National Renewable Energy Laboratory 

Innovation for Our Energy Future 

A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 

Establishing Benchmarks for 

DOE Commercial Building R&D 

and Program Evaluation 

Preprint 

M. Deru, B. Griffith, and P. Torcellini 

To be presented at ACEEE Summer Study 

Pacific Grove, California 

August 14−18, 2006 

Conference Paper 

NREL/CP-550-39834 

June 2006 
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1

 

Establishing Benchmarks for DOE Commercial Building 

R&D and Program Evaluation1

Michael Deru, Brent Griffith, and Paul Torcellini, National Renewable Energy Laboratory 

ABSTRACT 

The U.S. Department of Energy (DOE) Building Technologies Program and the DOE 

research laboratories conduct a great deal of research on building technologies. However, 

differences in models and simulation tools used by various research groups make it difficult to 

compare results among studies. The authors have developed a set of 22 hypothetical benchmark 

buildings and weighting factors for nine locations across the country, for a total of 198 buildings. 

The benchmark buildings are representative of new commercial building stock and meet the 

minimum requirements of ANSI/ASHRAE/IESNA Standard 90.1-2004. The benchmark 

building definitions are complete descriptions suitable for whole building energy simulations and 

are implemented in EnergyPlus input files. The complete benchmark definitions with 

documentation of all inputs will be available in a technical document published near the end of 

2006. The EnergyPlus input files are automatically created by preprocessor routines, which 

minimize errors and ensure that everyone has consistent implementation in the most recent 

version of EnergyPlus and the most recent version of the benchmarks. 

The benchmark buildings will form the basis for research on specific building 

technologies, energy code development, appliance standards, and measurement of progress 

toward the DOE energy goals. Having a common starting point allows us to better share and to 

compare research results and move forward in making more energy-efficient buildings. In 

addition, the benchmark buildings can be used with minor modifications to evaluate other energy 

efficiency programs and individual buildings. 

Introduction 

The U.S. Department of Energy (DOE) Building Technologies (BT) program has set the

aggressive goal of producing marketable net zero energy buildings (ZEBs) by 2025. The ZEB 

goal has been divided into intermediate goals of achieving energy savings of 30%, 50%, and 

70% compared to a building built to the minimum requirements of ANSI/ASHRAE/IESNA 

Standard 90.1-2004 (subsequently referred to as Standard 90.1-2004) (ASHRAE 2004a). 

Several analysis activities that will determine the best paths forward to reach these goals are 

under way. Coordinating these research activities and tracking progress toward the goals is 

greatly simplified if we start with common reference points. 

Several projects have created benchmark or prototypical buildings. The most familiar are 

from Lawrence Berkeley National Laboratory (LBNL), which developed a series of prototypical 

buildings over several years. Two papers by Huang and his colleagues present excellent 

1

 This work has been authored by an employee or employees of the Midwest Research Institute under Contract No. 

DE-AC36-99GO10337 with the U.S. Department of Energy. The United States Government retains and the 

publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow 

others to do so, for United States Government purposes.
2

summaries of this work and previous work (Huang et al. 1991; Huang and Franconi 1999). This 

project is based on some of this LBNL work. Two more recent efforts are a set of standardized 

energy simulation models for commercial buildings from the University of Massachusetts 

(Stocki et al. 2005) and a residential building benchmark from the DOE Building America 

program (NREL 2005). However, this project differs from previous efforts in that its purpose is 

to provide a baseline of new buildings that meet the minimum requirements of Standard 90.1-

2004 for analyzing combinations of present and future energy efficiency technologies. Previous 

work focused on characterizing the energy use in existing buildings, rather than looking at the 

ability for future technology to achieve much lower energy buildings. Additional benchmark 

buildings are being developed for existing buildings based on work from LBNL in a related 

project. The benchmarks for new and existing buildings will be coordinated to be compatible 

and consistent with national building data. 

These benchmark building descriptions will provide a common point of reference to 

measure the progress of DOE energy efficiency goals for commercial building research. They 

will be used for research in assessment studies, optimization work, controls, daylighting, 

ventilation and indoor air quality, and other areas. In addition, they may be used in the 

development of energy codes for buildings and building appliance standards. 

The benchmarks are meant to be hypothetical ideal buildings that meet the requirements 

of Standard 90.1-2004; they are not meant to model the performance of real buildings. The 

benchmark building definitions are not intended to act as targets to rate the energy performance 

of individual existing or proposed buildings. They are not intended to be used with building 

performance rating systems. Variations of the benchmark building definitions may be created in 

the future by other parties for such purposes, but that is not the objective of this project. 

