# #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 TECHNO LOGIES 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, ConsultantReview of Analyses in Support of ANOPR 11/17/2003 ii JOHN BRYANT, Consultant MICHAEL DAVIS, ConsultantReview of Analyses in Support of ANOPR 11/17/2003 i 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 Mu lti-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 Sy stems 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......... 27Review of Analyses in Support of ANOPR 11/17/2003 ii 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 th e 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 neve r been experimentally verified................ 33 4.2.6 The BLAST analysis includes rooftop ai r conditioners outside the range of capacities of the two clas ses 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 exclude s 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 Met hodology 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.................................................................................................................... .. 45Review of Analyses in Support of ANOPR 11/17/2003 iii 6.0 ASSESSMENT OF THE INCREMENT AL MARKUP OF COMMERCIAL......... 46 UNITARY AIR CONDITIONING EQUIPMENT PRICES .................................................. 46

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

6.2 Review of Assumptions and Appr oach Used to Calculate Markups ..................... 47

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

6.4 Sensitivity Analysis of Chan ges 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.......................................... 60Review of Analyses in Support of ANOPR 11/17/2003 iv EXECUTIVE SUMMARY

The Energy Policy and Conservation Act as amended (EPCA), 42 U.S.C. 6291-6317, establishes energy efficiency standards for certain commercia l unitary air conditioners and heat pumps that correspond to ASHRAE Standard 90.1-1992. ASHR AE 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 stri ngent levels of energy efficiency where technically feasible and economically justified ( National Energy Policy Report , May 2001).

DOE conducted in-depth technical analyses of th is matter. To support a more stringent standard than ASHRAE 90.1-1999, the DOE analysis must s how 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, w ith 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 no t 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 uni tary air conditioners and heat pumps in the range of 65,000 to 240,0000 Btu pe r 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 co mments. After considering the stakeholder inputs as they related to the t echnical 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, approxima tions 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 IR T 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;Review of Analyses in Support of ANOPR 11/17/2003 v 3. Commercial Unitary Air Conditioning Equipm ent Supply Fan Energy Use While Ventilating;
and

4. Assessment of the Incremental Markup of Co mmercial Unitary Air Conditioning Equipment
Prices.

Evaluating the Nation’s energy use and estimatin g the effects of proposed rulemaking on that energy use is not an exact science. The IRT’s ro le was not to re-do the DOE analyses but to judge whether they were reasonable and defendabl e; whether there were errors that should be corrected; and/or whether there were better proce dures, 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 whethe r the proposed modification was likely to significantly affect the results of the TSD.

The analyses by DOE (Headquarters, National Laborat ories, contractors, etc.) in the TSD were extensive, following a complex methodology. The IR T took a typical engineering approach to the review: what is wrong and needs to be fixed? This report does not desc ribe 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 tr ouble-shooting the analysis to make it more defendable. (We were not to, and did not, c onclude whether the proposed rulemaking was justified.) The IRT concluded that on the whol e 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 co mments 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 pr ocedures would not significantly affect the results. We recommend that the markup analysis stand as it is.

• The IRT felt that the exclusion of manufacturi ng 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 identifi ed 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 corr ected 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 St akeholder comments.

This report describes the IRT’s assessment of these issues, thei r significance on the validity of the analysis results, and reco mmendations for addressing them.Review of Analyses in Support of ANOPR 11/17/2003 1 1.0 BACKGROUND

The Energy Policy and Conservation AC T (EPCA) as amended, 42 U.S.C. 6291-6317, establishes energy efficiency standards for certa in 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 St andard 90.1-1999 or more st ringent levels of energy efficiency where technically feas ible 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 capacit y. 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, includi ng comments from the Air-Conditioning and Refrigeration Institute (ARI), American C ouncil for an Energy Efficient Economy (ACEEE), Natural Resources Defense Council (NRDC), Lennox, and Trane. After considering the stakeholder inputs as they rela ted to the technical analysis, DOE decided to assemble an independent review team (IRT) to pr epare an independent assessment of:

• Some of the significant comments received, and

• The overall methodology, assumptions, approxima tions and data used in the technical support document (TSD).

This report represents that re view of the analyses performe d in support of the DOE Advance Notice of Proposed Rulemaking (ANOPR) on En ergy Conservation Standards for Commercial Unitary Air Conditioners and Heat Pumps, and so me of the comments received by DOE. In midAugust 2003, DOE contracted with the IRT to eval uate 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 Equipm ent Supply Fan Energy Use While Ventilating;
and

4. Assessment of the Incremental Markup of Co mmercial Unitary Air Conditioning Equipment
Prices.Review of Analyses in Support of ANOPR 11/17/2003 2 2.0 SCOPE OF ANALYSIS AND TERMS OF REFERENCE

The IRT was headed by Sentech, Inc. and consiste d 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 Internationa l (PERI) (Allan Evan s, Ph. D., P.E.)

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

• Consultants: Dennis L. O’Neal, Ph.D., P.E.; Je rry Jackson, Ph. D.; John Bryant, Ph. D., P.E.; Michael Davis, EIT

Each of the four areas referenced in Section 1.0 was assi gned 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 unde r contract to Sentech

3. Modeling of ventilation and fan use – Consultant s, directed by Dennis O’Neal under contract
to Sentech

4. Incremental markup of A/C pricing – McNeil

Overall technical review, coor dination, and supplemental techni cal 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 a nd competent analysts may differ on techniques, data, assumptions, and approximations used. The object ives of this independent analysis were to review the DOE ANOPR analyses and to ev aluate 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 origin al analyses. It was not supposed to come up with the “answer,” but to dete rmine whether the underlying analys es 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 mo re appropriate data sets or simulation models that could potentially improve or co mplement the analysis.

Thus, the role of the IRT was to assess the anal ytical 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.Review of Analyses in Support of ANOPR 11/17/2003 3 The IRT was to focus on issues that would lik ely 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 comp etent 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 ge nerally-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 assessm ent approach (i.e., the team real ly 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 enti rely 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 techni cal analyses making up the TSD.Review of Analyses in Support of ANOPR 11/17/2003 4 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 lo ad shape characterization is to assess the energy and peak demand savings potential of di fferent 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 samp le buildings is a major factor in determining electricity costs for cooling with in 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 pa ckaged) air-conditioning equipment. This subset of 1,033 buildings wa s selected from the overall CBECS database according to certain criteria, and complemented w ith 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 developi ng a building set and the associated energy performance profile that is inte nded 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 an alysis 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 Mu lti-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 th is issue is a real one), and if DOE’s analysis is reasonable in terms of its methodology, assumptions, a nd data sources. The intention was not to determine if
DOE’s characterization of comm ercial unitary A/C use in bui ldings 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 we re taken by DOE, whether they were reasonable, and if other “reasonable” appro aches might have been adopted that could change the overall results of the analysis . This final point is criti cal, because an analysis of this scope must include many engineering judgments and a ssumptions, 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 a nd energy benefits associated with various efficiency levels), and whether these pieces of the analysis are sound. In cases where an issueReview of Analyses in Support of ANOPR 11/17/2003 5 appears to be valid but its signifi cance (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 perf ormed at a level where all rele vant 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 re view consisted primarily of assessing how DOE worked with whatever information was available. It is important to note that within the scope of this independent revi ew, 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 ma terials where possible. All of the resources used in conducting this revi ew 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 in stalled commercial uni tary air conditioning equipment?

What other data sources might o ffer further insights into the di stribution and use of commercial unitary air conditioning equipment?

What other methods might provide a better approach to sele cting 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 databa se and filtered it down to the 1,033 sample using criteria related to Princi pal Building Activity (PBA) and percentage of cooled space, as shown in the figure below 1

.

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 equipmentReview of Analyses in Support of ANOPR 11/17/2003 6 As the figure illustrates, the an alysis in support of the proposed rulemaking relies upon a subset of 1995 CBECS samples to characterize the distri bution and operation of unitary A/C systems in commercial buildings. The development of this s ubset 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 deve lopment process are assessed below:

• How effectively the building sample repres ents 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 de velop 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 th ese 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 th e distribution and use of unitary A/C systems?

