#20 

REVIEW OF ANALYSES IN SUPPORT OF PROPOSED 

U.S. DOE ANOPR 

ON ENERGY CONSERVATION STANDARDS 

FOR COMMERCIAL UNITARY AIR CONDITIONERS 

AND HEAT PUMPS 

NOVEMBER 17, 2003 

PREPARED FOR: 

U.S. DEPARTMENT OF ENERGY 

BUILDING TECHNOLOGIES PROGRAM 

PREPARED BY: 

SENTECH, Inc. 

7475 Wisconsin Avenue, Suite 900 

Bethesda, MD 20814 

and 

ENERGETICS, Inc. 

GEORGE REEVES ASSOCIATES, Inc. 

McNEIL TECHNOLOGIES, Inc. 

PRINCETON ENERGY RESOURCES INTERNATIONAL, Inc. 

DENNIS L. O’NEAL, Consultant 

JERRY JACKSON, Consultant 

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JOHN BRYANT, Consultant 

MICHAEL DAVIS, Consultant

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TABLE OF CONTENTS 

EXECUTIVE SUMMARY 

1.0 BACKGROUND ............................................................................................................... 1 

2.0 SCOPE OF ANALYSIS AND TERMS OF REFERENCE .......................................... 2 

3.0 REVIEW OF THE BUILDING ENERGY USE AND LOAD SHAPE 

CHARACTERIZATION ANALYSIS ............................................................................ 4 

3.1 Introduction................................................................................................................... 4 

3.2 Building Sample Approach .......................................................................................... 5 

3.2.1 Introduction............................................................................................................. 5 

3.2.2 Assessment.............................................................................................................. 6

3.2.3 Findings................................................................................................................... 9 

3.3 Omission of Industrial/Manufacturing Buildings.................................................... 10 

3.3.1 Introduction........................................................................................................... 10

3.3.2 Approach............................................................................................................... 10

3.3.3 Assessment............................................................................................................ 11 

3.3.4 Findings................................................................................................................. 16 

3.4 Electricity Prices for Multi-Building Facilities ........................................................ 17 

3.4.1 Introduction........................................................................................................... 18

3.4.2 Assessment............................................................................................................ 18 

3.4.3 Findings................................................................................................................. 18 

3.5 Distribution of Buildings in the 1,033 Sample.......................................................... 19 

3.5.1 Introduction........................................................................................................... 19

3.5.2 Assessment............................................................................................................ 19 

3.5.3 Findings................................................................................................................. 21 

3.6 Cooling Loads for Unitary A/C Systems that are not the Primary HVAC Sytsem21 

3.6.1 Introduction........................................................................................................... 21

3.6.2 Assessment............................................................................................................ 21 

3.6.3 Findings................................................................................................................. 22 

3.7 Summary...................................................................................................................... 22 

3.8 Resources Used in the Independent Review ............................................................. 24 

Appendix to 3.0: Estimates of Unitary A/C Energy Use in Commercial Buildings......... 27 

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4.0 EVALUATION OF THE BLAST ENGINEERING ANALYSIS .............................. 29 

4.1 Introduction................................................................................................................. 29 

4.2 Review of the Rooftop Model in BLAST .................................................................. 29 

4.2.1 The rooftop model in BLAST assumes a single compressor air conditioner with 

compressor unloading ........................................................................................... 30 

4.2.2 The temperature used for implementing head pressure controls is excessively high

............................................................................................................................... 30 

4.2.3 The analysis supporting the ANOPR did not factor in all the capacity-related

 variables in BLAST .............................................................................................. 31 

4.2.4 The rooftop model assumes a low saturated suction temperature ........................ 33 

4.2.5 The rooftop model in BLAST has never been experimentally verified................ 33 

4.2.6 The BLAST analysis includes rooftop air conditioners outside the range of 

capacities of the two classes being analyzed ........................................................ 34 

4.2.7 The maximum efficiency of the rooftops used in the analysis is too low............. 34 

4.2.8 Ignoring part load performance excludes many options that could improve energy 

use ......................................................................................................................... 35 

4.2.9 Summary............................................................................................................... 35 

4.3 Rooftop Efficiency and Controls ............................................................................... 36 

4.3.1 Efficiency Levels in ASHRAE Standards ............................................................ 36 

4.3.2 Window Characteristics........................................................................................ 36 

4.3.3 Economizer Use and Control Assumptions .......................................................... 37 

4.3.4 Summary............................................................................................................... 37 

4.4 Comparison of BLAST Simulation Methodology and CBECS Estimates of Energy 

Use .................................................................................................................................... 38 

4.4.1 CBECS Sample Building Issues ........................................................................... 38 

4.4.2 CBECS End Use Energy Use Information ........................................................... 40 

4.4.3 Preferred Methodology for Estimating CBECS A/C Use..................................... 40 

4.4.4 Calibrating the BLAST Model to individual buildings ........................................ 41 

4.4.5 Summary............................................................................................................... 42 

4.5 References.................................................................................................................... 43 

5.0 VENTILATION MODELING ...................................................................................... 44 

5.1 Introduction................................................................................................................. 44 

5.2 Assessment................................................................................................................... 44 

5.2.1 Airflow calculations in BLAST appear to be correct ........................................... 44 

5.2.2 Ventilation Rates................................................................................................... 44 

5.3 Summary...................................................................................................................... 45 

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6.0 ASSESSMENT OF THE INCREMENTAL MARKUP OF COMMERCIAL......... 46 

UNITARY AIR CONDITIONING EQUIPMENT PRICES .................................................. 46 

6.1 Introduction................................................................................................................. 46 

6.2 Review of Assumptions and Approach Used to Calculate Markups ..................... 47 

6.3 Potential Impact of Manufacturing/Industrial Sales on Markup Calculations.... 47 

6.4 Sensitivity Analysis of Changes in Markup Assumptions....................................... 48 

6.5 Other Markup Cost Factors ...................................................................................... 51 

6.6 Summary...................................................................................................................... 55 

7.0 SUMMARY CONCLUSIONS AND RECOMMENDATIONS ................................. 56 

Appendix to 7.0: Documents Received from DOE by the IRT.......................................... 60 

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EXECUTIVE SUMMARY 

The Energy Policy and Conservation Act as amended (EPCA), 42 U.S.C. 6291-6317, establishes 

energy efficiency standards for certain commercial unitary air conditioners and heat pumps that 

correspond to ASHRAE Standard 90.1-1992. ASHRAE revised Standard 90.1 in October 1999. 

Under EPCA, the U.S. Department of Energy (DOE) must determine whether to adopt the new 

ASHRAE/IESNA Standard 90.1-1999 or more stringent levels of energy efficiency where 

technically feasible and economically justified (National Energy Policy Report, May 2001). 

DOE conducted in-depth technical analyses of this matter. To support a more stringent standard 

than ASHRAE 90.1-1999, the DOE analysis must show that such a standard is technically 

feasible and economically justified. Some of the elements of this justification are: 

• Identifying which facilities will be affected by the proposed rulemaking; 

• Determining the electricity consumed by the covered appliances, with and without the 

proposed rulemaking; 

• Estimating the energy and operating cost savings that would result from the standard; and 

• Estimating the incremental markup of prices of more efficient air conditioners. 

Such analyses require data; assumptions and approximations; analysis procedures, including 

energy use simulation models; and life cycle cost analysis. This is not a precise process; 

competent professionals can differ on all aspects of this analysis. 

In January 2001, DOE published a final rule adopting ASHRAE/IESNA 90.1-1999 for 18 

product categories and decided to further evaluate commercial unitary air conditioners and heat 

pumps in the range of 65,000 to 240,0000 Btu per hour cooling capacity. In June 2001, DOE 

published a Draft Framework Document for Commercial Air Conditioner and Heat Pump 

Standards Rulemaking, and solicited public comment. In October 2001, a workshop was held on 

the proposed rulemaking and public/stakeholder comments. After considering the stakeholder 

inputs as they related to the technical analysis, DOE decided to assemble an independent review 

team (IRT) to prepare an independent assessment of: 

• Some of the significant comments received, and 

• The overall methodology, assumptions, approximations and data used in the Technical 

Support Document (TSD) for the proposed Advance Notice of Proposed Rulemaking 

(ANOPR) on Energy Conservation Standards for Commercial Unitary Air Conditioners and 

Heat Pumps. 

In mid-August 2003, DOE contracted with the IRT to evaluate the methods and data used by 

DOE and comments received in four areas: 

1. Assessment of the Buildings Sample Used to Represent Installed Commercial Unitary Air 

Conditioning Equipment; 

2. Comparison of BLAST Simulation and CBECS Estimates of Energy Use in Buildings by 

Commercial Unitary Air Conditioning Equipment; 

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3. Commercial Unitary Air Conditioning Equipment Supply Fan Energy Use While Ventilating; 

and 

4. Assessment of the Incremental Markup of Commercial Unitary Air Conditioning Equipment 

Prices. 

Evaluating the Nation’s energy use and estimating the effects of proposed rulemaking on that 

energy use is not an exact science. The IRT’s role was not to re-do the DOE analyses but to 

judge whether they were reasonable and defendable; whether there were errors that should be 

corrected; and/or whether there were better procedures, data or models to perform the analysis. 

Where the team concluded that an error had been made or there was a better alternative analysis 

procedure, we tried, at least qualitatively, to determine whether the proposed modification was 

likely to significantly affect the results of the TSD. 

The analyses by DOE (Headquarters, National Laboratories, contractors, etc.) in the TSD were 

extensive, following a complex methodology. The IRT took a typical engineering approach to 

the review: what is wrong and needs to be fixed? This report does not describe all that was, in 

our opinion, correct and sound, but rather focuses on deficiencies or errors. The IRT approached 

the task as a “Tiger Team” review of the DOE analysis; we were to point out problems we 

discovered, with the constructive objective of trouble-shooting the analysis to make it more 

defendable. (We were not to, and did not, conclude whether the proposed rulemaking was 

justified.) The IRT concluded that on the whole the DOE analyses were well thought out, logical, 

comprehensive, and professional. 

The IRT analyzed a number of issues, problems or errors in the TSD analysis related to the 

building sample, BLAST simulations, and the markup analysis. 

• The IRT concluded that the stakeholder comments on markups represent a legitimate 

difference of opinion, but the DOE analysis is basically sound and defendable. Plausible 

alternative methods and assumptions in the markup analysis and procedures would not 

significantly affect the results. We recommend that the markup analysis stand as it is. 

• The IRT felt that the exclusion of manufacturing sites from the analysis should be further 

evaluated, as this omission might have a significant effect on the analyses’ conclusions. 

• For the energy use analysis, the IRT identified several significant errors. Most can be 

easily corrected (e.g., correcting coefficients in the input data), some will take a bit more 

effort to correct (e.g., re-segmenting the test buildings’ zones to use appropriately-sized 

A/C). The IRT recommends that these be corrected and the simulations be re-run, as they 

will likely significantly change the analysis results. 

• However, there are serious limitations with BLAST (e.g., outdated compressor models, 

inability to do part load modeling, the need to validate BLAST rooftop A/C models). 

Some of these issues (but not all) were identified in the Stakeholder comments. 

This report describes the IRT’s assessment of these issues, their significance on the validity of 

the analysis results, and recommendations for addressing them. 

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1.0 BACKGROUND 

The Energy Policy and Conservation ACT (EPCA) as amended, 42 U.S.C. 6291-6317, 

establishes energy efficiency standards for certain commercial unitary air conditioners and heat 

pumps that correspond to ASHRAE Standard 90.1-1992. ASHRAE revised Standard 90.1 in 

October 1999. Under EPCA, the U.S. Department of Energy (DOE) must determine whether to 

adopt the new ASHRAE/IESNA Standard 90.1-1999 or more stringent levels of energy 

efficiency where technically feasible and economically justified (National Energy Policy Report, 

May 2001). 

DOE conducted in-depth technical analyses of this matter. In January 2001, DOE published a 

final rule adopting ASHRAE/IESNA 90.1-1999 for 18 product categories and decided to further 

evaluate commercial unitary air conditioners and heat pumps in the range of 65,000 to 240,0000 

Btu per hour cooling capacity. In June 2001, DOE published a Draft Framework Document for 

Commercial Air Conditioner and Heat Pump Standards Rulemaking, and solicited public 

comment. In October 2001, a workshop was held on the proposed rulemaking and 

public/stakeholder comments. 

Stakeholder input was received, including comments from the Air-Conditioning and 

Refrigeration Institute (ARI), American Council for an Energy Efficient Economy (ACEEE), 

Natural Resources Defense Council (NRDC), Lennox, and Trane. After considering the 

stakeholder inputs as they related to the technical analysis, DOE decided to assemble an 

independent review team (IRT) to prepare an independent assessment of: 

• Some of the significant comments received, and 

• The overall methodology, assumptions, approximations and data used in the technical 

support document (TSD). 

This report represents that review of the analyses performed in support of the DOE Advance 

Notice of Proposed Rulemaking (ANOPR) on Energy Conservation Standards for Commercial 

Unitary Air Conditioners and Heat Pumps, and some of the comments received by DOE. In midAugust 2003, DOE contracted with the IRT to evaluate the methods and data used by DOE and 

comments received in four areas: 

1. Assessment of the Buildings Sample Used to Represent Installed Commercial Unitary Air 

Conditioning Equipment; 

2. Comparison of BLAST Simulation and CBECS Estimates of Energy Use in Buildings by 

Commercial Unitary Air Conditioning Equipment; 

3. Commercial Unitary Air Conditioning Equipment Supply Fan Energy Use While Ventilating; 

and 

4. Assessment of the Incremental Markup of Commercial Unitary Air Conditioning Equipment 

Prices. 

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2.0 SCOPE OF ANALYSIS AND TERMS OF REFERENCE 

The IRT was headed by Sentech, Inc. and consisted of five companies and four consultants: 

• Energetics, Inc. (Energetics) (James Lyons, P.E.) 

• George Reeves Associates, Inc. (GRA) (George Reeves) 

• McNeil Technologies, Inc. (McNeil) (Kevin DeGroat, Jack Whittier) 

• Princeton Energy Resources International (PERI) (Allan Evans, Ph. D., P.E.) 

• Sentech, Inc. (Sentech) (Lawrence Markel) 

• Consultants: Dennis L. O’Neal, Ph.D., P.E.; Jerry Jackson, Ph. D.; John Bryant, Ph. D., P.E.; 

Michael Davis, EIT 

Each of the four areas referenced in Section 1.0 was assigned to one group: 

1. Buildings sample used for the analysis – Energetics 

2. BLAST modeling of building energy use and air-conditioner performance – Consultants, 

directed by Dennis O’Neal under contract to Sentech 

3. Modeling of ventilation and fan use – Consultants, directed by Dennis O’Neal under contract 

to Sentech 

4. Incremental markup of A/C pricing – McNeil 

Overall technical review, coordination, and supplemental technical analysis were provided by 

Sentech, with support from GRA and PERI. 

The IRT recognizes that evaluating the Nation’s energy use and estimating the effects of 

proposed rulemaking on that energy use is not an exact science. There is not a unique “correct” 

method to perform this analysis. Reasonable and competent analysts may differ on techniques, 

data, assumptions, and approximations used. The objectives of this independent analysis were to 

review the DOE ANOPR analyses and to evaluate the approach, assumptions, data, 

models/software, and engineering judgments used, to determine if they are reasonable, logical 

and defendable. 

The IRT was not tasked to re-do or duplicate the original analyses. It was not supposed to come 

up with the “answer,” but to determine whether the underlying analyses and/or the issues raised 

in the comments DOE has received are valid. In reviewing the ANOPR studies, it was foreseen 

that the IRT might recommend more appropriate data sets or simulation models that could 

potentially improve or complement the analysis. 

Thus, the role of the IRT was to assess the analytical processes used in the ANOPR studies and 

comment on: 

• The reasonableness of assumptions that were made; 

• The overall logic of the methodology; 

• The quality of the data and data analysis/simulation tools that were used; and 

• The validity of issues that have been raised with the analysis by stakeholder comments. 

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The IRT was to focus on issues that would likely change the results of the DOE analysis. 

However, short of re-doing the analysis, it was not possible to estimate the magnitude of 

differences in the results more than qualitatively. 

The IRT stresses again that reasonable and competent professionals may differ on these issues. 

Alternatives to the TSD analyses could result in a different answer but not necessarily a more 

correct answer. The IRT has not attempted to impose on the ANOPR analysis how the IRT 

would conduct the analysis, but rather whether the TSD used reasonable and defendable data 

and procedures and, if not, suggest alternative generally-accepted procedures and data that 

could be used. 

The next four sections each present an analysis of one of the four areas listed in Section 1.0. Due 

to the nature of the IRT’s assessment approach (i.e., the team really consisted of six sub-teams) 

and the rapid response required (i.e., two months), the formats of these sections vary, and the 

analyses between sections may not be entirely consistent or cross-referenced. 

Section 7.0 summarizes the results of Sections 3.0 through 6.0 and presents recommendations to 

DOE on modifications to the technical analyses making up the TSD. 

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3.0 REVIEW OF THE BUILDING ENERGY USE AND LOAD SHAPE 

CHARACTERIZATION ANALYSIS 

3.1 Introduction 

The purpose of DOE’s building energy use and load shape characterization is to assess the 

energy and peak demand savings potential of different equipment efficiency levels across the 

range of buildings that use unitary systems. The building energy use and load shape data that 

result from energy simulations of a set of sample buildings is a major factor in determining 

electricity costs for cooling within the life cycle cost analysis. 

In performing the building energy use and load shape characterization, DOE based its analysis 

on simulations run on a subset of buildings from the 1995 Commercial Buildings Energy 

Consumption Survey (CBECS) database that use unitary (or packaged) air-conditioning 

equipment. This subset of 1,033 buildings was selected from the overall CBECS database 

according to certain criteria, and complemented with further information as necessary to develop 

building profiles for the energy simulations. 

Unitary A/C systems are used in differing manners in a broad range of building types in many 

different climates, and no single “perfect” set of building data exists to characterize their 

performance in the real world. In developing a building set and the associated energy 

performance profile that is intended to characterize unitary A/C performance on a national level, 

it is inevitable that the necessary application of engineering judgments and assumptions will 

have some bearing on the overall analysis results. Therefore DOE requested that the independent 

review team (IRT) address the following issues: 

• Building Sample Approach 

• Omission of Industrial/Manufacturing Buildings 

• Electricity Prices for Multi-Building Facilities 

• Distribution of Buildings in the 1,033 Sample 

• Cooling Loads for Unitary A/C Systems that are not the Primary HVAC System 

These issues were highlighted by DOE based on its interaction with stakeholders and its insights 

into the analysis. The IRT assessed these issues with the intention of determining if certain 

concerns are valid (meaning that this issue is a real one), and if DOE’s analysis is reasonable in 

terms of its methodology, assumptions, and data sources. The intention was not to determine if 

DOE’s characterization of commercial unitary A/C use in buildings was the “correct” method, 

because in an analysis of this scope and magnitude no single correct method exists. 

Instead, this review focuses on the steps that were taken by DOE, whether they were reasonable, 

and if other “reasonable” approaches might have been adopted that could change the overall 

results of the analysis. This final point is critical, because an analysis of this scope must include 

many engineering judgments and assumptions, and one could suggest alternatives at every step 

of the analysis. What is most important, however, are those portions of the analysis that have 

bearing on the overall results (e.g., the economic and energy benefits associated with various 

efficiency levels), and whether these pieces of the analysis are sound. In cases where an issue 

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appears to be valid but its significance (i.e., its magnitude within the overall analysis) is hard to 

judge, the IRT offered suggestions on how to better determine the issue’s significance. 

The independent review was performed at a level where all relevant reference materials were 

thoroughly assessed in the context of the issues raised by DOE. Additional data sources were 

sought and used (if possible) to gain further insights into DOE’s work. In some cases, no 

additional data on a particular issue existed in a useful format, and the review consisted primarily 

of assessing how DOE worked with whatever information was available. It is important to note 

that within the scope of this independent review, no additional simulations or modeling exercises 

were conducted. Therefore, the findings of this review are based solely on an evaluation of 

DOE’s analysis complemented by other reference materials where possible. All of the resources 

used in conducting this review are listed in Section 3.8. 