Model Development 

Our goal is to represent the building types, sizes, and locations of 70% of the new 

building stock. Modeling all new buildings is impractical, so we select a small number of 

buildings and develop weighting factors to represent the entire building stock. The approach 

taken for this model is to use sector-wide data to determine an appropriate, average mix of 

buildings and then move this set to different locations to capture climate and geographic effects. 

The climate-sensitive variables in each building are varied by location according to the 

requirements in Standard 90.1-2004. The benchmark building set is composed of two parts—the 

building models (which consist of the energy modeling descriptions) and the national model 

(which consists of the sets of building types, locations, and weighting factors). 

There are very few data from the national building stock about the parameters that affect 

energy use. We have used the building characteristics data from the 2003 Commercial Building 

Energy Consumption Survey (CBECS) (EIA 2006) from the Energy Information Administration 

(EIA), population data from the 2000 Census (U.S. Census Bureau 2002), economic activity data 

from the 2002 Economic Census (U.S. Census Bureau 2005), and 2005 construction cost data 

(RSMeans 2004) to help construct the benchmark building model. 

Creating energy simulation files is very time consuming and mistakes are easily made. 

Analysis activities of the national building sectors involve creating hundreds or even thousands 

of input files. Computer programs were developed to autogenerate the energy simulation input 

files, and the benchmark buildings were defined by sets of rules that can be easily programmed. 
3

National Model 

The national model consists of determining sets of building types, locations, and 

weighting factors to represent 70% of the new commercial building construction. Moving 

beyond 70% of the building stock quickly increases the number of the benchmark buildings to 

include several buildings that represent small fractions of the total population. Development of 

the national model was divided into three efforts: building characteristics, locations, and 

weighting factors. The benchmark building types, sizes, and number of floors are based on 2003 

CBECS, the locations are based on U.S. census data, and the weighting factors were determined 

with the 2002 Economic Census and construction costs. The final set of benchmarks comprises 

22 representative buildings where each is modeled in nine locations for 198 models. 

The 2003 CBECS is a good source of statistical data for the characteristics of buildings in 

the commercial sector. It contains numerous variables for each of 4,820 buildings. However, 

the data set does present some problems. Modeling every building in the data set is considered 

too cumbersome for practical use in analyses of highly integrated technologies and practices that 

require detailed energy performance models be rerun numerous times. At the same time, the 

data set is too small to form a robust statistical representation of all commercial building types in 

all climates. In addition, the data set is for the existing building stock, but the benchmarks are 

intended to help analyze new and future construction. Finally, the 2003 CBECS data are still 

preliminary and did not include the energy performance data when this model was developed. 

The current set of benchmarks reduces the set of 4,820 to a set of 198 representative buildings. 

The methodology used here was selected to make maximum use of statistical data and to 

minimize the potential for personal bias on selecting the characteristics of buildings. 

The first step in reducing the data set was to select high-level parameters to categorize the 

4,820 buildings. The parameters include the year of construction, principal building activity 

(PBA), number of floors, and floor area. The parameters were selected in part because they have 

significant impact on the potential for achieving net-zero site energy use. To mimic new 

buildings, yet maintain a good statistical sample, only buildings constructed from 1994 to 2003 

were included. Of the 18 PBAs defined in CBECS, we eliminated four and combined others to 

form 10 building activities. We define a split between large and small buildings to be 20,000 ft2

. 

The number of floors determines the types of mechanical systems, and the floor area determines 

roof space available for deployment of photovoltaic systems and roof daylighting devices. We 

divided the buildings into groups by floor area and number floors (see Table 1). 

Once the sorting parameters were selected, the next step was to use the parameters to 

“slice” the set of newer buildings in the 2003 CBECS public use data set into 22 bins. Data in 

each bin were used to develop values for the average floor area and number of floors in the 

benchmark building models, as shown in Table 2. The percent of floor area column represents 

the percent of the weighted floor area in the reduced set of new buildings. 

