6-i

CHAPTER 6. BUILDING ENERGY USE AND END-USE LOAD

CHARACTERIZATION

TABLE OF CONTENTS

6.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.1.2 Methodology Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-2

6.2 ENGINEERING APPROACH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-3

6.2.1 Representative Building Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-4

6.2.2 CBECS Data (1992 and 1995) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-6

6.2.2.1 CBECS Data Used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-6

6.2.2.2 Building Characteristics in the 1033 Data Set . . . . . . . . . . . . . . . . . . . . 6-8

6.2.2.3 Engineering Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-11

6.2.3 Overview of Modeling Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-13

6.2.3.1 Space Air-Conditioning Load Modeling . . . . . . . . . . . . . . . . . . . . . . . 6-13

6.2.3.2 The Three-Story Prototype Building Model . . . . . . . . . . . . . . . . . . . . 6-15

6.2.4 Development of Equipment Efficiency Levels . . . . . . . . . . . . . . . . . . . . . . . . 6-16

6.2.5 Engineering Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-17

6.2.5.1 Development of Building Envelope Characteristics . . . . . . . . . . . . . . 6-17

6.2.5.2 Window-Wall Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-23

6.2.5.3 Aspect Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-25

6.2.5.4 Schedules and Load Densities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-27

6.2.5.5 Ventilation and Infiltration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-37

6.2.5.6 Economizer Use and Control Assumptions . . . . . . . . . . . . . . . . . . . . . 6-43

6.2.5.7 Heating and Cooling Setpoint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-43

6.2.5.8 Equipment Sizing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-44

6.2.6 GeoClimate Locations and TMY2 Station Assignments . . . . . . . . . . . . . . . . . 6-48

6.2.7 Simulation and Data Manipulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-50

6.3 SUMMARY OF RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-55

6.3.1 Comparison of Simulation End-Use Intensities to CBECS-Calculated EUIs . 6-55

6.3.2 Annual Building Energy Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-57

6.3.3 Comparison of Results to the Screening Analysis . . . . . . . . . . . . . . . . . . . . . . 6-59

LIST OF TABLES

Table 6.2.1 ANOPR Building Categories Versus CBECS PBA Categories . . . . . . . . . . . . 6-5

Table 6.2.2 CBECS Variables Used in the Characterization Process . . . . . . . . . . . . . . . . . . 6-6

Table 6.2.3 Characteristics of CBECS Buildings Used in the ANOPR Analysis . . . . . . . . 6-9

Table 6.2.4 Building Type Distributions in the 1033 Building Set . . . . . . . . . . . . . . . . . . 6-10

Table 6.2.5 Percentage of Represented Buildings by Floor Area for the 1033 Building 

Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-10

Table 6.2.6 Building Distribution (Percentage) by Building Type and Census Division 

for the 1033 Building Set (Top Number in Each Cell) and the Total 

Number of Buildings Represented (Bottom Number). . . . . . . . . . . . . . . . . . . 6-11

Table 6.2.7 Listing of Parameter File Variables and Sample Values from One Building . 6-12
6-ii

Table 6.2.8 EER Levels Selected for Building Simulations . . . . . . . . . . . . . . . . . . . . . . . . 6-16

Table 6.2.9 Building Envelope Characteristics Variables in CBECS95 Survey . . . . . . . . 6-18

Table 6.2.10 Categorization of CBECS Wall Construction Data . . . . . . . . . . . . . . . . . . . . . 6-20

Table 6.2.11 U-Factors for Wall Insulation Levels by Construction Category . . . . . . . . . . 6-21

Table 6.2.12 Estimated Fraction of Low-E Glass in Commercial Fenestration . . . . . . . . . . 6-22

Table 6.2.13 Window U-Factor and Shading Coefficient Combinations . . . . . . . . . . . . . . . 6-23

Table 6.2.14 Assigned WWR Weight for Each PBA in the 1995 Building Set . . . . . . . 6-24

Table 6.2.15 Weights for Random Assignment Within Each WWR Bin . . . . . . . . . . . . . . . 6-25

Table 6.2.16 Total Building Adjusted Floor Area for each Building Type and Aspect 

Ratio Bin (Millions of Square Feet) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-27

Table 6.2.17 Number of Hours a Building is Open Each Week . . . . . . . . . . . . . . . . . . . . . 6-30

Table 6.2.18 Number of Sample Buildings in Each Schedule Category . . . . . . . . . . . . . . . 6-30

Table 6.2.19 Peak Lighting Power Densities for Each of the Six Building Types . . . . . . . . 6-33

Table 6.2.20 Peak Plug Load Power Densities for Each of the Six Building Types . . . . . . 6-35

Table 6.2.21 Ventilation Rates Used for Each Building Type (cfm/ft2

) . . . . . . . . . . . . . . . . 6-38

Table 6.2.22 ASHRAE Standard 62-2001 Data for Education Building Spaces . . . . . . . . . 6-40

Table 6.2.23 ASHRAE Standard 62-2001 Data for Food Service Building Spaces . . . . . . 6-41

Table 6.2.24 ASHRAE Standard 62-2001 Data for Assembly Building Spaces . . . . . . . . . 6-42

Table 6.2.25 Development of Design-Day Data for BLAST Simulations . . . . . . . . . . . . . . 6-45

Table 6.2.26 Estimate of Capacity Purchased Versus Capacity Required for Two 

Manufacturers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-47

Table 6.2.27 TMY2 Stations by Census Region and Climate Zone, Including Alaska and 

Hawaii, and Excluding Puerto Rico and Guam . . . . . . . . . . . . . . . . . . . . . . . . 6-49

Table 6.2.28 Number of CBECS Buildings by Climate Zone and Census Region . . . . . . . 6-50

Table 6.3.1 Comparison of Annual Consumption for the Cooling and Fan Energy at Each 

of the 10 EER Levels, for the ANOPR Analysis and the 2000 DOE 

Screening Analysis. Percent Savings Based on EER=8.9 . . . . . . . . . . . . . . . 6-59

LIST OF FIGURES

Figure 6.1.1 Flow Diagram of the Load Shape Characterization Process . . . . . . . . . . . . . . . 6-3

Figure 6.2.1 Simplified Flow Diagram of the Simulation Methodology . . . . . . . . . . . . . . . 6-14

Figure 6.2.2 Typical Lighting Schedule for a Retail Store Open Monday Through Friday 6-29

Figure 6.3.2 Frequency distribution of the BLAST EUIs and the CBECS reported EUIs . 6-56

Figure 6.3.1 Plot of BLAST Cooling EUIs versus CBECS EUIs by Building 

Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-56

Figure 6.3.3 Comparison of BLAST and CBECS reported EUIs aggregated by 

GeoClimate region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-57

Figure 6.3.5 Change in BLAST Package Cooling EUIs with Increase in EER for Arbitrary 

Large and Small Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-58

Figure 6.3.4 Fraction of Simulated Annual Building Energy Use by Major End Use and 

Building Type. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-58

Figure 6.3.6 Weighted Cooling EUI Across all 1033 Buildings . . . . . . . . . . . . . . . . . . . . . 6-60

Figure 6.3.7 Comparison of Cooling Equipment Energy Use with DOE Screening 

Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-60
6-1

CHAPTER 6. BUILDING ENERGY USE AND END-USE LOAD

CHARACTERIZATION

6.1 INTRODUCTION

The building energy use and end-use load characterization analysis produced energy use

estimates and end-use load shapes for commercial unitary air-cooled air conditioners. The

energy use estimates enabled evaluation of energy savings from the operation of commercial

unitary air-conditioning equipment at various efficiency levels, while the end-use load shapes

allowed evaluation of the impact on electricity demand from the operation of commercial unitary

air-conditioning equipment. The analysis produced results from a variety of building types in

multiple climate locations in order to capture some of the diversity of use, and performance, of

commercial unitary air conditioning equipment.

6.1.1 Background

Previous analytic work focused on evaluating cost-effective levels of efficiency for

commercial unitary air-cooled air conditioning equipment. The American Society of Heating,

Refrigerating and Air-Conditioning Engineers, Inc. (ASHRAE) Standing Standards Project

Committee 90.1 (SSPC 90.1) performed the initial analysis in 1994 that led to the establishment

of equipment efficiency levels in ASHRAE Standard 90.1-1999. This analysis focused on two

building types (office and retail) situated in 11 climate locations to represent the diversity of

climate parameters influencing commercial building energy use. Building simulation results for

each of the building types provided the data to calculate full load equivalent operating hours

(FLEOHs). Then the SSPC 90.1 members weighted the FLEOH results by construction volume

estimates derived from DOE’s Commercial Building Energy Consumption Survey (CBECS)

data set.

In 1996, the Pacific Northwest National Laboratory (PNNL) performed an independent

analysis of efficiency levels of commercial unitary air-conditioning equipment, using an

expanded set of seven building types, but retaining the 11 climate locations used by ASHRAE. 

This work is documented in Preliminary Findings: Analysis of Commercial Space-Conditioning

and Storage Water-Heating Equipment Efficiencies.

1

 In addition to the expanded building types,

the analysis used different fundamental assumptions for the building simulations, including the

assumptions on the use of economizers and the method of sizing equipment, as well as the

scaling of the thermal loads in the simulated buildings to building sizes more representative of

the actual building population. Compared to the ASHRAE work, PNNL used different building

sizes and weights for each building type. The PNNL used a separate unitary air conditioning

system simulator based on a heat pump simulation model developed at Oak Ridge National

Laboratory (ORNL) for DOE. This simulator allowed the determination of energy use for the

air-conditioning equipment based on various design options that improved air conditioning

system efficiency.

A subsequent independent analysis by PNNL in 2000 provided a further evaluation of

efficiency levels of commercial unitary air conditioning equipment using the same seven
6-2

AnnualEnergyUse FLEOH EquipmentCapacity

Efficiency = ×

building types and 11 climate locations, with the exception that Tampa, Florida, replaced

Orlando, Florida, in the new climate set. The replacement of Orlando became necessary since

Typical Meteorological Year version 2 (TMY2) weather files were used in this analysis and the

Orlando weather station is no longer represented in the TMY2 files. The 2000 analysis is

documented in Screening Analysis for EPACT-Covered Commercial HVAC and Water-Heating

Equipment.

2

 Pacific Northwest National Laboratory updated the input assumptions for the

building simulations from the 1996 analysis based on projected changes to internal lighting

densities resulting from the greater use of electronic ballasts in the marketplace. Pacific

Northwest National Laboratory based the lighting densities on projected densities in 2015, and

updated its data sources from those used in the 1996 analysis (1992 CBECS) to the 1995 version

of the CBECS data set. It used separate simulations to reflect differences in air-conditioning

schedules for setback/setup and economizer/no economizer, and weighted the results based on

the reported use of setback and economizer systems in the CBECS data set. Finally, it developed

commercial unitary air-conditioning equipment energy usage directly from the building

simulations, using FLEOHs that were weighted by construction volume and building type.

In each of the three analyses described above, FLEOHs were used to estimate energy use

of cooling equipment, using the following equation:

The FLEOH is expressed in each of the analyses as the number of annual operating hours

at full capacity that a properly-sized particular air-conditioning unit would need to operate to

satisfy the annual cooling load of the building.

6.1.2 Methodology Overview

The purpose of the building energy use and load shape characterization analysis is to

assess the energy and peak demand savings potential of different equipment efficiencies across

the range of commercial building types and climate zones. The 1995 CBECS (CBECS95) data

set was the primary source used to develop the assumptions for building characteristics. The

Department used six building types: assembly, education, food service, office, retail, and

warehouse. The Department chose to analyze buildings that cooled 70 percent or more of the

total floor space with air-cooled unitary air-conditioning equipment. Each CBECS building was

modeled using the Building Loads and System Thermodynamics (BLAST) program. The

Department computed each building’s loads by simulating the same prototype three-story,

48,000-square foot (sf) building with five thermal zones per floor. The Department “scaled” the

results of that prototype’s simulation to match the specific geometry of the CBECS building

represented—e.g., conditioned floor area, aspect ratio, number of floors, number of thermal

zones per floor. The Department simulated the same buildings at 10 different energy efficiency

ratio (EER) levels. The end result was an hourly end-use energy profile of the following:
6-3

Characterization of

Building Stock:

Using CBECS

Data

Development and

Simulation of

Building Prototypes

Using BLAST

Energy

Time

Hourly End Use

Energy Profiles

Figure 6.1.1 Flow Diagram of the Load Shape Characterization Process

C air-cooled unitary air conditioning equipment;

C heating; 

C lights; 

C plug and miscellaneous loads; 

C package equipment fan; 

C other cooling; and

C other fan.

Figure 6.1.1 is a simplified flow diagram of this process.

6.2 ENGINEERING APPROACH

This section describes the engineering approach and assumptions used in the Advanced

Notice of Proposed Rulemaking (ANOPR) analysis. It includes discussion of how DOE

generated the air-conditioning loads, and descriptions of building types and characteristics, load

densities, and operating schedules. There is a presentation of the methodology for assigning

TMY2 stations to each of the 1033 buildings used in the analysis. Last, there is discussion of 

issues related to ventilation, equipment sizing, and economizer use.

6.2.1 Representative Building Types

Consistent with both the preliminary findings1

 and the screening analyses,2 DOE used a

subset of representative building types to characterize the energy use and loads for this analysis. 

Building types are characterized in CBECS using their reported principal building activity

(PBA). The Department identified six building types, made up of a combination of CBECS

PBA categories (see Table 6.2.1), based on their significance of energy use. These building types
6-4

typically use unitary air-cooled air-conditioning equipment, and the CBECS data set contained

sufficient information to characterize the building for modeling purposes. The six building types

selected included most of the top eight energy-using building types (with the exception of Health

Care and Lodging), based on CBECS data. The Department did not include the inpatient

segment of the Health Care building type because there were insufficient data to characterize the

buildings for the purpose of energy simulations; however, DOE did include the outpatient

segment of Health Care by adding these observations into the Office category, since these

buildings could be modeled similar to office buildings. The Department deleted Lodging

because the number of observations nationwide was small, and because these types of buildings

do not typically use unitary air-conditioning equipment. From a practical standpoint, typical

lodging units are usually air conditioned either with incremental units, such as packaged terminal

air conditioners (PTACs), or by terminal units connected to a central chilled water system.

