they are critical to the commercial success of the data set and company, while 

other fields are not as obviously trustworthy. Additionally, the commercial nature of the data set 

leads to questions regarding coverage of real estate assets unlikely to be sold, such as publicly 

owned schools, hospital campuses, and federally owned properties. Although ascertaining the 

 57 http://www.costar.com/
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accuracy of these entries in individual municipalities is relatively easy, testing the accuracy of 

these measures across the United States is significantly more challenging.

B.4 Model Coverage and Gaps

Only DOE prototype buildings can be modeled in ComStock, so building types in CBECS and 

CoStar must be mapped to these 16 supported building types. The overall building count and 

floor area in ComStock is determined by the CoStar to DOE prototype mapping. Those buildings 

are then populated with additional characteristics based on the CBECS to DOE prototype 

mapping. This core model coverage is shown at the center of Figure B-4 as the circle labeled 

ComStock. The commercial building gap model consists of those CBECS building types that can 

be mapped to CoStar but not to the DOE prototype buildings. This is represented as the light 

green area surrounding ComStock. Energy represented in CBECS but not in ComStock and

unmappable to CoStar is unmodeled but quantifiable; CoStar building types mapped to neither a 

DOE prototype nor CBECS are unrepresented and their energy use is completely unknown to 

this analysis. Those two slices of energy use are shown on the left and right sides of Figure B-4

respectively. Table B-1 and Table B-2 show the mapping between CoStar and DOE prototypes

and 2012 CBECS and DOE prototypes respectively, along with the total number of buildings of 

those types reported in the CONUS. Table B-3 lists those 2012 CBECS building types that do 

not map to a DOE prototype building, along with their estimated electricity use and the CoStar 

building types they map to, if applicable. 

Figure B-4. Relationship between major data sources and commercial model coverage 
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Table B-1. Mapping of CoStar Building Combo Codes and DOE Prototype Buildings

CoStar Combo Code DOE Prototype

Building

CONUS CoStar 

Building Count

Flex_Light Distribution warehouse 14,259

Flex_Light Manufacturing warehouse 21,252

Flex_Showroom strip_mall 6,205

Health Care_Assisted Living midrise_apartment 11,013

Health Care_Congregate Senior Housing midrise_apartment 1,055

Health Care_Continuing Care Retirement Community midrise_apartment 1,140

Health Care_Hospital hospital 4,700

Health Care_Rehabilitation Center outpatient 2,872

Health Care_Skilled Nursing Facility outpatient 6,009

Hospitality_Hotel large_hotel 29,876

Hospitality_Hotel Casino large_hotel 284

Hospitality_Motel small_hotel 23,072

Hospitality_Single Room Occupancy Hotel midrise_apartment 1,611

Industrial_Distribution warehouse 32,783

Industrial_Service warehouse 44,851

Industrial_Showroom warehouse 7,038

Industrial_Truck Terminal warehouse 6,605

Industrial_Warehouse warehouse 410,878

Multi-Family_Apartments midrise_apartment 528,468

Multi-Family_Dormitory midrise_apartment 3,424

Office_Industrial Live/Work Unit medium_office 2,101

Office_Loft/Creative Space midrise_apartment 8,617

Office_Medical outpatient 102,312

Office_Office Live/Work Unit small_office 10,315

Office_Office/Residential small_office 20,747

Retail_Bank small_office 55,825

Retail_Bar full_service_restaurant 15,715

Retail_Day Care Center primary_school 17,046

Retail_Department Store retail 2,937

Retail_Fast Food quick_service_restaurant 77,699

Retail_Freestanding retail 435,819

Retail_Garden Center retail 5,412

Retail_Restaurant full_service_restaurant 115,881

Retail_Storefront strip_mall 134,575

Retail_Storefront Retail/Office strip_mall 70,242

Retail_Storefront Retail/Residential strip_mall 120,904

Specialty_Airplane Hangar warehouse 2,899

Specialty_Post Office strip_mall 19,897
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CoStar Combo Code DOE Prototype

Building

CONUS CoStar 

Building Count

Specialty_Schools primary_school 12,642

Specialty_Self-Storage warehouse 29,984

Specialty_Sorority/Fraternity House midrise_apartment 529

Matched count 2,419,493

Flex_R&D no_match 9,699

Flex_Telecom Hotel/Data Hosting no_match 1,057

Multi-Family_Manufactured Housing/Mobile Home 

Park

no_match 14,135

Office_Telecom Hotel/Data Hosting no_match 976

Retail_Auto Dealership no_match 43,599

Retail_Auto Repair no_match 100,053

Retail_Bowling Alley no_match 2,392

Retail_Convenience Store no_match 46,419

Retail_Drug Store no_match 15,895

Retail_Funeral Home no_match 5,892

Retail_Health Club no_match 4,350

Retail_Movie Theatre no_match 2,811

Retail_Service Station no_match 62,194

Retail_Supermarket no_match 12,679

Retail_Truck Stop no_match 760

Retail_Veterinarian/Kennel no_match 7,076

Specialty_Airport no_match 410

Specialty_Auto Salvage Facility no_match 3,012

Specialty_Car Wash no_match 16,241

Specialty_Cement/Gravel Plant no_match 2,126

Specialty_Cemetery/Mausoleum no_match 570

Specialty_Chemical/Oil Refinery no_match 1,107

Specialty_Contractor Storage Yard no_match 3,244

Specialty_Correctional Facility no_match 412

Specialty_Drive-in Movie no_match 46

Specialty_Landfill no_match 144

Specialty_Lodge/Meeting Hall no_match 12,922

Specialty_Lumberyard no_match 698

Specialty_Marina no_match 2,134

Specialty_Movie/Radio/TV Studio no_match 959

Specialty_Parking Garage no_match 3,753

Specialty_Parking Lot no_match 3,422

Specialty_Police/Fire Station no_match 2,626

Specialty_Public Library no_match 1,498

Specialty_Radio/TV Transmission Facilities no_match 611
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CoStar Combo Code DOE Prototype

Building

CONUS CoStar 

Building Count

Specialty_Railroad Yard no_match 250

Specialty_Recycling Center no_match 414

Specialty_Religious Facility no_match 37,475

Specialty_Shelter no_match 1,134

Specialty_Shipyard no_match 283

Specialty_Trailer/Camper Park no_match 2,830

Specialty_Water Retention Facility no_match 59

Specialty_Water Treatment Facility no_match 496

Specialty_Winery/Vineyard no_match 1,429

Sports & Entertainment_Amusement Park no_match 659

Sports & Entertainment_Baseball Field no_match 525

Sports & Entertainment_Casino no_match 280

Sports & Entertainment_Golf Course/Driving Range no_match 3,973

Sports & Entertainment_Horse Stables no_match 948

Sports & Entertainment_Race Track no_match 401

Sports & Entertainment_Skating Rink no_match 978

Sports & Entertainment_Swimming Pool no_match 569

Sports & Entertainment_Theater/Concert Hall no_match 2,083

No match count 440,708

Industrial_Food Processing not_commercial 3,870

Industrial_Manufacturing not_commercial 105,632

Industrial_Refrigeration/Cold Storage not_commercial 1,928

Industrial_Telecom Hotel/Data Hosting not_commercial 1,216

Land_Commercial not_commercial 379

Land_Industrial not_commercial 59

Land_Residential not_commercial 23

Specialty_Utility Sub-Station not_commercial 1,524

Not commercial count 114,631

Total building count 2,974,832

a CoStar Combo Codes are derived from all possible combinations of Primary and Secondary property 

types within the CoStar data set.
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Table B-2. Mapping of CBECS Detailed Principal Building Activity (PBAPLUS) and DOE Prototype 

Buildings, and Associated Building Counts 

CBECS 

PBAPLUS 

Code PBAPLUS Description

DOE Prototype 

Building

CBECS Building Count 

(final weight for U.S.)a

2 Admin Office medium_office 558,062

3 Bank small_office 91,071

4 Government Office medium_office 113,270

5 Medical Office small_office 50,438

6 Mixed-use Office small_office 125,082

7 Other Office small_office 74,451

9 Distribution warehouse 151,361

10 Warehouse warehouse 426,972

18 Diagnostic outpatient 60,300

19 Clinic outpatient 86,855

27 College secondary_school 27,215

28 Primary School primary_school 189,038

29 High School secondary_school 42,708

30 Preschool primary_school 67,726

31 Other School primary_school 61,973

32 Fast Food quick_service_restaurant 92,313

33 Restaurant full_service_restaurant 178,595

35 Hospital hospital 9,579

36 Nursing midrise_apartment 29,535

37 Dorm midrise_apartment 24,647

38 Hotel large_hotel 29,982

39 Motel small_hotel 60,913

40 Other Lodging small_hotel 12,848

42 Retail retail 336,273

43 Other Retail strip_mall 58,821

50 Strip Mall strip_mall 162,687

53 Bar/Lounge full_service_restaurant 71,364

Matched count 3,194,076

8 Laboratory no_match 15,505

11 Self-storage no_match 208,836

12 Convenience Store no_match 79,108

13 Convenience + Gas no_match 51,652

14 Grocery Store no_match 44,734

15 Other Food Sale no_match 1,246

16 Fire/Police no_match 68,908

17 Other Safety no_match 8,655

20 Cold Storage no_match 8,499
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CBECS 

PBAPLUS 

Code PBAPLUS Description

DOE Prototype 

Building

CBECS Building Count 

(final weight for U.S.)a

21 Religious Worship no_match 411,799

22 Entertainment no_match 51,180

23 Library no_match 23,778

24 Recreation no_match 100,363

25 Social no_match 135,435

26 Other Assembly no_match 41,258

34 Other Food Service no_match 37,439

41 Vehicle Dealer no_match 43,167

44 Postal no_match 30,343

45 Repairs no_match 84,492

46 Vehicle Service no_match 214,001

47 Vehicle Storage no_match 176,142

48 Other Service no_match 113,568

49 Other no_match 109,260

51 Enclosed Mall no_match 1,379

52 Courthouse no_match 6,278

No match count 2,067,022

1 Vacant not_commercial 296,041

Not commercial count 296,041

Total building count 5,557,138

a Building counts are the total of the final weights associated with each instance of a CBECS PBAPLUS 

code. These values represent the entire U.S. (as opposed to the CONUS), as disaggregation by state is 

not feasible with CBECS data (whose highest level of granularity is division).
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Table B-3. Mapping of CBECS Detailed Principal Building Activity (PBAPLUS) and CoStar Combo 

Codes for those PBAPLUS Categories without a DOE Prototype Match

CBECS 

PBAPLUS 

Code

PBAPLUS 

Description CoStar Combo Code

2012 CBECS Annual 

Electricity Use 

(TWh)

8 Laboratory Flex: R&D 19.0

12 Convenience Store Retail: Convenience Store 14.5

13 Convenience + Gas Retail: Service Station 12.1

Retail: Truck Stop

14 Grocery Store Retail: Supermarket 33.7

16 Fire/Police Specialty: Police/Fire Station 6.8

17 Other Safety Specialty: Correctional Facility 8.0

21 Religious Worship Specialty: Religious Facility 23.8

22 Entertainment Sports & Entertainment: 

Theater/Concert Hall

20.2

23 Library Specialty: Public Library 11.6

24 Recreation Retail: Bowling Alley 24.8

Retail: Health Club

Sports & Entertainment: 

Skating Rink

Sports & Entertainment: 

Swimming Pool

41 Vehicle Dealer Retail: Auto Dealership 7.9

46 Vehicle Service Retail: Auto Repair 14.4

47 Vehicle Storage Specialty: Auto Salvage 

Facility

8.6

Specialty: Parking Garage

Gap Subtotal 205.4

1 Vacant no_match 7.7

11 Self-storage no_match 5.7

15 Other Food Sale no_match 0.7

20 Cold Storage no_match 12.7

25 Social no_match 9.7

26 Other Assembly no_match 14.0

34 Other Food Service no_match 5.0

44 Postal no_match 4.1

45 Repairs no_match 3.7

48 Other Service no_match 6.6

49 Other no_match 36.7

51 Enclosed Mall no_match 14.0

52 Courthouse no_match 6.7

Unmodeled Subtotal 127.3

Total: Gap and Unmodeled 332.7
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As shown in Table B-1, eight of the 102 combo codes defined by CoStar were determined not to 

be commercial building types. Fifty-three of the building combo codes cannot be adequately 

modeled using DOE prototype buildings. These unmatched building types account for 15% 

(440,708 buildings) of the total reported commercial buildings in the CONUS. Of the 53 CBECS 

PBAPLUS categories, 25 cannot be adequately modeled using DOE prototype buildings and 

one is not considered commercial. The unmatched building types account for 30% (1,596,473 

buildings) of the statistically weighted CBECS commercial building estimate.

There is inherent uncertainty in the mappings defined in Table B-1 and Table B-2. For instance, 

one school building may be more closely aligned with a secondary school while another more 

closely aligns with a primary school. The implications of this mapping uncertainty on the 

ComStock modeling process differ for CoStar and CBECS data. Because CoStar data are used to 

define modeled building types, the unmatched CoStar building types are used to determine the 

unmodeled subsectors within ComStock. Therefore, the gap model heavily depends on this 

mapping, and uncertainty propagation is a significant concern. Potential unknown errors 

in CBECS building type mappings to prototype building types will lead to potential 

misrepresentation of building characteristic distributions dependent on prototype building type.

B.5 Modeling Details

The dependencies defined in Table 5 are mapped to discrete conditional probability distributions 

based on the sample-space segmentations shown in Figure B-2. The result is a flattened 

definition of a 12-dimensional probabilistic sample space maintained in a predefined spreadsheet 

interface. The calculation of the tabular values relies on several data sets stored and maintained 

by geospatial data science research staff at NREL. The calculations are performed using several 

Python libraries and PostGIS functionality contained in a PostgreSQL server also maintained by 

the NREL Geospatial Data Science team.

The discretized sample space so created is sampled based on a predetermined sample density. 

For the purpose of this analysis, a sample density of 350,000 simulations across the CONUS was 

used, based on previous analysis performed by the NREL Residential Buildings group (Wilson et 

al. 2016). The sample density will be examined in more detail in future work, particularly to 

determine key convergence criteria at different geo-temporal scales.
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Appendix C. Industrial Sector Model Details

The Industrial Geospatial Analysis Tool for Energy Evaluation (IGATE-E) is a model that 

utilizes multiple data sources and statistical approaches to estimate the energy consumption of 

manufacturing plants across the United States. Originally developed by Oak Ridge National 

Laboratory in 2012, this tool has been used to investigate the potential for demand response and 

CHP at the plant level (Alkadi et al. 2013; Bhandari et al. 2018). Because industrial processes 

vary greatly, IGATE-E does not attempt direct simulation of loads, but rather compiles data from 

multiple sources and applies statistical techniques to estimate energy consumption down to the 

end-use level. Currently, IGATE-E is built in MATLAB with plans to release a web-based 

version in the future. IGATE-E’s user interface is shown in Figure C-1.

Figure C-1. IGATE-E Version 2.0

C.1 IGATE-E: Energy Consumption

The core functionality of IGATE-E involves estimating electricity and natural gas consumption 

for each manufacturing plant in the United States. To accomplish this, IGATE-E performs 

regression analyses of energy consumption versus number of employees using data from the 

Industrial Assessment Centers (IAC) database. Regression results are developed for individual 

industries based on their North American Industry Classification System (NAICS) or Standard 

Industrial Classification (SIC) system code. These systems are used to classify businesses 

according to their primary economic activity, specifically the type of product being produced.

Working within this framework, the basic premise of IGATE-E is that manufacturing facilities 

producing similar products (as categorized by their NAICS/SIC code) will utilize similar 

processes which ultimately have similar energy intensities (i.e., energy usage per product 

produced). In this case, number of employees are used as a proxy for the product being 
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produced.58 Within the model, a linear regression analysis is conducted by industry for every 

three- and four-digit NAICS/SIC code.59 An example of these results is shown in Figure C-2 for 

the animal food manufacturing industry (NAICS 3111).

Figure C-2. NAICS: 3111 (Animal Food Manufacturing) regression results

Although this approach is comprehensive, some limitations exist. First, the IAC Database only 

contains data for small- and medium-sized plants,60 limiting its relevance in industries where large 

manufacturing plants play a major role (DOE 2017a). Another limitation of the IAC Database is 

the age of its results, with assessment available back to 1981. Improvements in energy efficiency 

and labor productivity over the last 35 years may limit the accuracy of IGATE-E in industries 

where fewer recent assessments have been completed. For use in dsgrid, IGATE-E ignores 

assessments which do not provide information on a plant’s NAICS code. As a result of this 

requirement, IGATE-E only uses assessment data from 2002 onward, limiting problems that could 

have arisen from using older data. Additional data sources have been explored to supplement this 

data; however, the proprietary/competitive nature of plant level information has made it difficult to 

find additional data beyond what is available from the IAC. 

Using these regression results, energy consumption is estimated for individual plants based on 

employment information from the Manufacturers' News, Inc. (MNI) EZ Select database of 

approximately 294,000 manufacturing plants. These initial estimates are compared to the EIA’s 

Manufacturing Energy Consumption Survey (MECS) at the three-digit NAICS code level (Table 

C-1).

 58 IGATE-E originally utilized annual sales data for this purpose, however, inconsistencies in quality and the 

sporadic availability of this data limited its value.

59 Within IGATE-E, a requirement of five IAC assessments is specified to perform an acceptable regression. While 

most 4-digit industries have enough data to meet this requirement, those that do not utilize three-digit regressions 

instead.

60 Eligibility requirements to qualify for an IAC Assessment include energy bills between $100,000-/yr and 

2,500,000/yr.
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Table C-1. Comparison of IGATE-E estimates to 2014 MECS (Initial)

NAICS Manufacturing Type IGATE-E

(GWh)

2014 

MECS

(GWh)

Difference

(GWh)

Difference 

Relative to 

2014 

MECS (%)

311 Food manufacturing 77,186 76,701 485 0.6

312 Beverage and tobacco products 14,413 10,014 4,399 43.9

313 Textile mills 10,841 12,687 -1,846 -14.6

314 Textile product mills 19,013 2,748 16,265 591.9

315 Apparel manufacturing 2,118 803 1,315 163.8

316 Leather and allied products 2,000 316 1,684 532.9

321 Wood products 30,291 22,353 7,938 35.5

322 Paper manufacturing 36,490 99,474 -62,984 -63.3

323 Printing and related support 33,796 14,232 19,564 137.5

324 Petroleum and coal products 10,009 67,662 -57,653 -85.2

325 Chemical manufacturing 73,449 183,096 -109,647 -59.9

326 Plastics and rubber products 68,452 55,967 12,485 22.3

327 Nonmetallic mineral products 39,799 37,841 1,958 5.2

331 Primary metal manufacturing 85,595 138,437 -52,842 -38.2

332 Fabricated metal products 47,244 43,683 3,561 8.2

333 Machinery manufacturing 44,637 23,758 20,879 87.9

334 Computer and electronic products 24,385 32,864 -8,479 -25.8

335 Electrical equipment and 

components 14,229 11,764 2,465 21.0

336 Transportation equipment 42,729 45,584 -2,855 -6.3

337 Furniture and related products 9,155 5,169 3,986 77.1

339 Miscellaneous 39,298 8,863 30,435 343.4

Total 725,129 894,016 -168,887 -18.9

By comparing IGATE-E’s estimates to the 2014 MECS, multiple differences emerge. Industries 

where consumption is significantly underestimated include:

• 322: Paper

• 324: Petroleum and Coal Products

• 325: Chemicals

• 331: Primary Metals.
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In reviewing the MECS, these industries were found to have the highest energy intensities, 

suggesting that the lack of regression data for large manufacturing plants may be limiting 

IGATE-E’s accuracy in these cases. For industries where consumption is significantly 

overestimated, discrepancies in the number of establishments considered by MECS compared to 

IGATE-E may be the primary issue.61

While additional research is being conducted to understand these differences, IGATE-E’s initial 

estimates are adjusted by industry and census region to match the MECS. This is accomplished 

by scaling individual plant estimates so that aggregate consumption estimates match those from 

the 2014 MECS (Figure C-3). The primary drawback to this is that errors in the MECS, are 

ultimately reproduced by IGATE-E. Future efforts will focus on the further development of 

IGATE-E’s optimization approach to avoid “over adjusting” the initial estimates. 

Figure C-3. Comparison of IGATE-E estimates to 2014 MECS (optimized)

C.2 IGATE-E: Load Shapes

To estimate hourly annual load shapes, a regression analysis of peak electricity versus electricity 

data from the IAC Database is conducted. Next, an average load factor is estimated for each 

individual industry based on the slope of these regressions.

LLLLLLLL FF FF FF = 1

SSSS SSSS ∗ 8760

These estimates are used along with energy consumption estimates to determine a plant’s peak 

electricity demand. The resulting load factor estimates are shown in Figure C-4.

 61 For example, while the MNI database and the U.S. Census 2015 Statistics of U.S. Businesses 

(https://www.census.gov/programs-surveys/susb.html) largely agree on the number of manufacturing 

establishments, reporting 294,427 and 292,825 respectively; the 2014 MECS estimates 175,107 establishments.
124

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Figure C-4. IGATE-E load factor estimates by industry

To construct hourly annual load shapes, data from EPRI’s Load Shape Library is utilized. This 

online repository provides daily load shapes by sector and end use for various scenarios (i.e.,

weekday versus weekend and peak season versus off-peak season). To estimate energy 

consumption at the end-use level, data from the 2014 MECS is applied. Next, categories 

included in the EPRI Load Shape Library are matched to the most appropriate MECS end-use 

category (Table C-2).

Table C-2. End-Use and Load Shape Category Mapping

2014 MECS End-Use Category EPRI Load Shape Library Category

Conventional Boiler Use Other

Process Heating Process Heating

Process Cooling and Refrigeration Other

Machine Drive Machine Drives

Electro-Chemical Processes Other

Other Process Use Other

Facility HVAC HVAC

Facility Lighting Lighting

Other Facility Support Other

Onsite Transportation Other

Other Nonprocess Use Other

End Use Not Reported Other
125

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Finally, disaggregated load shapes are constructed for each industry based on end-use 

consumption estimates from the 2014 MECS and load shapes from the EPRI Load Shape Library

(Figure C-5). The resulting load shapes are either “stretched” or “flattened” on an industry-byindustry basis to match the load factor estimates derived previously. This approach is ultimately 

limited by the granularity and accuracy of the IAC Database, the MECS and the EPRI Load 

Shape Library.

Figure C-5. NAICS: 3111 (Animal Food Manufacturing) normalized load shape

Within IGATE-E, load shapes are applied at the individual plant-level based on a plant’s peak 

electricity demand. For use in dsgrid, plant-level peak demand estimates are compiled by county 

and NAICS code, with the resulting output being matched to the corresponding normalized load 

shape (with adjustments made to account for time zones and daylight-saving time policies).

C.3 Industrial Gap Model

Within the industrial sector, IGATE-E’s methodology has specifically been developed for the 

manufacturing subsector. Subsectors not covered by IGATE-E include agriculture, forestry, 

fishing and hunting; mining, quarrying, and oil and gas extraction; and construction. In dsgrid, 

these non-manufacturing subsectors are represented in the industrial gap model using annual 

electricity estimates from the AEO, with generic load shapes taken from the EPRI Load Shape 

Library. To develop county level results for these subsectors, AEO national estimates are first 

disaggregated to the state level based on employment data from the U.S. Census Bureau’s 

Statistics of U.S. Businesses, and then to the county level based on number of establishments 

data from the U.S. Census Bureau’s County Business Patterns (CBP).
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Appendix D. Transportation Sector Model Details

While dsgrid’s general transportation focus is on electrification of on-road transportation, this 

report focuses on constructing a model of historical electricity demand for the year 2012. The 

transportation sector accounted for less than 0.2% of total electricity consumption in 2012 (7 

TWh out of the total approximately 3,800 TWh consumed (see Figure 14) and vehicle 

electrification was still minimal (there were only approximately 75,000 PEV on the road in the 

country in 2012 [Cazzola et al. 2017]). Of the 7 TWh of electricity consumed for transportation 

in 2012, 6.6 TWh were used by intercity rail, transit rail, and commuter rail (EIA 2015a). Rail 

transportation is thus covered in dsgrid as a “gap”; this appendix describes the simple method 

used to estimate hourly electricity consumption profiles for electric transit in each state in 2012.

Methodologically, this appendix describes a process to disaggregate annual propulsion electric 

energy consumption estimates, obtained from the National Transit Database (NTD) (FTA 2017), 

spatially and temporally to generate a 50 x 8,784 matrix containing hourly energy consumption 

estimates for electric rail transit in every state in 2012.

D.1 The National Transit Database (NTD)

Formed in 1974, the National Transit Database (NTD) is a federal reporting program for transit 

agencies receiving federal funding under Federal Transit Administration (FTA) programs 5307 

(Urbanized Area Formula) and 5311 (Rural Area Formula) (FTA 2017). NTD contains data 

related to the financial, operation, and condition of U.S. transit systems. It also requires monthly 

operating and safety statistics reports from agencies that file as a “Full Reporter” (those 

operating more than 30 vehicles). The FTA submits annual NTD reports summarizing all transit 

services and safety data to Congress for review and use.

NTD data products include transit provider profiles, national transit summaries and trends, transit 

data tables, monthly data tables, and historical timeseries. Transit data tables are the source for 

data from the Annual Module – a comprehensive report outlining the operations of all transit 

services in a given year. For 2012, the NTD contains an annual report from 67 electric transit 

services covering 45 U.S. urban areas that were leveraged to produce the dsgrid energy 

consumption estimates.

To generate hourly load profiles, three tables from the NTD 2012 Annual Module were used: 

• 2012 Annual Databases Energy Consumption: Reports annual energy consumption 

(in kWh propulsion) for transit services.

• 2012 Annual Database Service: Reports annual operating hours and capacities (number 

of rail cars) for different scenarios, namely “Weekday,” “Saturday,” and “Sunday”

• 2012 Annual Database Agency UZAs: Transit agency-to-urbanized area (UZA) 

crosswalk.

D.2 Methods

The total annual energy consumption (from the 2012 Annual Databases Energy Consumption) 

data by transit service reported by NTD was disaggregated temporally and spatially to produce 

hourly energy consumption estimates for electric transit in every state.
127

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First, annual operating hours and capacities (from 2012 Annual Database Service) are used to 

compute an hourly utilization factor table, which is then used as a proxy to temporally 

disaggregate the annual energy consumption. An hourly utilization factor UUiiii is defined for each 

transit service ii and hour jj based on the number of vehicles nniiii, operating for transit service ii in 

hour jj, and on the number of operating minutes ttiiii, within that hour (e.g., if service starts at 7:30 

a.m., the corresponding ttiiii for the hour going from 7 a.m. to 8 a.m. would be 30 minutes):

UUiiii = nniiii

max nnii, � =1

=8764 ∙

ttiiii

60

The utilization factor varies between 0 and 1 and it represents the relative transit service load in 

each hour relative to the maximum service load conditions for that transit service (i.e., operating 

at max capacity for the full hour). Under the maximum load condition, UU = 1. Under a half load 

condition (e.g., full capacity for half an hour and half capacity for full hour), UU = 0.5. For no load 

UU = 0. The resulting utilization matrix is a 67 x 8,784 matrix with each cell containing UUii, where 

ii is a unique transit service and jj is the hour of the year (Table D-1).

Table D-1. Sample Utilization Matrix

1 2 3 ⋯ 8,784

ss11 UU1 1 UU1 2 UU1 3 UU1 ⋯ UU1 8784

ss22 UU2 1 UU2 2 UU2 3 UU2 ⋯ UU2 8784

⋮ UU⋮ 1 UU⋮ 2 UU⋮ 3 UU⋮ ⋯ UU⋮ 8764

ss6666 UU67 1 UU67 2 UU67 3 UU67 ⋯ UU67 8784

Hourly energy consumption estimates ii for each transit service ii and hour jj are then derived 

from the annual energy consumption estimates EEii for each transit service ii reported in the 2012 

Annual Databases Energy Consumption as:

ii = UUii

∑ UUii =8,784

=1

∙ EEii

Second, hourly energy consumption estimates are attributed to the appropriate urbanized area 

using the NTD’s transit agency-to-UZA crosswalk (2012 Annual Database Agency UZAs) and 

then aggregated spatially by state for dsgrid reporting. The 67 transit services considered in the 

NTD data set are located in 45 urban areas that belong to 30 states. Some urban areas, however, 

span multiple states, as illustrated in Figure D-6 for the Portland urban area. For those urban 

areas that span multiple states, the energy consumption was allocated across states based on topdown annual electricity sales for the transportation sector (EIA 2013a).
128

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Figure D-6. The Portland, Oregon (red)-Washington (blue) urban area spans two states

Finally, all operating times were standardized to EST to sync electrical load profiles across all 

U.S. time zones.

D.3 Results

The methodology described above is used to generate a 50 x 8,784 matrix containing hourly 

timeseries of transit rail hourly electricity in kWh, one for each of the fifty states. Figure D-7

shows a map of the 2012 annual electricity demand for rail transport in each state. The 

magnitude of electric rail transit service varies a lot across the country: high energy demand is 

concentrated in large urban areas; many states have little to no electric rail transit.

To illustrate the hourly load profiles corresponding to rail transport, Figure D-8 shows the load 

profiles over one sample week for three states: California (CA), New York (NY), and Florida 

(FL). Results show the significant differences between week-end and weekdays as well as the 

impact of changes of service during each day (intra-hourly changes are not fully captured by the 

NTD data set used for this analysis). 
129

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Table D-1 shows the first 12 hours of the state-level energy estimates (in kWh) used in dsgrid.

