NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Using Utility Load Data to 

Estimate Demand for Space 

Cooling and Potential for 

Shiftable Loads

Paul Denholm, Sean Ong, and Chuck Booten

Technical Report

NREL/TP-6A20-54509

May 2012
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Using Utility Load Data to 

Estimate Demand for Space 

Cooling and Potential for 

Shiftable Loads

Paul Denholm, Sean Ong, and Chuck Booten

Prepared under Task No. 0664.1202

Technical Report

NREL/TP-6A20-54509

May 2012
NOTICE

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iii

Acknowledgments

The authors would like to thank Nate Blair, Craig Christensen, Jan Kosny, and Robin 

Newmark for providing helpful comments and suggestions.
iv

Table of Contents

List of Figures..............................................................................................................................................v

List of Tables................................................................................................................................................v

1 Introduction ...........................................................................................................................................1

2 Methods .................................................................................................................................................3

2.1 Traditional Methods for Estimating Hourly Cooling Demands ..........................................3

2.2 A Simplified Top-Down Approach .....................................................................................4

3 Results and Discussion........................................................................................................................9

3.1 Cooling Profiles...................................................................................................................9

3.2 Comparisons to Bottom-Up Estimates...............................................................................10

3.3 Sensitivities........................................................................................................................12

4 Discussion and Conclusions.............................................................................................................13

References .................................................................................................................................................14

Appendix A: Base Case Regional Cooling Demand ..............................................................................17

Appendix B: National Energy Use Estimates .........................................................................................19

Residential Energy Consumption Survey (RECS) ..................................................................20

End-Use Consumption in Survey Households...................................................................21

Commercial Buildings Energy Consumption Survey (CBECS) .............................................22

National Energy Modeling System (NEMS)...........................................................................23
v

List of Figures

Figure 1. Seasonal demand patterns in Colorado (Public Service Company of Colorado–

East) ........................................................................................................................ 4

Figure 2. Demand difference within the cooling season (data from LADWP) .................. 5

Figure 3. Demand difference within the cooling season (data from Baltimore Gas & 

Electric)................................................................................................................... 6

Figure 4. Demand difference for the entire year in central Maryland (data from Baltimore 

Gas & Electric) ....................................................................................................... 7

Figure 5. Demand difference for the entire year in southern Florida (data from Progress 

Energy Florida-South) ............................................................................................ 8

Figure 6. Example of estimated cooling load patterns for Arizona Public Service and the 

state of Arizona in 2005.......................................................................................... 9

Figure 7. Example of estimated cooling load patterns for Florida, Illinois, and New York 

in 2005 .................................................................................................................. 10

Figure 8. Regional estimates of annual cooling demand .................................................. 12

Figure B-1. Energy use survey information analysis and flow......................................... 20

List of Tables

Table 1. Summary of National Cooling Demand Estimates and Comparison to Previous 

Estimates............................................................................................................... 10
1

1 Introduction

Space cooling is a significant use of energy in the United States, consuming about 10% of 

total electricity sales (EIA 2009). Cooling also drives the peak electricity demand and the 

associated need for peaking generation capacity. Peaking generators used to meet much 

of this demand are often less efficient than baseload generators. High demand on hot days 

also creates additional stress on electricity transmission and distribution (T&D)

infrastructure. These concerns have prompted greater interest in the control of space 

cooling loads to improve efficiency of the electric power sector. Options for control of 

cooling loads include the ability to temporarily reduce demand for cooling through 

demand response (DR) programs or the use of thermal energy storage (TES) to shift 

demand.

Both DR and TES are already used in some regions of the United States1 but can 

potentially be used at a greater scale to provide additional system benefits by enabling the 

integration of variable renewable generators (VG) such as solar and wind. DR can rapidly 

reduce electricity demand and provide operating reserves such as frequency regulation, 

contingency spinning reserves, and load-following reserves (Kueck et al. 2008).

2 Greater 

amounts of some of these ancillary services will be required with increased penetration of 

VG, and DR can potentially provide a relatively low-cost source of reserves. TES can 

also aid in renewable integration by changing load patterns over longer time scales. In the 

United States, the summertime demand for electricity peaks in the late afternoon, while 

wind energy often peaks in the evening (Denholm and Hand 2011). Correlation of solar 

energy production and load is greater but still limited—solar production peaks at noon 

while electricity demand peaks several hours later.