The building types selected for analysis were Assembly, Education, Food Services,

Office, Retail, and Warehouses (non-refrigerated). The Department further screened the 

individual CBECS buildings so that only buildings with 70 percent or more of their total floor

space air conditioned by unitary air conditioning equipment were included in the representative

building set. The Department based its threshold of 70 percent air-conditioned floor space on a

need to keep the sample size reasonable, yet representative of the building stock with unitary airconditioning equipment. A higher threshold (i.e., 90 percent air-conditioned floor space) would

have resulted in too small a sample; a lower threshold (i.e., less than 50 percent) would have

included buildings with limited unitary air-conditioning equipment for uses that would be

problematic in characterizing their loads accurately. The total number of observations in the six

building types meeting the 70 percent threshold was 1033. These buildings accounted for over

73 percent of the annual air-conditioning energy use and 67 percent of the square footage of

commercial buildings that air condition at least part of their floor space with unitary airconditioning equipment. The Department built up the building types being used for the EPAct

analysis from observations that are categorized in CBECS as PBAs. Table 6.2.1 provides a map

of the CBECS PBA to the Energy Policy Act of 1992 (EPAct) Building Category, and accounts

for the PBAs that DOE did not use.
6-5

Table 6.2.1 ANOPR Building Categories Versus CBECS PBA Categories

EPAct No. EPAct Building Category CBECS

PBA Code CBECS Principal Building Activity

1 Assembly (ASM) 13 Public Assembly

2 Education (EDU) 6 Education

3 Food Service (FDS) 15 Food Service (restaurant)

4 Office (OFF) 2

8

Office/Professional

Healthcare (outpatient)

5 Retail (RET) 3

23

24

25

26

Mercantile/Services

Strip Shopping

Enclosed Shopping Center/Mall

Retail (except Mall)

Service (except Food)

6 Warehouse (WHS) 5 Warehouses (non-refrigerated)

NA All Other PBAs 1

4

6

7

9

10

11

12

16

17

18

19

20

91

Vacant

Laboratory

Food Sales

Public Order and Safety

Industrial Processing/Manufacturing

Agricultural Purposes

Warehouses (refrigerated)

Religious Worship

Healthcare (inpatient)

Nursing Home

Lodging (hotel/motel/dorn)

Residential

Indoor Parking Garage

Other
6-6

6.2.2 CBECS Data (1992 and 1995)

The Department used the CBECS95 data set as the primary data source to develop the

assumptions for building characteristics for the purpose of modeling; DOE supplemented this

with the CBECS92 data set (for window-to-wall ratio and aspect ratio) as well as other sources,

when CBECS95 did not contain the needed data for the energy simulations. The CBECS data

sets are publicly available and provide statistically valid results from a periodic national survey

of commercial buildings and their energy suppliers performed by the U.S. Department of

Energy, Energy Information Administration (EIA). The results of the surveys are available as

downloadable reports and microdata files from the EIA website (www.eia.doe.gov). The

Department did not use CBECS99 data, which are the latest data available, because: 1) the

characteristics data were not initially available at the time of the analysis, and 2) there is

potential bias in the data because the new survey lacks data for any new buildings smaller than

10,000 square feet—a size category that includes buildings likely to use unitary air-conditioning

equipment. The previous CBECS data include characteristic and energy consumption

information for buildings 1,000 square feet or more.

6.2.2.1 CBECS Data Used 

In the CBECS95 survey, 5,766 buildings were surveyed and the sampled buildings were

given base weights (CBECS variable ADJWT6) that relate the sampled buildings to the entire

stock of commercial buildings in the United States. The DOE analysis used a subset of 1,033

buildings from the 5,766 buildings available. Included in the CBECS data are variables that

indicate the percentage of each building that is air conditioned (CBECS variable COOLP6), and

the percentage of the air-conditioned floor space in each building that is air conditioned by

unitary air-conditioning equipment (CBECS variable PKGCP6). The buildings chosen for

analysis were buildings that air conditioned 70percent or more of their floor space by unitary airconditioning equipment:

 (COOLP6*PKGCP6)/100>=70. 

The Department eliminated from the query a building with a CBECS weight of zero and

buildings with PBAs of “Lodging” and “Other.” Table 6.2.2 presents a list of the variables used

in the characterization process.

Table 6.2.2 CBECS Variables Used in the Characterization Process

Field Identifier Description

BLDGID6 CBECS building identifier

SQFT6 Square footage

ADJWT6 Adjusted weight

YRCON6 Year construction was completed
6-7

YRCONC6 Year of construction category

NFLOOR6 Number of floors

PBA6 Principal building activity

VACBA16 First previous/intended activity, used as PBA for vacant buildings

VACP6 Percent vacant

PORVAC6 Space vacant for at least 3 months

REGION6 Census region

CENDIV6 Census division

ZONE11 Expanded census division

CLIMATE6 Climate zone

TEMPAVG6 Average 1995 temperature (F)

TEMPSTD6 Standard deviation of 1995 temperature (F)

CDD656 Cooling degree-days (Base 65 F)

HDD656 Heating degree-days (Base 65 F)

MSA6 Metropolitan statistical area

NWKER6 Number of workers during main shift

WKHRS6 Total weekly hours open

WKHRSC6 Total weekly hours open category

OPNMF6 Typically open Mon. thru Fri.

OPNSAT6 Typically open on Saturdays

OPNSUN6 Typically open on Sundays

LTOHRP6 Percent lit during operating hours

LOHRPC6 Percent lit during operating hours categorical variable (e.g., 0-10%)

LNHRPC6 Percent lit during off-hours category
WKHRS6 Total weekly hours open

6-8

MAINCL6 Main equipment used for cooling

COOLP6 Percent cooled in 1995

COOLPC6 Percent cooled in 1995 category

PKGCL6 Packaged cooling [unitary air conditioning] units used

PKGCP6 Percent packaged cooling [unitary air conditioning] units

PCTBLDGPKGD Calculated field (COOLP6*PKGCL6)/100

HTPCP6 Percent cooled by heat pump 

HTPMPC6 Heat pump used for cooling 

ECN6 Economizer cycle 

RFCNS6 Roof construction material 

WLCNS6 Wall construction material 

RIN6 Roof or ceiling insulation 

WIN6 Exterior wall insulation 

ELCLBTU6 Electric cooling use (mBtu) 

LOADFAC6 Annual load factor 

PEAK6 Peak annual load 

PEAKS6 Peak summer load 

PEAKW6 Peak winter load 

SEASON6 Season of peak load 

6.2.2.2 Building Characteristics in the 1033 Data Set

For this analysis, it is important that the buildings selected are representative of the entire

building stock with unitary air-cooled air-conditioning equipment, and that there are no biases in

the building distributions.
6-9

Table 6.2.3 shows the number of buildings, total floor area, and total package [unitary

air-conditioning] equipment cooling energy reported by CBECS for various building

disaggregations. The second number in each cell in rows 3 and 4 (in parentheses) is the

percentage relative to the buildings reporting package cooling equipment (row 2).

Table 6.2.3 Characteristics of CBECS Buildings Used in the ANOPR Analysis

Number of

CBECS

Observations

Total Floor Space

Represented

(million sf)

Total Floor Space

Cooled with

Package

Equipment

(million sf)

Total Package

Cooling Energy

(Million Btu)

All CBECS

Observations 5,766 58,772 14,171 125,644,225

Buildings reporting

package cooling

equipment*

2,584 26,516 14,171 125,644,225

Buildings with at

least 70% floor

space cooled by

package

equipment*

1,206

(47%)

11,543

(44%)

10,967

(77%)

104,442,780

(83%)

Our six building

types meeting 70%

threshold

1,033

(40%)

9,914

(37%)

9,441

(67%)

91,426,091

(73%)

* Excluding buildings with no reported energy use 

The total number of observations in DOE’s six representative building types meeting the

70percent criteria is 1033. Approximately 70 percent of the 1033 buildings reported 100 percent

of the floor space air conditioned by unitary air-conditioning equipment. The six representative

building types selected account for over 73 percent of the annual cooling energy use and 67

percent of the square footage of commercial buildings that reported unitary air-conditioning

equipment.

Table 6.2.4 shows the relative percentages of the six representative building types in the

final sample by number of buildings, floor area, and unitary air conditioner cooling energy

consumption. In all three characterizations, retail and office building types are the most

prevalent building type.

Table 6.2.5 shows the distribution of building floor area for each of the six building types

for the 1033 building set. The buildings were binned by reported building floor area ranges, as

shown. 

Table 6.2.6 shows the percentage distribution of the CBECS observations by the nine

Census divisions. The top number in each cell is the number of buildings from the 1033
6-10

building set in that cell. The bottom number is the total number of buildings represented by

CBECS in that cell. The region populated with the most buildings is the Pacific (157,511

buildings), followed closely by the South Atlantic region (120,693 buildings) and the West

South Central region (120,465 buildings). The two least populated regions are the West North

Central region (33,398 buildings) and the New England region (33,480 buildings).

Table 6.2.4 Building Type Distributions in the 1033 Building Set

Building Type Percentage by Number

of Buildings

Percentage by Floor

Area

Percentage by Unitary

Air Conditioning

Energy Use

Assembly 10 8 5

Education 9 10 8

Food Service 14 7 16

Office 35 30 32

Retail 30 42 38

Warehouse 2 3 1

Total 100 100 100

Table 6.2.5 Percentage of Represented Buildings by Floor Area for the 1033 Building Set

Floor Area (sf) Assembly Education Food Service Office Retail Warehouse

# 10,000 37% 27 82 27 20 11

10,001 - 20,000 29 15 9 16 15 11

20,001 - 30,000 11 13 2 15 10 8

30,001 - 40,000 8 6 0 8 7 11

40,001 - 50,000 4 5 5 7 4 8

50,001 - 100,000 8 24 2 13 15 24

100,001 - 200,000 3 8 0 6 12 14

200,001- 300,000 0 0 0 2 5 5

300,000 - 400,000 0 1 0 2 2 0

400,001 - 500,000 0 0 0 2 1 3

500,001 -

1,000,000 00 0 246

> 1,000,000 0 0 0 0 4 0

Total 100 100 100 100 100 100
6-11

Table 6.2.6 Building Distribution (Percentage) by Building Type and Census Division for

the 1033 Building Set (Top Number in Each Cell) and the Total Number of

Buildings Represented (Bottom Number).

Census Division Assembly Education Food Service Office Retail Warehouse Total

East North

Central

16

15,329

6

1,458

10

18,189

55

25,599

55

41,563

3

318

145

102,455

East South

Central

10

17,638

8

7,651

5

9,586

20

40,070

37

27,012

3

113

83

102,069

Middle Atlantic 9

11,187

8

7,760

5

13,311

36

18,765

36

22,107

4

673

98

73,803

Mountain 7

7,440

7

2,956

2

3,115

23

12,277

12

14,095

0

0

51

39,883

New England 3

6,550

2

298

3

6,440

23

16,003

16

3,775

3

415

50

33,480

Pacific 10

9,552

19

28,765

10

21,685

90

53,792

74

42,070

7

1,647

210

157,511

South Atlantic 10

6,434

18

12,029

7

15,237

62

58,435

89

21,471

11

7,087

197

120,693

West North

Central

4

755

3

849

2

3,636

18

9,452

28

18,706

0

0

55

33,398

West South

Central

6

2,321

28

9,832

13

20,276

30

39,522

61

42,135

6

6,379

144

120,465

Total 75

77,206

99

71,597

57

111,473

357

273,916

408

232,933

37

16,633

1033

783,758

6.2.2.3 Engineering Database

Modeling commercial buildings and equipment using BLAST required assumptions

about the buildings’ envelope characteristics, internal loads, occupancy characteristics, and

activity schedules. The Department created a database to store the primary set of 1033 buildings

and associated characteristics from CBECS95 data, and to integrate this information with other

engineering parameters developed for the analysis. The engineering assumptions are discussed

in detail in section 7.2.6. 

From the database, the Department generated a parameter file (Parm file) of inputs for

BLAST. The Parm file consisted of input variables for simulation of each of the 1033 buildings

with 10 different EER values, for a total of 10,330 records. Table 6.2.7 shows the parameter 

values for one of the sample buildings. A unique run identifier traces each record to the CBECS
6-12

building identifier, the location of the building, and the EER case being analyzed. Appendix D

contains the full set of Parm file values for all of the 1033 buildings for one EER value.

Table 6.2.7 Listing of Parameter File Variables and Sample Values from One Building

Parm File Variable Sample Value

run_ID 11_ILG_1

wban_number 13781

city WILMINGTON_DE

bldg_type ret

sqft_scale 25000

floors_scale 2

WWR 0.04

aspect_ratio_scale 3

wall_const_fyi Sheet metal panels

roof_R_fyi 19

roof_U 0.051

wall_R_fyi 0

wall_U 1.18

fens_SC 0.94

fens_U 1.13

schedule_name RET_MFS

months_oper 12

light_power 1.88

equip_power 0.4

people_dens 2.222

vent_peak 0.25

infiltration 0.038

activity_level 0.425

econ Yes (only when

indicated by CBECS

dataset)

econ_type Enth
6-13

heat_SPT 70

cool_SPT 75

setback_T 60

setup_T 85

econ_hi_limit 65

base_fan_esp 0.75

fan_power_ratio 0.11

run_type design

roof_type builtup

wall_type metal

EER 8.5

over_sizing_factor 1.182225

pct_cooled_scale 100

pct_pkgd_cooling_scale 100

6.2.3 Overview of Modeling Methodology

Assessing the building energy use at specific equipment efficiency levels requires a

sequence of modeling exercises. Figure 6.1.1 shows the process used in this study to estimate

the annual energy consumption of the covered equipment at various efficiency levels. This

section provides the reader with an overall understanding of the hourly load simulation analysis

flow. Each of the process blocks in Figure 6.1.1 is described in more detail elsewhere in this

document. This section should help put the more detailed discussions in proper context. 