The next task was to determine a set of locations that represent the combination of new 

building activity and climate. After considering several methods of selecting locations, we used 

the following methodology to select the weather file locations. 
4

Table 1. Categorization of 2003 CBECS Data 

Bldg. No. PBA Floor Area, ft2 Number of Floors 

1 Office/Professional All 1 

2 Office/Professional All 2 to 4 

3 Office/Professional > 20,000 5 or more 

4 Warehouse ≤ 20,000 1 

5 Warehouse > 20,000 1 

6 Warehouse All 2 or more 

7 Education ≤ 20,000 1 

8 Education > 20,000 1 

9 Education All 2 or more 

10 Retail ≤ 20,000 1 

11 Retail > 20,000 1 

12 Retail All 2 or more 

13 Service and Safety All 1 

14 Service and Safety All 2 or more 

15 Food Services All All 

16 Health Care All 1 

17 Health Care All 2 or more 

18 Lodging All 1 to 4 

19 Lodging All 5 or more 

20 Assembly All 1 

21 Assembly All 2 or more 

22 Food Sales All All 

Table 2. Benchmark Buildings for New Construction 

Bldg. 

No. 

PBA Floor 

Area, ft2

Number of 

Floors 

Percent of Floor Area 

in Reduced Set 

1 Office 13,006 1 3.9 

2 Office 75,060 3 5.9 

3 Office 598,423 12 4.6 

4 Warehouse 9,974 1 8.9 

5 Warehouse 157,863 1 7.9 

6 Warehouse 292,667 2 6.0 

7 Education 7,658 1 2.3 

8 Education 122,735 1 4.3 

9 Education 107,506 3 6.7 

10 Retail 11,656 1 2.3 

11 Retail 94,911 1 4.4 

12 Retail 238,579 2 2.4 

13 Service and Safety 24,039 1 6.4 

14 Service and Safety 96,595 3 3.4 

15 Food Services 17,834 1 3.2 

16 Health Care 19,667 1 2.2 

17 Health Care 241,442 4 4.2 

18 Lodging 62,771 3 4.0 

19 Lodging 285,493 8 2.5 

20 Assembly 24,209 1 4.5 

21 Assembly 162,301 3 7.4 

22 Food Sales 23,902 1 2.6 

We choose the nine census divisions as the level of granularity, which is the finest 

geographic granularity available in the 2003 CBECS. CBECS only reports building information 
5

by census division to hide the identity of specific buildings. The next step was to determine the 

most appropriate weather file location, or site where typical year weather data (TMY2) are 

available, within each Census Division. This was done in the following manner: 

• A Graphical Information System (GIS) was used to associate 2000 census blocks to the 

nearest TMY2 site. The 2000 census population data were then aggregated by TMY2 site 

and state. All 216 TMY2 sites in the lower 48 states are included. A single TMY2 site may 

cross census division boundaries; this is accounted for by including the state lines in the 

organization of the data set. 

• The heating degree-day (base 65oF) (HDD65) and cooling degree-day (base 50oF) (CDD50) 

data for each TMY2 site were then assembled with the population data and used to determine 

the population-weighted, mean values for HDD65 and CDD50 for each of the nine census 

divisions. Table 3 shows the mean results. 

The specific TMY2 site that most closely matches the mean values for HDD65 and 

CDD50 were found by minimizing a combined error signal. The error signal was formulated by 

comparing the absolute value of the deviations between mean degree-days and the degree-days 

for each weather site. The combined error signals were calculated for all possible choices of 

weather sites within a census division and the one with the lowest error signal was chosen as the 

best match. This method does not take into account solar insolation, which is important for ZEB 

analysis. Table 3 shows the locations selected and the combined error signal. 

Table 3. Population-Weighted Weather Location Assignments by Census Division 

Census Division 

Mean 

HDD65 

oF-days 

Mean 

CDD50 

oF-days City State 

90.1-2004 

Climate 

Zone 

Combined 

Error Signal 

oF-days 

1 New England 6,176 2,665 Providence RI 5A 370 

2 Middle Atlantic 5,232 3,100 Atlantic City NJ 4A 161 

3 East North Central 6,430 2,897 South Bend IN 5A 122 

4 West North Central 6,496 3,340 Des Moines IA 5A 32 

5 South Atlantic 2,794 5,619 Columbia SC 3A 256 

6 East South Central 3,412 4,946 Huntsville AL 3A 180 

7 West South Central 2,211 6,448 Fort Worth TX 3A 202 

8 Mountain 4,425 3,908 Albuquerque NM 4B 641 

9 Pacific 2,727 4,017 Sacramento CA 3B 478 

Finally, we needed weighting factors for the 198 models to characterize the number of 

buildings that are similar to the benchmarks for each location. The 2003 CBECS appears to 

provide the data to develop values for the weighting factors; however, attempts to use these data 

failed because there are too few buildings to determine the geographic distribution of 

benchmarks. For example, 27 of the 198 models result in weights of zero because 2003 CBECS 

had no sample buildings. We therefore turned to the 2002 Economic Census (U.S. Census 

Bureau 2005) and cost models from 2005 Square Foot Costs (RSMeans 2004) to characterize the 

geographic distribution of new additions to the commercial building stock. 