The Commercial Buildings Energy Consumption Su rvey (CBECS) is a representative national survey that collects energy-related building ch aracteristics data and energy consumption and expenditures data for commercial buildings in the United States. CBECS offers a broad representative data source for the characteristic s of commercial buildings in the US. The HVAC
characteristics data contained in CBECS represent the best pub licly available, national-level
information on the distribution and use of unita ry A/C equipment in commercial buildings. Further, each sample in the database is we ighted to reflect how many similar buildings it represents in the ac tual 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 thor oughly 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 a nd 67% of the square footage of commercial buildings that cool at least pa rt of their floor space with unitary A/C equipment. The IRT concludes that even after filtering out certain bu ilding types and buildings that cool less than 70% of the floor space with uni tary equipment, these values indicate that the 1,033 sample effectively represent the distri bution of unitary equipment in commercial buildings.

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

The assessment of this question re quired a review of available da ta products from DOE and the Energy Information Administration (EIA), the res earch community, industry groups, and privateReview of Analyses in Support of ANOPR 11/17/2003 7 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 suppor t of the proposed rulemaking), EIA changed the way that the target population of “commercial bu ildings” 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 bu ildings situated on manufactu ring sites in the 1992 CBECS is noteworthy because of the omission of all industrial/manufacturi ng buildings from the rulemaking analysis (see Section 3.3), including co mmercial 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 i ndustrial 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 (A RI) is a major trade a ssociation in the HVAC industry and produces a Statisti cal Profile with data on HVAC equipment from manufacturers’ perspective. The Statistical Profile include s 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 wh ere specific types of equipment are deployed.

BOMA Experience Exchange Report The Building Owners and Managers Associati on’s (BOMA) 2003 Experience Exchange Report contains operating income and expense data on over 3,000 public and priv ate sector commercial properties throughout North America. Data is cl assified by categories su ch as building type, building class, building owners hip, 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 onl y HVAC-related information included in this database is the HVAC subset under the Maintenance and Repairs category.

California Non-Residential New C onstruction (NRNC) Baseline Report
The California Non-Residential New Construc tion Baseline Report is a study conducted on behalf of the California Board for Energy Efficien cy (CBEE). The study is based on surveys of the designers (architects and engi neers) of new buildings in Calif ornia, onsite audits, and DOE-2 building simulations. The onsite building audits ar e a collection of several sets of NRNC audits conducted during the 1990’s, and repr esent 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 Storag e” categories. The data for these buildings includes information on HVAC equipment type; this could be useful in assessi ng the distribution of unitary equipment across a range of building types at the state level. This information is publiclyReview of Analyses in Support of ANOPR 11/17/2003 8 available online in a database format. However, as a California data base, there is no accepted method to extrapolate it to the na tional level, either by itself or combined with CBECS. (That is,
it would introduce bias to add NR NC to CBECS in order to “be tter” characterize buildings of certain types or climate zones.)

Manufacturing Energy Consumption Survey (MECS) The MECS database, prepared by EIA, is a nation ally representative sa mple of manufacturing
establishments in the US. The most recent vers ion of MECS in 1998 had a sample size of almost 18,000 plants and collected energy-related data acr oss 36 industry groups and industries (based on the NAICS system). MECS data with releva nce 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 co llection of databases (bas ed in part on CBECS) developed from information on more than 800,000 individual utility customers throughout the US. MAISY contains energy data on both comm ercial and industrial customers, although the level of detail between these two gr oups 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 cu stomers, but not industrial buildings. Industrial buildings in MAISY do, howev er, have sample weights that allow for the estimation of national-level data based on the samples.

Given these characteristics, MAIS Y contains sufficient data to pe rform aggregated load profile comparisons between commercial an d 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 le ss detailed data (e.g., ut ility-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 th e primary data source for this analysis is
reasonable. No national-level data sets detailing the distri bution 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 cau tion, because specially augmenting the analysis with auxiliary data would likely introduce bias.

What other methods might be used to d evelop 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 star ting with the CBECS database and narrowing it down to the 1,033 sample was a sound method of developing a repr esentative commercial building set. A few
deviations from the method us ed are discussed below.Review of Analyses in Support of ANOPR 11/17/2003 9 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 pa rtially 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 approx imations needed to estimate these A/C loads would outweigh the appearance of increased “p recision” from modeli ng additional buildings. This, coupled with the fact that the 1,033 build ing set already characterizes a major portion of commercial buildings with unitary systems, relegate s this issue to a secondary consideration that would most likely not change the results of the analysis.

Consider using more up-to-date comm ercial building data from 1999 CBECS
The 1999 CBECS data set was not used for the bu ilding characterization analysis because the
entire micro-data set was not available in time fo r this analysis, and this database excludes new buildings less than 10,000 square feet. Usi ng the 1995 CBECS data help ed to address these issues but may also skew the distribution of build ing vintage towards older buildings. This could potentially influence building characteristics, bui lding loads, and the char acterization of unitary A/C equipment.

A distinct advantage to using the 1999 CBECS da ta is the inclusion of some new buildings (those greater than 10,000 square feet). Howe ver, disadvantages to using 1999 CBECS include the minimum size requirement of new buildings a nd the fact that the 1999 survey revisited many of the 1995 samples, so the data would contain a gr eat 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 indicati on 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 retrofits 2 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 re sulted in a sample representative of the populati on of unitary A/C equipment in commercial buildings. (This
section did not discuss the omission of industrial bu ildings; that is covered in the next section.)

Specific findings include:

• Using CBECS as the primary data source a nd starting point for developing the building sample utilized the best available single s ource of national-level data on HVAC and energy characteristics of commercial buildings

• The 1,033 sample is effective in characteriz ing the population of unitary A/C systems operating in commercial buildings

2 ARI Statistical Profile. Air-Conditioning and Refrigeration Institute, 2001.Review of Analyses in Support of ANOPR 11/17/2003 10 • Potential alternatives to the commercial building sample development process may be considered, but appear unlikely to ch ange the results of the analysis.

3.3 Omission of Industrial/Manufacturing Buildings

In developing the building sample for the build ing energy use and end-us e load characterization analysis, only commercial buildings were used. Buildings in the industr ial/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 te rms 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 da tabase contains 5,766 building samples with data on building type, build ing characteristics, and HVAC characteristics, including A/C equipment type.

In the 1995 edition of CBECS, no industrial buil dings 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 imp acts on the rulemaking analysis. First, it is possible that some buildings that use significan t amounts of unitary A/C cooling energy are not captured in the building set. S econd, it is also possible that th ese same buildings’ electricity prices may be different, affecti ng the energy analysis results. Ta ken 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 th e industrial buildings issue, two cr iteria determine if this issue is significant in the c ontext of the overall resu lts of the analysis. First, do industrial and manufacturing buildings consum e a significant proportion of the energy used by the unitary A/C systems covered in this rulemaking? Without sa tisfying this criteria, industrial unitary A/C energy use is a “small slice of the pie” and will have little bearing on th e overall results of the analysis, regardless of issues re lated 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 thes e criteria must be satisfied simultaneously . That is, it must be determined that t hose manufacturing/industrial buildings with lower tariffs are also buildings that use signi ficant energy for unitary A/C systems. ConcludingReview of Analyses in Support of ANOPR 11/17/2003 11 that some industrial buildings us e unitary systems and some industrial buildings have lower tariffs is not sufficient to establish significance. A correlation of these fact ors 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/m anufacturing 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 kWh 3 , although the interpre tation and manipulation of CBECS data can
lead to different estimates.

As part of this independent review, 1995 CBECS da ta was used to develop an approximate range of unitary A/C energy use in commercial buildin gs based on two separate analyses using CBECS data (Appendix to Section 3.0). The results of th e two approaches are within the same range, and indicate that annual energy use of unitary A/C systems in commercia l 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 MECS 4 and 1995
CBECS data to estimate that industrial build ings use 17.2 billion kWh annually for packaged A/C systems. This value, coupled with Fr azell’s 46.1 billion kWh estimate for commercial buildings, would indicate that roughly one-qua rter of all packaged A/C system energy consumption occurs in industr ial/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 consumption 5 is the same in the commercial and industrial building sectors. While the level of detail contai ned 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 consump tion figure must be viewed cautiously due to the scale of this assumption.

