National Energy Modeling System (NEMS)

Residential Demand Module

Independent Statistics & Analysis

www.eia.gov

U.S. Department of Energy

Washington, DC 20585

Last Updated 2020
U.S. Energy Information Administration | National Energy Modeling System Residential Energy Demand Module 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.
2

1. Introduction

The NEMS Residential Demand Module (RDM) generates projections of energy demand (also called

energy consumption) for the residential sector by end-use service, fuel type, and U.S. census division.

The RDM uses inputs from other NEMS modules, such as projected energy prices and macroeconomic 

indicators, along with input data from EIA’s Residential Energy Consumption Survey (RECS). The NEMS 

integrating module uses the RDM projections, along with input from other modules, to compute 

equilibrium energy prices and quantities.

The RDM projects residential sector energy demand in six sequential steps. These steps produce 

information on housing stocks, technology choices, appliance stocks, building shell integrity, distributed 

generation, and energy consumption. The module uses a stock-vintaging approach that projects 

household equipment stock and equipment efficiency over time.

2. Module Output

The RDM projects housing stock and energy consumption per housing unit and can produce a 

disaggregated projection of appliance stock and efficiency for the following types of equipment:

Forced-air furnaces (electric, natural gas, propane, and distillate fuel oil)

Hydronic heating systems/ boilers (natural gas, distillate fuel oil, and kerosene)

Heat pumps (electric air-source, ground-source, and natural gas)

Wood stoves (pellet)

Air conditioners (central and room)

Dishwashers

Water heaters (electric, natural gas, distillate fuel oil, propane, and solar)

Cooking ranges (electric, natural gas, and propane)

Clothes washers (front- and top-loading)

Clothes dryers (electric and natural gas)

Refrigerators (with top-, side-, and bottom-mounted freezers)

Standalone freezers (upright and chest)

Lighting (general service, linear fluorescent, reflector, and exterior)

Solar photovoltaic (PV) systems

Natural gas fuel cells

Small wind turbines

3. Modeling Methods

For each projection year, the RDM performs the following steps:

1. Generate a projection of housing stock based on the retirement of existing housing stock and the 

addition of new construction as determined in the NEMS Macroeconomic Activity Module (MAM).

2. Project current-year household energy-consuming equipment stock, accounting for housing 

demolitions and additions.

3. Project market shares for household equipment types and efficiencies by end-use service (e.g., 

space heating, cooking).
3

4. Project the current year’s equipment additions and replacements, for both existing homes and new 

construction vintages, based on estimated housing and equipment survival rates and projected 

equipment market shares.

5. Project energy efficiencies weighted by market share.

6. Project fuel consumption using Unit Energy Consumption (UEC) and the weighted efficiencies.

The primary module inputs include fuel prices, housing stock characteristics, housing starts, population, 

and technology characteristics. The technology characteristics used in the module include installed 

capital costs (in real dollars), equipment efficiency, and expected equipment lifetimes.

The NEMS RDM calculations are based on accounting principles and a representation of residential 

consumer economic behavior that generates projections of residential sector energy demand, appliance 

stocks, and market shares of household equipment. For each projection year, the RDM projects the 

stock of housing units and the corresponding energy-consuming equipment. The housing stock changes 

over time as houses are removed from the stock (demolished, retired, or converted) and new 

construction is added or converted to residential use. Similarly, the equipment stock changes each 

projection year as appliances fail and are replaced, through increases in the saturation of existing 

appliances, and as new technologies enter the market.

The RDM treats the year of the most recent RECS as a base year. Base-year information from the RECS 

forms the foundation of equipment and housing stock data for the RDM. Market share information from 

the RECS is used, along with data from the U.S. Census Bureau on characteristics of new housing, to 

estimate the number and type of replacements and additions to the equipment stock. The choice 

between the capital cost and the first year's operating cost determines the market share for each type 

of equipment. The RDM projects market shares as functions of the corresponding fuel prices, expected 

level of equipment usage, and equipment efficiency characteristics.

Log-linear regression coefficients (see Section B.1) are used to estimate the market shares of competing 

technologies within each of the ten major end-use service categories listed in Table 1. Market shares are 

projected for both new construction equipment decisions and replacement equipment decisions. The 

module’s technology choice calculations consider the relative installed capital and operating costs of 

each equipment type within the logit function to calculate the market shares of the technology within 

each service, region, and housing type. Several miscellaneous service categories, listed in Table 2, are 

modeled with a different approach that does not consider investment parameters such as cost or 

efficiency.

