### CHAPTER 7. ENERGY USE CHARACTERIZATION FOR RESIDENTIAL AIR-CONDITIONING AND HEAT PUMP EQUIPMENT

TABLE OF CONTENTS

7.1 Introduction

.................................................................................................................. .... 7-1
7.1.1 General Approach to the Energy Use Analysis – Residential

............................. 7-1
7.1.2 General Approach to the Energy Use Analysis – Commercial

............................ 7-3
7.1.3 Overview of Energy Us e Analysis Inputs

............................................................ 7-4
7.2 ANALYZED RECS HOUSEHOLDS

................................................................................ 7-4
7.3 BASELINE ANNUAL SPACE-COOLING ENERGY USE – RESIDENTIAL

............... 7-5
7.3.1 Methodology for Estimating Stock Annual Space-Cooling Energy Use

............. 7-6
7.3.2 Stock Annual Space-Cooling Energy Use

........................................................... 7-7
7.3.3 Stock Space-Cooling Efficiency (SEER)

............................................................. 7-9
7.3.4 Estimating Baseline Annual Space-Cooling Energy Use

.................................. 7-12
7.4 BASELINE ANNUAL SPACE-HEATING ENERGY USE FOR CHP EQUIPMENTRESIDENTIAL

................................................................................................................. 7- 13
7.4.1 Estimating Stock Annual Space-Heating Energy Use

....................................... 7-14
7.4.2 Stock Space-Heating Efficiency (HSPF)

........................................................... 7-16
7.4.3 Estimating Baseline Annual Space-Heating Energy Use

.................................. 7-18
7.5 BASELINE RESIDENTIAL COOLING AND HEATING RESULTS-ADJUSTED
FOR RECS SAMPLE WEIGHTS

.................................................................................... 7-19
7.6 BASELINE COMMERCIAL CAC AND CHP ENERGY USE

...................................... 7-20
7.7 OVERVIEW OF COMMERCIAL ENERGY SIMULATION APPROACH

................. 7-21
7.7.1 Simulation Tool Description

.............................................................................. 7-21
7.7.2 Efficiency Levels Analyzed and Corre sponding Simulation Parameters

.......... 7-22
7.7.3 Small Office Building Characteristics

............................................................... 7-23
7.7.4 Building Operating Characteristics

.................................................................... 7-24
7.7.5 Building Mechanical Systems and Equipment

.................................................. 7-27
7.8 BASELINE COMMERCIAL ENERGY DISTRIBUTION

............................................. 7-27
7.9 COMMERCIAL ENERGY FOR INTERMEDIATE EFFICIENCY LEVELS

............... 7-29
7.10 MARKET BASELINE SPACE-COOLING AND SPACE-HEATING EFFICIENCIES7-29
7.11 ESTABLISHMENT OF GEOGRAPHIC REGIONS FOR STANDARDS ANALYSIS
AND CORRESPONDING REGIONAL ENERGY CONSUMPTION BY
EFFICIENCY LEVEL

...................................................................................................... 7-32

7-i

### LIST OF TABLES

Table 7.3.1. The 30-Year Average CDD (1978–2007) and the 2005 CDD for the Nine Census Divisions and the Four Large States

........................................................ 7-7 Table 7.3.2. Weather Adjusted Space-Cooling Ener gy Consumption Statistics at Regional Level (CAC-Stock)

.............................................................................................. 7-8 Table 7.3.3. Weather Adjusted Space-Cooling Ener gy Consumption Statistics at Regional Level (CHP-Stock)

............................................................................................... 7-8
Table 7.3.4. Number of Observati ons for Each Ag e Group

...................................................... 7-10 Table 7.3.5. Average Annual Shipped Space-Cooling Efficiency

............................................. 7-12 Table 7.3.6. Baseline (13 SEER) Space-Coolin g Energy Consumption Statistics at Regional Level (CAC)

....................................................................................... 7-13 Table 7.3.7. Baseline (13 SEER) Space-Coolin g Energy Consumption Statistics at Regional Level (CHP)

........................................................................................ 7-13 Table 7.4.1. 30-Year Average HDD (1978–2007) and the 2005 HDD for the Nine Census Divisions and the Four Large States