3

TES has several advantages over electricity storage devices such as pumped hydro or 

batteries. Most importantly, it effectively stores energy at higher round-trip efficiencies.

Most electricity storage devices typically store energy with a total round-trip efficiency of 

less than 80% (Denholm et al. 2010). Thermal storage efficiency can be closer to 100%.

4

It can also be deployed at the point of use, decreasing need for transmission and lowering 

transmission losses (Gansler et al. 2001). The primary disadvantage of thermal storage is 

that it is tied to an end use, and the demand for cooling varies over multiple time scales.

Knowledge of the amount of energy that is shiftable over various time scales is a 

prerequisite to evaluating the potential large-scale benefits of cooling-related DR and 

TES. Understanding this hourly and daily variation in demand is particularly important to 

 1 Air conditioning load control is widely used by utilities to temporarily reduce demand. The role of 

controlled cooling for DR and load control applications has been widely studied. An example study with a 

brief literature review is provided by Yin et al. (2010). 2 DR can provide electricity services over multiple time scales. This includes ancillary services during any 

time it is available as well as reducing peak demand and associated capacity requirements.

3 Solar insolation peaks at noon; production from a photovoltaic system depends on system orientation. 4 The concept of “round-trip efficiency” is not easily defined in TES systems in part because it must be 

compared to the conventional alternative. However, by many measures the efficiency of TES is commonly 

cited as well above 90%, and sometimes higher than 100%, because a cold-storage-based cooling system 

can use less electricity than its conventional alternative (Willis and Parsonnet 2010; MacCracken 2003).
2 

understand the potential role of DR and TES as an enabling technology for grid 

integration of VG. 

While modeled and actual data for individual buildings exist, there is less data on the 

aggregated system demand for cooling and the total system-level potential for TES. In 

this paper, we utilize a simple “top-down” methodology to isolate hourly cooling demand 

using historical utility loads. By comparing demand on representative “mild” (low 

cooling demand) days to other days during the cooling season, we extract an estimate of 

the hourly cooling demand. We estimate the hourly and total cooling demand across 

various regions in the United States and compare estimates using our methodology to 

previous estimates of total cooling demand. 
3

2 Methods

The primary motivation of this work is to apply a simple, transparent, and reproducible 

method that would enable an understanding of the potential role of cooling-related DR

and TES in the large-scale deployment and integration of VG. Studies of wind and solar

integration have found that penetrations of 20%–30% (on an energy basis) can be 

accommodated by “low-cost” flexibility options such as changing operational practices 

and effectively utilizing markets (GE Energy 2010). Beyond this level, it becomes 

increasingly difficult to use VG due to the limited correlation of VG supply with normal 

loads (Denholm and Hand 2011). Energy storage of various types has been suggested to 

further enable VG integration. Thermal storage provides one option; in Demark, hot 

storage has been suggested due to the large amount of heating demand during cold, 

windy months (Blarke and Lund 2008). In the United States, the warmer climate may 

make cold storage economically viable. A large fraction of the U.S. population lives in 

regions where cooling is required during many months of the year.

Understanding the benefits and limits of VG in the grid requires time-series simulations, 

which include hourly (or sub-hourly) profiles of demand and VG sources such as wind 

and solar. These datasets are used in increasingly complex models that simulate the 

operation of the grid, including the present and future mix of conventional and VG and 

may include impacts of forecast error, transmission power flows, and the need for 

operating reserves (Milligan et al. 2010). As a result, incorporating DR or TES into these 

simulations requires an estimate of the hourly (or sub-hourly) demand for cooling energy. 

This is very different than conventional electricity storage technologies such as pumped 

hydro or batteries, which are completely controllable and largely independent of weather 

and human behavior.

2.1 Traditional Methods for Estimating Hourly Cooling Demands

We have found limited analyses or datasets that can provide the total, hourly demand for 

cooling over large areas and for recent years. We have found a number of studies that 

perform “bottom-up” simulations of individual buildings (Deru et al. 2011). These 

studies typically have the ability to isolate cooling load and even examine the benefits of 

storage to individual buildings. A review of methods to simulate building energy demand 

is provided by Swan and Ugursal (2009) and Kavgic et al. (2010). Bottom-up analyses 

use building simulation tools that include the hourly heating and cooling requirements for 

individual buildings using historical weather data (Hopkins et al 2011; Polly et al. 2011;

Hendron and Engebrecht 2010). These studies often aggregate a large number of 

buildings and have the ability to provide estimates of cooling demand over large regions.