The RDM also considers building shell integrity (e.g., as determined by insulation levels and window 

sealing) in the projection of end-use consumption. Because homeowners may improve insulation in 

response to higher energy prices, building shell integrity in existing homes is sensitive to real price 

increases for space conditioning fuels. The RDM projects residential sector energy consumption as a 

function of the equipment and housing stock, average unit energy consumption, weighted equipment 

characteristics, and building shell integrity improvements.
4

4. RDM Modeling Components

The RDM characterizes energy consumption using a series of algorithms that account for the stocks of 

housing and appliances, equipment market shares, and energy intensity. The module assesses the shifts 

of market shares between competing technologies based on fuel prices, equipment costs, and 

assumptions about the behavior of residential consumers.

The RDM is a sequential structured system of algorithms, with succeeding computations using the 

results from previously executed components as inputs. The module is composed of six components: 

housing stock projection, equipment technology choice, appliance stock projection, building shell 

integrity, distributed generation, and energy consumption.

In this section, we briefly describe each modeling component. Section 5 provides two examples of how 

the components are implemented for specific types of household equipment.

4.1. Housing Stock Projection Component

The Housing Stock Projection Component of the RDM received the following inputs:

Housing starts

Existing housing stock in the base year

Housing stock attrition rates

Housing floor area trends (new and existing)

The RDM divides U.S. housing units into two housing vintages: housing that existed in the base year (the 

year of the most recent RECS) and new construction built in years after the base year. The location and 

type of housing stock are the primary model drivers of the module’s outputs. The RDM uses data from 

the NEMS Macroeconomic Activity Module (MAM) to project new and existing housing for three 

dwelling types (single family homes, multifamily homes, and mobile homes) within each census division. 

Housing units are removed from the housing stock at a constant rate over time, based on an analysis of 

each building type’s historical growth and housing starts. For each projection year, the module assumes 

the removal of 0.3% of single family homes, 0.5% of multifamily homes, and 3.4% of mobile homes. The 

RDM also receives projections of new housing construction and square footage from the NEMS MAM.

The RDM assumes that a constant 1.2% share of existing housing is renovated each year, increasing the 

square footage of the conditioned (heated and cooled) living area by about one-third. Newly 

constructed homes are assumed to be larger than existing homes, which increases the heating and 

cooling loads in new construction. This assumption is based on a time-series analysis of RECS data, which 

shows increased conditioned floorspace per household over time, as well as increased prevalence of 

slightly higher ceilings.

4.2. Equipment Technology Choice Component

The Equipment Technology Choice Component simulates the behavior of consumers by projecting 

market shares for each available equipment type. The component receives the following inputs:

Equipment retail or replacement cost

Equipment subsidies
5

Equipment energy efficiency

Equipment penetration level (i.e., percent of households with that equipment)

Water usage factors

Fuel and equipment switching costs

Fuel costs

The module models new and replacement equipment decisions for each technology type. For new 

construction, the home heating fuel is determined by the relative life-cycle costs of all competing 

heating systems. The RDM assumes that a percentage of space heaters, air conditioners, water heaters, 

cooking ranges, and clothes dryers may be replaced with competing technologies in single-family 

homes. Projected switching is based on the retail cost of new equipment and the cost of switching 

technologies.

The RDM assumes that space heaters, air conditioners (heat pumps and central air conditioners), water 

heaters, cooking ranges, and clothes dryers may be replaced with competing technologies in singlefamily homes and that 20% of the replacement market in single-family homes is eligible to switch fuels 

in any projection year. The projected percentage that switch technologies depends on factors such as 

retail equipment cost and technology switching cost. Equipment units in multifamily and mobile homes 

are assumed to be replaced with the same technology.

The efficiency choices projected for residential equipment are based on parameters of a log-linear

regression model that relates equipment market shares to equipment life-cycle costs. The life-cycle 

costs include capital/installed (first cost) and discounted operating (annual fuel) costs. EIA analysts 

adjust the parameters to agree with the most recently available data. A log-linear regression model is 

also used to project market share weights of equipment types installed in new construction. The

projected market share weights are then used to compute composite average efficiencies for each 

service listed in Table 1 and Table 2. The technologies are distinguished by the service demand that they 

satisfy, by the fuel that they consume, and by their energy efficiency.

Energy efficiency can be defined as the ratio of service demand to energy input. For relatively simple 

devices such as space heaters or light bulbs, service demand is a unit of heat or light, respectively, and 

thus efficiency is described in terms of heat per unit energy (such as coefficient of performance (COP) or 

annual fuel utilization efficiency (AFUE)) or light per unit energy (lumens per watt). Because the RDM 

ultimately converts all energy, heat, and light measures to British thermal units (Btu), energy efficiency 

is considered unitless.