3.2 Building Sample Approach 

How well does the sample of buildings selected from CBECS represent the current operating 

conditions associated with the population of installed commercial unitary air conditioning 

equipment? 

What other data sources might offer further insights into the distribution and use of commercial 

unitary air conditioning equipment? 

What other methods might provide a better approach to selecting a representative set of 

buildings? 

3.2.1 Introduction

In developing the sample of 1,033 buildings used to characterize the operation of unitary A/C 

systems, DOE started with the 1995 CBECS database and filtered it down to the 1,033 sample 

using criteria related to Principal Building Activity (PBA) and percentage of cooled space, as 

shown in the figure below1

. 

 

 

1

 Chapter 6 of the ANOPR Technical Support Document gives more background information on the building sample 

development process. 

1995 CBECS – 

5,766 buildings 

Identification of 6 building types to 

use from the CBECS database 

Final set of 1,033 buildings made up of 

Assembly, Education, Food Service, 

Office, Retail, and Warehouse buildings 

Identify and combine PBA 

categories that use unitary A/C 

Selection of buildings where 

>70% of floor space cooled with 

unitary A/C equipment 

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As the figure illustrates, the analysis in support of the proposed rulemaking relies upon a subset 

of 1995 CBECS samples to characterize the distribution and operation of unitary A/C systems in 

commercial buildings. The development of this subset is an important factor in the overall 

analysis, because these are the buildings that ultimately portray where unitary systems are 

deployed and how they are operated. 

Three aspects of this sample development process are assessed below: 

• How effectively the building sample represents the population of unitary systems; 

• What other data might offer information on the distribution of unitary systems; and 

• What other methods might be used to develop a representative set of buildings. 

One prominent issue related to the building set is the omission of industrial and manufacturing 

buildings, as well as commercial buildings on these types of sites, which use unitary A/C 

systems. Since this issue has been raised as an independent topic it will be discussed separately 

in the next section (Section 3.3). 

3.2.2 Assessment

How well does the building sample represent the distribution and use of unitary A/C systems? 

The Commercial Buildings Energy Consumption Survey (CBECS) is a representative national 

survey that collects energy-related building characteristics data and energy consumption and 

expenditures data for commercial buildings in the United States. CBECS offers a broad 

representative data source for the characteristics of commercial buildings in the US. The HVAC 

characteristics data contained in CBECS represent the best publicly available, national-level 

information on the distribution and use of unitary A/C equipment in commercial buildings. 

Further, each sample in the database is weighted to reflect how many similar buildings it 

represents in the actual population of commercial buildings. 

Thus, by using the sample weights provided in CBECS, the 1,033 building sample can be 

assessed in terms of how thoroughly it captures those commercial buildings that use unitary 

cooling equipment. Based on DOE’s analysis of the sample, the buildings in the sample 

represent 73% of the annual A/C energy use and 67% of the square footage of commercial 

buildings that cool at least part of their floor space with unitary A/C equipment. The IRT 

concludes that even after filtering out certain building types and buildings that cool less than 

70% of the floor space with unitary equipment, these values indicate that the 1,033 sample 

effectively represent the distribution of unitary equipment in commercial buildings. 

What other data sources might offer information on the distribution of unitary systems? 

The assessment of this question required a review of available data products from DOE and the 

Energy Information Administration (EIA), the research community, industry groups, and private 

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entities. The most notable resources are discussed below, along with their data that are relevant 

to the rulemaking analysis. 

1992 Commercial Buildings Energy Consumption Survey

In the 1995 CBECS (the data set used in support of the proposed rulemaking), EIA changed the 

way that the target population of “commercial buildings” was defined. Two types of buildings 

included in the 1992 CBECS, parking garages and commercial buildings located on multibuilding manufacturing facilities, were excluded from CBECS starting in 1995. 

The inclusion of commercial buildings situated on manufacturing sites in the 1992 CBECS is 

noteworthy because of the omission of all industrial/manufacturing buildings from the 

rulemaking analysis (see Section 3.3), including commercial buildings on manufacturing sites. 

The 1992 CBECS data offers some insights into the distribution and use of unitary A/C 

equipment in commercial buildings on industrial sites – information that is not included in 1995 

CBECS or the rulemaking analysis. However, CBECS 1992 does not contain data on industrial 

or manufacturing buildings, just commercial buildings on these types of sites. This topic is 

discussed further in Section 3.3. 

ARI Statistical Profile

The Air-Conditioning and Refrigeration Institute (ARI) is a major trade association in the HVAC 

industry and produces a Statistical Profile with data on HVAC equipment from manufacturers’ 

perspective. The Statistical Profile includes information on cooling equipment shipments, 

efficiency levels, and trends over time. However, the data in the profile is highly aggregated and 

does not include information on the buildings where specific types of equipment are deployed. 

BOMA Experience Exchange Report

The Building Owners and Managers Association’s (BOMA) 2003 Experience Exchange Report 

contains operating income and expense data on over 3,000 public and private sector commercial 

properties throughout North America. Data is classified by categories such as building type, 

building class, building ownership, and hours of operation. The building expense data includes 

categories such as cleaning, repair and maintenance, and utility costs. The utility costs are 

disaggregated by energy source (e.g., electricity, fuel oil), but no further data is available beyond 

this level of detail. The only HVAC-related information included in this database is the HVAC 

subset under the Maintenance and Repairs category. 

California Non-Residential New Construction (NRNC) Baseline Report

The California Non-Residential New Construction Baseline Report is a study conducted on 

behalf of the California Board for Energy Efficiency (CBEE). The study is based on surveys of 

the designers (architects and engineers) of new buildings in California, onsite audits, and DOE-2 

building simulations. The onsite building audits are a collection of several sets of NRNC audits 

conducted during the 1990’s, and represent nearly 800 sites. While much of this data is focused 

on commercial buildings – particularly office, retail, schools, and public assembly – some 

samples within the database fall into the “General Commercial & Industrial Work” and 

“Commercial & Industrial Storage” categories. The data for these buildings includes 

information on HVAC equipment type; this could be useful in assessing the distribution of 

unitary equipment across a range of building types at the state level. This information is publicly 

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available online in a database format. However, as a California data base, there is no accepted 

method to extrapolate it to the national level, either by itself or combined with CBECS. (That is, 

it would introduce bias to add NRNC to CBECS in order to “better” characterize buildings of 

certain types or climate zones.) 

Manufacturing Energy Consumption Survey (MECS)

The MECS database, prepared by EIA, is a nationally representative sample of manufacturing 

establishments in the US. The most recent version of MECS in 1998 had a sample size of almost 

18,000 plants and collected energy-related data across 36 industry groups and industries (based 

on the NAICS system). MECS data with relevance to this rulemaking analysis include total 

HVAC energy (not disaggregated by cooling or heating), number of buildings onsite (starting in 

1998), and conditioned floor space (1994 MECS but not 1998). 

Market Analysis and Information System (MAISY) 

Market Analysis and Information System is a collection of databases (based in part on CBECS) 

developed from information on more than 800,000 individual utility customers throughout the 

US. MAISY contains energy data on both commercial and industrial customers, although the 

level of detail between these two groups varies. Load profile data for both types of buildings is 

contained in MAISY, and disaggregated hourly A/C loads are also provided for both types of 

customers. A/C equipment type is provided for commercial customers, but not industrial 

buildings. Industrial buildings in MAISY do, however, have sample weights that allow for the 

estimation of national-level data based on the samples. 

Given these characteristics, MAISY contains sufficient data to perform aggregated load profile 

comparisons between commercial and industrial customers on a state basis or utility area basis. 

Based on a preliminary review, the IRT concluded that MAISY data could complement or guide 

the DOE analysis in some areas (Section 3.3) but could not replace CBECS data. 

Many other data sets with smaller scale or less detailed data (e.g., utility-based studies), 

specialized data (e.g., only certain building types), or private (for sale) data are also available, 

but their level of detail and scope limits their usefulness to the rulemaking analysis. While a few 

of the data resources listed above might offer some complementary data that could help guide the 

rulemaking analysis, the use of 1995 CBECS as the primary data source for this analysis is 

reasonable. No national-level data sets detailing the distribution of unitary A/C equipment 

across all building types were found. The use of additional or complementary data in the 

analysis should also be viewed with some caution, because specially augmenting the analysis 

with auxiliary data would likely introduce bias. 

What other methods might be used to develop a representative set of buildings? 

The above discussions of available data and the level of representation provided by the 1,033 

building set indicate that starting with the CBECS database and narrowing it down to the 1,033 

sample was a sound method of developing a representative commercial building set. A few 

deviations from the method used are discussed below. 

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Consider including buildings where unitary systems conditioned < 70% of the floor area

By excluding those commercial buildings where unitary systems condition less than 70% of the 

floor area, the current analysis does not capture buildings where unitary systems are used in 

conjunction with other equipment or buildings that may be only partially cooled. While this 

exclusion might cause the analysis to miss some partial-load scenarios, it would be extremely 

difficult to predict the loads on unitary systems in these situations (see Section 3.6). The 

uncertainty introduced by assumptions and approximations needed to estimate these A/C loads 

would outweigh the appearance of increased “precision” from modeling additional buildings. 

This, coupled with the fact that the 1,033 building set already characterizes a major portion of 

commercial buildings with unitary systems, relegates this issue to a secondary consideration that 

would most likely not change the results of the analysis. 

Consider using more up-to-date commercial building data from 1999 CBECS 

The 1999 CBECS data set was not used for the building characterization analysis because the 

entire micro-data set was not available in time for this analysis, and this database excludes new 

buildings less than 10,000 square feet. Using the 1995 CBECS data helped to address these 

issues but may also skew the distribution of building vintage towards older buildings. This could 

potentially influence building characteristics, building loads, and the characterization of unitary 

A/C equipment. 

A distinct advantage to using the 1999 CBECS data is the inclusion of some new buildings 

(those greater than 10,000 square feet). However, disadvantages to using 1999 CBECS include 

the minimum size requirement of new buildings and the fact that the 1999 survey revisited many 

of the 1995 samples, so the data would contain a great deal of overlap. Within the scope of this 

review, the IRT cannot estimate the magnitude of the difference in results from the 1999 

CBECS. There is also no indication that the 1995 CBECS data are not representative of current 

commercial unitary A/C usage. These issues, plus the fact that roughly 60% of total sales in the 

HVAC equipment market are for replacement and retrofits2

 in existing buildings, likely reduce 

the significance of the building vintage issue that results from using 1995 CBECS. 

3.2.3 Findings

The approach used to develop the 1,033 building set was reasonable and resulted in a sample 

representative of the population of unitary A/C equipment in commercial buildings. (This 

section did not discuss the omission of industrial buildings; that is covered in the next section.) 

Specific findings include: 

• Using CBECS as the primary data source and starting point for developing the building 

sample utilized the best available single source of national-level data on HVAC and energy 

characteristics of commercial buildings 

• The 1,033 sample is effective in characterizing the population of unitary A/C systems 

operating in commercial buildings 

 

2

 ARI Statistical Profile. Air-Conditioning and Refrigeration Institute, 2001. 

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• Potential alternatives to the commercial building sample development process may be 

considered, but appear unlikely to change the results of the analysis. 

3.3 Omission of Industrial/Manufacturing Buildings 

In developing the building sample for the building energy use and end-use load characterization 

analysis, only commercial buildings were used. Buildings in the industrial/manufacturing sector 

that use unitary A/C systems were not included in the characterization of building energy use and 

load profiles. 

What is the significance of this omission in terms of overall unitary A/C energy use and end-use 

load shapes in the building characterization? 

3.3.1 Introduction

The building set used in this rulemaking analysis to characterize unitary A/C energy performance 

in buildings was developed from the 1995 CBECS database. This database contains 5,766 

building samples with data on building type, building characteristics, and HVAC characteristics, 

including A/C equipment type. 

In the 1995 edition of CBECS, no industrial buildings were included. This omission includes 

both industrial/manufacturing facilities as well as commercial buildings that are situated on 

industrial sites. 

The omission of these building types has two impacts on the rulemaking analysis. First, it is 

possible that some buildings that use significant amounts of unitary A/C cooling energy are not 

captured in the building set. Second, it is also possible that these same buildings’ electricity 

prices may be different, affecting the energy analysis results. Taken together, these two issues 

could potentially affect the load characterization, energy cost estimates, and ultimately the life 

cycle cost analysis of unitary A/C systems. 

3.3.2 Approach

In reviewing the significance of the industrial buildings issue, two criteria determine if this issue 

is significant in the context of the overall results of the analysis. First, do industrial and 

manufacturing buildings consume a significant proportion of the energy used by the unitary A/C 

systems covered in this rulemaking? Without satisfying this criteria, industrial unitary A/C 

energy use is a “small slice of the pie” and will have little bearing on the overall results of the 

analysis, regardless of issues related to load shapes and electricity prices for industrial buildings. 

Second, do industrial/manufacturing customers that use unitary A/C systems pay significantly 

different electricity prices for their cooling energy compared to commercial customers? For the 

omission of industrial buildings to be significant, both these criteria must be satisfied 

simultaneously. That is, it must be determined that those manufacturing/industrial buildings with 

lower tariffs are also buildings that use significant energy for unitary A/C systems. Concluding 

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that some industrial buildings use unitary systems and some industrial buildings have lower 

tariffs is not sufficient to establish significance. A correlation of these factors in the same subset 

of industrial buildings is necessary. 

3.3.3 Assessment

Do Industrial/Manufacturing Buildings use a significant quantity of unitary A/C energy 

relative to the commercial building stock? 

Understanding this issue requires having at least an approximation of unitary A/C energy use in 

1) commercial buildings, and 2) industrial/manufacturing buildings. One estimate of annual 

packaged A/C energy use in commercial buildings in the US, based on the 1995 CBECS 

database, is 46.1 billion kWh3

, although the interpretation and manipulation of CBECS data can 

lead to different estimates. 

As part of this independent review, 1995 CBECS data was used to develop an approximate range 

of unitary A/C energy use in commercial buildings based on two separate analyses using CBECS 

data (Appendix to Section 3.0). The results of the two approaches are within the same range, and 

indicate that annual energy use of unitary A/C systems in commercial buildings is in the range of 

39 billion kWh to 46 billion kWh. 

Determining unitary A/C energy consumption in industrial buildings is more challenging due to 

limited data. The Frazell analysis cited above uses a combination of 1998 MECS4

 and 1995 

CBECS data to estimate that industrial buildings use 17.2 billion kWh annually for packaged 

A/C systems. This value, coupled with Frazell’s 46.1 billion kWh estimate for commercial 

buildings, would indicate that roughly one-quarter of all packaged A/C system energy 

consumption occurs in industrial/manufacturing buildings. 

A major assumption used in arriving at the 17.2 billion kWh result is that the proportion of 

packaged A/C energy consumption to total HVAC energy consumption5 is the same in the 

commercial and industrial building sectors. While the level of detail contained in the MECS data 

necessitates an assumption of this type (MECS does not contain disaggregated data on cooling 

energy or cooling system type), the resulting energy consumption figure must be viewed 

cautiously due to the scale of this assumption. 

This assumption is extremely significant and somewhat counter-intuitive. Lower electricity 

tariffs tend to be offered to larger electricity users (see next assessment issue). A light industrial 

facility might have a similar proportion of its electricity consumed by unitary A/C as a 

commercial facility, but its electric rates will also be similar. A large industrial facility that might 

have lower electric rates is likely to use most of its electricity for industrial processes, with A/C 

energy consumption comprising a small portion of the overall load. Thus, those industrial 

buildings for which this assumption may be valid (light industrial facilities) are likely to pay 

 

3 A Preliminary Quantification. The Effects of Excluding Industrial/Manufacturing Sites from the DOE Rulemaking 

to Determine Efficiency Levels of Unitary Large Equipment. Charles Frazell, PE. March 2003. 4

 Manufacturing Energy Consumption Survey (MECS). Energy Information Administration, 1998. 

5

 Where total HVAC energy is defined as the sum of heating, ventilation, and air-conditioning energy 

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similar electric rates anyway, and for those industrial buildings with lower rates (larger, heavier 

industry), the assumption is probably not valid.

Other estimates of annual unitary A/C energy consumption place the total energy consumption 

of packaged rooftop units in the US at 76 billion kWh annually6. This estimate includes both 

commercial and industrial building unitary A/C energy, and appears to originate from an April 

2000 DOE screening analysis7 8

. However, the analysis used to arrive at this result is not 

transparent within either source, so this estimate of total energy consumption must be used with 

caution. 

As illustrated by the uncertainties associated with the figures above, representative national-level 

data on the HVAC characteristics of industrial buildings are scarce. Using a more qualitative 

approach to the issue of packaged A/C systems in industrial buildings, DOE adopts the position 

that the general size of industrial facilities would tend to preclude the widespread use of 

packaged systems in the 5 to 20 ton range. However, this claim is not supported adequately to 

conclude that industrial packaged A/C use is not significant. In fact, recent research in 

California concluded that there is little correlation between building size and system size, and 

that unit size is driven more by the size of the conditioned zone rather than building size9. And 

for those industrial buildings that do use unitary equipment, it is likely to be the equipment 

covered in this rulemaking since roughly 90% of all rooftop unit shipments fall into this range.6

 

While national-level data on the use and energy consumption of packaged A/C equipment in 

industrial/manufacturing buildings is lacking, some information is available for a subset of these 

buildings: commercial buildings on industrial/manufacturing sites. 

In 1995 CBECS, the Energy Information Administration (EIA) changed the way that the target 

population of commercial buildings was defined for the CBECS. A few types of buildings 

included in previous surveys (1989 and 1992) were excluded. One of these was commercial 

buildings located on multi-building manufacturing facilities. This category of building is not 

included in the DOE rulemaking analysis and represents one of the two types of industrial / 

manufacturing buildings left out of the analysis. The other type is actual industrial/ 

manufacturing buildings. 

While the 1995 CBECS database does not include commercial buildings on 

industrial/manufacturing sites, 1992 CBECS does include data for such buildings, that represent 

119,000 commercial buildings that are part of multi-building industrial/manufacturing 

 

6 Bridging the Efficiency Gap: Commercial Packaged Rooftop Air Conditioners. Shugars, J.; Coleman, P.; Payne, 

C.; and Van Wie McGrory, L. 

7 Screening Analysis for EPACT-Covered Commercial HVAC and Water-Heating Equipment. US Department of 

Energy, April 2000. 

8

 Communication with Phil Coleman of Lawrence Berkeley National Laboratory in October 2003. Mr. Coleman is a 

co-author of Bridging the Efficiency Gap: Commercial Packaged Rooftop Air Conditioners. 9 Background Research Summary. Integrated Energy Systems Productivity and Building Science. New Buildings 

Institute for the California Energy Commission Public Interest Energy Research (PIER) Program, 200l. 

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facilities10. A multi-building facility is defined as a group of two or more buildings on the same 

site owned or operated by the same entity. 

A preliminary analysis of 1992 CBECS performed for this independent review indicated that the 

total number of samples of commercial buildings on industrial/manufacturing facilities is 235, 

with over 22 million square feet of floor space. Of these building samples, over 92% of them (by 

number) fall into the Principal Building Activity (PBA) categories of Warehouse (36%), Office 

(34%), Vacant (14%), and Mercantile/Services (9%). The average building size in this subsample is 95,000 square feet. 

Of these buildings which had some form of air-conditioning, 39% of them used packaged A/C 

systems. About two-thirds of this subset of buildings used packaged A/C for 100% of the 

building’s cooling. 

Using the building sample weights provided in CBECS 1992 to develop a national-level 

estimate, over 35% of commercial buildings (by number) on industrial/manufacturing sites have 

no cooling of any kind. In 1995 CBECS about 26% of commercial buildings (by number) were 

not cooled11. According to 1994 MECS, roughly 32% of the enclosed floor space in industrial 

buildings was not conditioned (which means no controlled heating or cooling). In general, the 

two types of buildings left out of the rulemaking analysis appear more likely not to be cooled 

compared to commercial buildings. This trend does not support the assumption that the ratio of 

packaged A/C energy to total HVAC energy is the same in commercial and industrial buildings. 