.................................................................. 7-15 Table 7.4.2. Statistics of Annual Weather-Adju sted Space-Heating Energy Consumption for RECS-Stock CHP at Regional Level

........................................................... 7-15 Table 7.4.3. Average Annual H eating Pump HSPF

.................................................................. 7-18 Table 7.4.4. Baseline (13 SEER) Space-Heatin g Energy Consumption Statistics at Regional Level(CHP)

......................................................................................... 7-18
Table 7.7.1. CAC and CHP Blower and Characteristics Used in Modeling Commercial Cooling and Heating Energy Use

...................................................................... 7-23 Table 7.10.1. CAC and CHP Efficiency Di stributions Estimated for 2007

.............................. 7-31 Table 7.10.2. CAC and CHP Efficiency Distribu tions Estimated for Market Baseline

............ 7-32 Table 7.11.1. Proposed Climate Regions for Regional Standards

............................................. 7-33 Table 7.11.2. Split-System Air Conditioners: Aver age Annual Energy Use by Efficiency Level for Coil-Only Systems

............................................................................. 7-34 Table 7.11.3. Split-System Air Conditioners: Aver age Annual Energy Use by Efficiency Level—Blower-Coil Systems

............................................................................ 7-34 Table 7.11.4. Split-System Heat Pumps: Aver age Annual Energy Use by Efficiency
Level

.................................................................................................................. 7-35 Table 7.11.5. Single-Package Air Conditione rs: Average Annual Energy Use by Efficiency Level

................................................................................................. 7-35 Table 7.11.6. Single-Package Heat Pumps: Av erage Annual Energy Use by Efficiency
Level

.................................................................................................................. 7-36

LIST OF FIGURES

Figure 7.1.1. Flow Diagram for the Determina tion of Annual Space-Cooling Energy for
Residential CAC and CHPs

................................................................................. 7-2 Figure 7.1.2. Flow Diagram for the Determina tion of Annual Space-Heating Energy for
Residential CHPs

................................................................................................. 7-3Figure 7.1.3. Flow Diagram for the Determination of Annual Space-Cooling and SpaceHeating Energy for CAC and CH Ps in Commercial Use

.................................... 7-4

7-ii

### Figure 7.3.1. National Distribution of Weat her-Adjusted Space-Cooling Energy for
RECS Observations

............................................................................................. 7-9 Figure 7.3.2. National Distribution of Weathe r-Adjusted Space-Cooling Energy for CHPRECS Observations

............................................................................................. 7-9 Figure 7.3.3. The Distribution of CACs in the RECS Sample by Age Groups

......................... 7-10 Figure 7.3.4. The Distribution of CHPs in the RECS Sample by Age Groups

......................... 7-11 Figure 7.3.5. Distribution of RECS House holds with CACs by Vintage

.................................. 7-11 Figure 7.4.1. National Distribution of Weathe r-Adjusted Space-Heating Energy for RECS
Observations

...................................................................................................... 7-16 Figure 7.4.2. Distribution of Households with Heating-Performance Heat Pumps by Age Groups

................................................................................................................ 7-17 Figure 7.4.3. Distribution of RECS Households with Central Heat Pumps by Vintage

........... 7-17 Figure 7.5.1. Baseline National Cooling Energy Distribution for CAC – Residential

.............. 7-19 Figure 7.5.2. Baseline National Cooling Energy Distribution for CHP – Residential

.............. 7-19 Figure 7.5.3. Baseline National Heating Energy Distribution for CHP – Residential

............... 7-20 Figure 7.7.1. Small Office Building Design View

..................................................................... 7-24 Figure 7.7.2. Small Office Building HVAC Zoning

.................................................................. 7-24 Figure 7.7.3. Small Office Lighting Schedule

........................................................................... 7-25 Figure 7.7.4. Small Office Plug Load Schedule

........................................................................ 7-26 Figure 7.7.5. Small Office Occupancy Schedule

....................................................................... 7-26 Figure 7.7.6. Small Office Thermo stat Set Point Schedule

....................................................... 7-27 Figure 7.8.1. Baseline National Cooling Energy Distribution for CAC-Commercial, Weighted

............................................................................................................ 7-28 Figure 7.8.2. Baseline National Cooling Energy Distribution for CHP-Commercial, Weighted