For other equipment, service demand can be more difficult to quantify, or other factors beyond the 

primary service demand may contribute to a unit’s energy consumption. In the case of refrigerators, the 

primary service demand is the volume of interior space refrigerated, but features such as an icemaker or 

through-the-door water dispenser can add to the appliance’s unit energy consumption (UEC). Another 

example is televisions, where service demand may be described as the area of the visual display, but 

other factors such as power draw in standby and off modes affect their energy consumption. For this 

reason, some equipment is described by a UEC (typically in units of kilowatthours per year) rather than 

an energy-efficiency metric.
6

4.3. Appliance Stock Projection Component

The Appliance Stock Projection Component receives the following inputs:

Expected equipment minimum and maximum lifetimes

Base-year equipment stocks

Equipment saturation level (i.e., the number of units per household)

4.3.1. Appliance Stock Assumptions

The component projects the number of end-use appliances within all occupied housing units, tracking

equipment additions and replacements using the following assumptions:

• Some energy-consuming equipment is chosen based on its upfront cost compared to its 

operating costs (like water heaters and refrigerators) while some appliances are chosen based 

on factors not related to their energy consumption (such as security systems or coffee makers).

• Equipment lifetime is characterized by a survival function, which maps the proportion of 

surviving units against time, as illustrated in Figure 1. The RDM first estimates a linear survival 

function and then smoothes it using Weibull shape parameters, which are estimated from data 

on equipment lifetime ranges.

• The equipment contained in a retiring housing structure retires when the structure is removed 

from the housing stock (zero salvage value for equipment).

• Projected new home heating fuel shares are based on the U.S. Census Bureau's Characteristics 

of New Housing Survey of Construction microdata and vary over time due to changes in lifecycle cost for each of the heating system types.

• The choice of fuel for water heating and cooking is largely dependent on the choice of heating 

fuel.

• When old equipment is replaced, the type of fuel used for cooking and water heating is based 

on the percentage of homes able to switch fuels (e.g., homes with natural gas service may 

switch from electricity to natural gas) and a technology choice-switching algorithm similar to 

that described for space heating below.

Figure 1. Weibull and linear equipment survival rate functions
7

The RDM also incorporates the following assumptions regarding specific appliances:

4.3.2. Room and central air-conditioning units

The RDM disaggregates room and central air-conditioning units based on data for existing housing units. 

It uses the market penetration of room and central air systems (by census division and housing type), 

along with new housing construction data, to determine the number of new units of each type. The 

penetration rate for central air conditioning is estimated by means of time series analysis of RECS data. 

The RDM also uses RECS data to project the number of room air conditioners per household in new 

construction.

4.3.3. Solar water heaters

Market shares for solar water heaters are tabulated from the RECS microdata. The RDM assumes that, in 

solar water heating systems, solar energy provides 50% of the energy needed to satisfy hot water 

demand, and an electric back-up unit satisfies the remaining 50%.

4.3.4. Refrigerators and freezers

The RDM assumes fixed overall shares for three types of refrigerators based on freezer orientation (topmounted, side-mounted, or bottom-mounted freezer) and two types of freezers (upright and chest). 

Market shares are established based on RECS microdata.

4.3.5. Clothes washers

Because many efficiency features for clothes washers act to reduce the demand for hot water, the RDM

links clothes washer choice to the water heating service. Also, like refrigerators and freezers, multiple 

equipment orientations (top- and front-loading) are compared, with market shares developed from 

RECS microdata.

4.3.6. Clothes dryers

Based on an analysis of RECS data, the RDM assumes that clothes dryer market penetration increases 

over the projection period, with a terminal saturation level that is consistent with the market 

penetration of clothes washers.

4.4. Building Shell Integrity Component

The RDM models building shell integrity for existing and new housing. The Building Shell Integrity 

Component receives the following inputs:

Level of shell integrity (insulation and air tightness)

Price elasticity of shell integrity

Rate of improvement in existing housing shell integrity

Cost and efficiency of various building shell measures for new construction

The existing housing stock responds to rising prices of space conditioning fuels by improving shell 

integrity. Shell integrity improvements might range from relatively inexpensive measures (such as 

caulking and weather-stripping) to projects with substantial costs (such as window replacement). These 

improvements exhibit a one-way price response: more measures are installed as prices increase, but 

those measures are not undone when prices fall.
8

New housing stock also incorporates shell integrity improvements. The shell integrity of new housing is a 

function of capital and operating costs for several levels of total system efficiency. New housing stock 

includes homes that meet the 2009 International Energy Conservation Code (IECC), those that meet 

ENERGY STAR® criteria, those that qualify for federal tax credits for efficient shells, and those that 

include the most efficient commercially available building shell components, as well as some non-codecompliant homes. Building shell efficiency and heating, ventilation, and air-conditioning (HVAC) systems 

for new housing stock are modeled as functions of the life-cycle cost of competing building shell and 

HVAC packages.

