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

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Methodology for Analyzing the 

Technical Potential for Energy 

Performance in the U.S. 

Commercial Buildings Sector 

with Detailed Energy Modeling 

Preprint 

B. Griffith 

National Renewable Energy Laboratory 

D. Crawley 

U.S. Department of Energy

Presented at SimBuild 2006 Conference 

Cambridge, Massachusetts 

August 2−4, 2006 

Conference Paper 

NREL/CP-550-40124 

November 2006 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
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METHODOLOGY FOR ANALYZING THE TECHNICAL POTENTIAL FOR ENERGY 

PERFORMANCE IN THE U.S. COMMERCIAL BUILDINGS SECTOR WITH 

DETAILED ENERGY MODELINGI

Brent Griffith1 and Drury Crawley2

1

National Renewable Energy Laboratory, Golden, CO 

2

U.S. Department of Energy, Washington, DC 

 

I

 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 non-exclusive, 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. 

ABSTRACT

This paper summarizes a methodology for developing 

quantitative answers to the question, “How low can 

energy use go within the commercial buildings sector?” 

The basic process is to take each building in the 1999 

CBECS public use data files and create a baseline 

building energy model for it as if it were being built 

new in 2005 with code-minimum energy performance. 

The 1999 CBECS data form a statistical model of the 

commercial buildings sector by using a set of 5,430 

buildings with weighting factors to indicate how many 

more such buildings are represented by each member. 

For each building, we used 1999 CBECS data on floor 

area, number of floors, census division, basic climatic 

design criteria, principal building activity, and number 

of employees. The expanded building descriptions 

needed for detailed energy modeling with EnergyPlus 

were generated by applying ASHRAE Standard 90.1-

2004 and augmenting with data from Huang and 

Franconi (1999), probabilistic assignments, and other 

assumptions. The technical potential of energy design 

measures, for the sector as a whole, were then 

evaluated by altering and rerunning the energy models, 

comparing perturbed results to each baseline, and then 

aggregating performance metrics. The primary benefit 

of the method is that the CBECS weighting factors 

provide a robust way of aggregating national results 

from simulations of individual buildings. However, it 

is also challenging because it requires considerable 

computing resources. The methodology is 

recommended for future analyses when the results must 

properly reflect the national implications and when 

only a limited number of scenarios need to be 

investigated. 

INTRODUCTION

Buildings can be designed in such a way that a 

thorough application of energy-efficient design 

practices and technologies combined with photovoltaic 

(PV) on-site electricity generation might convert them 

from energy consumers to energy producers. The netzero point, where as much energy is produced as used 

each year, lies at the heart of one definition of the zeroenergy building (ZEB) concept. Choosing PV for onsite energy production is appealing, since the roofs of 

virtually all commercial buildings could be considered 

viable sites and PV systems have little adverse impact 

on buildings. 

The U.S. Department of Energy’s (DOE) Building 

Technologies (BT) program set a goal of creating the 

conditions for low- and zero-energy commercial 

buildings (LZEBs) to be market viable by 2025. 

However, such a goal is realistic for only a portion of 

the commercial sector, since some commercial 

buildings are far too large and power hungry to meet all 

their energy needs with on-site solar power. This begs 

two questions: What portion of the commercial sector 

could actually reach the ZEB goal? And how low can 

you realistically go across the sector as a whole? 

This paper describes a methodology for studying such 

questions by assessing the technical potential of sets of 

energy improvement technologies and practices in the 

commercial buildings sector. We use the term 

technical potential to refer to “maximum technology” 

scenarios that are used to estimate the limits of what is 

possible in the sector. As such, we do not include cost 

and economic analyses such as assessing market 

penetration or projecting how the sector might evolve. 

This technical potential study looks at what could 

1
happen if all the buildings were changed at once and 

does not track rates of adoption or model additions and

retirements of the building stock. We focused on

evaluating sets of known technologies and practices

and modeling the system interactions with detailed

engineering calculations. For this study, the authors 

used EnergyPlus (Crawley et al. 2001) to model the 

energy performance of a variety of commercial 

buildings in various subsectors and climates. 