One difficulty in working with three data sets is that each has a different classification 

system. Therefore, we had to map between the three data sets, which leads to some loss of 

information. For instance, the economic census contains nine construction types, and all the 

buildings were placed into these nine types. The construction costs for each type were 
6

determined by averaging the RSMeans costs that were mapped to each census construction type. 

The national cost data were then adjusted to each state by a cost modifier, which was determined 

from city data published in RSMeans. 

An initial test of the validity of this approach was completed. Using national average 

cost models and no regional modifiers, the area of new construction in 2002 came out to 2.06 

billion ft2

. Data tables from EIA’s AEO 2005 listed new floor area additions in 2002 at 2.09 

billion ft2

; therefore, we concluded that we could approach the calculations in this way. 

The next step was to model the distribution of the benchmark buildings within each group 

of categories for the type of construction used in the 2002 Economic Census with the percent of 

floor area data listed in Table 2 (derived from 2003 CBECS). The limitation of this approach is 

that the distribution of building types, within a type of construction group, is not sensitive to 

geographic location. However, there does not appear to be a robust way of capturing this with 

currently available statistical data. 

Finally, the weighting factors for the benchmark models were calculated from the 2002 

Economic Census data and RSMeans cost models and shown in Table 4. The nationwide new 

additions from this model correspond to 2.14 billion square feet, which is consistent with AEO 

2005 data. In addition, the percentage of total construction by census division matches closely 

with the economic value of construction by census division from Reed Construction Data (2005). 

 

Table 4. National Sector Model Weighting Factors 

Bldg. 

No. 

New 

England 

Middle 

Atlantic 

East 

North 

Central 

West 

North 

Central 

South 

Atlantic 

East 

South 

Central 

West 

South 

Central 

Mountain Pacific 

1 523 1314 1311 960 2526 481 1279 947 1603 

2 137 345 344 252 662 126 335 248 420 

3 13 34 34 25 65 12 33 24 41 

4 253 769 1040 596 1387 361 766 870 1219 

5 14 43 58 33 78 20 43 49 68 

6 6 18 24 14 32 8 18 20 28 

7 716 1242 1486 700 2100 451 1207 776 1128 

8 84 145 173 82 245 53 141 91 132 

9 149 258 308 145 436 94 250 161 234 

10 192 451 635 351 980 291 500 450 584 

11 45 106 149 82 230 68 118 106 137 

12 10 23 32 18 50 15 26 23 30 

13 258 608 857 473 1323 392 675 607 788 

14 34 80 113 63 175 52 89 80 104 

15 174 410 577 319 892 265 455 409 531 

16 140 330 453 271 603 251 337 223 293 

17 22 51 70 42 94 39 52 35 46 

18 97 92 112 65 291 53 122 164 157 

19 13 13 15 9 40 7 17 23 22 

20 99 187 415 202 507 159 365 200 276 

21 24 46 102 50 124 39 90 49 68 

22 106 248 350 193 540 160 276 248 322 

Building Descriptions 

Detailed energy models of buildings require many details that are not available from 

standard data sources. Appendix G in Standard 90.1-2004 provides guidance on envelope and 
7

equipment when creating a baseline building that meets the minimum requirements of the 

standard. However, many assumptions not covered in the standard have to be made to complete 

the modeling of the buildings. These assumptions include thermal zoning, aspect ratio, 

orientation, number of floors, window to wall ratios, HVAC types, and schedules. We have 

divided the building input into program, form, fabric, and equipment, which are shown in Table 

5 with some of the building parameters. 