Figure D-7. 2012 annual electricity demand for rail transport by state

Figure D-8. Sample electrical transit load profiles for California, New York, and Florida

Profiles are shown in local time to better compare the operational profiles of different states.
130

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Table D-1. Sample of Energy Estimation Matrix

State 1 2 3 4 5 6 7 8 9 10 11 12

AK 0 0 0 0 0 0 0 0 0 0 0 0

AL 0 0 0 0 0 0 0 0 0 0 0 0

AR 0 0 0 0 0 0 0 0 0 0 0 28

AZ 1,556 1,556 1,348 0 0 0 1,167 1,556 1,556 1,556 1,556 1,556

CA 43,027 42,460 41,480 35,874 19,017 0 3,569 14,936 25,193 25,744 25,744 43,027

CO 3,109 3,109 3,109 3,109 3,109 3,109 3,109 3,109 3,109 3,109 3,109 3,109

CT 26,709 26,709 26,709 19,142 17,806 26,709 26,709 26,709 26,709 26,709 26,709 26,709

DE 0 0 0 0 0 0 0 0 0 0 0 0

FL 0 0 0 0 0 5,431 5,431 5,431 5,431 5,431 5,431 5,431

GA 7,046 7,046 0 0 0 1,174 7,046 7,046 7,046 7,051 7,051 7,051

HI 0 0 0 0 0 0 0 0 0 0 0 0

IA 0 0 0 0 0 0 0 0 0 0 0 0

ID 0 0 0 0 0 0 0 0 0 0 0 0

IL 24,891 24,891 24,631 24,148 24,148 24,891 24,891 24,891 24,891 24,891 24,891 24,891

IN 1,389 1,389 555 0 0 926 1,389 1,389 1,389 1,389 1,389 1,389

KS 0 0 0 0 0 0 0 0 0 0 0 0

KY 0 0 0 0 0 0 0 0 0 0 0 0

LA 446 0 0 0 0 0 446 446 446 446 446 446

MA 19,594 4,089 0 0 0 3,607 19,594 19,594 19,594 19,594 19,594 19,594

MD 31,360 1,045 31,360 31,360 35,313 8,134 35,835 35,835 35,835 36,861 39,682 39,682

ME 0 0 0 0 0 0 0 0 0 0 0 0

MI 368 368 147 0 0 246 368 368 368 368 368 368

MN 1,697 1,697 1,697 1,697 1,697 1,697 1,697 1,697 1,697 1,697 1,697 1,697

MO 2,078 2,078 1,351 0 0 2,078 2,078 2,078 2,078 2,078 2,078 2,078

MS 0 0 0 0 0 0 0 0 0 0 0 39

MT 0 0 0 0 0 0 0 0 0 0 0 0
131

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State 1 2 3 4 5 6 7 8 9 10 11 12

NC 0 0 0 0 0 0 0 0 0 0 0 0

ND 0 0 0 0 0 0 0 0 0 0 0 0

NE 0 0 0 0 0 0 0 0 0 0 0 0

NH 0 0 0 0 0 0 0 0 0 0 0 0

NJ 29,158 29,081 27,605 3,892 797 16,630 29,158 29,158 29,158 29,158 29,158 29,158

NM 0 0 0 0 0 0 0 0 0 0 0 0

NV 1,112 1,112 1,112 1,112 1,112 1,112 0 0 0 0 1,112 1,112

NY 177,970 177,970 177,970 170,402 169,067 177,970 177,970 177,970 178,586 178,586 178,586 178,473

OH 3,157 1,692 0 761 3,157 3,157 3,157 3,157 3,157 3,157 3,157 3,157

OK 0 0 0 0 0 0 0 0 0 0 0 0

OR 3,834 3,834 3,730 3,715 2,105 0 1,795 3,715 3,715 3,715 3,804 3,834

PA 17,850 6,498 2,230 2,230 8,955 19,122 19,285 22,506 24,103 24,103 24,130 24,130

RI 0 0 0 0 0 0 0 0 0 0 0 0

SC 0 0 0 0 0 0 163 336 336 336 336 336

SD 0 0 0 0 0 0 0 0 0 0 0 0

TN 0 0 0 0 0 0 0 0 0 17 17 171

TX 8,522 8,287 6,050 0 5,647 8,067 8,317 8,522 8,522 8,522 8,522 8,522

UT 1,797 0 0 0 0 0 0 0 0 0 0 3,594

VA 0 0 0 0 0 0 0 0 0 0 0 407

VT 0 0 0 0 0 0 0 0 0 0 0 0

WA 2,632 2,632 2,488 2,259 647 0 552 1,142 1,835 2,483 2,510 2,576

WI 3,391 3,391 3,391 1,130 0 0 3,391 3,391 3,391 3,391 3,391 3,456

WV 0 0 0 0 0 0 0 0 0 0 0 0

WY 0 0 0 0 0 0 0 0 0 0 0 0
132

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Appendix E. Model Coverage Analysis Details

dsgrid requires the harmonization of multiple independent sectoral models and the representation 

of unmodeled sectors to completely depict present and potential future national electricity load. 

Accomplishing both requires both a calibration across sectoral models and a quantification of the 

contribution of unmodeled subsectors to total electricity and energy consumption.

To help meet these requirements, a top-down analysis of energy and electricity consumption 

was carried out across modeled and unmodeled subsectors. The results provide insights into the 

coverage of the dsgrid sectoral models, quantifying the relative contribution that each sectoral 

model can be expected to make to load totals while identifying gaps needing to be represented 

independently.

E.1 Method

Energy consumption estimates by subsector and end use were derived from the 2009 RECS (EIA 

2013d), the 2012 CBECS (EIA 2016a), and 2014 Manufacturing Energy Consumption Survey 

(EIA 2017a). These sector-specific survey data were used to determine relative contributions of 

individual subsectors and end uses to total sectoral energy use, in terms of both electricity and 

overall energy.

The absolute contribution of individual subsectors and end uses to national electricity and energy 

consumption was subsequently determined by applying a calibration factor to each sector 

survey’s energy use data, aligning each survey total with the AEO 2015 reported sectoral energy 

consumption for 2012. Because the ratio of electricity demand to total energy use differed 

between the sector surveys and their corresponding AEO sector values, a least-squares procedure

was applied to co-optimize the calibration factors for best-fit alignment with both sectoral 

electrical demand and sectoral total energy consumption.

As the industrial survey data (MECS) only covered manufacturing subsectors, the calibration 

factor to match to the AEO sector total was calculated with respect to only the manufacturing 

subcomponent of the AEO industrial sector data. The AEO non-manufacturing subcomponent—

which was further broken down into agriculture, construction, and mining subsectors—was 

added directly to the final data set (i.e., with a calibration factor of 1.0). A similar approach was 

applied to directly add AEO 2015’s 2012 subsectoral transport data to the final data set, given 

the absence of a transport sector survey.

Each subsector in the reference data set could then be tagged as either modeled (either in detail 

or with a coarse gap model) or unmodeled, yielding an estimate of the size and location of gaps 

between the bottom-up model results and national totals. The list of modeled and unmodeled 

subsectors is provided in Table E-1.
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Table E-1. Subsectors Categorized as Modeled in Detail, Sectoral Gap Model, or Unmodeled

Sector Modeled Subsectors 

(Detailed)

Modeled Subsectors 

(Gap)

Unmodeled 

Subsectors

Residential

Data source: RECS 

2009

Single Family Detached

Five+ Unit Apartment

Single Family Attached 

Two- to Four Unit 

Apartment

Mobile Home

—

Commercial

Data source: CBECS 

2012

Administrative/Professi

onal Office

Bank/Other Financial

Bar/Pub/Lounge

Clinic/Other Outpatient 

Health

College/University

Distribution/Shipping 

Center

Dormitory/Fraternity/So

rority

Elementary/Middle 

School

Fast Food

Government Office

Highschool

Hospital/Inpatient 

Health

Hotel

Medical Office—

Diagnostic

Medical Office—

NonDiagnostic

Mixed Use Office

Motel/Inn

Non-Refrigerated 

Warehouse

Nursing Home/Assisted 

Living

Other Classroom 

Education

Other Lodging

Other Office

Other Retail

Preschool/Daycare

Restaurant/Cafeteria

Retail Store

Strip Shopping Mall

Convenience Store

Convenience Store with 

Gas Station

Entertainment/Culture

Fire Station/Police 

Station

Grocery Store/Food 

Market

Laboratory

Library

Other Public Order and 

Safety

Recreation

Religious/Worship

Vehicle Dealership 

Showroom

Vehicle Service Repair 

Shop

Vehicle 

Storage/Maintenance

Courthouse/Probation 

Office

Enclosed Mall

Other

Other Food Sales

Other Food Service

Other Public Assembly

Other Service

Post Office/Postal 

Center

Refrigerated 

Warehouse

Repair Shop

Self-storage

Social/Meeting

Vacant
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Sector Modeled Subsectors 

(Detailed)

Modeled Subsectors 

(Gap)

Unmodeled 

Subsectors

Industrial

Data Sources:

MECS 2014 

(manufacturing) + AEO 

2015 (nonmanufacturing)

Food

Beverage and Tobacco 

Products

Textile Mills

Textile Product Mills

Apparel

Leather and Allied 

Products

Wood Products

Paper

Printing and Related 

Support

Petroleum and Coal 

Products

Chemicals

Plastics and Rubber 

Products

Nonmetallic Mineral 

Products

Primary Metals

Fabricated Metal 

Products

Machinery

Computer and 

Electronic Products

Electrical Equip., 

Appliances, and 

Components

Transportation 

Equipment

Furniture and Related 

Products

Miscellaneous

Agriculture

Construction

Mining

-

Transportation

Data source: AEO 2015

Bus Transportation

Commercial Light 

Trucks

Freight Trucks

Light-Duty Vehicles

Rail—Passenger Air

Lubricants

Military Use

Natural Gas Pipeline

Rail—Freight

Recreational Boats

Shipping—Domestic

Shipping—International
135

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E.2 Results

Electricity Gaps

Eighty percent of 2012 national electricity consumption is represented in one of dsgrid’s four 

core sectoral models. An additional 16% is represented via coarser gap models. Detailed model 

gaps are split primarily between the commercial, residential, and industrial sectors, with only a 

small share of unmodeled electrical load in the transportation sector (due to transportation’s 

small contribution to overall load in 2012). These breakdowns are shown in Figure E-1. 

Figure E-1. Proportion of annual electricity use in each sectoral modeling category

The two single-largest subsectors in terms of annual electrical load not captured by a detailed 

core model are mobile homes (residential sector) and mining (industrial sector). Apartment 

buildings with 2–4 units (residential), single-family attached dwellings (residential) and 

construction (industry) were other non-core model subsectors each contributing more than 1% to 

total national load. Though the commercial buildings sector accounts the largest amount of noncore-model load by sector, its gaps are spread out over a larger number of unmodeled or 

coarsely-modeled subsectors. The largest non-core model subsectors by electrical load are 

summarized in Table E-2 and Table E-3.
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Table E-2. Largest Subsectors Represented by Coarse Gap Models, by Total Electricity Use

Sector Gap-Modeled 

Subsector

Proportion of Sector 

Electricity Use (%)

Proportion of National 

Electricity Use (%)

Commercial Grocery Store /

Food Market

2.7 1.0

Recreation 2.0 0.7

Religious Worship 1.9 0.7

Residential Mobile Home 7.2 2.3

2–4 Unit Apartment 5.0 1.6

Single Family Attached 4.7 1.5

Industrial Mining 6.5 2.1

Construction 4.8 1.6

Agriculture 2.7 0.9

Transportation Rail-Passenger 94.6 0.2

Table E-3. Largest Unmodeled Subsectors, by Total Electricity Use

Sector Unmodeled 

Subsector

Proportion of Sector 

Electricity Use (%)

Proportion of National 

Electricity Use (%)

Commercial Other 3.0 1.0

Other Public Assembly 1.1 0.4

Enclosed Mall 1.1 0.4

Total Site Energy Gaps

Seventy-six percent of 2012 total national energy use is nominally captured by the four core 

sectoral models. Unlike with the electricity-only case, the largest modeling gaps are in transport 

and industry, which have more than twice as much unmodeled or coarsely-modeled energy as 

compared to the residential and commercial sectors. Figure E-2 provides a breakdown by sector 

and model type.
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Figure E-2. Annual total site energy use gaps in sectoral models

The largest gap-model subsectors once again include mining, construction and 2–4 unit 

apartments, with various transport subsectors (most notably air transport) becoming significant 

gaps when consumption is analyzed in terms of all site energy. The largest non-core model 

subsectors by total site energy are summarized in Table E-4 and Table E-5.
138

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Table E-4. Largest Subsectors Represented by Coarse Gap Models, by Total Site Energy Use

Sector Unmodeled Subsector Proportion of sector

site energy use (%)

Proportion of national 

site energy use (%)

Commercial Religious Worship 2.5 0.3

Grocery Store/Food Market 2.3 0.3

Recreation 2.1 0.3

Residential 2–4 Unit Apartment 6.7 1.2

Single Family Attached 5.4 0.9

Mobile Home 4.6 0.8

Industrial Mining 17.2 4.3

Construction 10.0 2.5

Agriculture 5.4 1.3

Transportation Passenger Rail 0.1 0.04

Table E-5. Largest Unmodeled Subsectors, by Total Site Energy Use

Sector Unmodeled Subsector Proportion of sector 

site energy use (%)

Proportion of national 

site energy use (%)

Commercial Other 2.4 0.3

Other Public Assembly 1.2 0.2

Transportation Air 8.9 3.9

NG Pipelines 2.9 1.3

Military Transport 2.7 1.2

International Shipping 2.5 1.1

Freight Rail 1.7 0.7

Electric or Electrifiable Site Energy Coverage Analysis

The coverage analysis also categorized sector end uses by their viability for electrification to 

estimate overall electrification potential relative to model coverage. Because our categorization 

(Table E-6) is aligned with Table 1.1 in Mai et al. (2018), “electrifiable” in this instance does not 

carry its vernacular meaning but is more a statement about whether electrification of the end use 

or subsector is being considered by the wider Electrification Futures Study (EFS). Subsectors 

without detailed survey data (transportation and non-manufacturing industrial) were treated as a 

single end use.
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Table E-6. Electrifiable and Non-Electrifiable End Uses by Sector

Sector Electrifiable End Uses End Uses Not Electrified 

in the EFS

Commercial Space Heating

Air Conditioning

Ventilation

Water Heating

Lighting

Cooking

Refrigeration

Office Equipment

Computing

Other

Residential Space Heating

Air Conditioning

Water Heating

Refrigeration

Other

Industrial Conventional Boiler Use

Process Heating

Process Cooling and 

Refrigeration

Machine Drive

Electro-Chemical Processes

Facility HVAC

Facility Lighting

End Use Not Reported

Mining

Agriculture 

Construction 

Other Facility Support

Onsite Transportation

Other Process Use

Other Nonprocess Use

Transportation Light-Duty Vehicles

Commercial Light Trucks

Freight Trucks

Bus Transportation

Rail – Freight

Rail – Passenger

Shipping – Domestic

Shipping – International

Air

Military Use

Recreational Boats

Lubricants

Natural Gas Pipelines
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We then summarize the proportion of site energy use that is either already electric, or is labeled 

as “electrifiable” above, and compare that to the remaining energy, that is, non-electric siteenergy use in categories labeled as “end uses not electrified in the EFS.” Based on these 

categorizations, 82% of national end-use energy use was considered electric or electrifiable, with 

91% of that energy represented by a detailed sector model, and 8% covered by a gap model. 

Table E-7 provides a cross-tabulation across model fidelity and electrifiability.

Table E-7. End-Use Energy Share by Model Detail and Electrifiability

Electric or Electrifiable Site 

Energy Use (%)

Non-Electric Energy whose 

Electrification is Not Considered 

in the EFS (%)

Detailed Model 74.2 2.1

Gap Model 6.1 7.3

Unmodeled 1.2 9.1
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Appendix F. Historical Year Model Results, CONUSLevel Description of Gap Models and Distributed 

Generation

The residential gap model is summarized by subsector and end use in the body of the report 

alongside the detailed residential modeling. The other gap models are characterized by reduced 

resolution along at least one dimension. We summarize them here in the form of summary tables 

and diurnal load shape plots.

F.1 Commercial Gaps

The commercial building gap model does not include end-use information. The load shape for 

each subsector in the gap model follows the overall commercial building load shape at whatever 

finest level resolution is available. As a result, at the CONUS level of aggregation it is 

approximately equal to the top line load shape seen in Figure 24. Analogously, to the extent that 

the commercial gap model shape varies by region, the regional shapes are visible in the 

commercial diurnal plots available by census division in Appendix G.

We summarize the commercial building gap model here in Table F-1 and Table F-2.

Table F-1. Commercial Gap Model Annual Summary for the CONUS in 2012,

by Subsector and Census Division

Census Division/

Subsector

South 

Atlantic Pacific

East 

North 

Central

West 

South 

Central

Mid 

Atlantic

New 

England

West 

North 

Central

East 

South 

Central Mountain Total

Grocery Store/Food 

Market (GWh) 2,474 9,889 2,810 3,789 8,356 2,634 876 1,417 1,209 33,452 

Recreation (GWh) 4,535 4,215 6,204 1,849 1,691 1,444 2,009 918 1,882 24,746 

Religious Worship 

(GWh) 5,970 2,423 2,791 3,036 3,379 651 1,377 3,181 935 23,743 

Entertainment Culture 

(GWh) 4,411 1,892 3,880 2,867 3,734 431 933 309 1,728 20,184 

Laboratory (GWh) 5,460 3,052 3,135 807 2,626 2,123 - 135 1,630 18,968 

Convenience Store 

(GWh) 3,695 804 2,111 2,089 1,503 1,370 1,162 661 1,074 14,468 

Vehicle Service 

Repair Shop (GWh) 3,485 1,182 2,452 2,077 1,005 877 2,249 761 266 14,353 

Convenience Store 

with Gas Station 

(GWh)

4,182 1,025 695 1,868 - 328 679 2,017 1,247 12,043 

Library (GWh) 1,841 501 1,231 5,432 680 1,130 129 552 122 11,619 

Vehicle Storage 

Maintenance (GWh) 1,399 2,486 1,736 318 492 526 688 598 263 8,507 

Other Public Order 

and Safety (GWh) 2,252 474 1,013 2,973 554 88 245 283 121 8,003 

Vehicle Dealership 

Showroom (GWh) 3,431 1,605 737 1,137 875 - 43 - - 7,828 

Fire Station/Police 

Station (GWh) 2,050 526 544 1,086 297 1,049 845 369 - 6,768 

Total (GWh) 45,186 30,074 29,340 29,328 25,191 12,650 11,235 11,202 10,476 204,682 
142

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Table F-2. Commercial Gap Model Annual Summary for the CONUS in 2012,

Proportions by Subsector and Census Division

Census 

Division/

Subsector

South 

Atlantic Pacific

East 

North 

Central

West 

South 

Central

Mid 

Atlantic

New 

England

West 

North 

Central

East 

South 

Central Mountain Total

Grocery 

Store/Food 

Market (%)

5.5 32.9 9.6 12.9 33.2 20.8 7.8 12.6 11.5 16.3 

Recreation (%) 10.0 14.0 21.1 6.3 6.7 11.4 17.9 8.2 18.0 12.1 

Religious 

Worship (%) 13.2 8.1 9.5 10.4 13.4 5.1 12.3 28.4 8.9 11.6 

Entertainment 

Culture (%) 9.8 6.3 13.2 9.8 14.8 3.4 8.3 2.8 16.5 9.9 

Laboratory (%) 12.1 10.1 10.7 2.8 10.4 16.8 - 1.2 15.6 9.3 

Convenience 

Store (%) 8.2 2.7 7.2 7.1 6.0 10.8 10.3 5.9 10.2 7.1 

Vehicle Service 

Repair Shop (%) 7.7 3.9 8.4 7.1 4.0 6.9 20.0 6.8 2.5 7.0 

Convenience 

Store with Gas 

Station (%)

9.3 3.4 2.4 6.4 - 2.6 6.0 18.0 11.9 5.9 

Library (%) 4.1 1.7 4.2 18.5 2.7 8.9 1.1 4.9 1.2 5.7 

Vehicle Storage 

Maintenance (%) 3.1 8.3 5.9 1.1 2.0 4.2 6.1 5.3 2.5 4.2 

Other Public 

Order and Safety 

(%)

5.0 1.6 3.5 10.1 2.2 0.7 2.2 2.5 1.2 3.9 

Vehicle 

Dealership 

Showroom (%)

7.6 5.3 2.5 3.9 3.5 - 0.4 - - 3.8 

Fire 

Station/Police 

Station (%)

4.5 1.8 1.9 3.7 1.2 8.3 7.5 3.3 - 3.3 

Total (%) 22.1 14.7 14.3 14.3 12.3 6.2 5.5 5.5 5.1 100.0 

The non-building commercial gap models for municipal water and outdoor lighting are 

summarized in Table F-3 and Table F-4 respectively. The corresponding load shapes for all of 

the CONUS are depicted in Figure F-1 and Figure F-2.
143

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Table F-3. Municipal Water Gap Model Annual Summary for the CONUS in 2012, Electricity Use 

and Proportions by Subsector and Census Division

Census Division

Public 

Water 

Supply 

(GWh)

Wastewater 

Treatment 

(GWh)

Total 

(GWh)

Public 

Water 

Supply (%)

Wastewater 

Treatment

(%) Total (%)

Pacific 15,827 4,637 20,465 77.3 22.7 26.2 

South Atlantic 7,534 5,802 13,335 56.5 43.5 17.1 

East North Central 5,836 4,495 10,331 56.5 43.5 13.2 

Mid Atlantic 5,140 3,959 9,099 56.5 43.5 11.6 

West South Central 4,576 3,524 8,100 56.5 43.5 10.4 

Mountain 2,778 2,140 4,918 56.5 43.5 6.3 

West North Central 2,579 1,986 4,565 56.5 43.5 5.8 

East South Central 2,318 1,785 4,103 56.5 43.5 5.3 

New England 1,817 1,400 3,217 56.5 43.5 4.1 

Total 48,405 29,726 78,131 62.0 38.0 100.0 

Figure F-1. Municipal water gap model diurnal load shapes
144

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Table F-4. Outdoor Lighting Gap Model Annual Summary for the CONUS in 2012, Electricity Use 

and Proportions by Subsector and Census Division

Census Division

Parking 

(GWh)

Roadway 

(GWh)

Total 

(GWh) Parking (%)

Roadway 

(%) Total (%)

South Atlantic 20,916 12,406 33,322 62.8 37.2 19.5 

Pacific 16,717 9,915 26,633 62.8 37.2 15.6 

East North Central 16,203 9,610 25,814 62.8 37.2 15.1 

Mid Atlantic 14,271 8,465 22,736 62.8 37.2 13.3 

West South Central 12,705 7,536 20,241 62.8 37.2 11.9 

Mountain 7,713 4,575 12,288 62.8 37.2 7.2 

West North Central 7,159 4,246 11,406 62.8 37.2 6.7 

East South Central 6,435 3,817 10,252 62.8 37.2 6.0 

New England 5,046 2,993 8,038 62.8 37.2 4.7 

Total 107,166 63,562 170,728 62.8 37.2 100.0 

Figure F-2. Outdoor lighting gap model diurnal load shapes for the CONUS in 2012
145

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F.2 Industrial Gaps

Similar to the commercial building gap model, the load shapes for the industrial gap model are 

essentially the same as the detailed modeling for the industrial sector taken as a whole, and no 

end-use specificity is provided. And, we summarize this model on an annual basis by census 

division in Table F-5.

Table F-5. Industrial Gap Model Annual Summary for the CONUS in 2012, Electricity Use and 

Proportions by Subsector and Census Division

Census Division

Mining, Quarrying, and Oil and Gas Extraction (GWh)Construction (GWh)Agriculture, Forestry, Fishing and Hunting (GWh)

Total 

(GWh)

Mining, Quarrying, and Oil and Gas Extraction (%)Construction (%)Agriculture, Forestry, Fishing and Hunting (%)

Total 

(%)

West South Central 40,738 9,497 3,644 53,879 75.6 17.6 6.8 29.3 

South Atlantic 7,229 12,226 7,833 27,288 26.5 44.8 28.7 14.8 

Pacific 3,336 9,128 11,720 24,183 13.8 37.7 48.5 13.1 

Mountain 14,883 5,335 1,985 22,203 67.0 24.0 8.9 12.1 

East North Central 3,941 7,975 2,565 14,481 27.2 55.1 17.7 7.9 

Mid Atlantic 4,835 7,886 1,338 14,060 34.4 56.1 9.5 7.6 

West North Central 5,220 4,730 2,193 12,144 43.0 39.0 18.1 6.6 

East South Central 4,798 3,305 3,194 11,297 42.5 29.3 28.3 6.1 

New England 354 2,728 1,356 4,439 8.0 61.5 30.6 2.4 

Total 85,335 62,811 35,830 183,975 46.4 34.1 19.5 100.0 

F.3 Transportation Gaps

The transportation gap model for electricity used for passenger rail is summarized in Table F-6. 

The CONUS load shape is plotted in Figure F-3.
146

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Table F-6. Transportation Gap Model for Passenger Rail Electricity, Annual Summary for the 

CONUS in 2012 by Census Division

Census Division Total GWh) Total (%)

Mid Atlantic 3,304 51.5 

South Atlantic 793 12.4 

New England 709 11.0 

Pacific 706 11.0 

East North Central 623 9.7 

Mountain 124 1.9 

West South Central 114 1.8 

West North Central 43 0.7 

East South Central 2 0.0 

Total 6,417 100.0 

Figure F-3. Transportation gap model diurnal load shapes for the CONUS in 2012
147

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F.4 Distributed Generation

The distributed generation model provides estimates of behind-the-meter generation from CHP

plants, other distributed thermal plants, and PV. This is done by sector and by state. The model is 

summarized for all of the CONUS in Table F-7. The diurnal generation patterns are shown in 

Figure F-4. CHP and distributed thermal plants are modeled mostly as baseload plants. The solar 

diurnal pattern is readily apparent in the DPV profiles.

Table F-7. Distributed Generation Model, Annual Summary for the CONUS in 2012

Sector/DG Type Industry Commercial Residential Total

CHP (GWh) 194,988 28,457 375 223,820 

Thermal DG (GWh) 6,782 561 — 7,343 

Distributed PV 

(GWh) 1,814 1,810 2,386 6,010 

Total (GWh) 203,584 30,828 2,761 237,173 

CHP (%) 95.8 92.3 13.6 94.4 

Thermal DG (%) 3.3 1.8 — 3.1 

Distributed PV (%) 0.9 5.9 86.4 2.5 

Total (%) 85.8 13.0 1.2 100.0 

Figure F-4. Distributed generation model diurnal generation patterns for CONUS in 2012
148

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Appendix G. Historical Year Model Results by 

Census Division

We present results analogous to those in Section 3, but at a finer level of geographic detail, 

namely at the census division level. The census divisions are defined by the U.S. Census Bureau. 

For the purposes of this report, we model only the contiguous United States. Those 48 states and 

the District of Columbia (Washington, D.C.) are listed in Table G-1 next to their corresponding

census division and region. The other two states, Alaska and Hawaii, are both in the Pacific 

census division.

Table G-1. States Listed by Census Region and Division

Census Region Census Division States

Midwest West North Central Iowa, Kansas, Minnesota, Missouri, Nebraska, North 

Dakota, South Dakota

East North Central Indiana, Illinois, Michigan, Ohio, Wisconsin

Northeast New England Connecticut, Maine, Massachusetts, New Hampshire, 

Rhode Island, Vermont

Mid Atlantic New Jersey, New York, Pennsylvania

South South Atlantic Delaware, District of Columbia, Florida, Georgia, 

Maryland, North Carolina, South Carolina, Virginia, 

West Virginia

East South Central Alabama, Kentucky, Mississippi, Tennessee

West South Central Arkansas, Louisiana, Oklahoma, Texas

West Mountain Arizona, Colorado, Idaho, New Mexico, Montana, 

Utah, Nevada, Wyoming

Pacific California, Oregon, Washington
149

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G.1 West North Central

Table G-2. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in 

dsgrid, West North Central Census Division

Component 

Type

Component

Name Residential Commercial Industrial Transport Total

Top-down Hourly load 360.6 

Derived T&D losses 21.3 

Top-down Annual energy 102.8 100.6 91.3 0.0 294.8 

dsgrid Distributed generation – 1.0 4.6 – 5.6 

dsgrid-core Gap models 11.8 27.2 12.1 0.0 51.2 

dsgrid-core Detailed sector models 83.7 65.2 69.3 – 218.2 

Derived Total site energy 102.8 101.6 95.9 0.0 300.4 

Derived Annual sector residuals 7.3 9.3 14.4 -0.0 31.0 

Derived Hourly residuals 75.5 

Figure G-1. Bottom-up detailed sectoral and gap model load compared to bulk-level historical 

hourly load plus DG estimates for the West North Central census division in 2012
150

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Figure G-2. dsgrid hourly residuals shown in context for the West North Central census division.