Whole-building energy performance modeling has been

in use for approximately 30 years, and researchers have

used such tools to represent large portions of the 

building stock. Some of the earliest studies were 

conducted by researchers at Pacific Northwest National 

Laboratory (Briggs, Crawley, and Belzer 1987; Briggs, 

Crawley, and Schliesing 1992; Crawley and Schliesing

1992). A more recent example is research by Huang

and Franconi (1999) at Lawrence Berkeley National

Laboratory, who built on this work. Moffat (2001) 

presents a good overview of these methods, which he

refers to as stock aggregation, in the context of life 

cycle analysis and community planning. Moffat 

equates such bottom-up methods with “normative 

futures analysis” and “backcasting.” Large-scale 

simulation studies are also common in the history of

developing and evaluating codes and standards. 

This paper presents a brief overview of a methodology

for evaluating energy performance across the entire 

U.S. commercial buildings sector. An implementation

of the methodology is described and sample results are 

presented from an evaluation of the potential for U.S. 

commercial buildings to reach a goal of annual net-zero

site energy use. 

METHODOLOGY 

The methodology can be divided into two separate

modeling domains: (1) the modeling used to describe 

the sector; and (2) the modeling used to describe 

individual buildings. 

Sector Modeling 

A national sector model is a set of building types, 

locations, and weighting factors. Each member of the 

set is a model of a single building that can be analyzed 

in detailed (forward) building energy modeling tools. 

The sector model is key to connecting annual energy

modeling tools to national assessments of the sectorwide performance potential. The national sector model

used here is based on a large (N = 5,375) set of

building models that was derived directly from a robust

statistical data set developed by the U.S. Department of

Energy’s Energy Information Agency (EIA). 

The EIA statistical data set used here is known as the 

1999 Commercial Buildings Energy Consumption 

Survey (CBECS) Public Use Data (EIA 2002). The 

basic process is to take each building in the 1999 

CBECS public use data files and create a baseline 

building energy model as if it were being built new in

2005 with code-minimum energy performance. The 

1999 CBECS includes data on 5,430 buildings and 

provides weighting factors to indicate how many more 

such buildings are represented by each entry to form a 

statistical model of the commercial buildings sector. 

For each building, we used CBECS data on floor area, 

number of floors, census division, basic climatic design

criteria, principal building activity, and number of

employees. We then augmented descriptions needed 

for simulation by a number of assignments based on

building standards for commercial buildings to

represent new stock. A key assumption here is that the 

CBECS weighting factors are still applicable, although 

many details of the survey buildings are unknown (to

the public) and therefore need to be generated 

synthetically. These details are part of the building

modeling domain discussed below. We also assume

that the mix of buildings in the sector will not change

over the years and that the same weighting factors will 

be used for future scenarios. The sector model used 

here excluded refrigerated warehouses, so the total 

number of baseline building models was 5,375 (down

from 5,430 in 1999 CBECS). 

The primary benefit of basing the sector model directly

on CBECS is that the weighting factors (the variable 

ADJUST7) provide a robust way of obtaining national

results from models of individual buildings. For 

example, to aggregate results for the national average 

for percent savings in net site energy use intensity

(netEUI or E) for a particular LZEB scenario, we use 

the weighting factor and floor area data from CBECS 

in the following manner: 

( )

( )

( )

( )

( )

( )

, ,

1 1

1 1

,

1

1

%savings

100

N N

i base i i i LZEB i i

i i

N N

i i i i

i i

N

i base i i

i

N

i i

i

E AW E AW

AW AW

E AW

AW

= =

= =

=

=

=

⎛ ⎞ ⎛ ⎞ ⎛ ⎜ ⎟ ⎜ ⎟ ⎜ ⎜ ⎟−⎜ ⎜ ⎟ ⎜

⎜ ⎝ ⎠ ⎝ ⎠ ⎝

⎞

⎟

⎟

⎟

⎟

⎠ ⋅

∑ ∑

∑ ∑

∑

∑

(1) 

where, 

i is the index for each individual model, 

2
E is the net energy use intensity, 

N is the total number of model results, 

A is the building floor area, and 

W is the weighting factor. 