Table 5. Input Categories with Partial Parameter Lists 

Program Form Fabric Equipment 

Location 

Total floor area 

Schedules 

Plug and process loads 

Lighting densities 

Ventilation needs 

Occupancy 

Number of floors 

Aspect ratio 

Window fraction 

Window locations 

Shading 

Floor height 

Orientation 

Exterior walls 

Roof 

Windows 

Interior partitions 

Internal mass 

HVAC system types 

Component efficiency 

Control settings 

Lighting fixtures 

Lamp types 

Daylighting controls 

Building Program. Some of the program parameter values are shown in Table 6. The activities 

and locations were determined in the process of analyzing the national building data as shown in 

Table 1. The occupancy rates in Table 6 are from Standard 90.1-1989 (ASHRAE 1989), except 

for the small warehouse and service and safety buildings, which were determined based on 

assumption. Ventilation requirements are determined from ASHRAE Standard 62.1-2004 

(ASHRAE 2004b). The building area method was used to determine lighting power densities 

from Standard 90.1-2004. Some of the benchmark buildings combine building types that have 

different lighting power allowances. In these cases, the most likely value was chosen. 

Determining the plug load intensity is difficult because there are very few data on this number. 

Thus, several assumptions had to be made based on experience with a small number of buildings 

and from previous work by Huang and Franconi (1999). 

The operating parameters in Table 6 are controlled with hourly schedules. Setting 

schedules is another problematical task; however, the benchmark buildings will be used in 

comparison to other simulations with the same schedules, which lower the importance of getting 

them exactly right. As a starting point, we use the schedules in the ANSI/ASHRAE/IESNA 

Standard 90.1-2004 User’s Guide (ASHRAE 2004c), which are slight modifications of the 

schedules included in Standard 90.1-1989. Experience with monitoring real buildings has shown 

that night and weekend plug loads are much higher than predicted by these schedules (Torcellini 

et al. 2004). We created separate schedules for plug loads and increased the plug load schedules 

during unoccupied periods by an additional 20% to 40%, depending on the building type. 

Fuel sources were limited to natural gas and electricity for the benchmark buildings. 

Utility costs are used to help analyze the effectiveness of energy efficiency and on-site energy 

production technologies. Utility rate schedules vary widely across the country, and capturing this 

variability is difficult. We have chosen to use the utility rate schedule for each benchmark 

location as an approximation. These rates are picked automatically by EnergyPlus. Finally, tax 

rates on utilities vary at the city and county government levels, and most utility companies do not 

publish tax rates with their tariffs because they vary within the service territory. We assumed 

that energy taxes are equal to the state sales tax rate plus 2%. 
8

Table 6. Benchmark Building Program Parameters 

Number Principal 

Building Activity 

Floor Area

(ft

2

) 

Occupancy 

(ft

2

/person) 

Ventilation 

(cfm/person) 

Lighting 

(W/ft

2

) 

Plug Loads 

(W/ft

2

) 

1 Office 13,006 275 17 1.0 1.3 1

2 Office 75,060 275 17 1.0 1.3 1

3 Office 598,423 275 17 1.0 1.3* 

4 Warehouse 9,974 5,000* 0.06 cfm/ft2 0.8 0.1*

5 Warehouse 157,863 15,000 0.06 cfm/ft2 0.8 0.1* 

6 Warehouse 292,667 15,000 0.06 cfm/ft2 0.8 0.1* 

7 Education 7,658 75 15 1.2 0.8**

8 Education 122,735 75 15 1.2 0.8** 

9 Education 107,506 75 15 1.2 0.8** 

10 Retail 11,656 300 16 1.5 0.5*

11 Retail 94,911 300 16 1.5 0.5* 

12 Retail 238,579 300 16 1.5 0.5* 

13 Service and Safety 24,039 300* 19* 1.0 1.0* 

14 Service and Safety 96,595 300* 19* 1.0 1.0* 

15 Food Services 17,834 100 10 1.5 2.25** 

16 Health Care 19,667 200 25 1.0 2.2** 

17 Health Care 241,442 200 25 1.2 2.2** 

18 Lodging 62,771 250 11 1.0 0.7** 

19 Lodging 285,493 250 11 1.0 0.7** 

20 Assembly 24,209 50 6 1.2 0.4** 

21 Assembly 162,301 50 6 1.2 0.4** 

22 Food Sales 23,902 300 15 1.5 1.5* 

* Assumption 

** Huang and Franconi 1999 

Building Form. The floor area and number of floors were determined from the weighted 

average values for each bin, as shown in Table 2. Obtaining robust statistical data on the other 

parameters is difficult, so we must generate the additional detail using assumptions (see Table 7). 

We define aspect ratio as the overall length in the east-west direction divided by the overall 

length in the north-south direction. Assumptions were used to create aspect ratios, floor-to-floor 

height, and plenum height where sufficient data were not available. A uniform distribution with 

five-degree increments was used to obtain values for the rotation parameter by random selection. 