6.2.3.1 Space Air-Conditioning Load Modeling

Assessment of the annual energy use of space air-conditioning equipment at various

efficiencies requires knowledge of the building loads of typical building types in various

climates. The Department used the following approach, illustrated in Figure 6.2.1, to generate

estimates of such loads:

C Identify a set of cities that adequately represents the diversity of climates in the United

States. The Department based this analysis on simulations of space air-conditioning

loads in 157 locations. 
6-14

Size equipment 

with BLAST 

design-day run

Simulate annual 

energy use

with BLAST

Scale results to 

match actual 

building

geometry

Adjust cooling

and fan energy 

use for less than 

100% packaged 

cooling

15-zone, 3-story Prototype

(48,000 sf)

CBECS – inferred

size and geometry

Figure 6.2.1 Simplified Flow Diagram of the Simulation Methodology

C Evaluate data on existing and new buildings to identify appropriate assumptions

regarding building construction (e.g., building type, size, number of floors, insulation

levels, window-to-wall ratios [WWRs]). The Department analyzed data from CBECS92

and CBECS95 to establish these assumptions.3,4 The Department based the important

input assumptions—internal load profiles, occupancy/activity schedules, equipment

control schedules, ventilation strategies, and envelope characteristics—on the review of

CBECS92 or CBECS95 data and other studies of real buildings. The analysis used a

generic 15-zone, three-story prototype building that is common to all building types. The

use and justification of the generic building approach is discussed below. See section

6.2.5 for a detailed discussion of the data analyzed.

C Employ a building energy simulation model to obtain estimates of building airconditioning loads for each relevant building type in each climate-representative city.

The Department used the BLAST program to calculate the building air-conditioning

loads.5
6-15

6.2.3.2 The Three-Story Prototype Building Model 

The Department simulated the cooling coil loads and the building energy use using the

BLAST hourly simulation tool,5 and performed the simulations using a generic building model

with fixed geometry. The generic building model is a three-story, 48,000 ft2

 building with 15

separate thermal zones (four perimeter zones and one core zone for each floor). The perimeter

zones have a 15-ft depth and the building model uses a fixed 1:1 aspect ratio.

Because of the extreme diversity in building size, shape, and other characteristics, even

within a particular building type category, it is generally difficult to identify a single prototype

building that is adequately representative of the geometry for the stock of buildings under

analysis. In this analysis, DOE transformed the loads and energy use for systems defined with

the generic building’s geometry to provide loads and energy use for an actual CBECS building

used for the analysis. This is because the analysis reported in this document requires

development of system loads representative of loads that would be experienced by unitary

equipment in a diversity of actual applications, not system loads from a specific building. 

The three-story, 15-zone generic building captures all the important zone types of the

relevant building class. The three-story prototype has a ground level, a roof level, and an

intermediate level. Buildings with more than three stories can be represented by multiplying the

middle-floor loads of the generic prototype. Further, each floor is represented by a core zone

(i.e., a zone that has no exterior walls or windows) and a perimeter zone facing each of the

cardinal directions. System loads for buildings with greater or lesser perimeter-to-core area

ratios can be developed by properly weighting the loads and energy use for the prototype

building’s zones. Buildings with more wall area facing one direction than others (i.e., buildings

with non-square aspect ratios) can be represented by scaling or weighting the generic building

zone loads to emphasize the dominant orientation(s).

Properly characterizing system loads from large classes of buildings requires eliminating

any orientation biases, because the actual building stock is oriented more or less randomly. 

Some analysts have approached this problem by simulating a square prototype with equal

glazing area facing each cardinal direction. However, most real buildings are not square and

loads based on a square prototype will improperly weight the influences of internal gains and

external weather on building loads. Another approach is to simulate a building with a realistic

aspect ratio in each of several orientations. This approach, of course, greatly increases the effort

to obtain the load estimates. A benefit of using the 15-zone prototype and properly weighting

the zone loads and energy use is that non-square buildings can be represented with a proper

balance of internal load and external (weather) drivers without arbitrarily biasing the results

toward a particular solar orientation. Because the building stock is more or less randomly

oriented, to eliminate any bias from the solar gains, zone load from all perimeters must be

equally weighted while perimeter-versus-core influence is adjusted to match the desired aspect

ratio.
6-16

6.2.4 Development of Equipment Efficiency Levels

The Department based the efficiency levels chosen for the simulations on assumptions

appropriate for the unitary air-conditioning equipment being analyzed. Two important baselines

to be analyzed were: 1) the current minimum manufacturing standard energy efficiency levels, as

established under Energy Policy and Conservation Act (EPCA) legislation, and 2) the minimum

energy-efficiency standards set forth by the updates to ASHRAE 90.1-1999—below which, by

statute, DOE cannot set standards. 

In addition, DOE analyzed two system capacities: 65,000–135,000 Btu/hr cooling

capacity air-cooled unitary air conditioners and 135,000–240,000 Btu/hr cooling capacity aircooled unitary air conditioners. The Department considered minimum efficiencies mandated by

EPCA and by ASHRAE, for both equipment capacities, to be important base efficiency levels to

be analyzed in the rulemaking.

The maximum efficiency analyzed in the rulemaking was an EER of 12 for both

equipment size categories. As of July 2002, the were no EER 12 products in these size

categories in the Air-Conditioning and Refrigeration Institute (ARI) directories of certified

equipment. However, there were products with an EER of 11.8 in the 65,000–135,000 Btu/h

capacity category, and products with an EER of 11.5 in the 135,000–240,000 Btu/h capacity

category. Because these represent the high end of the existing market, DOE particularly

examined these EER levels. 

To fill in the intermediate efficiency levels, between the baseline products and the highefficiency products currently available, the Department set efficiencies at 0.5 EER increments

(i.e., 10.0, 10.5, 11.0, 11.5). The Department analyzed all efficiencies above the ASHRAE 90.1-

1999 level for each equipment capacity category at these increments, up to 12.0 EER. Table

6.2.8 shows the EER increments simulated and the equipment capacities to which they applied.

Table 6.2.8 EER Levels Selected for Building Simulations

Efficiency

Case

EER

Level Source

Used in Energy Analysis

65-135

kBtu/h

135-240

kBtu/h

1 8.5 EPCA minimum for 135-240 kBtu/h x

2 8.9 EPCA minimum for 65-135 kBtu/h x

3 9.5 ASHRAE 90.1-1999 minimum for 135-240 kBtu/h x

4 10.0 0.5 EER increment x

5 10.1 ASHRAE 90.1-1999 minimum for 65-135 kBtu/h x x

6 10.5 0.5 EER increment x x

7 11.0 0.5 EER increment x x

8 11.5 Highest reported EER for 135-240 kBtu/h x x
6-17

9 11.8 Highest reported EER for 65-135 kBtu/h x x

10 12.0 0.5 EER increment x x

6.2.5 Engineering Assumptions

This section documents the methodologies and key assumptions used in generating the

engineering input parameters required for building load simulations, for buildings for which

characteristics data were not directly available from CBECS95 or other sources. 

6.2.5.1 Development of Building Envelope Characteristics

The Department assumed that the base building to be simulated was of slab-on-grade

construction with a flat, built-up roofing system. Built-up roofs represent the most common type

of roof used in commercial buildings, based on the CBECS95 survey data. The Department

inferred the remainder of the building envelope characteristics, starting from the CBECS95 data.

Table 6.2.9 shows the variables in CBECS95 that describe the building envelope

characteristics. 
6-18

Table 6.2.9 Building Envelope Characteristics Variables in CBECS95 Survey

CBECS95 Variable CBECS Description Values

WLCNS6 Wall Construction Window/vision glass

Decor/construction glass

Sheet metal panels

Pre-cast concrete panels

Masonry

Siding/shingles/shakes

No one major type

Don’t know

Not ascertained

RFCNS6 Roof Construction Wooden materials

Slate or tile

Singles (not wood)

Built-up

Metal surfacing

Single/multiple ply

Concrete roof

No one major type

Other

Refused

Don’t know

Not ascertained

RIN6 Roof or Ceiling Insulation

(presence of)

Inapplicable

Yes

No

Refused

Don’t know

Not ascertained

WIN6 Wall Insulation (presence of) Inapplicable

Yes

No

Refused

Don’t know

Not ascertained

STW6 Storm Windows or Doors Inapplicable

Yes

No

Refused

Don’t know

Not ascertained

TRG6 Tinted or Reflective Glass Inapplicable

Yes

No

Refused

Don’t know

Not ascertained
6-19

Wall Insulation Levels. The description of the wall construction and roof construction

variables in CBECS95 primarily describes the surface material for these portions of the building

envelope and not the actual construction. The Department made inferences about whether the

construction represented structural masonry construction or frame wall construction. Different

methods of insulation are commonly used for these two types of wall construction. 

There are few other studies of wall and roof insulation characteristics in existing

commercial buildings. The Department identified two studies. The first was a 1986 Pacific Gas

& Electric survey of on-site construction characteristics in existing buildings in California. The

results of this survey are reported in Akbari et al. 6 The second study was a review of

commercial building characteristics undertaken in 2000 by New Jersey Electric and Gas Utilities

to understand typical construction and renovation practices in their service area. 7 This study

observed building characteristics for a large number of newly constructed buildings and a

smaller number of building retrofits, and provided the frequency of occurrences for different

wall and roof R-values (thermal resistance values) from those observations. The study showed

the predominance of three insulation levels—R-6, R-11, and some R-19—for wall insulation,

and the predominance of R-19 or R-20 levels for roof insulation in new construction or retrofit

cases. Because the of the way the data are reported, neither study clearly indicates the likelihood

of an R-0 (uninsulated) roof or wall insulation level. Unfortunately, the New Jersey Electric and

Gas Utilities study did not clearly define the type of construction in the binning process.

The Department reviewed these studies but, because of their localized nature, it

recognized that it would be difficult to categorize either of their results as representative of U.S.

construction practices.

Given the unavailability of national data, the Department chose a simplified strategy for

determining wall and roof insulation levels that would capture some diversity without overcomplicating the analysis. The Department chose the wall and roof insulation levels as follows.

C For frame wall construction, if the individual building data in the CBECS95 survey

reported that wall insulation was present in the building, DOE assumed the wall

insulation to be an R-11 equivalent. R-11 is believed to be the most common R-value

historically for standard frame wall construction, since its thickness was designed to fill

wall cavities created by standard 3.5-inch wall construction.

C For structural masonry-type construction, there are three common alternatives for wall

insulation. The first is some type of continuous insulation (e.g., foam sheet) placed on

the wall surface, over which is placed some type external protective covering (e.g.,

stucco). The second is some type of fibrous batt-type insulation that would be pinned to

the interior wall surface. This type of insulation would commonly be framed by a nonstructural wall inside the space, commonly with only an inch or two of cavity thickness. 

The third method of insulation involves the construction of a standard 3.5-inch, nonstructural, wood or metal frame, which is placed on the inside surface to provide a cavity

for insulation. Once this 3.5-inch cavity is constructed, it generally makes economic

sense to fill the cavity with standard fibrous insulation. The cavities created in the second
6-20

and third alternatives would typically have an interior wall surface, such as gypsum

board, for aesthetic reasons. Combinations of cavity and continuous insulation exist, but

DOE did not consider these for this analysis. 

For this analysis, the Department assumed that there was a 50 percent probability that

wall insulation on a masonry-type wall would be an R-6 level (representing the equivalent of

approximately 1.5 inches of continuous foam or a 2-inch pinned insulation batt) and a 50 percent

probability that it would be an R-11 level (representing a standard 3.5-inch insulation batt). The

Department did not examine higher insulation levels. 

The R-value of the wall insulation does not completely describe the thermal performance

of the wall. In addition, DOE used the data on wall construction to determine the additional

impact of the balance of the wall assembly. The Department categorized each of the

construction types in CBECS95 as either mass, metal frame, wood frame, or metal building. It

estimated the probability that a given CBECS construction would fall into each of these

categories, as shown in Table 6.2.10.

Table 6.2.10 Categorization of CBECS Wall Construction Data

Generic Construction Category

WCNS6 Category Metal Frame Wood Frame Masonry Metal Building

Assumed probability of being in generic construction category

Sheet metal panels 0% 0% 0% 100%

Masonry 0% 0% 100% 0%

Window/vision glass 100% 0% 0% 0%

Decorative/construction glass 100% 0% 0% 0%

Pre-cast concrete panels 0% 0% 100% 0%

No one major type 25% 25% 25% 25%

Other 50% 50% 0% 0%

Siding/shingles/shakes 50% 50% 0% 0%

The Department assumed that the balance-of-assembly R-values for each generic

assembly included air films on both the interior and exterior surfaces, gypsum board on the

interior surface, and, for the metal and wood frame wall, 5/8-inch siding material. For the metal

building-type construction, DOE assumed only a sheet metal layer on the building exterior. For

masonry buildings, DOE assumed an external surface layer of brick.

The Department based the overall wall U-factor (thermal transmission factor) for each

generic construction category and wall insulation level on wall assembly U-factors shown in

Appendix A of ASHRAE 90.1-1999. These U-factors are shown in Table 6.2.11.
6-21

Table 6.2.11 U-Factors for Wall Insulation Levels by Construction Category

Generic Construction Category

Wall Insulation R-Value

(hr-sf-F/Btu)

Metal Frame Wood Frame Masonry Metal Building

Overall U-factor used in Analysis (Btu/hr/ft2

/°F)

0 0.352 0.292 0.580 1.180

6 NA NA 0.129 0.184

11 0.124 0.096 0.079 0.123

19 0.109 0.067 0.048 0.070

Roof Insulation. Built-up roofs can be constructed with essentially any insulation level desired.

Insulation is most commonly applied to the upper roof surface, but may also be found hung at

the underside of the roof deck or, less commonly, applied to the topside of a suspended ceiling. 

For this analysis, DOE assumed that all roofs reported as insulated by CBECS had the equivalent

of R-19 insulation, corresponding to an overall U-factor of 0.051 Btu/hr/ft2/°F. The Department

identified this as the single most common level.

Window Characteristics. The window characteristics of concern for the energy analysis are

primarily the overall window U-factors and the window shading coefficient (SC). These

characterize the performance of the windows in the building once the window-to-wall ratio has

been established. The Department did not try to characterize the performance of awnings or

separate window shading devices for this analysis, since CBECS does not indicate the size or

shading value of projections, nor the use of internal movable shading devices such as Venetian

blinds. 

The CBECS95 data set contains information on whether windows in the building use

tinted or reflective glass or other shading films (CBECS95 variable TRG6), whether storm

windows or doors are used (CBECS95 variable STW6), and whether either internal or external

shading devices or awnings are present (CBECS95 variable AWN6). 