To determine the fraction of commercial floor area that 

could reach the ZEB goal, we use the following:

( )

,

1 ,

1

0.0

0.0 0.0

N

i i i LZEB

i i LZEB

N

i i

i

AW if E

if E

Fraction ZEB

AW

=

=

⎛ ⎞ ≤

⎜ ⎟

> ⎝ ⎠ =

∑

∑

(2) 

Such summations can also be performed over subsets

of N to characterize portions of the sector to 

differentiate by characteristics such as principal 

building activity, geography, number of floors, and 

floor area. The aggregations are also a practical 

method of simplifying the results from large numbers 

of models. 

Building Modeling

Defining a building model for detailed, whole-building

energy modeling programs, such as EnergyPlus, 

requires considerable detail. Table 1 lists examples of

input parameters that are used to structure these myriad 

details into the following four categories: 

Program refers to the architectural program, which

describes how the building will be used and the 

services it must deliver to the occupants. From an

energy point of view, the program influences many

important drivers (climate, plug and process loads,

ventilation requirements, operating schedules, and

comfort tolerances) that will ultimately determine 

energy performance. 

Form refers to the geometry of the building and its 

elements, and has important energy implications that 

stem from how the building interacts with the sun and

ambient conditions. 

Fabric refers to the materials that are used to construct 

the building and involves insulation levels, glazing

systems, and thermal mass. 

Equipment includes HVAC equipment and lighting

systems and controls. Except for plug and process load 

equipment selected by the occupants, this includes all 

the energy-consuming equipment that is part of the 

building. 

Table 1 Input parameter categories

PROGRAM FORM FABRIC EQUIPMENT 

Facility

location 

Total floor 

area 

Schedules

Plug and 

process

loads 

Lighting 

levels 

Ventilation 

needs 

Occupancy

Site

constraints

Number 

of floors

Aspect

ratio 

Window

fraction 

Window

location 

Shading

Floor

height 

Azimuth 

Exterior 

walls 

Roof

Windows

Interior

partition 

Internal 

mass 

HVAC system

types 

Component 

efficiency

Control settings

Lighting 

fixtures 

Lamp types

Daylighting

controls

Inputs such as those listed in Table 1 are considered 

“high-level” parameters, which do not, or cannot, 

appear directly in the input file. These parameters 

often imply a one-to-many relationship and that rules 

are needed to translate the parameter to multiple model 

input values. High-level parameters can directly

represent an energy design measure separately from

how that measure needs to be represented to the 

simulation program. In this methodology, the focus of

the building modeling domain is to manage how the

high-level parameters are determined and then mapped

to the low-level inputs that are needed for detailed

energy modeling programs such as EnergyPlus. 

Available data in 1999 CBECS are quite limited in

contrast to the detailed input needed for energy models. 

This (and the fact that BT’s ZEB goal mainly addresses

new construction) led to a fundamental shift where the 

CBECS statistical model for existing stock was used as 

a proxy for a statistical model of the types of buildings

being built in 2005. So instead of the real building, we

used ASHRAE 90.1-2004 to help define many

important features that govern energy use for fabric and 

equipment. Table 2 presents a summary of the sources

used to assign values for key input parameters. 

CBECS masks the locations of buildings for anonymity

in the survey, but does provide data for the census 

division (CENDIV7) and values for heating degreedays (HDD657) and cooling degree-days (CDD657). 

The location assignment algorithm used here compares

these reported degree-day values to the degree-days for 

all the weather locations (with typical year weather 

data) in that census division and chooses the one with

the closest match. The building is then assigned to this 

weather location. 

3
Table 2 Assigning model details 

PARAMETER SOURCES 

Location and 

Weather 

Fit degree-day and Census Division 

data from CBECS 

Utility tariffs Gas:EIA; Electricity: TAP and utility 

web sites 

Envelope 

construction 

ANSI/ASHRAE/IESNA Standard 90.1-

2004 

Floor area and 

number floors 

1999 CBECS public use data 

Aspect ratio and 

azimuth 

Probabilistic assignments 

Glazing fractions Huang and Franconi 1999; assumptions 

People density Number of worker data from 1999 

CBECS; ANSI/ASHRAE/IESNA 

Standard 90.1-1989 

Lighting power 

density 

ANSI/ASHRAE/IESNA Standard 90.1-

2004 

Plug/process 

power density 

Assumed mean by activity type, 

probabilistic assignments 

Schedules Modified versions from ASHRAE 

Standard 90.1-1989 

Outside Air 

Ventilation 

ANSI/ASHRAE Standard 62.1-2004 

Modeling the energy cost implications for commercial 

buildings also requires that demand charges be 

calculated and tariff schedules be changed based on 

service capacity. A comprehensive data set of utility 

tariffs is needed to implement this methodology. Such 

a data set was developed that contains input objects for 

120 tariffs for 40 utility companies by synthesizing data 

from EIA, a Web-based central repository run by the 

Tariff Analysis Project (TAP) (http://tariffs.lbl.gov/), 

and utility company Web sites. 