Using the sample weights within 1992 CBECS further, the estimated national A/C cooling 

energy for packaged systems in commercial buildings on industrial/manufacturing facilities is 

4.3 trillion Btu, or 1.3 billion kWh. This estimate is much smaller than the values given above 

for commercial buildings. But the 1.3 billion kWh is just the estimated unitary cooling energy 

for the 2.1 billion square feet of commercial buildings on industrial/manufacturing sites, and 

does not include any unitary cooling energy for the 8.4 billion square feet of manufacturing floor 

space that is conditioned.12

The following conclusions can be drawn from this brief analysis of commercial buildings on 

industrial/manufacturing sites: 

• Buildings within this category are somewhat likely to be cooled with packaged A/C 

equipment if they are cooled.

• The estimated packaged A/C energy use in this subset of industrial/manufacturing buildings 

is small compared to commercial buildings, but this estimate does not include manufacturing 

floor space that is air-conditioned. 

 

10 CBECS 1992, Table A25 – Multibuilding, Number of Buildings and Floorspace, 1992. Energy Information 

Administration, 1992. 

11 CBECS 1995, Table 3 – Census Region, Number of Buildings and Floorspace, 1995. Energy Information 

Administration, 1995. 

12 MECS 1994, Table A7 – Enclosed Floorspace and Conditioned Floorspace. Energy Information Administration, 

1994. 

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• A large portion of these buildings (35%) are not cooled by any mechanical equipment. 

Using this data on commercial buildings on industrial/manufacturing sites, one could arrive at an 

estimate of overall industrial/manufacturing sector packaged A/C energy if adequate data existed 

on the distribution of unitary A/C equipment in industrial buildings. Unfortunately, nationallevel data of this type that could complement CBECS and MECS is not available. Limited data 

on cooling equipment and cooling energy profiles in industrial buildings is available (see Section 

3.2), which presents the challenge of how to assess the significance of this issue with incomplete 

information. 

Is the cost of electricity for industrial/manufacturing buildings significantly different 

compared to commercial buildings? 

The rulemaking analysis utilizes both a tariff-based approach and an hourly cost approach to 

arrive at the electricity cost estimates for the 1,033 building sample. The tariff approach to 

estimating electricity costs was based on a sample of tariffs from 90 utilities that serve 48.5% of 

commercial and industrial customers nationally. In most cases, DOE assigned a building to a 

tariff based on the peak demand (as simulated) over the previous 12 months. This tariff was then 

combined with the energy load profile for the building to determine electricity costs for cooling 

as well as the marginal energy cost savings that resulted from efficiency improvements. 

Stakeholders have expressed concern that industrial facilities with unitary A/C systems would 

operate on less expensive tariffs, and that the omission of these buildings skews the cooling costs 

higher. The result of skewing cooling costs higher is to make higher efficiency A/C systems 

more attractive in the analysis due to increased energy cost savings. 

 

Under the tariff pricing scheme used in the analysis, industrial buildings with packaged systems 

would face lower electricity costs for cooling if 1) these facilities were assigned to less expensive 

tariffs simply by virtue of being an industrial/manufacturing site, or 2) the load profiles of these 

facilities were of a magnitude and shape such that the facility qualified for a more advantageous 

tariff. 

In examining the first question, DOE found through research that utilities typically do not 

distinguish between commercial and industrial customers, per se, in their tariffs. Only 

occasionally did DOE find utilities that will offer different tariffs for different business types 

(e.g., industrial vs. commercial). In investigating a portion of these cases more closely, DOE 

found in all cases that although the tariffs had different names, the rates were in fact the same.13 

The IRT reviewed a sample of the utility tariffs collected under the rulemaking analysis.14 The 

findings confirm that tariffs typically are not determined by the type of customer but rather by 

the electric power demand and usage characteristics of a customer. This fact, coupled with the 

breadth of DOE’s utility tariff sample, indicates that tariffs based solely on business type are not 

a significant factor and would likely not translate directly into lower prices for industrial 

buildings on a scale that would alter the results of the analysis. 

 

13 ANOPR Technical Support Document, Section 8.2.3.1.2, “Tariff-based analysis” 

14 Archive of Utility Tariffs: http://eetd.lbl.gov/ea/ees/tariffs/index.php 

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The second question – whether industrial/manufacturing load profiles would qualify these 

facilities for less expensive utility tariffs that would reduce the marginal savings from increased 

efficiency levels – is more difficult to answer. The determination of which tariff a building is 

assigned in the DOE sample is driven by the facility’s demand, with higher demand levels 

corresponding to tariffs with less expensive unit energy charges (cents/kWh).15 Also, through its 

research DOE recognized that higher load factors (flatter loads) are associated with lower 

marginal rates (ANOPR Technical Support Document Section 8.2.3.1.2). 

In commercial buildings, air-conditioning loads can be a major contributor to the overall 

electrical load. Many of the other loads (lights, computers, communications) and occupants in 

commercial buildings serve to add to a building’s cooling load. Air-conditioning demand is 

therefore a major factor in overall building peak demand and helps to determine the tariff as well 

as demand charges ($/kW). Demand levels vary tremendously from site to site, and may be 

greater than or less than demand levels in industrial buildings on a site-by-site basis. Load 

factors in commercial buildings generally tend to be lower than those for industrial buildings due 

to differences in operations and usage schedules. A few samples of commercial building load 

factors provided in the ANOPR Technical Support Document (Technical Support Document 

Section 8.2.3.1.2) ranged from 33% for a warehouse to 54% for a restaurant. Other references 

such as the MAISY database contain load factors based on building type, such as a summer load 

factor range of 42% to 72% for Office buildings16. While generalized load factors are useful for 

qualitative comparisons of large sets of buildings (e.g., commercial vs. industrial), any 

quantitative analysis related to A/C energy, peak demand coincidence, and tariffs must be 

performed with actual site-specific load data. 

In industrial buildings, load factors are generally higher due to the types of processes conducted 

and operating schedules.17 For these same reasons, A/C loads in some industrial facilities are a 

very small contributor to overall building demand and may have little or no bearing on 

determination of the tariff or demand charges. The peak demand of an industrial facility may be 

driven exclusively by industrial operations. In the aluminum industry, for example, the total 

HVAC electrical energy (of which cooling is just one component) is just 1.5% of the total 

facility energy18. Further, the peak A/C load may or may not coincide with the peak load of the 

overall facility, meaning the peak demand savings from increased A/C efficiency levels would be 

treated differently than they are in commercial buildings. Other references show that the largest 

utility customers in some areas (which will tend to be industrial facilities) have average weekday 

load factors around 0.86, while much smaller facilities (which will include many commercial 

buildings) have a lower load factor of 0.70.19 Again, average load factors of this type are useful 

for general comparisons only. 

 

15 For a more complete explanation of tariff structures and the impact of demand and energy use on rates, see 

Section 8.2.3.1.2, “Tariff-based analysis” of the ANOPR Technical Support Document. 

16 MAISY Database. http://www.maisy.com/isight1.htm 

17 Opportunities for Microprocessor and Fuel Cell/ Gas Hybrid Systems in Industrial Applications, Arthur D. Little, 

January 2000. 

18 MECS 1998. Table N6.2 – End Uses of Fuel Consumption. Energy Information Administration, 1998. 

19 Market Monitor 2000 – A Report by the Division of Energy Resources. Commonwealth of Massachusetts, Office 

of Consumer Affairs and Business Regulation, February 2002. 

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These findings regarding load characteristics in industrial buildings, combined with DOE’s 

conclusion about the relationship between load factors and marginal rates, indicate that some 

industrial buildings (particularly those with high demands and high load factors) would have 

lower marginal rates within the rulemaking analysis. This in turn would reduce the energy cost 

saving benefits of increased A/C efficiency levels. Once again, this is only relevant to the 

analysis if these same buildings use a significant amount of electricity for unitary A/C. 

3.3.4 Findings

Unitary A/C Use in Industrial Buildings

Taking into account DOE’s data sources and analysis, external analyses and references, and 

rough estimates performed under this review, it is extremely difficult to arrive at a defensible 

estimate, using publicly available data, of unitary A/C energy use in those industrial and 

manufacturing buildings omitted from the analysis. In the absence of such an estimate, it is also 

difficult to judge the significance of unitary A/C operations in industrial buildings within the 

overall results of the analysis. 

Despite these constraints, the following factors do indicate that this issue is valid, even if its 

significance cannot be determined currently: 

• An estimate of 1.3 billion kWh of unitary A/C energy in commercial buildings on industrial 

sites. This is a small fraction of the commercial range, but it does not include any cooling 

energy for industrial or manufacturing floor space. The total floor space of this type of 

building is just 1⁄4 of the conditioned floor space within industrial buildings. 

• 79 billion kWh of HVAC electrical energy use in manufacturing buildings, some of which is 

consumed by unitary A/C systems. 

• Approximations of 17.2 billion kWh for industrial unitary A/C energy and 76 billion kWh of 

total packaged rooftop unit energy. While these estimates involve debatable assumptions 

(e.g., the proportion of A/C energy used in industrial facilities) and non-transparent analysis, 

respectively, even if they are both 30% too high it would still result in a meaningful portion 

of unitary A/C in industrial buildings. 

It must be stressed that the analysis cited in the stakeholder comments (Frazell 2003) assumes 

the same proportion of HVAC electricity will be used for unitary A/C in commercial and in 

industrial locations. This is probably not valid for those buildings that are primarily heavy 

manufacturing and have a significantly lower tariff than commercial buildings. For those light 

industrial buildings with loads similar to commercial buildings (i.e., those industrial buildings 

where this assumption may be more valid), their tariffs are also similar and the commercial life 

cycle cost analysis will apply. Since the estimate of 17.2 billion kWh of industrial unitary A/C 

electricity use rests upon this assumption, it is probably overstated. 

While DOE’s steps in the current analysis are reasonable given the available data resources, this 

issue has enough potential that DOE (possibly in conjunction with stakeholders) should 

determine if additional data or analysis could be developed with reasonable effort that would add 

clarity to the significance of this issue. That is, what intermediate steps could be taken to get a 

better gauge of the significance of industrial unitary A/C energy use in facilities with lower 

tariffs? 

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One possibility is to approach this issue from the opposite direction by reviewing the rulemaking 

analysis to determine how much industrial unitary energy use would be significant. That is, what 

minimum amount of industrial unitary A/C energy consumption would be enough to change the 

results of the analysis? This threshold for significance could then be compared to the rough 

estimates (e.g., 17.2 billion kWh) to determine if this issue warrants further examination. 

Different Electric Rates for Commercial and Industrial Buildings

DOE’s work in developing cost estimates for commercial buildings using its tariff methodology 

appears to be thorough and reasonable. As for the potential cost differences between industrial 

and commercial buildings, a review of DOE’s analysis, its tariff archive, and information on load 

profiles indicates that this issue is likely a valid one. That is, it is likely that some industrial 

buildings would pay lower marginal prices for electricity. 

The significance of this issue (e.g., does it change the results of the analysis) rests on how much 

lower industrial rates might be and how the A/C energy load relates to the whole building load in 

specific industrial facilities that use unitary systems. This issue is similar to the industrial 

building energy use issue discussed above, in that a diagnostic analysis should be considered that 

would offer more insight on the significance of the issue. The data available at this point only 

indicates that the issue is valid. 

One possibility would involve using load profile data for specific industrial facilities, using a 

data source such as MAISY (that contains disaggregated A/C data for industrial facilities with 

national-level weighting factors). This analysis could focus on facilities within industries that 

use significant amounts of HVAC energy (e.g., computers, electronics) to help assure that 

relevant industrial buildings are being assessed. The load profiles and energy use data from such 

buildings could be combined with DOE’s tariff database to arrive at energy cost estimates, which 

would in turn be compared to the commercial facilities in the rulemaking that are in the same 

utility districts. This type of approach would not characterize unitary A/C operation in industrial 

buildings, but it would provide an indication of the significance of cost differences between 

industrial and commercial buildings. 

3.4 Electricity Prices for Multi-Building Facilities 

In the building sample that DOE developed from the CBECS database, 373 of the 1,033 

buildings are associated with multi-building facilities. Would the average price of electricity 

paid by this 373 building sub-sample likely differ from the average price of electricity of the 

1,033 sample? 

How likely are buildings associated with multi-building facilities to be separately metered for 

electricity and how likely are they to be on a separate rate structure from the multi-building 

facility? 

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3.4.1 Introduction

The issue of metering practices for multi-building facilities is significant to the rulemaking 

analysis because if buildings in this situation are metered collectively with other buildings on 

site, then they may qualify for less expensive rates. Treating these buildings as single-metered, 

stand-alone units could potentially result in the overestimation of energy costs and marginal cost 

savings estimates for these buildings. 

3.4.2 Assessment

The IRT contacted a sample of utilities and other personnel experienced with metering practices 

to determine how multi-building facilities are typically metered and billed. 

While each of the groups contacted stated that there was no absolute rule for how multi-building 

facilities are handled, the general trend was to independently meter each building on a multibuilding site and to assign rate structures based on each individual building. Several 

explanations for this approach were provided: 

• Metering individual buildings allows facility and energy managers to benchmark building 

performance more effectively. 

• Metering individual buildings simplifies the management of leased-out buildings on a multibuilding facility. 

• As a site evolves over time, new meters are added as new buildings are constructed, resulting 

in a collection of individually metered buildings. 

A few limited examples were provided in which account managers at a utility would aggregate 

loads from multiple meters on a site into a single profile at the customer’s request. However, in 

general it was reported that buildings were metered and billed as stand-alone entities and not 

combined in any way with other buildings on the site. 

The experience of the IRT is that under electricity deregulation a growing number of national 

accounts (e.g., nation-wide franchises) and “load aggregators” are negotiating combined 

metering and favorable rates for multi-building sites and even for separate sites (that will 

purchase their electricity, and be metered and billed), as a group. Such agreements are still 

uncommon, and with the varied nature and rate of “deregulation initiatives” throughout the 

nation, there is no way to factor this into the analyses supporting the ANOPR. 

3.4.3 Findings

It is unlikely that the average price of electricity paid by the sub-sample of buildings on multibuilding sites would differ significantly from the average price for the 1,033 sample based on 

any issues related to metering multi-building facilities. Currently the general trend for multibuilding sites is to meter buildings separately and to determine rate structures based on 

individual buildings. 

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3.5 Distribution of Buildings in the 1,033 Sample 

To what extent does the distribution of buildings in the 1,033 building sample accurately 

represent the distribution of actual commercial buildings by regions and climate? 

3.5.1 Introduction

The 1995 CBECS sample was drawn to be representative of the commercial building population 

in the U.S. DOE asked the IRT to assess how representative the sample of 1,033 buildings drawn 

from 1995 CBECS was in terms of its geographic- and climate-based distribution. This issue 

focuses solely on the 1,033 building data set and how well its distribution represents 1995 

CBECS and the total (national) commercial building population. 

Within the context of the rulemaking analysis, by reducing the size of the sample used by 

selecting out buildings relevant to the analysis of commercial A/C, the same degree of statistical 

significance is not maintained when extrapolating to the national population as for the original 

complete 1995 CBECS data base. It is beyond the scope of the IRT to verify the various 

statistical parameters of the 1,033-building database, the robustness of these parameters relative 

to the overall CBECS database, and the implications of sub-dividing the 1,033 building sample 

into smaller sub-groups that are used to draw national-level conclusions. 

This question posed by DOE is a qualitative, not a statistical, one, and one can qualitatively

examine the sample of buildings used to characterize the operation of unitary A/C systems to see 

if all regions and climate zones are represented on an equitable basis. In general, the 1,033 

building sample should mimic the distribution of the commercial building population by region 

and climate if the variances of parameters from which the sample was drawn are similar for each 

region and/or climate zone subset. 

3.5.2 Assessment

Table 6.2.6 in the ANOPR Technical Support Document (TSD Section 6.2.2) provides an 

indication of how representative the distribution of the set of 1,033 buildings is in terms of 

region (census division) and building type. With similar levels of variability of data, regions 

with a larger building population such as the Pacific census division (157,511 buildings) would 

likely be more heavily sampled in the 1,033 set, while lower population regions such as the West 

North Central division (33,398 buildings) would likely be represented by fewer samples. The 

table below presents a portion of Table 6.2.6 from the Technical Support Document. 

Census Division # of Samples from the 

1,033 Building Set 

All Commercial 

Buildings 

Number of Actual Buildings per 

Sample Building 

East North Central 145 102,455 707 

East South Central 83 102,069 1230 

Middle Atlantic 98 73,803 753 

Mountain 51 39,883 782 

New England 50 33,480 670 

Pacific 210 157,511 750 

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South Atlantic 197 120,693 613 

West North Central 55 33,398 607 

West South Central 144 120,465 837 

 

The ratio of actual buildings in a region to the number of samples included in the 1,033 set 

(column 4) is expected to be approximately in the same range across the nine different census 

divisions (recognizing however that population size and variance within each division will 

dictate the sample size for assuring statistical significance when extrapolated to the national 

population). The results shown in the table above indicate that with one exception, the sample of 

1,033 buildings is similar to the distribution of the commercial building population by region. 

With the exception of the East South Central division, all of the regions are within about 30% of 

the average actual buildings to sample building ratio (772). While the East South Central region 

had a lower representation, this could potentially be due to the amount of variances of buildings 

within the region. 

After reviewing the DOE TSD, the IRT feels the set of 1,033 is effective in representing the 

regional distribution of the building population. However, as noted in Section 3.5.1 above, a 

national sample such as CBECS is designed to give statistically valid results for the overall (i.e., 

U.S.) commercial building population. Statistical significance and un-biased results are not 

assured for analyses of individual regions (or climate zones.)

In terms of the sample’s effectiveness in representing the distribution of actual commercial 

buildings by climate, the following table uses information from Table 6.2.28 in the ANOPR 

Technical Support Document (TSD Section 6.2.6) and Table 1 from 1995 CBECS Building 

Characteristics tables. 

Climate Zone 

1 2 3 4 5 

# of Samples from the 1,033 

Building Set 

71 210 209 302 241 

All Commercial 

Buildings 

493,000 975,000 1,070,000 1,103,000 937,000 

Number of Actual Buildings per 

Sample Building 6,900 4,600 5,100 3,700 3,900 

Climate Zones are defined in terms of Cooling Degree Days (CDD) and Heating Degree Days 

(HDD) 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 

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Review of Analyses in Support of ANOPR 11/17/2003 21

The table indicates that buildings in Climate Zone 1 have a somewhat lower proportion of 

buildings in the sample than the region’s proportion of commercial buildings in the Nation. (For 

this Climate Zone, which is the coldest zone, there is only 1 sample in the analysis set for every 

6,900 actual buildings, while in Climate Zone 4 there is 1 sample for every 3,700 actual 

buildings.) 

Despite this, the range of sample to actual building ratios is reasonable, especially when one 

considers that the CBECS building set has been simultaneously screened based on several other 

criteria to arrive at the 1,033 sample. Some of these screening criteria may have had the effect of 

sampling more heavily in Climate Zones where air-conditioning is most frequently used or 

where there is a larger variance in building types. 

3.5.3 Findings

Based on the qualitative analyses above, the buildings in the 1,033 sample seem to adequately 

represent the regional and climate-based distribution of the commercial building population in 

the United States. For the purpose of drawing conclusions about the national commercial 

building population, however, one must be very cautious about subdividing the overall CBECS 

sample and performing micro-simulations or -analyses where the results are very different by 

building type within a region (or climate zone), and then extrapolating the results to draw 

conclusions of national significance. This problem is discussed in terms of the BLAST 

simulation model in Section 4.0. 

3.6 Cooling Loads for Unitary A/C Systems that are not the Primary 

HVAC System 

Is it reasonable to assume that cooling loads are similar whether a unitary system conditions the 

majority or a minority of floor space in a building? 

