Short-Term Energy Outlook 

Retail Electricity Sales 

Module

www.eia.gov

U.S. Department of Energy

Washington, DC 20585

Last updated: January 2023
U.S. Energy Information Administration | Retail Electricity Sales Module i 

The U.S. Energy Information Administration (EIA), the statistical and analytical agency within the 

U.S. Department of Energy (DOE), prepared this report. By law, our data, analyses, and forecasts are 

independent of approval by any other officer or employee of the U.S. Government. The views in this 

report do not represent those of DOE or any other federal agencies.
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Table of Contents

Data Sources ................................................................................................................................................. 4

Electricity Retail Sales Module Inputs and Regression Models.................................................................... 6

Regional Retail Electricity Sales Model Structure ......................................................................................... 7

Residential sector retail sale estimations................................................................................................ 9

Commercial sector retail sale estimations .............................................................................................. 9

Industrial sector retail sale estimations................................................................................................. 10

Transportation sector retail sale estimations........................................................................................ 11

Total retail sales of electricity estimations............................................................................................ 11

Model adjustment factors..................................................................................................................... 11
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Table of Figures

Figure 1. U.S. census divisions ...................................................................................................................... 3

Figure 2. Structure of the Short-Term Energy Outlook’s Retail Electricity Sales Module .............................8
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Introduction

The Retail Electricity Sales Module is a component of the U.S. Short-Term Energy Model (USSTEM) 

within EIA’s Short-Term Integrated Forecasting System (STIFS). The module is designed to provide 

forecasts of retail electricity sales to customers for their own use (end-use customers) and not for resale 

to others. The module forecasts the amount of electricity sold to four end-use sectors (residential, 

commercial, industrial, and transportation) for the entire United States and for the nine U.S. census 

divisions (Figure 1). The Retail Electricity Sales Module is closely linked to the USSTEM Electricity Supply 

Module, where total retail sales act as an input for determining total U.S. electricity load and generation.

Some industrial and commercial establishments consume electricity they produce onsite. This type of 

electricity consumption is modeled in the Electricity Supply Module. 

Figure 1. U.S. census divisions

Data source: U.S. Census Bureau 
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Data Sources

The data input for the Retail Electricity Sales Module are by month. In most cases, retail sales data are 

converted to average per-day units for each month, so that the data can be integrated into other 

USSTEM modules. The primary EIA data sources for the Retail Electricity Sales Module are our:

• Electric Power Monthly

• Electricity Monthly Update

• Electric Power Annual

Retail electricity data in each of these publications are derived from three EIA surveys. The Annual 

Electric Power Industry Report (Form EIA-861) collects operational data and characteristics annually from 

a sample of electric utilities and energy service providers. Schedule 4 of this survey collects information 

from respondents about the amount of electricity sold to the retail customers in the four end-use 

sectors and the corresponding amount of revenue received during the past year.

Most investor-owned utilities and competitive retail electric suppliers (about 500 respondents) are 

required to file the Monthly Electric Power Industry Report (Form EIA-861M) each month. This survey 

collects retail revenues and sales to each end-use sector, and this observed data provide the basis for 

imputing the monthly sales of respondents who file annual, rather than monthly, reports (Forms EIA861/861S).

We send the short form of the Annual Electric Power Industry Report (Form EIA-861S) to approximately 

1,100 smaller utilities. This survey collects data on total retail revenue and sales of electricity during the 

year, but it does not request a breakdown of how much is sold to each end-use sector. Once every five 

years, the respondents of this survey fill out the full Annual Electric Power Industry Report (Form EIA861) in lieu of the abbreviated form.

In addition to the data published in the EIA sources listed above, the Retail Electricity Sales Module also 

uses data from outside sources. The Edison Electric Institute (EEI) compiles a Weekly Electric Output

report with data for nine regions of the United States. Although EEI’s regional mapping does not 

correspond exactly with census divisions, strong correlations exist between the EEI output data and EIA 

total retail sales. The Retail Electricity Sales Module incorporates information from weekly output to 

estimate electricity sales for the most recent months for which EIA monthly data are not yet available.

We obtain historical data and forecasts for cooling degree days and heating degree days from the 

National Oceanic and Atmospheric Administration (NOAA).1 Degree-day data are collected for individual 

states. We calculate weighted averages for each census division (and for the entire United States) using 

the states’ populations during each month, estimated by the U.S. Census Bureau. This averaging method 

differs from NOAA’s method, which uses a constant population weight for each state (currently based 

 1 Cooling degree days (CDD) are a measure of how warm a location is over a period of time compared with a base temperature, 

usually 65°F. The measure for CDD for a particular day is calculated as the positive deviation of the day’s average temperature 

from (usually) 65°F; if the average falls below 65°F, the value for CDD = 0. Heating degree days (HDD), a measure of the relative 

coldness of a location, are calculated as the deviation of daily average temperature below 65°F; HDD = 0 if the daily average 

temperature exceeds 65°F.
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on the 2010 census). We use the dynamic population weighting methodology for USSTEM to capture 

geographic shifts in population that affect energy consumption patterns.