The methodology also uses (pseudo) random number 

generation to capture much of the variability in 

buildings. We used probabilistic assignments (by 

applying the central limit theorem) to create many 

details such as distribution of plug and process loads, 

azimuth and aspect ratio, and infiltration. For plug and 

process, or interior electrical equipment, we assumed a 

normal distribution, and values for the mean and 

standard deviation of the peak power density. For 

azimuth and aspect ratio, we assumed a uniform 

distribution. 

Huang and Franconi (1999) provide values for glazing 

fractions for selected types of new-vintage buildings. 

These were used and then augmented with assumptions 

for building types that were not covered. 

1999 CBECS provides data on the number of workers 

(NWKER7), the total square footage (SQFT7), and 

various numbers for capacities (RWSEAT7, 

PBSEAT7, EDSEAT7, FDSEAT7, and HCBED7). 

ANSI/ASHRAE/IES Standard 90.1-1989 Section 13 

also provides recommendations for occupancy density. 

To determine occupancy levels, we used the number of 

workers specified in 1999 CBECS—except where 

nonemployees as well as employees would be expected 

to be in the buildings—in which case we used Standard 

90.1-1989. 

The methodology also requires sets of schedules that 

are common in energy modeling. ANSI/ASHRAE/IES 

Standard 90.1-1989 Section 13 includes schedules for 

use with the Energy Cost Budget method. In Standard 

90.1-1989 (with addenda) the lights and equipment 

shared the same schedules, but case study research and 

broad anecdotal evidence suggest that plug and process 

loads do not necessarily track lighting loads and that 

during off hours they are higher, probably because of 

increased use of information technology and security 

equipment. Therefore, new plug and process schedules 

were created, by assumption, for use with this 

methodology. These have nighttime levels that are 

20%−40% of daytime, depending on activity. 

Once the values for high-level parameters are obtained 

(from CBECS and other sources), then rule-based 

algorithms are needed to define exactly how each 

building is modeled from a set of high-level 

parameters. For such large numbers of models, 

preprocessing routines are needed to automatically 

generate complete input files for the modeling 

program. The main task of the preprocessor is to 

translate the high-level parameters into a description of 

the building geometry and combine that with all the 

other data needed to run a simulation. Geometry autobuilding of “shoebox” buildings is common in energy 

modeling and is applicable here. The preprocessor 

needs to implement various transformations, including 

thermal zoning, HVAC system configuration and 

operation, overhang creation, and the use of multipliers 

to reduce simulation execution time for buildings with 

large numbers of floors or large numbers of 

fenestration components. 

EXAMPLE RESULTS AND DISCUSSION

The methodology described above was applied to a 

study of how achievable the goal of net zero is in the 

commercial sector. Algorithms for performing 

assignments were developed and implemented in a 

preprocessor. 

Using a large set of models addresses the problem of 

how to credibly assess the sector as a whole, but it also 

presents severe technical challenges for how to deal 

with tens of thousands of simulations and the demand 

for computing resources and data management. These 

challenges were handled by constructing custom 

4
software that includes automatic creation of EnergyPlus

input files, database and routines for dispatching and

executing models on a large Linux cluster, mining 

EnergyPlus output files for results and storing them in a 

database, retrieving results from the database, and

reducing and analyzing results into manageable forms

for reporting. We used a 64-bit compiler for Linux to

compile EnergyPlus and make efficient use of a large 

cluster. 

A few results from a study that used the current

methodology are presented here for these four

scenarios: 

1. Base produced reference baseline results by

applying ANSI/ASHRAE/IESNA Standard 90.1-

2004 to define characteristics that affect energy

performance. 

2. Base w/PV examined how effective it would be to 

only add PV to the baseline buildings. The systems 

applied 10% efficient PV panels with an area equal 

to 50% of the total roof area for every building.