This assumption is extremely significant and so mewhat counter-intuiti ve. 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 fo r industrial processes, with A/C energy consumption comprising a small portion of the overall load. T hus, those industrial buildings for which this assumption may be valid ( light industrial faciliti es) 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 energyReview of Analyses in Support of ANOPR 11/17/2003 12 similar electric rates anyway, and for those industr ial 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 annually 6 . This estimate includes both commercial and industrial building unitary A/C en ergy, and appears to originate from an April 2000 DOE screening analysis 7 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 system s 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. Howeve r, this claim is not su pported 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 bu ilding size and system size, and that unit size is driven more by the size of the conditioned zone rather than building size 9 . 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 av ailable for a subset of these buildings: commercial buildings on industrial/manufacturing sites.

In 1995 CBECS, the Energy Information Administra tion (EIA) changed the way that the target population of commercial buildi ngs was defined for the CBECS . A few types of buildings included in previous surveys (1989 and 1992) we re excluded. One of these was commercial buildings located on multi-building manufacturing facilities. Th is category of building is not included in the DOE rulemaking analysis and repr esents one of the two types of industrial / manufacturing buildings left out of the anal ysis. 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 Comme rcial 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.Review of Analyses in Support of ANOPR 11/17/2003 13 facilities 10 . 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 fo r this independent revi ew indicated that the total number of samples of commercial buildi ngs 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 Activ ity (PBA) categories of Warehouse (36%), Office (34%), Vacant (14%), and Mer cantile/Services (9%). The aver age 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 commerci al buildings (by number) on indus trial/manufacturing sites have no cooling of any kind. In 1995 CBECS about 26% of commercial buildings (by number) were
not cooled 11 . According to 1994 MECS, roughly 32% of the enclosed floor space in industrial buildings was not conditioned (which means no c ontrolled heating or coo ling). 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 assump tion 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 estimate d national A/C cooling energy for packaged systems in commercial build ings 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 kW h 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 billio n 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/m anufacturing 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 – Mu ltibuilding, Number of Buildings and Fl oorspace, 1992. En ergy Information Administration, 1992.

11 CBECS 1995, Table 3 – Census Region, Number of Bu ildings and Floorspace, 19 95. Energy Information Administration, 1995.

12 MECS 1994, Table A7 – Enclosed Floorspace and Conditi oned Floorspace. Energy In formation Administration,
1994.Review of Analyses in Support of ANOPR 11/17/2003 14 • A large portion of these buildi ngs (35%) are not cooled by any mechanical equipment.

Using this data on commercial buildings on industri al/manufacturing sites, one could arrive at an
estimate of overall industrial/manuf acturing 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 av ailable. Limited data on cooling equipment and cooling en ergy profiles in industrial build ings is availa ble (see Section 3.2), which presents the challenge of how to asse ss the significance of this issue with incomplete
information.

Is the cost of electricity for industrial/manuf acturing buildings significantly different compared to commercial buildings?

The rulemaking analysis utilizes both a tariff-ba sed approach and an hourly cost approach to arrive at the electricity cost estimates for th e 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 bui lding to determine electricity costs for cooling as well as the marginal energy cost savings that resulted from efficiency improvements.

Stakeholders have expressed conc ern that industrial facilities with unitary A/C systems would operate on less expensive tariffs, and that the omi ssion of these buildings skews the cooling costs higher. The result of skewing cooling costs hi gher 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 anal ysis, 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/manu facturing site, or 2) the load profiles of these facilities were of a magnitude a nd shape such that the facility qualified for a more advantageous
tariff.

In examining the first question, DOE found thr ough research that utilities typically do not distinguish between commercia l and industrial customers, per se , in their tariffs. Only occasionally did DOE find utilities that will offe r 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 diff erent names, the rates were in fact the same. 13

The IRT reviewed a sample of the utility ta riffs collected under the rulemaking analysis. 14 The findings confirm that tariffs typi cally are not determined by the t ype 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 th at tariffs based solely on business type are not a significant factor and would likely not translate directly in to lower prices for industrial buildings on a scale that would a lter 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.phpReview of Analyses in Support of ANOPR 11/17/2003 15 The second question – whether industrial/manuf acturing 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 th e facility’s demand, with higher demand levels corresponding to tariffs w ith less expensive unit en ergy 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 load s can be a major contributor to the overall electrical load. Many of the ot her loads (lights, computers, communications) and occupants in commercial buildings serve to add to a build ing’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 ge nerally tend to be lower than t hose for industrial buildings due to differences in operations and usage schedules. A few samples of commercial building load factors provided in the ANOPR Technical S upport 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 factor s based on building type, such as a summer load factor range of 42% to 72% for Office buildings 16 . While generalized load factors are useful for qualitative comparisons of la rge sets of buildings (e.g., co mmercial vs. industrial), any quantitative analysis related to A/C energy, p eak demand coincidence, and tariffs must be performed with actual site-specific load data.

In industrial buildings, load fact ors 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 ma y have little or no bearing on determination of the tariff or demand charges. The peak demand of an indus trial facility may be driven exclusively by industrial operations. In the aluminum i ndustry, for example, the total HVAC electrical energy (of which cooling is just one component) is just 1.5% of the total facility energy 18 . Further, the peak A/C load may or may not coincide with the peak load of the overall facility, meaning the peak demand savings fr om increased A/C efficiency levels would be treated differently than they are in commercial buildings. Other re ferences 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 f acilities (which will in clude many commercial buildings) have a lower load factor of 0.70. 19 Again, average load factor s 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.Review of Analyses in Support of ANOPR 11/17/2003 16 These findings regarding load characteristics in industrial buildings, combined with DOE’s conclusion about the relationship between load fact ors and marginal rates, indicate that some industrial buildings (particularl y those with high demands and hi gh load factors) would have lower marginal rates within the rulemaking analys is. This in turn woul d reduce the energy cost saving benefits of increased A/C efficiency leve ls. Once again, this is only relevant to the analysis if these same buildi ngs use a significant amount of el ectricity for unitary A/C.

3.3.4 Findings

Unitary A/C Use in Industrial Buildings Taking into account DOE’s data sources and anal ysis, external analyses and references, and rough estimates performed under this review, it is extremely difficult to a rrive at a defensible estimate, using publicly available data, of un itary 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 comme rcial range, but it does not include any cooling energy for industrial or manuf acturing 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 manufacturi ng buildings, some of which is consumed by unitary A/C systems.

• Approximations of 17.2 billion kWh for industria l unitary A/C energy and 76 billion kWh of
total packaged rooftop unit energy. While thes e estimates involve de batable assumptions (e.g., the proportion of A/C energy used in indus trial 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 an alysis cited in the stakeholde r 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 pr obably not valid for those buildi ngs that are primarily heavy manufacturing and have a signifi cantly lower tariff than commer cial buildings. For those light industrial buildings with loads si milar 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 estim ate of 17.2 billion kWh of industrial unitary A/C electricity use rests upon this as sumption, it is probably overstated.

While DOE’s steps in the current analysis are re asonable 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?Review of Analyses in Support of ANOPR 11/17/2003 17 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 ener gy 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 Comm ercial and Industrial Buildings DOE’s work in developing cost estimates for commercial buildings us ing its tariff methodology appears to be thorough and reasonable. As for th e potential cost differe nces between industrial and commercial buildings, a review of DOE’s anal ysis, 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 ener gy load relates to the whole building load in specific industrial facilities that use unitary systems. This is sue is similar to the industrial building energy use issue discussed above, in that a diagnostic analys is should be considered that would offer more insight on the si gnificance of the issue. The da ta available at this point only
indicates that the issue is valid.

One possibility would involve usi ng load profile data for specific industria l 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, electro nics) to help assure that relevant industrial buildings ar e being assessed. The load profile s and energy use data from such buildings could be combined with DOE’s tariff data base to arrive at energy cost estimates, which would in turn be compared to the commercial faci lities 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 develope d from the CBECS database, 373 of the 1,033 buildings are associated with multi-building facil ities. 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?Review of Analyses in Support of ANOPR 11/17/2003 18 3.4.1 Introduction

The issue of metering practices for multi-buildi ng facilities is significant to the rulemaking analysis because if buildings in this situati on are metered collectively with other buildings on site, then they may qualify for less expensive rate s. 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 ot her 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 ap proach were provided:

• Metering individual buildings allows facility and energy managers to benchmark building performance more effectively.