4.5. Distributed Generation Component

The Distributed Generation Component projects adoption of residential solar photovoltaic (PV) systems, 

natural gas fuel cells, and small wind turbine systems for on-site generation to compete with purchased 

electricity in satisfying household electricity needs. The component receives the following inputs:

Equipment cost

Equipment subsidies

Equipment conversion efficiency

Solar insolation values

Macroeconomic indicators (e.g., household income)

Cross-sector capacity levels

System penetration parameters

Wind speeds

Grid interconnection limitations

The RDM uses a hurdle model (see Lent 2017) to project the number of new residential solar PV 

adoptions. It uses a cash-flow formulation (assuming that the decision to invest in a system is a function 

of the payback time) to project the adoption of natural gas fuel cells and small wind turbines. Electricity 

generated from these systems is deducted from total housing unit use, or sold back to the grid, if 

feasible.

4.6. Energy Consumption Component

4.6.1. Input Data

The RDM’s Energy Consumption Component calculates end-use consumption for each service and fuel 

type shown in Table 1 and Table 2. The consumption projections are products of the number of units in 

the equipment stock and the average technology unit energy consumption (UEC). The average UEC 

changes as the composition of the equipment stock changes over time. The Energy Consumption 

Component receives the following inputs:

Base-year unit energy consumption (UEC)

Population-weighted heating and cooling degree days

Population

Household size

Personal disposable income

Energy consumption can vary based on projected heating and cooling shell integrities, fuel prices, 

personal disposable income, housing unit sizes, and weather, as applicable to specific equipment and 
9

end-use services.The RDM incorporates the following assumptions regarding specific types of end-use 

consumption, as described in the following subsections.

4.6.2. Lighting

The RDM partitions lighting into four main categories of bulb type: general service, reflector, linear 

fluorescent, and exterior. Within the general service category, several hours of use bins further partition 

this category, allowing bulb choice to vary with the amount of time each fixture is used on an annual 

basis. For the reflector, exterior, and linear fluorescent categories, the RDM assumes an average hours 

of use value per DOE’s 2015 U.S. Lighting Market Characterization report. Within an application such as 

general service lighting, a fixed light output level (measured in lumens) is used, so that choices are 

among bulbs with similar light output. The RDM uses adjustment factors to benchmark shares of general 

service lighting technologies to agree with National Electrical Manufacturers Association (NEMA) lamp 

shipment indices.

4.6.3. Furnace fans and secondary heating

The RDM projects furnace fan energy consumption based on the number of housing units that have 

fossil fuel-fired central forced-air heating and the number of heating and cooling degree days.

The RDM projects the consumption of secondary heating fuels based on the share of housing units that 

use a secondary heating fuel and the unit energy consumption, adjusted to account for the housing unit 

shell integrity.

4.6.4. Miscellaneous electric loads and other/unspecified consumption

The RDM uses expected numbers of equipment units or devices and per-unit energy consumption to 

form projections of miscellaneous electric loads (MELs). These are not modeled at the same level of 

detail as other end uses. Consumption projections for some of these MELs are also affected by projected 

changes in square footage and disposable income.

Even with the consumption information collected in RECS, there is consumption attributed to 

unspecified uses, which are aggregated as other uses.

5. Examples of Modeling Component Implementation

Here we discuss the RDM’s implementation of the modeling components for three specific types of 

household equipment: space heating, lighting, and clothes washing equipment. The modeling methods 

for most household appliances are similar to those used for clothes washers, while some specialized

methods are applied for space heating and lighting.

5.1. Example 1: Space Heating Equipment

The RDM models the energy consumption of space heating equipment in two stages. For new 

construction, the first stage models the consumer’s choice of an HVAC/building shell combination 

package. Each package comprises heating/cooling equipment and a building shell integrity level 

(including an insulation level). The model accounts for five fuel-specific HVAC/shell packages, defined by

the energy efficiency code they are designed to meet: no code; the International Energy Conservation 

Code (IECC); or 30%, 40%, or 50% better than the IECC. For existing homes, the RDM models fuel choice 
10

in the first stage, while the second stage considers efficiency improvements in HVAC equipment for all 

homes.