............................................................................................................ 7-28 Figure 7.8.3. Baseline National Heating Energy Distribution for CHP-Commercial, Weighted

............................................................................................................ 7-29

7-iii

### 7.1 INTRODUCTION

The energy use analysis provides estimate s of the distribution of annual energy consumption for central air c onditioner (CAC) and central heat pump (CHP) equipment at the base case ( i.e. , with no new standards) and at efficien cy standard levels considered in the analysis. This information is used in the subs equent life-cycle cost (LCC) and payback period (PBP) analyses (chapter 8 of the technical s upport document [TSD]), which in turn provides the raw energy use data for the national impacts analys es (chapter 11 of the TSD). Both energy use
in the residential sector customers and to the commercial sector customer are considered. In the energy use analysis, the United States (U.S.) Department of Energy (DOE) developed energy consumption estimates for four equipment classes analyzed in the engineering analysis (chapter 5 of the TSD). For one equipment class, split-sy stem air conditioners, tw o installation methods were separately considered: coil-only, where th e indoor furnace fan is not replaced for higher efficiency equipment, and blower-coil, where the indoor blower is replaced, corresponding to the similar breakdown of this equipment cl ass in the engineering analysis.

7.1.1 General Approach to the Energy Use Analysis – Residential

The general approach adopted in the energy use analysis for CAC equipment used in residences was to utilize energy use and house hold characteristics data from the DOE Energy Information Agency (EIA) 2005 Residential Energy Consumpti on Survey (RECS) to provide estimates of cooling and heating energy fo r the sample households using CAC and CHP equipment. This estimate reflects household stoc k equipment efficiency levels. The energy use estimates are then adjusted to reflect long te rm average weather cond itions and then further adjusted to reflect equipment energy consump tion for the same households assuming that they were using new equipment as would be purchased in 2016 under a base case standards scenario.

A sample population of U.S. homes using CA C and CHP equipment was first identified from the RECS households surveyed. This sample provided a dataset of cooling and heating energy use for 2005 for households with CACs and CHPs. This data were then adjusted (weather-normalized) to reflect corresponding ener gy-use figures for typical long-term climate conditions. Historical efficiency data for shipped CAC and CHP equipment was used in conjunction with equipment age information from RECS to estimate the stock equipment efficiency for each household record used in the analysis. The annual energy consumption for equipment in the 2016 base case ( i.e. , no new standards) was then calculated for each equipment class by multiplying the weather-adjusted cooling or heating energy use for each RECS household by the ratio of the efficiency ( e.g. , SEER) of the stock unit in that household to that of an assumed efficiency for that household in th e 2016 base case. This 2016 base case provides an efficiency level likely to be purchased fo r each household assuming a new CAC or CHP purchase in that year. The 2016 efficiency levels used for each househol d were selected at random from a distribution of shippe d efficiencies developed for ea ch product class. This base case is also referred to as the market baseline case, since the efficiency distribution is representative of the current market for CAC and CHP equipment.

Once the base case cooling energy use was established for a selected RECS household record, the corresponding annual cooling energy consumption for higher efficiencies under new standard scenarios was calculated by multiply ing the cooling energy consumption for each

7-1

### household in the base case by the ratio of the seas onal energy efficiency ratio (SEER) in the base case to that of the SEER in the standards case. In the case of heating energy use for heat pumps the corresponding household annual heat pump heating energy consumption under new standard scenarios was calculated by multiplying the he at pump heating energy consumption for each household in the base case by the ratio of the heating seasonal performance factor (HSPF) in the base case to the HSPF in the standards case. Figure 7.1.1 and Figure 7.1.2 provide a flow diagram on calculation of cooling and heating energy for the residential energy use analysis.

