Short-Term Energy Forecasting Overview

Independent Statistics & Analysis

www.eia.gov

U.S. Department of Energy

Washington, DC 20585

Last Updated 2020
U.S. Energy Information Administration | Short-Term Energy Forecasting Overview i

This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and 

analytical agency within the U.S. Department of Energy. By law, EIA’s data, analyses, and forecasts are 

independent of approval by any other officer or employee of the United States Government. The views 

in this report therefore should not be construed as representing those of the U.S. Department of Energy 

or other federal agencies.
U.S. Energy Information Administration | Short-Term Energy Forecasting Overview 1

1. Introduction

The Short-Term Energy Outlook (STEO) is a monthly release of EIA’s forecasts of energy supply, 

consumption, and prices through the end of the next calendar year. At the end of each year, the 

forecast period is extended twelve months, so the forecasts cover a 13-to-24 month horizon. STEO 

includes forecasts for petroleum, natural gas, coal, renewables, electricity, biofuels, and carbon dioxide 

emissions. Most of the forecasts in STEO are based on an underlying historical data series reported in 

EIA statistical data release publications. STEO also includes forecasts from non-EIA sources for 

macroeconomic, weather, and other variables that are important drivers of energy markets. EIA 

forecasts data in STEO in monthly intervals and presents the data in monthly, quarterly, and annual

interval. The forecasts cover a 13-to-24 month horizon. 

EIA uses the Short-Term Integrated Forecasting System (STIFS) to create the forecasts published each 

month in STEO. STIFS comprises two main models, programmed in the Eviews software package. 

However, STIFS uses the output of several other models and forecasts, the results of which are fed 

exogenously to STIFS. The two main models of STIFS are 

• U.S. short-term energy model (USSTEM)

• Global short-term oil model (GSTOM)

USSTEM, which forecasts many more series than GSTOM, is the main engine of STIFS. Although USSTEM 

and GSTOM can be run independently, each includes information from the other. This document will 

mainly focus on the USSTEM part of STIFS, while referencing the global oil market model periodically 

when discussing the connections between the two models. EIA will release documentation for GSTOM in 

a separate report. 

2. Historical Data Inputs

With few exceptions, STEO publishes forecasts of data series that are reported in other EIA historical 

data publications. USSTEM generally accesses historical EIA data from

1) Internal Oracle databases 

2) Publicly available Microsoft Excel files on the EIA website

These data are then stored on a network drive for USSTEM to access. These historical data come mainly 

from 10 EIA publications:

• Petroleum Supply Monthly

• Petroleum Marketing Monthly

• EIA-914, Monthly Production Report

• Weekly Petroleum Status Report

• Natural Gas Monthly

• Weekly Natural Gas Storage Report

• Coal Quarterly

• Electric Power Monthly 
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• Monthly Energy Review

• Hourly Grid Monitor

In addition, USSTEM uses non-EIA data historical data from a number of sources:

• Refinitiv 

• S&P Global Market Intelligence: SNL Energy Data 

• IHS Markit 

• Oxford Economics

• U.S. Census Bureau

• U.S. Federal Highway Administration 

• U.S. Federal Aviation Administration

• U.S. Bureau of Labor Statistics

• U.S. Bureau of Economic Analysis 

• National Oceanic and Atmospheric Administration (NOAA)

• North American Electric Reliability Corporation (NERC)

• U.S. Regional Transmission Organizations (RTO) and Independent System Operators (ISO)

3. Exogenous Models and Forecasts Used by USSTEM

Although most of the forecasts EIA publishes in STEO are produced endogenously within USSTEM, the 

model also relies on several forecasts produced by outside sources:

• U.S. macroeconomics – IHS Markit

o For the U.S. macroeconomic forecast, EIA runs the IHS Markit model of the U.S. 

economy. For STEO, the only input EIA adjusts when running the IHS model is the oil 

prices, which are set to match EIA’s internally generated oil price forecast.

• Global macroeconomics – Oxford Economics

o This model provides macroeconomic data used mostly by GSTOM but also read by 

USTEMM.

• U.S. weather – NOAA

o EIA gets state-level heating degree day (HDD) and cooling degree day (CDD) history and 

forecasts from NOAA. EIA uses contemporaneous population weights to aggregate the 

state-level data to the regional and national levels published in STEO. For example, 

historical HDD for January 2010 are weighted using historical state populations from 

January 2010, and forecast HDD for January 2021 are weighted using forecast state 

populations for January 2021. So, changes in U.S. and regional HDD and CDD data 

represented in STEO over time reflect not only changes in temperature but also shifts in 

population.

4. USSTEM module components

Although EIA STEO analysts execute USSTEM by running a single Eviews program file, USSTEM comprises

several sub-modules, which are automatically executed each time the main USSTEM model is run. These 

sub-modules generally represent the various energy sources. Each module consists of econometric 
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linear regression and/or identity equations that are solved simultaneously within the Eviews software.

Indentity equations calculate values for some series that result from simple arithmetic relationships 

between other variables. 