5.1.1. Projecting HVAC unit operating costs

For each HVAC/shell package considered, the RDM projects a market share weight that accounts for 

both initial purchasing (or capital) costs and annual operating costs. For existing homes, new equipment 

operating cost, by housing type, is computed as a product of five factors accounting for the following 

effects:

• regional fuel price

• unit energy consumption

• a weather effect, based on heating degree days

• an efficiency adjustment, to account for equipment efficiency improvements

• a shell efficiency adjustment, to account for building shell improvements

For new construction, the operating cost for heating is computed similarly, but a second term is added 

to account for air conditioning costs, because the choice of heating and cooling technologies is linked.

5.1.2. Simulating price-induced technology change

The RDM Technology Choice Component can simulate fuel price-induced technology change, i.e., the 

tendency of manufacturers to make improved-efficiency HVAC technologies available earlier in response 

to sustained high fuel prices. The RDM first computes the ratio (PriceRatio) of a three-year average fuel 

price (during the high-price period) to the base-year fuel price. Then it computes the number of years 

(ShiftYears) that the availability of improved HVAC technologies should be shifted earlier as 

ShiftYears = 10 × (PriceRatio − 1).

5.1.3. Estimating market share weights for HVAC units

The RDM technology choice component projects the life-cycle cost for an equipment category as the 

sum of (1) the initial capital cost of the appliance, net of rebates and other incentives, and (2) the annual 

operating cost multiplied by a factor that adjusts for inflation, as well as for a delayed payment effect, 

over the expected number of years of the equipment’s operation.

The projected life-cycle cost is used as the independent variable in a log-linear regression model (see 

Section B.1) that projects a market share weight for each equipment category. The final weight for each 

projection year is a weighted average of (1) the weight for the previous projection year and (2) the 

predicted value of the weight, based on the regression coefficients. The RDM projects a final market 

share weight for each equipment category, housing type, census division, and projection year. It 

normalizes the final weights to force them to sum to unity within each census division and year. Based 

on historical data, the module then divides each category into more detailed equipment types and 

performs a similar weight calculation within each category, resulting in more detailed market share 

weights.

5.1.4. Appliance stock projection for HVAC units

The RDM Appliance Stock Projection Component projects the number of HVAC units in houses 

constructed since the base year as the product of the number of new houses constructed and the 

market share for the unit’s equipment category. To project the number of replacement units needed in 

houses built since the base year, the model multiplies the projected number of units by the fixed annual 

housing unit survival rate (0.997, 0.995, or 0.966 for single-family homes, multifamily homes, and mobile 
11

homes, respectively) and by the equipment survival rate characterized by a Weibull survival curve for 

each equipment and housing type in each census division.

To project the number of replacement units needed in housing units that existed in the base year, the 

RDM multiplies the total projected number of existing HVAC units by the housing survival rate and a 

projected equipment retirement rate. Through a similar calculation, the RDM then accounts for the 

number of units that will be replaced twice during the projection period. The module then uses a 

Weibull function to smooth the survival curve for each equipment type within each census division.

For the HVAC units that are replaced, the RDM next projects the proportion that will be replaced by 

units of a different technology type (e.g., the proportion of distillate fuel oil furnaces replaced by natural 

gas furnaces). To project the number of HVAC units eligible to switch technologies, the module applies a 

fixed estimated proportion for each equipment category. Then, because we assume that the decision to 

switch technologies is based on a comparison of equipment life-cycle costs, the module uses a log-linear 

regression with a technology switching weight as the dependent variable and equipment life-cycle cost 

as the sole independent variable. The RDM normalizes the switching weights to force them to sum to 

unity within each equipment category, housing type, and projection year. The module then reconciles 

the switching from and switching to categories, allocating the projected units to the correct equipment 

classes.

Finally, for each equipment category, the RDM adds the projected numbers of surviving HVAC units 

across years from the base year through the projection year. The sums are computed first for units 

surviving from the base year and then for units added as replacements since the base year. 

5.1.5. Shell integrity component for HVAC units

The Shell Integrity Component uses index values to track increases in the energy efficiency of building 

shells over time, relative to the base year. Each index is computed as

Index = 1 − Proportional Increase in Efficiency Since the Base Year.

For example, an index value of 0.3 represents a 0.7 (or 70%) increase in efficiency since the base year—

the maximum increase that the RDM projects.

Because the RDM assumes only increases (never decreases) in building shell efficiency over time, the 

index values for a housing category may decrease but cannot increase over the projection period. For 

each housing category, census division, HVAC fuel, and projection year, the RDM calculates the shell 

integrity index for housing units that existed in the base year as the product of three factors:

• the shell integrity for the base year

• a parameter representing the annual increase in existing shell efficiency due to technology 

improvements

• a price elasticity function based on prices from the current projection year and the previous two 

years
12

The use of the three-year price elasticity function reflects the assumption that consumers adopt shell 

improvements based on multi-year price trends.