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

• As a site evolves over time, new meters are ad ded as new buildings are constructed, resulting
in a collection of indivi dually 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 mete red 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 electri city deregulation a grow ing number of national accounts (e.g., nation-wide franchises) and “l oad 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 bi lled), 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 electric ity 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 faci lities. Currently the general trend for multibuilding sites is to meter buildings separately and to determine rate structures based on individual buildings.Review of Analyses in Support of ANOPR 11/17/2003 19 3.5 Distribution of Buildings in the 1,033 Sample

To what extent does the dist ribution of buildings in the 1,033 building sample accurately represent the distribu tion of actual commercial buildi ngs by regions and climate?

3.5.1 Introduction

The 1995 CBECS sample was drawn to be repres entative of the commer cial 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 distri bution 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 rele vant to the analysis of commercial A/ C, the same degree of statistical significance is not maintained when extrapolatin g to the national populati on as for the original complete 1995 CBECS data base. It is beyond th e scope of the IRT to verify the various statistical parameters of the 1,033-building database, the robustne ss of these parameters relative to the overall CBECS database, and the implicat ions of sub-dividing th e 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 charact erize 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 Docu ment (TSD Section 6.2.2) provides an indication of how representative the distributi on of the set of 1,033 build ings is in terms of region (census division) and buildin g type. With similar levels of variability of data, regions with a larger building populati on such as the Pacific census division (157,511 buildings) would likely be more heavily sampled in the 1,033 set, wh ile lower population regions such as the West North Central division (33,398 build ings) would likely be represen ted 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 750Review of Analyses in Support of ANOPR 11/17/2003 20 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 th e number of samples in cluded 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 varian ce within each division will dictate the sample size for assuring statistical significance when extra polated to the national population). The results shown in the table above indicate that w ith 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 divi sion, all of the regions ar e within about 30% of the average actual buildings to sample building ra tio (772). While the East South Central region had a lower representation, this co uld potentially be due to the am ount of variances of buildings within the region.

After reviewing the DOE TSD, the IRT feels th e set of 1,033 is effective in representing the regional distribution of the bu ilding 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 populatio n. Statistical significance a nd un-biased results are not assured for analyses of indivi dual regions (or climate zones.)

In terms of the sample’s effectiveness in repr esenting the distribution of actual commercial buildings by climate, the following table us es information from Table 6.2.28 in the ANOPR Technical Support Document (T SD 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 Cooli ng 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 HDDReview of Analyses in Support of ANOPR 11/17/2003 21 The table indicates that buildings in Climat e Zone 1 have a somewhat lower proportion of buildings in the sample than the region’s proporti on of commercial buildings in the Nation. (For this Climate Zone, which is the coldest zone, ther e 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 build ing 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 th ese 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 th e 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 subdividi ng the overall CBECS sample and performing micro-simulations or -ana lyses where the results are very different by building type within a region (or climate zone), and then extrapolati ng the results to draw conclusions of national signifi cance. This problem is discussed in terms of the BLAST
simulation model in Section 4.0.

3.6 Cooling Loads for Unitary A/C Syst ems that are not the Primary HVAC System

Is it reasonable to assume that cooling loads ar e 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 aski ng: will the cooling a nd 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 build ing of smaller size?

3.6.2 Assessment

Whether the two 5-ton systems in the example a bove 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?Review of Analyses in Support of ANOPR 11/17/2003 22 • How is the unitary system that conditions onl y 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 sc hedules, operation/activity schedules, humidity controls, economizer operations, ventilation requirements, and lo ad diversity influence the operation of both systems?

When faced with these questions it becomes appare nt 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 operati onal 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 th eir operations within the analysis.

It should also be noted that modeling only syst ems that condition 70% or more of a building’s floor space resulted in a sample set that captu red 73% of the annual A/C energy in commercial buildings that use unitary A/C systems. This f act, coupled with the increased uncertainties of characterizing equipment operation in buildings w ith a minority of floor space cooled by unitary equipment, justifies the decision to focus on bu ildings 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 equi pment. Characterizing the operation of unitary systems in buildings where they condition a mino rity of the floor space would involve more uncertainty in assumptions and judgments. This uncertainty, combined w ith the fact that the “70% and over” building sample captures a larg e cross-section of buildings with unitary equipment, led the IRT to conclude that selectin g 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 equipmen t in commercial buildings.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 di stribution of unitary A/C equipment across all building types (including industrial) were found.

• Some auxiliary data resources exist that coul d help guide the rulemaking analysis and offer insights into the signific ance of other issues high lighted in this review.

• Potential alternatives to the commercial building sample development process may be considered, but appear unlikely to ch ange the results of the analysis.

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

• The claim that excluding industrial buildings fr om 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 th e analysis. The available data on unitary A/C energy usage in industrial buildi ngs indicates that these build ings 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 equipm ent in the industrial sector based on the general size of industrial build ings is not adequately supporte d in the rulemaking analysis and runs contrary to othe r recent building research.

• Estimates of unitary A/C energy in commercial buildings on i ndustrial sites are small but not insignificant. These types of buildings, comb ined with actual industrial and manufacturing buildings, represent buildings th at 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 distribut ion of unitary A/C equipment in industrial buildings prevents
the determination of whether excl uding this portion of A/C energy is significant (e.g., would it change the results of the analysis were it in cluded?). Additional effo rts to arrive at an
estimate of how much indus trial unitary energy use would be significant could help to clarify
this issue.

Omission of Industrial Buildings – Imp act on Load Profile and Energy Costs

• The claim that industrial and commercial build ings would pay different prices for energy
based on the load characteristics of these two types of buildings is valid . This finding isReview 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 re sults 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 poin t the significance has no t been determined, but additional analysis involving industrial energy and load da ta and the rulemaking tariff database could potentially help to clarify the issue.

• Tariffs based on business type (e.g., an “industria l” tariff) are not a si gnificant 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 Prac tices 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 electr icity paid by the sub-sa mple 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 th e commercial building population.

• Within the context of the energy use simulati ons, caution must be exercised when taking the national CBECS sample, subdividing it, performi ng simulations, and extrapolating the results
to draw national-level conc lusions. (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 c ool all or most of the building.

• Including only those buildings that cool a majo rity of floor space with unitary systems in the building sample captures a broad cross-secti on of commercial build ings with unitary equipment and is a reasonable approach.

3.8 Resources Used in the Independent Review

1992 Commercial Buildings Energy Consumption Su rvey. Energy Information Administration,
1992.Review of Analyses in Support of ANOPR 11/17/2003 25 1994 Manufacturing Energy Consumption Surve y. Energy Information Administration, 1994.

1995 Commercial Buildings Energy Consumption Su rvey. Energy Information Administration,
1995.

1998 Manufacturing Energy Consumption Surve y. Energy Information Administration, 1998.

1999 Commercial Buildings Energy Consumption Su rvey. Energy Information Administration,
1999.

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

ARI Statistical Profile . Air-Conditioning and Refrig eration Institute, 2001.

A Preliminary Quantification. The Effects of Ex cluding Industrial/Manufac turing Sites from the DOE Rulemaking to Determine Efficiency Levels of Unitary Large Equipment. Charles Frazell,
PE. March 2003.

Background Research Summary. Integrated Ener gy Systems Productivity and Building Science. New Buildings Institute for the California Energy Commission Public Interest Energy Research (PIER) Program, 200l.

Bridging the Efficiency Gap: Commerci al Packaged Rooftop Air Conditioners . Shugars, J.; Coleman, P.; Payne, C.; and Van Wie Mc Grory, L., LBL-46190, ACEEE 2000 Summer School, www.dc.lbl.gov/~payne/publica tions/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 E nd Use Load Characterization Analysis , DOE presentation, www.eere.energy.gov/buildingsapp liance_standards/commercial/ac_hp.html

Commercial Unitary Air Conditioner and Heat Pu mp Life Cycle Cost Analysis: Inputs and
Results. Lawrence Berkeley National La boratory (presentation), 2003. http://www.eere.energy.gov/buildingsappl iance_standards/commercial/ac_hp.html

Commercial Unitary Air Conditioner and Heat Pump Framework. DOE, 2001.

