World Energy Projection System (WEPS): 

Commercial Demand Model

Independent Statistics & Analysis

www.eia.gov

U.S. Department of Energy

Washington, DC 20585

Last Updated 2020
U.S. Energy Information Administration | World Energy Projection System: Commercial Demand Model 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.
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Contents

1. Introduction .............................................................................................................................................. 2

2. Description of the Projection Method ...................................................................................................... 2

3. Deriving the Parameters........................................................................................................................... 4

4. Adjusting the Projection Method for Unusual Conditions........................................................................ 4
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1. Introduction

The WEPS Commercial Demand Model projects energy consumption by businesses, institutions, and 

service organizations. Most commercial energy use occurs in buildings or structures, supplying services 

such as space heating, water heating, lighting, cooking, and cooling. Energy consumed for services not 

associated with buildings, such as for traffic lights and city water and sewer services, is also categorized 

as commercial energy use. The model projects commercial sector energy consumption for a number of 

energy sources in each of the WEPS regions over the projection horizon. The Commercial Demand 

Model projects energy consumption for the following energy sources:

• Motor gasoline

• Distillate fuel

• Residual fuel

• Kerosene

• Liquid Petroleum Gases (LPG)

• Natural gas

• Coal

• Electricity

• District Heat (steam or hot water)

• Biomass

• Solar

To project commercial energy consumption, we assume that the following factors determine 

consumption changes over time:

• Changes in services gross output, defined as the total dollar value of services provided by 

commercial establishments, adjusted to reflect purchasing power parity

• Changes in commercial energy prices

• The sensitivity of energy consumption to changes in services gross output and prices

• A linear trend

Increased services gross output generally leads to higher consumption, while price increases encourage 

consumption declines. The sensitivity of energy consumption to changes in services gross output and 

prices varies by region, fuel, and time period and is represented by an elasticity parameter. The linear 

trend accounts for other factors such as energy efficiency improvements, energy conservation efforts, 

and policy effects.

2. Description of the Projection Method

The projection method for commercial energy consumption is similar to the method used for residential 

energy consumption. For each WEPS region and for each fuel and projection year, we calculate a 

multiplicative index representing the change in commercial energy consumption that results from the 

projected change in services gross output since the base year. We then compute similar factors to 

represent the effects of changes in commercial energy prices and a linear trend. To arrive at a projection 

for the current year, we multiply the base year’s energy consumption estimate by the product of these 

three factors: 
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This Year’s Consumption = (Base Year’s Consumption)

× (Index of Change Due to Services Gross Output Changes)

× (Index of Change Due to Price Changes)

× (Change Due to a Long-term Trend).

To compute Last Year’s Consumption, we start with an actual estimate for the base year, the most 

recent historical year for which data are available. We update the estimate sequentially for each year in 

the projection period. The WEPS Global Activity Model provides the necessary projections of services 

gross output, and various other WEPS models provide commercial fuel price projections.

We set the Index of Change Due to Changes in Services Gross Output equal to 1 for the base year. Then, 

for each region, fuel, and projection year, we calculate an index value by updating the previous year’s 

value as follows:

This Year

'

s Index = �Last Year

'

s Index�

Lag Effect Parameter × �

This Year's Output

Base Year's Output�

Output Elasticity

,

where “Output” refers to services gross output. The Lag Effect Parameter indicates the effect of the

previous year’s index value on the current year’s index value (a measure of consistency), while the 

Output Elasticity indicates the impact of services gross output change on the commercial consumption 

of the particular fuel. For example, a value of 0 for these parameters indicates no effect, while a value of 

1 indicates a strong effect. We derive the parameter values by analyzing projections from EIA’s U.S. 

National Energy Modeling System (NEMS) and applying adjustments for the WEPS regions, as discussed 

in Section 3. The values of services gross output represent the total value of commercial output for the 

region, adjusted for purchasing power parity.

Similarly, for each fuel, we set the Index of Change Due to Price Change equal to 1 for the base year. 

Then, for each region and projection year, we calculate an index value by updating the previous year’s 

value as follows:

This Year

'

s Index = �Last Year

'

s Index�

Lag Effect Parameter × �

This Year's Price

Base Year's Price�

Price Elasticity

.