Examples include estimates for California (Brown and Koomey 2002) and Texas (Heiple 

and Sailor 2008). We also identified one study that estimates the cooling demand for the 

entire United States (Huang and Broderick 2000). A limitation of bottom-up models is 

their data and computational intensity, which require detailed estimates of building stock 

characteristics and complete meteorological data for the simulation years. Use of actual 

year data (as opposed to typical meteorological year data) is important if the data is to be 
4

used for grid integration analysis. Grid simulations typically use the load and real or 

simulated wind and solar data for one or more of the corresponding years.5

An alternative to bottom-up approaches are top-down methods that attempt to determine 

the load patterns of large groups of consumers. There are a number of statistical 

approaches that can be used to correlate historical temperature data with total load (FERC 

2009; Valor 2001). Utilities commonly use commercial software packages that project 

total load based on weather forecasts and historical demand patterns combined with 

heuristics and experience to produce accurate day-ahead forecasts of total load (Kueck et 

al. 2008).

2.2 A Simplified Top-Down Approach

The method we apply here provides a very simple top-down approach of estimating total 

cooling load. The method uses historical hourly utility loads and relies on the differences 

in demand between cool and warmer days. Figure 1 shows the hourly electricity demand 

for a utility in Colorado for three weeks in 2005 and the framework for our methodology.

Several patterns are visible, including a relatively low demand in the spring and 

somewhat increased demand in the winter characterized by two daily peaks. Each week 

starts on a Monday; somewhat reduced demand can be observed on the weekend (the last 

two days). The greatest demand is during the summer, dominated by a cooling demand 

that peaks in the late afternoon and into the early evening.

Figure 1. Seasonal demand patterns in Colorado (Public Service Company of Colorado–

East)

While the most dramatic difference between load patterns is seasonal, there are still 

variations in the total and peak demand within a season, driven by differences in weather. 

 5 For example, the Western Wind and Solar Integration Study (GE Energy 2010) performed simulations 

based on wind, solar, and load patterns in the years 2004–2006. Simulations incorporating cooling demand 

and thermal storage would require corresponding data for these three years. 

0

1000

2000

3000

4000

5000

6000

7000

0 24 48 72 96 120 144 168

Demand (MW)

Hour

Summer Spring Winter
5

Figure 2 demonstrates this difference and provides the basis for our methodology. The 

figure superimposes the hourly load reported by the Los Angeles Department of Water 

and Power (LADWP) from May 4, 2005, and June 17, 2005. If these two days are 

equivalent in all other factors, the only difference in demand between them should be 

space cooling driven by temperature. As a result, subtracting the difference is the 

incremental hourly and total cooling load.

Figure 2. Demand difference within the cooling season (data from LADWP)

Theoretically, this approach can be repeated by subtracting the total hourly demand from 

a base or reference day in which there is little or no cooling demand from all other days

within the cooling season. This should produce an hourly cooling demand, which can be 

summed to estimate the annual demand.

There are a number of limitations to this simplistic approach. Most obviously there are 

significant variations in demand patterns based on factors other than weather. There is 

inherently reduced demand for electricity during weekends and holidays. So the increased 

demand between a Saturday and a weekday may reflect factors other than temperature.

This effect can potentially be captured by establishing an appropriate “base” day (e.g., 

comparing a hot weekend day to a cooler weekend day).

Figure 3 illustrates this approach conceptually, showing the hourly load for central 

Maryland between June 1 and June 21, 2005. At the bottom is the assumed base demand 

profile (no cooling) with the same shape on weekdays but a different (lower) base 

0

1000

2000

3000

4000

5000

6000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Load (MW)

Hour

Estimated Cooling Load Low Demand Day
6

demand on weekends. Demand greater than the base demand pattern is assumed to be 

cooling demand.

Figure 3. Demand difference within the cooling season (data from Baltimore Gas & 

Electric)

To generate a reference (non-cooling) day, our base-case approach was to pick separate 

reference days for weekdays, Sundays, and Saturdays. For weekdays, we picked the five

non-holiday6 days with the lowest demand and excluded the absolute lowest two of these 

five. These exclusions should help reduce the effect of poor data, power outages, or other 

anomalies for the reference day. We then generated an hourly profile for a “non-cooling 

day” by averaging the hourly profiles of the remaining three days. This generated a 

composite 24-hour base demand for each utility. We repeated the process for weekends, 

separately picking the lowest three Sundays and Saturdays, dropping the absolute lowest,

and generating a composite hourly demand for each day. Each of these three-day profiles 

(weekday, Saturday, and Sunday) was then subtracted from all the other days during the 

cooling season.