The same PV systems are used in the LZEB 2005 

scenario. 

3. LZEB 2005 examined what can be achieved when

an aggressive package of currently available 

technologies and practices is applied. Each

building was reoriented and elongated along an

east-west axis for good daylighting and passive

solar design. Exterior fixed overhangs were added 

to south-facing glazing. Tubular daylighting 

devices were added to core zones. ZEB

technologies for the facade were modeled by

adding superinsulation and superwindows. The 

HVAC system was changed to a centralized, 

chilled-water-based variable air volume system

with economizer and heat recovery ventilation on

the outside air system. Lighting power density

(LPD) was reduced 17% from ASHRAE 2004 by

assuming the use of super T8 lighting. The

coefficient of performance (COP) of all water-towater chillers was set at 6.0. Gas heating

efficiency was raised from 80% in the baseline 

models to 95% by assuming the use of condensing

boilers. 

4. LZEB 2025 predicted the energy savings with all 

the same measures as the LZEB 2005 scenario

with somewhat higher component performances

than are currently available, but that would seem to 

offer reasonable outcomes after 20 years of R&D. 

This scenario includes a doubling of the efficiency

in rooftop PV panels and a slight improvement in

inverter efficiency. LPD levels were reduced by

50% from ASHRAE 2004. Chiller COP increased 

from 6.0 to 6.5 (by assumption), and heating

efficiency increased from 0.95 to 0.97 (by

assumption). Fan static pressure decreased 

slightly, and minor improvements in HVAC 

systems are forecast.

ZEB Potential 

We used Equation 2 to evaluate the results for the three 

alternative energy performance scenarios and to 

determine how much of the commercial sector could 

reach the ZEB goal. The results are shown in Figure 1. 

The ZEB goal was found to be achievable for portions

of this sector—with 2005 technologies and practices, 

22% of commercial buildings could reach net zero. 

Projections for technology improvements by 2025 

increase this percentage to 64%. Calculated according

to floor area, rather than by number of buildings, the 

percentages that can reach the ZEB goal are 23% for 

2005 and 53% for 2025. 

5

23

53

7

64

22

0

20

40

60

80

100

Base

w/PV

LZE

B

2005

LZE

B

2025

Percent able to reach Z

E

B g

o

al

Floor area # of buildings

Figure 1 Percentage of the U.S. commercial sector that

can reach the ZEB goal for various scenarios

We used Equation 1 to compare the three alternative 

scenarios to the base scenario and to determine the 

average level of energy savings. These results are 

presented in Figure 2 in terms of net savings (which

include PV power systems) and total savings (which

include only changes in efficiency). 

The “LZEB 2005” scenario examined what can be 

achieved if an aggressive package of current

technologies and practices to improve energy efficiency

was also applied. This scenario showed that the 

amount of floor area that could reach ZEB reaches 23% 

and, for the sector as a whole, 69% of site energy could 

be saved in this manner. 

Under the “LZEB 2025” scenario, we modeled two 

main performance differences—a doubling of the 

5
output available from rooftop PV panels and a 50%

reduction in LPD. Under this scenario, 53% of the 

commercial floor area could achieve net zero and, for 

the sector as a whole, 105% of the site energy could be 

saved or 5% more energy could be produced than

consumed by all the buildings in the sector. 

28

69

41 48

105

0

0

20

40

60

80

100

120

140

Ba

s

e

w/

P

V

LZE

B

2005

LZE

B

20

P

25

ercent savin

g

s in site en

erg

y

Net (with PV)

Total (consumption)

Figure 2 Percent savings in site energy for the 

U.S. commercial sector for various scenarios 

(100% represents ZEB)

Characteristics That Influence ZEB 

The characteristics that influence the ability to reach

the ZEB goal can be examined. For example, the 

sector model buildings can be binned by the number of

floors or the total floor area and then reapplying

Equation 2 to the resulting subsets of N. Figure 3

shows the percent of commercial floor area that can

reach ZEB as a function of the number of floors.

Figure 4 shows the percent of commercial floor area 

that can reach ZEB as a function of the total floor area 

of each building. Figures 3 and 4 are plots known as

“XYSize,” where the size of the plot symbols

represents the probability distribution function of the 

individual bins versus the X-axis; the Y-axis represents

the result for that bin. The results show that number of

floors is a much stronger indicator of ability to reach

ZEB than is overall size. 