Macroeconomic data such as population, GDP, income, employment, and industrial production are 

incorporated as explanatory variables in many USSTEM modules. The macroeconomic forecasts are 

generated by models IHS Market developed. IHS Markit updates its national macroeconomic forecasts 

monthly using its model of the U.S. economy. We re-run the IHS Markit model to produce 

macroeconomic forecasts that are consistent with our Short-Term Energy Outlook energy price 

forecasts.
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Electricity Retail Sales Module Inputs and Regression Models

The Electricity Retail Sales Module forecasts regional retail electricity sales for five end-use sectors:

• The residential sector

• The commercial sector

• The industrial sector

• The transportation sector

• All end-use sectors combined

Census division regions for retail electricity are:

• East North Central

• East South Central

• Middle Atlantic

• Mountain

• New England

• Pacific Contiguous

• Pacific Noncontiguous

• South Atlantic

• West North Central

• West South Central

The Retail Electricity Sales Module differentiates between the contiguous states (California, Oregon, and 

Washington) and the noncontiguous states (Alaska and Hawaii) in the Pacific Census Division. Because of 

this differentiation, the census division definitions differ slightly from the standard ones that are shown 

in Figure 1.
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Regional Retail Electricity Sales Model Structure

We designed the USSTEM Retail Electricity Sales Module to forecast sales to each end-use sector within 

the nine census division areas (Figure 2).

2 Direct use of electricity by power generators is modeled 

separately in the USSTEM Electricity Supply Module because that electricity is produced and consumed 

onsite. We estimate retail sales in each regional area individually and then aggregate them into U.S. 

totals for each end-use sector and a U.S. retail sales total across all end-use sectors.

The Retail Electricity Sales Module uses linear regression models to estimate coefficients that represent 

relationships between important variables, for example, supply, demand, number of customers, and 

prices. Some regression models include monthly dummy variables to capture the normal seasonality in 

the data series and to account for specific historical months for which the data reflect infrequent and 

unpredictable events such as hurricanes, fuel disruptions, or other factors. Most models also include a 

lagged dependent variable term to account for any autocorrelation in the estimation.

 2 The Retail Electricity Sales Module divides the Pacific Census Division into two separate areas: California, Oregon, and 

Washington and Hawaii and Alaska.
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Figure 2. Structure of the Short-Term Energy Outlook’s Retail Electricity Sales Module

Personal

Income

Residential 

Electricity

Price

Cooling and 

Heating 

Degree Days

Electricity 

Consumption

Per Customer

Retail Sales of 

Electricity to the 

Residential Sector

Number of 

Residential 

Customers

Commercial 

Electricity

Price

Retail Sales of 

Electricity to the 

Commercial 

Sector

Service-Sector

Employment

Retail Sales of 

Electricity to the 

Transportation 

Sector

Industrial 

Production 

Index

Industrial 

Electricity

Price

Industrial 

Natural Gas

Price

Retail Sales of 

Electricity to the 

Industrial

 Sector

Total Retail Sales 

of Electricity to 

All Sectors

Data Source: U.S. Energy Information Administration

*Oval shapes include independent variables whereas rectangle shapes include dependent variables. 
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Residential sector retail sale estimations 

The Retail Electricity Sales Module models residential electricity consumption on a per-customer basis to 

analyze changes in the average monthly electricity bill. Disaggregating the data in this way also allows 

better modeling of the effects of changes in energy efficiency on retail electricity sales.

Average residential consumption per customer for each census division is defined as the total monthly 

retail sales of electricity to the residential sector in the region divided by the total number of residential 

customers. A residential customer corresponds to an individual electric meter. In some cases, more than 

one household may share a meter. Some electricity customers represent seasonal residences. 

We collect annual data on the total retail sales and number of customers in the Annual Electric Power 

Industry Report (Form EIA-861). Historical estimates of growth in the number of customers for the most 

recent year are calculated using responses from our Monthly Electric Power Industry Report (EIA-861M), 

which collects retail sales and customer data from a sample of annual EIA-861 respondents. We 

calculate the forecast annual growth in the number of residential customers using the growth rate of 

the forecast number of households as projected by IHS Markit.

For each census division, the Retail Electricity Sales Module estimates average consumption per 

residential customer as a function of five independent variables:

• Retail residential electricity price in the census division, cents per kilowatthour

• Consumer price index in the census division

• Personal disposable income in the census division, nominal dollars

• Cooling degree days in the census division 

• Heating degree days in the census division

Increases in the real residential electricity price should place downward pressure on customers’ 

electricity consumption. In many cases, however, the responsiveness of consumption to changes in 

prices is relatively insignificant. The most important factor affecting retail sales of electricity to the 

residential sector is weather. The numbers of cooling and heating degree days are significantly

correlated with residential consumption. The electricity sales data we collected in some cases reflect 

billing cycles that do not correspond with calendar months. Therefore, one-month lagged observations 

for heating and cooling degree days are included as explanatory variables.