A detailed look at variable STW6 in the original survey indicates that this variable

represents the use of storm windows, storm doors, thermal pane glass, or double- or triple-pane

glass. Very little triple-pane glass is used in commercial building in the U.S. For this analysis,

if other-than-single pane glazing was reported used in the building (CBECS variable STW6 =

“Yes”), DOE characterized the building windows as double-pane glazing.

The degree of tinting in tinted windows is unknown from the CBECS data. For this

analysis, DOE characterized any reference to tinting as equivalent to standard gray- or greentinted glass. These represent the most common commercial glass tints in use. 8

An important step in estimating both the U-factor and the SC for glazing is determining

whether a low-emissivity (low-e) surface is used in the glazing. This attribute is not
6-22

characterized by the CBECS95 data, and would typically be found only in newer construction

(post 1980), or in replaced windows. Current estimates are that approximately 20 percent of the

glazing sold for commercial buildings has low-e coating. 8

 Virtually all of this low-e coated

glass is dual-pane to ensure longevity of the low-e coating. For this analysis, DOE assumed a

linear growth for the commercial low-e glass market, from an initial product availability in 1985

through providing 20 percent of the double-pane market in 2000. Table 6.2.12 shows the

Department’s estimates of the probability of any double-pane window constructed between 1985

and 1995 having a low-e coating.

Table 6.2.12 Estimated Fraction of Low-E Glass in Commercial Fenestration

Year of

Construction

Low-E Fraction

1985 1.2%

1986 2.4%

1987 3.5%

1988 4.7%

1989 5.9%

1990 7.1%

1991 8.2%

1992 9.4%

1993 10.6%

1994 11.8%

1995 12.9%

Because low-e glazing generally helps to reduce heating loads in a building, the

Department assumed that it is not used in the southernmost climate zone identified by CBECS

(>2000 cooling degree-days (CDD), <4000 heating degree days (HDD). The result of the

application of this approach resulted in only a few percent of the buildings actually modeled

having low-e glazing.

The CBECS information with respect to the presence of awnings provided virtually no

detail on the size or shading capability of awnings. Because no other source of information on

this capability could be identified, DOE did not address this variable in this analysis.

The Department created a table of U-factor and SC combinations for each possible

occurrence of tinted glass or multi-pane windows. It assigned the presence of low-e coatings on

multipane glazing using a probability curve based on the market fractions for low-e glazing

reported in Table 6.2.12. Table 6.2.13 shows the selected U-factor and SC combinations.
6-23

Table 6.2.13 Window U-Factor and Shading Coefficient Combinations

Multipane Tinted or

reflective glass

Low-E Assumed Characteristics

Window U-Factor Window SC Glass Type

Yes Yes Yes 0.59 0.48 Double Glazed, Grey Tint,

e=0.10, 1/4inch space,

SHGC=0.42

Yes Yes No 0.69 0.54 Double Glazed, Heat Absorbing,

1/4inch space, SHGC=0.47

Yes No Yes 0.59 0.57 Double Glazed, Clear, e=0.10,

1/4inch space, SHGC=0.50

Yes No No 0.69 0.8 Double Glazed, Clear, 1/4inch

space, SHGC=0.70

No Yes No 1.13 0.68 Single Glazed, Heat Absorbing,

SHGC 0.59

No No No 1.13 0.94 Single Glazed, Clear, SHGC

0.82

6.2.5.2 Window-Wall Ratio 

The Department assigned a WWR to each of the 1033 buildings, using a random

assignment based on a weighted distribution derived from the CBECS92 data set for each of the

six building categories. The first weighted random assignment determined which WWR bin was

assigned to the building. The second random assignment determined the actual WWR assigned

to the building. 

No information on the WWR was available in the CBECS95 data. However, in the

CBECS92 data, a “percent glass on exterior” variable was reported for each building in one of

the five bins shown below: 

C 10 percent or less

C 11–25 percent

C 26–50 percent

C 51–75 percent

C 76 –100 percent

The Department extracted an equivalent 1033-building set from the CBECS92 database,

using the same criteria as it did in generating the 1033-building set from the CBECS95 data. 

Using this new building set, DOE determined the frequency of occurrence of WWRs for each

building type. It used these as a proxy for the WWR bin frequency in the 1995 building set. The

Department based the frequency of occurrence of each building type in each of the WWR bins

on the total adjusted building weight (total number of buildings represented by the CBECS

building). It further differentiated three of the PBAs by building size because there was an
6-24

observed difference in WWR between large and small buildings for those building types. The

Department defined the breakpoint between small and large buildings as the building size

associated with the median of the cumulative frequency distribution of CBECS92 building

weights, ordered by building size for each building type. The median size is 6,000 ft2 for

education and office buildings, and 4,500 ft2

 for retail buildings.

Table 6.2.14 shows the weights developed from the CBECS92 data for each WWR bin

by building type and size category. After DOE assigned the WWR bin to each building, it used a

second weighted random assignment to assign a specific WWR value within each of the WWR

bins. Table 6.2.15 shows the second set of weights for this assignment.

Table 6.2.14 Assigned WWR Weight for Each PBA in the 1995 Building Set

Building Type WWR Bin

10% 11-25% 26-50% 51-75% 76-100%

Assembly 0.73 0.19 0.04 0.04 0.00

Education > 6000sf 0.43 0.41 0.14 0.02 0.00

Education # 6000sf 0.68 0.20 0.12 0.00 0.00

Food Service 0.44 0.27 0.22 0.07 0.00

Office > 6000sf 0.39 0.30 0.19 0.08 0.04

Office # 6000sf 0.57 0.24 0.17 0.02 0.00

Retail > 4500sf 0.46 0.26 0.26 0.02 0.00

Retail # 4500sf 0.57 0.24 0.17 0.02 0.00

Warehouse 0.88 0.08 0.04 0.00 0.00
6-25

Table 6.2.15 Weights for Random Assignment Within Each WWR Bin

WWR Bin Assigned WWR Weight

10% or less 0% 0.00

2% 0.10

4% 0.15

6% 0.20

8% 0.25

10% 0.30

11-25% 15% 0.33

20% 0.34

25% 0.33

26-50% 30% 0.20

35% 0.20

40% 0.20

45% 0.20

50% 0.20

51-75% 55% 0.20

60% 0.20

65% 0.20

70% 0.20

75% 0.20

76-100% 80% 0.50

85% 0.30

90% 0.15

95% 0.05

100 0.00

6.2.5.3 Aspect Ratio

The Department randomly assigned an aspect ratio to each of the CBECS buildings to

be modeled, based on a weighted distribution derived from the CBECS92 data for each of the six

building categories. The Department further split the Retail and Office buildings into a small

and large category at 4500 ft2 and 6000 ft2, respectively, using the criteria described previously

for WWR assignments. 
6-26

AspectRatio BuildingLength

BuildingWidth =

The Department calculated the actual aspect ratio for the CBECS92 data from the

reported length and width of the building. The CBECS92 buildings were also classified as

“Rectangle” or “Rect/Square with Courtyard.” The Courtyard buildings did not have the

length and width reported. The CBECS95 data did not report either the building length and

width or whether it was a courtyard building or not.

The aspect ratio is defined as the reported building length divided by the building

width:

Appendix E contains data on the frequency of occurrence of the reported aspect ratio

for each of the 18 bins for each of the six building types. Table 6.2.16 shows the total

adjusted floor area for each of the building types.

For those buildings assigned a courtyard aspect ratio, DOE calculated the aspect ratio

using the procedures described in Appendix F. 

The Department compared the distribution of aspect ratios for different-size buildings

to identify any differences due to building size. It found a notable difference in the

Education, Retail, and Office PBAs. The Department used the same size breakpoint that it

used in the WWR ratio to divide these building types into small and large categories.
6-27

Table 6.2.16 Total Building Adjusted Floor Area for each Building Type and Aspect

Ratio Bin (Millions of Square Feet)

Aspect 

Ratio Bin

Aspect Ratio

Range mm

ASM EDU FDS OFF-Lg OFF-Sm RET-Lg RET-Sm WHS

1.0 < 1.05 58.7 18.0 114.5 231.4 21.4 282.4 29.1 0

1.1 # 1.05 to < 1.15 13.8 0 12.8 28.8 18.1 61.0 1.7 0

1.2 # 1.15 to 1.25 0 4.1 10.5 61.7 0 5.2 10.1 21.4

1.3 #1.25 to 1.35 44.0 74.5 49.9 232.4 8.3 94.4 29.6 0

1.4 #1.35 to 1.45 39.7 0 62.4 72.8 60.1 174.5 3.8 0

1.5 #1.45 to 1.55 27.5 30.2 25.3 166.1 7.2 181.8 0 0

1.6 #1.55 to 1.65 0.8 85.2 6.8 5.1 17.1 60.6 9.8 0

1.7 #1.65 to 1.75 1.7 34.6 0 93.1 123.4 151.6 7.8 81.6

1.8 #1.75 to 1.85 99.6 0 0 0 0 159.1 0 0

1.9 #1.85 to 1.95 0 45.6 0 16.8 90.3 58.9 0 0

2.0 #1.95 to 2.05 89.8 4.2 47.9 197.9 56.7 385.7 7.3 59.0

2.33 #2.05 to 2.75 87.7 83.0 87.9 401.3 99.7 640.2 17.4 5.2

3.0 #2.75 to 3.25 66.5 1403 91.7 192.0 25.7 215.5 9.0 7.8

3.5 #3.25 to 3.75 7.5 19.7 9.8 113.2 0 295.8 1639 4.8

4.0 #3.75 to 4.25 14.7 81.0 24.3 50.2 15.4 203.0 17.8 5.8

4.5 #4.25 to 4.75 104.9 0 0 72.7 1.6 176.6 21.5 0

5.0 #4.75 to 5.25 4 24.5 31.8 33.9 15.8 22.9 11.5 0

10.0 #5.25 to 15.5 162.5 315.3 70.4 197.3 2.9 345.9 14.2 55.0

20.0 #15.5 to 25.5 13.4 62.5 0 214.8 1.6 187.6 0 22.4

30.0 #25.5 to 35.5 1.9 9.3 0 11.3 0 119.6 0 41.7

40.0 #35.5 0 0 0 0 0 95.0 0 0

6.2.5.4 Schedules and Load Densities

Modeling the commercial buildings and equipment using the BLAST simulation tool5

required assumptions about the buildings’ internal loads, key envelope characteristics,

occupancy characteristics, and ventilation rates, and the heating, ventilating, and

air-conditioning (HVAC) and service water-heating (SWH) equipment operation schedules. 

This section describes the building characteristics that DOE used to estimate the loads for the

representative building types selected for the technical analysis. 

For heating and cooling equipment, the annual energy use is primarily a function of

the heating or cooling loads the equipment must meet. These loads can vary by hour of the

day, day of the week, and time of the year. The variations are driven by factors such as the
6-28

type of building in which the equipment is installed, the activities and internal loads (lighting,

occupant, and receptacle loads) in the building, and the building’s internal and external

environmental conditions, ventilation rates, and HVAC control strategy. 

Lighting and Plug Load Profiles. Lighting and plug loads represent a significant

fraction of building internal loads for many representative building types. Typically, these

loads are represented by a peak power density (in watts per square foot or watts per

conditioned zone being modeled) and a profile that describes the hourly magnitude with

respect to the peak (as shown in Figure 6.2.2). These profiles are typically “hat-shaped” with

a “crown” representing the peak period and a “brim” representing the off-hours (base-load). 

In between the “crown” and “brim” is a transition period representing the period between

when the light and plug loads first start to increase and when these loads are fully “on” during

the “crown” period. The significant characteristics of the profile are the duration of the

“crown,” the duration of the transition period, and the relative magnitude of the off-hour

“brim” with regard to the peak.

The peak load and profile are unique for each building, but much similarity exists for

buildings of the same type and occupancy. While each building is unique, building

performance researchers often choose a representative description of the lighting and plug

loads for simulation purposes. In the energy-efficiency standards arena, ASHRAE’s SSPC

90.1 has developed several typical profiles for use in their energy cost budget (ECB)

compliance method, which estimates the annual energy consumption of a design building. 

These profiles, while not representative of any particular building, reflect the professional

judgment of the members of the committee about the appearance of plug and light profiles for

various building types. This judgment is influenced by another major source of information: 

metered data from individual buildings. These data are available from several sources and are

usually collected by utilities or government agencies for use in energy conservation and

demand-side management programs. Some of them are discussed below.
6-29

Adjusted Detailed Analysis Schedule

0

0.2

0.4

0.6

0.8

1

1.2

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour-Of-Day

Fraction of Full Load

Weekday

Retail L M-F

Figure 6.2.2 Typical Lighting Schedule for a Retail Store

Open Monday Through Friday

The End-Use Load and Consumer Assessment Program (ELCAP) developed profiles

from metered data for several commercial building types.9, 10, 11 Taylor and Pratt (1989)10

provide average profiles for several building types, Taylor (1992)9 provides more detailed

average information for five building types, and Taylor and Pratt (1990)11 provide similar

information for individual retail and office buildings. Kasmar (1992)12 provides useful profile

information for several building types. Metering the DOE Headquarters Building by the

Federal Energy Management Program (FEMP) provided plug and light profiles for this

building, as documented in Halverson et al (1994).13

The lighting and plug load profiles in Standard 90.1-1989 (ASHRAE 1989a) were

modified through ASHRAE’s addenda process. ASHRAE’s proposed Addendum 90.lj to

Standard 90.1-1989 (referred to as “Addendum 90.1j”) revises several simulation profiles by

increasing the magnitude of off-hour loads. The modified ASHRAE load shapes were the

basis for the previous screening analysis work2

 and, therefore, are the basis for this analysis,

as well as for the six representative building types.

For the screening analysis,2

 PNNL developed lighting and equipment load profiles

from the profiles recommended by ASHRAE for each of the building types considered. 

Where metered data were available,11 PNNL used observed peak and base loads to scale the

profiles to reflect loads observed in “real” buildings. For the details on how the profiles were

adjusted using the peak and base loads, refer to the screening analysis report.2
6-30

For this analysis, the peak densities remained unchanged compared to the Screening

Analysis. However, DOE adjusted the profiles as necessary to the duration of “on” periods to

reflect typical occupancy patterns observed in the CBECS data.3, 4

Building Schedules. The lighting, plug load, HVAC, and occupancy schedules in a

building differ widely by building type. Although there could be some variations in schedules

within the same class of buildings, for this analysis DOE used an average schedule to

represent a single building type. The analysis of the CBECS954 database indicated that there

are five distinct building schedules: (1) open 24 hours a day and seven days a week (24/7);

(2) open business hours Monday through Friday; (3) open business hours Monday through

Saturday; (4) open business hours Monday through Friday and Sunday; (5) open business

hours all week. The Department computed the average number of hours a building is open

each week by further analyzing the CBECS95 database for each of the five schedule types. 