The other critical assumption in our method is choosing the cooling season; simply 

subtracting the lowest load day from all days would mischaracterize heating, lighting, and 

other loads during the non-cooling season. 

The cooling season for each utility was based on historical temperature and cooling 

degree data. Temperature data was obtained and assigned to each utility, and the cooling 

season in the base case is defined as any day with an average temperature of 65°F or 

higher. This is a conservative approximation that captures the majority of the cooling 

season. This includes the majority of the commercial cooling load; although, for 

 6 Days excluded from being considered as “reference” days were: New Year’s Day, Martin Luther King, 

Presidents’, Memorial, Independence, Labor, Columbus, Veteran’s, Thanksgiving, Christmas, and New 

Year’s Eve.

0

1000

2000

3000

4000

5000

6000

7000

6/1/2005 6/3/2005 6/5/2005 6/7/2005 6/9/2005 6/11/2005 6/13/2005 6/15/2005 6/17/2005 6/19/2005 6/21/2005

Demand (MW)

Cooling Demand

Base (Non-Cooling) Demand

Weekday Weekend
7

simplicity, it deliberately ignores parts of the commercial cooling season, which in some 

cases can be year-round. Figure 4 illustrates the cooling season, base demand, and 

cooling demand for an entire year, along with the daily average temperature. It should be 

noted that this method actually produces a series of cooling days as opposed to a 

continuous season. The figure also shows the daily average temperature.

Figure 4. Demand difference for the entire year in central Maryland (data from Baltimore 

Gas & Electric)

Figure 4 shows that isolating the cooling season is important to avoid characterizing 

heating loads as cooling. In some locations in the southern United States, cooling may be 

required during much of the year. This introduces a potential source of error in those 

locations, especially due to seasonal variations in load due to lighting. The most extreme 

case is southern Florida, as shown in Figure 5, where cooling is required nearly yearround. (The sharp drop in demand in October is due to a hurricane that produced power 

failures.)

0

1000

2000

3000

4000

5000

6000

7000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Demand (MW)

0

10

20

30

40

50

60

70

80

90

Average Daily Temperature (F)

Non-Cooling Season Demand Base Demand (Cooling Season)

Cooling Demand (Cooling Season) Temperature
8

Figure 5. Demand difference for the entire year in southern Florida (data from Progress 

Energy Florida-South)

We applied this methodology to 300 utilities, market sub-regions, or balancing areas in 

the United States for the years 2005 and 2006. Hourly load data was obtained from Platts, 

although this data is publically available from the Federal Energy Regulatory 

Commissions (FERC) Form 714 filings. The total annual load in this dataset is 

3,928 TWh for 2005 and 3,936 TWh for 2006. This compares well with U.S. Energy 

Information Administration (EIA) estimates of electric sector total generation (excluding 

industrial and commercial self-generation) for the lower 48 states of 3,929 TWh and 

3,941 TWh in 2005 and 2006, respectively, or a difference of less than 1%.7 Processing 

of the data was performed in a Microsoft Excel/VBA environment. It should be noted 

that this load data is measured at the “busbar,” which is essentially at the point of 

generation, and includes losses in the T&D system, which will impact a comparison of 

our results to end-use estimates, as discussed in the next section. 

 7 This is calculated by taking the total electric sector generation (including utilities, independent power 

producers, and electric sector co-generation) plus imports, excluding commercial and industrial generators, 

whose load is not “seen” by utilities, and subtracting Hawaii and Alaska. This means that industrial and 

commercial cooling loads met by self generation are not included in this analysis but represent a very small 

fraction of total cooling demand (EIA 2007).

0

1000

2000

3000

4000

5000

6000

7000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Demand (MW)

0

10

20

30

40

50

60

70

80

90

100

Average Daily Temperature (F)

Non-Cooling Season Demand Base Demand (Cooling Season)

Cooling Demand (Cooling Season) Temperature
9

3 Results and Discussion

3.1 Cooling Profiles

The methods produce an hourly cooling load for each of the 300 load regions in the 

United States. This data could be aggregated to various geographical regions such as 

states or census regions.8 Figure 6 provides an example of an hourly cooling profile for 

an entire year for both single utility service territory (Arizona Public Service) and all 

utilities in Arizona aggregated to the state level.