Communications with Alan Swenson. Energy Info rmation Administration, September – October

2003.Review of Analyses in Support of ANOPR 11/17/2003 26 Communications with Craig Drumheller, former ly of Public Service E&G. September 2003.

Communications with Jean Lupin acci. Energy Star, October 2003.

Communications with Jerry Jack son. Market Analysis and Information System (MAISY), September 2003.

Communications with Jim Raba. DOE Office of Building Technologies, September – October

2003.

Communications with Leslie Latuse k. Allegheny Power, October 2003.

Communications with Martha Johnson. Ener gy 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 Indu strial 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 Indu strial Equipment: Energy Conservation Standards for Commercial Unitary Air Conditi oners 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 th e Division of Energy Resources. Commonwealth of Massachusetts, Office of Consumer Affa irs 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/ Ga s Hybrid Systems in Industrial Applications, Arthur D. Little, January 2000

Screening Analysis for EPACT-Covered Co mmercial HVAC and Water-Heating Equipment . US Department of Energy, April 2000.Review of Analyses in Support of ANOPR 11/17/2003 27 

### A ppendix to 3.0 Estimates of Unitary A/C Energy Use in Commercial Buildings

Approach #1 From 1995 CBECS Table 3 (in the En ergy 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. In stead, 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 Centra l Chillers represent 19.4% of the total re ported electrical cooling energy (Column B), the reported cooling energy for buildings with Centra l 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)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 to tal square footage of 26, 628 million square feet (using this square footage offers a generous estima te 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 kWhReview of Analyses in Support of ANOPR 11/17/2003 29 4.0 EVALUATION OF THE BL AST ENGINEERING ANALYSIS

4.1 Introduction

The Department of Energy (DOE) is prepari ng an Advance Notice of Proposed Rulemaking (ANOPR) for minimum efficiency standards fo r unitary air conditioners. The ANOPR and supporting documentation will set minimum efficien cy standards. This section summarizes a review of several aspects of the DOE analyses suppor ting 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 pr essure controls are activated;

• Modeling the saturated c ondensing 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 efficien cy levels in ASHRAE 90.1-1999;

• Assumptions on use of low-emissivity gl ass in commercial construction; and

• Assumptions about economizer use.

(3) A review of the use of CBECS data to char acterize 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 compre ssor 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.Review of Analyses in Support of ANOPR 11/17/2003 30 The model in BLAST has some significant restrictions that directly affect the DOE analysis. In addition, the BLAST analysis used to support th e ANOPR appears to ha ve some assumptions
that should be reevaluated.

4.2.1 The rooftop model in BLAST assumes a single compressor ai r conditioner with compressor unloading

A single compressor rooftop unit is a reasonable assumption for r ooftop units with a capacity under 10 tons (120,000 Btu/h), but it is not a reasonable assump tion for larger units. The ANOPR includes units with capacit ies up to 20 tons. Many of th e units between 10 and 20 tons have multiple compressors. The BLAST rooftop model is not capable of modeling these types of rooftops. As currently confi gured, 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 act ual 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 e ffect 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 cy linder 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 th e 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 th e part load performance of multip le compressor rooftops found in the larger capacity class.

The part load model in BLAST assumes the co mpressor has cylinder unloading. Discussions with several manufacturers indicate that many of the smaller rooft op units use hermetic compressors, which do not have unloading capabi lities. For these units, the compressor will cycle on and off as the cooling load decreases be low the steady state capac ity of the unit. We would expect the part load effici ency of a unit that cycles on a nd off to be less than one with cylinder unloading. If the part load on/off cycl ing performance could be characterized, it would be possible to define a set of coeffici ents that could be used in BLAST.

4.2.2 The temperature used for implementing he ad 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 pr oper refrigerant control in the sy stem. 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 pressu re controls are oftenReview of Analyses in Support of ANOPR 11/17/2003 31 implemented by either varying the speed of the c ondenser fan or cycling the condenser fan(s) on
and off.

In BLAST, head pressure controls are im plemented by specifying the minimum saturated condensing temperature (MSCT). This is th e 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 temp erature and heat transfer char acteristics of the condenser. We would expect 100oF to corres pond 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 fo r MSCT is high. A review of some literature
from manufacturers as well as some discussion s 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 condensi ng 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 incr ease in energy use will depend on the cooling load and number of hours that the outdoor temper atures are between 45 and 75oF. There may be some parts of the country (South Florida or Sout h Texas) where units would not even need head pressure controls or they would be used sparin gly because temperatures rarely get below 40oF.

The sample BLAST deck shows that the default MS CT was used because it never was reset. The default value should not be used. We strongly recommend that the anal ysts contact the major rooftop manufacturers to determ ine: (1) a reasonable range of outdoor temperatures that their units operate before needing head pressure cont rols, 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 no t 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 DFLPR) (1 LOAD SCT + + = OADB (1)

where,

OADB = outside air dr y 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 coo ling capacity of approxima tely 500,000 Btu/hr (40+Review of Analyses in Support of ANOPR 11/17/2003 32 tons). The description in th e Hittle (1982) paper makes it cl ear that CUA varies with the capacity of the unit. Both the BLAST documenta tion 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 to ns. The impact on the analysis is twofold. First, an underestimate of the SCT will decrease th e estimate of the energy use of the rooftop unit. The power of the unit is dependent on the difference between the saturated condensing temperatur e 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 imp act is that an estimat e of a smaller SCT will force BLAST to implement head pressure contro ls at a higher outdoor te mperature 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 95 oF. Using Equation 1, BLAST would calculate

SCT as:

F 97.9 F 2.9 F 95

F h r kBtu 27.43 0.326) (1 hr Btu 60000

F 95 SCT ° = ° + ° =

° ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

+

+ ° = (2)

In reality, the CUA for a 5 ton unit should be ab out 3.31 kBtu/(hoF). A mo re realistic calculated
value of SCT would be:

F 119 F 4 2 F 95

F h r kBtu 3.31 0.326) (1 hr Btu 60000

F 95 SCT ° = ° + ° =

° ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

+

+ ° = (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 cont rols would already be in use when the outdoor temperature is 95oF! In reality, this would not happen because th e 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 th e 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 t oo small. We believe all BLAST runs used to support the analysis of the ANOPR will need to be rerun with mo re reasonable values for the CUA for each rooftop used in the buildings. Give n that some of the buildings modeled have 15 or more rooftops, individual C UAs 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 estimate d and input into BLAST fo r 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.Review of Analyses in Support of ANOPR 11/17/2003 33 4.2.4 The rooftop model assumes a low saturated suction temperature

The user can input the “Design Sa turated Suction Temperature” (DSST ) or use the default value. The input file provided to the IRT used the defa ult value of the DSST, wh ich is 40oF. BLAST
calculates the estimated Saturation Suction Temperature, SST, based on the DSST, the amount of loading on the evaporator (CPLR), and the unloade r 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 Te xas A&M University (TAM U) laboratory and have
found that the SSTs for these units varied from 40 to 45oF when the en tering 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 temperatur e (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 us ed. Thus, if the SST is smaller than it should be, it would contribute to a larger difference be tween 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 te sting 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 l earned that the rooftop model has never been experimentally verified. While a similar chiller model (1983) developed by CERL went through a limited vali dation, 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 th e correct trends/values. Test data will also provide some assurance of the accuracy of the m odel. For residential sized air conditioners, the analyst has the option of using two well known a nd verified models fr om either Oak Ridge National Laboratories (ORNL) or the National Institute for St andards 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 th at their estimates of performance are ± 1% or
±20%. Besides the deficiencies already pointed out in the mode l, we think it is important DOE verify the accuracy of the rooftop model with ex perimental test data. Because there are two classes of rooftop units, at a minimum verifica tion should be done for a unit in each of the size classes. Failure to provide at least some mini mum verification of the rooftop model with test data will lead to doubt of the validity of the BL AST 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 appe ar reasonable, and its results are used and accepted for HVAC studies. However, the standard of analysis for a building owner who wishesReview of Analyses in Support of ANOPR 11/17/2003 34 to evaluate various A/C models, an engineeri ng team trying to estimate the value of improved A/C efficiency, or an Energy Services Comp any performing an audit to determine which efficiency measures to implement is not nearly as rigorous as the standa rds 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 concerne d that without experi mental 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 ai r 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 mo del 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 pack aged 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 st andard 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 th at appropriately sized r ooftops 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 roofto ps used in the analysis is too low