Figure G-3. Historical and dsgrid load duration curves for the West North Central census division 

in 2012

The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of the 

detailed sector and gap models.
151

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Table G-3. Residential Subsectors, Summary of Electricity by End Use for the West North Central 

Census Division in 2012

Subsector

Interior 

Equipment 

(GWh)

Space 

Cooling 

(GWh)

Interior 

Lights 

(GWh)

Space 

Heating 

(GWh)

Fans 

(GWh)

Water 

Systems 

(GWh)

Exterior 

Lights 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Single 

Family 

Detached

32,662 14,179 10,298 8,672 7,152 7,499 2,092 76 — 82,630 

Mobile 

Home 2,008 717 416 364 342 490 68 4 — 4,409 

Single 

Family 

Attached

1,730 618 359 313 295 422 58 4 — 3,798 

Apartment 

in Building 

2 to 4 Units

1,224 470 463 272 520 — 561 47 16 3,573 

Midrise 

Apartment 

Building

347 143 144 98 165 — 173 15 5 1,089 

Total 37,970 16,126 11,680 9,719 8,474 8,411 2,952 145 21 95,498 

Table G-4. Residential Electricity Proportions by Subsector and End Use for the West North 

Central Census Division in 2012

Subsector

Interior 

Equipment 

(%)

Space 

Cooling 

(%)

Interior 

Lights 

(%)

Space 

Heating 

(%)

Fans 

(%)

Water 

Systems 

(%)

Exterior 

Lights 

(%)

Pumps 

(%)

Heat 

Rejection 

(%)

Total 

(%)

Single Family 

Detached 39.5 17.2 12.5 10.5 8.7 9.1 2.5 0.1 — 86.5 

Mobile Home 45.5 16.3 9.4 8.2 7.8 11.1 1.5 0.1 — 4.6 

Single Family 

Attached 45.5 16.3 9.4 8.2 7.8 11.1 1.5 0.1 — 4.0 

Apartment in 

Building 2 to 

4 Units

34.3 13.2 13.0 7.6 14.6 — 15.7 1.3 0.5 3.7 

Midrise 

Apartment 

Building

31.9 13.1 13.2 9.0 15.1 — 15.8 1.3 0.4 1.1 

Total 39.8 16.9 12.2 10.2 8.9 8.8 3.1 0.2 0.0 100.0 
152

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Figure G-4. Residential electricity use diurnal patterns by season, modeled for the West North 

Central census division 2012
153

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Table G-5. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End

Use for the West North Central Census Division in 2012

Subsector

Interior 

Lights 

(GWh)

Interior 

Equipment 

(GWh)

Fans 

(GWh)

Space 

Cooling 

(GWh)

Exterior 

Lights 

(GWh)

Space 

Heating 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Strip Mall 11,541 1,690 3,224 1,578 2,365 701 9 3 21,111 

Large Office 4,116 4,746 3,346 2,436 826 114 393 134 16,111 

Standalone 

Retail Store 3,882 1,213 1,452 733 594 715 2 1 8,590 

Medium Office 1,139 1,301 941 690 404 67 59 18 4,618 

Warehouse 1,175 472 601 120 576 679 5 2 3,631 

Small Office 829 1,139 575 382 323 128 5 3 3,385 

Full Service 

Restaurant 597 1,332 331 250 167 93 4 1 2,776 

Large Hotel 368 613 405 286 99 71 10 3 1,855 

Hospital 391 432 215 182 28 — 35 13 1,296 

Primary School 319 252 152 102 40 72 7 3 947 

Outpatient 

Treatment 

Facility

150 221 89 66 54 — 12 5 596 

Small Hotel 52 75 9 6 36 8 1 0 187 

Quick Service 

Restaurant 6 35 12 7 3 1 0 0 64 

Total 24,565 13,522 11,352 6,838 5,516 2,650 541 186 65,168 
154

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Table G-6. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for 

the West North Central Census Division in 2012

Subsector

Interior 

Lights 

(%)

Interior 

Equipment 

(%)

Fans 

(%)

Space 

Cooling 

(%)

Exterior 

Lights 

(%)

Space 

Heating 

(%)

Pumps 

(%)

Heat 

Rejection 

(%)

Total 

(%)

Strip Mall 54.7 8.0 15.3 7.5 11.2 3.3 0.0 0.0 32.4 

Large Office 25.6 29.5 20.8 15.1 5.1 0.7 2.4 0.8 24.7 

Standalone 

Retail Store

45.2 14.1 16.9 8.5 6.9 8.3 0.0 0.0 13.2 

Medium Office 24.7 28.2 20.4 14.9 8.7 1.4 1.3 0.4 7.1 

Warehouse 32.4 13.0 16.5 3.3 15.9 18.7 0.1 0.0 5.6 

Small Office 24.5 33.7 17.0 11.3 9.6 3.8 0.2 0.1 5.2 

Full Service 

Restaurant

21.5 48.0 11.9 9.0 6.0 3.4 0.2 0.1 4.3 

Large Hotel 19.8 33.0 21.9 15.4 5.3 3.8 0.6 0.2 2.8 

Hospital 30.2 33.4 16.6 14.0 2.2 — 2.7 1.0 2.0 

Primary 

School

33.7 26.6 16.1 10.8 4.2 7.6 0.7 0.3 1.5 

Outpatient 

Treatment 

Facility

25.2 37.1 14.9 11.0 9.1 — 2.0 0.8 0.9 

Small Hotel 27.7 40.3 4.8 3.0 19.4 4.4 0.3 0.1 0.3 

Quick Service 

Restaurant

9.2 54.4 18.2 11.1 5.4 1.7 0.0 0.0 0.1 

Total 37.7 20.7 17.4 10.5 8.5 4.1 0.8 0.3 100.0 
155

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Figure G-5. Commercial electricity use diurnal patterns by season, modeled for the West North 

Central census division 2012
156

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Table G-7. Industrial Manufacturing Subsectors, Summary of Model Results for the West North Central Census Division in 2012

Subsector

Machine Drive (GWh)Process Heating (GWh)Process Cooling And Refrigeration (GWh)Facility HVAC (GWh)Facility Lighting (GWh)Electro Chemical Processes (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Animal Slaughtering and Processing 2,652 263 1,611 516 454 14 111 123 189 136 57 26 6,151 

Pesticide, Fertilizer, and Other Agricultural Chemical 

Manufacturing 2,790 181 486 319 205 857 78 61 107 69 13 13 5,178 

Plastics Product Manufacturing 1,957 586 292 370 346 — — 83 18 — 16 — 3,668 

Petroleum and Coal Products Manufacturing 2,688 113 140 126 84 48 24 33 25 37 0 3 3,321 

Iron and Steel Mills and Ferroalloy Manufacturing 859 1,103 47 200 140 734 50 38 15 63 5 9 3,263 

Basic Chemical Manufacturing 1,720 111 290 192 124 511 46 36 64 41 8 8 3,150 

Converted Paper Product Manufacturing 1,898 99 42 111 97 21 86 27 117 13 5 — 2,515 

Grain and Oilseed Milling 961 95 577 185 163 5 40 44 68 49 20 9 2,217 

Pulp, Paper, and Paperboard Mills 1,599 83 35 93 81 17 71 23 97 11 4 — 2,115 

Animal Food Manufacturing 892 89 548 175 154 5 38 42 64 46 19 9 2,081 

Pharmaceutical and Medicine Manufacturing 1,008 65 172 113 73 304 27 22 38 25 5 5 1,856 

Other Fabricated Metal Product Manufacturing 741 254 55 263 166 66 61 57 — — 12 3 1,677 

Aerospace Product and Parts Manufacturing 629 179 82 305 204 11 80 64 14 25 23 19 1,634 

Architectural and Structural Metals Manufacturing 688 236 51 247 155 62 57 53 — — 11 3 1,565 

Dairy Product Manufacturing 668 66 397 128 113 3 27 30 47 34 14 6 1,534 

Printing and Related Support Activities 837 64 116 263 131 18 22 44 13 — 17 — 1,526 

Other Nonmetallic Mineral Product Manufacturing 797 378 58 97 74 26 32 23 13 — 6 2 1,506 

Cement and Concrete Product Manufacturing 669 318 49 82 62 22 27 19 11 — 5 2 1,267 

Foundries 298 381 16 69 48 251 17 13 5 22 2 3 1,125 

Other Chemical Product and Preparation Manufacturing 561 36 95 63 40 167 15 12 21 14 3 2 1,029 

Soap, Cleaning Compound, and Toilet Preparation 

Manufacturing 521 34 90 59 38 159 14 11 20 13 2 2 965 

Fruit and Vegetable Preserving and Specialty Food 

Manufacturing 408 40 248 79 70 2 17 19 29 21 9 4 947 

Other Food Manufacturing 400 40 239 77 68 2 17 18 28 20 8 4 921 

Bakeries and Tortilla Manufacturing 369 37 222 71 63 2 15 17 26 19 8 4 853 

Coating, Engraving, Heat Treating, and Allied Activities 351 121 27 127 80 32 30 28 — — 6 2 803 
157

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Subsector

Machine Drive (GWh)Process Heating (GWh)Process Cooling And Refrigeration (GWh)Facility HVAC (GWh)Facility Lighting (GWh)Electro Chemical Processes (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Motor Vehicle Manufacturing 294 84 39 145 97 5 38 31 6 12 11 9 773 

Motor Vehicle Parts Manufacturing 287 82 37 140 93 5 37 29 6 12 10 9 747 

Nonferrous Metal (except Aluminum) Production and 

Processing 196 250 10 44 31 160 11 8 3 14 1 2 731 

Other General Purpose Machinery Manufacturing 308 71 30 151 103 7 17 27 7 — 7 — 729 

Glass and Glass Product Manufacturing 385 182 28 46 35 13 15 11 6 — 3 1 724 

Agriculture, Construction, and Mining Machinery 

Manufacturing 297 69 28 143 98 7 16 26 7 — 7 — 697 

Semiconductor and Other Electronic Component 

Manufacturing 148 77 74 155 59 15 47 31 6 14 2 12 639 

Other Wood Product Manufacturing 461 39 4 46 37 2 7 10 11 7 2 2 628 

Resin, Synthetic Rubber, and Artificial Synthetic Fibers and 

Filaments Manufacturing 320 21 54 36 23 95 9 7 12 8 1 1 586 

Paint, Coating, and Adhesive Manufacturing 308 20 53 35 23 94 9 7 12 8 1 1 570 

Other Miscellaneous Manufacturing 171 60 37 126 73 4 10 25 8 — 3 1 519 

Alumina and Aluminum Production and Processing 137 175 7 31 22 113 8 6 2 10 1 1 512 

Rubber Product Manufacturing 259 78 39 49 46 — — 11 2 — 2 — 486 

Motor Vehicle Body and Trailer Manufacturing 182 52 24 89 60 3 23 19 4 7 7 6 477 

Beverage Manufacturing 171 20 94 56 44 1 12 16 8 36 10 2 470 

Other Electrical Equipment and Component Manufacturing 128 105 29 76 45 36 17 10 2 — 3 — 451 

Navigational, Measuring, Electromedical, and Control 

Instruments Manufacturing 102 53 52 109 42 10 33 22 4 10 1 9 449 

Forging and Stamping 196 67 15 70 44 17 16 15 — — 3 1 445 

Household and Institutional Furniture and Kitchen Cabinet 

Manufacturing 199 15 7 82 68 1 — 14 5 — 4 2 397 

Other Transportation Equipment Manufacturing 136 39 18 67 45 2 17 14 3 6 5 4 356 

Sugar and Confectionery Product Manufacturing 151 15 92 29 26 1 6 7 11 8 3 1 351 

Industrial Machinery Manufacturing 147 34 14 71 48 3 8 13 3 — 3 — 345 

Medical Equipment and Supplies Manufacturing 103 36 22 75 43 2 6 15 5 — 2 1 309 

Metalworking Machinery Manufacturing 123 29 12 60 41 3 7 11 3 — 3 — 292 
158

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Subsector

Machine Drive (GWh)Process Heating (GWh)Process Cooling And Refrigeration (GWh)Facility HVAC (GWh)Facility Lighting (GWh)Electro Chemical Processes (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Machine Shops; Turned Product; and Screw, Nut, and Bolt 

Manufacturing 122 42 9 44 28 11 10 10 — — 2 1 279 

Commercial and Service Industry Machinery Manufacturing 117 27 11 57 39 3 6 10 3 — 3 — 276 

Steel Product Manufacturing from Purchased Steel 73 93 4 16 12 60 4 3 1 5 0 1 272 

Communications Equipment Manufacturing 61 32 31 65 25 6 20 13 2 6 1 5 268 

Engine, Turbine, and Power Transmission Equipment 

Manufacturing 112 26 11 54 37 3 6 10 3 — 3 — 263 

Lime and Gypsum Product Manufacturing 136 65 10 17 13 5 6 4 2 — 1 0 258 

Electrical Equipment Manufacturing 71 58 17 43 25 20 9 6 1 — 2 — 253 

Ventilation, Heating, Air-Conditioning, and Commercial 

Refrigeration Equipment Manufacturing 76 18 7 37 25 2 4 7 2 — 2 — 180 

Veneer, Plywood, and Engineered Wood Product 

Manufacturing 131 11 1 13 10 1 2 3 3 2 0 1 177 

Spring and Wire Product Manufacturing 77 26 6 28 17 7 6 6 — — 1 0 175 

Computer and Peripheral Equipment Manufacturing 29 15 15 32 12 3 10 7 1 3 0 3 130 

Railroad Rolling Stock Manufacturing 48 14 6 24 16 1 6 5 1 2 2 2 127 

Office Furniture (including Fixtures) Manufacturing 60 4 2 25 21 0 — 4 1 — 1 1 121 

Other Textile Product Mills 61 8 8 15 12 0 1 3 6 — 3 — 115 

Boiler, Tank, and Shipping Container Manufacturing 51 17 4 18 11 4 4 4 — — 1 0 114 

Household Appliance Manufacturing 30 25 7 18 10 8 4 2 0 — 1 — 105 

Ship and Boat Building 38 11 5 19 13 1 5 4 1 2 1 1 100 

Clay Product and Refractory Manufacturing 44 21 3 5 4 1 2 1 1 — 0 0 84 

Seafood Product Preparation and Packaging 27 3 17 5 5 0 1 1 2 1 1 0 63 

Cutlery and Handtool Manufacturing 27 9 2 10 6 2 2 2 — — 0 0 62 

Manufacturing and Reproducing Magnetic and Optical 

Media 14 7 7 15 6 1 4 3 1 1 0 1 60 

Fabric Mills 23 3 1 6 3 0 1 1 1 — 0 — 39 

Other Leather and Allied Product Manufacturing 24 3 1 4 4 0 0 1 0 — 0 — 38 

Audio and Video Equipment Manufacturing 8 4 4 8 3 1 2 2 0 1 0 1 33 

Electric Lighting Equipment Manufacturing 9 7 2 5 3 2 1 1 0 — 0 — 31 
159

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Subsector

Machine Drive (GWh)Process Heating (GWh)Process Cooling And Refrigeration (GWh)Facility HVAC (GWh)Facility Lighting (GWh)Electro Chemical Processes (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Fiber, Yarn, and Thread Mills 13 2 1 3 2 0 0 0 0 — 0 — 21 

Textile and Fabric Finishing and Fabric Coating Mills 11 2 1 3 1 0 0 0 0 — 0 — 18 

Footwear Manufacturing 11 1 1 2 2 0 0 0 0 — 0 — 18 

Cut and Sew Apparel Manufacturing 7 1 0 5 3 — — 1 0 — 0 — 16 

Leather and Hide Tanning and Finishing 9 1 1 1 2 0 0 0 0 — 0 — 15 

Tobacco Manufacturing 5 1 3 2 1 0 0 0 0 1 0 0 14 

Other Furniture Related Product Manufacturing 6 0 0 3 2 0 — 0 0 — 0 0 13 

Sawmills and Wood Preservation 5 0 0 1 0 0 0 0 0 0 0 0 7 

Textile Furnishings Mills 3 0 0 1 1 0 0 0 0 — 0 — 5 

Hardware Manufacturing 2 1 0 1 0 0 0 0 — — 0 0 4 

Apparel Accessories and Other Apparel Manufacturing 0 0 0 0 0 — — 0 0 — 0 — 0 

Total 33,716 7,201 7,007 6,863 4,852 4,080 1,492 1,440 1,201 831 400 222 69,305 
160

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Figure G-6. Industrial electricity use diurnal patterns by season, modeled for the West North 

Central census division 2012
161

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Table G-8. Distributed Generation Model, Annual Summary the West North Central Census 

Division in 2012 by Sector and DG Type

Sector/DG Type Industry Commercial Residential Total

CHP (GWh) 4,512 938 — 5,450 

Distributed PV (GWh) 47 46 29 121 

Thermal DG (GWh) 6 7 — 13 

Total (GWh) 4,565 991 29 5,585 

CHP (%) 98.8 94.7 — 97.6 

Distributed PV (%) 1.0 4.6 100.0 2.2 

Thermal DG (%) 0.1 0.7 — 0.2 

Total (%) 81.7 17.7 0.5 100.0 

Figure G-7. Distributed Generation Model Diurnal Generation Patterns, West North Central census 

division 2012
162

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G.2 East North Central

Table G-9. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in 

dsgrid, East North Central Census Division

Component 

Type

Component

Name Residential Commercial Industrial Transport Total

Top-down Hourly load 585.4 

Derived T&D losses 27.6 

Top-down Annual energy 188.6 184.9 202.2 0.6 576.4 

dsgrid Distributed generation 0.0 4.8 17.1 – 22.0 

dsgrid-core Gap models 24.6 65.5 14.5 0.6 105.2 

dsgrid-core Detailed sector models 174.5 164.8 190.3 – 529.7

Derived Total site energy 188.7 189.7 219.4 0.6 598.4 

Derived Annual sector residuals -10.4 -40.6 14.5 -0.0 -36.4

Derived Hourly residuals 59.4

Figure G-8. Bottom-up detailed sectoral and gap model load compared to bulk-level historical 

hourly load plus DG estimates for the East North Central census division in 2012
163

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Figure G-9. dsgrid hourly residuals shown in context for the East North Central census division.

Figure G-10. Historical and dsgrid load duration curves for the East North Central census division 

in 2012

The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of the 

detailed sector and gap models.
164

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Table G-10. Residential Subsectors, Summary of Electricity by End Use for the East North Central

Census Division in 2012

Subsector

Interior 

Equipment 

(GWh)

Space 

Cooling 

(GWh)

Interior 

Lights 

(GWh)

Space 

Heating 

(GWh)

Fans 

(GWh)

Water 

Systems 

(GWh)

Exterior 

Lights 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Single 

Family 

Detached

70,344 24,364 22,774 20,152 15,792 13,782 4,612 177 — 171,997 

Apartment 

in Building 

2 to 4 Units 

4,013 1,169 1,649 1,020 1,501 — 1,987 115 41 11,495 

Single 

Family 

Attached 

3,235 970 698 564 587 616 114 8 — 6,793 

Mobile 

Home 2,995 898 647 522 544 571 105 7 — 6,289 

Midrise 

Apartment 

Building

821 255 362 233 331 — 433 26 9 2,472 

Total 81,409 27,656 26,130 22,492 18,755 14,969 7,252 333 50 199,046 

Table G-11. Residential Electricity Proportions by Subsector and End Use for the East North 

Central Census Division in 2012

Subsector

Interior 

Equipment 

(%)

Space 

Cooling 

(%)

Interior 

Lights 

(%)

Space 

Heating 

(%)

Fans 

(%)

Water 

Systems 

(%)a

Exterior 

Lights 

(%)

Pumps 

(%)

Heat 

Rejection 

(%)

Total 

(%)

Single Family 

Detached 40.9 14.2 13.2 11.7 9.2 8.0 2.7 0.1 — 86.4 

Apartment in 

Building 2 to 

4 Units 

34.9 10.2 14.3 8.9 13.1 — 17.3 1.0 0.4 5.8 

Single Family 

Attached 47.6 14.3 10.3 8.3 8.6 9.1 1.7 0.1 — 3.4 

Mobile Home 47.6 14.3 10.3 8.3 8.6 9.1 1.7 0.1 — 3.2 

Midrise 

Apartment 

Building

33.2 10.3 14.7 9.4 13.4 — 17.5 1.0 0.4 1.2 

Total 40.9 13.9 13.1 11.3 9.4 7.5 3.6 0.2 0.0 100.0 
165

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Figure G-11. Residential electricity use diurnal patterns by season, modeled for the East North 

Central census division 2012
166

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Table G-12. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End

Use for the East North Central Census Division in 2012

Subsector

Interior 

Lights 

(GWh)

Interior 

Equipment 

(GWh)

Fans 

(GWh)

Space 

Cooling 

(GWh)

Exterior 

Lights 

(GWh)

Space 

Heating 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Strip Mall 27,091 3,940 7,049 2,755 5,541 1,636 21 8 48,041 

Large Office 12,175 13,949 9,235 6,076 2,466 408 916 350 45,574 

Standalone 

Retail Store 11,202 3,460 3,748 1,495 1,702 1,769 11 4 23,390 

Medium Office 2,540 2,890 1,975 1,086 903 192 80 29 9,696 

Warehouse 3,198 1,271 1,423 164 1,529 1,680 10 4 9,279 

Small Office 2,194 3,002 1,429 774 857 318 7 5 8,585 

Full Service 

Restaurant 1,653 3,730 886 556 463 229 8 3 7,527 

Large Hotel 952 1,530 862 607 233 105 44 18 4,350 

Hospital 1,048 1,475 581 493 81 — 82 32 3,793 

Primary School 717 572 309 171 92 111 6 3 1,980 

Outpatient 

Treatment 

Facility

554 818 218 109 198 — 21 9 1,927 

Small Hotel 166 221 20 11 102 14 1 0 537 

Quick Service 

Restaurant 16 96 30 17 9 1 0 0 170 

Total 63,506 36,955 27,766 14,315 14,176 6,463 1,206 463 164,850 
167

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Table G-13. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for 

the East North Central Census Division in 2012

Subsector

Interior 

Lights 

(%)

Interior 

Equipment 

(%)

Fans 

(%)

Space 

Cooling 

(%)

Exterior 

Lights 

(%)

Space 

Heating 

(%)

Pumps 

(%)

Heat 

Rejection 

(%)

Total 

(%)

Strip Mall 56.4 8.2 14.7 5.7 11.5 3.4 0.0 0.0 29.1 

Large Office 26.7 30.6 20.3 13.3 5.4 0.9 2.0 0.8 27.6 

Standalone 

Retail Store 47.9 14.8 16.0 6.4 7.3 7.6 0.0 0.0 14.2 

Medium Office 26.2 29.8 20.4 11.2 9.3 2.0 0.8 0.3 5.9 

Warehouse 34.5 13.7 15.3 1.8 16.5 18.1 0.1 0.0 5.6 

Small Office 25.6 35.0 16.6 9.0 10.0 3.7 0.1 0.1 5.2 

Full Service 

Restaurant 22.0 49.6 11.8 7.4 6.1 3.0 0.1 0.0 4.6 

Large Hotel 21.9 35.2 19.8 13.9 5.4 2.4 1.0 0.4 2.6 

Hospital 27.6 38.9 15.3 13.0 2.1 — 2.2 0.8 2.3 

Primary 

School 36.2 28.9 15.6 8.6 4.6 5.6 0.3 0.1 1.2 

Outpatient 

Treatment 

Facility

28.7 42.5 11.3 5.7 10.3 — 1.1 0.5 1.2 

Small Hotel 31.0 41.2 3.7 2.1 19.0 2.7 0.2 0.1 0.3 

Quick Service 

Restaurant 9.4 56.4 18.0 10.1 5.5 0.5 0.0 0.0 0.1 

Total 38.5 22.4 16.8 8.7 8.6 3.9 0.7 0.3 100.0 
168

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Figure G-12. Commercial electricity use diurnal patterns by season, modeled for the East North 

Central census division 2012
169

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Table G-14. Industrial Manufacturing Subsectors, Summary of Model Results for the East North Central Census Division in 2012

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Facility Lighting (GWh)Process Cooling And Refrigeration (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Iron and Steel Mills and Ferroalloy Manufacturing 9,491 12,185 2,205 8,105 1,548 524 549 423 166 694 58 97 36,047 

Plastics Product Manufacturing 6,129 1,834 1,157 — 1,082 916 — 261 57 — 49 — 11,484 

Motor Vehicle Parts Manufacturing 3,624 1,030 1,760 61 1,178 473 461 371 78 147 132 112 9,429 

Basic Chemical Manufacturing 4,544 293 507 1,349 326 765 122 96 169 109 20 20 8,320 

Petroleum and Coal Products Manufacturing 6,346 266 298 114 198 331 56 77 58 88 1 8 7,841 

Converted Paper Product Manufacturing 5,228 272 306 57 267 117 236 75 322 35 14 — 6,928 

Pulp, Paper, and Paperboard Mills 4,913 255 285 53 249 109 219 70 299 32 13 — 6,499 

Foundries 1,467 1,879 338 1,237 237 80 84 65 25 106 9 15 5,541 

Pharmaceutical and Medicine Manufacturing 2,848 184 321 858 206 486 78 61 107 69 13 13 5,244 

Other Fabricated Metal Product Manufacturing 2,081 713 740 184 466 154 172 159 — — 33 10 4,711 

Dairy Product Manufacturing 1,763 174 337 9 297 1,048 72 80 123 89 37 17 4,046 

Animal Slaughtering and Processing 1,657 164 322 9 283 1,006 70 77 118 85 36 16 3,843 

Resin, Synthetic Rubber, and Artificial Synthetic Fibers and 

Filaments Manufacturing 1,994 128 222 593 143 336 54 42 74 48 9 9 3,652 

Other Nonmetallic Mineral Product Manufacturing 1,787 848 217 59 166 130 72 51 30 — 13 5 3,377 

Motor Vehicle Manufacturing 1,281 366 632 22 422 171 167 134 28 53 48 41 3,364 

Pesticide, Fertilizer, and Other Agricultural Chemical 

Manufacturing 1,689 109 193 519 124 294 47 37 65 42 8 8 3,134 

Coating, Engraving, Heat Treating, and Allied Activities 1,363 469 494 124 311 104 115 107 — — 22 6 3,116 

Architectural and Structural Metals Manufacturing 1,359 466 488 122 307 102 113 105 — — 22 6 3,090 

Other Chemical Product and Preparation Manufacturing 1,628 105 182 486 117 276 44 34 61 39 7 7 2,987 

Nonferrous Metal (except Aluminum) Production and 

Processing 793 1,009 178 646 125 42 44 34 13 55 5 8 2,951 

Forging and Stamping 1,291 442 461 115 290 96 107 99 — — 20 6 2,928 

Printing and Related Support Activities 1,468 113 462 31 230 203 39 76 23 — 30 — 2,675 

Fruit and Vegetable Preserving and Specialty Food 

Manufacturing 1,063 105 207 5 182 646 45 49 76 55 23 10 2,465 

Bakeries and Tortilla Manufacturing 971 96 188 5 165 585 40 45 69 49 21 9 2,244 

Other General Purpose Machinery Manufacturing 880 204 431 21 294 85 49 78 20 — 21 — 2,084 
170

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Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Facility Lighting (GWh)Process Cooling And Refrigeration (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Soap, Cleaning Compound, and Toilet Preparation 

Manufacturing 1,111 72 126 340 81 193 31 24 43 27 5 5 2,058 

Steel Product Manufacturing from Purchased Steel 548 697 123 448 87 29 30 23 9 38 3 5 2,042 

Glass and Glass Product Manufacturing 1,060 502 127 35 97 76 42 30 17 — 7 3 1,997 

Paint, Coating, and Adhesive Manufacturing 1,004 65 114 306 73 174 28 22 38 25 5 5 1,858 

Other Food Manufacturing 767 76 148 4 130 459 32 35 54 39 16 7 1,766 

Alumina and Aluminum Production and Processing 458 584 103 376 73 24 25 20 8 32 3 5 1,711 

Engine, Turbine, and Power Transmission Equipment 

Manufacturing 707 163 342 17 234 67 39 61 16 — 17 — 1,663 

Metalworking Machinery Manufacturing 701 162 342 17 233 68 39 62 16 — 17 — 1,655 

Rubber Product Manufacturing 837 251 159 — 148 126 — 36 8 — 7 — 1,571 

Cement and Concrete Product Manufacturing 827 393 101 28 77 61 34 24 14 — 6 2 1,566 

Grain and Oilseed Milling 672 66 129 3 114 403 28 31 47 34 14 6 1,549 

Semiconductor and Other Electronic Component 

Manufacturing 344 179 361 34 138 172 110 73 13 32 4 29 1,487 

Other Wood Product Manufacturing 986 84 100 4 80 9 14 21 24 14 3 5 1,344 

Animal Food Manufacturing 513 51 101 3 88 315 22 24 37 27 11 5 1,197 

Motor Vehicle Body and Trailer Manufacturing 445 127 218 8 146 59 57 46 10 18 16 14 1,162 

Other Electrical Equipment and Component Manufacturing 325 266 192 90 114 74 42 26 5 — 8 — 1,143 

Machine Shops; Turned Product; and Screw, Nut, and Bolt 

Manufacturing 499 172 180 45 113 38 42 39 — — 8 2 1,138 

Other Miscellaneous Manufacturing 355 125 261 8 150 77 20 53 17 — 6 3 1,076 

Industrial Machinery Manufacturing 456 105 220 11 151 43 25 40 10 — 11 — 1,072 

Navigational, Measuring, Electromedical, and Control 

Instruments Manufacturing 220 115 235 22 89 112 72 48 9 21 3 19 965 

Beverage Manufacturing 342 41 112 2 89 187 24 31 15 73 19 4 938 

Other Transportation Equipment Manufacturing 352 100 172 6 115 46 45 36 8 14 13 11 919 

Spring and Wire Product Manufacturing 375 129 136 34 85 28 32 29 — — 6 2 856 

Sugar and Confectionery Product Manufacturing 366 36 71 2 63 223 15 17 26 19 8 4 851 

Aerospace Product and Parts Manufacturing 309 88 150 5 100 40 39 32 7 13 11 10 804 
171

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Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Facility Lighting (GWh)Process Cooling And Refrigeration (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Household and Institutional Furniture and Kitchen Cabinet 

Manufacturing 396 29 163 3 135 13 — 27 10 — 9 4 789 

Electrical Equipment Manufacturing 221 182 133 63 79 52 30 18 4 — 6 — 787 

Commercial and Service Industry Machinery Manufacturing 275 63 133 7 91 26 15 24 6 — 6 — 646 

Agriculture, Construction, and Mining Machinery 

Manufacturing 267 62 129 6 88 25 15 23 6 — 6 — 627 

Medical Equipment and Supplies Manufacturing 177 62 129 4 74 38 10 26 9 — 3 1 534 

Veneer, Plywood, and Engineered Wood Product 

Manufacturing 303 26 30 1 24 3 4 6 7 4 1 1 411 

Ventilation, Heating, Air-Conditioning, and Commercial 

Refrigeration Equipment Manufacturing 170 40 84 4 57 17 10 15 4 — 4 — 404 

Boiler, Tank, and Shipping Container Manufacturing 175 60 61 15 38 12 14 13 — — 3 1 392 

Clay Product and Refractory Manufacturing 200 95 25 7 19 15 8 6 3 — 1 1 379 

Office Furniture (including Fixtures) Manufacturing 185 14 77 1 64 6 — 13 5 — 4 2 371 

Household Appliance Manufacturing 100 82 59 27 35 23 13 8 2 — 2 — 350 

Lime and Gypsum Product Manufacturing 185 88 22 6 17 13 7 5 3 — 1 1 349 

Cutlery and Handtool Manufacturing 91 31 33 8 21 7 8 7 — — 1 0 208 

Communications Equipment Manufacturing 46 24 50 5 19 24 15 10 2 5 1 4 204 

Railroad Rolling Stock Manufacturing 77 22 38 1 26 10 10 8 2 3 3 2 203 

Ship and Boat Building 76 22 38 1 26 10 10 8 2 3 3 2 203 

Other Textile Product Mills 101 13 25 1 20 13 1 5 9 — 5 — 193 

Electric Lighting Equipment Manufacturing 49 40 29 14 17 11 6 4 1 — 1 — 173 

Fabric Mills 57 8 14 0 7 4 2 1 2 — 1 — 96 

Computer and Peripheral Equipment Manufacturing 21 11 23 2 9 11 7 5 1 2 0 2 92 

Audio and Video Equipment Manufacturing 18 9 19 2 7 9 6 4 1 2 0 1 77 

Seafood Product Preparation and Packaging 31 3 6 0 5 19 1 1 2 2 1 0 71 

Manufacturing and Reproducing Magnetic and Optical 

Media 16 9 17 2 7 8 5 3 1 2 0 1 71 

Textile and Fabric Finishing and Fabric Coating Mills 34 5 9 0 4 2 1 1 1 — 1 — 58 

Other Leather and Allied Product Manufacturing 36 5 6 0 6 2 0 1 0 — 0 — 57 
172

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Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Facility Lighting (GWh)Process Cooling And Refrigeration (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Other Furniture Related Product Manufacturing 25 2 10 0 8 1 — 2 1 — 1 0 49 

Fiber, Yarn, and Thread Mills 22 3 6 0 3 1 1 1 1 — 0 — 37 

Sawmills and Wood Preservation 21 2 2 0 2 0 0 0 0 0 0 0 28 

Cut and Sew Apparel Manufacturing 10 1 7 — 4 0 — 1 1 — 0 — 25 

Footwear Manufacturing 11 1 2 0 2 1 0 0 0 — 0 — 18 

Tobacco Manufacturing 6 1 2 0 2 3 0 1 0 1 0 0 16 

Leather and Hide Tanning and Finishing 8 1 1 0 1 0 0 0 0 — 0 — 12 

Textile Furnishings Mills 6 1 1 0 1 1 0 0 1 — 0 — 11 

Hardware Manufacturing 2 1 1 0 0 0 0 0 — — 0 0 5 

Apparel Accessories and Other Apparel Manufacturing 0 0 0 — 0 0 — 0 0 — 0 — 1 

Total 85,656 29,375 18,636 16,809 13,003 12,565 4,141 3,839 2,517 2,247 954 593 190,334 
173

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Figure G-13. Industrial electricity use diurnal patterns by season, modeled for the East North 

Central census division 2012
174

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Table G-15. Distributed Generation Model, Annual Summary the East North Central Census 

Division in 2012 by Sector and DG Type

Sector/DG Type Industry Commercial Residential Total

CHP (GWh) 16,658 4,757 0 21,416 

Thermal DG (GWh) 439 32 — 471 

Distributed PV (GWh) 39 38 21 97 

Total (GWh) 17,135 4,828 21 21,985 

CHP (%) 97.2 98.5 2.2 97.4 

Thermal DG (%) 2.6 0.7 — 2.1 

Distributed PV (%) 0.2 0.8 97.8 0.4 

Total (%) 77.9 22.0 0.1 100.0 

Figure G-14. Distributed Generation Model Diurnal Generation Patterns, East North Central

census division 2012
175

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G.3 New England

Table G-16. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in 

dsgrid, New England Census Division

Component 

Type

Component

Name Residential Commercial Industrial Transport Total

Top-down Hourly load 126.0 

Derived T&D losses 3.5 

Top-down Annual energy 47.2 45.4 27.8 0.6 121.0 

dsgrid Distributed generation 0.1 2.7 7.5 – 10.3 

dsgrid-core Gap models 8.0 23.9 4.4 0.7 37.1 

dsgrid-core Detailed sector models 42.7 58.7 30.4 – 131.8 

Derived Total site energy 47.3 48.1 35.4 0.6 131.4 

Derived Annual sector residuals -3.4 -34.5 0.6 -0.1 -37.5

Derived Hourly residuals -36.6

Figure G-15. Bottom-up detailed sectoral and gap model load compared to bulk-level historical 

hourly load plus DG estimates for the New England census division in 2012
176

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Figure G-16. dsgrid hourly residuals shown in context for the New England census division.