Thermal zones were set up with one to five zones per floor. The glazing fractions were set to the 

values used by Huang and Franconi (1999), except where they exceeded the maximum values 

specified in Standard 90.1-2004 Table G3.1. The window locations are determined by the sill 

height, which is set to 3.6 ft, and the edge offset, which was fixed at 0.16 ft. 

Building Fabric. The fabric of the building includes the construction type and thermal 

properties of the walls, roof, floor, and windows. Construction types are defined from Standard 

90.1-2004 Table G3.1 and the thermal properties are from Tables 5.5-1 to 5.5-8. 

Building Equipment. Standard 90.1-2004 Appendix G specifies HVAC equipment to use for 

baseline buildings in Tables G3.1.1A and G3.1.1B. This information is repeated in Table 8 for 

each benchmark building, with the assumption that all buildings use natural gas as a heating fuel. 

There are two exceptions in the current benchmark buildings from the Appendix G 

recommendations. Benchmark building numbers 5 and 11 are assigned packaged single zone 

systems, which is common practice for these building types. 
9

Table 7. Building Form Parameters 

Bldg. 

No. Aspect 

Ratio Azimuth 

Floor-toFloor 

Height (ft) 

Plenum 

Height 

(ft) 

Zones per 

Floor 

Perimeter 

Zone Depth 

(ft) 

Glazing 

Fraction 

1 1.2 295 13 4 5 15 0.40*

2 2.2 40 13 4 5 15 0.40* 

3 1.2 235 13 4 5 15 0.40* 

4 1.1 295 15 N/A 1 N/A 0.03**

5 2.2 55 20 N/A 5 20 0.03** 

6 2.2 295 15 N/A 5 20 0.03** 

7 1.1 195 13 4 5 N/A 0.18** 

8 5.0 275 13 4 5 20 0.18** 

9 3.0 350 13 4 5 20 0.18** 

10 1.1 30 15 N/A 1 N/A 0.15** 

11 2.2 160 23 N/A 5 20 0.15** 

12 1.5 195 18 N/A 5 20 0.15** 

13 2.2 245 18 N/A 5 15 0.15†

14 1.5 170 15 N/A 5 15 0.15†

15 1.3 20 13 4 5 20 0.175**

16 3.5 260 13 4 5 15 0.25** 

17 2.0 185 13 4 5 15 0.25** 

18 3.5 45 12 3 5 20 0.21** 

19 2.5 255 12 3 5 20 0.35** 

20 1.2 5 18 3 5 20 0.15†

21 3.0 250 15 3 5 20 0.15†

22 1.5 170 20 N/A 5 15 0.15** 

* Limits of Standard 90.1-2004 Table G3.1 

** Huang and Franconi 1999 

†

Assumption 

Equipment sizing is determined according to Standard 90.1-2004 Section G3.1.2.2. 

Minimum, nominal coefficient of performance values are taken from Standard 90.1-2004. 

Performance curves and HVAC system models are used to model how performance might vary 

when operating away from the nominal operation point using the models available in 

EnergyPlus. Minimum theoretical efficiencies are taken from Standard 90.1-2004, and the 

efficiency of gas heating coils is set to 0.80. Economizer operation is determined from building 

size and climate zone per Section G3.1.2.6. The minimum ventilation rates were determined 

from ANSI/ASHRAE Standard 62.1-2004 Table 6-1. 

Preliminary Validation of the Benchmarks 

This benchmark model was compared to a larger sector model from an earlier study, 

referred to as the Assessment (Griffith et al. 2006). The Assessment study created EnergyPlus 

simulation files for 5,375 building models that corresponded directly with the buildings in the 

1999 CBECS public use data set (all activities except refrigerated warehouses). The routines 

from the Assessment were used to make a comparison between these models and the benchmark 

models to generate several EnergyPlus input files. There are some differences in these models 

and the benchmark models—primarily in the HVAC systems. The “LZEB 2005” scenario uses 

routines that alter the base models by applying a comprehensive package of year 2005 efficiency 
10

technologies and practices and PV systems. Table 9 provides a summary of key characteristics 

along with results from EnergyPlus simulations. 

Table 8. Building HVAC Equipment 

Bldg. 