These hours are tabulated in Table 6.2.17. The Department also computed the number of

buildings in each of the five schedule categories by building type; these are listed in Table

6.2.18. 

Table 6.2.17 Number of Hours a Building is Open Each Week 

Building Type 24/7

(247)

MondayFriday

(M-F)

Monday -

Friday and

Saturday

(MFS)

Monday -

Friday and

Sunday

(MFU)

Monday -

Friday and

Saturday and

Sunday

(ALL)

Assembly 168 45 49 36 83

Education 168 48 79 49 71

Food Service 168 77 88 NA 91

Office 168 48 61 45 86

Retail 168 45 59 NA 80

Warehouse 168 48 63 94 84

Table 6.2.18 Number of Sample Buildings in Each Schedule Category

Building Type 24/7

(247)

MondayFriday

(M-F)

Monday -

Friday and

Saturday

(MFS)

Monday -

Friday and

Sunday

(MFU)

Monday -

Friday and

Saturday and

Sunday

(ALL)

Assembly 1 15 9 5 35

Education 1 70 11 5 8

Food Service 3 2 7 NA 41

Office 40 202 92 3 10

Retail 30 8 76 NA 277

Warehouse 6 19 5 2 1

Total 86 316 202 15 372
6-31

Adjustment of Modified ASHRAE Profiles. Using the modified ASHRAE profiles

used for the screening analysis work2 and the number of hours the buildings are open each

week (see Table 6.2.17), DOE generated new profiles for lighting, plug loads, and occupancy. 

The steps followed in the adjustment process are listed below:

C First, DOE estimated the number of hours the building is open each day. For example,

the total number of open hours for the schedule category "ALL" is 100. The number

of hours the building is open each day is estimated by dividing 100 by 7 (number of

days in the "ALL" category), which equals 14.2. The Department rounded the result

off to the nearest hour—in this example, the result is 14. For this schedule category,

the building must be open 14 hours a day to meet the weekly open hours. 

C Second, because it was adjusting pre-existing profiles, DOE selected a schedule

threshold value (possible values between 0 and 1.0) to be used in determining whether

the building was open. A schedule threshold is defined as a level or value above

which the schedule is considered "open" and counted toward the number of hours the

schedule is “open.”

C Third, DOE chose the start time (i.e., when the building is occupied). In almost all

cases, DOE used the start times from the modified ASHRAE profiles. 

C Fourth, DOE estimated the number of open hours using the threshold value established

in the second step by counting the hours when the schedule threshold was exceeded. If

the number of hours was greater than the hours established in the first step, DOE

stretched the modified ASHRAE profile to provide longer hours of operation; for

fewer hours of operation, DOE shrunk the profile.

Appendix G contains tables of the schedule profiles for each of the six building types

and five schedule types.

Lighting Densities. The lighting peak power densities were identical to the values

used for the screening analysis.2 Because of the importance of lighting energy in commercial

building loads and the rapid changes in lighting technology, DOE based the lighting peak

densities on a projection of future lighting power. This section provides a brief description of

how DOE established those values.

For each representative building, DOE estimated the lighting density the building

would use in the year 2015. The Department derived the year 2015 lighting power density

estimates using a combination of interior space type lighting models and estimated future

lighting technology and application trends. This combination allowed the estimates to be

based on individual components of a lighting power density (design elements, technologies,

and application where known) rather than an escalation of any existing power density values. 

A major input in developing the 2015 estimates was the estimated market shares of

electronic and magnetic fluorescent lamp ballasts in 2015. The Department developed these
6-32

estimates using the national energy savings (NES)l model with a 2027 base case. The

Department used the ballast model and an internal Lawrence Berkeley National Laboratory

(LBNL) spreadsheet, developed to supplement the NES, to run energy-efficiency standards

scenarios to estimate magnetic ballast shipments. The Department assumed that the 2027

base scenario covered renovated buildings whose magnetic ballasts would be removed at a

fixture turnover rate of once every 16 years. In accordance with the LBNL spreadsheet, DOE

added to the electronic ballasts in the existing stock a number of new magnetic ballasts each

year, as projected by the NES model; this number goes to zero by a certain year under the

standard. The Department assumed that the new building growth rate would be 1.07 percent

per user; it based this assumption on projections of historical commercial and industrial floor

space (including Federal buildings) from 1980 to 2030, and data developed by building type

by Regional Economic Research, Inc. (RER), San Diego, California.14

The results show that, by 2015, magnetic ballasts are likely to make up 30 percent of

the total fluorescent lamp ballast market under the anticipated standards. Fluorescent lamp

systems represent 77 percent of lighted floor space, according to CBECS95.3

The interior space models used in this derivation were the same as those developed

for the ASHRAE Standard 90.1-1999 lighting standards. Lighting design professionals and

practitioners consider these models to represent good quality and innovative lighting design. 

Although these models do not represent the mix of existing building lighting designs, interest

in good quality design and human factor effects of the workplace environment are expected to

dominate much of the building stock by 2015. By 2015, a portion (estimated up to 16

percent) of the building stock will consist of new buildings constructed with these design

elements in mind. Further, a larger portion of the existing building stock is expected to be

retrofitted with partially- or fully-redesigned lighting. This redesign would be primarily

caused by replacing degraded 15+ year-old systems, and building redesigns for other

purposes, such as new tenants and changing building space functions.

The ASHRAE Standard 90.1-1999 lighting standard models also include specific

characteristics appropriate for year 2015 estimation. These models incorporate the upper

range of energy-efficient equipment and ignore equipment that is generally on the trailing

edge of the market as it is replaced with newer, more efficient, and upgraded versions. The

models use the current leading-edge T8 fluorescent lamp technology as the basis for all linear

fluorescent applications. The current trend in the development of smaller, cheaper, better

compact fluorescent lamp (CFL) products could lead building lighting design and retrofits

toward the application of CFLs in most previous incandescent applications. The models

automatically incorporate compact fluorescent technology wherever it is possible to replace

incandescent systems. The models also incorporate halogen lamps as replacements for

incandescent lamps, and small metal halide lamps as replacements for some fluorescent lamp

applications where appropriate (e.g., retail buildings).

The Department did not consider the impact of building lighting controls. Because

the installed wattage would not change, no data existed to support an estimate of the impact of
6-33

lighting controls on overall HVAC system performance. The HVAC systems would likely be

designed for the full (undimmed) lighting load.

Table 6.2.19 lists the lighting power densities used for the six representative building

types. Brief descriptions of the profiles and the power densities follow.

Table 6.2.19 Peak Lighting Power Densities for Each of the Six Building Types

Building Type Power Density (W/ft2

)

Assembly 1.59

Education 1.45

Food Service 1.75

Office 1.32

Retail 1.88

Warehouse 1.19

Building Type: Assembly

Discussion of Profile: Assembly includes museums, dance halls, auditoriums,

gymnasiums, sports facilities, and churches. The assumed assembly building lighting profile

is hat-shaped, with a peak lighting integrity in 2015 estimated at 1.59 watts per square foot

(W/ft2

). Metered data were not available for this building type.

Building Type: Education

Discussion of Profile: Education includes school buildings. The typical school

building lighting profile is hat-shaped. Usually, the lights are either all on or all off in a

school building. Note that school buildings include elementary schools that may be expected

to operate from 8 a.m. to 5 p.m., secondary schools that may have significant extracurricular

activities at night, and colleges and universities that may have evening classes. This building

type is highly varied.

Measured peak load estimates range from 0.9 W/ft2

 to 2.3 W/ft2, with most estimates

falling between 1.4 W/ft2 and 2.3 W/ft2. Off-hour base-load estimates range from 0 W/ft2 to

0. 1 W/ft2

, with 0. 1 W/ft2 providing a representative average. The estimated peak lighting

intensity in the year 2015 was 1.45 W/ft2.
6-34

Building Type: Food Service

Discussion of Profile: The typical restaurant building lighting profile is hat-shaped,

with all lights either on or off. The lights are on when food is being served. Obviously, a

24-hour restaurant will have a much different profile than a lunch counter that serves only

breakfast and lunch.

Measured peak-load estimates range from 1.2 W/ft2 to 2.0 W/ft2. Off-hour base-load

estimates range from 0.14 W/ft2 to 0.8 W/ft2. The estimated peak lighting intensity in the year

2015 was 1.75 W/ft2.

Building Type: Office

Discussion of Profile: The typical office building lighting profile is the classic hat

shape, with a single peak period occurring for most of the working day and a lower off-hour

period. The peak period is typically six to ten hours in duration. A transition period between

the peak and off-hour period is typically one to three hours, depending on occupant behavior

and lighting control schemes. Office buildings are typically active Monday through Friday,

with minimal activity on Saturdays and even less on Sundays.

Measured peak working day estimates range from 0.7 W/ft2

 to 1.9 W/ft2, with most of

the estimates falling in the range of 1.3 W/ft2 to 1.5 W/ft2. The base off-hour load estimates

range from 0 W/ft2

 to 0.6 W/ft2, with most metered estimates in the range of 0.2 W/ft2 to 0.4

W/ft2. The estimated peak lighting intensity in the year 2015 was 1.32 W/ft2.

Building Type: Retail

Discussion of Profile: The typical retail building lighting profile is hat-shaped. 

Usually, the lights in a retail building are on if the business is open and off if the business is

closed. The peak period is typically 10 hours in duration. Retail buildings are typically active

all days of the week, with reduced hours on Sundays.

Peak lighting load estimates range from 1.1W/ft2

 to 2.9 W/ft2, with metered results

tending to fall in the lower part of the range. Off-hour base-load estimates range from 0 W/ft2

to 0.3 W/ft2, with metered data indicating 0.1 W/ft2 to 0.3 W/ft2. The estimated peak lighting

intensity in the year 2015 was 1.32 W/ft2

.

Building Type: Warehouse

Discussion of Profile: The typical warehouse building lighting profile is hat-shaped. 

Usually, the lights in a warehouse are on if the business is open and off if the business is

closed. Weekends show minimal loads.
6-35

The peak load estimates range from 0.4 W/ft2 to 0.7 W/ft2, with most values falling

near 0.6 W/ft2. Off-hour base-load estimates range from 0 W/ft2 to 0.2 W/ft2, with 0.1 W/ft2

representing a suitable average. The estimated peak lighting intensity in the year 2015 was

1.19 W/ft2

.

Plug Load Densities. The plug load power densities for six representative building

types are shown in Table 6.2.20, and a brief description of the profiles and the power densities

follow.

Table 6.2.20 Peak Plug Load Power Densities for Each of the Six Building Types

Building Type Power Density (W/ft2)

Assembly 0.19

Education 0.48

Food Service 1.20

Office 0.64

Retail 0.40

Warehouse 0.15

Building Type: Assembly

Discussion of Profile: The typical assembly building plug load profile is hat-shaped.

Metered data were not available for this building type. The assumed peak and

off-hour base loads for the screening analysis were 0.19 W/ft2

 and 0.01 W/ft2, respectively.

Building Type: Education

Discussion of Profile: The typical education building plug load profile is

hat-shaped.

Because the sample size for metered data was very small, the proposed Standard

90.1-1999 profiles and associated peak and off-hour base loads were used for the screening

analysis. The peak and off-hour base loads for the screening analysis were 0.475 W/ft2

 and

0.01 W/ft2

, respectively.

Building Type: Food Service

Discussion of Profile: The typical food service building plug load profile is

hat-shaped. Obviously, a 24-hour restaurant will have a much different profile than a lunch

counter that serves only breakfast and lunch.
6-36

The screening analysis used the ASHRAE peak and off-hour base-load values. The

peak and off-hour base loads for the screening analysis were 1.2 W/ft2 and 0.5 W/ft2,

respectively.

Building Type: Office

Discussion of Profile: The typical office building plug load profile is the classic hat

shape, with a single peak period occurring for most of the working day and a lower off-hour

period. The peak period is typically six to ten hours. A transition period between the peak

and off-hour period is typically one to three hours, depending on occupant behavior. Office

buildings are typically active Monday through Friday, with minimal activity on Saturdays and

even less on Sundays.

Peak-load estimates range from 0.2 W/ft2

 to 0.8 W/ft2, with most falling in the range

of 0.6 W/ft2 to 0.8 W/ft2. Off-hour base-load estimates range from 0 W/ft2 to 0.4 W/ft2, with

many falling near 0.3 W/ft2

. The peak and off-hour base loads for the screening analysis were

0.64 W/ft2

 and 0.3 W/ft2, respectively.

Building Type: Retail

Discussion of Profile: The typical retail building plug profile is hat-shaped.

Peak-load estimates range from 0.2 W/ft2 to 0.6 W/ft2. Off-hour base loads range

from 0 W/ft2

 to 0.2 W/ft2. The peak and off-hour base loads for the screening analysis were

0.4 W/ft2 and 0.1 W/ft2, respectively.

Building Type: Warehouse

Discussion of Profile: The typical warehouse building plug load profile is

hat-shaped. 

Metered data indicate that relatively little difference exists between the peak and

off-hour base plug loads in a warehouse, so the peak and off-hour base loads for the screening

analysis were 0.15 W/ft2

 and 0.1 W/ft2, respectively.

Building HVAC Operation and Occupancy Assumptions. For the BLAST loads

calculations, DOE assumed the occupant activity level to be 425 British thermal units per

hour (Btu/h) per person for all building types, except food services and warehouses. For food

services, DOE assumed the activity level to be 550 Btu/h per person; for warehouses, the

activity level was 1,000 Btu/h per person. The Department derived these values from data in

the ASHRAE Fundamentals Handbook 1993. It did not vary the occupant activity levels with

climate.
6-37

6.2.5.5 Ventilation and Infiltration

The Department based the ventilation rate assumptions on the acceptable ventilation

rates provided by ASHRAE Standard 62-2001 15 (Table 2) for each building type. The

Department entered the ventilation for each building into the Parm files as estimated cubic

feet of outdoor air per minute, per square foot of building floor area (cfm/ft2).