Figure G-17. Historical and dsgrid load duration curves for the New England census division in 

2012

The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of the 

detailed sector and gap models.
177

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Table G-17. Residential Subsectors, Summary of Electricity by End Use for the New England

Census Division in 2012

Subsector

Interior 

Equipment 

(GWh)

Interior 

Lights 

(GWh)

Space 

Cooling 

(GWh)

Water 

Systems 

(GWh)

Fans 

(GWh)

Space 

Heating 

(GWh)

Exterior 

Lights 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Single 

Family 

Detached

20,595 6,646 4,302 3,476 2,620 2,089 1,369 384 — 41,481 

Apartment 

in Building 

2 to 4 Units

1,812 780 417 — 672 624 936 54 17 5,312 

Single 

Family 

Attached 

1,115 232 200 194 106 85 38 14 — 1,984 

Midrise 

Apartment 

Building

403 182 92 — 147 130 216 12 4 1,186 

Mobile 

Home 421 88 76 73 40 32 14 5 — 748 

Total 24,347 7,927 5,086 3,743 3,584 2,959 2,574 470 21 50,712 

Table G-18. Residential Electricity Proportions by Subsector and End Use for the New England

Census Division in 2012

Subsector

Interior 

Equipment 

(%)

Interior 

Lights

(%)

Space 

Cooling

(%)

Water 

Systems 

(%)

Fans 

(%)

Space 

Heating 

(%)

Exterior 

Lights 

(%)

Pumps 

(%)

Heat 

Rejection 

(%)

Total 

(%)

Single Family 

Detached 49.6 16.0 10.4 8.4 6.3 5.0 3.3 0.9 — 81.8 

Apartment in 

Building 2 to 

4 Units

34.1 14.7 7.8 — 12.6 11.7 17.6 1.0 0.3 10.5 

Single Family 

Attached 56.2 11.7 10.1 9.8 5.3 4.3 1.9 0.7 — 3.9 

Midrise 

Apartment 

Building

34.0 15.3 7.8 — 12.4 10.9 18.2 1.0 0.3 2.3 

Mobile Home 56.2 11.7 10.1 9.8 5.3 4.3 1.9 0.7 — 1.5 

Total 48.0 15.6 10.0 7.4 7.1 5.8 5.1 0.9 0.0 100.0 
178

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Figure G-18. Residential electricity use diurnal patterns by season, modeled for the New England

census division 2012
179

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

Table G-19. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End

Use for the New England Census Division in 2012

Subsector

Interior 

Lights 

(GWh)

Interior 

Equipment 

(GWh)

Fans 

(GWh)

Exterior 

Lights 

(GWh)

Space 

Cooling 

(GWh)

Space 

Heating 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Large Office 5,538 6,426 4,104 1,108 2,274 134 362 126 20,070 

Strip Mall 8,646 1,265 2,163 1,771 603 546 5 2 15,000 

Standalone 

Retail Store 3,729 1,190 1,051 567 334 602 4 1 7,478 

Medium Office 1,401 1,598 1,026 499 497 87 43 14 5,164 

Small Office 798 1,100 492 312 228 112 3 2 3,047 

Warehouse 808 313 293 383 27 418 2 1 2,244 

Full Service 

Restaurant 474 1,063 252 132 113 68 2 1 2,105 

Large Hotel 312 502 287 77 175 35 15 5 1,407 

Hospital 261 372 191 20 116 — 23 9 992 

Outpatient 

Treatment 

Facility 

174 256 81 62 40 — 8 3 624 

Primary School 154 123 82 21 35 12 2 1 429 

Small Hotel 49 72 4 36 2 4 0 0 168 

Quick Service 

Restaurant 2 11 4 1 1 0 — — 19 

Total 22,347 14,290 10,029 4,989 4,445 2,017 468 163 58,748 
180

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Table G-20. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for 

the New England Census Division in 2012

Subsector

Interior 

Lights 

(%)

Interior 

Equipment 

(%)

Fans 

(%)

Exterior 

Lights 

(%)

Space 

Cooling 

(%)

Space 

Heating 

(%)

Pumps 

(%)

Heat 

Rejection 

(%)

Total 

(%)

Large Office 27.6 32.0 20.4 5.5 11.3 0.7 1.8 0.6 34.2 

Strip Mall 57.6 8.4 14.4 11.8 4.0 3.6 0.0 0.0 25.5 

Standalone 

Retail Store 49.9 15.9 14.1 7.6 4.5 8.1 0.0 0.0 12.7 

Medium Office 27.1 30.9 19.9 9.7 9.6 1.7 0.8 0.3 8.8 

Small Office 26.2 36.1 16.1 10.2 7.5 3.7 0.1 0.1 5.2 

Warehouse 36.0 14.0 13.0 17.1 1.2 18.6 0.1 0.0 3.8 

Full Service 

Restaurant 22.5 50.5 11.9 6.3 5.4 3.2 0.1 0.0 3.6 

Large Hotel 22.2 35.7 20.4 5.5 12.4 2.5 1.0 0.3 2.4 

Hospital 26.3 37.5 19.3 2.0 11.7 — 2.3 0.9 1.7 

Outpatient 

Treatment 

Facility 

28.0 41.1 12.9 9.9 6.4 — 1.3 0.5 1.1 

Primary School 35.9 28.6 19.1 4.9 8.1 2.7 0.5 0.2 0.7 

Small Hotel 28.9 43.0 2.5 21.6 1.3 2.4 0.2 0.1 0.3 

Quick Service 

Restaurant 9.4 55.5 19.4 5.6 7.6 2.4 — — 0.0 

Total 38.0 24.3 17.1 8.5 7.6 3.4 0.8 0.3 100.0 
181

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Figure G-19. Commercial electricity use diurnal patterns by season, modeled for the New England

census division 2012
182

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Table G-21. Industrial Manufacturing Subsectors, Summary of Model Results for the New England Census Division in 2012

Subsector

Machine Drive (GWh)Facility HVAC (GWh)Process Heating(GWh)Process Cooling And Refrigeration (GWh)Electro Chemical Processes (GWh)Facility Lighting (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Pharmaceutical and Medicine Manufacturing 2,044 230 132 349 615 148 56 44 77 50 9 9 3,763 

Pulp, Paper, and Paperboard Mills 1,458 85 76 32 16 74 65 21 89 10 4 — 1,929 

Basic Chemical Manufacturing 830 92 53 140 246 60 22 17 31 20 4 4 1,519 

Plastics Product Manufacturing 760 143 227 114 — 134 — 32 7 — 6 — 1,424 

Converted Paper Product Manufacturing 1,029 60 54 23 11 53 46 15 63 7 3 — 1,363 

Other Chemical Product and Preparation Manufacturing 626 70 40 106 187 45 17 13 23 15 3 3 1,149 

Semiconductor and Other Electronic Component 

Manufacturing 245 257 127 122 24 98 78 52 9 23 3 20 1,059 

Other Nonmetallic Mineral Product Manufacturing 543 66 258 39 18 50 22 16 9 — 4 2 1,026 

Resin, Synthetic Rubber, and Artificial Synthetic Fibers and 

Filaments Manufacturing 542 60 35 91 161 39 15 11 20 13 2 2 993 

Navigational, Measuring, Electromedical, and Control 

Instruments Manufacturing 192 205 100 98 19 78 63 42 7 19 2 16 843 

Aerospace Product and Parts Manufacturing 322 156 92 42 5 105 41 33 7 13 12 10 838 

Soap, Cleaning Compound, and Toilet Preparation 

Manufacturing 355 40 23 62 109 26 10 8 14 9 2 2 658 

Printing and Related Support Activities 351 110 27 48 7 55 9 18 5 — 7 — 639 

Nonferrous Metal (except Aluminum) Production and 

Processing 168 38 214 9 137 27 9 7 3 12 1 2 625 

Paint, Coating, and Adhesive Manufacturing 333 38 22 58 102 24 9 7 13 8 2 2 617 

Pesticide, Fertilizer, and Other Agricultural Chemical 

Manufacturing 330 38 21 58 101 24 9 7 13 8 2 2 613 

Petroleum and Coal Products Manufacturing 486 23 20 25 9 15 4 6 4 7 0 1 600 

Other Wood Product Manufacturing 437 44 37 4 2 35 6 9 10 6 1 2 596 

Other Electrical Equipment and Component Manufacturing 169 100 139 39 47 59 22 14 3 — 4 — 594 

Other Miscellaneous Manufacturing 158 116 56 34 3 67 9 23 8 — 3 1 478 

Beverage Manufacturing 173 57 21 95 1 45 12 16 8 37 10 2 476 

Iron and Steel Mills and Ferroalloy Manufacturing 121 28 155 7 103 20 7 5 2 9 1 1 459 

Glass and Glass Product Manufacturing 243 29 115 17 8 22 10 7 4 — 2 1 458 
183

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

Subsector

Machine Drive (GWh)Facility HVAC (GWh)Process Heating(GWh)Process Cooling And Refrigeration (GWh)Electro Chemical Processes (GWh)Facility Lighting (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Dairy Product Manufacturing 191 37 19 113 1 32 8 9 13 10 4 2 438 

Fabric Mills 236 60 34 15 0 29 7 6 8 — 3 — 399 

Other Fabricated Metal Product Manufacturing 176 63 60 13 16 39 15 13 — — 3 1 399 

Ship and Boat Building 133 67 38 18 2 45 18 14 3 6 5 4 353 

Architectural and Structural Metals Manufacturing 142 51 49 11 13 32 12 11 — — 2 1 322 

Cement and Concrete Product Manufacturing 168 20 80 12 6 16 7 5 3 — 1 1 318 

Coating, Engraving, Heat Treating, and Allied Activities 137 50 47 10 12 31 12 11 — — 2 1 313 

Bakeries and Tortilla Manufacturing 119 23 12 72 1 20 5 5 8 6 3 1 275 

Medical Equipment and Supplies Manufacturing 89 65 31 19 2 37 5 13 4 — 2 1 268 

Other General Purpose Machinery Manufacturing 112 55 26 11 3 37 6 10 3 — 3 — 265 

Foundries 67 15 86 4 57 11 4 3 1 5 0 1 253 

Fruit and Vegetable Preserving and Specialty Food 

Manufacturing 102 20 10 62 1 17 4 5 7 5 2 1 237 

Communications Equipment Manufacturing 50 53 26 26 5 20 16 11 2 5 1 4 219 

Industrial Machinery Manufacturing 90 44 21 9 2 30 5 8 2 — 2 — 212 

Fiber, Yarn, and Thread Mills 123 31 18 8 0 15 4 3 4 — 2 — 208 

Textile and Fabric Finishing and Fabric Coating Mills 123 31 18 8 0 15 4 3 4 — 2 — 208 

Commercial and Service Industry Machinery Manufacturing 87 42 20 8 2 29 5 8 2 — 2 — 205 

Forging and Stamping 86 31 29 6 8 19 7 7 — — 1 0 194 

Computer and Peripheral Equipment Manufacturing 42 46 22 22 4 18 14 10 2 4 1 4 189 

Steel Product Manufacturing from Purchased Steel 50 11 64 3 41 8 3 2 1 4 0 0 186 

Metalworking Machinery Manufacturing 68 33 16 7 2 23 4 6 2 — 2 — 162 

Electrical Equipment Manufacturing 40 24 33 9 11 14 5 3 1 — 1 — 143 

Animal Slaughtering and Processing 61 12 6 37 0 10 3 3 4 3 1 1 140 

Other Food Manufacturing 58 11 6 34 0 10 2 3 4 3 1 1 132 

Motor Vehicle Parts Manufacturing 49 24 14 6 1 16 6 5 1 2 2 2 129 

Household and Institutional Furniture and Kitchen Cabinet 

Manufacturing 53 22 4 2 0 18 — 4 1 — 1 1 105 

Alumina and Aluminum Production and Processing 28 6 35 1 23 4 2 1 0 2 0 0 103 
184

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

Subsector

Machine Drive (GWh)Facility HVAC (GWh)Process Heating(GWh)Process Cooling And Refrigeration (GWh)Electro Chemical Processes (GWh)Facility Lighting (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Seafood Product Preparation and Packaging 41 8 4 25 0 7 2 2 3 2 1 0 97 

Rubber Product Manufacturing 48 9 14 7 — 9 — 2 0 — 0 — 91 

Sugar and Confectionery Product Manufacturing 37 7 4 23 0 6 2 2 3 2 1 0 86 

Machine Shops; Turned Product; and Screw, Nut, and Bolt 

Manufacturing 37 14 13 3 3 8 3 3 — — 1 0 85 

Engine, Turbine, and Power Transmission Equipment 

Manufacturing 35 17 8 3 1 11 2 3 1 — 1 — 81 

Electric Lighting Equipment Manufacturing 23 13 19 5 6 8 3 2 0 — 1 — 81 

Audio and Video Equipment Manufacturing 18 19 9 9 2 7 6 4 1 2 0 2 78 

Spring and Wire Product Manufacturing 32 12 11 2 3 7 3 3 — — 1 0 73 

Lime and Gypsum Product Manufacturing 32 4 15 2 1 3 1 1 1 — 0 0 60 

Veneer, Plywood, and Engineered Wood Product 

Manufacturing 38 4 3 0 0 3 1 1 1 1 0 0 51 

Cutlery and Handtool Manufacturing 18 6 6 1 2 4 2 1 — — 0 0 41 

Grain and Oilseed Milling 16 3 2 10 0 3 1 1 1 1 0 0 38 

Ventilation, Heating, Air-Conditioning, and Commercial 

Refrigeration Equipment Manufacturing 16 8 4 2 0 5 1 1 0 — 0 — 38 

Other Textile Product Mills 19 5 3 2 0 4 0 1 2 — 1 — 37 

Motor Vehicle Body and Trailer Manufacturing 14 7 4 2 0 5 2 1 0 1 1 0 37 

Animal Food Manufacturing 15 3 2 9 0 3 1 1 1 1 0 0 35 

Household Appliance Manufacturing 10 6 8 2 3 3 1 1 0 — 0 — 34 

Clay Product and Refractory Manufacturing 18 2 8 1 1 2 1 1 0 — 0 0 34 

Manufacturing and Reproducing Magnetic and Optical 

Media 6 6 3 3 1 2 2 1 0 1 0 0 26 

Agriculture, Construction, and Mining Machinery 

Manufacturing 10 5 2 1 0 3 1 1 0 — 0 — 24 

Other Transportation Equipment Manufacturing 9 4 2 1 0 3 1 1 0 0 0 0 23 

Motor Vehicle Manufacturing 9 4 2 1 0 3 1 1 0 0 0 0 22 

Office Furniture (including Fixtures) Manufacturing 8 3 1 0 0 3 — 1 0 — 0 0 16 

Cut and Sew Apparel Manufacturing 5 3 1 0 — 2 — 0 0 — 0 — 12 
185

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

Subsector

Machine Drive (GWh)Facility HVAC (GWh)Process Heating(GWh)Process Cooling And Refrigeration (GWh)Electro Chemical Processes (GWh)Facility Lighting (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Sawmills and Wood Preservation 8 1 1 0 0 1 0 0 0 0 0 0 11 

Other Leather and Allied Product Manufacturing 7 1 1 0 0 1 0 0 0 — 0 — 10 

Tobacco Manufacturing 4 1 0 2 0 1 0 0 0 1 0 0 10 

Boiler, Tank, and Shipping Container Manufacturing 4 1 1 0 0 1 0 0 — — 0 0 9 

Other Furniture Related Product Manufacturing 3 1 0 0 0 1 — 0 0 — 0 0 7 

Footwear Manufacturing 4 1 0 0 0 1 0 0 0 — 0 — 6 

Railroad Rolling Stock Manufacturing 2 1 1 0 0 1 0 0 0 0 0 0 5 

Textile Furnishings Mills 1 0 0 0 0 0 0 0 0 — 0 — 2 

Hardware Manufacturing 1 0 0 0 0 0 0 0 — — 0 0 1 

Leather and Hide Tanning and Finishing 1 0 0 0 0 0 0 0 0 — 0 — 1 

Total 15,143 3,214 3,020 2,280 2,168 2,022 758 641 528 329 144 110 30,356 
186

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Figure G-20. Industrial electricity use diurnal patterns by season, modeled for the New England

census division 2012
187

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Table G-22. Distributed Generation Model, Annual Summary the New England Census Division in 

2012 by Sector and DG Type

Sector/DG Type Industry Commercial Residential Total

CHP (GWh) 7,412 2,581 20 10,013 

Distributed PV (GWh) 110 109 66 284 

Thermal DG (GWh) 18 9 — 28 

Total (GWh) 7,540 2,699 85 10,325 

CHP (%) 98.3 95.6 23.1 97.0 

Distributed PV (%) 1.5 4.0 76.9 2.8 

Thermal DG (%) 0.2 0.4 — 0.3 

Total (%) 73.0 26.1 0.8 100.0 

Figure G-21. Distributed Generation Model Diurnal Generation Patterns, New England

census division 2012
188

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G.4 Mid Atlantic

Table G-23. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in 

dsgrid, Mid Atlantic Census Division

Component 

Type

Component

Name Residential Commercial Industrial Transport Total

Top-down Hourly load 395.0 

Derived T&D losses 17.3 

Top-down Annual energy 132.2 161.5 69.5 3.9 367.2 

dsgrid Distributed generation 0.7 9.6 33.7 – 43.9 

dsgrid-core Gap models 30.6 57.0 14.1 3.3 105.0 

dsgrid-core Detailed sector models 110.2 145.2 89.7 – 345.0 

Derived Total site energy 132.9 171.1 103.2 3.9 411.1 

Derived Annual sector residuals -7.9 -31.1 -0.5 0.6 -38.9

Derived Hourly residuals -28.4

Figure G-22. Bottom-up detailed sectoral and gap model load compared to bulk-level historical 

hourly load plus DG estimates for the Mid Atlantic census division in 2012
189

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Figure G-23. dsgrid hourly residuals shown in context for the Mid Atlantic census division.

Figure G-24. Historical and dsgrid load duration curves for the Mid Atlantic census division in 

2012

The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of the 

detailed sector and gap models.
190

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Table G-24. Residential Subsectors, Summary of Electricity by End Use for the Mid Atlantic

Census Division in 2012

Subsector

Interior 

Equipment 

(GWh)

Interior 

Lights 

(GWh)

Space 

Cooling 

(GWh)

Space 

Heating 

(GWh)

Fans 

(GWh)

Water 

Systems 

(GWh)

Exterior 

Lights 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Single 

Family 

Detached

44,354 15,035 14,443 10,818 8,646 7,692 3,095 586 — 104,668 

Single 

Family 

Attached 

6,916 1,514 2,042 899 1,024 1,082 247 64 — 13,788 

Apartment 

in Building 

2 to 4 Units

4,415 1,797 1,542 1,120 1,829 — 2,161 159 54 13,076 

Midrise 

Apartment 

Building 

1,775 785 688 435 791 — 935 80 25 5,514 

Mobile 

Home 1,866 409 551 242 276 292 67 17 — 3,719 

Total 59,325 19,540 19,266 13,515 12,566 9,066 6,505 906 79 140,766 

Table G-25. Residential Electricity Proportions by Subsector and End Use for the Mid Atlantic

Census Division in 2012

Subsector

Interior 

Equipment 

(%)

Interior 

Lights 

(%)

Space 

Cooling 

(%)

Space 

Heating 

(%)

Fans 

(%)

Water 

Systems 

(%)a

Exterior 

Lights 

(%)

Pumps 

(%)

Heat 

Rejection 

(%)

Total 

(%)

Single Family 

Detached 42.4 14.4 13.8 10.3 8.3 7.3 3.0 0.6 — 74.4 

Single Family 

Attached 50.2 11.0 14.8 6.5 7.4 7.8 1.8 0.5 — 9.8 

Apartment in 

Building 2 to 

4 Units

33.8 13.7 11.8 8.6 14.0 — 16.5 1.2 0.4 9.3 

Midrise 

Apartment 

Building 

32.2 14.2 12.5 7.9 14.3 — 17.0 1.4 0.5 3.9 

Mobile Home 50.2 11.0 14.8 6.5 7.4 7.8 1.8 0.5 — 2.6 

Total 42.1 13.9 13.7 9.6 8.9 6.4 4.6 0.6 0.1 100.0 
191

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

Figure G-25. Residential electricity use diurnal patterns by season, modeled for the Mid Atlantic

census division 2012
192

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

Table G-26. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End

Use for the Mid Atlantic Census Division in 2012

Subsector

Interior 

Lights 

(GWh)

Interior 

Equipment 

(GWh)

Fans 

(GWh)

Exterior 

Lights 

(GWh)

Space 

Cooling 

(GWh)

Space 

Heating 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Strip Mall 33,372 4,833 7,535 6,797 2,980 1,995 25 10 57,547 

Large Office 9,146 10,950 6,715 1,859 5,378 187 819 267 35,320 

Standalone 

Retail Store 7,303 2,311 2,240 1,102 906 1,012 5 2 14,882 

Medium Office 2,735 3,117 2,044 974 1,288 159 109 34 10,458 

Small Office 1,918 2,621 1,237 750 702 258 8 5 7,500 

Warehouse 1,954 785 758 957 103 927 6 2 5,491 

Full Service 

Restaurant 1,208 2,720 669 338 371 174 6 2 5,489 

Large Hotel 780 1,237 567 196 469 62 36 13 3,360 

Hospital 494 686 315 39 246 8 45 17 1,850 

Primary School 531 427 236 70 137 72 6 3 1,482 

Outpatient 

Treatment 

Facility

393 592 173 141 101 — 19 8 1,426 

Small Hotel 87 133 22 60 12 12 1 0 326 

Quick Service 

Restaurant 7 39 12 4 6 0 0 0 69 

Total 59,927 30,452 22,523 13,287 12,699 4,865 1,085 362 145,200 
193

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Table G-27. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for 

the Mid Atlantic Census Division in 2012

Subsector

Interior 

Lights 

(%)

Interior 

Equipment 

(%)

Fans 

(%)

Exterior 

Lights 

(%)

Space 

Cooling 

(%)

Space 

Heating 

(%)

Pumps 

(%)

Heat 

Rejection 

(%)

Total 

(%)

Strip Mall 58.0 8.4 13.1 11.8 5.2 3.5 0.0 0.0 39.6 

Large Office 25.9 31.0 19.0 5.3 15.2 0.5 2.3 0.8 24.3 

Standalone 

Retail Store 49.1 15.5 15.1 7.4 6.1 6.8 0.0 0.0 10.2 

Medium Office 26.2 29.8 19.5 9.3 12.3 1.5 1.0 0.3 7.2 

Small Office 25.6 35.0 16.5 10.0 9.4 3.4 0.1 0.1 5.2 

Warehouse 35.6 14.3 13.8 17.4 1.9 16.9 0.1 0.0 3.8 

Full Service 

Restaurant 22.0 49.6 12.2 6.2 6.8 3.2 0.1 0.0 3.8 

Large Hotel 23.2 36.8 16.9 5.8 14.0 1.8 1.1 0.4 2.3 

Hospital 26.7 37.1 17.0 2.1 13.3 0.4 2.4 0.9 1.3 

Primary School 35.9 28.8 15.9 4.7 9.2 4.9 0.4 0.2 1.0 

Outpatient 

Treatment 

Facility

27.5 41.5 12.1 9.9 7.1 — 1.3 0.5 1.0 

Small Hotel 26.5 40.8 6.7 18.4 3.6 3.5 0.4 0.1 0.2 

Quick Service 

Restaurant 9.7 57.5 18.1 5.7 8.3 0.5 0.0 0.1 0.0 

Total 41.3 21.0 15.5 9.2 8.7 3.4 0.7 0.2 100.0 
194

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Figure G-26. Commercial electricity use diurnal patterns by season, modeled for the Mid Atlantic

census division 2012
195

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Table G-28. Industrial Manufacturing Subsectors, Summary of Model Results for the Mid Atlantic Census Division in 2012

Subsector

Machine Drive (GWh)Process Heating (GWh)Electro Chemical Processes (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Conventional Boiler Use (GWh)Other Facility Support (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Pharmaceutical and Medicine Manufacturing 7,779 502 2,342 876 1,328 564 212 293 166 189 35 35 14,322 

Basic Chemical Manufacturing 5,090 328 1,511 567 857 366 137 189 107 122 23 22 9,320 

Iron and Steel Mills and Ferroalloy Manufacturing 2,341 3,006 2,000 544 129 382 135 41 104 171 14 24 8,893 

Pulp, Paper, and Paperboard Mills 3,538 184 38 205 78 180 158 216 50 23 9 — 4,680 

Plastics Product Manufacturing 2,025 606 — 382 303 357 — 19 86 — 16 — 3,794 

Converted Paper Product Manufacturing 2,785 145 30 163 62 142 125 171 40 19 8 — 3,690 

Other Chemical Product and Preparation 

Manufacturing

1,769 114 528 198 299 127 48 66 37 43 8 8 3,245 

Soap, Cleaning Compound, and Toilet Preparation 

Manufacturing

1,749 113 535 199 303 128 48 67 38 43 8 8 3,241 

Petroleum and Coal Products Manufacturing 2,075 87 37 97 108 65 18 19 25 29 0 3 2,563 

Resin, Synthetic Rubber, and Artificial Synthetic 

Fibers and Filaments Manufacturing

1,369 88 407 153 231 98 37 51 29 33 6 6 2,507 

Other Nonmetallic Mineral Product Manufacturing 940 446 31 114 68 87 38 16 27 — 7 3 1,775 

Printing and Related Support Activities 917 71 19 288 127 144 24 14 48 — 19 — 1,671 

Pesticide, Fertilizer, and Other Agricultural 

Chemical Manufacturing

842 55 258 96 147 62 23 32 18 21 4 4 1,562 

Semiconductor and Other Electronic Component 

Manufacturing

336 175 33 352 168 134 107 13 71 32 4 28 1,452 

Glass and Glass Product Manufacturing 691 327 23 83 49 63 27 11 19 — 5 2 1,301 

Paint, Coating, and Adhesive Manufacturing 695 45 212 79 120 51 19 27 15 17 3 3 1,287 
196