3.6.1 Introduction

This issue is essentially asking: will the cooling and electrical energy loads seen by a 5-ton 

unitary A/C unit that conditions 10% of the floor space in large building be the same as the loads 

experienced by a 5-ton unitary system that cools an entire building of smaller size? 

3.6.2 Assessment

Whether the two 5-ton systems in the example above experience the same cooling loads depends 

on a number of issues, including: 

• How closely were both units matched with the cooling load of the space that they are 

intended to condition? 

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Review of Analyses in Support of ANOPR 11/17/2003 22

• How is the unitary system that conditions only a minor portion of the floor space integrated 

with the other HVAC systems on-site? How completely are the conditioned spaces 

separated? 

• How do factors such as cooling set-point schedules, operation/activity schedules, humidity 

controls, economizer operations, ventilation requirements, and load diversity influence the 

operation of both systems? 

When faced with these questions it becomes apparent that it is quite difficult to say with much 

certainty that the loads faced by these two systems would be similar. In considering the factors 

listed above, DOE decided to focus the analysis on those systems that condition a majority of 

floor space, incorporating realistic judgments and assumptions regarding their operations as 

necessary. 

Those systems that condition a minority of the floor space would generally introduce more 

uncertainty in assumptions regarding their operational characteristics. Unitary systems that are 

deployed in buildings with other A/C systems may be operated in many ways, which makes it 

hard to accurately characterize their operations within the analysis. 

It should also be noted that modeling only systems that condition 70% or more of a building’s 

floor space resulted in a sample set that captured 73% of the annual A/C energy in commercial 

buildings that use unitary A/C systems. This fact, coupled with the increased uncertainties of 

characterizing equipment operation in buildings with a minority of floor space cooled by unitary 

equipment, justifies the decision to focus on buildings with 70% or more floor space cooled by 

unitary systems. 

3.6.3 Findings

It is difficult to judge whether cooling loads would be similar in buildings with a majority versus 

a minority of floor space cooled by unitary equipment. Characterizing the operation of unitary 

systems in buildings where they condition a minority of the floor space would involve more 

uncertainty in assumptions and judgments. This uncertainty, combined with the fact that the 

“70% and over” building sample captures a large cross-section of buildings with unitary 

equipment, led the IRT to conclude that selecting only buildings with a majority of floor space 

conditioned by unitary systems was a reasonable decision. 

3.7 Summary 

In the independent review of issues related to the building sample used in the rulemaking 

analysis, these findings were made: 

Building Sample Development and Representation

• The 1,033 building sample set developed from 1995 CBECS effectively represents the 

distribution of unitary equipment in commercial buildings. 

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Review of Analyses in Support of ANOPR 11/17/2003 23

• The use of 1995 CBECS as the primary data source for the building sample development is 

reasonable. 

• No national-level data sets detailing the distribution of unitary A/C equipment across all 

building types (including industrial) were found. 

• Some auxiliary data resources exist that could help guide the rulemaking analysis and offer 

insights into the significance of other issues highlighted in this review. 

• Potential alternatives to the commercial building sample development process may be 

considered, but appear unlikely to change the results of the analysis. 

Omission of Industrial Buildings – Impact on Unitary A/C Energy Use 

• The claim that excluding industrial buildings from the analysis leaves a meaningful portion 

of unitary A/C energy out of the rulemaking analysis is valid – meaning that the claim is 

legitimate and this issue has some impact on the analysis. The available data on unitary A/C 

energy usage in industrial buildings indicates that these buildings use a meaningful amount 

of unitary system cooling energy. This merits further analysis. However, the quantity of 

industrial building unitary A/C energy is unlikely to be as large as estimated in the 

stakeholder comments. 

• Downplaying the use of unitary cooling equipment in the industrial sector based on the 

general size of industrial buildings is not adequately supported in the rulemaking analysis 

and runs contrary to other recent building research. 

• Estimates of unitary A/C energy in commercial buildings on industrial sites are small but not 

insignificant. These types of buildings, combined with actual industrial and manufacturing 

buildings, represent buildings that use unitary A/C equipment that were not included in the 

rulemaking analysis. 

• Rough approximations of unitary A/C energy in industrial buildings indicate that packaged 

systems in these buildings consume a meaningful amount of energy, even if the estimates in 

the stakeholder comments are overstated to some extent. 

• The lack of data on the distribution of unitary A/C equipment in industrial buildings prevents 

the determination of whether excluding this portion of A/C energy is significant (e.g., would 

it change the results of the analysis were it included?). Additional efforts to arrive at an 

estimate of how much industrial unitary energy use would be significant could help to clarify 

this issue. 

Omission of Industrial Buildings – Impact on Load Profile and Energy Costs

• The claim that industrial and commercial buildings would pay different prices for energy 

based on the load characteristics of these two types of buildings is valid. This finding is 

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Review of Analyses in Support of ANOPR 11/17/2003 24

based on a review of the rulemaking analysis, the tariff archive, and general information on 

load profiles. 

• The significance of this issue (would it change the results of the analysis) depends on how 

much lower industrial rates might be and how many industrial facilities that use unitary A/C 

equipment would pay lower rates. At this point the significance has not been determined, but 

additional analysis involving industrial energy and load data and the rulemaking tariff 

database could potentially help to clarify the issue. 

• Tariffs based on business type (e.g., an “industrial” tariff) are not a significant factor and 

would likely not translate into lower prices for industrial buildings on a scale that would alter 

the results of the analysis. 

Electricity Prices and Metering Practices for Multi-Building Facilities

• The general trend for multi-building sites is to meter buildings separately and to determine 

rate structures based on individual buildings. 

• It is unlikely that the average price of electricity paid by the sub-sample of buildings on 

multi-building sites (373) would differ significantly from the average price for the 1,033 

sample based on any issues related to metering multi-building facilities. 

Distribution of the Building Sample by Region and Climate

• The distribution of buildings in the 1,033 sample adequately represents the regional- and 

climate-based distribution of the commercial building population. 

• Within the context of the energy use simulations, caution must be exercised when taking the 

national CBECS sample, subdividing it, performing simulations, and extrapolating the results 

to draw national-level conclusions. (Addressed in Section 4.0 of this report) 

Cooling Loads in Buildings where Unitary A/C Cools Only a Minor Portion of the Space

• Characterizing the operation of unitary systems in buildings where they condition a minority 

of the floor space would involve more far-reaching assumptions and judgments, compared to 

buildings where unitary systems cool all or most of the building. 

• Including only those buildings that cool a majority of floor space with unitary systems in the 

building sample captures a broad cross-section of commercial buildings with unitary 

equipment and is a reasonable approach. 

3.8 Resources Used in the Independent Review 

1992 Commercial Buildings Energy Consumption Survey. Energy Information Administration, 

1992. 

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1994 Manufacturing Energy Consumption Survey. Energy Information Administration, 1994. 

1995 Commercial Buildings Energy Consumption Survey. Energy Information Administration, 

1995. 

 

1998 Manufacturing Energy Consumption Survey. Energy Information Administration, 1998. 

1999 Commercial Buildings Energy Consumption Survey. Energy Information Administration, 

1999. 

Archive of Utility Tariffs. DOE. http://eetd.lbl.gov/ea/ees/tariffs/index.php 

ARI Statistical Profile. Air-Conditioning and Refrigeration Institute, 2001.

A Preliminary Quantification. The Effects of Excluding Industrial/Manufacturing Sites from the 

DOE Rulemaking to Determine Efficiency Levels of Unitary Large Equipment. Charles Frazell, 

PE. March 2003. 

Background Research Summary. Integrated Energy Systems Productivity and Building Science. 

New Buildings Institute for the California Energy Commission Public Interest Energy Research 

(PIER) Program, 200l. 

Bridging the Efficiency Gap: Commercial Packaged Rooftop Air Conditioners. Shugars, J.; 

Coleman, P.; Payne, C.; and Van Wie McGrory, L., LBL-46190, ACEEE 2000 Summer School, 

www.dc.lbl.gov/~payne/publications/PackageRooftopAC.pdf 

California Non-Residential New Construction Characteristics Database. Architectural Energy 

Corporation, 1999. 

Comments on the Energy Conservation Program for Consumer Products: Energy Conservation 

Standards for Commercial Unitary Air Conditioners and Heat Pumps. Docket Number: EERM/STD-01-375. DOE. 

Commercial Building Energy Use and End Use Load Characterization Analysis, DOE 

presentation, www.eere.energy.gov/buildingsappliance_standards/commercial/ac_hp.html 

Commercial Unitary Air Conditioner and Heat Pump Life Cycle Cost Analysis: Inputs and 

Results. Lawrence Berkeley National Laboratory (presentation), 2003. 

http://www.eere.energy.gov/buildingsappliance_standards/commercial/ac_hp.html 

Commercial Unitary Air Conditioner and Heat Pump Framework. DOE, 2001. 

Communications with Alan Swenson. Energy Information Administration, September – October 

2003. 

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Review of Analyses in Support of ANOPR 11/17/2003 26

Communications with Craig Drumheller, formerly of Public Service E&G. September 2003. 

Communications with Jean Lupinacci. Energy Star, October 2003. 

Communications with Jerry Jackson. Market Analysis and Information System (MAISY), 

September 2003. 

Communications with Jim Raba. DOE Office of Building Technologies, September – October 

2003. 

Communications with Leslie Latusek. Allegheny Power, October 2003. 

Communications with Martha Johnson. Energy Information Administration, September – 

October 2003. 

Communications with Ms. McDonald. Baltimore Gas and Electric, October 2003. 

Communications with Phil Coleman. Lawrence Berkeley National Laboratory. September – 

October 2003. 

Draft Advance Notice of Proposed Rulemaking and Notice of Public Hearing. Energy 

Conservation Program for Commercial and Industrial Equipment: Energy Conservation 

Standards for Commercial Unitary Air Conditioners and Heat Pumps, DOE (not published). 

Draft Advance Notice of Proposed Rulemaking and Notice of Public Hearing. Energy 

Conservation Program for Commercial and Industrial Equipment: Energy Conservation 

Standards for Commercial Unitary Air Conditioners and Heat Pumps. Technical Support 

Document. DOE (not published). 

Guidelines for Energy-Efficient Commercial Unitary HVAC Systems. Consortium for Energy 

Efficiency, 2001. 

Market Monitor 2000 – A Report by the Division of Energy Resources. Commonwealth of 

Massachusetts, Office of Consumer Affairs and Business Regulation, February 2002. 

Non-Residential New Construction Baseline Study. RLW Analytics, 1999. 

Public Workshop on a Rulemaking to Consider Energy Efficiency Standards for Commercial 

Unitary Air Conditions and Heat Pumps. (transcript). US Department of Energy. 

Opportunities for Microprocessor and Fuel Cell/ Gas Hybrid Systems in Industrial Applications, 

Arthur D. Little, January 2000 

Screening Analysis for EPACT-Covered Commercial HVAC and Water-Heating Equipment. US 

Department of Energy, April 2000. 

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Review of Analyses in Support of ANOPR 11/17/2003 27

Appendix to 3.0 Estimates of Unitary A/C Energy Use in Commercial Buildings 

Approach #1 

From 1995 CBECS Table 3 (in the Energy End Uses Section of CBECS) 

 Column A Column B Column C 

 

Electrical Cooling Energy 

Consumption in Buildings with 

This Type of Cooling 

Equipment*

Percentage of Total 

Reported Electrical 

Cooling Energy by 

Equipment Type 

Adjusted Electrical 

Cooling Energy by 

Equipment Type *** 

 (trillion BTU) (trillion BTU) 

Residential Type 

Central AC 65 12.4% 42.0 

Heat Pumps 62 11.8% 40.1 

Individual AC 66 12.5% 42.7 

District Chilled Water 4 0.8% 2.6 

Central Chiller 102 19.4% 65.9 

Packaged AC Units 205 39.0% 132.5 

Swamp Coolers 16 3.0% 10.3 

Other 6 1.1% 3.9 

 

Total 526 340.0 

Total Electrical Cooling 

Energy in Commercial 

Buildings (trillion BTU)** 340 

 

* This is NOT the energy used by the given type of equipment. Instead, it is the electrical cooling energy 

 used by buildings that contain this type of equipment. 

 The cooling energy for buildings with multiple types of cooling equipment will be counted in more than 1 

category. 

** From Table 3. Electricity Consumption by End Use, 1995. *** The adjusted value calculates the cooling energy broken out by equipment type by proportionally adjusting the 

 reported values in Column A to account for the "double-counted" cooling energy. 

For example, since buildings with Central Chillers represent 19.4% of the total reported electrical cooling energy (Column 

B), the reported cooling energy for buildings with Central Chillers was adjusted downward by 19.4% of the "doublecounted" energy = 526 - 340 = 186. So the Central Chiller cooling energy was adjusted from 102 - .194(186) = 65.9 

132.5 trillion BTU (for packaged A/C) = 38.8 billion kWh in site electricity (3412 Btu/kWh) 

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Review of Analyses in Support of ANOPR 11/17/2003 28

Approach #2

Using 1995 CBECS Table 2. 

Energy End-Use Intensities for Sum of Major Fuels

 

Cooling EUI for buildings with packaged A/C units = 7.9 kBTU/sq foot 

Packaged A/C units are used in buildings with a total square footage of 26, 628 million square feet 

(using this square footage offers a generous estimate of unitary energy use because these buildings 

are not cooled exclusively with unitary systems)

An extreme upper limit for packaged A/C energy use is therefore

(7900 BTU/ sq ft) * (26, 628 million sq ft) = 210.36 trillion BTU 

 = 61.6 billion kWh 

However, since packaged A/C systems do not cool all of the square footage 

in these buildings, it is more reasonable to use an estimate of how much floor space 

is actually conditioned by unitary systems. Using Table 3C - "Main Cooling Equipment, 

Cooled Floorspace" from A Look at Commercial Buildings in 1995: Characteristics, 

Energy Consumption, and Energy Expenditures (EIA), 

the total cooled floor space in commercial buildings is: 36,001 million sq ft 

According to this same report on p. 27, packaged A/C units 

cooled 55% of all cooled floorspace, or: 19,800 million sq ft 

Using this area conditioned by unitary systems with the Cooling 

EUI of 7.9 kBTU/sq foot, the estimated energy use = 156.42 trillion BTU 

= 45.8 billion kWh 

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Review of Analyses in Support of ANOPR 11/17/2003 29

4.0 EVALUATION OF THE BLAST ENGINEERING ANALYSIS 

4.1 Introduction 

The Department of Energy (DOE) is preparing an Advance Notice of Proposed Rulemaking 

(ANOPR) for minimum efficiency standards for unitary air conditioners. The ANOPR and 

supporting documentation will set minimum efficiency standards. This section summarizes a 

review of several aspects of the DOE analyses supporting the ANOPR, including: 

(1) The rooftop air conditioning model used in BLAST 

• BLAST’s inability to model multiple compressor air conditioning units; 

• The temperature at which head pressure controls are activated; 

• Modeling the saturated condensing temperature; 

• Modeling the saturated suction temperature; 

• Validation of the BLAST model; 

• Capacities of air conditioners simulated in the DOE analyses; 

• Assumptions about the maximum efficiency of commercially-available unitary air 

conditioners; and 

• Inability to model part load performance. 

(2) Some of the primary assumptions used by DOE for the analyses to support the ANOPR 

• Correcting citation of A/C efficiency levels in ASHRAE 90.1-1999; 

• Assumptions on use of low-emissivity glass in commercial construction; and 

• Assumptions about economizer use. 

(3) A review of the use of CBECS data to characterize the commercial buildings and energy use 

for the analyses supporting the ANOPR 

• Statistical significance of results obtained by simulating performance of buildings in 

small sub-samples of CBECS; 

• Estimation of air conditioning energy use from CBECS data; 

• Suggested method to resolve this issue; and 

• Calibrating the BLAST model to individual buildings. 

Each issue is discussed below: 

4.2 Review of the Rooftop Model in BLAST 

Rooftops are modeled in BLAST as single compressor direct expansion (DX) condensing units. 

The original DX model was published by Hittle in 1982. The equations described in the DX 

condensing unit HELP files of the BLAST computer program are essentially the same as those 

outlined in the model by Hittle (1982). There are minor differences in some of the constants and 

a normalization routine to estimate the full load power ratio. 

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The model in BLAST has some significant restrictions that directly affect the DOE analysis. In 

addition, the BLAST analysis used to support the ANOPR appears to have some assumptions 

that should be reevaluated. 

4.2.1 The rooftop model in BLAST assumes a single compressor air conditioner with 

compressor unloading

A single compressor rooftop unit is a reasonable assumption for rooftop units with a capacity 

under 10 tons (120,000 Btu/h), but it is not a reasonable assumption for larger units. The 

ANOPR includes units with capacities up to 20 tons. Many of the units between 10 and 20 tons 

have multiple compressors. The BLAST rooftop model is not capable of modeling these types of 

rooftops. As currently configured, BLAST should not be used to model the larger capacity 

rooftops. With multiple compressors, the part load performance should be much better than 

would be for single speed, single compressor units. Thus, the actual energy use of the baseline 

and improved efficiency units will both be less than predicted by BLAST. 

In Section 4.2 of the TSD, a multiple compressor rooftop unit is portrayed as an option that can 

improve the annual energy use, but have no effect on the nominal full load EER. Multiple 

compressor units are more the norm in the larger rooftop class. Thus, the baseline unit should 

have multiple compressors in it. The compressor model in BLAST has a simple routine that 

simulates cylinder unloading in single speed compressors. As was pointed out in comments by 

Lennox (2003), the BLAST part load model of cylinder unloading has EER trends as a function 

of temperature that are different than the performance of a multiple compressor rooftop. If 

BLAST cannot model a multiple compressor rooftop unit, then it should not be used to predict 

the energy use or savings of the larger class of rooftops. 

We believe that this is a significant deficiency in the analysis and needs to be addressed. One 

way to address the deficiency in BLAST is to develop a multi-compressor rooftop model. This 

will take time to develop the model, validate it, and implement it in BLAST. Without a new 

model, BLAST will not capture the part load performance of multiple compressor rooftops found 

in the larger capacity class. 

The part load model in BLAST assumes the compressor has cylinder unloading. Discussions 

with several manufacturers indicate that many of the smaller rooftop units use hermetic 

compressors, which do not have unloading capabilities. For these units, the compressor will 

cycle on and off as the cooling load decreases below the steady state capacity of the unit. We 

would expect the part load efficiency of a unit that cycles on and off to be less than one with 

cylinder unloading. If the part load on/off cycling performance could be characterized, it would 

be possible to define a set of coefficients that could be used in BLAST. 

4.2.2 The temperature used for implementing head pressure controls is excessively high

Head pressure controls are used in commercial air conditioners to artificially keep the head 

pressure high when the outdoor temperature is low. This maintains a sufficient pressure across 

the expansion device to ensure proper refrigerant control in the system. The amount of pressure 

required across the expansion device will depend on whether it is a thermal expansion valve, 

fixed short-tube, or electronic expansion valve. For rooftops, head pressure controls are often 

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implemented by either varying the speed of the condenser fan or cycling the condenser fan(s) on 

and off. 

In BLAST, head pressure controls are implemented by specifying the minimum saturated 

condensing temperature (MSCT). This is the minimum refrigerant temperature that the 

condenser will operate at during the simulation. Normally, as the outdoor temperature drops, the 

condensing temperature will also drop in an air conditioning system. The default value for the 

MSCT in BLAST is 100 oF(37.8 oC). BLAST calculates a saturation temperature in the 

condenser based on the outdoor temperature and heat transfer characteristics of the condenser. 