Section 3.5.1 of the TSD provides a description of the efficiency le vels of the rooftop classes. The maximum technologically feasible design th at DOE chose to consider was one with an Energy Efficiency Ratio (EER) of 12.0. Th e Consortium for Energy Efficiency (CEE 2003) publishes summary tables of Air Conditioning and Re frigeration Institute (ARI) data for rooftops by manufacturer. The data include capacity, IP LV 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 17.0Review of Analyses in Support of ANOPR 11/17/2003 35 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 ro oftop model with an EER of 13.5 (price $4,325) and a 115,000 Btu/h m odel with an EER of 13.4 (price $5,525). There are units that can be purchased today with e fficiencies as much as 12% higher than 12.0.
This is similar to the residen tial air conditioner/heat pump analys is in the early 1980s. At that time, DOE published that the maximum technologically feasib le SEER for residential air
conditioners was 10.4 when there was an air c onditioner 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 e fficiency EER should be before publishing the ANOPR.

4.2.8 Ignoring part load performance exclude s 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 compresso rs was discussed earlier. Given that rooftop units are typically much more complicated th an residential units and must, in many cases, operate at part load conditions fo r a large fraction of the year, it seems inconsistent for the DOE to incorporate part load performance in resi dential air conditioner standards and ignore it for
commercial air conditioner standards. Reside ntial equipment performance is based on the Seasonal Energy Efficiency Ratio (SEER). The commercial equipment equivalent to SEER is the Integrated Part Load Value (IPLV). Reside ntial units with multiple speed compressors, fans, variable speed compressors, all can be tested, performance quantified, and assigned an SEER. To ignore the many options availabl e to rooftop manufacturers to improve part load performance and annual energy usage while having to just ify 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 mark et in 2003 with similar EERs (CEE 2003). The units with the higher IP LV would use significantly less energy than those with lower IPLVs but have the same EER. The TIAX report illustrate s 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 curren tly incapable of handlin g 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. Differen tiating 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 equipmen t 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 th at can be combined with the building models in BLAST to estimate the annual energy use for cooling of rooftop air conditioners. The analysisReview of Analyses in Support of ANOPR 11/17/2003 36 used to support the ANOPR used BLAST. Ther e were a number of limita tions 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), th e 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 re striction in BLAST is that it only handles single compressor r ooftop 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 system s 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 BL AST 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 effici ency 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 leve ls were published as part of ASHRAE 90.1- 1999, they are scheduled to go into effect two year s 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 correctio ns may be needed for references to Table 6.2.8 EER Levels Selected for Building Si mulations 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-emissiv ity (low-e) glazing in co mmercial 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, unl ess required by building codes, only very knowledgeable owners would have low-e glass installed in their newly constructed commercial buildings. Until the la te 1990’s, the premium for the upgrade from standard to low-e glazin g was several dollars/ft 2 of glass area. Beginni ng in the early 2000’s, many states began to adopt the In ternational Code Counc il’s (ICC) family of building codes. In those states that adopted the en tire code set, it would also include the International Energy Conservation Code (IECC). Depending on HDD or CDD climate data, the IECC could requireReview of Analyses in Support of ANOPR 11/17/2003 37 the use of low-e glass. The ICC has no such pr ovision for commercial construction. So, it is restated that even this modest application pe rcentage 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 elimin ate the entire South eastern portion of the United States, which includes Florida and Texas. These two states represent a considerable portion of the air-conditioning ma rket 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). Conve rsely, 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 “originall y were going to use fixed, dry-bul b economizers” for the analysis. They decided not to use that control strategy because of “pro blems” with BLAST’S simulation of that type of control when a pplied 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 availa ble and is also the most expens ive control option. This range (7.5 – 15 ton) represents a very common applicati on 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 instal lation. It is significantly easier to install this type of equipment than a compar able capacity central system. As such, this range of equipment is usually specified (by the owne r or engineer) to be a pretty basic package with a minimum of expensive add-on options.

A second issue has to do with whether economizer s should be applied in all buildings. For example, in hot and humid climates, the applicab ility of an economizer operation is limited at best. The ASHRAE 90.1-1999 essentially eliminat es 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 woul d be required to have an economizer installed on any size HVAC system. DOE’s assu mption of economizer use is probably overstated and would lead to an overs tatement 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 assumpti ons used in the analysis. Whatever assumptions are eventually used should have adequate justif ication to ensure they do not inadvertently bias the energy use calculations.Review of Analyses in Support of ANOPR 11/17/2003 38 4.4 Comparison of BLAST Simulation Methodology and CBECS Estimates of Energy Use

In this section, the BLAST energy simulati on methodology is discussed along with the CBECS estimates of energy use. An alternative me thodology for estimating energy use from the CBECS
data base is also discussed.

Previous analysis related to unita ry air conditioners used “typi cal” 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 serv es 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 an alysis of a representative sample of small, medium and large buildings. These differences exis t 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 analys es 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 stra tegy for the “average” system, but the effects of the DSM program on over- and unde r-sized units were very diffe rent, and did not “average” out to the effects of the typical or medium-sized uni t. As a result, the implementation of a program designed with this aggregation e rror analysis was less load reducti on and a greater drop-out rate than expected.

Aggregation bias can be minimized by increasi ng the number of “typical building types” and climate zones. The application of BLAST analys is to individual CBECS buildings reflects an attractive methodological framework for improving on previous analysis approaches. This use of a large number of individual bu ildings 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 populati on using weights that reflect the representation of each building within the population. Howeve r, increasing the number of bu ilding types within the limited CBES data sample will introduce bias error, as described in Section 4.4.1.

Application of the micro-simula tion approach can substantially improve the accuracy of results and provide much greater deta il concerning individual issues related to the analysis. Unfortunately, the analysis used to support th e 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 ar e described in the following four sections. Modifications of the analysis required to rectify the deficien cies 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 precisionReview of Analyses in Support of ANOPR 11/17/2003 39 targets of the survey are achieve d 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 commer cial 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 ha s 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 Tabl e 6.2.28 of the TSD. The 25 cells with CBECS buildings vary from 164 buildings in the largest to 5 in the sma llest. 10 of the 25 cells have fewer than 20 sample buildings. To see the po tential problem in attempting to distinguish among energy use characteristics (includi ng load shapes) with this sample , consider the following. The
East South Central census divisi on includes three climate zones, 3, 4 and 5 with 14, 53 and 16 sample buildings, respectively. If a sufficient nu mber of each of the 6 bu ilding 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 us e 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 char acterization of energy use for each of the 6 x 25 = 150 segments. For instance, given these sma ll samples, there might be 0 restaurants in climate zone 3, 2 restaurants in climate zone 4 and 3 restaurants in clim ate zone 5 providing a
bias towards climate characteristics of zone 5.

Given the importance of the sample characteristic s 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 th en applying each building in the subdivision to each utility in that subdivision to increase the sample size.