Figure 6. Example of estimated cooling load patterns for Arizona Public Service and the 

state of Arizona in 2005

These profiles show expected trends, such as the greater relative need for cooling in

warmer locations, but also reveal a variety of phenomenon that can be “spot-checked” for 

some basic validation. For example, Figure 7 shows the cooling demand in Florida, 

Illinois, and New York in 2005. Of note is the dramatic decrease in cooling demand in 

Florida beginning at about hour 7,100 (October 25). This is due to dramatically reduced 

temperatures and local power outages associated with Hurricane Wilma. While this 

demonstrates a general indication of the well-established correlation between temperature 

and cooling demand, it does not explicitly validate the accuracy of our methods. For 

some basic indication of the usefulness of this approach, we also compared total annual 

cooling demand with other estimates, as discussed in the following section.

 8 Many of the transmission areas cross state boundaries. To aggregate to the state level, GIS techniques 

were used to assign the population (and corresponding loads) between the various states.

0

2000

4000

6000

8000

10000

12000

1 2 3 4 5 6 7 8 9 10 11 12

Cooling Demand (MW)

Month of Year

Arizona

Arizona Public Service
10

Figure 7. Example of estimated cooling load patterns for Florida, Illinois, and New York in 

2005

3.2 Comparisons to Bottom-Up Estimates

We found a limited set of data to compare to our approach—primarily datasets generated 

by the EIA, including the Residential Energy Consumption Survey (RECS), Commercial 

Building Energy Consumption Survey (CBECS), and Annual Energy Outlook (AEO).

9

Appendix B describes these sources and estimation methods in more detail. Table 1

summarizes the annual cooling demand estimates using our methods aggregated to the 

national level, with comparisons to previous estimates using bottom-up methods.

Table 1. Summary of National Cooling Demand Estimates and Comparison to Previous 

Estimates

Source Space Cooling 

Demand (GWh)

% Difference

from Base Case 

(at load)

Notes 

2005 2006 2005 2006

Base Case 

Results

(busbar)

444 423 NA NA

Base Case 

Results (at 

load)

413 393 NA NA This value is the busbar case multiplied 

by 0.93

AEO 2007 378 NA -8.5% NA Annual Energy Outlook

AEO 2008 393 369 -4.8% -6.1%

AEO 2009 NA 401 NA 2.0%

RECS/

CBECS

399 NA

-3.4%

Sum of residential cooling from 2005 

and commercial cooling from 2003

Two sets of base case results are provided. The first row provides the estimates using the 

base dataset, measured at the point of generation. These values include both electricity 

 9 These sources are all available from the EIA via www.eia.gov.

0

5000

10000

15000

20000

25000

30000

0 2000 4000 6000 8000

Cooling Demand (MW)

Hour of Year

Florida

Illinois

New York
11

consumed by the end user, as well as losses in T&D. The second row provides an

estimate of the actual end-use cooling demand by removing T&D losses. This distinction 

is based on where electricity is measured. When measured at the point of generation 

(using our method using reported load data), these estimates include electricity 

effectively consumed by space cooling due to T&D losses, which average about 7% 

nationally (EIA 2012). However, bottom-up space cooling estimates are generated at the 

point of load, or what a customer would see at the meter. For a better comparison, we 

removed a T&D loss factor of 7% and base comparisons of previous estimates to the 

values in the second row. It should be noted that losses are largely a function of the load 

(since resistive losses are proportional to the square of the current). Since space cooling is 

associated with periods of peak demand, we would expect the losses associated with 

cooling to be higher than average, which would potentially result in our adjusted 

estimates being slightly higher than estimates that do not account for T&D losses.

10

Overall, our top-down estimates are similar to but somewhat higher than estimates in the 

literature. We can hypothesize at least one reason for our method producing higher net 

cooling loads (in addition to the effect of marginal T&D losses at a rate greater than 

average). Previous estimates of space cooling generally include only residential and 

commercial buildings. While this should capture the large majority of cooling, it likely 

does not capture cooling demands in industrial facilities. Our estimates of space cooling 

include all buildings, including industrial buildings and any other space cooling systems 

that are not captured in reported estimates. We would expect industrial buildings to be a 

relatively small fraction of total cooling demand, but we could find no estimates in the 

literature.

Alternatively, we can think of at least one source of underestimation in our methods. In 

some buildings, particularly those with high internal gains, we would anticipate space 

cooling to have some constant level of demand, even during relatively cool temperatures.