Seven module components are endogenous to USSTEM:

• Electricity demand models

o Electricity retail prices

o Electricity retail sales

o Electricity load

• Electricity supply models

o Electricity generation

o Fuel consumption by generators

o Fuel costs for power generators

o Wholesale electricity prices

• Renewable energy models

o Renewable energy capacity

o Renewable energy consumption

o Distributed solar generation

• Natural gas models

o Natural gas supply

o Natural gas demand and inventories

o Natural gas prices

• Petroleum models

o Jet fuel

o Residual fuel

o Distillate fuel 

o Motor gasoline 

o Other petroleum products 

o Hydrocarbon gas liquids

o Petroleum refinery supply

o Petroleum product prices

• Coal model

o Coal production, consumption, stocks, and trade

• CO2 emissions model

o Emissions by fuel source

EIA maintains the model objects and associated equations in an Eviews database, which are read in 

when USSTEM is executed. Once USSTEM is executed, it solves all of the equations in the model objects 

simulatenously to produce the forecasts.

EIA also produces several key forecasts using models that are outside of the USSTEM framework. These 

forecasts are done outside of USSTEM because they require modeling techniques or data compiling 

complexities that do not fit neatly into the econometric structure of USSTEM. Although they are 

produced outside of USSTEM, these forecasts include many of the foundational variables used in 

USSTEM that are both key energy market variables and also important explantory variables in regression

models.
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• Global oil production and consumption

o These forecasts come from the GSTOM component of STIFS.

• Brent and WTI crude oil spot prices 

o EIA uses two models as bases for oil price forecasts. The first is a pooling model that 

provides a single Brent price path that is an average of five separate models that use 

both EIA and non-EIA oil market data. The second is a linear regression model based on 

inputs from the STEO global oil market forecast. Ultimately, EIA considers the output of 

the two models as guides and constructs a price forecast using analyst judgment.

• U.S. crude oil and natural gas production

o Using the crude oil and Henry Hub natural gas spot price forecasts as inputs, EIA 

generates crude oil and natural gas production forecasts using a linear regression model 

in Eviews. This model forecasts regional-level production using historical well-level 

production based on decline rate data. 

• U.S. liquefied natural gas (LNG) imports and exports

o As the United States steadily increases its LNG export capacity, LNG exports will be a 

function of this capacity, which is difficult for a model to forecast. EIA analysts track 

export project construction and determine utilization rates for new export capacity. 

• U.S. electricity generation and wholesale electricity prices

o EIA uses LCG Consulting’s UPLAN software, which is an integrated modeling tool for 

simulating electricity industry operations and power markets on an hourly basis. 

Analysts run UPLAN scenarios using data from USDEMM and data provided by LCG. 

USSTEM uses the model solution output from UPLAN to produce the monthly STEO 

electricity supply forecasts. 

5. Solving the Model

When the Eviews software executes USSTEM, it first reads the historical data, exogenous forecasts, and 

EIA forecasts produced out of USSTEM, and then it solves the model for the endogenous forecast 

variables. EVIEWS uses the Gauss-Seidel iterative method to solve USSTEM.

USSTEM contains nearly 600 equations to forecast variables representing different energy sources and 

market activities. Most of the equations are linear regression models involving historical EIA data series. 

Many of these series are published with a two-month lag (for example, data for January are published at 

the end of March). For some data series, EIA publishes weekly data, in which case USSTEM uses this 

weekly data to fill in the intervening months between the last monthly data point and the forecast. 

Often STEO analysts adjust the weekly data point using analyst judgment because these series are 

generally considered preliminary, but the monthly data are considered final and are never adjusted. The 

system aggregates data from EIA’s hourly grid monitor to provide recent monthly estimates for some 

electricity data series.

Whether or not preliminary data are available to provide estimates of recent historical data, USSTEM

will prepare modeled data estimates for any historical month for which no data exist and then execute 

the forecasting equations for any future month. The following are two examples of the progression from 

actual data to forecast in a hypothetical STEO published in April. 
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Historical data availability STEO data series

Last monthly 

data

Weekly data 

(yes/no)

January February March April

Example 1 January yes historical monthly weekly weekly forecast

Example 2 January no historical monthly forecast forecast forecast

The accuracy of a forecast created using coefficients estimated from historical data depends on the 

relationships among the historical data series, as represented by the coefficients, holding in the future. 

In reality, energy markets are constantly evolving, and prior estimated relationships often lose their 

forecasting significance. As a result, STEO analysts regularly monitor market developments to account 

for events not reflected in the historical data. In addition, exogenous factors outside of available data 

sets (for example, regulatory changes) are often known and can affect forecasts. Many equations in 

USSTEM have associated adjustment factors (add factors) that STEO analysts use to reflect expected 

energy market changes that may not be adequately reflected by the current structure of the model 

equations. Add factors are stored in an Eviews database file and read in when USSTEM is executed. 

As a general practice, EIA analysts who work on STEO reestimate the forecasting equations once a year. 

However, some equations are reestimated more frequently and some less frequently.1 In the 

reestimation process, analysts consider whether the set of independent variables is still appropriate 

from a standpoint of both description and statistical significance. Analysts also consider the estimation 

period to ensure it captures a period for which the historical data relationships observed are relevant for 

the current forecast period. 

 1 For details on recommended updating frequencies for STEO regression models, see “Estimating Re-fitting Frequencies for 

Short-term Energy Models” by Lent, J. and George, R. (2019), available from EIA on request.
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Model diagram