For two of the building types (Retail and Warehouse), the ventilation rate in ASHRAE

Standard 62-2001 is based on defined quantities of outdoor cfm/ft2; DOE directly translated

these into the parameter files for its simulations. Conversations with former and current

ASHRAE Standard 62 Chairmen both confirmed that this was a reasonable interpretation of

the ASHRAE Standard 62 requirements for these spaces. For the remaining four building

types (Office, Food Service, Education, and Public Assembly), the ventilation rates are

prescribed in terms of cfm/person, and therefore an estimate of the ventilation rate must be

based on an estimate of the space occupancy.

The CBECS provides information on the number of workers during the peak shift for

all building types; however, this value reasonably reflects building occupancy for only one of

the building types: Offices. For the other buildings types (e.g., Retail), occupancy is not

driven by workers, but by other users of the building (e.g., customers, students, or the general

public). The Department based occupancy estimates used for establishing ventilation rates for

the other building types on ASHRAE Standard 62-2001 estimates of peak space

occupancy/1000 ft2

 of “net occupiable space,” shown in Table 2 of ASHRAE Standard 62-

2001. As defined by ASHRAE Standard 62-2001, net occupiable space is the floor area of an

occupiable space defined by the inside surface of its walls but excluding shafts, column

enclosures, and other permanently enclosed, inaccessible, and unoccupiable areas. 

Obstructions in the space such as furnishings, and display or storage racks, whether temporary

or permanent, may not be deducted from the space area. Hence, net occupiable space is

roughly equivalent to building floor area for most of the building types examined. For the

purposes of this study, DOE equates net occupiable space with building floor area.

Issues develop when estimating the design occupancy. ASHRAE Standard 62-2001

states explicitly that, where occupant density differs from that in Table 2, the designer should

use the per-occupant ventilation rates for the anticipated occupancy load. However, in most

cases, the building designer still must determine the anticipated occupancy load for the space. 

Different sources for estimates of the design occupancy that are used by engineers for

ventilation design include: recommended ASHRAE Standard 62 peak occupancies, fire code

exit occupancies, peak occupancies defined by other code minimums (e.g., California’s Title

24), and estimated peak occupancy loads from the building owner. Previous consultation with

design engineers suggested that the ASHRAE Standard 62-2001 peak occupancies are likely

the most representative of current engineering design practice, unless other building codes

have predefined more specific methodology for establishing occupancy-based ventilation

rates (as is done in Title 24). 
6-38

The ASHRAE Standard 62-2001 ventilation design occupancies are commonly well

above the reported CBECS workers-per-peak-shift requirements for office buildings, and are

generally above the average daily occupancy rates used in the development of the occupant

load schedules (see discussion of schedules in Section 7.2.6.5). Concern over this

discrepancy led to further discussion of the use of the ASHRAE Standard 62-2001 occupancy

estimates with other professional design engineers. Follow-up conversations with practicing

engineers confirmed that the ASHRAE Standard 62-2001 design occupancies are likely the

most common occupancy estimates used by design professionals today, particularly given the

current attention paid to indoor air quality issues. 

It is important to define the difference between the occupancy rates used in

establishing the ventilation requirements for the building (and thus the ventilation-driven

loads on the HVAC system) and the occupancy rates used in establishing the occupancydriven thermal loads on the building. The Department used the occupancy schedules for the

building simulation to drive the occupant thermal (and latent) load additions to the space in a

way that reasonably estimates those loads for different hours of the year. However, the

occupancy rates used in establishing the ventilation requirements are “design” values that

define the required ventilation capability in the design. The Department assumes that this

ventilation level is fixed for the year and does not vary with occupancy. Control systems do

exist that would allow variation of the ventilation level in the space, dependent on occupancy

(demand control ventilation), but DOE has not examined them in this study.

In addition, it is important to note that ASHRAE Standard 62-2001 provides for

adjustments for space types that are occupied intermittently. If the occupancy peak is

expected to last less than three hours, the outdoor air-flow rate may be determined on the

basis of average occupancy, provided that average occupancy used for the determination of

ventilation is not less than one-half the maximum. Space types such as auditoriums and food

service buildings can take advantage of average occupancy considerations to reduce the

ventilation requirements.

Table 6.2.21 shows the ventilation rates used in the study for each building type. The

strategy used to set ventilation rates for each building type is discussed below.

Table 6.2.21 Ventilation Rates Used for Each Building Type (cfm/ft2

)

Building Type Ventilation Rate (cfm/ft2

)

Assembly 0.890

Education 0.450

Food Service 0.600

Office 0.140

Retail Avg 0.283; Range 0.210 to 0.300 

Warehouse 0.050
6-39

Office Building

The Department based the ventilation rate on a typical office building described by

ASHRAE Standard 62-2001. The ASHRAE Standard 62-2001, Table 2, lists four space types

under “Offices,” one being “Office Space” with a maximum occupancy of seven

persons/1000 ft2 net occupiable space, and three being “Reception Areas,”

“Telecommunication Centers/Data Entry Areas,” and “Conference Rooms” for higher-density

occupancy (50–60 persons/1000 ft2

 net occupiable space). For all but reception areas,

ASHRAE Standard 62-2001 recommends 20 cfm/person for ventilation. For the reception

areas, it recommends 15 cfm/person. For the purposes of this study, DOE used an average

occupancy of seven persons /1000 ft2

 to set the ventilation requirements for all of the office

buildings examined. Using 20 cfm/person results in an average ventilation rate of 0.14

cfm/ft2

. There are areas of a building that require higher ventilation and areas that require

lower ventilation, but based on the previously noted discussions with practicing engineers, it

was apparent that assuming the entire space as “office” would provide ventilation rates

reasonably representative of building totals without requiring detailed space-by-space

breakdowns. The ventilation rate chosen for this analysis is the same ventilation rate that was

used for office buildings in the original 1994 ASHRAE analysis of the cost-effective

efficiency levels for cooling equipment, based on the ASHRAE 90.1 Mechanical

Subcommittee’s professional judgement.

Education Buildings

ASHRAE Standard 62-2001 has a category for education buildings where it lists the

space types shown in Table 6.2.22.
6-40

Table 6.2.22 ASHRAE Standard 62-2001 Data for Education Building Spaces

Education Space Type Peak Occupancy (Person/ft)2* cfm/person Calc. cfm/ft2*

Classroom 50 15 0.75

Laboratories 30 20 0.6

Training Shop 30 20 0.6

Music Rooms 50 15 0.75

Libraries 20 15 0.3

Locker Rooms** 0.5

Corridors** 0.1

Auditoriums 150 15 2.25

Smoking Lounges 70 60 4.2

* Net occupiable space, no modification for intermittent occupancy.

** Only cfm/ft2

 value provided in ASHRAE Standard 62-2001

Most of the space of a typical elementary school is composed of classrooms, followed

closely by corridors, libraries, and office space. Secondary schools are more likely to have

other facilities, such as training shops, music rooms, and science laboratories. Separate

auditoriums also become more prevalent in secondary schools. Elementary schools often

have smaller gymnasium spaces that also serve as auditoriums or cafeterias. 

The Department did not identify a typical breakdown of space types for the entire

education category for this work. However, discussions with designers revealed that,

frequently, corridor spaces in elementary schools did not receive significant separate

ventilation, because these were temporary-use spaces (e.g., when students were in corridors

they were not in classroom). Typical average ventilation requirements for school buildings

reported by designers appeared to be in the 0.3–0.7 cfm/ft2

 range. To provide a reasonable

estimate, DOE used the following methodology: A simple average of the “teaching-like

spaces” in Table 6.2.22 (Classrooms, Laboratories, Training Shops, Music Rooms, Libraries)

yields a typical ventilation requirement of 0.6 cfm/ft2

. The Department assumed that

approximately 70 percent of the building floor space is composed of spaces such as this. The

remaining 30 percent is composed of corridors or office space, for which DOE assumed a 0.1

cfm/ft2

 value. An area-weighted average of these yields a typical ventilation rate of 0.45

cfm/ft2 for the building, which falls in the range of values reported by design professionals. 

The Department used this value for the air-conditioning loads analysis.

Food Service Buildings

The ASHRAE Standard 62-2001 has a category for food service buildings where it

lists the space types shown in Table 6.2.23.
6-41

Table 6.2.23 ASHRAE Standard 62-2001 Data for Food Service Building Spaces

Food Service Space Type Peak Occupancy (Person/ft2) * cfm/person Calc. cfm/ft2*

Dining Rooms 70 20 1.40

Cafeteria/Fast Food 100 20 2.0

Bars/Cocktail Lounge 100 30 3.0

Kitchen/Cooking 20 15 0.3

* Net occupiable space, no modification for intermittent occupancy

The ASHRAE Standard 62-2001 notes that exhaust flow from a kitchen should

typically be approximately 1.5 cfm/ft2. This is important because a common ventilation

strategy for a restaurant (particularly a fast food restaurant) is to supply virtually all the

outside air into the dining area, and exhaust virtually all of the building through the kitchen

cooking hoods. A second common strategy is to bring in unconditioned air to provide for any

needed makeup air to the cooking hoods. Both of these strategies make understanding food

service ventilation more complicated than simply providing an area-weighting of the space

types shown above. 

For the purposes of this study, the Department estimated restaurant ventilation levels

assuming two different scenarios. The first scenario was a fast food building with

approximately 60 percent dining area and 40 percent kitchen; the second scenario was a sitdown type restaurant with 80 percent dining area and 20 percent kitchen. In both cases, DOE

assumed that the ventilation for the kitchen space was not provided by the primary spaceconditioning unit for that space (e.g., ventilation loads on the building come through the

dining space ventilation requirements). The Department also assumed that the peak

occupancy periods would be of short enough duration to take advantage of a 50 percent

reduction in flow rate. This practice results in: 

Building ventilation = Dining area space fraction × Dining area occupancy

density × Ventilation rate/person × 50 percent reduction

for intermittent use

For the fast food restaurant:

Ventilation = 60% × 100 persons/1000 ft2

 × 20 cfm/person × 50% = 600 cfm/1000 ft2

For the sit down restaurant:

Ventilation = 80% × 70 persons/1000 ft2 × 20 cfm/person × 50% = 560 cfm/1000 ft2

Both of the above examples would result in kitchen exhaust rates that would meet the

ASHRAE Standard 62-2001 1.5 cfm/ft minimum. For the purposes of this analysis, the

Department adopted a typical exhaust rate of 0.6 cfm/ft2 for all food service buildings.
6-42

Assembly Buildings

Assembly buildings are a tremendously diverse group. However, in general, they are

characterized by the need to respond to very high and generally intermittent peak occupancy

rates. Table 6.2.24 shows a list of typical assembly building space types from ASHRAE

Standard 62-2001.

Table 6.2.24 ASHRAE Standard 62-2001 Data for Assembly Building Spaces

Assembly Building Space Type

Peak Occupancy

(Person/ft2

)* cfm/person Calc. cfm/ft2*

Theater Lobby 150 20 3.0

Auditoriums 150 15 2.25

Stages, Studios 70 15 1.05

Bowling Alleys 70 25 1.75

Ballrooms 100 25 2.5

Playing Floors (Gymnasium) 30 20 0.6

Swimming Pools** 0.5

Ice Arena** 0.5

Game Rooms 70 25 1.75

Spectator Areas 150 15 2.25

Transportation Waiting Room 100 15 1.5

Platforms 100 15 1.5

* Net occupiable space, no modification for intermittant occupancy

** Only cfm/ft2

 value provided in ASHRAE Standard 62-2001

An examination of all the assembly building types with an occupancy listed shows an

average peak occupancy from ASHRAE Standard 62-2001 of 99 persons/ft2. Ventilation rates

vary from 15 to 25 cfm/person. No particular breakdown of these space types in the general

population is known, nor is this a complete list of all assembly building space types. Again,

DOE used a simple strategy to estimate the typical ventilation rate that might be experienced

by assembly buildings under ASHRAE Standard 62-2001. The Department estimated the 

ventilation rate for assembly buildings by assuming that, on average, 90 percent of the

building space would be available for public assembly and 10 percent would be unoccupied or

not separately vented space. The Department assumed the average ventilation rate to be 20

cfm/person, and gave all space designs a 50 percent credit for intermittent occupancy. This

results in 0.89 cfm/ft2

 for assembly spaces. The Department used this value to represent a

typical ventilation rate for assembly buildings for the purposes of this study.
6-43

Retail buildings

The ASHRAE Standard 62-2001 has straightforward requirements for retail buildings. 

Ground-level floors for retail building spaces have a requirement of 0.3 cfm/ft2 ventilation. 

Upper-floor levels of retail spaces are required to have 0.2 cfm/ft2

 ventilation rates. By using

these figures in conjunction with the number-of-floors variable published in CBECS

(NFLOORS6), the Department was able to estimate the cfm/ft2

 requirement for each retail

building in the DOE 1033-building subset. The Department used this methodology to

estimate retail building ventilation requirements. 

Warehouse buildings

The ASHRAE Standard 62-2001 has a requirement of 0.05 cfm/ft2

 for warehouse

spaces. The Department used this value for all warehouse buildings in the 1033-building set.

6.2.5.6 Economizer Use and Control Assumptions

The CBECS95 data set has a single variable (ECN6) that indicates whether the

building in question has an economizer to provide free cooling. The survey provides no

indication of the control or operation status of the economizer, or whether it is used on all

building systems. Comments from the industry indicated that enthalpy-based economizers

had been available for a long time and were a reasonable assumption for humid climates. For

the ANOPR analysis, DOE based all economizers used in the building system simulation on

differential enthalpy economizers. This control decision has relatively little impact in dry

climates, where economizers are expected to be more prevalent, but is expected to reduce

cooling loads somewhat in humid climates, in comparison with other possible economizer

control strategies.