No. System Type Fan Control Cooling Type Heating Type 

1 PSZ-AC constant volume direct expansion fossil fuel furnace 

2 Packaged VAV with reheat VAV direct expansion hot water fossil fuel boiler 

3 VAV with reheat VAV chilled water hot water fossil fuel boiler 

4 PSZ-AC constant volume direct expansion fossil fuel furnace 

5 PSZ-AC constant volume direct expansion fossil fuel furnace 

6 VAV with reheat VAV chilled water hot water fossil fuel boiler 

7 PSZ-AC constant volume direct expansion fossil fuel furnace 

8 Packaged VAV with reheat VAV direct expansion hot water fossil fuel boiler 

9 VAV with reheat VAV chilled water hot water fossil fuel boiler 

10 PSZ-AC constant volume direct expansion fossil fuel furnace 

11 PSZ-AC constant volume direct expansion fossil fuel furnace 

12 VAV with reheat VAV chilled water hot water fossil fuel boiler 

13 PSZ-AC constant volume direct expansion fossil fuel furnace 

14 Packaged VAV with reheat VAV direct expansion hot water fossil fuel boiler 

15 PSZ-AC constant volume direct expansion fossil fuel furnace 

16 PSZ-AC constant volume direct expansion fossil fuel furnace 

17 VAV with reheat VAV chilled water hot water fossil fuel boiler 

18 PSZ-AC constant volume direct expansion fossil fuel furnace 

19 VAV with reheat VAV chilled water hot water fossil fuel boiler 

20 PSZ-AC constant volume direct expansion fossil fuel furnace 

21 VAV with reheat VAV chilled water hot water fossil fuel boiler 

22 PSZ-AC constant volume direct expansion fossil fuel furnace 

Table 9. Comparison of Benchmark and Assessment Sector Models 

Characteristics and Results Benchmark Model Assessment Model 

Scope New construction in 2002 Existing stock as of 1999 

Number of models 198 5,375 

Number of weather locations 9 77 

Weighted floor area (billion ft2) 2.09 66.46 

Weighted roof area (billion ft2) 1.33 39.96 

Ratio of roof area to floor area 0.64 0.60 

Base average EUI (kBtu/ft2·yr) 49.3 49.7 

LZEB 2005 average total EUI (kBtu/ft2·yr) 33.1 29.3 

LZEB 2005 average net EUI—includes PV (kBtu/ft2·yr) 14.6 15.7 

LZEB 2005 average percent savings in total site EUI (%) 33.7 39.5 

LZEB 2005 average percent savings in net site EUI (%) 74.0 82.4 

The results show that the response of the two sector models is similar. For the base case 

scenario, the average EUIs differ by only 0.4 kBtu/ft2

·yr. For the LZEB 2005 scenario, the total 

EUIs differ by 3.8 kBtu/ft2

·yr and the net EUIs differ by 1.1 kBtu/ft2·yr or 13% higher and 7% 

lower. The Assessment sector model tends to produce higher efficiency improvements and 

lower PV production. The results are encouraging in that the smaller set of benchmark models 

performs similarly to the larger Assessment set. There is no “truth” standard here, only a 

comparison of two sector models that employ hundreds or thousands of simulations. However, 
11

more research is needed to ensure that the benchmark sector model will perform adequately in 

the ZEB context. 

Conclusions 

Energy modeling as a representation of the entire commercial building sector is a 

complex task; however, with some simplifying assumptions and current computing power, 

reasonable models can be made of this large set of buildings. DOE research goals require that 

we use extensive energy simulations to assess the energy use and energy efficiency potentials on 

a national basis instead of on individual buildings. We have established a set of benchmark 

buildings and weighting factors to represent new commercial buildings in the United States. The 

benchmark building descriptions have been established as set of rules that can be expanded into 

EnergyPlus input files by two computer programs. These programs provide rapid and consistent 

implementation of the benchmark building descriptions, and allow unlimited variations to be 

implemented to analyze different technologies. 

We recognize that this benchmark model is not perfect and could be improved. Complete 

data for developing the benchmark buildings are simply not available and several assumptions 

have to be made. More data are needed on the current building stock and on operations within 

the buildings to minimize the number of assumptions and improve the models. This set of 

benchmark buildings is intended to be viewed as version 1; updated versions will be developed 

as more building data are made available, as more analysis is completed with the benchmarks, 

and as analysis requirements change. 

Acknowledgments 

We would like to thank Drury Crawley (DOE) for his guidance on this project and Donna 

Heimiller (NREL) for her assistance with the GIS analysis. This work was supported by the 

Assistant Secretary for Energy Efficiency and Renewable Energy, Building Technologies 

Program of the U.S. Department of Energy under Contract No. DE-AC36-99-GO10337. 