Essentially, the analysis reuses individual buildin g records for different utilities, presumably running the BLAST analysis in e ach utility location for each bu ilding. While some of the elements of this process are r easonable, 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-weigh ting the building reco rds 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 accu racy of the total sample, treating it as a stratified sample. If necessary, merge the 1992 CBECS data with th e 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 build ing records for different regions and utility service areas toReview of Analyses in Support of ANOPR 11/17/2003 40 expand the sample representation is desirable (after rerunning the BLAST model with the appropriate weather data), thes e building records must have ne w weights developed in a poststratification process based on count s 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 conditi oning energy use intensities (EUI’s). Chapter 6, Section 6.3.1 of the analys is documentation provides a comp arison 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 calibra tion process which applies the Facility Energy Decision Screening (FEDS) system to model each individual reco rd’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 th e 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 a nd peak kW) which exists for most 1995 CBECS
records can be used to estimate air conditioni ng EUIs (including fan use) for each record. Ordinary least square regression s can be run using monthly kWh- and kW-dependent variables (a technique called “seemingly unrelat ed regression” can be used to combine the two estimations and improve the estimation efficiency) and with weather variables, squa re 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 anal ysis 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 deco mposition of weather- and non-weather-sensitive loads
takes into account the use of both air conditioni ng and space heating in some months. This approach will provide a more accu rate estimate of air conditioning energy use compared to that reported in the CBECS records, because the CBECS process had to calibra te to space heating air conditioning and other end uses while the statistical process ca n be specified to provide a primary estimate of air conditioning.Review of Analyses in Support of ANOPR 11/17/2003 41 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 Mo del to individual buildings

The micro-simulation modeling process that appl ies 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 prototyp e-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 CBEC S building used for the analysis. This is because the analysis reported in this docum ent requires development of system loads representative of an actual build ing, not a typical building.”

The BLAST modeling, while incorporating info rmation from each building record in each simulation, completely ignores by far the most important opportunity to develop “ system loads representative of an actual build ing, 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 a nd CBECS EUI’s (as a pr oxy, 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 exte nding to the upper ri ght in the Figure.

Each of the buildings modeled with BLAST shou ld 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 seri es of heuristic decision rules, th e 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 el ectricity 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 insula tion levels, ventilation rate s, thermostat setting, occupancy or other factors. However, calibra ting to actual air conditi oning electricity use by modifying some of these paramete rs provides a modeled record that at least has the correct air conditioning electricity use. Deci sion rules can be altered and sens itivity 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 perf ormed providing an estimation result consistent
with Maximum Likelihood Estimation.Review of Analyses in Support of ANOPR 11/17/2003 42 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 correlati on between the two data sets be comes 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 ag gregate results reflect on ly 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 buildi ng discrepancies exist.

Ignoring the BLAST/actual EUI di screpancies ignores valuable information in the analysis process and elevates the argument of exactly offs etting 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 accu rate than analysis of a few prototypes. The DOE analysis seems to argue that less accurate an alysis 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 impr ovement over previously applied analytical approaches. However, the current applicati on 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.Review of Analyses in Support of ANOPR 11/17/2003 43 The impact of these deficiencies, which may be si gnificant, 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 in corporating 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 electri city 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”, Buildi ng Sciences Series 155, National Bureau of Standards, Gaithersburg, MD.

Hittle, D. 1982, “An Algorithm for Modeling a Di rect 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, “Dev elopment and Validation of a Reciprocating Chiller Model for Hourly Energy Analysis Programs”, ASHRAE Transactions , American Society of Heating, Refrigerating, and Air C onditioning 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 Pu mp Design Tool – Mark IV User’s Guide”, ORNL/CON-343, Oak Ridge Nationa l Laboratory, Oak Ridge, TN.

TIAX, 2003, “Energy Efficient Rooftop Air-Cond itioner: Design, Development, and Field Testing”, Final Report, TIAX LLC, Acorn Park, Cambridge, MA 02140.Review of Analyses in Support of ANOPR 11/17/2003 44 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 th at should be applied the building
models. The issues raised in stakeholder co mments include questions about the airflow calculations used in BLAST and the applica tion of ASHRAE Standa rd 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) rega rding 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 ca lculation of power i nput is the value of condenser U-factor (CUA). This variable was discussed earlier in the context of the saturated condensing temperature (SCT). CUA is determ ined from detailed manufacturer’s performance data on a unit. These performance data must not include indoor (evapo rator) fan power in the power data. If this CUA is used, then the co rrect power (without indoor fan) for the rooftop will
be calculated in BLAST and airflo w 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 stan dard in the United States regul ating ventilation rates for new construction. However, unless this standard ha s been adopted by local m unicipal 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 Ve ntilation 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 Ventilati on (DCV) or take advantage of scheduled ventilation for buildings with known and regular occ upancies and schedules. DCV uses CO 2 concentration in a conditioned space as a surrogate for occupancy. If the CO 2 concentration is high, that indicates a correspondingly high occupancy rate, and the HVAC equipment will adjust to deliver the appropriate amount of fresh out door air. Scheduled ventila tion 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 thatReview of Analyses in Support of ANOPR 11/17/2003 45 outside air is modeled is thr ough the use of an econo mizer option. Demand-, or especially occupancy-controlled ventilation could be mode led 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 cons truction is that the total cooling flowrate is approximately 1.0 cfm/ft 2 . Thus, the assembly area listed with a ventilation airflow rate at 0.89
cfm/ft 2 means that 89% of the total air flow is ventil ation or fresh outside ai r. Particularly in southern climates, most commercial rooft op packaged equipment cannot accommodate the
additional moisture load with this much ventilation. A value of 0.45 cfm/ft 2 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 practic e. Additionally, smaller packaged systems do not have the dehumidification capacity to meet coin cident cooling and ventilation loads.

It is not clear why the DOE uses CBECS occupa ncy 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 availa ble 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 cal culations in the DOE TSD are done correctly. However, the ventilation rates used by DOE in its models correspond to an overly-aggressive and unrealistic applica tion 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.Review of Analyses in Support of ANOPR 11/17/2003 46 6.0 ASSESSMENT OF THE INCREMENT AL MARKUP OF COMMERCIAL UNITARY AIR CONDITIONING EQUIPMENT PRICES

6.1 Introduction

As a result of numerous st akeholder comments, DOE request ed 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 eq uipment markups consistent with the existing practice in the industry, and is there any research in published literature th at 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 mech anical contractors and small mechanical
contractors? Uneven distribution of the co sts between different regions or states?

The review considered the data and met hods 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 ar e the revenue per dollar cost of goods required to cover all costs and profit

• Incremental markups are a function of the increa se in the cost of e quipment and only include
the variable costs of a firm – all costs ex cluding 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 m echanical contractors, baseline markup of 1.7, incremental markup of 1.37

o 32.5% of replacement sold through large m echanical contractors, baseline markup of 1.55, incremental markup of 1.29Review 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 acc ounts, 1⁄2 of normal distribution chain markup

at 1.71

For 30% of sales involving new construction:

• 50% of new sold through small mechanical cont ractors, 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 distri bution chain at 1.80

• No general contractor involvement, and therefore no general contractor markup

6.2 Review of Assumptions and Appr oach Used to Calculate Markups

The IRT has reviewed the methodology and assump tions 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 analys is 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 a nd 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 te rms 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 mar kup calculations. The appr oach described in the analysis was applied consistently. All labor-rel ated costs were excluded from the calculation of incremental markups, while all other costs were in cluded in the incremental markup calculations.

Although there are no data to back up the estim ate of the markup on national accounts, the logic of the argument that it would fall somewhere be tween the manufacturer’s price for the equipment and the price to a customer using one of the othe r distribution chains is logical. Splitting the difference and taking half is some what arbitrary, but th ere is not enough data to create a better
estimate.

6.3 Potential Impact of Manufacturing/I ndustrial 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 industria l sector is an important factor that should be included in the economic impact analysis of the new rule. For th e markup analysis the main issue this raises is
whether sales to the industri al sector break down into th e same proportions of new and replacement units, and from there into the same proportions with regard to shares sold throughReview of Analyses in Support of ANOPR 11/17/2003 48 wholesalers versus national acc ounts, and from wholesalers to large and small mechanical
contractors. There is no liter ature that would argue for cha nging 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 se rved by these ty pes of unitary air conditioning units involve similar construction practices and similar cont ractor arrangements. The current description of the distribu tion chains is practical for both.