While this would affect our estimates of total demand, it would probably have less impact 

on the desired goal (estimating shiftable thermal load) especially if this demand were 

constant.

There are likely additional sources of error, particularly in regions with longer cooling 

seasons as noted earlier. Seasonal changes in lighting demand patterns could have a 

significant impact when comparing a cool winter day when days are shorter (but still in 

the cooling season) to a hot summer day. This impact will be relatively small in places 

where we compare days that are only a few weeks or months apart but could be larger in 

locations like Florida.

The estimates can be compared to RECS/CBECS data at the regional as well as national 

level. Figure 8 compares the results from our approach to these previous estimates. The 

first column represents the summed estimates from RECS 2003 and CBECS 2005, while 

the other two columns use our base case assumptions measured at the load site. These 

results cannot be directly compared since they represent different years. This is especially 

 10 For example, a 1999 estimate of T&D losses for California was 8.1% on average and 8.6% on peak 

(Brown and Koomey 2002).
12

important given the year-to-year variability of weather, which can be observed in Table 1

by the 5%–6% decrease in cooling demand from 2005 to 2006 shown both in the AEO 

2008 estimates and using our approach. This comparison does illustrate that this topdown method for estimating cooling energy works reasonably well across all regions of 

the country.

Figure 8. Regional estimates of annual cooling demand

3.3 Sensitivities

Overall, as indicated by Table 1, our estimates are typically within 10% of previous 

estimates, with limitations described previously. As noted earlier, our methods are driven 

in part by the choice of reference days and cooling season. To determine the sensitivity of 

our results to the cooling season assumption, we evaluated the impact of changing the 

temperature threshold assumption. This allows an estimate of the amount of cooling load

that is not captured in the base case 65°F threshold for the cooling season. Specifically, 

we set the threshold for the cooling season to any day with an average temperature of 

60°F and 70°F, compared to the base case of 65°F. This is essentially equivalent to 

capturing a larger or smaller percentage of the commercial cooling season. At the 60°F 

threshold, more commercial cooling is included while the residential cooling load will 

change very little since residential electric heating loads will be minor and almost the 

entire cooling season will have already been captured at the 65°F threshold. At the 70°F 

threshold, some amount of both residential and commercial cooling load will not be 

included in the estimate. The lower temperature threshold increased the total annual 

cooling demand by about 5% for both 2005 and 2006. The higher temperature decreased

the total annual cooling demand by about 8% and 11% for 2005 and 2006, respectively.

0

20

40

60

80

100

120

140

Annual Cooling Demand (TWh)

CBECS/RECS Base 2005 Base 2006
13

4 Discussion and Conclusions

Given the amount of space cooling and its coincidence with peak demand, it is an 

important source of both responsive demand and shiftable load via TES. Understanding 

the potential large-scale deployment of these technologies, as well as their role in 

integrating renewable energy, requires knowledge of the demand for cooling over 

multiple time scales. We applied a simple method to estimate hourly space cooling 

demand using historical utility load data. Subtracting hourly demand for a cool 

“reference” day led to demand estimates that are within 10% of previous estimates.

Application of these profiles to DR and TES requires a number of assumptions and 

caveats. First, there are obvious market adoption issues associated with these 

technologies. Our method generates a profile for cooling across customer classes as 

opposed to bottom-up methods that isolate loads to individual buildings or building types.

Adoption of DR or TES might be more common among large industrial or commercial 

customers whose demand profiles are different from residential profiles. A more accurate 

approach may be to combine top-down and bottom-up models to provide a starting point 

for estimations of cooling profiles that could be used for regional studies of responsive or 

shiftable cooling demand.
14

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17

Appendix A: Base Case Regional Cooling Demand

Total A/C Load 

(GWh)

Peak A/C Load 

(MW)