References 

ASHRAE. (1989). Energy Efficient Design of New Buildings Except Low-Rise Residential 

Buildings. ANSI/ASHRAE/IESNA Standard 90.1-1989. Atlanta, GA: American Society of 

Heating, Refrigerating and Air-Conditioning Engineers. 

ASHRAE. (2004a). Energy Standard for Buildings Except Low-Rise Residential Buildings.

ANSI/ASHRAE/IESNA Standard 90.1-2004. Atlanta, GA: American Society of Heating, 

Refrigerating and Air-Conditioning Engineers. 

ASHRAE. (2004b). Ventilation for Acceptable Indoor Air Quality. ANSI/ASHRAE Standard 

62.1-2004. Atlanta, GA: American Society of Heating, Refrigerating and Air-Conditioning 

Engineers. 

ASHRAE. (2004c). ANSI/ASHRAE/IESNA Standard 90.1 User’s Manual. Atlanta, GA: 

American Society of Heating, Refrigerating and Air-Conditioning Engineers. 
12

EIA. (2005). Annual Energy Outlook 2005. Washington, DC: Energy Information 

Administration. www.eia.doe.gov/oiaf/aeo. 

EIA. (2006). 2003 Commercial Building Energy Consumption Survey. Washington, DC: 

Energy Information Administration. www.eia.doe.gov. 

Griffith, B.T.; P. Torcellini, N. Long, D. Crawley, and J. Ryan. (2006). Assessment of the 

Technical Potential for Achieving Zero-Energy Commercial Buildings. Submitted for 

Proceedings of the 2006 ACEEE Summer Study on Energy Efficiency in Buildings, 13−18 

August 2006, Pacific Grove, CA. Washington, DC: ACEEE American Council for an 

Energy-Efficient Economy; 

Huang, J., H. Akbari, L. Rainer, and R. Ritshard. (1991). 481 Prototypical Commercial 

Buildings For 20 Urban Market Areas. Berkeley, CA: Lawrence Berkeley National 

Laboratory. 

Huang, J. and E. Franconi. (1999). Commercial Heating and Cooling Loads Component 

Analysis. LBL-37208. Berkeley, CA: Lawrence Berkeley National Laboratory. 

NREL (2005). Building America Research Benchmark Definition. Updated December 29, 

2004. Golden, CO: National Renewable Energy Laboratory. 

RSMeans. (2004). 2005 Square Foot Costs, 26th Annual Edition. Kingston, MA: Reed 

Construction Data, Inc. 

Stocki, M., D.C. Curcija, and M.S. Bhandari. (2005). The Development of Standardized Whole 

Building Simulation Assumptions for Energy Analysis for a Set of Commercial Buildings 

(draft May 31 2005). Amherst, MA: University of Massachusetts. 

Torcellini, P.A.; M. Deru, B. Griffith, N. Long, S. Pless, R. Judkoff, and D.B. Crawley. (2004). 

Lessons Learned from Field Evaluation of Six High-Performance Buildings. Proceedings of 

the 2004 ACEEE Summer Study on Energy Efficiency in Buildings, 22-27 August 2004, 

Pacific Grove, California. Washington, DC: ACEEE American Council for an EnergyEfficient Economy; pp. 3/325−3/337; NREL Report No. CP-550-36545. 

U.S. Census Bureau. (2002). 2000 US Census. Washington, DC: U.S. Department of 

Commerce. 

U.S. Census Bureau. (2005). 2002 Economic Census: Sector 23: Construction: Geographic 

Area Series: Value of Construction Work for Establishments by Type of Construction: 2002. 

Washington, DC: U.S. Department of Commerce. http://factfinder.census.gov/servlet/ 

IBQTable?_bm=y&-geo_id=&-ds_name=EC0223A6&-_lang=en, accessed March 1, 2006. 
F1147-E(12/2004) 

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Establishing Benchmarks for DOE Commercial Builidng R&D and 

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The U.S. Department of Energy (DOE) Building Technologies Program and the DOE research laboratories conduct a 

great deal of research on building technologies. However, differences in models and simulation tools used by 

various research groups make it difficult to compare results among studies. The authors have developed a set of 22 

hypothetical benchmark buildings and weighting factors for nine locations across the country, for a total of 198 

buildings. 

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benchmark building; commercial building 

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