For housing units built after the base year, the RDM calculates an annual shell integrity index as the 

weighted average of the shell integrity indices associated with the available HVAC packages that use a 

particular fuel. The weights in the weighted average are the market share weights computed in the 

Technology Choice Component. The module then uses annual market share weights to average these 

index values over the years between the base year and the projection year, providing an annual average 

shell integrity index for each fuel, projection year, and census division.

5.1.6. Energy consumption component for HVAC units

The RDM’s Energy Consumption Component begins by projecting a unit energy consumption (UEC) value 

for each HVAC equipment category, census division, and projection year. For new HVAC equipment, the 

projected UEC is a product of the following factors:

• equipment category efficiency (heat output /fuel input)

• equipment market share weight, projected by the Technology Choice Component

• average efficiency of the base-year stock for the equipment category (heat output /fuel input)

• UEC for the base-year stock of the equipment category for the census division and housing type

(MMBtu/year)

• an adjustment for the projected number of heating degree days in the year

• an adjustment to account for the increasing conditioned floor space in housing units during the 

projection period

The RDM uses the same method to calculate a UEC for each category of replacement HVAC equipment, 

except that the adjustment factor for increasing conditioned floor space is not applied. For equipment 

surviving from the base year, the module adjusts the base year UEC values to account for changes in 

heating degree days and the increase in conditioned floor space. For each census division, equipment 

category, and projection year, the RDM then computes an average UEC, weighting the equipment 

vintages (new, replacement, and surviving) by their market shares.

Finally, for each HVAC fuel considered, the RDM combines data from all equipment categories that use 

the specific fuel. The module computes an average UEC by fuel, census division, and projection year. It

first performs the calculation for the base year. For each equipment category and each vintage (new, 

replacement, and surviving), the module computes a product of the following factors:

• number of units in the stock

• UEC

• building shell integrity for the specific fuel

The RDM adds the products across the three equipment vintages and multiplies the sum by the same 

three-year price elasticity factor used by the Shell Integrity Component. The RDM performs a similar 

calculation for each year of the projection period, except that, for each equipment category and vintage, 
13

the product of the three factors above is further multiplied by a rebound effect factor. The rebound 

effect adjusts the three-year price elasticity for the effect of increased energy efficiency in the 

equipment category. For each equipment category, census division, and projection year, the RDM 

computes the rebound effect based on the product of the following factors:

• equipment efficiency for the projection year

• equipment market share weight, projected by the Technology Choice Component

• average equipment efficiency for the base year

The rebound effect increases the projected energy service demand as the equipment efficiency 

increases, reflecting consumers’ tendency to use more services when their equipment uses energy more 

efficiently. The UEC by fuel, census division, and projection year is the RDM’s final output for HVAC 

equipment.

5.2. Example 2: Clothes Washing Equipment

Clothes washing (and dishwashing) energy consumption is composed of electricity consumption and 

water heating demand. The RDM accounts for changes in clothes washer efficiency on water heating 

consumption.

5.2.1. Projecting clothes washer operating costs

For each clothes washer considered, the RDM projects a market share weight that accounts for both 

initial purchasing (or capital) costs and annual operating costs. For both existing and new homes, 

equipment operating cost, by housing type, is computed as a product of four factors accounting for the 

following effects:

• regional fuel price

• unit energy consumption

• projection year equipment efficiency

• base-year stock-average equipment efficiency

5.2.2. Estimating market share weights for clothes washers

The RDM technology choice component functions similarly to that for space heating and cooling; 

however, shares of top- versus front-loading clothes washers remain fixed at the RECS base-year levels 

(76% and 24%, respectively). The model can also account for increasing penetration of clothes washers 

and dishwashers into new construction (as seen in the newest RECS homes between surveys). Because 

penetration of clothes washers appears to be flat in recent years, no increase is currently assumed.

5.2.3. Energy consumption component for clothes washers

Because clothes washers consume only one fuel directly (i.e., electricity), the energy consumption 

calculation is simpler than the calculation for most of the other end uses: total consumption is a sum 

product of number of clothes washers of each vintage and their respective UECs.