We would expect 100oF to correspond to an outdoor temperature of about 75oF. If the calculated 

saturation temperature is less than 100 oF, then BLAST will automatically keep the refrigerant 

condensing temperature at 100 oF. This value for MSCT is high. A review of some literature 

from manufacturers as well as some discussions with their engineers indicated that current 

rooftop units are designed to operate down to at least 45 oF outdoor temperature without head 

pressure controls. At this outdoor temperature, the condensing temperature would correspond to 

nominally 65 to 70 oF, depending on the design of the condenser. Thus, the MSCT for these 

units would be 65 to 70oF, not 100oF. The impact of using the default value (100oF) for MSCT 

versus a more reasonable 65 to 70oF is that the energy use calculated in BLAST will be 

unrealistically high. The actual percentage increase in energy use will depend on the cooling 

load and number of hours that the outdoor temperatures are between 45 and 75oF. There may be 

some parts of the country (South Florida or South Texas) where units would not even need head 

pressure controls or they would be used sparingly because temperatures rarely get below 40oF. 

The sample BLAST deck shows that the default MSCT was used because it never was reset. The 

default value should not be used. We strongly recommend that the analysts contact the major 

rooftop manufacturers to determine: (1) a reasonable range of outdoor temperatures that their 

units operate before needing head pressure controls, and (2) how head pressure controls are 

implemented in the units (i.e., actual saturation temperatures, whether fan cycling, etc.). While 

we have provided suggested values here, we feel reasonable values of MSCT should be 

developed and fully documented. 

4.2.3 The analysis supporting the ANOPR did not factor in all the capacity-related

variables in BLAST 

In the BLAST rooftop model, the saturated condensing temperature (SCT) is calculated using the 

equation: 

( )

CUA

LOAD(1 DFLPR) SCT + =OADB+ (1) 

where, 

OADB = outside air dry bulb temperature 

LOAD = total load on the condensing unit 

DFLPR = design full-load power ratio 

CUA = condenser U-factor 

The default value of CUA is 27.43 kBtu/(hr oF). The documentation in the help files clearly 

shows that this CUA is for a unit with a cooling capacity of approximately 500,000 Btu/hr (40+ 

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tons). The description in the Hittle (1982) paper makes it clear that CUA varies with the 

capacity of the unit. Both the BLAST documentation and Hittle (1982) show how this value can 

be estimated from manufacturer's data. The impact of using the default value is an underestimate 

of the condensing temperature for all units with capacities less than 40 tons. The impact on the 

analysis is twofold. First, an underestimate of the SCT will decrease the estimate of the energy 

use of the rooftop unit. The power of the unit is dependent on the difference between the 

saturated condensing temperature and the saturated suction temperature (SST). An 

underestimate of the SCT will produce a smaller (SCT – SST), which will decrease the estimated 

power consumption of the unit. The second impact is that an estimate of a smaller SCT will 

force BLAST to implement head pressure controls at a higher outdoor temperature than it would 

with a reasonable value of SCT. For example, assume a 5 ton (60,000 Btu/hr) rooftop unit is 

being modeled and the outdoor temperature is 95oF. Using Equation 1, BLAST would calculate 

SCT as: 

95 F 2.9 F 97.9 F

hr F

kBtu 27.43

(1 0.326) hr

Btu 60000

SCT 95 F = ° + ° = °

°

⎟

⎠

⎞ ⎜

⎝

⎛ +

= ° + (2) 

In reality, the CUA for a 5 ton unit should be about 3.31 kBtu/(hoF). A more realistic calculated 

value of SCT would be: 

95 F 24 F 119 F

hr F

kBtu 3.31

(1 0.326) hr

Btu 60000

SCT 95 F = ° + ° = °

°

⎟

⎠

⎞ ⎜

⎝

⎛ +

= ° + (3) 

BLAST relies on the SCT calculation and MSCT to determine when head pressure controls 

should be engaged. Using the default value of CUA along with the default value of MSCT 

(100oF) would mean that the head pressure controls would already be in use when the outdoor 

temperature is 95oF! In reality, this would not happen because the head pressure controls would 

not engage because the actual SCT would really be about 120oF, not 97.9oF. 

Using the default CUA is a major error in the BLAST analysis used to support the ANOPR. 

Given that the largest rooftop unit covered by the ANOPR is 20 tons, every one of the units 

simulated will have an estimated SCT that is too small. We believe all BLAST runs used to 

support the analysis of the ANOPR will need to be rerun with more reasonable values for the 

CUA for each rooftop used in the buildings. Given that some of the buildings modeled have 15 

or more rooftops, individual CUAs consistent with the capacity of the units must be estimated 

and input for each unit. Hittle (1982) and the BLAST HELP files provide illustrations of how 

this can be done using manufacturers’ published performance data. CUA data for each of the 

rooftop units used in a building must be estimated and input into BLAST for the baseline rooftop 

units. We would also expect the CUAs to vary between the lower efficiency baseline units and 

the higher efficiency units. High efficiency units might have larger condensers, high 

performance fins, etc. that would provide a larger CUA than a lower efficiency unit. Thus, CUAs 

also need to also be developed for each of the higher efficiency units analyzed. 

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4.2.4 The rooftop model assumes a low saturated suction temperature

The user can input the “Design Saturated Suction Temperature” (DSST) or use the default value. 

The input file provided to the IRT used the default value of the DSST, which is 40oF. BLAST 

calculates the estimated Saturation Suction Temperature, SST, based on the DSST, the amount of 

loading on the evaporator (CPLR), and the unloader throttling range (UTR). The default value 

of the UTR is 4oF. Using the default DSST, with the default CPLR and UTR produces a SST 

with a minimum of 36oF and a maximum of 40oF. The IRT has previously measured SSTs for 

smaller (3 to 10 tons) package units in the Texas A&M University (TAMU) laboratory and have 

found that the SSTs for these units varied from 40 to 45oF when the entering air temperature 

across the evaporator was 80oF and outdoor temperature was 95°F. This would indicate that the 

DSST should potentially be adjusted up about 5°F. 

In BLAST, the saturated condensing temperature (SCT) and the SST are ultimately used to 

estimate the power used by the compressor. The larger the difference between the SCT and SST, 

the larger the amount of energy used. Thus, if the SST is smaller than it should be, it would 

contribute to a larger difference between the SCT and SST, which would increase the estimated 

energy use. Correcting this problem is relatively simple in that the default value for the SST 

should not be used. Manufacturers should have sample data of saturated suction temperature 

from data they collect during testing of their rooftop units. These data could provide more 

realistic values for input into BLAST. 

4.2.5 The rooftop model in BLAST has never been experimentally verified

The rooftop model implemented in BLAST dates back to a model developed by Hittle (1982). 

One of the recommendations in that paper was that the model needed to be experimentally 

verified. From a follow-up phone conversation with Hittle (2003), it was learned that the rooftop 

model has never been experimentally verified. While a similar chiller model (1983) developed 

by CERL went through a limited validation, the rooftop model never was. We believe using an 

engineering model that has not been verified with test data opens DOE to significant criticism. 

Engineers build models of processes and equipment, but rely on test data to make corrections in 

the model and to ensure the model is predicting the correct trends/values. Test data will also 

provide some assurance of the accuracy of the model. For residential sized air conditioners, the 

analyst has the option of using two well known and verified models from either Oak Ridge 

National Laboratories (ORNL) or the National Institute for Standards and Technology (NIST) 

(Rice 1991 and Domanski and Didion 1983). Both models have been used and compared against 

experimental test data. Without any experimental verification of the rooftop model in BLAST, it 

will be difficult for the analysts (and DOE) to say that their estimates of performance are ± 1% or 

±20%. Besides the deficiencies already pointed out in the model, we think it is important DOE 

verify the accuracy of the rooftop model with experimental test data. Because there are two 

classes of rooftop units, at a minimum verification should be done for a unit in each of the size 

classes. Failure to provide at least some minimum verification of the rooftop model with test 

data will lead to doubt of the validity of the BLAST analysis as well as all the economic analysis 

that depends on it. 

This is not to say that the BLAST program is not valid. That BLAST has been widely used for 

over 20 years illustrates that its simulations appear reasonable, and its results are used and 

accepted for HVAC studies. However, the standard of analysis for a building owner who wishes 

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to evaluate various A/C models, an engineering team trying to estimate the value of improved 

A/C efficiency, or an Energy Services Company performing an audit to determine which 

efficiency measures to implement is not nearly as rigorous as the standards DOE must meet to 

justify regulations of the importance and breadth of the proposed NOPR. The IRT does not feel 

BLAST is invalid; we are concerned that without experimental validation of the rooftop model, it 

might be difficult to defend the analysis in a public hearing. 

4.2.6 The BLAST analysis includes rooftop air conditioners outside the range of 

capacities of the two classes being analyzed

After reviewing the example BLAST input files for two different buildings, the files indicate that 

a separate packaged rooftop unit was used for each thermal zone in the building regardless of the 

zone size. This approach does not match the real world implementation of packaged rooftop 

cooling systems. A proper application of the model would have used a single rooftop to serve 

the loads for several smaller zones. If the point of the BLAST simulation is to evaluate units in 

the five to twenty ton capacity ranges, then the total loads handled by the units should be in that 

range of capacities. Table 4-1 below shows the packaged units used in one of the simulations. 

The capacity of the units ranged in size from 2.3 to 17 tons. Only three of the rooftops are 

actually covered by the proposed standard for the two classes of rooftops. All of the other 12 

zones are handled by residential-sized equipment. Either the equipment needs to be modeled 

differently or the buildings should be rezoned so that appropriately sized rooftops can be used to 

cool the building. 

Table 4-1 – Capacity of the air conditioners used in the 15 zones in one of the BLAST input files 

4.2.7 The maximum efficiency of the rooftops used in the analysis is too low

Section 3.5.1 of the TSD provides a description of the efficiency levels of the rooftop classes. 

The maximum technologically feasible design that DOE chose to consider was one with an 

Energy Efficiency Ratio (EER) of 12.0. The Consortium for Energy Efficiency (CEE 2003) 

publishes summary tables of Air Conditioning and Refrigeration Institute (ARI) data for rooftops 

by manufacturer. The data include capacity, IPLV and EER on current units on the market. 

Zone Tons 

1 2.8

2 3.3

3 2.3

4 3.0

5 16.3

6 3.7

7 4.4

8 3.0

9 4.1

10 16.4

11 3.8

12 4.5

13 3.1

14 4.0

15 17.0

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These data indicate that for the 75 to 135 kBtu/h capacities, there are 4 units with EERs over

12.0. In fact, the highest is 13.5. For the capacity range from 135 to 240 kBtu/h, there were six 

units with an EER of 12.0 or above. The DOE Unitary Air-conditioner Program (UAP), under its 

Unitary Air Conditioner Technology Procurement, lists an 88,000 Btu/h rooftop model with an 

EER of 13.5 (price $4,325) and a 115,000 Btu/h model with an EER of 13.4 (price $5,525). 

There are units that can be purchased today with efficiencies as much as 12% higher than 12.0. 

This is similar to the residential air conditioner/heat pump analysis in the early 1980s. At that 

time, DOE published that the maximum technologically feasible SEER for residential air 

conditioners was 10.4 when there was an air conditioner with a SEER of 14.0 on the market 

(O’Neal, 1985). The DOE should closely re-examine the issue of what the maximum 

technologically feasible efficiency EER should be before publishing the ANOPR. 

4.2.8 Ignoring part load performance excludes many options that could improve energy 

use

In Section 4.2 of the TSD, a statement was made that “there are technologies available in the 

marketplace which typically have no impact on the EER (nominal full-load) rating, but which 

may improve annual energy performance.” A list is then provided of technologies that DOE 

would not consider. The issue of multiple compressors was discussed earlier. Given that rooftop 

units are typically much more complicated than residential units and must, in many cases, 

operate at part load conditions for a large fraction of the year, it seems inconsistent for the DOE 

to incorporate part load performance in residential air conditioner standards and ignore it for 

commercial air conditioner standards. Residential equipment performance is based on the 

Seasonal Energy Efficiency Ratio (SEER). The commercial equipment equivalent to SEER is 

the Integrated Part Load Value (IPLV). Residential units with multiple speed compressors, fans, 

variable speed compressors, all can be tested, performance quantified, and assigned an SEER. 

To ignore the many options available to rooftop manufacturers to improve part load performance 

and annual energy usage while having to justify the minimum standards on the basis of 

calculated annual energy use and dollar savings is inconsistent. Units with similar EERs can 

have dramatically different IPLVs. For example, in a recent report by TIAX (2003), they 

designed and tested three nominal 10 ton rooftop units that had EERs of between 9.3 and 10.8, 

but which had IPLVs that ranged from 12.8 to 14.1. These IPLVs are about 20 to 30% higher 

than similarly sized units on sold on the market in 2003 with similar EERs (CEE 2003). The 

units with the higher IPLV would use significantly less energy than those with lower IPLVs but 

have the same EER. The TIAX report illustrates that EER alone should not be used as the 

performance criteria for rooftop units. The IPLV is in the existing test procedure and can easily 

be reported by the manufacturer. BLAST is currently incapable of handling most of the options 

listed in Section 4.2 of the TSD. Thus, a new model would need to be developed that could 

include these options. Differentiating IPLV from EER also requires more sophisticated 

consideration of electric energy and demand pricing. (While the IRT feels that A/C efficiency 

standards for this class of equipment are better based on IPLV than EER, DOE is required by 

statute to base this rulemaking on EER.) 

4.2.9 Summary

BLAST includes a rooftop air conditioner model that can be combined with the building models 

in BLAST to estimate the annual energy use for cooling of rooftop air conditioners. The analysis 

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used to support the ANOPR used BLAST. There were a number of limitations of the analysis 

that were discussed. Some of these, such as specification of the condenser U-factor (CUA), the 

value of the design saturated suction temperature (DSST), the maximum saturated condensing 

temperature (MSCT), the maximum technological efficiency, and the use of rooftops outside the 

size range of the two capacity classes, can readily be corrected for future analysis with BLAST. 

However, there were several items that we believe make BLAST a poor choice for the 

engineering analysis tool to support the ANOPR. The biggest restriction in BLAST is that it 

only handles single compressor rooftop systems and models them with cylinder unloading. 

While the rooftops in the smaller class typically use single compressors, they are hermetic 

compressors and will usually use on/off cycling to control capacity. The IRT expects the part 

load performance of hermetic compressor systems to be different than that modeled with 

BLAST. Because many rooftops in the larger capacity class have two compressors, the part 

load rooftop model in BLAST will not provide a good representation of the larger capacity 

rooftops. 

The IRT recommends that the verification of the rooftop model is important to provide 

confidence in the engineering analysis. The BLAST analysis provides the energy use and cost 

estimates that will be used to justify the setting of the minimum standards. Thus, it is important 

to have a model that has been verified. 

4.3 Rooftop Efficiency and Controls 

In this section, basic assumptions and data regarding rooftop efficiency and controls are 

discussed. 

4.3.1 Efficiency Levels in ASHRAE Standards

 

In Figure 5.5.1 of the DOE TSD analysis, EER levels listed for the 7.5 and 15 ton units are 

labeled as ASHRAE 90.1-1999. While these levels were published as part of ASHRAE 90.1-

1999, they are scheduled to go into effect two years after the 1999 Standard is published, i.e., in 

2001. Therefore, the label in Figure 5.5.1 should read “2001 EER levels in ASHRAE 90.1-

1999” not “ASHRAE 90.1-1999”. Similar corrections may be needed for references to 

Table 6.2.8 EER Levels Selected for Building Simulations for Efficiency Cases 3 and 5. 

It is straightforward to correct these labels. 

4.3.2 Window Characteristics 

This section describes the use of low-emissivity (low-e) glazing in commercial construction. 

DOE assumed a linear growth of the application of low-e glass from 1.2% in 1985 to a 12.9% 

level in 1995, and on to an assumed current level of 20% market share. Even the linear growth 

leading to 20% is likely optimistic. From the IRT’s experience, unless required by building 

codes, only very knowledgeable owners would have low-e glass installed in their newly 

constructed commercial buildings. Until the late 1990’s, the premium for the upgrade from 

standard to low-e glazing was several dollars/ft2

 of glass area. Beginning in the early 2000’s, 

many states began to adopt the International Code Council’s (ICC) family of building codes. In 

those states that adopted the entire code set, it would also include the International Energy 

Conservation Code (IECC). Depending on HDD or CDD climate data, the IECC could require 

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the use of low-e glass. The ICC has no such provision for commercial construction. So, it is 

restated that even this modest application percentage for low-e application in commercial 

construction is optimistic. 

In addition, the DOE assumed that low-e glazing was not used in their analysis for climate zones 

with < 4000 HDD and > 2000 CDD. This would eliminate the entire Southeastern portion of the 

United States, which includes Florida and Texas. These two states represent a considerable 

portion of the air-conditioning market in the U.S. Assuming low-e for heating only in the 

northern U.S. would underestimate the impact of high efficiency electric cooling equipment 

(lower load in building). Conversely, assuming use of low-e in southern climates as well would 

reduce the apparent cooling load on the HVAC equipment and allow for more efficient 

operation. In either case, any use of low-e glazing is generally on construction involving only 

very well informed owners. 

4.3.3 Economizer Use and Control Assumptions

DOE states that they “originally were going to use fixed, dry-bulb economizers” for the analysis. 

They decided not to use that control strategy because of “problems” with BLAST’S simulation 

of that type of control when applied to “certain systems”. While it is true that differential 

enthalpy control is available for economizer control, our experience is that it is very seldom

chosen as an option on most smaller (7.5 – 15 ton) packaged equipment. It is usually the most 

sophisticated control strategy available and is also the most expensive control option. This range 

(7.5 – 15 ton) represents a very common application for small commercial construction and is the 

preferred choice for economic reasons. It is “cheap,” low first-cost equipment. The savings are 

mostly in the labor required to complete an installation. It is significantly easier to install this 

type of equipment than a comparable capacity central system. As such, this range of equipment 

is usually specified (by the owner or engineer) to be a pretty basic package with a minimum of 

expensive add-on options. 

A second issue has to do with whether economizers should be applied in all buildings. For 

example, in hot and humid climates, the applicability of an economizer operation is limited at 

best. The ASHRAE 90.1-1999 essentially eliminates the requirement for economizer in any area 

of the southeastern United States. Table 6.3.1 Minimum System Size for Which an Economizer 

is Required (ASHRAE 90.1-1999) has several entries for “N.R.” that stands for “No 

Requirement”. According to the weather data listed in Normative Appendix D, Table D-1, U.S. 

Climatic Data, there is not a single location in the state of Florida that would be required to have 

an economizer installed on any size HVAC system. DOE’s assumption of economizer use is 

probably overstated and would lead to an overstatement of the opportunity for “free” cooling 

through the use of an economizer. 

4.3.4 Summary

This section summarized some of the assumptions used in the BLAST analysis. These related to 

the window characteristics and economizer use and control. The IRT recommends that the DOE 

take a closer look at these items and the assumptions used in the analysis. Whatever assumptions 

are eventually used should have adequate justification to ensure they do not inadvertently bias 

the energy use calculations. 

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4.4 Comparison of BLAST Simulation Methodology and CBECS Estimates of 

Energy Use 

In this section, the BLAST energy simulation methodology is discussed along with the CBECS 

estimates of energy use. An alternative methodology for estimating energy use from the CBECS 

data base is also discussed. 

Previous analysis related to unitary air conditioners used “typical” building prototypes in energy 

use modeling analysis. The greatest level of detail in this analysis included seven building types 

and eleven climate zones (Chapter 6 of the DOE Documentation). While this analysis approach 

is attractive in its simplicity and certainly serves a purpose in the early stages of the kind of 

national modeling to which it was applied, its results suffer from a well known deficiency called 

aggregation bias 

Aggregation bias shows up, for instance, when analysis of a “typical” office building yields 

results that are different than results based on analysis of a representative sample of small, 

medium and large buildings. These differences exist even when the average characteristics of all 

of the buildings corresponds to characteristics of the typical building. This type of error 

frequently occurred, for example, in analyses of demand side management programs that 

attempted to decrease diversified A/C load by cycling air conditioners. Modeling an “average” 

building would provide an “optimal” cycling strategy for the “average” system, but the effects of 

the DSM program on over- and under-sized units were very different, and did not “average” out 

to the effects of the typical or medium-sized unit. As a result, the implementation of a program 

designed with this aggregation error analysis was less load reduction and a greater drop-out rate 

than expected. 