RECS Baseline SEER Market Baseline
Stock Household (SEER 13) SEER Standard Level Annual Cooling

Estimated SEER
of Stock
CAC or CHP Distribution SEER Energy (Weather Normalized)

RECS Age of Household
CAC or CHP Baseline Market Baseline Standard Level Annual Cooling Annual Cooling Annual Cooling Energy Use Energy Use Energy Use

AHRI Shipment Average
SEER
CAC and CHP



### Figure 7.1.1. Flow Diagram for the Determin ation of Annual Space-Cooling Energy for
Residential CAC and CHPs

7-2RECS
Stock Household Annual Heating Energy (Weather Normalized)

RECS Age of Household
CHP Baseline SEER (HSPF 7.7)

Baseline Annual Heating Energy Use Market Baseline
HSPF Distribution

Market Baseline Annual Heating Energy Use Standard Level
HSPF

Standard Level Annual Heating Energy Use Estimated
HSPF of Stock
CHP

AHRI Shipment Average HSPF by Vintage
CHP



### Figure 7.1.2. Flow Diagram for the Determin ation of Annual Space-Heating Energy for
Residential CHPs

7.1.2 General Approach to the Energy Use Analysis – Commercial

DOE also developed estimates for the annual energy use for CAC and CHP sold into the commercial building market. The commercial market is estimated to use approximately seven percent of all CAC and CHP equipment. For the commercial analysis, DOE relied on the use of the EnergyPlus building simulation software to estimate the energy consumption of this equipment at four specific efficiency levels-fro m baseline to the max t ech level-for 237 climates around the United States for a typical commercia l application, a small office building. DOE extracted equipment sizing, supply fan, condenser and heater energy use from these simulations. DOE then used these results to develop es timates of annual energy use for CAC and CHP equipment at other efficiency levels defined in the engineering analysis via interpolation between the efficiency levels simulated. Figure 7.1.3 provi des a flow diagram for the calculation of CAC and CHP cooling and heating energy for the commercial energy use analysis.

7-3

### Figure 7.1.3. Flow Diagram for the Determination of Annual Space-Cooling and SpaceHeating Energy for CAC and CHPs in Commercial Use

7.1.3 Overview of Energy Use Analysis Inputs

The following are the principal inputs to the residential energy use analysis:

• 

### Household cooling and heating energy from 2005 RECS

• 

### CAC and CHP equipment age information for the RECS households

• 

### Historical state av erage monthly cooling and hea ting degree day data (1978–2007)

• 

### Historical average cooling and heating efficiency data for CAC and CHP from AHRI

• 

### Base case efficiency distributions for 2016 developed for this analysis

• 

### Engineering efficiency levels to be analyzed

• 

### The following are the principal inputs to the commercial energy use analysis:

• 

### Engineering efficiency levels to be analyzed

• 

### Location specific weather file data for use with building simulation tool

• 

### Population-based weighting factors for each weather file

7.2 ANALYZED RECS HOUSEHOLDS

The 2005 RECS includes data for 4,382 housing units, of which 2,317 were reported to have “CAC only” cooling systems. These include 416 housing units reporting their CAC unit also being cooling-performance heat pumps. Th e number of households actually included in the LCC and PBP analyses were 1,854 for the CAC systems and 339 for heat pumps. Out of the 1901 (2317-416) households that could have been potentially considered for analysis of CAC energy consumption, 47 were not included in the analysis for the following reasons:

• 

### the CAC system was reported as not being used (40 households), and

Small Office Building Model EnergyPlus Simulation Tool

CAC or CHP
Characteristics for
Four Selected Efficiency Levels TMY Weather
Data Bldg. Annual Cooling, Heating, Fan Energy Use Zone Peak Equipment Sizes

Annual Cooling, Heating, Fan Energy Use for Selected Efficiency Levels (Normalized to 3-Ton unit) Market Baseline Annual Cooling , Heating, and Fan Energy Use Market Baseline
SEER /HSPF
Distribution Standard Level
SEER/HSPF

Standard Level Annual Cooling , Heating, and Fan Energy Use

7-4

### • 

### the household reported a “heat pump” being the main heating equipment, contradicting a second response that their CAC system is not a heat pump (7 households).

For the seven households with conflicting res ponses, it was considered that the response regarding the main heating equipment being a heat pump should override the secondary response
and these records were included in the set for analyzing energy consumption for heat pumps.

For the heat pumps, out of the 423 (416+7) candidate households, DOE removed 84 for the following reasons:

• 

### The system was reportedly not being used (6 households).

• 

### The survey responses reported a non-furnace heating equipment ( i.e. , heating stove, fireplace, built-in wall furnaces etc. being the main heating equipment), thus implying that the heating energy use reported for the household should not be primarily attributed to the heat pump (17 households).