Given the relationship between hot water-consuming end uses such as clothes washers and 

dishwashers, changes in equipment efficiency affect not only the electricity used for clothes washing, 

but also water heating fuel consumption. The RDM’s approach accounts for the following:
14

• weighted average load adjustment of new clothes washers with respect to water heating load in 

the current year by housing type and census division

• the equipment weight for each type of new equipment

• the fraction of hot water needed to provide the same level of service relative to the base-year 

average

• the number of and water heating consumption for clothes washers for each type of water 

heating fuel type by census division and building type for each vintage of equipment

• the share of and UEC for each type of water heating fuel type by census division and building 

type for all vintages of equipment

• the number of clothes washers by census division and building type for all vintages of 

equipment

• the adjustment to the water heating UEC to account for the efficiency of clothes washers with 

respect to hot water load by census division and building type for all vintages of equipment

Table 1. Major services and equipment in NEMS Residential Demand Module

End-Use Equipment Efficiency Metric

1. Space Heating

Air-Source Heat Pump Heating Seasonal Performance Factor 

(HSPF)

Electric Furnace COP

Distillate Fuel Oil Boiler Annual Fuel Utilization Efficiency (AFUE)

Distillate Fuel Oil Furnace AFUE

Ground-Source Heat Pump COP

Kerosene Furnace AFUE

Propane Furnace AFUE

Natural Gas Boiler AFUE

Natural Gas Furnace AFUE

Natural Gas Heat Pump COP

Wood Stove COP

2. Air Conditioning

Air-Source Heat Pump Seasonal Energy Efficiency Ratio (SEER)

Central Air Conditioner SEER

Ground-Source Heat Pump Energy Efficiency Ratio (EER)

Natural Gas Heat Pump COP

Room Air Conditioner EER

3. Water Heating

Electricity Water Heater Energy Factor (EF)

Distillate Fuel Oil Water Heater EF

Propane Water Heater EF

Natural Gas Water Heater EF
15

Solar Water Heater EF

4. Cooking

Electric Cooking range kilowatthours per year (kWh / yr)

Propane Cooking range Thermal Efficiency (Btu Out / Btu In)

Natural Gas Cooking range Thermal Efficiency (Btu Out / Btu In)

Table 1. Major services and equipment in NEMS Residential Demand Module (continued)

End-Use Equipment Efficiency Metric

5. Clothes Drying

Electric Clothes Dryer EF

Natural Gas Clothes Dryer EF

6. Clothes Washing

Clothes Washer kWh / cycle (motor), Modified Energy 

Factor (MEF), Water Factor (WF)

7. Dishwashing

Dishwasher EF, WF

8. Refrigeration

Refrigerator w/ top freezer (21 cubic 

foot capacity)

kWh / yr

Refrigerator w/ side freezer (26 

cubic foot capacity)

kWh / yr

Refrigerator w/ bottom freezer (25 

cubic foot capacity)

kWh / yr

9. Freezing

Chest Freezer (15 cubic foot 

capacity)

kWh / yr

Upright Freezer (automatic defrost, 

20 cubic foot capacity)

kWh / yr

10. Lighting

General Service – Compact 

Fluorescent Lamp (CFL)

lumens per watt

General Service – Incandescent and 

Halogen

lumens per watt

General Service – Light-Emitting 

Diode (LED)

lumens per watt

Linear Fluorescent (T12) lumens per watt

Linear Fluorescent (T8) lumens per watt

Linear Fluorescent (LED) lumens per watt

Reflector (Incandescent) lumens per watt

Reflector (Halogen) lumens per watt
16

Reflector (CFL) lumens per watt

Reflector (LED) lumens per watt

Exterior (Incandescent) lumens per watt

Exterior (Halogen) lumens per watt

Exterior (LED) lumens per watt

Exterior (High Pressure Sodium) lumens per watt
17

Table 2. Miscellaneous services and equipment in NEMS Residential Demand Module

End-Use Equipment Efficiency Metric

Televisions and Related Equipment

Televisions kWh / yr

Set-top Boxes kWh / yr

Video Game Consoles kWh / yr

Home Theater Systems kWh / yr

DVD / VCR players kWh / yr

Personal Computers and Related Equipment

Desktops kWh / yr

Laptops kWh / yr

Monitors kWh / yr

Networking Equipment (modems and routers) kWh / yr

Secondary Heat

Coal MMBtu / yr

Distillate Fuel Oil MMBtu / yr

Electric MMBtu / yr

Natural Gas MMBtu / yr

Kerosene MMBtu / yr

Propane MMBtu / yr

Wood MMBtu / yr

Furnace Fans and Boiler Circulation Pumps

Furnace fans kWh / yr

Other Uses

Ceiling Fans kWh / yr

Coffee Makers kWh / yr

Dehumidifiers kWh / yr

Microwaves kWh / yr

Pool Heaters & Pumps kWh / yr

Portable Electric Spas kWh / yr

Rechargeable Electronics kWh / yr

Security System kWh / yr

Wine Coolers kWh/ yr

Unspecified: Distillate Fuel Oil MMBtu / yr

Unspecified: Electric MMBtu / yr

Unspecified: Natural Gas MMBtu / yr

Unspecified: Propane MMBtu / yr

Distributed Generation

Solar Photovoltaic Electrical Efficiency

Natural Gas Fuel Cell Electrical Efficiency

Small Wind Turbine Electrical Efficiency
18

6. Data Quality

Here we discuss the quality of the data collected through EIA’s Residential Energy Consumption Survey 

(RECS). These data form the basis of the historical housing stock, appliance stock, and technology 

information that drives the NEMS RDM.