6.2.5.7 Heating and Cooling Setpoint

The CBECS95 data set contains no information on internal building operating

temperatures, but does contain a variable that indicates the reduction of heating in off hours

(RDHTNF6) or reduction in cooling during off hours (RDCLNF6). No indication is provided

as to whether this reduction is achieved via thermostatic setback or setup or equipment

shutdown. For the purpose of this analysis, the Department assumed a 70°F heating setpoint

and a 75°F cooling setpoint during occupied hours. If a reduction of heating in off hours was

indicated, DOE assumed a 10°F temperature setback (to 60°F) for off hours. If a reduction of

cooling during off hours was indicated, DOE assumed a 10°F temperature setup (to 85°F) for

off hours. If neither heating nor cooling reduction was indicated during off hours, the

Department assumed occupied-hour control setpoints for all hours.
6-44

6.2.5.8 Equipment Sizing

The term “sizing” refers to the method used to determine the cooling capacity for air

conditioning and the heating capacity for heating equipment selected for use in serving a

given building thermal zone. It is commonly a two-step process. The first step is the

mathematical determination of the cooling or heating load on a given zone. This is commonly

done by calculating the thermal load for a set of environmental conditions. The second step is

the selection of equipment that will meet the calculated design thermal load from the discrete

equipment capacities available for purchase.

Design-Day Conditions. For the ANOPR analysis, DOE calculated thermal loads for

sizing using the “design systems” algorithm built into BLAST. These algorithms create

cooling and heating “design days” based on user entries for summer and winter design

conditions. The data needed for the design days include the maximum and minimum

temperatures for the day, coincident wet bulb temperature, barometric pressure, the wind

speed and direction, the clearness index (0.0 = total cloud cover, 1.0 = clear skies), and a

precipitation index indicating either snow or rain.

The Department created both summer and winter design days for each location, based

on available “design” temperature and weather condition assumptions. By default, BLAST

immediately creates two design days for cooling and two for heating. The first design days

are assumed to have weekday (Monday) operating schedules and the second are assumed to

have a weekend (Sunday) operating schedule. The model assumes that the summer design

days occur on July 21 and the winter design days occur on January 21. Table 6.2.25 shows

the sources for the necessary BLAST design-day weather parameters. The Department used

climate data for design-days presented in the ASHRAE Fundamentals Handbook 2001. The

column label for each variable in that table is shown in Table 6.2.25. The clearness index

variable is set to 1.0 for the summer design day and 0.0 for the winter design day, to provide

extreme conditions. 
6-45

Table 6.2.25 Development of Design-Day Data for BLAST Simulations

Design Day BLAST Design-Day Parameter Source Data*

Winter Date Jan 21

Dry Bulb (HI) 99% Heating Dry Bulb (Heat Col 2b) + 1°F

Dry Bulb (LOW) 99% Heating Dry Bulb (Heat Col 2b)

Wet Bulb Wet bulb temperature corresponding to 30% RH and 99%

Heating Dry Bulb (Heat Col 2B) + 1°F

Barometric Pressure Std Pressure (Heat Col 1f)

Wind Speed 1 % Windspeed for coldest month (Heat Col 4c)

Wind Direction Wind Direction Corresponding to 99.6% dry bulb

temperature (Heat Col 5b)

Clearness Index Set to 0 (representing extreme low clearness index and

hence solar)

Summer Date Jul 21

Dry Bulb (HI) 1% Dry Bulb (Cooling Col 2c)

Dry Bulb (LOW) 1% Dry Bulb (Cooling Col 2c) – Mean Daily Dry Bulb

Range During Hottest Month (Cooling Col 5)

Wet Bulb 1% Mean Coincident Wet Bulb Condition (Col 2d)

Barometric Pressure Std Pressure (Heat Col 1f)

Wind Speed Wind Speed Corresponding to 0.4% Drybulb Condition

(Heat Col 5c)

Wind Direction Wind direction corresponding to 0.4% Drybulb condition

(Heat Col 5d)

Clearness Index Set to 1 (representing extreme high clearness index)

Annual Time Zone From TMY File

*Column references refer to column labels in ASHRAE Fundamentals Handbook 2001, Chap 27 Table 1a

Equipment Oversizing Factor. For the second step in sizing the equipment, it was

necessary to estimate how much a given system would likely be sized above that required by

the thermal loads. There is significant anecdotal evidence that many cooling systems are

sized well above what is required to meet the design thermal loads in the space. However,

just because a system may be significantly oversized in practice does not necessarily mean

that it should be modeled as such for the ANOPR analysis. DOE’s analysis depends on

balancing the first costs for higher-efficiency equipment with the energy cost savings from

higher-efficiency equipment. Increasing the efficiency of equipment that is grossly oversized

will be more expensive than increasing the efficiency of equipment that is properly sized and

this would impact the economics of higher efficiency equipment. It is clear, however, that the

discrete capacities of unitary equipment that can be purchased will result in the equipment

capacity purchased being somewhat larger than the design loads for the building. 
6-46

To provide some assessment of oversizing, DOE examined two product lines of yearround unitary packaged equipment from different manufacturers. The Department identified

ranges of discrete capacities between each product and the next-larger-capacity product for

each manufacturer, and selected the midpoint of each capacity range as the average system

size within that capacity range. The Department assumed that shipments of future products

covered under this rulemaking could be apportioned across all capacity ranges, based on the

ratio of the size of each capacity range relative to the overall capacity range for covered

products. With this simplifying assumption, it was possible to provide a weighted average of

the capacity required by the building and the average size of the units that would need to be

purchased if the purchaser stayed within a given manufacturer’s product line. Table 6.2.26

shows the results of this simplified analysis. In the case of Manufacturer A, the average of the

capacity purchased was 11.8 percent higher than the average capacity required. For

Manufacturer B, the average of the capacity purchased was 14.7 percent higher than the

average capacity required. 
6-47

Table 6.2.26 Estimate of Capacity Purchased Versus Capacity Required for Two

Manufacturers

MFG A

Capacities Available 

(kBtu/h)

Avg. Capacity within

Capacity Range (kBtu/h)

Required Capacity

Purchased (kBtu/h)

Estimated Fraction of Shipped

Products

65 68.0 71 0.036

71 79.5 88 0.102

88 89.0 90 0.012

90 105.0 120 0.180

120 125.0 130 0.060

130 134.0 138 0.048

138 159.0 180 0.251

180 206.0 232 0.311

232 NA NA NA

Wt. Average Capacity

Required

Wt. Average Capacity

Purchased

Ratio of Avg. Capacity

Purchased vs Capacity

Required

148.5 166.0 1.118

MFG B

Capacities Available 

(kBtu/h)

Avg. Capacity within

Capacity Range (kBtu/h)

Required Capacity

Purchased (kBtu/h)

Estimated Fraction of Shipped

Products

65 68.5 72 0.041

72 78.5 85 0.078

85 85.5 86 0.006

86 87.0 88 0.012

88 94.5 101 0.078

101 109.0 117 0.096

117 118.0 119 0.012

119 154.5 190 0.425

190 191.0 192 0.012

192 212.0 232 0.240

232 NA NA NA

Wt. Average Capacity

Required

Wt. Average Capacity

Purchased

Ratio of Capacity Purchased

vs Capacity Required 

148.5 170.3 1.147

Limitations on obtaining additional data—such as the sales volume of these and other

manufacturers, sales between product lines, the actual shipments by product capacity range
6-48

for manufacturers, and the frequency with which a purchaser of equipment might specify

products from different manufacturers—suggested that further investigation using this

methodology would not provide greater accuracy. Instead, DOE used the analysis to estimate

the range of oversizing that might occur due to the discrete sizes available from

manufacturers. For the ANOPR analysis, DOE selected a random oversizing factor for each

building, assuming a uniform distribution of oversizing factors between 0 percent and 25

percent of design capacity requirements. The Department applied the selected oversizing

factor to all equipment used in the building. 

6.2.6 GeoClimate Locations and TMY2 Station Assignments

The Department assigned each of the 1033 buildings meeting the criteria for inclusion

in the analysis to a GeoClimate region, defined as the union of the nine Census Divisions and

the five climate zones reported in CBECS95. The climate zones are defined by ranges of

HDD values and CDD values, as shown below:

Zone 1: <2000 CDD, >7000 HDD

Zone 2: <2000 CDD, 5500–7000 HDD

Zone 3: <2000 CDD, 4000–5499 HDD

Zone 4: <2000 CDD, <4000 HDD

Zone 5: >2000 CDD, <4000 HDD

The Department assigned each TMY2 station to a GeoClimate region, based on the

station’s HDD and CDD values and locations by city and state. This resulted in 45 potential

GeoClimate regions (nine census divisons × five climate zones). However, because some of

the climate zones do not naturally occur in some census divisions and not every GeoClimate

region had a TMY2 station, the final number of GeoClimate regions selected for this analysis

was 26. For example, Climate Zone 1, with more than 7000 HDD, is found only in census

divisions encompassing the northern tier of states or regions of the mountainous western U.S.

Thus, there is a set of TMY2 stations and CBECS buildings in each of the 26

GeoClimate regions. Within each census/CBECS cell, DOE assigned a relative weight to

each TMY2 station, based on the population of cities that can be shown to be most

climatically similar to that TMY2 location. For this, the Department used the United States

Geological Survey (USGS)’s Populated Places (PPL) database. The PPL database contains

information for more than 165,000 locations ("features," in USGS parlance). For each

feature, the database lists location, known latitude and longitude, elevation, and population

(for smaller places without a known population, the database assigns a population of one). To

map all 165,000 PPL locations to a best representative TMY2 station, DOE used a second

climate dataset of 4775 National Oceanographic and Atmospheric Administration (NOAA)

cooperative observer stations. First, DOE mapped each PPL location to the best NOAA

station; then it mapped each NOAA station to the best TMY2 station. The two-step mapping

was necessary because the PPL data have no climatic information. The NOAA stations

provided enough geographical coverage to allow mapping each PPL to a NOAA station,

knowing only location (by latitude and longitude) and elevation. The PPL-to-NOAA
6-49

mapping algorithm chose, for each PPL location, the closest NOAA station—unless that

station differed in elevation by more than 300 feet. If the difference in elevation of the closest

NOAA station was more than 300 feet, the algorithm chose, from among the next 20 closest

stations, the one with the closest elevation.

Mapping the NOAA stations to TMY2 stations was more involved and relied on

proximity, elevation, and HDD indicators. As a result of mapping a PPL to a TMY2 within

each Census/CBECS cell, the total population of NOAA stations pointing at each TMY2

station was known. Dividing the total population mapped to each TMY2 by the total

population of all TMY2s in the U.S. Census Bureau/CBECS cell gave the final relative

weights.

The Department assigned each of the CBECS buildings in the same GeoClimate

region to one of the TMY2 stations in that GeoClimate region by a random assignment

process, based on the population-weighted distribution. 

Table 6.2.27 lists the number of TMY2 stations in each of these regions, and Table

6.2.28 lists the number of CBECS buildings in the same geographic regions. A zero entry

means that the climate zone did not occur in that census division. Appendix H contains a list

of the number of buildings assigned to each TMY2 station assignment, and Appendix I shows

the TMY2 assignments for each of the 1033 buildings. The Department assigned at least one

CBECS observation to 157 of the 239 TMY2 locations.

Table 6.2.27 TMY2 Stations by Census Region and Climate Zone, Including Alaska

and Hawaii, and Excluding Puerto Rico and Guam

Census Division Climate Zone Total

12345

East North Central 14 15 1 0 0 30

East South Central 0 0 5 8 1 14

Middle Atlantic 5 8 4 0 0 17

Mountain 21 9 4 0 3 37

New England 4 4 0 0 0 8

Pacific 19 5 8 7 6 45

South Atlantic 0 1 10 13 8 32

West North Central 16 7 6 0 0 29

West South Central 0 0 3 4 18 25

Total 79 49 41 32 36 237
6-50

Table 6.2.28 Number of CBECS Buildings by Climate Zone and Census Region

Census Division Climate Zone Total

12345

East North Central 20 113 12 0 0 145

East South Central 0 0 14 53 16 83

Middle Atlantic 0 33 65 0 0 98

Mountain 5 25 0 5 16 51

New England 17 33 0 0 0 50

Pacific 8 0 26 164 12 210

South Atlantic 0 0 64 47 86 197

West North Central 21 6 28 0 0 55

West South Central 0 0 0 33 111 144

Total 71 210 209 302 241 1033

6.2.7 Simulation and Data Manipulation

This section describes the process used to simulate energy consumption for 10,330

hypothetical buildings. Given 1033 unique building descriptions, DOE modeled each at ten

different air-conditioning equipment EERs. The Department then used the output of the

simulation process to model the relevant CBECS sample.

The Department modeled each CBECS building using BLAST. The Department

developed the BLAST inputs from CBECS95 data, where available; for parameters not

available in CBECS95, DOE used random draws from distributions of possible values, and

sound engineering judgment.

The Department used additional strategies to facilitate the modeling of so many

unique buildings. First, it computed each building’s loads by simulating the same prototype

building: a three-story, 48,000 square-foot building with five thermal zones per floor. The

Department “scaled” the results of that prototype’s simulation to match the specifics of the

CBECS building in question, including conditioned floor area, aspect ratio, number of floors,

and number of thermal zones per floor. The scaling procedure was described in detail in

Barwig et. al1

 and will not be repeated here. Second, DOE programmed into the computer

software the “administrative” aspects of developing simulation input files and post-processing

output. Third, DOE managed the processing steps using a date-based dependency-checking

software tool. Finally, DOE ran the simulations on a small, 18-Central Processing Unit

(CPU) computing cluster.
6-51

The simulation/analysis process for each CBECS building has 10 steps:

1. Develop BLAST design-day input file (BLAST input file, BLIN file) for sizing

equipment.

2. Run BLAST design-day simulation.

3. Extract equipment sizing results from the BLAST design-day run output.

4. Modify the design-day BLIN files (from step 1) for an annual simulation by inserting

the equipment sizing results from steps 1–3.

5. Run BLAST annual simulation.

6. Reformat hourly BLAST output into comma-separated-value format.

7. Extract system sizing results from the annual-run BLAST output.

8. “Scale” annual-run hourly outputs and sizing results to match specific CBECS

building geometries.

9. Aggregate across building zones to get building-total results.

10. Adjust building totals to account for less-than-100 percent air-conditioned buildings

and buildings partially air conditioned with non-unitary air-conditioning equipment.

Each of the above steps is described in detail below. Most steps are straightforward

data manipulations that warrant mention only because they have to be done so many times. 

Steps 8, 9, and 10 involve the most substantive manipulations. Figure 6.2.1 shows a

simplified flow diagram of the simulation process.

Step 1 – Develop BLAST design-day input file.