• 

### Some households indicated a warm air furnace as the main heating equipment and no electrical energy use for space heating implying that the heat pump function is not used at all or that the RECS may be reporting the “heat pump” energy consumption or presence in error (36 households), and

• 

### Some households report a warm air furnace as well as heat pump, but questions on usage of any complementary fuel were skipped. Th is led to an ambiguity about whether the heating energy use reported from the conditional demand mode l was reported as representative of a heat pump or an electric furnace. Barring more detail on the analysis of these records by EIA, they were remove d from consideration in DOE’s analysis (25 households).

7.3 BASELINE ANNUAL SPACE-COOLING ENERGY USE – RESIDENTIAL

In order to estimate the energy savings for higher standards, DOE first established estimates of cooling and heating energy consumption for baseline equipment based on energy consumption in the current stock and product effi ciency assumptions appropriate to the stock equipment and the efficiency of baseline products . The energy consumption of CACs and CHPs in the equipment stock is related to the capacity and efficiency of these products, the climate, and individual household use patterns. The efficiency of the equipmen t in the building stock also varies depending on the vintage. The energy cons umption of residential air-conditioners and heat pumps in the current stock and estimates of the efficiency in the current stock are used in conjunction with the efficiencies of baseline e quipment to provide an estimate of energy consumption of baseline equipmen t. The baseline annual space-c ooling energy consumption for
residential air conditioners a nd cooling-performance heat pumps was estimated for each RECS observation using equation 7.3.1.

SEER

res

_ stock 

### UEC = UEC × Eq. 7.3.1

res

_ base

_ c res

_ stock

_ c 

### SEER base



### Where:

7-5

### UEC res_base_c 

### = the baseline annual unit energy consumption for space-cooling,
UEC res_stock_c 

### = the annual unit energy consumption space-cooling associated with the stock equipment,
SEER base 

### = the space-cooling efficiency associated with the baseline equipment,
and
SEER res_stock_c 

### = the space-cooling efficiency associated with the stock equipment.

Thus, once the annual space-cooling energy consumption associated with the stock equipment, UEC res_stock_c 

### , and the space-cooling efficiency, SEER res_stock_c 

### , are determined; the baseline annual space-cooling energy consump tion is estimated. In the next section, the methodology for obtaining UEC res_stock_c 

### and SEER res_stock 

### are discussed.

7.3.1 Methodology for Estimating Stoc k Annual Space-Cooling Energy Use

The stock annual space cooling energy cons umption was estimated based on data from the 2005 RECS. This survey contains data on household characteristics, end use energy consumption, general location in the United States (limited to location within census division and four large states; New York, Florida, Texas and California), and a pproximate heating and cooling degree days for each sample location for the year of the survey. The latter two statistics are purposefully kept general or sufficiently masked by EIA to preclude identification of the surveyed residences.

The household energy consumption for the indi vidual surveyed households in RECS are obtained by EIA from the energy suppliers via monthly energy bill records. EIA uses a conditional demand analysis (CDA) 1 

### model to further breakdown the household energy use into end-use energy consumption estimates ( e.g. , cooling, heating, lighting, and hot water energy) which it reports as annual end-use energy in th e survey results. The cooling energy use for the
selected households with CAC or CHP products represents th e cooling energy consumption associated with the stock equipment for 2005. Because 2005 may not be representative of typical weather for that location, the cooling energy cons umption associated with the stock equipment in the RECS households are first adjusted (wea ther normalized) using 30-year-average (1978– 2007) cooling degree day (CDD) data (base 65 °F) and Eq. 7.3.2:

CDD 30 _ yr

_ avg 

### UEC

res

_ stock

_ c 

### = UEC

res

_ stock

_ c

_ non

− adj 

### × CDD 

### Eq. 7.3.2

res

_ stock _ 2005



### Where:

UEC res_stock_c_non-adj 

### = annual space-cooling energy consumption associated with the stock equipment, extracted dir ectly from RECS household data,
UEC res_stock_c 

### = annual space-cooling consumption associated with the stock equipment, adjusted by using 30-year average CDD data for the RECS location,
CDD res_stock_2005 

### = CDD in 2005 for the specific Ce nsus Division or state where the housing unit is located, and
CDD 30_yr_avg 

### = 30-year average CDD (1978–2007) for the specific Census
Division or state where th e housing unit is located.

7-6

### 7.3.2 Stock Annual Space-Cooling Energy Use

DOE first weather-normalized the cooling en ergy use data for the selected RECS households to better reflect long term aver age weather conditions for each location. DOE
obtained CDD data for each state on a monthly basis for 1978–2007 from the National Oceanic and Atmospheric Administration (NOAA) Na tional Climatic Data Center (NCDC). 2 

### DOE aggregated the monthly data for each year to produce annual CDD data for each state. These were then further aggregated geographically to the specific census divisions or four large state geographic regions used by RECS. These states in cluded in each of these geographic regions are
shown in Table 7.3.1.