2005 2006 2005 2006

Alabama 9,179 9,922 6,297 6,618

Arizona 15,157 16,200 10,031 11,016

Arkansas 5,403 5,911 3,915 4,567

California 24,047 27,350 24,282 32,389

Colorado 2,692 2,803 4,015 3,914

Connecticut 2,630 2,337 3,087 3,600

Delaware 2,188 2,003 2,060 2,251

Florida 59,730 45,235 24,386 22,249

Georgia 19,242 21,628 14,686 15,610

Idaho 1,771 2,318 1,924 2,204

Illinois 16,055 13,582 16,101 17,387

Indiana 8,035 6,621 7,193 7,624

Iowa 3,676 3,239 4,131 4,271

Kansas 4,883 4,849 3,962 4,302

Kentucky 5,661 5,162 4,837 5,466

Louisiana 12,325 11,603 7,214 6,429

Maine 426 438 705 826

Maryland 3,743 3,426 3,524 3,850

Massachusetts 4,240 3,909 5,412 6,615

Michigan 13,104 10,168 14,809 16,694

Minnesota 5,092 5,068 5,808 6,195

Mississippi 8,477 5,719 4,302 3,467

Missouri 11,856 10,195 10,046 10,375

Montana 316 452 592 700

Nebraska 3,419 3,100 3,341 3,439

Nevada 5,527 6,304 4,625 4,600

New Hampshire 667 533 835 959

New Jersey 10,637 9,636 11,185 12,632

New Mexico 2,201 2,075 1,711 1,565

New York 14,992 12,633 13,277 16,054

North Carolina 7,345 7,251 6,276 6,907

North Dakota 710 830 1,010 1,263

Ohio 12,786 11,232 13,736 15,402

Oklahoma 9,859 9,782 6,750 6,986

Oregon 959 1,192 2,495 3,585

Pennsylvania 11,462 10,252 10,969 13,215

Rhode Island 815 781 1,109 1,400

South Carolina 17,007 16,349 15,331 14,058
18

South Dakota 548 567 699 610

Tennessee 15,290 16,375 13,315 14,611

Texas 65,074 70,171 35,565 38,668

Utah 1,909 2,213 2,038 2,263

Vermont 381 306 464 543

Virginia 17,450 15,968 15,141 16,806

Washington 1,469 1,440 2,660 3,571

West Virginia 736 650 828 946

Wisconsin 2,732 2,439 3,081 3,460

Wyoming 393 390 573 614
19 

Appendix B: National Energy Use Estimates

The most common methods of estimating national end uses of energy stem from surveys 

and analyses conducted by the EIA. There are two surveys relevant in this context: RECS 

and CBECS. The process by which this information is analyzed and used is given in 

Figure B-1. The surveys are independent and each has two components: the user 

component and the supplier component. The user component is meant to gather 

information about the buildings and how they are used. The supplier component is 

directed to the utilities that service the particular building and are for gathering total 

energy use data. 

The data analysis is different for RECS and CBECS. RECS uses an approach that 

incorporates less building physics (such as estimating building heat loss coefficients) than 

the CBECS analysis. However, both use non-linear regression analysis (in the case of 

CBECS this is only for certain fuels) to determine the effects of various parameters on 

building energy use and to estimate end-use consumption. 

This information forms the basis for the National Energy Modeling System (NEMS), 

which projects future energy use for the U.S. economy as a whole. This is then compiled 

and released in the AEO. 
20

Figure B-1. Energy use survey information analysis and flow

Residential Energy Consumption Survey (RECS)

RECS consists of information collected on thousands of homes across the United States;

the 2009 survey included more than 12,000 homes in 16 states. The homes are randomly 

chosen to enable statistical analysis and extrapolation of the data to the entire nation. The 

household survey collects information via approximately 150 questions regarding the 

physical characteristics of the house, how it is used, what sort of equipment and 

appliances it has and household characteristics. The energy supplier survey is sent to all 

energy suppliers for homes that are part of the survey; it documents energy supplied to 

the household via different fuels. 

The survey data is compiled and used to estimate national end-use consumption in two 

stages: 
21

1. Use non-linear statistical techniques to estimate end-use consumption in survey 

households 

2. Extrapolate using data from (1) to estimate regional and national end-use 

consumption. 

End-Use Consumption in Survey Households 

The process for estimating end-use consumption in survey households begins by splitting 

estimates according to fuel type: electricity, natural gas, fuel oil, liquefied petroleum gas,

and kerosene (EIA 1999). The regression procedure is replicated for each fuel type 

independently. Each fuel has multiple end uses that consume it. The primary list of end

uses is:

• Space heating

• Space cooling

• Water heating

• Refrigerators

• Appliances.

Electricity usage is affected by all of the uses above; other fuels are only affected by a 

subset. For example, fuel oil is only used for space heating, water heating, and 

appliances. For the specific case of electricity, further end uses are also defined: 

• Lighting

• Cooking

• Dishwashers

• Clothes dryer

• Freezers.