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Electro Chemical Processes (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Conventional Boiler Use (GWh)Other Facility Support (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Nonferrous Metal (except Aluminum) Production 

and Processing

340 433 277 76 18 54 19 6 14 24 2 3 1,266 

Other Wood Product Manufacturing 799 68 3 81 8 64 12 19 17 12 3 4 1,089 

Dairy Product Manufacturing 461 45 2 88 274 78 19 32 21 23 10 4 1,058 

Navigational, Measuring, Electromedical, and 

Control Instruments Manufacturing

213 111 21 228 109 87 70 8 46 21 2 18 934 

Beverage Manufacturing 337 40 2 110 184 87 23 15 31 72 19 4 925 

Other Miscellaneous Manufacturing 302 107 6 222 66 128 17 15 45 — 5 2 916 

Other Electrical Equipment and Component 

Manufacturing

260 213 72 153 59 91 34 4 21 — 6 — 913 

Architectural and Structural Metals Manufacturing 374 128 34 134 28 84 31 — 29 — 6 2 851 

Foundries 220 281 185 51 12 36 13 4 10 16 1 2 830 

Bakeries and Tortilla Manufacturing 345 34 2 67 208 59 14 24 16 18 7 3 797 

Other Fabricated Metal Product Manufacturing 339 116 30 120 25 76 28 — 26 — 5 2 767 

Cement and Concrete Product Manufacturing 395 188 13 48 29 37 16 7 11 — 3 1 749 

Fruit and Vegetable Preserving and Specialty 

Food Manufacturing

317 31 2 62 192 54 13 23 15 16 7 3 734 

Animal Slaughtering and Processing 297 29 2 58 180 51 12 21 14 15 6 3 689 

Other Food Manufacturing 244 24 1 47 146 41 10 17 11 12 5 2 561 

Motor Vehicle Parts Manufacturing 208 59 3 101 27 68 27 4 21 8 8 6 542 

Other General Purpose Machinery Manufacturing 208 48 5 102 20 70 12 5 18 — 5 — 493 

Medical Equipment and Supplies Manufacturing 156 55 3 114 33 66 9 7 23 — 3 1 470 
197

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Electro Chemical Processes (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Conventional Boiler Use (GWh)Other Facility Support (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Steel Product Manufacturing from Purchased 

Steel

124 157 101 28 7 20 7 2 5 9 1 1 461 

Fabric Mills 257 37 0 65 16 32 8 9 7 — 4 — 433 

Forging and Stamping 185 64 17 66 14 42 15 — 14 — 3 1 421 

Coating, Engraving, Heat Treating, and Allied 

Activities

184 63 17 67 14 42 16 — 14 — 3 1 420 

Communications Equipment Manufacturing 93 49 9 100 48 38 31 4 20 9 1 8 409 

Aerospace Product and Parts Manufacturing 150 43 3 73 20 49 19 3 15 6 5 5 390 

Clay Product and Refractory Manufacturing 204 97 7 25 15 19 8 3 6 — 1 1 388 

Electrical Equipment Manufacturing 107 89 31 65 25 38 14 2 9 — 3 — 383 

Fiber, Yarn, and Thread Mills 225 32 0 56 14 27 7 8 6 — 3 — 378 

Industrial Machinery Manufacturing 159 37 4 77 15 52 9 4 14 — 4 — 373 

Alumina and Aluminum Production and 

Processing

97 124 80 22 5 15 5 2 4 7 1 1 364 

Sugar and Confectionery Product Manufacturing 150 15 1 29 92 26 6 11 7 8 3 1 349 

Commercial and Service Industry Machinery 

Manufacturing

144 33 3 69 14 47 8 3 12 — 3 — 338 

Metalworking Machinery Manufacturing 140 32 3 68 14 47 8 3 12 — 3 — 332 

Engine, Turbine, and Power Transmission

Equipment Manufacturing

135 31 3 65 13 45 7 3 12 — 3 — 318 

Railroad Rolling Stock Manufacturing 106 30 2 52 14 35 14 2 11 4 4 3 278 

Animal Food Manufacturing 119 12 1 23 73 20 5 9 6 6 3 1 277 

Textile and Fabric Finishing and Fabric Coating 

Mills

162 23 0 41 10 20 5 6 4 — 2 — 273 
198

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Electro Chemical Processes (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Conventional Boiler Use (GWh)Other Facility Support (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Rubber Product Manufacturing 142 42 — 27 21 25 — 1 6 — 1 — 266 

Veneer, Plywood, and Engineered Wood Product 

Manufacturing

172 15 1 17 2 14 2 4 4 2 1 1 233 

Household and Institutional Furniture and Kitchen 

Cabinet Manufacturing

105 8 1 43 4 36 — 3 7 — 2 1 209 

Lime and Gypsum Product Manufacturing 97 46 3 12 7 9 4 2 3 — 1 0 184 

Motor Vehicle Body and Trailer Manufacturing 67 19 1 33 9 22 9 1 7 3 2 2 175 

Other Transportation Equipment Manufacturing 66 19 1 32 9 22 9 1 7 3 2 2 174 

Machine Shops; Turned Product; and Screw, Nut, 

and Bolt Manufacturing

73 25 7 26 6 17 6 — 6 — 1 0 167 

Computer and Peripheral Equipment 

Manufacturing

36 19 4 40 19 15 12 1 8 4 0 3 163 

Grain and Oilseed Milling 70 7 0 14 42 12 3 5 3 4 1 1 162 

Agriculture, Construction, and Mining Machinery 

Manufacturing

66 15 2 32 6 22 4 1 6 — 2 — 155 

Spring and Wire Product Manufacturing 57 20 5 21 4 13 5 — 5 — 1 0 131 

Manufacturing and Reproducing Magnetic and 

Optical Media

30 15 3 31 15 12 10 1 6 3 0 2 129 

Electric Lighting Equipment Manufacturing 34 28 9 20 8 12 4 1 3 — 1 — 120 

Audio and Video Equipment Manufacturing 25 13 2 26 12 10 8 1 5 2 0 2 108 

Cut and Sew Apparel Manufacturing 36 5 — 26 2 15 — 2 3 — 1 — 90 

Ship and Boat Building 34 10 1 17 5 11 5 1 4 1 1 1 90 
199

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Electro Chemical Processes (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Conventional Boiler Use (GWh)Other Facility Support (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Ventilation, Heating, Air-Conditioning, and 

Commercial Refrigeration Equipment 

Manufacturing

36 8 1 17 3 12 2 1 3 — 1 — 84 

Boiler, Tank, and Shipping Container 

Manufacturing

34 12 3 12 2 7 3 — 3 — 1 0 76 

Other Textile Product Mills 38 5 0 9 5 8 0 4 2 — 2 — 73 

Motor Vehicle Manufacturing 27 8 0 14 4 9 4 1 3 1 1 1 72 

Office Furniture (including Fixtures) Manufacturing 35 3 0 15 1 12 — 1 2 — 1 0 71 

Tobacco Manufacturing 23 3 0 7 12 6 2 1 2 5 1 0 61 

Cutlery and Handtool Manufacturing 22 8 2 8 2 5 2 — 2 — 0 0 50 

Seafood Product Preparation and Packaging 16 2 0 3 10 3 1 1 1 1 0 0 36 

Household Appliance Manufacturing 10 8 3 6 2 3 1 0 1 — 0 — 35 

Sawmills and Wood Preservation 19 2 0 2 0 2 0 0 0 0 0 0 26 

Other Leather and Allied Product Manufacturing 12 2 0 2 1 2 0 0 0 — 0 — 19 

Other Furniture Related Product Manufacturing 8 1 0 3 0 3 — 0 1 — 0 0 16 

Footwear Manufacturing 4 1 0 1 0 1 0 0 0 — 0 — 7 

Textile Furnishings Mills 4 0 0 1 0 1 0 0 0 — 0 — 7 

Leather and Hide Tanning and Finishing 2 0 0 0 0 0 0 0 0 — 0 — 4 

Hardware Manufacturing 1 0 0 0 0 0 0 — 0 — 0 0 3 

Apparel Accessories and Other Apparel 

Manufacturing

0 0 — 0 0 0 — 0 0 — 0 — 1 

Total 44,898 9,637 9,001 7,770 6,633 5,134 1,823 1,565 1,553 1,055 349 249 89,667 
200

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Figure G-27. Industrial electricity use diurnal patterns by season, modeled for the Mid Atlantic

census division 2012
201

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Table G-29. Distributed Generation Model, Annual Summary the Mid Atlantic Census Division in 

2012 by Sector and DG Type

Sector/DG Type Industry Commercial Residential Total

CHP (GWh) 33,096 9,015 349 42,460 

Distributed PV (GWh) 542 544 318 1,403 

Thermal DG (GWh) 36 40 — 76 

Total (GWh) 33,674 9,599 667 43,940 

CHP (%) 98.3 93.9 52.3 96.6 

Distributed PV (%) 1.6 5.7 47.7 3.2 

Thermal DG (%) 0.1 0.4 — 0.2 

Total (%) 76.6 21.8 1.5 100.0 

Figure G-28. Distributed Generation Model Diurnal Generation Patterns, Mid Atlantic

census division 2012
202

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G.5 South Atlantic

Table G-30. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in 

dsgrid, South Atlantic Census Division

Component 

Type

Component

Name Residential Commercial Industrial Transport Total

Top-down Hourly load 810.7 

Derived T&D losses 41.8 

Top-down Annual energy 336.8 305.6 139.4 1.3 783.0 

dsgrid Distributed generation – 3.0 30.7 – 33.6 

dsgrid-core Gap models 63.8 91.8 27.3 0.8 183.7 

dsgrid-core Detailed sector models 271.1 251.1 174.0 – 696.2 

Derived Total site energy 336.8 308.6 170.0 1.3 816.7 

Derived Annual sector residuals 1.9 -34.4 -31.3 0.5 -63.3

Derived Hourly residuals -79.2

Figure G-29. Bottom-up detailed sectoral and gap model load compared to bulk-level historical 

hourly load plus DG estimates for the South Atlantic census division in 2012
203

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Figure G-30. dsgrid hourly residuals shown in context for the South Atlantic census division.

Figure G-31. Historical and dsgrid load duration curves for the South Atlantic census division in 

2012

The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of the 

detailed sector and gap models.
204

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Table G-31. Residential Subsectors, Summary of Electricity by End Use for the South Atlantic

Census Division in 2012

Subsector

Interior 

Equipment 

(GWh)

Space 

Cooling 

(GWh)

Space 

Heating 

(GWh)

Interior 

Lights 

(GWh)

Water 

Systems 

(GWh)

Fans 

(GWh)

Exterior 

Lights 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Single 

Family 

Detached

89,096 61,900 32,942 28,115 27,691 22,142 5,716 39 — 267,640 

Mobile 

Home 11,787 6,684 2,873 2,423 3,965 2,322 394 7 — 30,454 

Single 

Family 

Attached 

8,164 4,630 1,990 1,678 2,747 1,608 273 5 — 21,094 

Apartment 

in 

Building 2 

to 4 Units

4,030 2,083 466 1,735 — 1,640 2,083 164 74 12,274 

Midrise 

Apartment 

Building

1,081 613 131 485 — 461 578 52 24 3,424 

Total 114,157 75,909 38,402 34,435 34,403 28,173 9,043 266 98 334,886 

Table G-32. Residential Electricity Proportions by Subsector and End Use for the South Atlantic

Census Division in 2012

Subsector

Interior 

Equipment 

(%)

Space 

Cooling 

(%)

Space 

Heating 

(%)

Interior 

Lights 

(%)

Water 

Systems 

(%)

Fans 

(%)

Exterior 

Lights 

(%)

Pumps 

(%)

Heat 

Rejection 

(%)

Total 

(%)

Single Family 

Detached 33.3 23.1 12.3 10.5 10.3 8.3 2.1 0.0 — 79.9 

Mobile Home 38.7 21.9 9.4 8.0 13.0 7.6 1.3 0.0 — 9.1 

Single Family 

Attached 38.7 21.9 9.4 8.0 13.0 7.6 1.3 0.0 — 6.3 

Apartment in 

Building 2 to 

4 Units

32.8 17.0 3.8 14.1 — 13.4 17.0 1.3 0.6 3.7 

Midrise 

Apartment 

Building

31.6 17.9 3.8 14.2 — 13.5 16.9 1.5 0.7 1.0 

Total 34.1 22.7 11.5 10.3 10.3 8.4 2.7 0.1 0.0 100.0 
205

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Figure G-32. Residential electricity use diurnal patterns by season, modeled for the South Atlantic

census division 2012
206

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Table G-33. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End

Use for the South Atlantic Census Division in 2012

Subsector

Interior 

Lights 

(GWh)

Interior 

Equipment 

(GWh)

Space 

Cooling 

(GWh)

Fans 

(GWh)

Exterior 

Lights 

(GWh)

Space 

Heating 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Large Office 18,354 21,049 16,108 14,570 3,736 255 2,149 915 77,135 

Strip Mall 30,416 4,409 5,428 5,947 6,198 1,019 26 13 53,456 

Standalone 

Retail Store 17,273 5,332 4,287 4,651 2,607 915 13 6 35,085 

Medium Office 4,289 4,902 3,473 3,017 1,528 85 208 89 17,591 

Small Office 4,150 5,696 2,808 2,813 1,622 196 25 18 17,328 

Large Hotel 2,961 4,783 3,259 2,694 761 76 168 74 14,774 

Warehouse 4,398 1,746 513 1,140 2,099 631 10 8 10,544 

Full Service 

Restaurant 2,122 4,733 1,377 1,193 592 105 14 6 10,143 

Hospital 1,678 2,131 1,715 1,153 126 — 258 146 7,206 

Primary 

School 1,160 925 625 558 149 46 21 13 3,496 

Outpatient 

Treatment 

Facility

912 1,340 311 338 328 — 48 26 3,303 

Small Hotel 248 343 45 39 163 11 5 3 857 

Quick Service 

Restaurant 19 115 33 37 11 1 0 0 217 

Total 87,979 57,504 39,982 38,150 19,920 3,340 2,943 1,318 251,137 
207

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Table G-34. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for 

the South Atlantic Census Division in 2012

Subsector

Interior 

Lights 

(%)

Interior 

Equipment 

(%)

Space 

Cooling 

(%)

Fans 

(%)

Exterior 

Lights 

(%)

Space 

Heating 

(%)

Pumps 

(%)

Heat 

Rejection 

(%)

Total 

(%)

Large Office 23.8 27.3 20.9 18.9 4.8 0.3 2.8 1.2 30.7 

Strip Mall 56.9 8.2 10.2 11.1 11.6 1.9 0.0 0.0 21.3 

Standalone 

Retail Store 49.2 15.2 12.2 13.3 7.4 2.6 0.0 0.0 14.0 

Medium Office 24.4 27.9 19.7 17.2 8.7 0.5 1.2 0.5 7.0 

Small Office 24.0 32.9 16.2 16.2 9.4 1.1 0.1 0.1 6.9 

Large Hotel 20.0 32.4 22.1 18.2 5.1 0.5 1.1 0.5 5.9 

Warehouse 41.7 16.6 4.9 10.8 19.9 6.0 0.1 0.1 4.2 

Full Service 

Restaurant 20.9 46.7 13.6 11.8 5.8 1.0 0.1 0.1 4.0 

Hospital 23.3 29.6 23.8 16.0 1.7 — 3.6 2.0 2.9 

Primary 

School 33.2 26.5 17.9 16.0 4.3 1.3 0.6 0.4 1.4 

Outpatient 

Treatment 

Facility

27.6 40.6 9.4 10.2 9.9 — 1.4 0.8 1.3 

Small Hotel 28.9 40.0 5.3 4.5 19.0 1.3 0.6 0.3 0.3 

Quick Service 

Restaurant 8.9 53.0 15.2 17.0 5.2 0.4 0.1 0.1 0.1 

Total 35.0 22.9 15.9 15.2 7.9 1.3 1.2 0.5 100.0 
208

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Figure G-33. Commercial electricity use diurnal patterns by season, modeled for the South 

Atlantic census division 2012
209

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Table G-35. Industrial Manufacturing Subsectors, Summary of Model Results for the South Atlantic Census Division in 2012

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Conventional Boiler Use (GWh)Other Facility Support (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Pulp, Paper, and Paperboard Mills 15,867 824 921 172 351 805 708 967 225 105 43 — 20,988 

Iron and Steel Mills and Ferroalloy 

Manufacturing 3,372 4,329 784 2,879 186 550 195 59 150 247 21 35 12,806 

Petroleum and Coal Products Manufacturing 9,798 410 460 176 512 306 86 90 119 136 1 12 12,106 

Basic Chemical Manufacturing 6,211 400 692 1,844 1,046 446 167 231 131 149 28 27 11,372 

Plastics Product Manufacturing 5,056 1,513 955 — 756 893 — 47 215 — 40 — 9,475 

Converted Paper Product Manufacturing 6,491 338 380 71 145 331 293 400 93 43 18 — 8,603 

Pharmaceutical and Medicine Manufacturing 4,378 283 493 1,318 748 317 119 165 94 106 20 20 8,060 

Resin, Synthetic Rubber, and Artificial Synthetic 

Fibers and Filaments Manufacturing 2,691 173 300 800 454 194 72 100 57 65 12 12 4,930 

Fiber, Yarn, and Thread Mills 2,849 408 709 0 176 348 86 98 71 — 41 — 4,788 

Pesticide, Fertilizer, and Other Agricultural 

Chemical Manufacturing 2,470 160 282 759 430 181 69 95 54 61 11 11 4,584 

Animal Slaughtering and Processing 1,919 190 373 10 1,165 328 80 137 89 98 41 19 4,450 

Other Wood Product Manufacturing 3,222 274 325 13 31 260 47 77 69 47 11 15 4,389 

Other Chemical Product and Preparation 

Manufacturing 1,938 125 217 578 328 140 52 72 41 47 9 9 3,556 

Printing and Related Support Activities 1,936 149 609 41 267 304 51 30 101 — 39 — 3,527 

Other Nonmetallic Mineral Product 

Manufacturing 1,728 820 209 57 126 160 70 29 49 — 12 5 3,266 
210

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Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Conventional Boiler Use (GWh)Other Facility Support (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Soap, Cleaning Compound, and Toilet 

Preparation Manufacturing 1,397 90 159 427 242 102 39 54 30 35 6 6 2,588 

Fabric Mills 1,526 219 385 0 96 189 47 53 39 — 23 — 2,576 

Nonferrous Metal (except Aluminum) 

Production and Processing 648 824 145 527 34 102 36 11 28 45 4 6 2,410 

Motor Vehicle Parts Manufacturing 851 242 413 14 111 277 108 18 87 35 31 26 2,214 

Rubber Product Manufacturing 1,113 333 211 — 167 197 — 10 48 — 9 — 2,088 

Architectural and Structural Metals 

Manufacturing 869 298 312 78 65 196 72 — 67 — 14 4 1,975 

Veneer, Plywood, and Engineered Wood 

Product Manufacturing 1,408 119 140 5 13 112 20 33 29 20 5 6 1,910 

Paint, Coating, and Adhesive Manufacturing 1,011 65 115 308 175 74 28 39 22 25 5 5 1,871 

Aerospace Product and Parts Manufacturing 702 199 340 12 91 228 89 15 72 28 26 22 1,824 

Other Fabricated Metal Product Manufacturing 735 252 261 65 54 165 61 — 56 — 11 3 1,664 

Other Electrical Equipment and Component 

Manufacturing 470 385 278 130 107 164 61 8 38 — 12 — 1,653 

Cement and Concrete Product Manufacturing 847 403 103 28 62 79 34 14 24 — 6 3 1,605 

Beverage Manufacturing 580 69 190 3 317 150 40 26 53 123 33 7 1,591 

Alumina and Aluminum Production and 

Processing 400 510 90 328 21 64 22 7 17 28 2 4 1,494 

Glass and Glass Product Manufacturing 774 366 93 25 55 71 31 13 22 — 5 2 1,458 
211

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Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Conventional Boiler Use (GWh)Other Facility Support (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Other Food Manufacturing 588 58 113 3 352 100 24 41 27 30 12 6 1,353 

Textile and Fabric Finishing and Fabric Coating 

Mills 746 107 189 0 47 93 23 26 19 — 11 — 1,262 

Bakeries and Tortilla Manufacturing 541 54 105 3 326 92 23 38 25 28 12 5 1,250 

Semiconductor and Other Electronic 

Component Manufacturing 287 149 301 28 143 115 91 11 61 27 3 24 1,241 

Foundries 322 412 74 271 18 52 18 6 14 23 2 3 1,216 

Fruit and Vegetable Preserving and Specialty 

Food Manufacturing 512 51 100 3 311 88 21 36 24 26 11 5 1,187 

Motor Vehicle Manufacturing 441 126 218 8 59 145 57 10 46 18 16 14 1,158 

Household and Institutional Furniture and 

Kitchen Cabinet Manufacturing 539 40 221 4 18 183 — 13 37 — 12 5 1,072 

Dairy Product Manufacturing 458 45 88 2 272 77 19 32 21 23 10 4 1,052 

Navigational, Measuring, Electromedical, and 

Control Instruments Manufacturing 236 123 252 24 121 96 77 9 51 23 3 20 1,034 

Other Miscellaneous Manufacturing 322 114 237 7 70 137 19 16 48 — 6 2 976 

Other Textile Product Mills 490 65 121 3 61 98 4 46 23 — 23 — 933 

Ship and Boat Building 307 88 155 5 42 103 41 7 33 13 12 10 815 

Other General Purpose Machinery 

Manufacturing 309 72 151 8 30 103 17 7 27 — 7 — 732 

Coating, Engraving, Heat Treating, and Allied 

Activities 320 110 116 29 24 73 27 — 25 — 5 2 731 
212

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Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Conventional Boiler Use (GWh)Other Facility Support (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Electrical Equipment Manufacturing 201 166 121 57 47 71 27 3 17 — 5 — 715 

Communications Equipment Manufacturing 160 84 172 16 83 65 53 6 35 16 2 14 707 

Animal Food Manufacturing 292 29 57 2 179 50 12 21 14 15 6 3 681 

Steel Product Manufacturing from Purchased 

Steel 179 227 40 146 9 28 10 3 8 13 1 2 666 

Engine, Turbine, and Power Transmission 

Equipment Manufacturing 274 63 132 7 26 90 15 6 24 — 6 — 644 

Industrial Machinery Manufacturing 262 60 126 6 25 86 14 6 23 — 6 — 615 

Tobacco Manufacturing 211 25 68 1 112 54 14 9 19 44 12 2 571 

Commercial and Service Industry Machinery 

Manufacturing 219 51 106 5 21 72 12 5 19 — 5 — 515 

Medical Equipment and Supplies Manufacturing 167 59 121 4 36 70 9 8 24 — 3 1 502 

Lime and Gypsum Product Manufacturing 265 126 32 9 19 25 11 4 8 — 2 1 501 

Forging and Stamping 198 68 71 18 15 44 16 — 15 — 3 1 449 

Sugar and Confectionery Product 

Manufacturing 183 18 36 1 111 31 8 13 9 9 4 2 424 

Clay Product and Refractory Manufacturing 222 106 27 8 16 21 9 4 6 — 2 1 422 

Motor Vehicle Body and Trailer Manufacturing 141 40 69 2 19 46 18 3 15 6 5 4 368 

Grain and Oilseed Milling 158 16 30 1 95 27 7 11 7 8 3 2 364 

Agriculture, Construction, and Mining Machinery 

Manufacturing 134 31 65 3 13 44 7 3 12 — 3 — 315 
213

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Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Conventional Boiler Use (GWh)Other Facility Support (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Textile Furnishings Mills 164 22 41 1 21 33 1 15 8 — 8 — 313 

Ventilation, Heating, Air-Conditioning, and 

Commercial Refrigeration Equipment 

Manufacturing

128 30 63 3 12 43 7 3 11 — 3 — 303 

Seafood Product Preparation and Packaging 126 13 25 1 78 22 5 9 6 7 3 1 294 

Computer and Peripheral Equipment 

Manufacturing 66 35 72 7 35 27 22 3 15 7 1 6 294 

Cut and Sew Apparel Manufacturing 111 14 80 — 5 47 — 6 9 — 2 — 274 

Metalworking Machinery Manufacturing 106 25 52 3 10 35 6 2 9 — 3 — 251 

Spring and Wire Product Manufacturing 106 36 38 10 8 24 9 — 8 — 2 1 242 

Other Transportation Equipment Manufacturing 85 24 42 1 11 28 11 2 9 3 3 3 223 

Household Appliance Manufacturing 62 51 36 17 14 22 8 1 5 — 2 — 217 

Electric Lighting Equipment Manufacturing 57 46 33 15 13 20 7 1 5 — 1 — 198 

Boiler, Tank, and Shipping Container 

Manufacturing 80 27 28 7 6 18 6 — 6 — 1 0 180 

Sawmills and Wood Preservation 126 11 13 0 1 10 2 3 3 2 0 1 171 

Railroad Rolling Stock Manufacturing 65 18 32 1 9 21 8 1 7 3 2 2 170 

Office Furniture (including Fixtures) 

Manufacturing 84 6 35 1 3 29 — 2 6 — 2 1 168 

Machine Shops; Turned Product; and Screw, 

Nut, and Bolt Manufacturing 47 16 17 4 4 11 4 — 4 — 1 0 107 
214

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Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Conventional Boiler Use (GWh)Other Facility Support (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Other Furniture Related Product Manufacturing 47 3 19 0 2 16 — 1 3 — 1 0 93 

Audio and Video Equipment Manufacturing 16 8 17 2 8 6 5 1 3 2 0 1 70 

Manufacturing and Reproducing Magnetic and 

Optical Media 15 8 16 1 7 6 5 1 3 1 0 1 64 

Cutlery and Handtool Manufacturing 23 8 8 2 2 5 2 — 2 — 0 0 52 

Other Leather and Allied Product Manufacturing 16 2 3 0 1 3 0 0 1 — 0 — 26 

Footwear Manufacturing 5 1 1 0 0 1 0 0 0 — 0 — 9 

Hardware Manufacturing 4 1 1 0 0 1 0 — 0 — 0 0 8 

Apparel Accessories and Other Apparel 

Manufacturing 2 0 2 — 0 1 — 0 0 — 0 — 6 

Leather and Hide Tanning and Finishing 2 0 0 0 0 0 0 0 0 — 0 — 4 

Apparel Knitting Mills 1 0 1 — 0 0 — 0 0 — 0 — 2 

Total 94,480 17,959 15,331 11,431 11,302 10,543 3,599 3,354 3,047 1,788 787 409 174,029 
215

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Figure G-34. Industrial electricity use diurnal patterns by season, modeled for the South Atlantic

census division 2012
216

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

Table G-36. Distributed Generation Model, Annual Summary the South Atlantic Census Division 

in 2012 by Sector and DG Type

Sector/DG Type Industry Commercial Residential Total

CHP (GWh) 30,000 2,751 — 32,752 

Thermal DG (GWh) 553 111 664 

Distributed PV (GWh) 114 114 86 314 

Total (GWh) 30,667 2,976 86 33,730 

CHP (%) 97.8 92.4 — 97.1 

Thermal DG (%) 1.8 3.7 — 2.0 

Distributed PV (%) 0.4 3.8 100.0 0.9 

Total (%) 90.9 8.8 0.3 100.0 

Figure G-35. Distributed Generation Model Diurnal Generation Patterns, South Atlantic

census division 2012
217

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G.6 East South Central

Table G-37. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in 

dsgrid, East South Central Census Division

Component 

Type

Component

Name Residential Commercial Industrial Transport Total

Top-down Hourly load 332.8 

Derived T&D losses 17.8 

Top-down Annual energy 114.5 82.8 123.2 0.0 320.5 

dsgrid Distributed generation – 0.5 17.9 – 18.3 

dsgrid-core Gap models 16.6 25.6 11.3 0.0 53.5 

dsgrid-core Detailed sector models 88.8 54.7 89.4 – 232.9 

Derived Total site energy 114.5 83.2 141.1 0.0 338.8 

Derived Annual sector residuals 9.0 3.0 40.4 0.0 52.4 

Derived Hourly residuals 33.2 

Figure G-36. Bottom-up detailed sectoral and gap model load compared to bulk-level historical 

hourly load plus DG estimates for the East South Central census division in 2012
218

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Figure G-37. dsgrid hourly residuals shown in context for the East South Central census division.