Commercial sector retail sale estimations

As in the residential sector, consumption of electricity in the commercial sector is also highly sensitive to

fluctuations in weather conditions. Space cooling, space heating, ventilation, and lighting are the 

primary uses of electricity in the commercial sector. Electricity used for cooking and refrigeration in 

food-service establishments and for computers and other equipment in office buildings account for the 

rest.

The Retail Electricity Sales Module estimates aggregate retail commercial sales of electricity for each 

census division. The number of customers is not included in the commercial sector regression models, 

like in the residential sales regression models. Electric utilities responding to EIA surveys will sometimes 

reclassify customers as commercial or industrial depending on their electricity consumption, not 
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necessarily on the type of business they conduct. So, the number of commercial sector customers can 

sometimes fluctuate widely from year to year.

Total retail electricity sales to the commercial sector in each census division (billion kilowatthours per 

day) are modeled as a function of regional private sector employment, weather (as measured by degree

days), and commercial electricity prices. The regression model for each census division includes six

explanatory variables:

• Retail commercial electricity price in the census division, cents per kilowatthour

• Consumer price index in the census division

• Personal disposable income in the census division, nominal dollars

• Cooling degree days in the census division

• Heating degree days in the census division

• Private sector employment in the census division

Theoretically, the coefficient on the commercial electricity price variable should be negative, but for 

most of the regional models, the effect of price changes is not significant. All of the regression models 

contain significant coefficients for the degree day variables, but the lagged series are not as robust such 

that does not have as good performance for data drawn from a wide range of probability distributions. 

Growth in private sector employment is a significant explanatory variable in most of the commercial 

electricity sales models.

Industrial sector retail sale estimations

Macroeconomic conditions primarily determine electricity use in the industrial sector. Space heating and 

cooling needs are minimal for industrial facilities, in contrast to the residential and commercial sectors. 

Most of the electricity consumed in the industrial sector is used to facilitate manufacturing processes. 

The chemical, primary metals, and food industries purchase nearly half the electricity sold to the 

industrial sector. Many manufacturing facilities can generate their own electric power, which they can 

use to supplement or substitute for retail electricity purchases if the cost of self-generation is low 

compared with the retail price of electricity.

The most important driver of retail sales to the industrial sector is the overall level of economic activity, 

but, different industries will expand at different rates. To reflect average economic activity, USSTEM 

uses an electricity-weighted industrial production index, which averages the industrial production 

indexes of various industries with weights based on the relative amounts of electricity they purchase. 

Another significant explanatory variable is the regional retail price of purchased electricity for the 

industrial sector. The regional industrial price of natural gas also is included in the model equations 

because this fuel could be considered a substitute for purchased electricity for those manufacturers that 

can produce their own electricity, often with generating units fueled by natural gas. At the census 

division level, the module estimates retail sales of electricity to the industrial sector as a function of 

three explanatory variables:

• Retail industrial electricity price in the census division, cents per kilowatthour

• Consumer price index in the census division

• U.S. electricity-weighted industrial production index (previously described)
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The coefficient on the retail electricity price should be negative, and for most of the regions, the 

estimated coefficients are of the correct sign and are statistically different from zero. The coefficient on 

the natural gas price variables is positive for most regions and significant in about half of the regions. 

The electricity-weighted industrial production index is also significant and positive for most regions. We 

include dummy variables for certain years to account for when one or more large customers are 

reclassified to different end-use sectors. This effect is most evident in the New England Census Division.

Transportation sector retail sale estimations

Retail sales of electricity to the transportation sector consist primarily of sales to public transit operators 

such as subway, light-rail, and streetcar systems. The overall transportation-sector electricity sales are

very small compared with the amount sold to other end-use sectors, but the number of customers is 

also much smaller. Transportation sales can be quite volatile, so forecasting transportation sales can be 

difficult. However, transportation electricity use follows some seasonal patterns, with especially large 

spikes in January and February. Electricity sales to the transportation sector are also assumed to be 

positively related to overall commercial employment.

For each census division, the Retail Electricity Sales Module estimates total retail sales of electricity to 

the transportation sector as a function of two explanatory variables:

• Private sector employment in the census division, millions

• Monthly dummy variables (the January and February peak is captured in the constant term)

Total retail sales of electricity estimates 

The total retail electricity sold in each region is simply equal to the sum of the electricity sold to all of the 

four end-use sectors.

Model adjustment factors

Most regression models in USSTEM have associated add factors, which allow manual adjustments to the 

modeled output. The add factors allow additive adjustments to the forecasts of the model’s dependent 

variable to reflect analyst judgment about issues that might not be adequately portrayed in the model, 

such as extended power outages, extreme weather events, etc.