When considering electricity, the magnitude of the baseline-estimated energy 

consumption of each of these 10 end uses is adjusted based on physical characteristics,

such as age and type of equipment, as well as usage patterns that can be estimated from 

the survey data. The importance of these adjustments and the portion of the fuel that is 

consumed in each survey household by the 10 end uses are determined using a non-linear 

regression analysis. The process is iterative but is summarized below for electricity

(Cureg 2012).

1. Assume that electricity consumption is affected by all possible relevant variables 

that are included in the consumer survey. The regression is performed using all 

end uses of a given fuel (and their adjustments) simultaneously.

2. Perform non-linear regression to determine the magnitude of these adjustments. 

An adjustment is essentially a multiplier for each variable. If the magnitude of an 

adjustment is not statistically different from zero, that variable does not affect 

consumption for that fuel. The variable is removed and the regression is repeated. 
22

3. Step 2 is repeated until all remaining adjustments are significant. This minimizes 

the error between actual and estimated consumption of each fuel and provides the 

information needed to estimate each of the 10 end uses (or fewer if considering 

fuels other than electricity).

4. Actual consumption of each fuel is known for each survey household from the 

energy supplier survey; a scale factor is included to adjust the estimated 

consumption to match the actual consumption.

The building characteristics collected from the survey and the end uses that are estimated 

using this technique form the basis of the Residential Sector Demand Module of NEMS 

to project residential energy usage in the future.

Commercial Buildings Energy Consumption Survey (CBECS)

CBECS is similar to RECS except that it concerns non-industrial or manufacturing 

commercial buildings. Over 5,000 buildings are included in the survey and there is also a 

component to the survey for energy suppliers, similar to RECS. Four fuels are 

considered: electricity, natural gas, fuel oil, and district energy (supplying energy to 

multiple buildings from a single generation source). Similar to RECS, each fuel has 

multiple end uses that consume it. The primary list of end uses is:

• Space heating 

• Cooling 

• Ventilation 

• Lighting 

• Water heating 

• Cooking 

• Refrigeration 

• Office equipment 

o Computers

o Other electronic plug load 

• Other uses.

The end-use consumption estimation is similar conceptually to the RECS methodology 

for natural gas and fuel oil. The steps for the regression analysis are reprinted below (EIA 

2003): 

1. Develop engineering algorithms that estimate end-use consumption for each 

building based on the survey parameters. Included in the engineering algorithm 

development is the specification of default parameters based on secondary 

sources.

2. Fit degree-day regression models to monthly consumption data for individual 

cases, as a basis for refining certain engineering parameters.
23

3. Fit a regression model to calibrate and adjust the engineering estimates.

4. Apply the fitted regression model to each CBECS case.

5. Re-scale the estimated end uses for each CBECS case to match the total 

consumption on the record for that case.

The regression analysis for electricity and district energy proved overly constraining and 

unreliable. Therefore, estimates were made for end uses from detailed engineering 

models. These models directly incorporate important building physics to estimate end-use 

consumption, unlike the RECS regressions or the CBECS regressions for natural gas and 

fuel oil. This includes estimating building shell heat transfer coefficients, building 

physical characteristics (e.g., conditioned floor area, number of floors, window 

characteristics, and floor height), equipment efficiency, outside surface temperature, 

ventilation rates, latent heat (relative humidity), and building usage (hours/day). 

There is still a scaling factor applied to the estimates for individual buildings to match the 

estimated total fuel usage to energy supplier data for that building. A weighting factor is 

applied to each of the individual buildings that is used to extrapolate to national energy 

use and end-use consumption estimation. The results form the basis for the Commercial 

Building Module for NEMS.

National Energy Modeling System (NEMS)

Specific RECS and CBECS data inputs into NEMS are: housing stock characteristics, 

existing equipment stock characteristics, fuel type for end uses (e.g., gas or electric heat 

for space cooling), end-use consumption estimates, and market share of particular 

technologies (i.e., number of households with central air conditioning, room air 

conditioning, or none) (EIA 2011a; EIA 2011b). These data are combined with heating 

degree day and cooling degree day information to adjust for weather-related effects 

during the particular year the survey data were collected. Other factors, such as fuel costs, 

policy changes, equipment and housing replacement, expected consumer behavior, 

building envelope integrity, and distributed energy generation, are incorporated to obtain 

forecasts of 21 different end uses of energy in homes and 10 end uses for commercial 

buildings for up to 30 years in the future. This information is compiled to generate the 

AEO (EIA 2008).