Figure G-38. Historical and dsgrid load duration curves for the East South Central census division in 

2012

The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of the 

detailed sector and gap models.
219

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

Table G-38. Residential Subsectors, Summary of Electricity by End Use for the East South Central

Census Division in 2012

Subsector

Interior 

Equipment 

(GWh)

Space 

Cooling 

(GWh)

Space 

Heating 

(GWh)

Interior 

Lights 

(GWh)

Water 

Systems 

(GWh)

Fans 

(GWh)

Exterior 

Lights 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Single 

Family 

Detached

29,574 17,358 14,166 9,205 8,997 7,185 1,864 10 — 88,359 

Mobile 

Home 4,398 2,137 1,464 906 1,402 852 147 2 — 11,307 

Apartment 

in Building 

2 to 4 Units

1,144 577 120 490 — 502 592 46 15 3,485 

Single 

Family 

Attached 

716 348 238 147 228 139 24 0 — 1,841 

Midrise 

Apartment 

Building

145 79 15 64 — 71 76 7 2 459 

Total 35,977 20,498 16,003 10,811 10,628 8,749 2,704 64 17 105,451 

Table G-39. Residential Electricity Proportions by Subsector and End Use for the East South 

Central Census Division in 2012

Subsector

Interior 

Equipment 

(%)

Space 

Cooling 

(%)

Space 

Heating 

(%)

Interior 

Lights 

(%)

Water 

Systems 

(%)

Fans 

(%)

Exterior 

Lights 

(%)

Pumps 

(%)

Heat 

Rejection 

(%)

Total 

(%)

Single Family 

Detached 33.5 19.6 16.0 10.4 10.2 8.1 2.1 0.0 — 83.8 

Mobile Home 38.9 18.9 12.9 8.0 12.4 7.5 1.3 0.0 — 10.7 

Apartment in 

Building 2 to 

4 Units

32.8 16.6 3.4 14.1 — 14.4 17.0 1.3 0.4 3.3 

Single Family 

Attached 38.9 18.9 12.9 8.0 12.4 7.5 1.3 0.0 — 1.7 

Midrise 

Apartment 

Building

31.7 17.2 3.2 13.9 — 15.5 16.6 1.5 0.5 0.4 

Total 34.1 19.4 15.2 10.3 10.1 8.3 2.6 0.1 0.0 100.0 
220

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Figure G-39. Residential electricity use diurnal patterns by season, modeled for the East South 

Central census division 2012
221

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

Table G-40. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End

Use for the East South Central Census Division in 2012

Subsector

Interior 

Lights 

(GWh)

Interior 

Equipment 

(GWh)

Fans 

(GWh)

Space 

Cooling 

(GWh)

Exterior 

Lights 

(GWh)

Space 

Heating 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Strip Mall 8,545 1,240 1,875 1,419 1,750 442 7 3 15,281 

Standalone 

Retail Store 5,387 1,778 1,613 1,270 819 350 5 2 11,224 

Large Office 2,289 2,642 1,635 1,758 441 40 239 87 9,131 

Small Office 1,060 1,456 744 647 414 72 6 4 4,403 

Medium Office 861 985 605 619 305 18 44 16 3,452 

Warehouse 1,045 416 330 102 514 267 2 1 2,678 

Full Service 

Restaurant 517 1,172 303 295 145 45 3 1 2,481 

Large Hotel 516 793 442 477 131 26 24 9 2,419 

Hospital 372 521 294 338 30 — 59 30 1,645 

Primary School 325 256 147 144 41 27 4 2 946 

Outpatient 

Treatment 

Facility

213 312 60 43 76 — 7 3 714 

Small Hotel 72 97 4 3 42 6 1 0 225 

Quick Service 

Restaurant 6 34 13 10 3 0 0 0 66 

Total 21,209 11,702 8,064 7,124 4,710 1,292 401 160 54,663 
222

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Table G-41. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for 

the East South Central Census Division in 2012

Subsector

Interior 

Lights 

(%)

Interior 

Equipment 

(%)

Fans 

(%)

Space 

Cooling 

(%)

Exterior 

Lights 

(%)

Space 

Heating 

(%)

Pumps 

(%)

Heat 

Rejection 

(%) Total (%)

Strip Mall 55.9 8.1 12.3 9.3 11.5 2.9 0.0 0.0 28.0 

Standalone 

Retail Store 48.0 15.8 14.4 11.3 7.3 3.1 0.0 0.0 20.5 

Large Office 25.1 28.9 17.9 19.3 4.8 0.4 2.6 1.0 16.7 

Small Office 24.1 33.1 16.9 14.7 9.4 1.6 0.1 0.1 8.1 

Medium Office 24.9 28.5 17.5 17.9 8.8 0.5 1.3 0.5 6.3 

Warehouse 39.0 15.5 12.3 3.8 19.2 10.0 0.1 0.0 4.9 

Full Service 

Restaurant 20.9 47.2 12.2 11.9 5.9 1.8 0.1 0.0 4.5 

Large Hotel 21.3 32.8 18.3 19.7 5.4 1.1 1.0 0.4 4.4 

Hospital 22.6 31.7 17.9 20.5 1.8 — 3.6 1.8 3.0 

Primary School 34.4 27.0 15.5 15.3 4.3 2.8 0.5 0.2 1.7 

Outpatient 

Treatment 

Facility

29.8 43.7 8.3 6.0 10.7 — 1.0 0.5 1.3 

Small Hotel 32.1 43.0 1.9 1.5 18.7 2.5 0.2 0.1 0.4 

Quick Service 

Restaurant 8.7 51.8 19.3 15.0 5.1 0.0 0.1 0.0 0.1 

Total 38.8 21.4 14.8 13.0 8.6 2.4 0.7 0.3 100.0 
223

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

Figure G-40. Commercial electricity use diurnal patterns by season, modeled for the East South 

Central census division 2012
224

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Table G-42. Industrial Manufacturing Subsectors, Summary of Model Results for the East South Central Census Division in 2012

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Facility Lighting (GWh)Process Cooling And Refrigeration (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Iron and Steel Mills and Ferroalloy 

Manufacturing 3,749 4,813 871 3,202 612 207 217 167 66 274 23 38 14,239 

Pulp, Paper, and Paperboard Mills 6,809 354 395 74 345 151 304 97 415 45 18 — 9,006 

Petroleum and Coal Products Manufacturing 5,907 247 277 106 185 308 52 72 54 82 1 8 7,299 

Basic Chemical Manufacturing 3,437 221 383 1,021 247 579 92 72 128 82 15 15 6,294 

Converted Paper Product Manufacturing 3,477 181 203 38 178 78 157 50 214 23 9 — 4,608 

Plastics Product Manufacturing 2,103 629 397 — 371 314 — 89 20 — 17 — 3,941 

Motor Vehicle Parts Manufacturing 1,292 367 628 22 420 169 165 132 28 52 47 40 3,362 

Resin, Synthetic Rubber, and Artificial Synthetic 

Fibers and Filaments Manufacturing 1,819 117 203 541 131 307 49 38 68 44 8 8 3,333 

Animal Slaughtering and Processing 988 98 192 5 169 600 41 46 70 51 21 10 2,291 

Other Wood Product Manufacturing 1,666 141 168 7 134 16 24 36 40 24 6 8 2,269 

Foundries 489 626 113 412 79 27 28 22 8 35 3 5 1,846 

Pesticide, Fertilizer, and Other Agricultural 

Chemical Manufacturing 966 63 110 297 71 168 27 21 37 24 4 4 1,792 

Motor Vehicle Manufacturing 676 193 334 12 223 90 88 71 15 28 25 21 1,776 

Nonferrous Metal (except Aluminum) 

Production and Processing 433 551 97 352 68 23 24 18 7 30 3 4 1,611 

Alumina and Aluminum Production and 

Processing 413 526 93 339 66 22 23 18 7 29 2 4 1,541 
225

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Facility Lighting (GWh)Process Cooling And Refrigeration (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Other Chemical Product and Preparation 

Manufacturing 696 45 78 208 50 118 19 15 26 17 3 3 1,276 

Rubber Product Manufacturing 666 199 126 — 118 100 — 28 6 — 5 — 1,250 

Printing and Related Support Activities 664 51 209 14 104 92 18 35 10 — 13 — 1,210 

Other Nonmetallic Mineral Product 

Manufacturing 587 279 71 19 54 43 24 17 10 — 4 2 1,110 

Steel Product Manufacturing from Purchased 

Steel 232 295 52 189 37 12 13 10 4 16 1 2 864 

Pharmaceutical and Medicine Manufacturing 447 29 50 134 32 76 12 10 17 11 2 2 822 

Architectural and Structural Metals 

Manufacturing 352 121 126 32 79 26 29 27 — — 6 2 801 

Other Fabricated Metal Product Manufacturing 332 114 118 29 74 25 27 25 — — 5 2 752 

Cement and Concrete Product Manufacturing 392 186 48 13 37 29 16 11 7 — 3 1 743 

Beverage Manufacturing 251 30 82 1 65 137 17 23 11 53 14 3 688 

Veneer, Plywood, and Engineered Wood 

Product Manufacturing 505 43 50 2 40 5 7 11 12 7 2 2 685 

Soap, Cleaning Compound, and Toilet 

Preparation Manufacturing 357 23 41 109 26 62 10 8 14 9 2 2 662 

Bakeries and Tortilla Manufacturing 273 27 53 1 46 164 11 13 19 14 6 3 630 

Fiber, Yarn, and Thread Mills 362 52 90 0 44 22 11 9 12 — 5 — 609 

Household Appliance Manufacturing 163 134 96 45 57 37 21 13 3 — 4 — 571 
226

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Facility Lighting (GWh)Process Cooling And Refrigeration (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Paint, Coating, and Adhesive Manufacturing 285 18 32 87 21 49 8 6 11 7 1 1 527 

Glass and Glass Product Manufacturing 263 124 32 9 24 19 10 7 4 — 2 1 495 

Motor Vehicle Body and Trailer Manufacturing 179 51 88 3 59 24 23 19 4 7 7 6 468 

Other Electrical Equipment and Component 

Manufacturing 128 105 76 36 45 29 17 10 2 — 3 — 452 

Household and Institutional Furniture and 

Kitchen Cabinet Manufacturing 223 17 92 2 76 8 — 15 5 — 5 2 444 

Other Food Manufacturing 189 19 36 1 32 113 8 9 13 10 4 2 436 

Animal Food Manufacturing 183 18 36 1 32 113 8 9 13 10 4 2 427 

Fruit and Vegetable Preserving and Specialty 

Food Manufacturing 178 18 35 1 30 108 7 8 13 9 4 2 413 

Dairy Product Manufacturing 176 17 34 1 30 105 7 8 12 9 4 2 404 

Navigational, Measuring, Electromedical, and 

Control Instruments Manufacturing 91 47 97 9 37 46 30 20 4 9 1 8 398 

Other General Purpose Machinery 

Manufacturing 165 38 81 4 55 16 9 15 4 — 4 — 391 

Textile and Fabric Finishing and Fabric Coating 

Mills 224 32 57 0 28 14 7 6 8 — 3 — 380 

Aerospace Product and Parts Manufacturing 145 41 70 2 47 19 18 15 3 6 5 4 377 

Forging and Stamping 160 55 57 14 36 12 13 12 — — 3 1 362 

Coating, Engraving, Heat Treating, and Allied 

Activities 152 52 55 14 35 12 13 12 — — 2 1 347 
227

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Facility Lighting (GWh)Process Cooling And Refrigeration (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Other Miscellaneous Manufacturing 113 40 83 2 48 24 6 17 5 — 2 1 341 

Grain and Oilseed Milling 143 14 27 1 24 86 6 7 10 7 3 1 329 

Other Textile Product Mills 169 22 42 1 34 21 1 8 16 — 8 — 322 

Electrical Equipment Manufacturing 78 64 47 22 28 18 10 6 1 — 2 — 277 

Lime and Gypsum Product Manufacturing 141 67 17 5 13 10 6 4 2 — 1 0 267 

Semiconductor and Other Electronic 

Component Manufacturing 60 31 63 6 24 30 19 13 2 6 1 5 258 

Ship and Boat Building 90 26 46 2 30 12 12 10 2 4 3 3 241 

Sawmills and Wood Preservation 176 15 18 1 14 2 3 4 4 3 1 1 240 

Clay Product and Refractory Manufacturing 119 57 15 4 11 9 5 3 2 — 1 0 226 

Industrial Machinery Manufacturing 80 18 38 2 26 8 4 7 2 — 2 — 187 

Engine, Turbine, and Power Transmission 

Equipment Manufacturing 76 17 37 2 25 7 4 7 2 — 2 — 178 

Fabric Mills 105 15 27 0 13 7 3 3 4 — 2 — 178 

Ventilation, Heating, Air-Conditioning, and 

Commercial Refrigeration Equipment 

Manufacturing

73 17 36 2 24 7 4 6 2 — 2 — 172 

Metalworking Machinery Manufacturing 68 16 33 2 23 7 4 6 2 — 2 — 161 

Spring and Wire Product Manufacturing 67 23 24 6 15 5 6 5 — — 1 0 152 

Agriculture, Construction, and Mining Machinery 

Manufacturing 65 15 31 2 21 6 4 6 1 — 2 — 152 
228

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Facility Lighting (GWh)Process Cooling And Refrigeration (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Medical Equipment and Supplies Manufacturing 46 16 33 1 19 10 3 7 2 — 1 0 139 

Sugar and Confectionery Product 

Manufacturing 57 6 11 0 10 35 2 3 4 3 1 1 132 

Seafood Product Preparation and Packaging 55 5 11 0 9 34 2 3 4 3 1 1 128 

Commercial and Service Industry Machinery 

Manufacturing 54 12 26 1 18 5 3 5 1 — 1 — 126 

Tobacco Manufacturing 44 5 14 0 11 24 3 4 2 9 2 1 120 

Cut and Sew Apparel Manufacturing 40 5 29 — 17 2 — 3 2 — 1 — 100 

Computer and Peripheral Equipment 

Manufacturing 22 11 24 2 9 12 7 5 1 2 0 2 97 

Communications Equipment Manufacturing 19 10 20 2 8 10 6 4 1 2 0 2 82 

Machine Shops; Turned Product; and Screw, 

Nut, and Bolt Manufacturing 35 12 13 3 8 3 3 3 — — 1 0 81 

Office Furniture (including Fixtures) 

Manufacturing 34 3 14 0 12 1 — 2 1 — 1 0 68 

Electric Lighting Equipment Manufacturing 19 15 11 5 6 4 2 1 0 — 0 — 66 

Textile Furnishings Mills 32 4 8 0 6 4 0 2 3 — 2 — 61 

Railroad Rolling Stock Manufacturing 22 6 11 0 7 3 3 2 0 1 1 1 59 

Audio and Video Equipment Manufacturing 13 7 14 1 5 7 4 3 1 1 0 1 57 

Boiler, Tank, and Shipping Container 

Manufacturing 25 9 9 2 6 2 2 2 — — 0 0 57 
229

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Electro Chemical Processes (GWh)Facility Lighting (GWh)Process Cooling And Refrigeration (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Other Transportation Equipment Manufacturing 20 6 10 0 7 3 3 2 0 1 1 1 53 

Manufacturing and Reproducing Magnetic and 

Optical Media 9 5 10 1 4 5 3 2 0 1 0 1 40 

Cutlery and Handtool Manufacturing 12 4 4 1 3 1 1 1 — — 0 0 28 

Other Furniture Related Product Manufacturing 7 0 3 0 2 0 — 0 0 — 0 0 13 

Other Leather and Allied Product Manufacturing 8 1 1 0 1 0 0 0 0 — 0 — 12 

Leather and Hide Tanning and Finishing 1 0 0 0 0 0 0 0 0 — 0 — 2 

Footwear Manufacturing 1 0 0 0 0 0 0 0 0 — 0 — 1 

Total 45,442 12,010 7,491 7,487 5,293 5,113 1,850 1,538 1,505 1,060 377 240 89,405 
230

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Figure G-41. Industrial electricity use diurnal patterns by season, modeled for the East South 

Central census division 2012
231

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

Table G-43. Distributed Generation Model, Annual Summary the East South Central Census 

Division in 2012 by Sector and DG Type

Sector/DG Type Industry Commercial Residential Total

CHP (GWh) 16,553 450 — 17,002 

Thermal DG (GWh) 1,298 — — 1,298 

Distributed PV (GWh) 18 18 6 43 

Total (GWh) 17,869 468 6 18,343 

CHP (%) 92.6 96.1 — 92.7 

Thermal DG (%) 7.3 — — 7.1 

Distributed PV (%) 0.1 3.9 100.0 0.2 

Total (%) 97.4 2.6 0.0 100.0 

Figure G-42. Distributed Generation Model Diurnal Generation Patterns, East South Central

census division 2012
232

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

G.7 West South Central

Table G-44. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in 

dsgrid, West South Central Census Division

Component 

Type

Component

Name Residential Commercial Industrial Transport Total

Top-down Hourly load 591.1 

Derived T&D losses 32.3 

Top-down Annual energy 208.2 191.4 158.4 0.1 558.0 

dsgrid Distributed generation 0.0 1.8 58.6 – 60.4 

dsgrid-core Gap models 27.1 57.7 53.9 0.1 138.8 

dsgrid-core Detailed sector models 176.8 138.6 130.5 – 445.9 

Derived Total site energy 208.2 193.2 217.0 0.1 618.4 

Derived Annual sector residuals 4.3 -3.1 32.6 -0.0 33.7 

Derived Hourly residuals 13.7 

Figure G-43. Bottom-up detailed sectoral and gap model load compared to bulk-level historical 

hourly load plus DG estimates for the West South Central census division in 2012
233

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

Figure G-44. dsgrid hourly residuals shown in context for the West South Central census division.

Figure G-45. Historical and dsgrid load duration curves for the West South Central census division in 

2012

The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of the 

detailed sector and gap models.
234

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Table G-45. Residential Subsectors, Summary of Electricity by End Use for the West South Central

Census Division in 2012

Subsector

Interior 

Equipment 

(GWh)

Space 

Cooling 

(GWh)

Interior 

Lights 

(GWh)

Fans 

(GWh)

Space 

Heating 

(GWh)

Water 

Systems 

(GWh)

Exterior 

Lights 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Single 

Family 

Detached

56,869 54,123 17,698 16,906 13,616 12,823 3,560 3 — 175,598 

Mobile 

Home 6,305 5,076 1,316 1,537 889 1,416 214 0 — 16,753 

Apartment 

in Building 

2 to 4 Units

2,027 1,320 803 963 168 — 973 76 38 6,369 

Single 

Family 

Attached 

1,495 1,203 312 364 211 336 51 0 — 3,971 

Midrise 

Apartment 

Building

370 271 158 191 28 — 190 19 9 1,236 

Total 67,066 61,993 20,288 19,961 14,911 14,575 4,988 99 48 203,928 

Table G-46. Residential Electricity Proportions by Subsector and End Use for the West South 

Central Census Division in 2012

Subsector

Interior 

Equipment 

(%)

Space 

Cooling 

(%)

Interior 

Lights 

(%)

Fans 

(%)

Space 

Heating 

(%)

Water 

Systems 

(%)

Exterior 

Lights 

(%)

Pumps 

(%)

Heat 

Rejection 

(%)

Total 

(%)

Single Family 

Detached 32.4 30.8 10.1 9.6 7.8 7.3 2.0 0.0 — 86.1 

Mobile Home 37.6 30.3 7.9 9.2 5.3 8.5 1.3 0.0 — 8.2 

Apartment in 

Building 2 to 

4 Units

31.8 20.7 12.6 15.1 2.6 — 15.3 1.2 0.6 3.1 

Single Family 

Attached 37.6 30.3 7.9 9.2 5.3 8.5 1.3 0.0 — 1.9 

Midrise 

Apartment 

Building

30.0 21.9 12.8 15.4 2.2 — 15.3 1.6 0.7 0.6 

Total 32.9 30.4 9.9 9.8 7.3 7.1 2.4 0.0 0.0 100.0 
235

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

Figure G-46. Residential electricity use diurnal patterns by season, modeled for the West South 

Central census division 2012
236

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

Table G-47. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End

Use for the West South Central Census Division in 2012

Subsector

Interior 

Lights 

(GWh)

Interior 

Equipment 

(GWh)

Space 

Cooling 

(GWh)

Fans 

(GWh)

Exterior 

Lights 

(GWh)

Pumps 

(GWh)

Space 

Heating 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Large Office 10,320 12,109 10,791 8,549 2,103 1,228 38 638 45,777 

Strip Mall 16,687 2,427 3,948 3,408 3,418 14 299 8 30,209 

Standalone 

Retail Store 9,816 3,146 3,155 3,009 1,498 10 432 5 21,072 

Small Office 1,712 2,347 1,319 1,255 668 11 64 8 7,383 

Medium Office 1,682 1,928 1,573 1,256 599 86 19 40 7,182 

Warehouse 2,804 1,117 475 828 1,375 7 347 5 6,959 

Full Service 

Restaurant 1,233 2,772 960 725 343 9 48 4 6,094 

Large Hotel 1,137 1,854 1,359 1,197 282 38 32 17 5,916 

Hospital 1,022 1,280 950 801 77 156 — 81 4,368 

Primary 

School 575 457 344 276 73 15 10 8 1,758 

Outpatient 

Treatment 

Facility

382 566 147 165 138 23 — 13 1,433 

Small Hotel 78 117 11 8 61 1 3 1 280 

Quick Service 

Restaurant 17 99 34 33 10 1 0 0 194 

Total 47,463 30,218 25,067 21,510 10,645 1,599 1,294 829 138,625 
237

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

Table G-48. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for 

the West South Central Census Division in 2012

Subsector

Interior 

Lights 

(%)

Interior 

Equipment 

(%)

Space 

Cooling 

(%)

Fans 

(%)

Exterior 

Lights 

(%)

Pumps 

(%)

Space 

Heating 

(%)

Heat 

Rejection 

(%)

Total 

(%)

Large Office 22.5 26.5 23.6 18.7 4.6 2.7 0.1 1.4 33.0 

Strip Mall 55.2 8.0 13.1 11.3 11.3 0.0 1.0 0.0 21.8 

Standalone 

Retail Store 46.6 14.9 15.0 14.3 7.1 0.0 2.0 0.0 15.2 

Small Office 23.2 31.8 17.9 17.0 9.0 0.2 0.9 0.1 5.3 

Medium Office 23.4 26.8 21.9 17.5 8.3 1.2 0.3 0.6 5.2 

Warehouse 40.3 16.0 6.8 11.9 19.8 0.1 5.0 0.1 5.0 

Full Service 

Restaurant 20.2 45.5 15.7 11.9 5.6 0.2 0.8 0.1 4.4 

Large Hotel 19.2 31.3 23.0 20.2 4.8 0.6 0.5 0.3 4.3 

Hospital 23.4 29.3 21.8 18.3 1.8 3.6 — 1.9 3.2 

Primary 

School 32.7 26.0 19.6 15.7 4.2 0.8 0.6 0.4 1.3 

Outpatient 

Treatment 

Facility

26.7 39.5 10.3 11.5 9.6 1.6 — 0.9 1.0 

Small Hotel 27.7 41.7 3.9 2.9 21.8 0.4 1.2 0.2 0.2 

Quick Service 

Restaurant 8.6 50.9 17.6 17.3 5.0 0.3 0.1 0.2 0.1 

Total 34.2 21.8 18.1 15.5 7.7 1.2 0.9 0.6 100.0 
238

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

Figure G-47. Commercial electricity use diurnal patterns by season, modeled for the West South 

Central census division 2012
239

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

Table G-49. Industrial Manufacturing Subsectors, Summary of Model Results for the West South Central Census Division in 2012

Subsector

Machine Drive (GWh)Process Heating (GWh)Electro Chemical Processes (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Petroleum and Coal Products Manufacturing 17,126 717 307 804 894 535 151 208 158 237 2 22 21,161 

Basic Chemical Manufacturing 10,318 664 3,064 1,150 1,737 741 277 217 384 247 46 46 18,892 

Iron and Steel Mills and Ferroalloy 

Manufacturing 3,482 4,470 2,974 809 192 568 201 155 61 255 21 36 13,224 

Pulp, Paper, and Paperboard Mills 5,965 310 65 346 132 303 266 85 364 39 16 — 7,890 

Plastics Product Manufacturing 2,990 895 — 565 447 528 — 127 28 — 24 — 5,602 

Converted Paper Product Manufacturing 4,049 211 44 237 90 207 182 58 249 27 11 — 5,366 

Resin, Synthetic Rubber, and Artificial Synthetic 

Fibers and Filaments Manufacturing 2,403 155 715 268 405 173 65 51 89 58 11 11 4,402 

Other Chemical Product and Preparation 

Manufacturing 1,878 121 560 210 318 135 51 40 70 45 8 8 3,445 

Animal Slaughtering and Processing 1,425 141 7 277 865 244 60 66 101 73 31 14 3,305 

Pesticide, Fertilizer, and Other Agricultural 

Chemical Manufacturing 1,582 103 486 181 276 116 44 34 61 39 7 7 2,936 

Other Wood Product Manufacturing 1,664 141 7 168 16 134 24 36 40 24 6 8 2,266 

Steel Product Manufacturing from Purchased 

Steel 509 648 416 115 27 81 28 22 9 36 3 5 1,897 

Printing and Related Support Activities 993 76 21 312 137 156 26 52 16 — 20 — 1,809 

Pharmaceutical and Medicine Manufacturing 970 63 292 109 166 70 26 21 37 24 4 4 1,786 
240

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Electro Chemical Processes (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Architectural and Structural Metals 

Manufacturing 734 252 66 263 55 166 61 57 — — 12 3 1,670 

Other Fabricated Metal Product Manufacturing 720 246 64 256 53 161 59 55 — — 11 3 1,629 

Other Nonmetallic Mineral Product 

Manufacturing 831 394 28 101 60 77 34 24 14 — 6 2 1,571 

Semiconductor and Other Electronic 

Component Manufacturing 355 185 35 373 177 142 113 75 14 33 4 30 1,535 

Nonferrous Metal (except Aluminum) 

Production and Processing 402 512 328 90 21 64 22 17 7 28 2 4 1,497 

Aerospace Product and Parts Manufacturing 549 156 9 266 72 178 70 56 12 22 20 17 1,427 

Soap, Cleaning Compound, and Toilet 

Preparation Manufacturing 764 49 234 87 133 56 21 17 29 19 4 3 1,415 

Rubber Product Manufacturing 676 202 — 128 102 120 — 29 6 — 5 — 1,268 

Agriculture, Construction, and Mining Machinery 

Manufacturing 506 117 12 243 48 166 27 44 11 — 12 — 1,186 

Cement and Concrete Product Manufacturing 609 289 20 74 45 57 25 18 10 — 4 2 1,152 

Foundries 304 390 256 70 17 49 17 13 5 22 2 3 1,149 

Alumina and Aluminum Production and 

Processing 265 338 217 60 14 42 15 11 4 19 2 3 990 

Paint, Coating, and Adhesive Manufacturing 531 34 162 60 92 39 15 12 20 13 2 2 983 

Beverage Manufacturing 334 40 2 109 182 86 23 31 15 71 19 4 915 
241

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Electro Chemical Processes (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Veneer, Plywood, and Engineered Wood 

Product Manufacturing 669 57 3 66 6 53 9 14 16 10 2 3 908 

Fruit and Vegetable Preserving and Specialty 

Food Manufacturing 387 38 2 75 235 66 16 18 28 20 8 4 898 

Motor Vehicle Manufacturing 307 88 5 151 41 101 40 32 7 13 11 10 806 

Other Food Manufacturing 342 34 2 66 205 58 14 16 24 17 7 3 789 

Grain and Oilseed Milling 321 32 2 62 192 54 13 15 23 16 7 3 739 

Bakeries and Tortilla Manufacturing 319 32 2 62 192 54 13 15 23 16 7 3 736 

Glass and Glass Product Manufacturing 384 182 13 46 27 35 15 11 6 — 3 1 723 

Other General Purpose Machinery 

Manufacturing 282 65 7 138 27 94 16 25 6 — 7 — 669 

Coating, Engraving, Heat Treating, and Allied 

Activities 291 100 26 106 22 66 25 23 — — 5 1 665 

Motor Vehicle Parts Manufacturing 252 72 4 123 33 82 32 26 5 10 9 8 657 

Other Electrical Equipment and Component 

Manufacturing 174 143 48 103 40 61 23 14 3 — 4 — 613 

Dairy Product Manufacturing 263 26 1 50 156 44 11 12 18 13 6 2 603 

Animal Food Manufacturing 251 25 1 49 155 43 11 12 18 13 5 2 586 

Other Miscellaneous Manufacturing 191 67 4 141 42 81 11 28 9 — 3 1 580 
242

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Electro Chemical Processes (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Navigational, Measuring, Electromedical, and 

Control Instruments Manufacturing 126 66 13 135 65 51 41 27 5 12 1 11 554 

Electrical Equipment Manufacturing 146 120 42 88 34 52 19 12 2 — 4 — 519 

Lime and Gypsum Product Manufacturing 269 128 9 33 20 25 11 8 4 — 2 1 508 

Other Textile Product Mills 238 32 1 59 30 48 2 11 22 — 11 — 454 

Communications Equipment Manufacturing 86 45 9 92 44 35 28 19 3 8 1 7 377 

Motor Vehicle Body and Trailer Manufacturing 129 37 2 63 17 42 17 13 3 5 5 4 337 

Commercial and Service Industry Machinery 

Manufacturing 139 32 3 67 13 46 8 12 3 — 3 — 328 

Railroad Rolling Stock Manufacturing 123 35 2 61 17 41 16 13 3 5 5 4 324 

Household and Institutional Furniture and 

Kitchen Cabinet Manufacturing 160 12 1 66 5 54 — 11 4 — 4 2 319 

Ship and Boat Building 118 34 2 60 16 40 16 13 3 5 5 4 315 

Spring and Wire Product Manufacturing 124 43 11 45 9 28 10 10 — — 2 1 282 

Engine, Turbine, and Power Transmission 

Equipment Manufacturing 116 27 3 56 11 38 6 10 3 — 3 — 272 

Industrial Machinery Manufacturing 115 26 3 55 11 38 6 10 3 — 3 — 270 

Forging and Stamping 115 39 10 41 9 26 10 9 — — 2 1 261 

Computer and Peripheral Equipment 

Manufacturing 55 29 6 61 29 23 19 12 2 6 1 5 248 
243

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Electro Chemical Processes (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Fabric Mills 143 21 0 36 9 18 4 4 5 — 2 — 241 

Sugar and Confectionery Product 

Manufacturing 103 10 1 20 63 18 4 5 7 5 2 1 240 

Ventilation, Heating, Air-Conditioning, and 

Commercial Refrigeration Equipment 

Manufacturing

93 22 2 46 9 31 5 8 2 — 2 — 222 

Clay Product and Refractory Manufacturing 117 56 4 14 9 11 5 3 2 — 1 0 222 

Medical Equipment and Supplies Manufacturing 72 25 2 52 15 30 4 10 3 — 1 1 216 

Cut and Sew Apparel Manufacturing 79 10 — 57 3 33 — 7 4 — 1 — 195 

Textile and Fabric Finishing and Fabric Coating 

Mills 104 15 0 26 7 13 3 3 4 — 2 — 177 

Seafood Product Preparation and Packaging 71 7 0 14 44 12 3 3 5 4 2 1 165 

Metalworking Machinery Manufacturing 54 13 1 26 5 18 3 5 1 — 1 — 128 

Boiler, Tank, and Shipping Container 

Manufacturing 55 19 5 19 4 12 4 4 — — 1 0 124 

Machine Shops; Turned Product; and Screw, 

Nut, and Bolt Manufacturing 54 19 5 20 4 12 5 4 — — 1 0 124 

Fiber, Yarn, and Thread Mills 61 9 0 15 4 7 2 2 2 — 1 — 103 

Office Furniture (including Fixtures) 

Manufacturing 45 3 0 19 2 15 — 3 1 — 1 0 89 

Other Transportation Equipment Manufacturing 34 10 1 17 4 11 4 3 1 1 1 1 88 
244

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Electro Chemical Processes (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Sawmills and Wood Preservation 54 5 0 5 1 4 1 1 1 1 0 0 73 

Electric Lighting Equipment Manufacturing 20 16 5 12 5 7 3 2 0 — 0 — 70 

Household Appliance Manufacturing 19 16 5 11 4 7 3 2 0 — 0 — 68 

Cutlery and Handtool Manufacturing 19 6 2 7 1 4 2 1 — — 0 0 43 

Other Leather and Allied Product Manufacturing 21 3 0 3 1 4 0 1 0 — 0 — 33 

Tobacco Manufacturing 12 1 0 4 6 3 1 1 1 2 1 0 32 

Audio and Video Equipment Manufacturing 7 3 1 7 3 3 2 1 0 1 0 1 28 

Other Furniture Related Product Manufacturing 14 1 0 6 0 5 — 1 0 — 0 0 27 

Textile Furnishings Mills 11 1 0 3 1 2 0 1 1 — 1 — 22 

Footwear Manufacturing 8 1 0 1 0 1 0 0 0 — 0 — 12 

Manufacturing and Reproducing Magnetic and 

Optical Media 2 1 0 2 1 1 1 0 0 0 0 0 9 

Apparel Accessories and Other Apparel 

Manufacturing 2 0 — 1 0 1 — 0 0 — 0 — 5 

Leather and Hide Tanning and Finishing 2 0 0 0 0 0 0 0 0 — 0 — 3 

Hardware Manufacturing 1 0 0 0 0 0 0 0 — — 0 0 2 

Total 70,703 13,876 10,661 10,395 8,684 7,160 2,422 2,155 2,102 1,516 472 324 130,471 
245

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

Figure G-48. Industrial electricity use diurnal patterns by season, modeled for the West South 

Central census division 2012
246

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

Table G-50. Distributed Generation Model, Annual Summary the West South Central Census 

Division in 2012 by Sector and DG Type

Sector/DG Type Industry Commercial Residential Total

CHP (GWh) 55,861 1,787 0 57,648 

Thermal DG (GWh) 2,692 35 — 2,727 

Distributed PV (GWh) 15 14 30 60 

Total (GWh) 58,568 1,837 30 60,435 

CHP (%) 95.4 97.3 0.2 95.4 

Thermal DG (%) 4.6 1.9 — 4.5 

Distributed PV (%) 0.0 0.8 99.8 0.1 

Total (%) 96.9 3.0 0.1 100.0 

Figure G-49. Distributed Generation Model Diurnal Generation Patterns, West South Central

census division 2012
247

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G.8 Mountain

Table G-51. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in 

dsgrid, Mountain Census Division

Component 

Type

Component

Name Residential Commercial Industrial Transport Total

Top-down Hourly load 289.4 

Derived T&D losses 15.0 

Top-down Annual energy 94.9 98.2 82.3 0.1 275.4 

dsgrid Distributed generation – 1.0 3.6 – 4.5 

dsgrid-core Gap models 12.4 27.7 22.2 0.1 62.4 

dsgrid-core Detailed sector models 84.4 65.3 36.5 – 186.1 

Derived Total site energy 94.9 99.1 85.9 0.1 280.0 

Derived Annual sector residuals -1.9 6.2 27.2 -0.0 31.5 

Derived Hourly residuals 29.1 

Figure G-50. Bottom-up detailed sectoral and gap model load compared to bulk-level historical 

hourly load plus DG estimates for the Mountain census division in 2012
248

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Figure G-51. dsgrid hourly residuals shown in context for the Mountain census division.