In estimating annual CDD for each census divi sion, the annual CDD for the states in each division are weighted according to the population in the component states for the specific year. Population data from 1978 to 2007 were deve loped based on 1980, 1990, and 2000 U.S. Census Bureau data, 3 

### with estimates for other years obtained by linear interpolation or extrapolation as necessary. The resulting 30-year average CDD an d the 2005 CDD for the regions are shown in
Table 7.3.1.

Table 7.3.1. The 30-Year Average CDD (1978–2007) and the 2005 CDD for the Nine Census Divisions and the Four Large States

Division States 30-Year Average CDD 2005 CDD

1: New England Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont 462 579

2: Middle Atlantic New Jersey, Pennsylvania 737 991

3: East North Central Indiana, Illinois, Michigan, Ohio, Wisconsin 758 983

4: West North Central Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota 989 1,171

5: South Atlantic Delaware, District of Columbia, Georgia, Maryland, North Carolina, South Carolina, Virginia, West Virginia 1,382 1,486

6: East South Central Alabama, Kentucky, Mississippi, Tennessee 1,620 1,747

7: West South Central Arkansas, Louisiana, Oklahoma 2,215 2,402

8: Mountain Arizona, Colorado, Idaho, New Mexico, Montana, Utah, Nevada, Wyoming 1,318 1,498

9: Pacific Alaska, Hawaii, Oregon, Washington 226 226 Four Large States New York 654 943

California 949 837

Texas 2,711 2,908

Florida 3,505 3,436



### Based on the 2005 RECS annual space-cooling energy consumption data and the CDD data, the weather-adjusted a nnual space-cooling energy consumption for each household was obtained by using Eq. 7.3.2. Statistics showing the weather-adjusted annual space-cooling energy consumption for the households that have air- conditioners are shown in Table 7.3.2. Statistics showing the weather-adjusted annual space-coolin g energy consumption for the households that have heat pumps are shown in

7-7

### Table 7.3.3. The frequency distribution of the weather-adjusted annual household energy consumption for space-cooling for CAC and CH P for the RECS observations are shown in Figure 7.3.1 and Figure 7.3.2.

Table 7.3.2. Weather Adjusted Spa ce-Cooling Energy Consumption Statistics at Regional Level (CAC-Stock)

Division or State Number of
Observations Min
kilowatt hours per

year (kWh/yr) Max kWh/yr Average kWh/yr

New England 50 56 4,186 1,229

Middle Atlantic 127 289 5,420 1,650

East North Central 346 130 9,459 1,477

West North Central 224 207 9,882 2,067

South Atlantic 233 152 8,628 2,952

East South Central 173 331 11,573 3,653

West South Central 79 647 12,359 4,070

Mountain 130 65 26,078 3,167

Pacific 23 89 3,610 1,273

New York 34 333 5,361 1,295

California 160 36 17,335 1,835

Texas 181 44 29,326 5,780

Florida 94 1,508 14,688 6,104

National 1,854 36 29,326 2,851



### Table 7.3.3. Weather Adjusted Spa ce-Cooling Energy Consumption Statistics at Regional Level (CHP-Stock)

Division or State Number of
Observations Min kWh/yr Max kWh/yr Average kWh/yr New England 1 432 432 432

Middle Atlantic 10 214 2,182 1,460

East North Central 20 455 6,938 2,315

West North Central 12 234 5,634 2,413

South Atlantic 108 445 11,059 3,442

East South Central 54 1,157 11,586 3,968

West South Central 6 1,246 9,573 4,545

Mountain 23 3,220 12,893 7,194

Pacific 13 78 3,221 1,060

New York 1 761 761 761

California 11 312 6,449 2,492

Texas 15 2,166 11,363 6,423

Florida 65 1,805 17,380 6,738

National 339 78 17,380 4,264

7-80% 2% 4% 6% 8% 10% 12% 14% 16% 18%Percentage of Obersavtions

Weather-Adjusted CAC Cooling Energy from RECS (kWh/yr)