Development of the 10,330 BLAST input files is facilitated by a PNNL-developed

software program known as gparm. Written in the Perl language, gparm works much like the

“mail merge” function available in many word processors. It combines a template BLIN file

containing replaceable parameter tags with parameter values held in a separate parameter file. 

The template file is in legal BLAST input language format, except for the replaceable tags. 

The parameter file is a rectangular table (in comma-separated-value format) with as many

rows are there are runs (10,330 in this case) and as many columns as there are parameters to

vary. Gparm repeatedly reads values from the next row of the parameter file, “fills in” the

template with those values, and writes out an associated BLIN file.

Appendix J shows a text listing of one filled-in BLIN file.
6-52

Steps 2 and 3 – Run BLAST design-day simulation and extract relevant results.

The Department combines each design-day BLIN file with the appropriate weather file

and simulates it using the BLAST software. The relevant results for each of the 15 zones

from this design-day simulation are supply and exhaust-air flowrates in cubic feet per minute,

heating peak, and sensible, latent, and total cooling peaks. These values are taken from the

two BLAST output reports “Fan System Parameters” and “System Load Summary.”

At this stage in the processing, fan power ratios (the ratio of fan power draw to cooling

system capacity) are also calculated and stored for later use. The peak fan power values are

taken from BLAST’s design-day output files with a “dday03" and/or “dday04" filename

extension. (The 03 and 04 represent weekend and weekday runs.)

Step 4 – Convert design-day BLIN file to annual-run BLIN file.

The Department performs annual BLAST runs with the exact same input (BLIN) files

as for design-day runs, but with the now-known system capacities inserted and the appropriate

run control statements modified. This process is automated with a Perl script (des2ann.pl).

Steps 5 and 6 – Run annual BLAST simulation and reformat hourly output.

The Department runs the annual BLAST simulation as before, and pipes the hourly

output (which comes out of BLAST in files named with a “.rwd” extension) through a

reformatting script to produce a simple, rectangular, comma-separated-value file. This file

contains 8760 rows (one per hour of the year) and 120 columns (eight output variables for

each of 15 simulated zones). The output variables collected for each zone include lighting,

equipment (electric appliances and other “internal gains”), heating and cooling space loads,

heating and cooling energy, fan power, and building-total electric.

Step 7 – Extract system sizes from annual run output.

BLAST re-estimates system sizes during the annual run, even though they were

specified in the input and taken from the design-day runs (steps 2 and 3). The annual-runbased capacities often differ slightly from the design-day-based capacities because fan

flowrates, and consequently ventilation loads, differ between the two runs. The annual-runbased capacities better reflect the system operation as simulated, so DOE collects them here

and uses them henceforth.

Step 8 – Scale annual hourly output to specific CBECS geometries.

The scaling process transforms the simulation results for the 15-zone prototype to the

specific geometry of a particular CBECS building. Although this strategy was originally

conceived as a means to leverage a single simulation to accurately represent multiple
a

 The scaling matrix can have negative elements for several reasons. For example, computing the loads for a one-story

building requires summing loads from the bottom and top floors of the 15-zone prototype and subtracting those from

the middle floor. Similar strategies are applied to accurately mix core and perimeter loads when scaling to a building

with a different perimeter-zone depth.

6-53

buildings, this work uses a one-to-one correspondence between BLAST runs and CBECS

buildings. It would be simpler, and arguably a bit more accurate, to simulate each building

directly, forgoing the need for scaling. However, since both the preliminary findings1

 and the

screening analysis2

 used the scaling procedure, DOE used it again here to maintain context

and facilitate comparisons across those analyses.

The scaling procedure is designed to accurately transform simulated building loads for

the 15-zone prototype into corresponding loads for a building with different geometry (square

footage, number of floors, aspect ratio, and number of zones per floor). The procedure is not

intended to transform building energy consumption (i.e., after accounting for heating and

cooling system performance). The previous analyses cited above were based only on building

loads, so the scaling process was a natural fit. However, this work uses BLAST to estimate

system performance (efficiency variations with part-load ratio and ambient temperature/

humidity), which requires additional steps in the scaling process.

The scaling of energy results for the 15-zone prototype to specific CBECS geometries

proceeds as follows:

A. Scale BLAST loads as before (see Preliminary Findings1

).

B. Infer hour-by-hour system EER by finding the ratio between energy consumption and

cooling load.

C. At each hour, transform the 15 zonal EERs to CBECS building-specific zone EERs,

using a modified version of the scaling procedure. This modified version uses the

same scaling matrix as used in Step A, but with negative elements set to zero. This

has the effect of computing a weighted-average EER for each hour, based only on

those zones in the 15-zone prototype that contribute positively to each scaled-to zone

in the CBECS building.a

D. Compute cooling energy by dividing the scaled-to loads (Step A) by the averaged

EERs (Steps B and C).

E. Scale lighting and equipment energy consumption, using the same scaling matrix as

for cooling loads. Because no efficiency adjustments are involved, the energy results

can be scaled directly. Also, since all schedules and intensities are identical on a persquare-foot basis, the scaling is simply a matter of maintaining the same end use

intensities (EUIs) or energy-per-square-foot, between the 15-zone prototype and the

CBECS building.
6-54

F. Scale cooling equipment capacities and fan power ratios using the same scaling matrix

as for cooling loads; store for later use.

Step 9 – Aggregate all energy end uses across zones.

This step sums, at each hour, the CBECS building-specific end-use energy values across

zones to obtain building totals.

Step 10 – Adjust building-level results for partially-cooled buildings.

Many CBECS buildings in the sample of 1033 are not 100 percent air conditioned. Also, the

sections of buildings that are air conditioned are often partially air conditioned with other

kinds of air-conditioning equipment that are not affected by this rulemaking. This final

processing step adjusts the building’s total end-use energy values to account for the relevant

air-cooled unitary air-conditioning fractions. This involves three steps:

A. The Department reduces cooling energy and corresponding fan energy proportionately

to account for only the fraction of the building that is cooled by unitary airconditioning equipment according to CBECS.

B. The Department creates an “other cooling” column to account for sections of the

building that are air conditioned by non-unitary air-conditioning systems. This is

computed by multiplying the simulated cooling energy of the baseline-efficiency

building by the fraction of floor space cooled by non-unitary air conditioning

equipment. The Department uses the baseline efficiency (EER = 8.9) run for all

buildings because there is no way to know the actual efficiency of the non-unitary airconditioning equipment.

C. The Department creates an “other fan energy” column to account for fan energy use in

portions of the building not cooled by unitary air-conditioning equipment. This is

computed by multiplying the building’s simulated fan energy by the fraction of floor

space not air conditioned by unitary air-conditioning equipment. The Department

assumes that the entire building requires air movement, including those portions that

are not air conditioned at all.
6-55

6.3 SUMMARY OF RESULTS

For heating and air-conditioning equipment, the annual energy use is a function of the

heating and cooling loads the equipment must meet. The Department used the BLAST hourly

simulation program to calculate building (zone) loads using the generic three-story prototype

building. The Department used the coil loads from BLAST as generic estimates of zone loads

(core versus perimeter, ground versus roof) of the different building types. The Department

scaled the zone coil load estimates to equivalent coil loads for each of the 1033 buildings in

the representative building set, then converted them to energy consumption required to meet

that load, using the heating and cooling system efficiency.

6.3.1 Comparison of Simulation End-Use Intensities to CBECS-Calculated EUIs

The Department compared the EUIs from the building model simulations with those 

from the CBECS data to determine how close simulations came to real building data.

The CBECS EUIs are not based on metered data. Instead, EIA derived the CBECS

cooling energy from the reported total annual electric energy using simulations. The cooling

EUI is then calculated from the CBECS-derived cooling energy estimate and the total

building floorspace. The air-conditioning EUIs were calculated from the BLAST simulation

output, scaled to the actual building size. For this purpose, the comparison was for a cooling

system with an EER of 8.9.

Figure 6.3.1 represents a comparison of the cooling EUIs (kBtu/sf/yr) from the

BLAST simulations and the CBECS data aggregated by building type and weighted by square

footage. For all building types combined, the BLAST cooling EUI estimates slightly exceed

the CBECS EUI estimates. The largest variations occur in assembly and food service

buildings, while the smallest variations occur in the office buildings.

Figure 6.3.2 shows the frequency distribution of cooling EUIs for the same two data

sets described above. The shape of the distribution is similar, but with a shift toward higher

cooling EUIs in the simulated results. At the individual building level comparison, there

appears to be little correlation between the simulated EUIs and CBECS EUIs. Given all the

unknowns and assumptions made in characterizing the building, DOE expected this result. 

Next, DOE aggregated the cooling EUIs by climate regions (see Figure 6.3.3). It

appears that, when the results are aggregated into a small number of groups consisting of

many buildings within a given climate region, the correlation between the two data sets

appears more favorable. When aggregating the results by GeoClimate regions, for the vast

majority of the regions, the simulated cooling EUIs are greater than the CBECS-reported

EUIs, which is similar to the trend shown by the frequency distribution in Figure 6.3.2.
6-56

Frequency Distribution of Cooling EUIs

0

50

100

150

200

250

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140

Cooling EUI Bin (kBtu/sf/yr)

Number of Observations

BLAST Total Cooling

CBECS Elect Cooling

Figure 6.3.2 Frequency distribution of the BLAST EUIs and the CBECS

 reported EUIs

Cooling Energy - Square Footage Weighted

0

5

10

15

20

25

Retail

Office

Education

Assembly

Food Service

Warehouse

All

EUI (kBtu/sq.ft./yr)

BLAST Simulation CBECS95

Figure 6.3.1 Plot of BLAST Cooling EUIs versus CBECS EUIs by

 Building Type
6-57

Total Cooling Weighted EUI Comparison by GeoClimate Region

0

5

10

15

20

25

30

0 5 10 15 20 25 30

BLAST EUI (kBtu/sf/yr)

CBECS EUI (kBtu/sf/yr)

Mountain-5

Figure 6.3.3 Comparison of BLAST and CBECS reported EUIs 

 aggregated by GeoClimate region

6.3.2 Annual Building Energy Use

The Department weighted the annual energy consumption from the simulations for

each of the individual buildings using the CBECS Adjusted Weight that reflects the portion of

the square footage each building represents at the national level. The simulated energy enduse splits for each of the six building categories analyzed in this analysis are shown in Figure

6.3.4 for the baseline cooling equipment (Case 2—8.9 EER). Shown in this figure is the

fraction of the represented building’s total weighted energy consumption for each of the five

major energy end uses. The fuel source for heating is natural gas; all other end uses are

electric.

Figure 6.3.5 shows the change in cooling EUI for two specific buildings in the

representative building set, as the equipment efficiency increases from an EER of 8.5 to an

EER of 12.0. These two buildings are an arbitrarily-selected small retail building and a large

office building in different climate regions. The EUIs do not include the fan energy.
6-58

Midland-Odessa TX / OFF_MFS / 100% Cooled / 25,000SF

0.0

2.0

4.0

6.0

8.0

10.0

12.0

8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 12.5

EER

Cooling EUI (kBth/sf/year)

Chicago IL / RET_ALL / 98% Cooled / 325,000 SF 

Figure 6.3.5 Change in BLAST Package Cooling EUIs with Increase

in EER for Arbitrary Large and Small Buildings

7%

14% 13% 14% 16% 15%

62%

37%

29%

13% 13% 14%

3%

8%

21%

22%

11% 10%

18%

27%

27%

37% 50% 52%

10% 13% 11% 14% 9% 9%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

ASM EDU FDS OFF RET WHS

Fan

Lighting

Equipment

Heating

Cooling

Figure 6.3.4 Fraction of Simulated Annual Building Energy Use by 

 Major End Use and Building Type.
6-59

6.3.3 Comparison of Results to the Screening Analysis

Table 6.3.1 presents the national energy savings estimates across all building types

and efficiency levels analyzed in this analysis. Using the electric consumption of the

combined, air-cooled unitary air conditioning and component fan per ton of cooling capacity

as a common metric to compare the DOE screening analysis, it can be seen that the

methodology used in this ANOPR analysis is generating slightly higher energy consumption

than the previous DOE screening analysis. However, the incremental savings compared to the

baseline equipment (EER=8.9) are approximately the same. The comparison of the Cooling +

Fan EUI versus EER is shown in Figure 6.3.6 and the energy use per ton is shown in Figure

6.3.7.

Table 6.3.1 Comparison of Annual Consumption for the Cooling and Fan Energy at

Each of the 10 EER Levels, for the ANOPR Analysis and the 2000 DOE

Screening Analysis. Percent Savings Based on EER=8.9 

EER

ANOPR Agg.

Wt. Annual

Cooling+Fan

(MMBtu/yr)

Energy Use

per Ton

ANOPR

(kWh/ton/yr)

Energy Use

per Ton

Screening

Analysis

(kWh/ton/yr)

Percent

Savings

ANOPR

(kWh/ton/yr)

Percent 

Savings

Screening

Analysis

(kWh/ton/yr)

EUI

Annual

Cooling +

Fan 

(kBtu/sf/yr)

8.5 175,983,907 2223 2170 4.6% 4.7% 19.5

8.9 167,941,046 2126 2072 -- -- 18.6

9.5 157,268,739 1997 1942 -6.1% -6.3% 17.4

10.0 149,436,865 1901 1845 -10.6% -11.0% 16.6

10.1 147,966,760 1883 1826 -11.4% -11.9% 16.4

10.5 142,422,945 1816 1757 -14.6% -15.2% 15.8

11.0 136,057,580 1738 1677 -18.3% -19.1% 15.1

11.5 130,266,270 1666 1604 -21.6% -22.6% 14.4

11.8 127,058,276 1627 1563 -23.5% -24.6% 14.1

12.0 125,025,284 1602 1537 -24.6% -25.5% 13.9
6-60

Cooling EUI versus EER

0

5

10

15

20

25

8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 12.5 13.0

EER

Cooling EUI (kBtu/sf/yr)

Figure 6.3.6 Weighted Cooling EUI Across all 1033 Buildings

Energy Use per Ton vs. EER

0

500

1000

1500

2000

2500

8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 12.5 13.0

EER

Energy Use (kWh/yr/ton)

ANOPR

Screening

Figure 6.3.7 Comparison of Cooling Equipment Energy Use with DOE

Screening Analysis
6-61

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