Figure G-52. Historical and dsgrid load duration curves for the Mountain census division in 2012

The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of the 

detailed sector and gap models.
249

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

Table G-52. Residential Subsectors, Summary of Electricity by End Use for the Mountain Census Division in 2012

Subsector

Interior 

Equipment 

(GWh)

Space 

Cooling 

(GWh)

Interior 

Lights 

(GWh)

Fans 

(GWh)

Space 

Heating 

(GWh)

Water 

Systems 

(GWh)

Exterior 

Lights 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Single Family 

Detached 32,880 15,722 10,540 8,833 7,934 5,447 2,136 58 — 83,551 

Mobile Home 2,988 1,046 628 643 502 522 102 4 — 6,435 

Apartment in Building 

2 to 4 Units 1,009 397 430 519 156 — 514 24 5 3,053 

Single Family 

Attached 1,332 466 280 287 224 233 45 2 — 2,868 

Midrise Apartment 

Building 274 95 123 140 40 — 146 8 2 827 

Total 38,482 17,727 12,002 10,422 8,856 6,201 2,944 95 6 96,735 

Table G-53. Residential Electricity Proportions by Subsector and End Use for the Mountain Census Division in 2012

Subsector

Interior 

Equipment 

(%)

Space 

Cooling 

(%)

Interior 

Lights 

(%) Fans (%)

Space 

Heating 

(%)

Water 

Systems 

(%)a

Exterior 

Lights (%)

Pumps 

(%)

Heat 

Rejection 

(%) Total (%)

Single Family Detached 39.4 18.8 12.6 10.6 9.5 6.5 2.6 0.1 — 86.4 

Mobile Home 46.4 16.3 9.8 10.0 7.8 8.1 1.6 0.1 — 6.7 

Apartment in Building 

2 to 4 Units 33.0 13.0 14.1 17.0 5.1 — 16.8 0.8 0.2 3.2 

Single Family Attached 46.4 16.3 9.8 10.0 7.8 8.1 1.6 0.1 — 3.0 

Midrise Apartment 

Building 33.1 11.5 14.8 17.0 4.8 — 17.7 0.9 0.2 0.9 

Total 39.8 18.3 12.4 10.8 9.2 6.4 3.0 0.1 0.0 100.0 
250

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Figure G-53. Residential electricity use diurnal patterns by season, modeled for the Mountain

census division 2012
251

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

Table G-54. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End

Use for the Mountain Census Division in 2012

Subsector

Interior 

Lights 

(GWh)

Interior 

Equipment 

(GWh)

Fans 

(GWh)

Space 

Cooling 

(GWh)

Exterior 

Lights 

(GWh)

Space 

Heating 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Strip Mall 9,437 1,379 2,464 1,231 1,927 399 10 2 16,849 

Large Office 3,463 4,073 3,177 1,874 726 62 284 67 13,726 

Standalone 

Retail Store 4,309 1,421 1,715 861 665 544 1 0 9,514 

Medium Office 1,357 1,546 1,203 741 482 37 45 11 5,423 

Warehouse 1,634 651 716 199 804 630 3 1 4,638 

Small Office 1,114 1,523 872 557 435 115 5 2 4,623 

Full Service 

Restaurant 724 1,616 447 310 202 70 3 1 3,372 

Large Hotel 648 1,024 718 445 163 60 31 6 3,094 

Hospital 460 704 283 189 38 — 32 9 1,714 

Primary 

School 330 266 177 118 43 18 5 1 958 

Outpatient 

Treatment 

Facility

261 381 123 60 94 — 11 3 933 

Small Hotel 97 142 9 4 71 15 1 0 339 

Quick Service 

Restaurant 6 36 13 8 4 0 — — 67 

Total 23,839 14,761 11,918 6,596 5,653 1,949 429 105 65,251 
252

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

Table G-55. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for 

the Mountain Census Division in 2012

Subsector

Interior 

Lights 

(%)

Interior 

Equipment 

(%)

Fans 

(%)

Space 

Cooling 

(%)

Exterior 

Lights 

(%)

Space 

Heating 

(%)

Pumps 

(%)

Heat 

Rejection 

(%)

Total 

(%)

Strip Mall 56.0 8.2 14.6 7.3 11.4 2.4 0.1 0.0 25.8 

Large Office 25.2 29.7 23.1 13.7 5.3 0.5 2.1 0.5 21.0 

Standalone 

Retail Store 45.3 14.9 18.0 9.0 7.0 5.7 0.0 0.0 14.6 

Medium Office 25.0 28.5 22.2 13.7 8.9 0.7 0.8 0.2 8.3 

Warehouse 35.2 14.0 15.4 4.3 17.3 13.6 0.1 0.0 7.1 

Small Office 24.1 33.0 18.9 12.1 9.4 2.5 0.1 0.0 7.1 

Full Service 

Restaurant 21.5 47.9 13.3 9.2 6.0 2.1 0.1 0.0 5.2 

Large Hotel 20.9 33.1 23.2 14.4 5.3 1.9 1.0 0.2 4.7 

Hospital 26.8 41.0 16.5 11.0 2.2 — 1.9 0.5 2.6 

Primary 

School 34.4 27.8 18.5 12.3 4.5 1.9 0.5 0.2 1.5 

Outpatient 

Treatment 

Facility

28.0 40.8 13.2 6.4 10.1 — 1.1 0.4 1.4 

Small Hotel 28.5 41.9 2.6 1.3 20.9 4.6 0.2 0.0 0.5 

Quick Service 

Restaurant 9.0 53.4 19.9 12.1 5.3 0.3 — — 0.1 

Total 36.5 22.6 18.3 10.1 8.7 3.0 0.7 0.2 100.0 
253

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

Figure G-54. Commercial electricity use diurnal patterns by season, modeled for the Mountain

census division 2012
254

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Table G-56. Industrial Manufacturing Subsectors, Summary of Model Results for the Mountain Census Division in 2012

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Electro Chemical Processes (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Petroleum and Coal Products 

Manufacturing 3,212 134 151 168 100 58 28 39 30 45 0 4 3,969 

Plastics Product Manufacturing 1,435 429 271 214 253 — — 61 13 — 11 — 2,689 

Iron and Steel Mills and Ferroalloy 

Manufacturing 697 894 162 38 114 595 40 31 12 51 4 7 2,645 

Pulp, Paper, and Paperboard Mills 1,602 83 93 35 81 17 71 23 98 11 4 — 2,119 

Converted Paper Product Manufacturing 1,161 60 68 26 59 13 52 17 71 8 3 — 1,539 

Semiconductor and Other Electronic 

Component Manufacturing 313 163 328 156 125 31 100 66 12 29 4 26 1,352 

Other Wood Product Manufacturing 892 76 90 9 72 4 13 19 21 13 3 4 1,216 

Basic Chemical Manufacturing 644 41 72 108 46 191 17 14 24 15 3 3 1,179 

Pharmaceutical and Medicine 

Manufacturing 620 40 70 106 45 187 17 13 23 15 3 3 1,142 

Other Nonmetallic Mineral Product 

Manufacturing 533 253 65 39 49 18 22 15 9 — 4 2 1,008 

Dairy Product Manufacturing 421 42 81 250 71 2 17 19 29 21 9 4 967 
255

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Electro Chemical Processes (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Aerospace Product and Parts 

Manufacturing 361 103 175 47 117 6 46 37 8 15 13 11 938 

Nonferrous Metal (except Aluminum) 

Production and Processing 250 319 56 13 40 204 14 11 4 17 1 2 932 

Navigational, Measuring, Electromedical, 

and Control Instruments Manufacturing 205 107 219 105 83 21 67 45 8 20 2 17 898 

Pesticide, Fertilizer, and Other Agricultural 

Chemical Manufacturing 419 27 48 73 31 129 12 9 16 10 2 2 778 

Animal Slaughtering and Processing 325 32 63 197 56 2 14 15 23 17 7 3 753 

Beverage Manufacturing 246 29 81 135 64 1 17 23 11 52 14 3 675 

Cement and Concrete Product 

Manufacturing 356 169 43 26 33 12 14 10 6 — 3 1 675 

Architectural and Structural Metals 

Manufacturing 281 96 101 21 63 25 23 22 — — 4 1 639 

Communications Equipment Manufacturing 132 69 142 68 54 13 44 29 5 13 2 11 583 

Fruit and Vegetable Preserving and 

Specialty Food Manufacturing 242 24 47 147 41 1 10 11 17 12 5 2 561 

Other Fabricated Metal Product 

Manufacturing 246 84 87 18 55 22 20 19 — — 4 1 556 

Glass and Glass Product Manufacturing 273 129 33 20 25 9 11 8 4 — 2 1 514 

Computer and Peripheral Equipment 

Manufacturing 114 60 125 61 47 12 39 26 5 11 1 10 511 
256

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Electro Chemical Processes (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Soap, Cleaning Compound, and Toilet 

Preparation Manufacturing 267 17 30 46 20 82 7 6 10 7 1 1 495 

Other Chemical Product and Preparation 

Manufacturing 269 17 30 46 19 80 7 6 10 6 1 1 494 

Veneer, Plywood, and Engineered Wood 

Product Manufacturing 361 31 36 3 29 1 5 8 8 5 1 2 489 

Other Miscellaneous Manufacturing 144 51 106 31 61 3 8 21 7 — 3 1 438 

Printing and Related Support Activities 230 18 72 32 36 5 6 12 4 — 5 — 419 

Bakeries and Tortilla Manufacturing 178 18 34 107 30 1 7 8 13 9 4 2 411 

Animal Food Manufacturing 156 15 31 96 27 1 7 7 11 8 3 2 363 

Other Food Manufacturing 136 13 26 81 23 1 6 6 10 7 3 1 313 

Foundries 80 103 18 4 13 67 5 4 1 6 0 1 302 

Coating, Engraving, Heat Treating, and 

Allied Activities 114 39 41 9 26 10 10 9 — — 2 1 260 

Grain and Oilseed Milling 109 11 21 65 19 1 5 5 8 6 2 1 251 

Household and Institutional Furniture and 

Kitchen Cabinet Manufacturing 117 9 48 4 40 1 — 8 3 — 3 1 232 

Motor Vehicle Parts Manufacturing 78 22 38 10 25 1 10 8 2 3 3 2 202 

Alumina and Aluminum Production and 

Processing 52 66 12 3 8 43 3 2 1 4 0 1 194 
257

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Electro Chemical Processes (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Medical Equipment and Supplies 

Manufacturing 63 22 46 14 27 1 4 9 3 — 1 0 190 

Paint, Coating, and Adhesive 

Manufacturing 91 6 10 16 7 28 3 2 3 2 0 0 168 

Lime and Gypsum Product Manufacturing 83 39 10 6 8 3 3 2 1 — 1 0 156 

Sugar and Confectionery Product 

Manufacturing 66 7 13 41 11 0 3 3 5 3 1 1 154 

Commercial and Service Industry 

Machinery Manufacturing 65 15 32 6 22 2 4 6 1 — 2 — 154 

Other General Purpose Machinery 

Manufacturing 57 13 28 6 19 1 3 5 1 — 1 — 134 

Other Electrical Equipment and Component 

Manufacturing 37 30 22 8 13 10 5 3 1 — 1 — 129 

Motor Vehicle Body and Trailer 

Manufacturing 48 14 23 6 16 1 6 5 1 2 2 1 124 

Steel Product Manufacturing from 

Purchased Steel 33 42 7 2 5 27 2 1 1 2 0 0 123 

Agriculture, Construction, and Mining 

Machinery Manufacturing 51 12 25 5 17 1 3 4 1 — 1 — 119 

Rubber Product Manufacturing 56 17 11 8 10 — — 2 1 — 0 — 105 

Resin, Synthetic Rubber, and Artificial 

Synthetic Fibers and Filaments 

Manufacturing

54 3 6 9 4 16 1 1 2 1 0 0 100 
258

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Electro Chemical Processes (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Industrial Machinery Manufacturing 38 9 18 4 13 1 2 3 1 — 1 — 90 

Other Textile Product Mills 46 6 11 6 9 0 0 2 4 — 2 — 87 

Electrical Equipment Manufacturing 23 19 14 5 8 7 3 2 0 — 1 — 83 

Clay Product and Refractory Manufacturing 40 19 5 3 4 1 2 1 1 — 0 0 75 

Audio and Video Equipment Manufacturing 17 9 18 9 7 2 6 4 1 2 0 1 75 

Forging and Stamping 30 10 11 2 7 3 2 2 — — 0 0 67 

Manufacturing and Reproducing Magnetic 

and Optical Media 11 6 12 6 5 1 4 2 0 1 0 1 49 

Engine, Turbine, and Power Transmission 

Equipment Manufacturing 20 5 10 2 7 0 1 2 0 — 0 — 48 

Machine Shops; Turned Product; and 

Screw, Nut, and Bolt Manufacturing 21 7 8 2 5 2 2 2 — — 0 0 47 

Other Transportation Equipment 

Manufacturing 18 5 9 2 6 0 2 2 0 1 1 1 47 

Metalworking Machinery Manufacturing 19 4 9 2 6 0 1 2 0 — 0 — 46 

Spring and Wire Product Manufacturing 18 6 7 1 4 2 2 1 — — 0 0 41 

Seafood Product Preparation and 

Packaging 16 2 3 10 3 0 1 1 1 1 0 0 38 

Tobacco Manufacturing 10 1 3 6 3 0 1 1 0 2 1 0 28 

Boiler, Tank, and Shipping Container 

Manufacturing 13 4 4 1 3 1 1 1 — — 0 0 28 
259

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Electro Chemical Processes (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Fiber, Yarn, and Thread Mills 16 2 4 1 2 0 0 0 1 — 0 — 27 

Railroad Rolling Stock Manufacturing 10 3 5 1 3 0 1 1 0 0 0 0 25 

Sawmills and Wood Preservation 17 1 2 0 1 0 0 0 0 0 0 0 23 

Other Furniture Related Product 

Manufacturing 11 1 4 0 4 0 — 1 0 — 0 0 22 

Ventilation, Heating, Air-Conditioning, and 

Commercial Refrigeration Equipment 

Manufacturing

7 2 4 1 3 0 0 1 0 — 0 — 18 

Ship and Boat Building 7 2 3 1 2 0 1 1 0 0 0 0 18 

Office Furniture (including Fixtures) 

Manufacturing 9 1 4 0 3 0 — 1 0 — 0 0 17 

Electric Lighting Equipment Manufacturing 5 4 3 1 2 1 1 0 0 — 0 — 17 

Cutlery and Handtool Manufacturing 7 3 3 1 2 1 1 1 — — 0 0 17 

Household Appliance Manufacturing 4 3 2 1 1 1 0 0 0 — 0 — 13 

Motor Vehicle Manufacturing 5 1 2 1 2 0 1 0 0 0 0 0 12 

Cut and Sew Apparel Manufacturing 5 1 3 0 2 — — 0 0 — 0 — 12 

Fabric Mills 7 1 2 0 1 0 0 0 0 — 0 — 11 

Textile and Fabric Finishing and Fabric 

Coating Mills 4 1 1 0 1 0 0 0 0 — 0 — 7 

Other Leather and Allied Product 

Manufacturing 4 0 1 0 1 0 0 0 0 — 0 — 6 
260

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

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Electro Chemical Processes (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Onsite Transportation (GWh)Other Nonprocess Use (GWh)Total (GWh)

Textile Furnishings Mills 1 0 0 0 0 0 0 0 0 — 0 — 2 

Leather and Hide Tanning and Finishing 1 0 0 0 0 0 0 0 0 — 0 — 1 

Footwear Manufacturing 1 0 0 0 0 0 0 0 0 — 0 — 1 

Apparel Accessories and Other Apparel 

Manufacturing 0 0 0 0 0 — — 0 0 — 0 — 0 

Total 18,435 4,253 3,631 2,814 2,375 1,983 864 770 572 455 162 144 36,45

9
261

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

Figure G-55. Industrial electricity use diurnal patterns by season, modeled for the Mountain

census division 2012
262

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

Table G-57. Distributed Generation Model, Annual Summary the Mountain Census Division in 2012 

by Sector and DG Type

Sector/DG Type Industry Commercial Residential Total

CHP (GWh) 3,080 669 — 3,749 

Distributed PV (GWh) 247 246 445 938 

Thermal DG (GWh) 247 38 — 285 

Total (GWh) 3,574 953 445 4,972 

CHP (%) 86.2 70.2 — 75.4 

Distributed PV (%) 6.9 25.9 100.0 18.9 

Thermal DG (%) 6.9 4.0 — 5.7 

Total (%) 71.9 19.2 8.9 100.0 

Figure G-56. Distributed Generation Model Diurnal Generation Patterns, Mountain census division 2012
263

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G.9 Pacific

Table G-58. Annual Electricity Load for 2012 in Terawatt-Hours, Top-Down and Represented in 

dsgrid, Pacific Census Division

Component 

Type

Component

Name Residential Commercial Industrial Transport Total

Top-down Hourly load 418.7 

Derived T&D losses 21.9 

Top-down Annual energy 144.5 179.6 86.5 0.7 411.3 

dsgrid Distributed generation 1.4 6.5 30.0 – 37.9 

dsgrid-core Gap models 23.0 77.2 24.2 0.7 125.1 

dsgrid-core Detailed sector models 137.1 163.4 83.3 – 383.8 

Derived Total site energy 145.9 186.1 116.5 0.7 449.2 

Derived Annual sector residuals -14.2 -54.5 9.0 0.0 -59.7

Derived Hourly residuals -74.2

Figure G-57. Bottom-up detailed sectoral and gap model load compared to bulk-level historical 

hourly load plus DG estimates for the Pacific census division in 2012
264

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

Figure G-58. dsgrid hourly residuals shown in context for the Pacific census division.

Figure G-59. Historical and dsgrid load duration curves for the Pacific census division in 2012

The dsgrid modeled load duration curve consists of the distributed generation model subtracted from the sum of the 

detailed sector and gap models.
265

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Table G-59. Residential Subsectors, Summary of Electricity by End Use for the Pacific

Census Division in 2012

Subsector

Interior 

Equipment 

(GWh)

Space 

Heating 

(GWh)

Interior 

Lights 

(GWh)

Space 

Cooling 

(GWh)

Fans 

(GWh)

Water 

Systems 

(GWh)

Exterior 

Lights 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Single 

Family 

Detached

54,567 21,167 17,893 16,840 11,922 8,438 3,592 9 — 

134,428 

Mobile 

Home

3,880 803 876 1,001 673 480 143 0 — 7,855 

Single 

Family 

Attached 

3,783 783 854 976 656 468 139 0 — 7,659 

Apartment 

in Building 

2 to 4 Units

2,673 378 1,127 831 1,012 — 1,352 95 19 7,487 

Midrise 

Apartment 

Building

957 127 419 267 344 — 500 32 6 2,652 

Total 65,861 23,257 21,168 19,915 14,606 9,386 5,725 137 26 160,080 

Table G-60. Residential Electricity Proportions by Subsector and End Use for the Pacific census 

division in 2012

Subsector

Interior 

Equipment 

(%)

Space 

Heating 

(%)

Interior 

Lights 

(%)

Space 

Cooling 

(%)

Fans 

(%)

Water 

Systems 

(%)

Exterior 

Lights 

(%)

Pumps 

(%)

Heat 

Rejection 

(%)

Total 

(%)

Single Family 

Detached 40.6 15.7 13.3 12.5 8.9 6.3 2.7 0.0 — 84.0 

Mobile Home 49.4 10.2 11.1 12.7 8.6 6.1 1.8 0.0 — 4.9 

Single Family 

Attached 49.4 10.2 11.1 12.7 8.6 6.1 1.8 0.0 — 4.8 

Apartment in 

Building 2 to 

4 Units

35.7 5.1 15.0 11.1 13.5 — 18.1 1.3 0.3 4.7 

Midrise 

Apartment 

Building

36.1 4.8 15.8 10.1 13.0 — 18.8 1.2 0.2 1.7 

Total 41.1 14.5 13.2 12.4 9.1 5.9 3.6 0.1 0.0 100.0 
266

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Figure G-60. Residential electricity use diurnal patterns by season, modeled for the Pacific

census division 2012
267

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

Table G-61. Commercial Subsectors, Summary of Electricity in Detailed Energy Modeling by End

Use for the Pacific Census Division in 2012

Subsector

Interior 

Lights 

(GWh)

Interior 

Equipment 

(GWh)

Fans 

(GWh)

Space 

Cooling 

(GWh)

Exterior 

Lights

(GWh)

Space 

Heating 

(GWh)

Pumps 

(GWh)

Heat 

Rejection 

(GWh)

Total 

(GWh)

Large Office 14,163 16,430 9,870 6,847 2,900 105 1,148 239 51,703 

Strip Mall 24,987 3,628 4,582 2,139 5,105 546 14 4 41,006 

Standalone 

Retail Store 9,557 3,110 2,808 1,348 1,462 787 5 1 19,077 

Medium Office 3,487 3,971 2,301 1,422 1,241 77 114 25 12,638 

Warehouse 3,888 1,550 970 281 1,882 702 5 2 9,279 

Small Office 2,187 2,976 1,477 867 854 162 11 4 8,537 

Large Hotel 1,516 2,334 1,240 917 368 33 98 19 6,526 

Full Service 

Restaurant 1,378 3,077 785 402 385 82 7 1 6,117 

Hospital 975 1,274 621 456 75 — 86 22 3,508 

Outpatient 

Treatment 

Facility 

687 1,017 258 110 243 — 22 6 2,343 

Primary 

School 648 519 304 195 84 53 12 3 1,818 

Small Hotel 227 307 32 17 144 13 3 1 743 

Quick Service 

Restaurant 10 57 19 9 6 0 0 0 101 

Total 63,709 40,249 25,265 15,011 14,748 2,561 1,524 328 163,396 
268

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Table G-62. Commercial Electricity Proportions of Detailed Models by Subsector and End Use for 

the Pacific Census Division in 2012

Subsector

Interior 

Lights 

(%)

Interior 

Equipment 

(%)

Fans 

(%)

Space 

Cooling 

(%)

Exterior 

Lights 

(%)

Space 

Heating 

(%)

Pumps 

(%)

Heat 

Rejection 

(%)

Total 

(%)

Large Office 27.4 31.8 19.1 13.2 5.6 0.2 2.2 0.5 31.6 

Strip Mall 60.9 8.8 11.2 5.2 12.5 1.3 0.0 0.0 25.1 

Standalone 

Retail Store 50.1 16.3 14.7 7.1 7.7 4.1 0.0 0.0 11.7 

Medium Office 27.6 31.4 18.2 11.3 9.8 0.6 0.9 0.2 7.7 

Warehouse 41.9 16.7 10.4 3.0 20.3 7.6 0.1 0.0 5.7 

Small Office 25.6 34.9 17.3 10.2 10.0 1.9 0.1 0.1 5.2 

Large Hotel 23.2 35.8 19.0 14.1 5.6 0.5 1.5 0.3 4.0 

Full Service 

Restaurant 22.5 50.3 12.8 6.6 6.3 1.3 0.1 0.0 3.7 

Hospital 27.8 36.3 17.7 13.0 2.1 — 2.4 0.6 2.1 

Outpatient 

Treatment 

Facility 

29.3 43.4 11.0 4.7 10.4 — 1.0 0.3 1.4 

Primary 

School 35.7 28.5 16.7 10.7 4.6 2.9 0.7 0.2 1.1 

Small Hotel 30.5 41.3 4.2 2.3 19.4 1.8 0.4 0.1 0.5 

Quick Service 

Restaurant 9.5 56.3 19.2 9.0 5.7 0.2 0.0 0.0 0.1 

Total 39.0 24.6 15.5 9.2 9.0 1.6 0.9 0.2 100.0 
269

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Figure G-61. Commercial electricity use diurnal patterns by season, modeled for the Pacific

census division 2012
270

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Table G-63. Industrial Manufacturing Subsectors, Summary of Model Results for the Pacific Census Division in 2012

Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Electro Chemical Processes (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Other Nonprocess Use (GWh)Onsite Transportation(GWh)Total (GWh)

Petroleum and Coal Products Manufacturing 6,454 270 303 337 202 116 57 78 59 89 8 1 7,974 

Plastics Product Manufacturing 3,389 1,014 640 506 598 — — 144 32 — — 27 6,350 

Iron and Steel Mills and Ferroalloy 

Manufacturing 1,628 2,090 378 90 266 1,390 94 73 28 119 17 10 6,184 

Semiconductor and Other Electronic 

Component Manufacturing 1,387 722 1,457 693 555 137 442 295 53 131 116 16 6,003 

Converted Paper Product Manufacturing 3,723 194 218 83 190 41 168 53 229 25 — 10 4,934 

Pulp, Paper, and Paperboard Mills 3,702 192 215 82 188 40 165 53 226 24 — 10 4,896 

Pharmaceutical and Medicine Manufacturing 1,780 115 200 304 129 536 49 38 67 43 8 8 3,276 

Aerospace Product and Parts Manufacturing 980 278 475 128 318 16 124 100 21 40 30 36 2,547 

Navigational, Measuring, Electromedical, and 

Control Instruments Manufacturing 578 302 617 295 235 58 189 126 22 56 49 7 2,534 

Beverage Manufacturing 887 105 290 485 230 5 61 81 40 188 11 50 2,433 

Fruit and Vegetable Preserving and Specialty 

Food Manufacturing 942 93 183 572 161 5 40 44 67 48 9 20 2,185 

Other Wood Product Manufacturing 1,309 111 132 13 106 5 19 28 31 19 6 4 1,783 

Other Food Manufacturing 701 69 135 420 119 4 29 32 49 35 7 15 1,615 

Dairy Product Manufacturing 680 67 130 404 115 3 28 31 47 34 6 14 1,561 

Communications Equipment Manufacturing 320 167 344 165 131 33 105 70 13 31 28 4 1,410 

Basic Chemical Manufacturing 693 45 77 117 50 206 19 15 26 17 3 3 1,270 

Foundries 327 419 75 18 53 276 19 14 6 24 3 2 1,235 
271

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Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Electro Chemical Processes (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Other Nonprocess Use (GWh)Onsite Transportation(GWh)Total (GWh)

Veneer, Plywood, and Engineered Wood 

Product Manufacturing 875 74 87 8 69 3 12 18 20 12 4 3 1,187 

Animal Slaughtering and Processing 497 49 97 302 85 3 21 23 35 25 5 11 1,152 

Computer and Peripheral Equipment 

Manufacturing 256 135 281 136 106 27 87 58 10 26 23 3 1,146 

Glass and Glass Product Manufacturing 560 265 67 40 51 18 22 16 9 — 2 4 1,055 

Architectural and Structural Metals 

Manufacturing 463 159 166 35 104 41 39 36 — — 2 7 1,053 

Other Nonmetallic Mineral Product 

Manufacturing 532 252 64 39 49 18 21 15 9 — 2 4 1,005 

Nonferrous Metal (except Aluminum) 

Production and Processing 265 338 60 14 42 216 15 11 4 19 3 2 987 

Bakeries and Tortilla Manufacturing 416 41 80 251 71 2 17 19 29 21 4 9 961 

Soap, Cleaning Compound, and Toilet 

Preparation Manufacturing 474 31 54 82 35 145 13 10 18 12 2 2 879 

Pesticide, Fertilizer, and Other Agricultural 

Chemical Manufacturing 468 30 53 82 34 144 13 10 18 12 2 2 868 

Manufacturing and Reproducing Magnetic and 

Optical Media 191 99 201 96 77 19 61 41 7 18 16 2 828 

Other Fabricated Metal Product Manufacturing 337 115 120 25 75 30 28 26 — — 2 5 763 