Aggregation bias can be minimized by increasing the number of “typical building types” and 

climate zones. The application of BLAST analysis to individual CBECS buildings reflects an 

attractive methodological framework for improving on previous analysis approaches. This use 

of a large number of individual buildings as the basis for analysis is often referred to as microsimulation. Micro-simulation provides analysis of individual buildings whose results can be 

aggregated back to the population using weights that reflect the representation of each building 

within the population. However, increasing the number of building types within the limited 

CBES data sample will introduce bias error, as described in Section 4.4.1. 

Application of the micro-simulation approach can substantially improve the accuracy of results 

and provide much greater detail concerning individual issues related to the analysis. 

Unfortunately, the analysis used to support the ANOPR is seriously flawed in a number of 

aspects, seriously calling into question the accuracy of energy use, customer cost and most other 

aspects of the analysis. These deficiencies are described in the following four sections. 

Modifications of the analysis required to rectify the deficiencies are also provided. 

4.4.1 CBECS Sample Building Issues

The 1995 CBECS database was used as a basis for developing a sample of actual buildings using 

Unitary Air Conditioning Equipment. The CBECS survey is a well-designed and reliable source 

of information on US commercial utility customers. As with all surveys, the statistical precision 

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targets of the survey are achieved only for variables that were used in designing the sample. 

That is, CBECS has been designed to provide national and regional estimates of commercial 

sector buildings and energy use. Once one drills down to find more specific information, the 

precision of associated estimates declines because the sample of these customer types is much 

smaller than the sample of all buildings for which the survey was designed. For example, the 

95% confidence interval around total US commercial floor space is +/- 3.4% (sample of 5765 

buildings). Drilling down to determine floor space for food service buildings, one finds a 95% 

confidence interval of +/- 12.2 % (sample of 141 buildings). The food service floor space served 

by packaged air conditioning has a 95% confidence interval of +/- 17.08% (sample of 67 

buildings). 

There are an even larger number of “GeoClimate” regions formed from the intersection of census 

division and climate zones as indicated in Table 6.2.28 of the TSD. The 25 cells with CBECS 

buildings vary from 164 buildings in the largest to 5 in the smallest. 10 of the 25 cells have 

fewer than 20 sample buildings. To see the potential problem in attempting to distinguish among 

energy use characteristics (including load shapes) with this sample, consider the following. The 

East South Central census division includes three climate zones, 3, 4 and 5 with 14, 53 and 16 

sample buildings, respectively. If a sufficient number of each of the 6 building types exists in 

each of the climate zones, no problem exists; however, the size of samples are too small to 

permit this situation. Since the CBECS sample was not designed to answer the relevant question 

in this analysis (i.e., building and energy use characteristics for the 6 building types and 25 

GeoClimate zones) there is no assurance that, with these small samples, the collection of 

buildings will provide an acceptably accurate characterization of energy use for each of the 6 x 

25 = 150 segments. For instance, given these small samples, there might be 0 restaurants in 

climate zone 3, 2 restaurants in climate zone 4 and 3 restaurants in climate zone 5 providing a 

bias towards climate characteristics of zone 5. 

Given the importance of the sample characteristics with respect to accuracy of all of the policy 

results, the analysts should have addressed these issues carefully in their analysis and 

documentation. 

DOE does recognize this problem indirectly in its economic analysis by assigning each building 

in the simulation sample to a subdivision, and then applying each building in the subdivision to 

each utility in that subdivision to increase the sample size. 

Essentially, the analysis reuses individual building records for different utilities, presumably 

running the BLAST analysis in each utility location for each building. While some of the 

elements of this process are reasonable, the effort described above which applies all of the 

sample buildings in a geographic region to each geographic subset of the region (i.e., the utility 

service area) without re-weighting the building records creates a misrepresentation of the 

required micro-simulation sampling process, and one cannot determine the impact of these errors 

on the accuracy of the of the analysis results. 

The most serious deficiencies in the analysis process can be rectified with the following efforts: 

(1) Calculate the statistical accuracy of the total sample, treating it as a stratified sample. If 

necessary, merge the 1992 CBECS data with the 1995 CBECS data. This would more than 

double the sample of buildings, significantly improving the statistical reliability of the results and 

(2) While “reusing” individual building records for different regions and utility service areas to 

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expand the sample representation is desirable (after rerunning the BLAST model with the 

appropriate weather data), these building records must have new weights developed in a poststratification process based on counts of establishments by employee size in the states and utility 

service areas. 

4.4.2 CBECS End Use Energy Use Information

One issue that has arisen in the evaluation of the BLAST-predicted cooling estimates is the 

discrepancy in the BLAST and CBECS air conditioning energy use intensities (EUI’s). Chapter 

6, Section 6.3.1 of the analysis documentation provides a comparison of BLAST and CBECS air 

conditioning EUI’s. The report states that “The CBECS EUIs were derived from the reported 

total annual electric cooling energy,” which seems to suggest that the CBECS air conditioning 

EUI’s for each CBECS record are metered data. However, end use EUI’s for each of the 

CBECS building records are estimated in a calibration process which applies the Facility Energy 

Decision Screening (FEDS) system to model each individual record’s space heating, cooling, 

ventilation, lighting, and water heating energy use with detailed building engineering simulations 

A summary of this process is provided at http://www.eia.doe.gov/emeu/cbecs/tech_end_use.html

The calibration process used monthly billing data which is available for most CBECS billing 

records to calibrate the FEDS model and to adjust each record to reflect reported annual 

electricity use. 

Since BLAST-derived air conditioning energy use estimates were not calibrated to CBECS 

energy use or monthly billing information (see the section below), one would expect the CBECS 

air conditioning EUI’s to be more accurate, a priori. However, an even more accurate estimate 

of each of the CBECS buildings air conditioning energy use can be developed reasonably easily 

as indicated in the next section. 

4.4.3 Preferred Methodology for Estimating CBECS A/C Use

The fifteen months of billing data (kWh and peak kW) which exists for most 1995 CBECS 

records can be used to estimate air conditioning EUIs (including fan use) for each record. 

Ordinary least square regressions can be run using monthly kWh- and kW-dependent variables (a 

technique called “seemingly unrelated regression” can be used to combine the two estimations 

and improve the estimation efficiency) and with weather variables, square feet, operating hours 

and other variables as independent variables. This regression technique has been used for more 

than twenty years as a first step in audit analysis where a building’s kWh and kW are separated 

into “weather-sensitive” and “non-weather sensitive” energy use. Applying this process to 

CBECS records would establish the extent to which BLAST over-estimates air conditioning 

EUI’s, if that is, in fact, the case. 

It should be noted that this statistical decomposition of weather- and non-weather-sensitive loads 

takes into account the use of both air conditioning and space heating in some months. This 

approach will provide a more accurate estimate of air conditioning energy use compared to that 

reported in the CBECS records, because the CBECS process had to calibrate to space heating air 

conditioning and other end uses while the statistical process can be specified to provide a 

primary estimate of air conditioning. 

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This process should also be undertaken in support of the following improvement in BLAST 

modeling. (See ASHRAE Guideline 14-2002 for procedures.) 

4.4.4 Calibrating the BLAST Model to individual buildings

The micro-simulation modeling process that applies the BLAST model to a sample of actual 

buildings has many advantages, including the direct representation of the diversity of buildings 

in the population along with the minimization of aggregation error which tends to accompany 

analysis of prototype-based analysis. 

This focus is clear in Chapter 6 of the documentation (Section 6.2.3.2 ): “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 an actual building, not a typical building.” 

The BLAST modeling, while incorporating information from each building record in each 

simulation, completely ignores by far the most important opportunity to develop “system loads 

representative of an actual building, not a typical building” when it fails to calibrate the BLAST 

simulation for each building to monthly billing data and estimated air conditioning kWh and 

peak kW contributions (see section above). 

The comparison of BLAST air conditioning EUI’s and CBECS EUI’s (as a proxy, at this point, 

for actual EUI’s) presented by DOE (Figure 6.3.1 below) shows little correlation. Assuming that 

the CBECS EUI’s are just moderately accurate suggests that the BLAST model output for each 

record bears little relationship to the actual air conditioning electricity used by the building. Most 

of the dots in Figure 6.3.1 should lie around the line extending to the upper right in the Figure. 

Each of the buildings modeled with BLAST should have been calibrated using the building’s 

actual monthly billing data and statistically-based estimates of air conditioning electricity use. 

While this calibration requires a series of heuristic decision rules, the end result is far preferable 

to what was accomplished with the current analysis. For instance, if the BLAST simulation of a 

CBECS record shows 30% more air conditioning electricity use than what is consistent with 

monthly billing data and statistically-based air conditioning electricity use, we don’t know to 

what extent the discrepancy is a function of insulation levels, ventilation rates, thermostat setting, 

occupancy or other factors. However, calibrating to actual air conditioning electricity use by 

modifying some of these parameters provides a modeled record that at least has the correct air 

conditioning electricity use. Decision rules can be altered and sensitivity analysis can be run to 

determine the impact of these rules on the analysis results. To the extent that the decision rules 

are formalized, parameter searches can be performed providing an estimation result consistent 

with Maximum Likelihood Estimation. 

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Results shown in another Figure (6.3.3), which aggregates result for the six building types and 

climate regions, is used by DOE as the basis for the following statement: “It appears that, when 

the results are smoothed by aggregating the EUIs into a small number of groups consisting of 

many buildings, the correlation between the two data sets becomes more favorable, lending 

credibility to the simulation methodology.” This statement is simply not correct. While the dots 

fall along the diagonal line in this chart, these aggregate results reflect only the fact that the 

BLAST results show greater air conditioning use in hotter climates and greater air conditioning 

for the appropriate building types. These results do not validate the BLAST analysis when such 

large individual building discrepancies exist. 

Ignoring the BLAST/actual EUI discrepancies ignores valuable information in the analysis 

process and elevates the argument of exactly offsetting errors to be a cornerstone of the BLAST 

analysis. This process turns the advantages of the sample-building-based micro-simulation 

analysis on its head. Micro-simulation provides more accurate results in part because analysis of 

a large number of individual buildings is more accurate than analysis of a few prototypes. The 

DOE analysis seems to argue that less accurate analysis of the individual buildings will lead to a 

more accurate final analysis. 

4.4.5 Summary

The basic methodological framework of applying an engineering heat load model to individual 

CBECS building records is a significant improvement over previously applied analytical 

approaches. However, the current application of this methodology is seriously flawed with 

respect to the sampling requirements of this methodology as well as its failure to calibrate 

BLAST to the individual building records. 

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The impact of these deficiencies, which may be significant, can be corrected with the suggestions 

provided above. Determining the size and nature of the errors resulting from these analysis 

problems can only be determined empirically by incorporating the suggestions offered above. 

However, a relatively simple statistical analysis of CBECS data can determine the size of the 

error in BLAST-estimated air conditioning electricity use (see the section above entitled 

Preferred Methodology for Estimating CBECS AC Use).

4.5 References 

CEE 2003, “Efficiency Listings - January, 2003”, Consortium for Energy Efficiency Web Site 

for ARI Data on Rooftop units, http://www.cee1.org/com/hecac/ac_tiers/eff_toc.htm. 

Domanski, P. and Didion, D. 1983, “Computer Modeling of the Vapor Compression Cycle with 

Constant Flow Area Expansion Devices”, Building Sciences Series 155, National Bureau of 

Standards, Gaithersburg, MD. 

Hittle, D. 1982, “An Algorithm for Modeling a Direct Expansion Air-Cooled Condensing Unit”, 

ASHRAE Transactions, American Society of Heating, Refrigerating, and Air Conditioning 

Engineering, Vol. 88, Pt. 2. 

Hittle, D. 2003, Phone conversation on Sept. 30, 2003. 

Lennox, 2003. “DOE Commercial Rulemaking Issues”, document dated March 10, 2003. 

Leverenz, D. J. and Bergan, N.E., 1983, “Development and Validation of a Reciprocating 

Chiller Model for Hourly Energy Analysis Programs”, ASHRAE Transactions, American Society 

of Heating, Refrigerating, and Air Conditioning Engineering, Vol. 89, Pt. 1A. 

O’Neal, D.L., Ed., 1985, “The Appliance Standards Analysis: An Evaluation and 

Recommendations”, Report of the Appliance Standards Analysis Review Group, Report 

submitted to Lawrence Berkeley Laboratory, Berkeley, CA. 

Rice, C.K., 1991, “The ORNL Modulating Heat Pump Design Tool – Mark IV User’s Guide”, 

ORNL/CON-343, Oak Ridge National Laboratory, Oak Ridge, TN. 

TIAX, 2003, “Energy Efficient Rooftop Air-Conditioner: Design, Development, and Field 

Testing”, Final Report, TIAX LLC, Acorn Park, Cambridge, MA 02140. 

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5.0 VENTILATION MODELING 

5.1 Introduction 

This section reviews the methodology used in the TSD for BLAST modeling of indoor 

ventilation, use of ventilation fans, and ventilation standards that should be applied the building 

models. The issues raised in stakeholder comments include questions about the airflow 

calculations used in BLAST and the application of ASHRAE Standard 62-2001 in setting 

minimum ventilation rates for commercial buildings. 

5.2 Assessment 

5.2.1 Airflow calculations in BLAST appear to be correct

One of the criticisms from Lennox (2003) regarding the BLAST energy use calculations of 

rooftops is that they were concerned that BLAST was overestimating fan power because of 

potential double counting the fan power. Because fan power is continuous during occupied 

hours, double counting it would increase total cooling energy use significantly. The IRT 

examined BLAST and it appears to be calculating airflow correctly if the rooftop data are input 

correctly. What is particularly crucial for correct calculation of power input is the value of 

condenser U-factor (CUA). This variable was discussed earlier in the context of the saturated 

condensing temperature (SCT). CUA is determined from detailed manufacturer’s performance 

data on a unit. These performance data must not include indoor (evaporator) fan power in the 

power data. If this CUA is used, then the correct power (without indoor fan) for the rooftop will 

be calculated in BLAST and airflow power will not be doubled counted. This is a subtle point 

in the BLAST input file, but must be followed. 

5.2.2 Ventilation Rates

ASHRAE 62-2001 is a default standard in the United States regulating ventilation rates for new 

construction. However, unless this standard has been adopted by local municipal building codes, 

it does not have the same legal standing as “code”. The ventilation rates used by DOE and as 

shown in Table 6.2.2.1, Peak Ventilation Values Used in Various Analyses, are aggressive. As 

with the application of low-e glass in commercial construction, informed owners are going to ask 

for Demand Controlled Ventilation (DCV) or take advantage of scheduled ventilation for 

buildings with known and regular occupancies and schedules. DCV uses CO2 concentration in a 

conditioned space as a surrogate for occupancy. If the CO2 concentration is high, that indicates a 

correspondingly high occupancy rate, and the HVAC equipment will adjust to deliver the 

appropriate amount of fresh outdoor air. Scheduled ventilation is obtained through simple 

control of outside air dampers from minimum to “design” based on the building’s occupancy 

schedule. 

In our experience, many consulting engineers would agree that this is not the usual case and that 

the less informed owner will opt for the fixed rate of ventilation air. BLAST only seems to allow 

for either a fixed percentage or a fixed volume of outside ventilation air. The only other way that 

EE-RM-STD-01-375 COMMENT 20
Review of Analyses in Support of ANOPR 11/17/2003 45

outside air is modeled is through the use of an economizer option. Demand-, or especially 

occupancy-controlled ventilation could be modeled by applying a schedule modifier to the fixed 

volume input in BLAST. This option might require the modification of the BLAST code. 

A high ventilation rate can place an enormous operational burden on smaller (7.5 ton) systems. 

A very common “rule of thumb” for commercial construction is that the total cooling flowrate is 

approximately 1.0 cfm/ft2

. Thus, the assembly area listed with a ventilation airflow rate at 0.89 

cfm/ft2

 means that 89% of the total air flow is ventilation or fresh outside air. Particularly in 

southern climates, most commercial rooftop packaged equipment cannot accommodate the 

additional moisture load with this much ventilation. A value of 0.45 cfm/ft2

 was used for 

education facilities. This represents almost half of the total airflow for a nominally sized unit. 

Again, these ventilation rates, while meeting the intent of ASHRAE 62, are not necessarily 

strictly adhered to in practice. Additionally, smaller packaged systems do not have the 

dehumidification capacity to meet coincident cooling and ventilation loads. 

It is not clear why the DOE uses CBECS occupancy data for office buildings in the building 

cooling and heating loads analysis, but goes on to use occupancy levels from Table 2 of the 

ASHRAE Standard 62-2001 for ventilation analysis. It would seem more prudent to use actual 

occupancy data for any buildings that are available in the CBECS database. Use of the Tabular 

data is the most conservative value and would overestimate the amount of ventilation required 

for those buildings. 

5.3 Summary 

The IRT concludes that the BLAST airflow calculations in the DOE TSD are done correctly. 

However, the ventilation rates used by DOE in its models correspond to an overly-aggressive 

and unrealistic application of the new ASHRAE Standard 62-2001 to the Nation’s commercial 

buildings. The BLAST simulations should be re-done using lesser rates of ventilation. 

EE-RM-STD-01-375 COMMENT 20
Review of Analyses in Support of ANOPR 11/17/2003 46

6.0 ASSESSMENT OF THE INCREMENTAL MARKUP OF COMMERCIAL 

UNITARY AIR CONDITIONING EQUIPMENT PRICES 

6.1 Introduction 

As a result of numerous stakeholder comments, DOE requested that the IRT review the 

procedures used in the TSD to estimate the incremental markup of A/C prices for higher 

efficiency units. 

This portion of the analysis was focused on the following issues: 

• Are the assumptions used to calculate the equipment markups consistent with the existing 

practice in the industry, and is there any research in published literature that would support or 

challenge DOE’s assumptions? 

• Would a change in assumptions or data have a significant impact on the results of the 

analysis? 

• Are there important areas of uncertainty that could have a significant impact on DOE’s 

conclusions, based on a sensitivity analysis? 

• Are there other cost factors outside of the scope of DOE’s analysis that could have a 

significant impact, such as regional variations in costs or significant code or siting issues in 

states or localities that represent significant shares of the market? 

• Are there economic implications DOE may have overlooked, such as an uneven distribution 

of the costs among national accounts, large mechanical contractors and small mechanical 

contractors? Uneven distribution of the costs between different regions or states? 

The review considered the data and methods behind the following key assumptions and 

calculations: 

• 30% of sales are for new installations 

• 70% of sales are to replace old equipment 

At each stage the markup has two components: 

• Baseline markups are a function of the cost of equipment and are the revenue per dollar cost 

of goods required to cover all costs and profit 

• Incremental markups are a function of the increase in the cost of equipment and only include 

the variable costs of a firm – all costs excluding labor and occupancy in this analysis 

For 70% of sales involving replacement units: 

• 82.5% sold through wholesalers, baseline markup of 1.36, incremental of 1.11 

o 50% of replacement sold through small mechanical contractors, baseline markup 

of 1.7, incremental markup of 1.37 

o 32.5% of replacement sold through large mechanical contractors, baseline markup 

of 1.55, incremental markup of 1.29 

EE-RM-STD-01-375 COMMENT 20
Review of Analyses in Support of ANOPR 11/17/2003 47

 82.5% that passes through mechanical contractors also passes through a 

general contractor, baseline markup of 1.24, incremental markup of 1.13. 

• 17.5% of replacement sold through national accounts, 1⁄2 of normal distribution chain markup 

at 1.71 

For 30% of sales involving new construction: 

• 50% of new sold through small mechanical contractors, baseline markup of 1.48, incremental 

markup of 1.26 

• 32.5% of new sold through large mechanical contractors, baseline markup of 1.35, 

incremental markup of 1.18 

• 17.5% of new sold through national accounts, 1⁄2 of normal distribution chain at 1.80 

• No general contractor involvement, and therefore no general contractor markup 

6.2 Review of Assumptions and Approach Used to Calculate Markups 

The IRT has reviewed the methodology and assumptions and has not found any information that 

would directly challenge DOE’s analysis of the distribution channels for unitary air conditioning 

equipment. The literature search conducted for this stage of the analysis has not uncovered any 

comparable information that would undermine DOE’s basic assumptions. DOE’s analysis is 

characterizing the industry on a national basis. Other studies and data sources only deal with 

portions of the distribution channels and markets for equipment, and have not been comparable. 