Although an N of 5,375 seems quite large, the sector 

model does not have enough members to hold up well

when binned by more than one characteristic. For 

example, if the results were examined by number of

floors and floor area at the same time, many bins would 

have too few members for the analysis to be 

statistically valid. 

Figure 3 Percentage of floor area that can reach ZEB 

as a function of number of floors in building: 

LZEB 2025 Scenario

Figure 4 Percentage of floor area that can reach ZEB 

as a function of total floor area in building: 

LZEB 2025 Scenario

Limitations

The main disadvantage of this method is that the large 

number of models increases computing requirements, 

which precludes the evaluation of large numbers of

scenarios with different technologies and practices. 

This points to a fundamental tension between modeling

the national implications of technologies and practices 

and modeling a number of options for them. With

current computing capabilities, for instance, the total 

number of simulations needs to be kept reasonably low

(<100,000). The tension here can be somewhat

alleviated with supercomputing and mass data storage 

resources, but the issue will persist because the number 

6
of models tends to grow geometrically and can easily 

swamp even the largest computing and storage 

capabilities. As a result, this methodology is 

recommended for future analyses when the results must 

properly reflect the national implications and when 

only a limited number of scenarios need be 

investigated. However, more efficient methods are 

needed with a relatively smaller set of buildings (<200) 

so researchers can investigate larger numbers of 

scenarios with different technologies and practices 

(parametric input perturbations). 

The 2003 CBECS public use data should become 

available in 2006, and any future such modeling based 

on CBECS should use the new data set rather than the 

1999 CBECS. 

CONCLUSION

A methodology was presented, and is suggested as 

useful, for evaluating the whole-sector energy 

performance potentials for commercial buildings. The 

method consists of sector modeling and building 

modeling. Basing the sector model directly on CBECS 

provides a solid statistical basis that instills confidence 

that the results properly reflect the entire sector. 

Modeling the energy performance of individual 

buildings requires numerous details to be synthesized 

that are not available from the sector model. 

Generating the necessary detail is inexact and 

cumbersome, but it is possible. 

The primary benefit of the method is that the CBECS 

weighting factors provide a robust way of aggregating 

national results from simulations of individual 

buildings. However, it is also challenging because it 

requires considerable computing resources. The 

methodology is recommended for future analyses when 

the results must properly reflect the national 

implications and when only a limited number of 

scenarios need to be investigated. 

ACKNOWLEDGMENTS

This work was supported by the Building Technologies 

Program within the Office of Energy Efficiency and 

Renewable Energy at the U.S. Department of Energy. 

The authors would like to thank the numerous people 

who contributed effort, ideas, and reviews to the study, 

including Nicholas Long, Paul Torcellini, Ron Judkoff, 

Craig Christensen, Mary Colvin, Michael Deru, John 

Ryan, and Terry Logee. 

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Building Stocks. Background report from the 

International Energy Agency’s Annex 31 EnergyRelated Environmental Impact of Buildings. 

Available from www.annex31.com/. 

7
F1147-E(12/2004) 

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November 2006 

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Conference Paper 

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5a. CONTRACT NUMBER

DE-AC36-99-GO10337 

5b. GRANT NUMBER

4. TITLE AND SUBTITLE

Methodology for Analyzing the Technical Potential for Energy 

Performance in the U.S. Commercial Buildings Sector with Detailed 

Energy Modeling 

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

NREL/CP-550-40124 

5e. TASK NUMBER

BEC66001 

6. AUTHOR(S)

B. Griffith and D. Crawley 

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National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401-3393 

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NREL/CP-550-40124 

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NREL 

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National Technical Information Service 

U.S. Department of Commerce 

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14. ABSTRACT (Maximum 200 Words)

This paper summarizes a methodology for developing quantitative answers to the question, “How low can energy use 

go within the commercial buildings sector?” The basic process is to take each building in the 1999 CBECS public 

use data files and create a baseline building energy model for it as if it were being built new in 2005 with codeminimum energy performance. 

15. SUBJECT TERMS

commercial buildings; energy performance; zeb; zero energy building 

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