Printing and Related Support Activities 405 31 127 56 64 9 11 21 6 — — 8 738 

Coating, Engraving, Heat Treating, and Allied 

Activities 317 109 115 24 72 29 27 25 — — 2 5 725 
272

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Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Electro Chemical Processes (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Other Nonprocess Use (GWh)Onsite Transportation(GWh)Total (GWh)

Other Chemical Product and Preparation 

Manufacturing 368 24 41 62 26 110 10 8 14 9 2 2 674 

Cement and Concrete Product Manufacturing 329 156 40 24 31 11 13 10 6 — 1 2 623 

Resin, Synthetic Rubber, and Artificial Synthetic 

Fibers and Filaments Manufacturing 311 20 35 52 22 92 8 7 12 7 1 1 570 

Alumina and Aluminum Production and 

Processing 147 187 33 8 23 120 8 6 2 10 1 1 548 

Other Miscellaneous Manufacturing 175 62 129 38 74 4 10 26 9 — 1 3 531 

Paint, Coating, and Adhesive Manufacturing 260 17 30 45 19 79 7 6 10 6 1 1 482 

Grain and Oilseed Milling 193 19 37 116 33 1 8 9 14 10 2 4 445 

Motor Vehicle Parts Manufacturing 167 47 81 22 54 3 21 17 4 7 5 6 435 

Rubber Product Manufacturing 224 67 42 34 40 — — 10 2 — — 2 420 

Steel Product Manufacturing from Purchased 

Steel 101 129 23 5 16 83 6 4 2 7 1 1 378 

Other General Purpose Machinery 

Manufacturing 159 37 78 15 53 4 9 14 4 — — 4 377 

Audio and Video Equipment Manufacturing 83 43 88 42 33 8 27 18 3 8 7 1 361 

Medical Equipment and Supplies Manufacturing 119 42 86 25 50 3 7 17 6 — 1 2 357 

Household and Institutional Furniture and 

Kitchen Cabinet Manufacturing 170 13 70 6 58 1 — 12 4 — 2 4 339 

Forging and Stamping 148 51 53 11 33 13 12 11 — — 1 2 336 

Lime and Gypsum Product Manufacturing 168 80 20 12 16 6 7 5 3 — 1 1 318 

Animal Food Manufacturing 134 13 26 82 23 1 6 6 10 7 1 3 313 
273

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Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Electro Chemical Processes (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Other Nonprocess Use (GWh)Onsite Transportation(GWh)Total (GWh)

Seafood Product Preparation and Packaging 129 13 25 79 22 1 5 6 9 7 1 3 301 

Other Electrical Equipment and Component 

Manufacturing 84 69 50 19 29 23 11 7 1 — — 2 297 

Industrial Machinery Manufacturing 125 29 60 12 41 3 7 11 3 — — 3 294 

Commercial and Service Industry Machinery 

Manufacturing 124 29 60 12 41 3 7 11 3 — — 3 292 

Sugar and Confectionery Product 

Manufacturing 124 12 24 76 21 1 5 6 9 6 1 3 289 

Clay Product and Refractory Manufacturing 149 71 18 11 14 5 6 4 3 — 0 1 284 

Ship and Boat Building 97 28 49 13 33 2 13 10 2 4 3 4 258 

Motor Vehicle Manufacturing 90 26 45 12 30 2 12 9 2 4 3 3 237 

Motor Vehicle Body and Trailer Manufacturing 73 21 36 10 24 1 9 8 2 3 2 3 191 

Machine Shops; Turned Product; and Screw, 

Nut, and Bolt Manufacturing 79 27 29 6 18 7 7 6 — — 0 1 181 

Other Transportation Equipment Manufacturing 60 17 30 8 20 1 8 6 1 2 2 2 158 

Metalworking Machinery Manufacturing 66 15 32 6 22 2 4 6 2 — — 2 157 

Agriculture, Construction, and Mining Machinery 

Manufacturing 58 13 28 5 19 1 3 5 1 — — 1 135 

Spring and Wire Product Manufacturing 59 20 21 4 13 5 5 5 — — 0 1 134 

Electrical Equipment Manufacturing 35 29 21 8 13 10 5 3 1 — — 1 126 

Other Textile Product Mills 52 7 13 7 10 0 0 2 5 — — 2 99 

Boiler, Tank, and Shipping Container 

Manufacturing 40 14 14 3 9 3 3 3 — — 0 1 90 
274

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Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Electro Chemical Processes (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Other Nonprocess Use (GWh)Onsite Transportation(GWh)Total (GWh)

Textile and Fabric Finishing and Fabric Coating 

Mills 53 8 13 3 7 0 2 1 2 — — 1 89 

Sawmills and Wood Preservation 64 5 6 1 5 0 1 1 2 1 0 0 87 

Office Furniture (including Fixtures) 

Manufacturing 41 3 17 1 14 0 — 3 1 — 0 1 82 

Cut and Sew Apparel Manufacturing 26 3 18 1 11 — — 2 1 — — 0 63 

Electric Lighting Equipment Manufacturing 17 14 10 4 6 5 2 1 0 — — 0 61 

Engine, Turbine, and Power Transmission 

Equipment Manufacturing 25 6 12 2 8 1 1 2 1 — — 1 59 

Fabric Mills 33 5 8 2 4 0 1 1 1 — — 0 56 

Ventilation, Heating, Air-Conditioning, and 

Commercial Refrigeration Equipment 

Manufacturing

23 5 11 2 8 1 1 2 1 — — 1 54 

Fiber, Yarn, and Thread Mills 26 4 6 2 3 0 1 1 1 — — 0 43 

Household Appliance Manufacturing 12 10 7 3 4 3 2 1 0 — — 0 43 

Cutlery and Handtool Manufacturing 17 6 6 1 4 2 1 1 — — 0 0 38 

Other Furniture Related Product Manufacturing 14 1 6 0 5 0 — 1 0 — 0 0 28 

Tobacco Manufacturing 6 1 2 3 1 0 0 1 0 1 0 0 15 

Textile Furnishings Mills 4 1 1 1 1 0 0 0 0 — — 0 8 

Hardware Manufacturing 4 1 1 0 1 0 0 0 — — 0 0 8 

Other Leather and Allied Product Manufacturing 4 1 1 0 1 0 0 0 0 — — 0 7 

Footwear Manufacturing 3 0 0 0 0 0 0 0 0 — — 0 4 

Railroad Rolling Stock Manufacturing 1 0 1 0 0 0 0 0 0 0 0 0 3 
275

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Subsector

Machine Drive (GWh)Process Heating (GWh)Facility HVAC (GWh)Process Cooling And Refrigeration (GWh)Facility Lighting (GWh)Electro Chemical Processes (GWh)Other Process Use (GWh)Other Facility Support (GWh)Conventional Boiler Use (GWh)End Use Not Reported (GWh)Other Nonprocess Use (GWh)Onsite Transportation(GWh)Total (GWh)

Apparel Accessories and Other Apparel 

Manufacturing 0 0 0 0 0 — — 0 0 — — 0 1 

Leather and Hide Tanning and Finishing 0 0 0 0 0 0 0 0 0 — — 0 0 

Total 40,613 9,504 9,040 6,821 5,648 4,194 2,276 1,917 1,340 1,168 411 385 83,319 
276

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Figure G-62. Industrial electricity use diurnal patterns by season, modeled for the Pacific

census division 2012
277

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Table G-64. Distributed Generation Model, Annual Summary the Pacific Census Division in 2012 

by Sector and DG Type

Sector/DG Type Industry Commercial Residential Total

CHP (GWh) 27,816 5,508 5 33,330 

Distributed PV (GWh) 683 681 1,385 2,749 

Thermal DG (GWh) 1,493 288 — 1,780 

Total (GWh) 29,992 6,477 1,391 37,859 

CHP (%) 92.7 85.0 0.4 88.0 

Distributed PV (%) 2.3 10.5 99.6 7.3 

Thermal DG (%) 5.0 4.4 — 4.7 

Total (%) 79.2 17.1 3.7 100.0 

Figure G-63. Distributed Generation Model Diurnal Generation Patterns, Pacific

census division 2012
278

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Appendix H. Weather File Methodology

Many building energy analyses rely on typical meteorological year (TMY) weather files to 

capture expected energy efficiency impacts over long time horizons. Building energy model 

calibration requires actual meteorological year (AMY) weather files matched to the period for 

which measured data are available (Hale et al. 2014), but otherwise, analysis with AMY weather 

files is relatively uncommon.

For this project, to produce load data that are usable in grid models with significant penetrations 

of wind and solar generation, we require AMY weather files for an appropriate historical year. 

These files were generated by transforming data from the National Solar Radiation Database 

(NSRDB)62 into the format and units required by EnergyPlus weather (EPW) files (Wilcox 2012; 

DOE 2017c) (Figure H-1). 

Figure H-1. Example of alignment of integrated AMY NSRDB solar radiation profiles with EPW time 

conventions as demonstrated using a TMY EPW file

NSRDB data are available at half-hourly resolution and represent an instantaneous measurement. 

In contrast, EPW files are typically hourly, with an end-of-period interpretation for data points. 

Thus, we apply trapezoidal rule averaging and integrating methods that use three NSRDB data 

points to construct every EPW data point. With t representing the time for which we are 

constructing an EPW data point these methods are:

Average:

EPW(tt) = 0.25 ∙ NSRDB(tt − 1h) + 0.5 ∙ NSRDB(tt − 0.5h) + 0.25 ∙ NSRDB(tt)

Integrate:

EPW(tt) = 0.25h ∙ NSRDB(tt − 1h) + 0.5h ∙ NSRDB(tt − 0.5h) + 0.25h ∙ NSRDB(tt)

 62 See https://nsrdb.nrel.gov/.
279

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The field mappings of EPW and NSRDB, along with the methods used to convert between the 

two are summarized in Table H-1.

Table H-1. Mapping to EnergyPlus Weather File Fields from NSRDB Fields

EPW NSRDB Conversion 

Factor

Summary 

Method Field Units Field Units

Relative Humidity % Relative Humidity % - Average

Snow Depth cm Snow Depth m 100 cm/m Average

Dew Point 

Temperature °C Dew Point 

Temperature K °C = K – 273.15 Average

Surface Pressure Pa Surface Pressure mbar 100 Pa/mbar Average

Dry Bulb Temperature °C Surface Temperature K °C = K – 273.15 Average

Precipitable Water mm Total Precipitable 

Water mm - Average

Wind Speed m/s Wind Speed m/s - Average

Wind Direction ° Wind Direction ° - Average

Global Horizontal 

Radiation (GHR) Wh/m2 Global Horizontal 

Irradiance (GHI) W/m2 - Integrate

Direct Normal 

Radiation (DNR) Wh/m2 Direct Normal 

Irradiance (DNI) W/m2 - Integrate

Diffuse Horizontal 

Radiation (DHR) Wh/m2 Diffuse Horizontal 

Irradiance (DHI) W/m2 - Integrate

In addition to handling unit and data point interpretation conversions, the weather file time 

extents were made to match the needs of producing a data set that covers 2012 for the entire 

CONUS, using EST as our basis for defining the exact extents of the year. To ensure full 

coverage, an additional day of simulation—December 31, 2011 local time—was added. The 

EnergyPlus output timeseries were subsequently processed to drop hours falling outside 

2012, EST.
280

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Appendix I. Combined Heat and Power and 

Distributed Thermal Model

To develop an estimate of hourly generation from combined heat and power (CHP) and other 

distributed thermal generators, we start by merging EIA data available at the plant and unit level. 

EIA Form 860 provides unit level prime mover and capacity information, with nameplate, 

summer, and winter capacity specified in MW (EIA 2013a). We aggregate this data to the plantprime mover level, and then join it with the EIA Form 923 generation and fuel data after 

aggregating the latter over fuel type. At the end of this process, we have capacity and generation 

data for each plant-prime mover combination, along with the code for the primary fuel used 

during the year. EIA Form 923 also provides information on sector—both a coarse specification 

and a NAICS code—as well as whether the plant provides CHP services, name and location 

information (EIA 2018, 923).

The source and disposition table of EIA Form 923 provides further details by plant that let us 

estimate how much generation is used behind-the-meter. Quantities of electricity generated on 

site and imported from the grid are reported: the sum is the total source electricity. The 

disposition of that electricity is then reported in terms of station use, direct use on site (this is 

what we interpret as the behind-the-meter use of electricity), sales (retail and for resale), tolling 

agreements and other outgoing electricity (EIA 2018). From these data, we estimate the behindthe-meter fraction as 

ffbehind-the-meter = Direct Use (MWh)

Net Source (MWh) = Direct Use (MWh)Total Source (MWh) − Station Use (MWh).

At this point we filter the plants down to just those that are non-utility CHP and other thermal 

plants. The filters we apply are:

• Sector – Plants that are commercial or industrial, CHP and not-CHP

• Prime mover – Other, hydro, wind, and PV are removed. All other prime movers are 

classified as CHP/Thermal DG.63 PV is covered by the distributed PV model. The other 

categories we remove are of potential interest but are excluded because of their small 

capacity and less available data (e.g., at the hourly level). As shown in Table I-1, this is 

not a severe limitation, as 98% of the generation reported falls in our aggregated 

CHP/Thermal DG category.

• Capacity and net generation – Some plants are reported as having zero capacity. These 

are removed, as are plants that report negative net generation for those prime movers 

classified as CHP/Thermal DG. However, we retain plants with zero net generation to 

represent capacity that is available but not used on a regular basis.

Following the application of the filters, we have data on 847 plants. Next, we determine for 

which of these plants a CEMS hourly profile is available. This join is done based on the Plant ID

and yields 69 plants with a CEMS profile; however, only 23 of those have a non-zero profile. For 

 63 The EIA prime mover codes included in our CHP/distributed thermal model are: CA, CS, CT (Combined Cycle), 

FC (Fuel Cell), GT (Combustion (Gas) Turbine, including Jet Engines), IC (Internal Combustion Engine), and ST 

(Steam Turbine).
281

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each of those plants we calculate the capacity factor of the profile in two ways. The “standard 

capacity factor” is calculated by dividing the annual generation represented in the profile by the 

profile’s maximum value, that is, by assuming the plant’s capacity is equal to the maximum 

output achieved in the annual profile. The “alternate capacity factor” is computed by dividing the 

annual generation in the CEMS profile by the plant capacity reported on EIA Form 860. Both of 

these capacity factors are retained and used in the process of matching modeled plants to CEMS 

profiles and then transforming those profiles to match the capacity factors prescribed for each 

modeled plant.

Table I-1. Commercial and Industrial Power Plant Capacity and Generation by Prime Mover, 

per EIA Forms 860 and 923

Generator

Type

Nameplate

Capacity (GW)

Net

Generation (TWh)

Generation

(%)

CHP/Thermal DG 34.52 154.12 98%

Hydro 0.69 2.37 2%

Other 0.11 0.31 0%

PV 0.12 0.14 0%

Wind 0.04 0.07 0%

Total 35.48 157.02 100%

The DOE Combined Heat and Power Installation Database (ICF Inc. and DOE 2016) lists CHP 

capacity in kW by city, state, facility name, application, NAICS code, operational year, prime 

mover, and fuel. For our purposes, we filter out CHP facilities for whom the application is 

“utilities,” and remove units installed after 2012. We also map NAICS codes to sector: 

residential, commercial, or industrial. At the end of this process, we have information on 3,687 

units in place as of 2012. They are fairly evenly split between small (< 1 MW and therefore not 

eligible for EIA reporting) and large (1 MW or greater) units, coming in at 1,951 units and 1,736 

units respectively. In principle, one might expect the number of large CHP units in the CHP DB

to be the same as the number of CHP units reported in the EIA data sources explained above. 

However, the EIA data sources only report 687 CHP plants, that is, 40% of the expected 1,736. 

The difference between data sources similarly holds if we look at capacity instead of number of 

installations (Table I-2). Therefore, we model CHP capacity from the CHP DB, and distributed 

thermal generation units reported in the EIA data sources (as such units are not listed in the CHP 

DB).

Table I-2. CHP and Thermal DG Capacity Described in EIA Forms 860 and 923 (EIA) and in the DOE 

Combined Heat and Power Installation Database (CHP DB)a

Capacity Type Commercial (MW) Industrial (MW) Residential (MW) Total (MW)

EIA DG 781 2,499 — 3,280 

EIA CHP 2,542 28,032 44 30,618 

CHP DB >= 1 MW 9,330 69,088 97 78,515 

CHP DB < 1 MW 344 137 29 510 

a Capacity included in the CHP/Thermal DG model is shaded orange.
282

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We assign behind-the-meter generation quantities to the CHP DB entries by performing a match 

between the CHP DB and EIA meta-data. In particular, we attempt to match operational data to 

CHP DB capacity on the basis of

• Sector information – First by Residential, Commercial, Industrial, then by increasingly 

specific NAICS codes (starting with three-digit and progressing up to five-digit if 

possible)

• Prime mover and fuel type – We first attempt to match just on prime mover. If that is 

successful and there are still multiple potential matches, we then attempt to match on 

prime mover-fuel type pairs. In both cases, the mapping that is applied between EIA 

codes and the CHP DB designations are listed in Table I-3.

• Capacity – For CHP DB plants of 1 MW or larger, we filter EIA plants based on 

capacity, for tighter and tighter bounds. For each run of the filter, the EIA plants that are 

kept are those whose capacities are between ff ∙ CCCHP DB and (1⁄ff) ∙ CCCHP DB, where f is a 

fraction that starts at 0.5 and can go as high as 0.99, and CCCHP DB is the capacity of the 

plant for which we are defining operational data.

• Location – The only location filter applied is state

These filters are applied in a particular order, starting with those aspects that we are most 

interested in matching, and proceeding to match at finer and finer levels as long as multiple 

potential EIA matches remain (Algorithm 1). 

Table I-3. Mapping Between EIA and CHP DB Prime Mover and Fuel Type Codesa

EIA CHP DB

Prime Mover Code Fuel Type Code Prime Mover Fuel Class

IC DFO Reciprocating Engine OIL

GT DFO Combustion Turbine OIL

ST SUB Boiler/Steam Turbine COAL

CA NG Combined Cycle NG

GT NG Combustion Turbine NG

ST AB Boiler/Steam Turbine BIOMASS

ST BIT Boiler/Steam Turbine COAL

ST BLQ Boiler/Steam Turbine WAST

ST DFO Boiler/Steam Turbine OIL

ST NG Boiler/Steam Turbine NG

IC NG Reciprocating Engine NG

ST WDS Boiler/Steam Turbine WOOD

CS NG Combined Cycle NG

CA LFG Combined Cycle BIOMASS

IC OBG Reciprocating Engine BIOMASS

ST PUR Boiler/Steam Turbine WAST
283

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EIA CHP DB

Prime Mover Code Fuel Type Code Prime Mover Fuel Class

ST OG Boiler/Steam Turbine OTR

ST OTH Boiler/Steam Turbine OTR

CT NG Combined Cycle NG

GT JF Combustion Turbine OIL

CA DFO Combined Cycle OIL

ST WH Boiler/Steam Turbine WAST

GT OG Combustion Turbine OTR

ST BFG Boiler/Steam Turbine WAST

ST MSB Boiler/Steam Turbine BIOMASS

ST LIG Boiler/Steam Turbine COAL

ST RFO Boiler/Steam Turbine OIL

CA BIT Combined Cycle COAL

CA WH Combined Cycle WAST

GT BIT Combustion Turbine COAL

All acronyms are defined in the acronyms list on page vi.

Algorithm 1—Match EIA Operational Data to CHP DB Instances

Input: CHP DB Instance to Match, 

 Set of 687 CHP Plants with EIA Data

No additional screens are applied as soon as the filtered subset is down to 1.

1: Screen on 3-digit NAICS

2: If no matches:

3: Screen on sector (Residential, Commercial, Industrial)

4: Screen on prime mover

5: If prime mover matched:

6: Screen on prime mover and fuel type

7: If CHP DB Capacity 1 MW or greater:

8: Screen capacity with f = 0.5

9: Screen on 4-digit NAICS

10: Screen on state

11: Screen on 5-digit NAICS

12: While f < 0.99:

13: Screen on capacity with f = 1 – (1 – f)/2

Output: Subset of EIA CHP Plants that Match the CHP DB Instance
284

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The matching granularity varies quite a bit across instances. Only 3% of instances achieved 

matches at the closest level possible: a sectoral match at the five-digit NAICS code level, and 

also matched on prime mover and fuel, state, and capacity (with f ≥ 0.5). Plants achieving that 

level of match are generally associated with the most common large-scale CHP applications: 

pulp and paper, wastewater treatment, refining, chemicals, food processing, and primary metals. 

Even so, those plants often ended up matched to a few or a handful of EIA plants, rather than a 

single one. In contrast, 1,369 of the 3,687 CHP DB plants considered did end up with a single 

match. With only one residential CHP plant in the EIA data, all 233 instances of residential CHP 

listed in the CHP DB were mapped to that instance. In contrast, getting down to a single match 

happened in many different ways for commercial and industrial plants. In some cases, there was 

only one EIA plant matching at the three-digit NAICS code level. In others the state was also 

matched, or the prime mover and fuel, or just the prime mover. The types of match achieved for 

each filter category are summarized in Table I-4. Once the final set of matching EIA CHP plants 

was returned for a given CHP DB plant, a single one was selected at random and the CHP DB 

plant’s capacity factor and behind the meter fraction assigned accordingly.

Table I-4. Results of Matching CHP Plants in the DOE CHP DB with CHP Plant Reports in 2012 EIA 

Form 860 and Form 923 Data

Sector 

Match

No. 

Plants

Plants

(%)

Prime 

Mover 

Match

No. 

Plants

Plants 

(%)

Size 

Match

No. 

Plants

Plants 

(%)

Location 

Match

No. 

Plants

Plants 

(%)

Sector 1,124 30 None 1,055 29 None 2,403 65 None 1,942 53

NAICS 

threedigit1,875 51 Prime 

Mover 431 12 0.5 684 19 State 1,745 47

NAICS 

fourdigit235 6

Prime 

Mover & 

Fuel

2,201 60 0.75 299 8

NAICS 

fivedigit453 12 0.875 154 4

> 0.875 147 4

Similar, but abbreviated, matching processes were then applied to select plants with CEMS data 

from which to derive an hourly generation profile. These processes were slightly different 

depending on whether the plant being matched was a CHP DB plant, or a distributed thermal 

plant from the EIA data. The two matching processes used are summarized in Algorithm 2 and 

Algorithm 3 respectively. In both cases, the final profile is chosen by comparing the capacity 

factor of the plant being matched to the two capacity factors associated with the CEMS profile. 

Both the chosen CEMS Plant Id, and which CEMS capacity factor (standard or alternate) should 

be used as a starting point for generating the modeled plant’s profile, are noted.
285

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Finally, a profile for each modeled plant is created by transforming the selected CEMS plant 

profile to match the modeled plant’s capacity factor and then multiplying the result by the 

behind-the-meter fraction. The CEMS profile transformation proceeds by representing the profile 

in terms of hourly capacity factors, finding the minimum generation point of the profile (the 

minimum over all hours when generation is non-zero), and then decomposing the profile into a 

baseload and non-baseload part. The non-baseload part is equal to the original profile minus the 

minimum generation point, truncated to be non-negative. This is retained as the “shape” of the 

profile. We then compute a new minimum generation point such that that level of generation plus 

the “shape” results in the desired annual capacity factor. And that is the returned profile if the 

desired capacity factor is large enough. If the desired capacity factor is smaller than that achieved 

by retaining only the “shape” of the profile, we instead multiply the original profile by a simple 

scaling factor (that is necessarily less than one).

The CEMS profiles that were used to feed this process are shown in Figure 15. The resulting 

aggregated CHP and distributed thermal profiles are depicted in Figure F-4 for all of the 

CONUS. Census division level plots are available in Appendix G.

Algorithm 2—Match CEMS Profiles to CHP DB Instances

Input: CHP DB Instance to Match, 

 Set of 23 Plants with non-zero CEMS Data

No additional screens are applied as soon as the filtered subset is down to 1.

1: Screen on EIA Operational Match is one of the 23 CEMS Plants

2: Screen on 3-digit NAICS

3: If no matches:

4: Screen on sector (Residential, Commercial, Industrial)

4: Screen on prime mover

5: If prime mover matched:

6: Screen on prime mover and fuel type

Output: Subset of CEMS Plants that Match the CHP DB Instance

Algorithm 3—Match CEMS Profiles to EIA Distributed Thermal Instances

Input: EIA Distributed Thermal Instance to Match, 

 Set of 23 Plants with non-zero CEMS Data

No additional screens are applied as soon as the filtered subset is down to 1.

1: Screen on this plant is one of the 23 CEMS Plants

2: Screen on 2-digit NAICS

3: If no matches:

4: Screen on sector (Residential, Commercial, Industrial)

5: Screen on 3-digit NAICS

4: Screen on prime mover

Output: Subset of CEMS Plants that Match the EIA Distributed Thermal 

Instance
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Appendix J. Raw State-Level System Loss Factors

Table J-1 provides the raw state-level data referenced and plotted in Section 2.3.4.1. Large 

differences between the so-called Reported and Estimated loss factors indicate some 

combination of discrepancies in data reported to EIA and FERC, or in our allocation of hourly 

load data from planning regions, to transmission nodes, and then up to the state level. For one 

example of the latter type of discrepancy, any load that is metered in a different state than the 

associated transmission node is almost certain to have been miscounted, that is, assigned to the 

state in which the transmission node resides rather than the state in which the meter resides.

Table J-1. Raw State-Level Loss Factors (a) Based on EIA Form 861 Data Alone (Reported), 

(b) Based on Comparing FERC Form 714 and EIA Form 861 Data (Estimated), and (c) Their 

difference (Estimated – Reported) 

State

EIA Annual 

Site 

Energy 

(TWh)

EIA Annual 

Losses 

(TWh)

Reported

Data Loss 

Factor (%)

FERC 

Hourly

Load 

(TWh)

FERC –

EIA Loss 

Estimate 

(TWh)

Estimated 

Loss 

Factor (%)

Estimated 

– Reported 

(% pt)

RI 7.7 0.4 5.0 4.9 -2.8 -36.2 -41.1

DC 11.4 1.3 11.2 8.0 -3.4 -29.6 -40.8

WY 17.1 0.3 1.8 11.7 -5.4 -31.5 -33.3

IN 105.3 3.4 3.2 80.4 -24.9 -23.6 -26.8

VT 5.5 0.4 6.6 5.1 -0.4 -7.8 -14.4

CO 57.4 4.3 7.5 56.7 -0.7 -1.2 -8.7

KY 89.1 3.6 4.1 86.1 -3.1 -3.5 -7.6

NM 23.2 1.2 5.4 22.7 -0.5 -2.1 -7.5

FL 221.6 14.0 6.3 220.1 -1.5 -0.7 -7.0

AR 47.0 3.5 7.4 47.7 0.7 1.6 -5.8

UT 29.8 0.3 0.9 28.4 -1.4 -4.7 -5.6

GA 131.3 8.8 6.7 133.0 1.7 1.3 -5.4

NE 30.9 2.3 7.5 31.7 0.8 2.5 -5.0

DE 11.6 0.8 6.9 11.9 0.3 2.2 -4.7

WA 94.2 4.4 4.6 94.3 0.1 0.1 -4.5

CA 269.0 12.9 4.8 271.5 2.5 0.9 -3.9

OR 48.1 6.3 13.2 52.9 4.8 9.9 -3.3

KS 40.5 2.8 6.9 42.1 1.6 4.1 -2.9

ME 11.7 0.1 1.2 11.5 -0.1 -0.9 -2.2

MI 105.0 5.6 5.4 109.4 4.3 4.1 -1.2

OH 153.0 13.1 8.5 164.5 11.5 7.5 -1.0

AL 86.3 4.6 5.3 90.2 3.8 4.4 -0.9

VA 108.0 3.5 3.3 111.1 3.1 2.9 -0.4

LA 84.9 4.0 4.7 88.8 3.9 4.6 -0.1
287

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State

EIA Annual 

Site 

Energy 

(TWh)

EIA Annual 

Losses 

(TWh)

Reported

Data Loss 

Factor (%)

FERC 

Hourly

Load 

(TWh)

FERC –

EIA Loss 

Estimate 

(TWh)

Estimated 

Loss 

Factor (%)

Estimated 

– Reported 

(% pt)

PA 144.9 7.7 5.3 152.6 7.7 5.3 0.0

NC 128.4 9.4 7.3 138.0 9.6 7.5 0.1

OK 59.9 5.6 9.4 65.8 5.8 9.8 0.4

SC 78.1 3.0 3.8 81.6 3.6 4.6 0.8

TN 96.5 6.5 6.8 103.9 7.4 7.7 0.9

TX 366.2 19.4 5.3 388.8 22.6 6.2 0.9

NJ 75.1 3.1 4.2 79.2 4.2 5.5 1.4

CT 29.5 0.8 2.7 30.9 1.4 4.7 2.1

MS 48.5 2.9 6.0 52.7 4.2 8.7 2.7

MN 68.3 4.3 6.3 75.5 7.3 10.7 4.4

AZ 75.2 4.8 6.4 83.7 8.6 11.4 5.0

WI 69.0 2.3 3.3 75.2 6.2 8.9 5.6

NV 35.3 1.0 2.9 38.3 3.0 8.5 5.7

IL 144.1 3.6 2.5 156.0 11.9 8.2 5.8

NY 147.2 5.8 3.9 163.1 15.9 10.8 6.9

MA 55.7 1.3 2.3 61.1 5.3 9.6 7.3

MD 61.9 2.3 3.8 69.9 8.0 13.0 9.2

NH 10.9 0.5 4.6 12.5 1.6 14.7 10.1

ID 23.7 1.5 6.1 28.5 4.8 20.1 13.9

ND 14.8 1.8 12.0 19.3 4.5 30.6 18.6

SD 11.9 0.5 3.8 14.7 2.8 23.4 19.6

WV 30.8 0.0 0.0 37.1 6.2 20.3 20.2

MO 82.7 6.2 7.5 107.5 24.9 30.1 22.6

MT 13.9 0.6 4.6 19.5 5.6 40.5 35.9

IA 45.8 1.8 3.9 69.7 23.9 52.1 48.2
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