DOE’s choice of national data from the industry and the Census Bureau appears to be the best 

choice for this analysis because of the need for markup calculations based on consistent 

definitions and roughly comparable data sets in terms of the time frame the data covers and clear 

distinctions between the steps in the distribution chain that are being characterized. Other data 

sources or approaches considered would involve difficulties in trying to combine information 

from multiple sources involving different assumptions and source data. 

The IRT examined the tables in Appendix L of the DOE analysis in detail to review the logic of 

the data included in the incremental cost markup calculations. The approach described in the 

analysis was applied consistently. All labor-related costs were excluded from the calculation of 

incremental markups, while all other costs were included in the incremental markup calculations. 

Although there are no data to back up the estimate of the markup on national accounts, the logic 

of the argument that it would fall somewhere between the manufacturer’s price for the equipment 

and the price to a customer using one of the other distribution chains is logical. Splitting the 

difference and taking half is somewhat arbitrary, but there is not enough data to create a better 

estimate. 

6.3 Potential Impact of Manufacturing/Industrial Sales on Markup Calculations 

A potentially important factor in the market analysis is addressed in Section 3.3 of this report: to 

what extent sales to the industrial sector is an important factor that should be included in the 

economic impact analysis of the new rule. For the markup analysis the main issue this raises is 

whether sales to the industrial sector break down into the same proportions of new and 

replacement units, and from there into the same proportions with regard to shares sold through 

EE-RM-STD-01-375 COMMENT 20
Review of Analyses in Support of ANOPR 11/17/2003 48

wholesalers versus national accounts, and from wholesalers to large and small mechanical 

contractors. There is no literature that would argue for changing the proportions, and based on 

the analysis in Section 3.3 the best assumption is that the supply chains would remain unaltered 

as far as the proportions of new and replacement sales, and how the equipment is distributed. 

Light industrial customers and the types of commercial buildings served by these types of unitary 

air conditioning units involve similar construction practices and similar contractor arrangements. 

The current description of the distribution chains is practical for both. 

6.4 Sensitivity Analysis of Changes in Markup Assumptions 

This element of the review focuses on whether a change in markup assumptions would have a 

significant impact on the results of the analysis. To accomplish this the markup estimates were 

varied based on the distribution analyses of the markups, and then the changes were input into 

the Life Cycle Cost impact analysis spreadsheets used in the DOE analysis. The range of 

markups tested was limited to values within two standard deviations of the national averages 

developed in the report. This was chosen as one bound on the variability of the markup 

calculations. Assuming a normal distribution, using a value two standard deviations from the 

mean in effect picks a value at the high end of the distribution of values used to calculate the 

mean, a value that is at the outer limits of 95% of the values within the distribution. The only 

exception was for the wholesaler incremental markup, where the probability distribution used in 

the original analysis was truncated to avoid having a markup less than zero. In this case a 

markup at the level of the mean plus .82 standard deviations was used, to parallel the assumption 

used in the original analysis. The original markup assumptions and the values used in the 

sensitivity tests are shown in Table 6.1. The net result was an increase in the overall baseline 

markup of 24% and an increase in the overall incremental markup of 15%. 

Table 6.2 shows some of the results of the change in markup levels for the changes described 

above, for an extreme case in which the original overall baseline and incremental markup were 

doubled, and for a doubling of the overall baseline markup. The “Engineering” tab in the 

lcc_cuac_tariff.xls workbook developed for the original analysis in Chapter 7 of the TSD was 

used to calculate the values in the table. Note that except for the values in the last two columns, 

which only use the baseline multipliers, the values for equipment price are a function of both the 

baseline and incremental markup values applied to the manufacturer price and cost. So while the 

relationship between manufacturer price and equipment price in Table 6.2 are derived from the 

values in Table 6.1, the multipliers have to be applied to both the manufacturer price (baseline) 

and the differences in manufacturer costs (incremental) to arrive at equipment price. For 

example the value for the 7.5 ton unit at the EER10.5 efficiency level is derived from 

multiplying the price of the baseline unit used for comparison ($2098) times the baseline markup 

for new construction (2.66) for a value of $5,586. The incremental markup is derived by 

multiplying the difference in price between the baseline unit and the EER10.5 unit ($2,145 - 

$2,098=$47) which is then multiplied by the new construction markup for small mechanical 

contractors (1.68 * $47 = $79). Adding the incremental calculation to the baseline calculation 

arrives at the markup price of $5,665. The new construction markups are the highest in the 

distribution chain, so these sensitivities are the highest levels of equipment costs used in the 

analysis. Other markups would be lower. 

EE-RM-STD-01-375 COMMENT 20
Review of Analyses in Support of ANOPR 11/17/2003 49

The sensitivity markups increase the price of the 7.5 ton units between $1,796 and $2,004, and 

the price of the 15 ton units between $3,387 and $3,799. However, the change in the price 

differential between the baseline unit (EER 10.1 for the 7.5 ton unit and EER 9.5 for the 15 ton 

unit) is a maximum of $208 for the 7.5 ton unit and a maximum of $380 for the 15 ton unit. The 

differentials in price are the main drivers for differences in life cycle costs, payback period and 

the economic and energy indicators used in the analysis. Because the price differentials between 

the baseline unit and the high efficiency units are relatively small even at the higher limits of the 

likely distribution of markups, pursuing greater accuracy in the markups is unlikely to have 

much impact on the overall analysis. The small price differentials are a product of the fact that 

the markups are applied to derive the price of the baseline units as well as the higher efficiency 

units. Therefore, changes in the markups result in only minor changes in the price difference 

between units. The price differentials are more sensitive to the incremental markups because 

they are not applied to the baseline units – they are driven completely by the difference in price 

between the baseline units and the higher efficiency units. 

As shown in the last column of Table 6.2, doubling the overall baseline markup only creates a 

price differential of $704 for the 7.5 ton EER12 unit, and $1,283 for the 15 ton EER 12 unit. 

There is no indication that the baseline markup estimates are off by anywhere near that large an 

amount. Doubling both the incremental and baseline overall markup creates larger price 

differences, up to $1,616 for the 7.5 ton unit and $2,947 for the 15 ton units. Incremental 

markups that are off by a factor of two are even less likely than the baseline markup doubling. 

The lifecycle cost spreadsheets for both the hourly and tariff electricity price models were run 

several times with both the original and the sensitivity analysis markups using the Crystal Ball 

2000 forecasting features. None of the runs turned up consistent, significant shifts in the original 

analysis results. 

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Review of Analyses in Support of ANOPR 11/17/2003 50

Table 6.1: Original Markups and Markups Plus Two Times Standard Deviation 

Original Markups for Commercial Unitary A/C and Heat Pumps

NEW CONSTRUCTION REPLACEMENT OVERALL

Baseline Baseline Baseline

Small Large Nat. Small Large Nat.

Mech. Mech. Acct. Mech. Mech. Acct.

Distributor 1.36 1.36 1.69 1.36 1.36 1.60 1.41

General Contractor 1.24 1.24 1.00 1.00 1.00 1.00 1.06

Mech. Contractor 1.48 1.35 1.00 1.70 1.55 1.00 1.48

Sales Tax 1.07 1.07 1.07 1.07 1.07 1.07 1.07

Overall 2.66 2.42 1.80 2.47 2.24 1.71 2.31

NEW CONSTRUCTION REPLACEMENT OVERALL

Incremental Incremental Incremental

Small Large Nat. Small Large Nat.

Mech. Mech. Acct. Mech. Mech. Acct.

Distributor 1.11 1.11 1.27 1.11 1.11 1.24 1.13

General Contractor 1.13 1.13 1.00 1.00 1.00 1.00 1.03

Mech. Contractor 1.26 1.18 1.00 1.37 1.29 1.00 1.25

Sales Tax 1.07 1.07 1.07 1.07 1.07 1.07 1.07

Overall 1.68 1.59 1.35 1.63 1.53 1.32 1.56

Sensitivity Markups for Commercial Unitary A/C and Heat Pumps

NEW CONSTRUCTION REPLACEMENT OVERALL

Baseline Baseline Baseline

Small Large Nat. Small Large Nat.

Mech. Mech. Acct. Mech. Mech. Acct.

Distributor 1.58 1.58 1.69 1.58 1.58 1.86 1.62

General Contractor 1.34 1.34 1.00 1.00 1.00 1.00 1.08

Mech. Contractor 1.57 1.43 1.00 1.80 1.64 1.00 1.55

Sales Tax 1.06 1.06 1.06 1.06 1.06 1.06 1.06

Overall 3.52 3.20 1.79 3.02 2.75 1.97 2.86

NEW CONSTRUCTION REPLACEMENT OVERALL

Incremental Incremental Incremental

Small Large Nat. Small Large Nat.

Mech. Mech. Acct. Mech. Mech. Acct.

Distributor 1.20 1.20 1.27 1.20 1.20 1.35 1.22

General Contractor 1.22 1.22 1.00 1.00 1.00 1.00 1.05

Mech. Contractor 1.33 1.26 1.00 1.46 1.37 1.00 1.32

Sales Tax 1.06 1.06 1.06 1.06 1.06 1.06 1.06

Overall 2.06 1.95 1.34 1.85 1.74 1.43 1.79

Increase in Baseline Markup 24%

Increase in Incremental Markup 15%

EE-RM-STD-01-375 COMMENT 20
Review of Analyses in Support of ANOPR 11/17/2003 51

6.5 Other Markup Cost Factors 

This portion of the markup review examines whether there are other cost factors outside of the 

scope of DOE’s analysis that could have a significant impact, such as regional variations in costs 

or significant code or siting issues in states or localities that represent significant shares of the 

market. Information on state variations in markup estimates is included in Appendix L of the 

DOE analysis and was used in calculating the distribution and standard deviation of markups at 

the wholesaler, mechanical contractor and general contractor elements of the distribution chain. 

As Appendix L reports, the relative standard deviation in the wholesale baseline markup is 8.2%; 

the general contractor relative standard deviation is 3.9%; and the mechanical contractor relative 

standard deviation is 3.1%. The frequency distribution for the markup values is fairly tight. The 

state data in Appendix L on wholesale baseline markups (Table L.5.1), general contractor 

baseline markups (Table L.5.2), and mechanical contractor markups (Table L.5.3) was used 

during this review to examine whether there were significant variations in markup levels by 

state. 

These data have several limitations that obstruct a direct comparison with the original analysis. 

First, they do not include the level of detail needed to break the mechanical contractors down 

into small and large within each state. Second, many states are missing information for one or 

Table 6.2: Equipment Price Impacts of Changes in Markups 

Descrip.

Manuf. Price 

($2001)

Equip. Price 

($2001) 

Original

Equip. Price 

($2001) 

Sensitivity

Price 

Difference 

with 

Sensitivity

Change in 

Price 

Differentials 

Sensitivity

Equip. Price 

($2001) 2* 

both 

Markups

Change in 

Price 

Differentials 

2* Both 

Markups

Equip. Price 

2* Baseline

Change in 

Price 

Differentials 

2* Baseline

EER=9.5 $2,098 $5,586 $7,382 $1,796 $11,173 $11,173

EER=10.0 $2,098 $5,586 $7,382 $1,796 $0 $11,173 $0 $11,173 $0

EER=10.1 $2,098 $5,586 $7,382 $1,796 $0 $11,173 $0 $11,173 $0

EER=10.5 $2,145 $5,665 $7,479 $1,814 $18 $11,331 $140 $11,252 $61

EER=11.0 $2,237 $5,821 $7,670 $1,849 $53 $11,641 $415 $11,407 $181

EER=11.5 $2,390 $6,077 $7,985 $1,908 $112 $12,153 $868 $11,663 $378

EER=11.8 $2,525 $6,305 $8,264 $1,960 $164 $12,609 $1,272 $11,891 $554

EER=12.0 $2,641 $6,499 $8,503 $2,004 $208 $12,997 $1,616 $12,085 $704

Descrip.

Manuf. Price 

($2001)

Equip. Price 

($2001) 

Original

Equip. Price 

($2001) 

Sensitivity

Price 

Difference 

with 

Sensitivity

Change in 

Price 

Differentials 

Sensitivity

Equip. Price 

($2001) 2* 

both 

Markups

Change in 

Price 

Differentials 

2* Both 

Markups

Equip. Price 

2* Baseline

Change in 

Price 

Differentials 

2* Baseline

EER=9.5 $3,957 $10,537 $13,924 $3,387 $21,073 $21,073

EER=10.0 $4,019 $10,641 $14,052 $3,411 -$8 $21,282 -$60 $21,178 -$26

EER=10.1 $4,039 $10,675 $14,094 $3,419 $0 $21,350 $0 $21,212 $0

EER=10.5 $4,122 $10,813 $14,264 $3,450 $32 $21,627 $245 $21,350 $107

EER=11.0 $4,291 $11,098 $14,613 $3,515 $96 $22,195 $749 $21,634 $326

EER=11.5 $4,570 $11,566 $15,188 $3,622 $203 $23,132 $1,578 $22,103 $688

EER=11.8 $4,823 $11,993 $15,713 $3,720 $301 $23,985 $2,334 $22,529 $1,017

EER=12.0 $5,029 $12,338 $16,137 $3,799 $380 $24,677 $2,947 $22,875 $1,283

System = 15-ton unit

System = 7.5-ton unit

EE-RM-STD-01-375 COMMENT 20
Review of Analyses in Support of ANOPR 11/17/2003 52

all of the tables. This did not affect the original analysis because it used the average values for 

wholesale, general contractor and mechanical contractors, but it precludes developing overall 

markups by state that are directly comparable to the original analysis. Still, 26 states had enough 

information to create rough estimates of the total baseline markup involving a wholesaler, a 

general contractor and a mechanical contractor to allow comparisons between states. As shown 

in Table 6.3, the average of the overall baseline markups calculated by state was 2.58; this is 

between the 2.42 and 2.66 overall markup calculated for new construction involving small and 

large mechanical contractors derived in the study. The standard deviation is 7.7%, or .2 as an 

absolute value. 

The state baseline markups in Table 6.3 are arranged in ascending order, with the states with the 

lowest overall markups at the top of the table. The baseline markup calculations in this analysis 

and in the original study were not weighted or adjusted to account for potential differences in the 

level of sales through different state supply chains – they only use the average level of markup. 

There is a potential problem if states that account for a larger proportion of sales also have a 

higher than average markup level. First, these states could experience a disproportionate share of 

the economic impact of increased equipment costs caused by raising efficiency standards 

because they will experience higher prices. Second, the national average markups may be 

inaccurate because they mask important regional variations in markups. 

To illustrate this issue, Table 6.3 also includes information on the proportion of total general 

contractor construction value attributed to each state in the census data used for the markup 

analysis. California accounts for the largest share of construction value in the census, at 9.4%, 

and has the third highest overall markup level. However, the California markup level is still only 

8% higher than the average, which is within one standard deviation of the mean value. New 

Jersey and South Carolina are the only states with higher baseline markup levels, and together 

they account for only 4.7% of mechanical contractor sales. This indicated that some of the 

variations in the markup levels do occur in states with large equipment markets, but it gave little 

indication as to whether the differences were significant. 

To examine whether these differences might have made a significant difference in the baseline 

markups, the baseline markup calculations for wholesalers, general contractors and mechanical 

contractors were recalculated by weighting each state’s markup by its share of overall national 

sales. Table 6.4 summarizes the results of the weighting. The table is divided into sections for 

wholesale, general contractor and mechanical contractor markups. The first column in each 

section shows the markup calculation used in the original study. The second column shows the 

contribution that state’s markup would make to a weighted calculation, the product of 

multiplying that state’s share of national sales times its average state markup. When the 

weighted calculations are added together and compared to an average of the state markups, the 

results show a very small, less than 1%, difference between the two approaches. The conclusion 

is that the approach used to estimate baseline markups may have masked some variation in 

markup levels from state to state, but the differences are very small and would not affect the 

overall conclusions of the analysis or result in significant state-level differences.

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Review of Analyses in Support of ANOPR 11/17/2003 53

States with 

Available Data

Wholesale 

Baseline 

Markup

General 

Contractor 

Baseline 

Markup

Mechanical 

Contractor 

Baseline 

Markup Sales Tax

Overall 

Markup

Proportion of Value 

of Construction, 

General Contractor

Oregon 1.23 1.17 1.38 1.06 2.11 1.55%

Maryland 1.24 1.26 1.39 1.06 2.30 3.10%

Colorado 1.34 1.16 1.41 1.06 2.32 1.94%

Arizona 1.3 1.18 1.43 1.06 2.33 1.79%

Tennessee 1.35 1.17 1.4 1.06 2.34 2.22%

Missouri 1.36 1.13 1.46 1.06 2.38 2.12%

Virginia 1.34 1.2 1.42 1.06 2.42 2.42%

Wisconsin 1.24 1.27 1.48 1.06 2.47 2.42%

Michigan 1.31 1.22 1.48 1.06 2.51 3.83%

Louisiana 1.35 1.23 1.47 1.06 2.59 1.18%

Texas 1.39 1.24 1.43 1.06 2.61 6.86%

Indiana 1.36 1.26 1.44 1.06 2.62 2.77%

New York 1.38 1.21 1.48 1.06 2.62 6.06%

Alabama 1.35 1.2 1.53 1.06 2.63 1.41%

Arkansas 1.32 1.27 1.48 1.06 2.63 0.67%

Illinois 1.39 1.21 1.48 1.06 2.64 5.35%

Kentucky 1.36 1.25 1.47 1.06 2.65 1.23%

Ohio 1.39 1.25 1.44 1.06 2.65 4.41%

Minnesota 1.48 1.18 1.45 1.06 2.68 2.00%

Florida 1.44 1.23 1.45 1.06 2.72 4.89%

Connecticut 1.42 1.21 1.5 1.06 2.73 1.15%

North Carolina 1.47 1.2 1.47 1.06 2.75 3.43%

Washington 1.49 1.16 1.52 1.06 2.78 2.17%

California 1.51 1.21 1.45 1.06 2.81 9.37%

New Jersey 1.4 1.35 1.46 1.06 2.92 3.22%

South Carolina 1.5 1.33 1.39 1.06 2.94 1.36%

Wyoming 1.42 1.06 0.15%

Vermont 1.49 1.06 0.17%

South Dakota 1.41 1.06 0.20%

Alaska 1.49 1.06 0.22%

North Dakota 1.85 1.44 1.06 0.26%

Rhode Island 1.4 1.06 0.28%

Montana 1.44 1.36 1.06 0.28%

Hawaii 1.55 1.06 0.30%

Maine 1.27 1.46 1.06 0.31%

West Virginia 1.4 1.06 0.35%

New Hampshire 1.44 1.06 0.42%

New Mexico 1.38 1.06 0.45%

Delaware 1.53 1.06 0.47%

Idaho 1.43 1.06 0.61%

Mississippi 1.21 1.45 1.06 0.64%

Nebraska 1.43 1.06 0.67%

Oklahoma 1.35 1.52 1.06 0.85%

Nevada 1.64 1.42 1.06 1.01%

Utah 1.19 1.49 1.06 1.03%

Iowa 1.21 1.51 1.06 1.08%

Kansas 1.41 1.06 1.14%

Massachusetts 1.42 1.06 2.52%

Georgia 1.36 1.5 1.06 3.10%

Pennsylvania 1.24 1.46 1.06 4.58%

National Markup 1.390 1.222 1.451 2.58

Table 6.3: State Markup Calculations 

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Review of Analyses in Support of ANOPR 11/17/2003 54