The Price Elasticity indicates the effect of price change on commercial consumption of the particular 

fuel.

We calculate the Change Due to a Long-term Trend as a simple multiplicative growth factor. We set the 

factor equal to 1 for the base year, and we set it equal to a target value for the final projection year 

(currently 2050). We then compute the annual growth rate by linear interpolation.

We adjust the commercial consumption projections for the first two projection years to agree with 

those published in EIA’s Short-Term Energy Outlook.
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The method described above is used for all fuels except biomass and solar, primarily because no price 

data are available for these fuels for the WEPS regions. Very little solar energy is used in commercial 

activities. Although a larger amount of biomass is used in some regions, much of it is unmarketed (e.g., 

wood gathered in rural areas) and therefore not captured in EIA’s historical international energy data. 

3. Deriving the Parameters

The Elasticities and the Lag Effect Parameters in the model are largely based on the behavior of 

projections from the NEMS Commercial Model and adapted to the WEPS regions. To develop these 

parameters, we analyze the projections from a previous year’s Annual Energy Outlook Reference case

and compare them to those from the corresponding High and Low Economic Growth cases and the 

corresponding High and Low Oil Price cases.

For example, we estimate the Output Elasticity parameters for each year and fuel by examining the 

differences in projected fuel consumption between the Reference case and the High Economic Growth 

case, relative to the projected changes in services gross output. We then repeat the process, examining 

differences between the Reference case and the low Economic Growth case projections. Because the 

estimated Income Elasticity parameters from the two analyses differ, EIA analysts generally compute the 

model parameter by averaging the two values. In cases where the resulting parameter seems 

inappropriate, analysts use alternative elasticity estimates, e.g., based on data from other fuels or 

sectors, along with expert judgment, to arrive at an acceptable parameter value.

We estimate the price elasticities in essentially the same manner, using different NEMS projections that 

reflect changes in specific fuel prices and comparing the projected changes in fuel consumption to those 

projected for the Reference case. In general, the elasticity for each fuel is an average of estimated 

elasticities for that fuel, but analysts use expert judgment when the NEMS-based elasticities don’t seem 

appropriate. 

4. Adjusting the Projection Method for Unusual Conditions

Although the basic method described above provides reasonable projections for most fuels, regions, and 

WEPS cases, EIA analysts adjust the method as needed to account for unusual conditions. 

Adjusting the Trend Factor

Under some conditions, a straight-line trend might not be appropriate for a particular projection, 

because we expect a different structural or behavioral trend. For example, consumption of a specific 

fuel in a specific region might have been recently growing very rapidly and may therefore be expected to 

reach saturation, resulting in a moderation in the trend. The model allows analysts to modify the trend

factor by adding an inflection point to the linear trend.

Accounting for Fuel Substitution in the High World Oil Price (HWOP) Case

When the price of one fuel increases relative to the prices of other fuels, some commercial 

establishments switch to the cheaper fuel. Because the basic method described in Section 2 involves no 

fuel cross-price elasticities, it doesn’t generally account for fuel substitution. In the HWOP case, 

however, we modify the method to ensure that the projected decline in petroleum-based fuel 
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consumption (from the Reference case level) results in increases in the projected consumption of other 

fuels.

We assume that 50% of the petroleum-based fuel consumption decrease in the HWOP case (relative to 

the Reference case) will be replaced by increases in consumption of other fuels. We estimate the 

substitution over the years in the projection period (currently from the base year to 2050) so that it 

starts at 0 in in the base year and gradually increases to its full value five years after the base year. The 

annual proportion then remains the same to the end of the projection period.

Once we determine, for each year, the amount of petroleum-based fuel consumption to be replaced by 

consumption of other fuels, we allocate the total amount to natural gas, coal, and electricity, based on 

the previously projected relative shares of each of these fuels. For example, if 100 trillion British thermal 

units (Btu) requires substitution, and the respective shares of natural gas, coal, and electricity are 0.4, 

0.0, and 0.6, then natural gas will increase by 40 trillion Btu, coal will be unchanged, and electricity will 

increase by 60 trillion Btu.