### Figure 7.3.1. National Distribution of Weath er-Adjusted Space-Coo ling Energy for RECS
Observations

0% 2% 4% 6% 8% 10% 12% 14%Percentage of Obersavtions

Weather-Adjusted CHP Cooling Energy from RECS (kWh/yr)



### Figure 7.3.2. National Distribution of Weath er-Adjusted Space-Coo ling Energy for CHPRECS Observations

7.3.3 Stock Space-Cooling Efficiency (SEER)

To estimate annual space-cooling energy consumption data at the baseline and higher efficiency levels, DOE relies on the cooling and heating energy calculated for the stock households and the historic space-cooling efficiency levels of the stock equipment, SEER res_stock



### . The space-cooling efficiency of stock equipment is related to the vintage of the equipment. In the 2005 RECS database, the age of the equipment is reported in te rms of age groups and not the specific vintage year. The five ag e groups are “less than 2 years old,” “2 to 4 years old,” “5 to

7-9

### 9 years old,” “10 to 19 years old,” and “20 years or older.” The da ta also include one additional age category: “as old as the home.” In RECS the y ears of construction of each residence for older homes are also reported in age bands though the sp ecific year of constructi on is indicated for the newer homes. DOE assumed that the age of the CAC system, within a given age group in the general population would be approx imately uniformly distributed throughout the range of the age band. For example, for the CAC systems in the “less than 2 years old” age group, it was assumed that 50 percent of heat pumps were one year old and the other 50 percent were 2 years old. Similar technique was used in ascertaining the probable vintage year of the home and the equipment when the equipment was reported to be the age of the home. The resulting age
distribution of the CACs and CHPs in the RECS households identified for analysis are shown in Table 7.3.4 and graphically in Figu re 7.3.3 and Figure 7.3.4, respectively.

Table 7.3.4. Number of Observations for Each Age Group
Equipment Type Less than 2
Years Old 2 to 4
Years Old 5 to 9
Years Old 10 to 19
Years Old 20 Years
or Older Total

Central Air 243 321 472 502 316 1854
Conditioners 13.1% 17.3% 25.5% 27.1% 17.0% 100% Cooling-Performance 57 61 104 89 28 339 Heat Pump 16.8% 18.0% 30.7% 26.3% 8.3% 100%

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%Percent Of households

Less than 2 2 to 4 years 5 to 9 years 10 to 19 20 years or years old old old years old older

Age of CAC Systems



### Figure 7.3.3. The Distribution of CACs in the RECS Sample by Age Groups

Once the age group in which the household e quipment falls into was established, DOE estimated the vintage of the equipment for each household in each age group by random assignment using the uniform age distributi on assumption for each age group and the known 2005 survey year. For the 20-year and older ag e group, all equipment were assumed to be between 20 and 29 years old with the actual vintage assigned using a uniform distribution. The

7-10

### resulting vintage distribution of the residential air conditioners and heat pumps in the overall sample is shown in Figure 7.3.5.

DOE estimated the stock cooling SEER for each household using equipment vintage and average shipped efficiency for each vintage year. The latter was developed from AHRI data 4 

### and
is shown in Table 7.3.5.

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0%

Less than 2 years

old 2 to 4 years old 5 to 9 years old 10 to 19 years old 20 years
or older

Age of Heat Pump SystemsPercent Of households



### Figure 7.3.4. The Distribution of CHPs in the RECS Sample by Age Groups

0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0%1,9761,9781,9801,9821,9841,9861,9881,9901,9921,9941,9961,9982,0002,0022,004

Vintage year of CAC Equipm entPercent of Households



### Figure 7.3.5. Distribution of RECS Hous eholds with CACs by Vintage

7-11

### Table 7.3.5. Average Annual Shipped Space-Cooling Efficiency

Year Central A/C (SEER) Heat Pump (SEER)