Baseline Markup

Contribution to 

Baseline Markup, 

Weighted by Share 

of Total Sales

Baseline 

Markup

Contribution to 

Baseline Markup, 

Weighted by Share 

of Total Sales

Baseline 

Markup

Contribution to 

Baseline Markup, 

Weighted by Share 

of Total Sales

Alabama 1.35 0.034 1.2 0.0298 1.53 0.0215

Alaska 1.49 0.0033

Arizona 1.3 0.029 1.18 0.0256 1.43 0.0255

Arkansas 1.32 0.012 1.27 0.0136 1.48 0.0100

California 1.51 0.126 1.21 0.1469 1.45 0.1358

Colorado 1.34 0.022 1.16 0.0321 1.41 0.0273

Connecticut 1.42 0.018 1.21 0.0168 1.5 0.0172

Delaware 1.53 0.0072

Florida 1.44 0.120 1.23 0.0682 1.45 0.0709

Georgia 1.36 0.063 1.5 0.0465

Hawaii 1.55 0.0047

Idaho 1.43 0.0087

Illinois 1.39 0.059 1.21 0.0623 1.48 0.0791

Indiana 1.36 0.043 1.26 0.0274 1.44 0.0398

Iowa 0.000 1.21 0.0123 1.51 0.0163

Kansas 1.41 0.0160

Kentucky 1.36 0.025 1.25 0.0131 1.47 0.0180

Louisiana 1.35 0.030 1.23 0.0126 1.47 0.0174

Maine 1.27 0.0026 1.46 0.0045

Maryland 1.24 0.031 1.26 0.0431 1.39 0.0431

Massachusetts 1.42 0.0357

Michigan 1.31 0.055 1.22 0.0387 1.48 0.0567

Minnesota 1.48 0.031 1.18 0.0335 1.45 0.0290

Mississippi 1.21 0.0144 1.45 0.0093

Missouri 1.36 0.029 1.13 0.0345 1.46 0.0309

Montana 1.44 0.003 1.36 0.0039

Nebraska 1.43 0.0096

Nevada 1.64 0.0261 1.42 0.0143

New Hampshire 1.44 0.0060

New Jersey 1.4 0.052 1.35 0.0396 1.46 0.0470

New Mexico 1.38 0.0062

New York 1.38 0.064 1.21 0.0926 1.48 0.0897

North Carolina 1.47 0.081 1.2 0.0435 1.47 0.0503

North Dakota 1.85 0.001 0.0000 1.44 0.0037

Ohio 1.39 0.061 1.25 0.0480 1.44 0.0634

Oklahoma 1.35 0.021 0.0000 1.52 0.0128

Oregon 1.23 0.032 1.17 0.0212 1.38 0.0213

Pennsylvania 1.24 0.0588 1.46 0.0669

Rhode Island 1.4 0.0039

South Carolina 1.5 0.031 1.33 0.0149 1.39 0.0189

South Dakota 1.41 0.0028

Tennessee 1.35 0.067 1.17 0.0306 1.4 0.0311

Texas 1.39 0.142 1.24 0.1053 1.43 0.0980

Utah 1.19 0.0170 1.49 0.0153

Vermont 1.49 0.0026

Virginia 1.34 0.044 1.2 0.0370 1.42 0.0343

Washington 1.49 0.033 1.16 0.0330 1.52 0.0330

West Virginia 1.4 0.0049

Wisconsin 1.24 0.029 1.27 0.0300 1.48 0.0357

Wyoming 1.42 0.0021

Overall 1.39 1.388 1.222 1.2251 1.4514 1.4519

% Difference, Weighted versus Average -0.20% 0.29% 0.03%

Wholesale General Contractor Mechanical Contractor

Table 6.4: State by State Comparison, Markups Weighted by Share of Total Sales Versus Averaged Markup 

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Review of Analyses in Support of ANOPR 11/17/2003 55

The analysis also considered whether the proportion of sales going through the distribution 

chains is the same from state to state and region to region, and whether variations might have a 

significant impact. In areas where for some reason markups tend to be higher than the national 

average there might be a tendency for customers to favor one distribution chain over another or, 

more likely, for customers served by a more expensive distribution chain to delay replacement. 

At this point there are no means of testing whether there are significant variations because the 

wholesaler and contractor data also include their work with other types of equipment. However, 

based on analyses of the state-specific information on markups in Appendix L, the IRT sees no 

major disparities that would encourage a significant shift in how unitary air conditioning 

equipment sales are made in any particular states. 

There may be a related issue at the national level that DOE should consider – whether a change 

in the price of equipment at the manufacturer may shift the proportion of sales for new versus 

replacement equipment, and/or shift the proportion of sales made through the different 

distribution chains. For systems going into new construction the markup on equipment is 

unlikely to change the distribution chain; the contractors are involved in far more than installing 

the air conditioning unit and are not likely to be chosen primarily because of the cost of the unit 

they propose. However, the replacement market may change as a result of increased equipment 

costs, as customers may delay replacements longer than under current conditions, and those at 

the end of the distribution chains with the highest markup may be more inclined to delay. That 

would affect overall sales and revenue, but it could also mean a disproportionate impact on the 

small mechanical contractors who tend to be in the distribution chain with the highest markups. 

That issue falls outside of the review of the markup analysis, but is worth considering in the 

overall impact analysis for the rule. 

On the issue of siting or code issues associated with unitary air conditioning equipment that 

meets the new efficiency standards, we have found no evidence that the new standards would 

result in a significant change in the way the equipment operates or in its dimension and sizes that 

would affect how it is sited or the required permitting. 

6.6 Summary 

The review of the markup calculations used in the original analysis found no major flaws in the 

approach. The IRT also found that because of the way the markups are applied to both the 

baseline units used for comparison and the higher efficiency units, there would have to be 

unrealistically large errors in the magnitude of the markup estimates to have any significant 

impact on the conclusions of the study. There is no indication that the markup calculations 

should change that much. Finally, while the averaging of state data on markups used in the 

original study masks some state-level variations in markup levels and therefore in the impacts of 

higher efficiency standards from state to state, the variations do not appear to be significant 

enough to have a major effect on the conclusions of the DOE analysis. 

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7.0 SUMMARY CONCLUSIONS AND RECOMMENDATIONS 

The Independent Review Team (IRT) was charged with reviewing the analyses in support of the 

ANOPR for setting energy efficiency standards for commercial unitary air conditioners and heat 

pumps in the range of 65,000 to 240,0000 Btu per hour cooling capacity (the Technical Support 

Document – TSD) and selected stakeholder comments and analyses. The objectives of the 

independent review were to assess the analytical processes used and comment on: 

• The reasonableness of assumptions that were made; 

• The overall logic of the methodology; 

• The quality of the data and data analysis/simulation tools that were used; and 

• The validity of issues that have been raised with the analysis by stakeholder comments. 

To support a more stringent standard than ASHRAE 90.1-1999, the DOE analysis must show 

that such a standard is technically feasible and economically justified. This requires: 

• Identifying which facilities will be affected by the proposed rulemaking; 

• Determining the electricity consumed by the covered appliances, with and without the 

proposed rulemaking; 

• Estimating the energy and operating cost savings that would result from the standard; and 

• Estimating the incremental markup of prices of more efficient air conditioners. 

Such analyses require data, assumptions and approximations, analysis procedures (including 

energy use simulation models), and life cycle cost analysis. This is not a precise process; 

competent professionals can differ on all aspects of this analysis. The IRT’s role was not to re-do 

the DOE analysis but to judge whether it was reasonable and defendable; whether there were 

errors that should be corrected; and/or whether there were better procedures, data or models to 

perform the analysis. Where the team concluded that an error had been made or there was a 

better alternative analysis procedure, we tried, at least qualitatively, to determine whether the 

proposed modification was likely to significantly affect the results of the TSD. The IRT was 

specifically asked to look at four areas: 

• The use of a subset of the CBECS 1995 database to characterize unitary A/C use and costs in 

buildings; 

• The procedures used to estimate the amount of electricity used for A/C, especially the use of 

the BLAST model applied to building types indicated by the CBECS data; 

• The modeling of ventilation in the subject buildings; and 

• The estimation of incremental price increase for more efficient A/C units. 

Sections 3.0 through 6.0 have described these issues, the analysis procedures used by the IRT, 

and our conclusions. This section summarizes those conclusions and, in the Appendix to 7.0, 

lists the documents received by the IRT from DOE to carry out the assessment. 

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Table 7-1 – Summary of IRT Conclusions 

 

Sect. Issue Recommendation DOE 

Effort 

Required 

Effect 

On TSD 

Conclusions 

III CBECS 

3.2 Use of CBECS 

1995 

IRT agrees with use of CBECS 1995. There is no 

more appropriate data base. Relevant commercial 

facilities are adequately represented. 

None N/A 

3.3 Exclusion of 

industrial facilities 

Additional assessment of this issue is warranted, 

to determine the magnitude of energy used by 

unitary A/C in industrial facilities with 

significantly lower electric rates. IRT feels this, if 

significant, is likely to be less significant than 

suggested in the stakeholder comments. 

Moderate – Possibly 

significant 

3.4 Modeling multibuilding facilities IRT feels this is not a significant factor in the TSD 

analysis. 

None Not significant 

3.5 Sample distribution The 1,033 building sample adequately represents 

the distribution of commercial buildings by region 

and climate. 

None N/A 

3.6 Unitary A/C not 

primary 

The IRT feels no additional modeling is needed to 

specifically include those instances where the 

unitary A/C is not the primary HVAC system 

None N/A 

IV BLAST modeling 

4.2.1 Single compressor 

model, no part load 

cycling 

Large portion, if not most, of the covered units 

now use multiple compressors and perform much 

more efficiently than BLAST predicts. 

Recommend developing updated multicompressor and part load cycling compressor 

models for BLAST 

Extensive 

effort to 

develop 

and test 

model 

– Significant 

4.2.2 High temp. for 

head pressure 

controls 

Determine more accurate minimum saturated 

condensing temperature and re-run BLAST 

simulations 

Moderate ? Significant 

4.2.3 Condenser U-factor Default condenser U-factor value used gives 

unrealistically low saturated condensing 

temperature, significantly underestimating A/C 

energy use. Use correct CUA values and re-run 

BLAST simulations 

Moderate + Significant 

4.2.4 Saturated suction 

temperature 

Default value of SST is too low (by about 5 

degrees F). Obtain more accurate SST from 

manufacturers’ data and re-run BLAST 

simulations. 

Moderate – Significant 

4.2.5 Experimental 

verification 

BLAST rooftop model has not been verified 

experimentally. While this does not invalidate the 

results, it leaves them vulnerable to challenge in a 

public hearing forum. IRT recommends 

experimental verification for A/C in each size 

class. 

Significant Unknown 

4.2.6 A/C capacities 

simulated 

Many of the BLAST simulations used A/C units 

outside of the 65 – 240 kBtu/h range. Re-zone the 

model buildings and re-run BLAST simulations.. 

Moderate Unknown 

4.2.7 Maximum 

commercial A/C 

efficiency 

Maximum efficiency of commercially-available 

A/C is too low (max. EER of 12.0 used). Units 

with EER of 13.5 are available. 

Minimal Revise charts in 

TSD Chapter 8 

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4.2.8 Part load 

performance 

BLAST cannot model many of the options 

available to commercial A/C units, such as 

multiple speed compressors and fans. This 

seriously underestimates expected efficiency of 

A/C units. A new model should be developed. 

Extensive – Significant 

4.3.1 ASHRAE 90.1-

1999 /2001 EERs 

Correct labeling of ASHRAE Standard EER levels Minimal None 

4.3.2 Low-e windows TSD overstates use of low-emissivity glazing. Reestimate and re-run BLAST simulations. Moderate + Possibly 

Significant 

4.3.3 Economizer TSD overstates use of economizer. Remove 

economizer and re-run BLAST simulations. 

Minimal to 

Moderate 

+ Possibly 

Significant 

4.4 BLAST 

simulations of 

CBECS cells 

IRT feels the simulation of so many building types 

and climate zones within the1,033 building 

sample, and then extrapolating to draw national 

conclusions, may have introduced bias and 

aggregation error. A re-calculation of the 

weighting factors may be needed. This should be 

investigated. 

Moderate 

to 

investigate. 

Unknown, 

possibly 

significant 

V. Ventilation 

5.2.1 BLAST airflow The IRT feels the BLAST airflow calculations are 

correct. 

None N/A 

5.2.2 ASHRAE 62-2001 The higher ventilation rates required by ASHRAE 

Standard 62-2001 have been applied in the 

modeling more aggressively than they are applied 

in existing buildings. Reduce ventilation 

requirements and re-run BLAST simulations. 

Minimal to 

Moderate – Probably 

Significant 

VI. Markup 

6.2 – 

6.5 

Markup procedures IRT sees no indication that the markup analysis 

should be modified. 

None N/A 

Table Explanation:

The Table is a “checklist” of the issues examined by the Independent Review Team (IRT), not meant to take the place of each Chapter’s more 

comprehensive conclusions/findings. 

Column 1, Sect., lists the section(s) in the report where the issue is covered. 

Column 2, Issue, gives an abbreviated or keyword indication of the relevant issue(s). Sometimes several “points” were grouped together 

because they were related. 

Column 3, Recommendation, gives the summary, conclusion and/or recommendation of the analysis related to each issue. This is a not an 

attempt to summarize all the ramifications of the analysis. It is a short answer to the question “What does the IRT’s recommendation mean 

about the technical support document’s (TSD) analysis?” 

Column 4, DOE Effort Required, is the IRT’s qualitative assessment of how extensive an effort it will be for DOE to comply with our 

recommendation; that is, to perform additional analyses, simulations, sensitivity analyses, etc. 

Column 5, Effect on TSD Conclusions, is a qualitative assessment of how much the IRT thinks this issue will affect the results of the TSD. 

• “N/A” (not applicable) where the IRT is recommending no additional or corrected analysis. 

• “Unknown” means that without doing the analysis we don’t know how big an effect this could have. That does not mean it is not 

potentially a large effect. 

• “Significant” means that this correction or additional analysis will likely have a major effect on the results. 

+ means that the recommended change will tend to favor more efficient A/C, more stringent standards. (E.g., such as when the change 

 will result in the air conditioners modeled using more energy than currently shown by the TSD.) 

– means that the recommended change will tend to reduce the operating costs of A/C, showing less benefit from more stringent 

standards. (Such as when the change will result in the air conditioners modeled using less energy than currently shown by the TSD.) 

? is used in one entry (4.2.2) where the IRT’s recommended change will increase energy use in one way and decrease it in another; 

 the net effect is not known. 

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The IRT has identified a number of issues, problems or errors in the TSD analysis related to the 

building sample, BLAST simulations, and the markup analysis. 

The foremost building sample issue is the omission of industrial buildings with unitary A/C 

systems. This requires some additional assessment. 

Many of the BLAST-related issues should be resolved by obtaining more accurate modeling 

parameters (usually easy to obtain) and re-running the BLAST simulations. However, BLAST 

has some serious limitations in its ability to model modern A/C units, especially compressors and 

control options, that are being installed in the U.S. Correcting this requires development and 

validation of new A/C and component models or use of another simulation model. Such model 

development is a major undertaking, but more accurate modeling of the covered A/C is expected 

to have a significant effect on the TSD results. 

The IRT sees no need to modify the DOE estimates of unitary air conditioner price markups. 

The “Recommendation,” “DOE Effort Required” and “Effect on TSD Conclusions” columns in the Table are an attempt 

 to give a shorthand guide to the IRT’s recommendations: 

• DOE TSD analysis was fine, no change in TSD. 

• DOE TSD analysis needs to be looked at more closely to see if potential inaccurate assumptions will have a 

 significant effect on the results. 

• DOE TSD analysis might be a little “off,” but we don’t think it’s significant, probably resulting in small 

changes, if any, in TSD conclusions.

• DOE TSD analysis has a significant error or defect. Correcting it is important, will likely have a significant effect 

 on the TSD conclusions, will require a (large/small) amount of effort, and the revised analysis will result in 

 (more/less/unknown) justification for a stricter standard.

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Appendix to 7.0 Documents Received from DOE by the IRT 

“Draft Advance Notice of Proposed Rulemaking and Notice of Public Hearing. Energy 

Conservation Program for Commercial and Industrial Equipment: Energy Conservation 

Standards for Commercial Unitary Air Conditioners and Heat Pumps,” ANOPR-CUAC 06-30D03 GJR-JDR v2EWORD, DOE (not published), received August 1, 2003. 

“Draft Advance Notice of Proposed Rulemaking and Notice of Public Hearing. Energy 

Conservation Program for Commercial and Industrial Equipment: Energy Conservation 

Standards for Commercial Unitary Air Conditioners and Heat Pumps; Technical Support 

Document,” DOE (not published). 

Chapters 1, 2, 5, 6, 12, 13, 14, 15, 16 dated July 28, 2003 

Chapter 3 dated June 17,2003 

Chapter 4 dated March 20, 2003 

Chapter 7 dated March 21, 2003 

Chapter 8 dated July 21, 2003 

Chapter 9 dated July 31,2003 

Chapter 10 dated July 25, 2003 

Chapter 11 dated January 22, 2003 

Appendices E, F, G, H, I, J, K, L dated November 21, 2002 

Appendices M, N, O, P dated December 4, 2002 

Appendix Q dated July 21, 2003 

Lennox, “DOE Commercial Rulemaking Issues,” March 10, 2003 

Charles W. Frazell, PE, “A Preliminary Quantification: The Effects of Excluding 

Industrial/Manufacturing Sites from the DOE Rulemaking to Determine Efficiency Levels of 

Unitary Large Equipment,” March 19, 2003. 

“Comments on the Energy Conservation Program for Consumer Products: Energy Conservation 

Standards for Commercial Unitary Air Conditioners and Heat Pumps,” Docket Number: EERM/STD-01-375, as of August 1, 2003, DOE. 

Sample BLAST input deck: 1012_DTW_3.ann.blin 

DOE presentations and reports from the EERE-Buildings Web site: 

http://www.eere.energy.gov/buildings/appliance_standards/commercial/ac_hp.html

Presentations:

• Bryan Berringer, “Introduction,” U.S. DOE Workshop on Standards for Commercial 

Unitary Air Conditioners and Heat Pumps, October 1, 2001, DOE. 

• “Commercial Building Energy Use and End Use Load Characterization Analysis,” 

September 2002, DOE. 

• Lawrence Berkeley National Laboratory, “Commercial Unitary Air Conditioner and Heat 

Pump Life Cycle Cost Analysis: Inputs and Results, December 2002. 

• TIAX, LLC, “Engineering Analysis: Cost-Efficiency Curves; commercial Unitary Air 

Cooled Air conditioners and Air Source Heat Pumps, January 2002. 

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• National Energy Savings Spreadsheet, March 31, 2003 (revised July 25, 2003), DOE 

• Life Cycle Cost Analysis Spreadsheet Model (Tariff-Based Electricity Prices), March 31, 

2003 (revised July 24, 2003), DOE 

• Life Cycle Cost Analysis Spreadsheet Model (Hourly Based Electricity Prices), March 

31, 2003 (revised July 24, 2003), DOE 

Report:

“Commercial Unitary Air Conditioner and Heat Pump Rulemaking Framework,” DOE, June 

13, 2001. 

Federal Register Notices by Office of Energy Efficiency and Renewable Energy, US 

Department of Energy:

• Proposed Rule: "Energy Efficiency Program for Commercial and Industrial Equipment: 

Test Procedures and Efficiency Standards for Commercial Warm Air Furnaces; 

Efficiency Certification, Compliance, and Enforcement Requirements for Commercial 

Heating, Air Conditioning and Water Heating Equipment," December 13, 1999. 

• "Test Procedures and Efficiency Standards for Commercial Air Conditioners and Heat 

Pumps," August 9, 2000. 

• “Energy Efficiency Program for Commercial and Industrial Equipment: Efficiency 

Standards for Commercial Heating, Air Conditioning and Water Heating Equipment,” 

March 1, 2000. 

• Proposed Rules: “Energy Efficiency Program for Consumer Products: Energy Efficiency 

Conservation Standards for Commercial Unitary Air Conditioners and Heat Pumps,” 10 

CFR Part 430, docket EE-RM/STD-01-375, August 17, 2001. 

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