The RECS data collection procedure relies on two instruments: the housing unit survey and the energy 

supplier survey. Data are collected from a representative sample of housing units through personal 

interviews. Billing data are then collected through mail questionnaires from the companies supplying 

energy to the participating housing units, provided that authorization is obtained from the housing 

units. The results of the housing unit and energy supplier surveys are presented in the U.S. Department 

of Energy documentation of the RECS.

Stage I of RECS consists of personal interviews. The sample for the interviews is drawn from the 

population of housing units occupied as primary residences in the 50 states and the District of Columbia.

The sample design process comprises of five steps that disaggregate the geographic scope into housing 

clusters of approximately five housing units to be surveyed.

The interview responses provide information on housing characteristics, fuels used, housing unit 

measurements, fuel bills, kitchen appliances, home appliance and electronics, space heating, air 

conditioning, water heating, miscellaneous uses, residential transportation, household characteristics, 

and energy expenditure assistance. Housing unit respondents are also asked to sign authorization forms 

to allow access to their billing records with energy suppliers.

Stage II of the survey design consists of a mail questionnaire for energy suppliers of the housing units 

interviewed in Stage I. In Stage II, EIA contacts suppliers of residential electricity, natural gas, distillate 

fuel oil, kerosene, and propane. EIA asks each supplier to provide billed quantities and expenditures for 

the housing units interviewed in Stage I.

Quality assurance begins with a manual verification of the interview data from Stage I. The 

questionnaires are checked for completeness and consistency. Interview responses are compared to 

energy supplier data, and respondents are contacted in the event that an inconsistency persists. These 

data collection and verification procedures ensure the quality of the survey data.

The RECS data on space heating and air-conditioning usage are adjusted across census divisions by 

heating and cooling degree day factors to account for potential deviations relative to the temperatures 

(and their corresponding degree days) during the RECS survey period. Thirty years of historical heating 

and cooling degree days for each state are used to establish a linear trend of heating and cooling degree 

days at the state level. This 30-year trend informs the projection of state-level degree days, which are

aggregated to the census division level using state-level population projections. Projected changes in 

degree days are thus intended to reflect projected shifts in population among states as well as 

continuing changes in historical degree day data.
19

Bibliography

Database of State Incentives for Renewables & Efficiency (DSIRE), 

http://programs.dsireusa.org/system/program. Raleigh, NC, accessed September 2017.

Leidos, Inc., Review of Distributed Generation and Combined Heat and Power Technology Performance 

and Cost Estimates and Analytic Assumptions for the National Energy Modeling System, 

https://www.eia.gov/analysis/studies/buildings/distrigen/. Washington, DC, May 2016.

Lent, J. “Using Hurdle Models for Long-term Projections of Residential Solar Photovoltaic Systems 

Installations,” available from EIA upon request, 2017.

U.S. Census Bureau, Characteristics of New Housing, https://www.census.gov/construction/chars/. 

Washington, DC, accessed June 2018.

U.S. Department of Energy, Solid-State Lighting Program, 2015 U.S. Lighting Market Characterization, 

https://www.energy.gov/eere/ssl/2015-us-lighting-market-characterization. Washington, DC, November 

2017.

U.S. Energy Information Administration, Analysis and Representation of Miscellaneous Electric Loads in 

NEMS, http://www.eia.gov/analysis/studies/demand/miscelectric/. Washington, DC, January 2014.

U.S. Energy Information Administration, Assessing Existing Energy Efficiency Program Activity, 

https://www.eia.gov/analysis/studies/buildings/efficiencyprogram/. Washington, DC, June 2018.

U.S. Energy Information Administration, Residential End Uses: Historical Efficiency Data and Incremental 

Installed Costs for Efficiency Upgrades, https://www.eia.gov/analysis/studies/residential/. Washington, 

DC, June 2017.

U.S. Energy Information Administration, Residential Energy Consumption Survey 2015, DOE/EIA0314(2015), Washington, DC, 2018.

U.S. Energy Information Administration, Updated Buildings Sector Appliance and Equipment Costs and 

Efficiency, http://www.eia.gov/analysis/studies/buildings/equipcosts/. Washington, DC, November 

2016.