6.4 Sensitivity Analysis of Ch anges 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 spreadshee ts used in the DOE analysis. The range of markups tested was limited to valu es within two standard devia tions 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, us ing 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 th e distribution. The only exception was for the wholesaler incremental mar kup, where the probability distribution used in the original analysis was truncated to avoid ha ving 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 ne t 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 resu lts 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 baseli ne markup. The “Engineering” tab in the lcc_cuac_tariff.xls workbook developed for the orig inal analysis in Chapter 7 of the TSD was
used to calculate the values in th e 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 Ta ble 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 manufactur er 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 usedfor 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 un it 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 increm ental calculation to the baseline calculation arrives at the markup price of $5,665. The ne w construction markups are the highest in the distribution chain, so these sens itivities are the highest levels of equipment costs used in the analysis. Other markups would be lower.Review of Analyses in Support of ANOPR 11/17/2003 49 The sensitivity markups increase the price of the 7.5 ton uni ts 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 di fferences in life cycle costs, payback period and the economic and energy indicators used in the an alysis. Because the pric e 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 a ccuracy 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 th e 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 estim ates are off by anywhere near that large an amount. Doubling both the incr emental 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 ev en less likely than the baseline markup doubling. The lifecycle cost spreadsheets for both the hourl y and tariff electricity price models were run several times with both the origin al and the sensitivity analysis markups using the Crystal Ball 2000 forecasting features. None of the runs turned up consistent, signi ficant shifts in the original analysis results.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%Review of Analyses in Support of ANOPR 11/17/2003 51 6.5 Other Markup Cost Factors

This portion of the markup review examines whet her there are other cost factors outside of the scope of DOE’s analysis that coul d have a significant impact, such as regional variations in costs or significant code or siting issues in states or localities that re present significant shares of the
market. Information on state variations in ma rkup estimates is included in Appendix L of the DOE analysis and was used in calculating the di stribution 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 whol esale baseline markup is 8.2%; the general contractor relative sta ndard deviation is 3.9%; and the mechanical contractor relative
standard deviation is 3.1%. The frequency distribution for the mar kup values is fairly tight. The state data in Appendix L on whol esale 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 we re significant variati ons 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 unitReview of Analyses in Support of ANOPR 11/17/2003 52 all of the tables. This did not affect the origin al analysis because it used the average values for wholesale, general contractor a nd 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 baselin e markups calculated by state was 2.58; this is between the 2.42 and 2.66 overall markup calcula ted 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 Tabl e 6.3 are arranged in ascending orde r, with the states with the lowest overall markups at the top of the table. Th e baseline markup calcula tions in this analysis and in the original study were not weighted or ad justed to account for potential differences in the level of sales through different st ate 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 st ates could experience a di sproportionate share of the economic impact of increased equipment co sts caused by raising efficiency standards because they will experience hi gher prices. Second, the nati onal average markups may be inaccurate because they mask important regional variations in markups.

To illustrate this issue, Table 6.3 also incl udes information on the pr oportion of total general
contractor construction va lue attributed to each state in th e census data used for the markup analysis. California accounts for th e largest share of construction value in the census, at 9.4%, and has the third highest overall markup level. Ho wever, 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 w ith higher baseline markup levels, and together they account for only 4.7% of mechanical contract or 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 diffe rences were significant.

To examine whether these differences might have made a significant difference in the baseline markups, the baseline markup calculations for w holesalers, general cont ractors 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 calcul ation, the product of multiplying that state’s share of national sale s times its average state markup. When the weighted calculations are added together and compared to an av erage of the state markups, the results show a very small, less than 1%, diffe rence between the two approaches. The conclusion is that the approach used to estimate baseli ne markups may have masked some variation in markup levels from state to state, but the diffe rences are very small a nd would not affect the
overall conclusions of the an alysis or result in significant state-level differences.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%

V irginia 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 CalculationsReview 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 Weight ed by Share of To tal Sales Versus Averaged MarkupReview of Analyses in Support of ANOPR 11/17/2003 55 The analysis also considered whether the pr oportion of sales going th rough the distribution chains is the same from state to state and regi on to region, and whether variations might have a significant impact. In areas where for some r eason markups tend to be higher than the national average there might be a tendency for customers to favor one distributi on chain over another or, more likely, for customers served by a more expe nsive distribution chain to delay replacement. At this point there are no means of testing wh ether there are significant variations because the
wholesaler and contractor data also include their work with ot her types of equipment. However, based on analyses of the state-specific inform ation on markups in Appendix L, the IRT sees no major disparities that would encourage a sign ificant 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 shoul d consider – whether a change in the price of equipment at th e manufacturer may shift the proportion of sales for new versus replacement equipment, and/or shift the pr oportion of sales made through the different distribution chains. For systems going into new construction the markup on equipment is unlikely to change the distributi on chain; the contractors are invol ved in far more than installing the air conditioning unit and are not likely to be c hosen 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 l onger than under current conditions, and those at
the end of the distributio n chains with the highest markup may be more inclined to delay. That would affect overall sales and revenue, but it c ould 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 ma rkup 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 equi pment 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 f ound 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 highe r efficiency units, there would have to be unrealistically large errors in the magnitude of the markup estim ates to have any significant impact on the conclusions of the study. Ther e is no indication that the markup calculations should change that much. Finally, while the aver aging of state data on markups used in the original study masks some state-le vel variations in markup levels and therefore in the impacts of higher efficiency standards from state to state, the variations do not a ppear to be significant enough to have a major effect on the conclusions of the DOE analysis.Review of Analyses in Support of ANOPR 11/17/2003 56 7.0 SUMMARY CONCLUSIONS AND RECOMMENDATIONS

The Independent Review Team (IRT) was charged w ith reviewing the analyses in support of the ANOPR for setting energy efficien cy standards for commercial uni tary air conditioners and heat pumps in the range of 65,000 to 240,0000 Btu pe r hour cooling capacity (the Technical Support Document – TSD) and selected stakeholder comments and analyses. The objectives of the independent review were to assess the an alytical 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 AS HRAE 90.1-1999, the DOE analysis must show
that such a standard is tec hnically feasible and economically justified. This requires:

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

• Determining the electricity consumed by the covered appliances, w ith and without the proposed rulemaking;

• Estimating the energy and operating cost savings that would result fr om 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 co st 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 th ere 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 resu lts 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 el ectricity 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 desc ribed these issues, the analysis procedures used by the IRT,
and our conclusions. This section summarizes t hose conclusions and, in the Appendix to 7.0, lists the documents received by the IRT fr om DOE to carry out the assessment.Review of Analyses in Support of ANOPR 11/17/2003 57 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 th is 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 temperatur e 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 8Review of Analyses in Support of ANOPR 11/17/2003 58 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 e xpected 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 ve ntilation 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 in dication that the markup analysis
should be modified. None N/A

Table Explanation:

The Table is a “checklist” of the issues examined by the Independ ent Review Team (IRT), not meant to take the place of each Cha pter’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 recommend ation mean about the technical support do cument’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 an alyses, 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. Th at 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 cha nge 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, show ing less benefit from mo re 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 wi ll increase energy use in one way and decrease it in another ;

the net effect is not known.Review of Analyses in Support of ANOPR 11/17/2003 59 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 omi ssion of industrial buildin gs 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-r unning the BLAST simulations. However, BLAST has some serious limitations in its ability to m odel 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 accu rate 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 estimat es 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 potentia l 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 si gnificant, probably resulting in small changes, if any, in TSD conclusions.

• DOE TSD analysis has a significa nt error or defect. Correcting it is importa nt, will likely have a significant effect on the TSD conclusions, will require a (large/sma ll) amount of effort, and the revised analysis will result in (more/less/unknown) jus tification for a stricter standard.Review of Analyses in Support of ANOPR 11/17/2003 60 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 Conditi oners 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 Conditi oners 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 Rulema king Issues,” March 10, 2003

Charles W. Frazell, PE, “A Preliminary Qu antification: The Effects of Excluding Industrial/Manufacturing Sites from the DOE Rule making 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 fr om 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 Standard s 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, “Com mercial Unitary Air Conditioner and Heat Pump Life Cycle Cost Analysis : Inputs and Results, December 2002.

• TIAX, LLC, “Engineering Analysis: Cost-Effi ciency Curves; commercial Unitary Air
Cooled Air conditioners and Air Source Heat Pumps, January 2002.Review of Analyses in Support of ANOPR 11/17/2003 61 • National Energy Savings Spreadsheet, Ma rch 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 Mode l (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 Ener gy 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, a nd 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 Progra m for Commercial and Industr ial 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.