1976 7.15 6.87

1977 7.18 6.89

1978 7.41 7.24

1979 7.45 7.34

1980 7.51 7.51

1981 7.73 7.70

1982 8.30 7.79

1983 8.44 8.23

1984 8.70 8.45

1985 8.84 8.56

1986 8.87 8.70

1987 8.95 8.93

1988 9.11 9.13

1989 9.23 9.26

1990 9.25 9.41

1991 9.44 9.73

1992 10.43 10.56

1993 10.52 10.82

1994 10.58 10.89

1995 10.64 10.93

1996 10.63 10.95

1997 10.62 10.93

1998 10.23 10.52

1999 10.86 11.24

2000 10.95 11.23

2001 11.07 11.31

2002 11.07 11.32

2003 11.22 11.48

2004 11.32 11.58

2005 11.33 11.69



### 7.3.4 Estimating Baseline Annual Space-Cooling Energy Use

For each class of equipment, the baseline annual space-cooling energy consumption for each equipment type (air-conditioner or heat pump) was estimated by using Eq. 7.3.2. The key inputs are the weather-adjusted annual space-c ooling energy consumption derived from the 2005 RECS data, the stock space-cooling efficiency estimates for air conditioners and heat pumps from Table 7.3.5, and the baseline cooling efficiency, 13 SEER.

Statistics for the baseline space-cooling energy consumption for RECS households at regional and national level are shown in Table 7.3.6 for air conditioners and Table 7.3.7 for heat

pumps.

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### Table 7.3.6. Baseline (13 SEER) Space-Cooling Ener gy Consumption Statistics at Regional Level (CAC)

Division or State Number of
Observations Min kWh/yr Max kWh/yr Average kWh/yr New England 50 45 3,526 987

Middle Atlantic 127 246 4,550 1,270

East North Central 346 104 7,967 1,148

West North Central 224 145 8,324 1,593

South Atlantic 233 119 7,513 2,314

East South Central 173 247 10,078 2,864

West South Central 79 433 10,115 3,082

Mountain 130 53 21,344 2,536

Pacific 23 78 3074 1,029

New York 34 229 3645 1,016

California 160 24 15,095 1,440

Texas 181 30 25,536 4,520

Florida 94 1,234 12,372 4,860

National 1,854 24 25,536 2,231



### Table 7.3.7. Baseline (13 SEER) Space-Cooling Ener gy Consumption Statistics at Regional Level (CHP)

Division or State Number of
Observations Min kWh/yr Max kWh/yr Average kWh/yr New England 1 249 249 249

Middle Atlantic 10 118 1,823 1,236

East North Central 20 323 5,566 1,882

West North Central 12 190 4,911 1,962

South Atlantic 108 320 9,417 2,779

East South Central 54 909 9,465 3,203

West South Central 6 973 6,797 3,568

Mountain 23 2,424 8,876 5,616

Pacific 13 68 2,339 842

New York 1 663 663 663

California 11 263 5,492 1,960

Texas 15 1,473 9,191 5,278

Florida 65 1,311 13,677 5,339

National 339 68 13,677 3,412



### 7.4 BASELINE ANNUAL SPACE-HEATING ENERGY USE FOR CHP EQUIPMENT- RESIDENTIAL

The baseline annual space-heating energy c onsumption for the CHP system is estimated following procedures similar to that for estimating the baseline annual space-cooling energy consumption. The baseline annual space-heating energy consumption for residential heat pumps is estimated by using the following equation:

HSPF

res

_ stock 

### UEC = UEC × Eq. 7.4.1

res

_ base

_ h res

_ stock

_ h 

### HSPF base

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### Where,

UEC res_base_h 

### = the baseline annual space-heating energy consumption
UEC res_stock_h 

### = the annual space-heating energy c onsumption associated with the stock heat pump
HSPF res_stock_h 

### = the space-heating efficiency associated with the stock equipment
HSPF base 

### = the space-heating efficiency associated with baseline equipment

7.4.1 Estimating Stock Annual Space-Heating Energy Use

The annual space-heating energy consumpti on for the CHP systems in the stock was
obtained from the 2005 RECS database for the heat pump sample identified. To account for the variation in weather conditions for the year 2005 from statistical nor mals, the annual spaceheating energy consumption in 2005 was adjust ed by using the following equation:

HDD 30 _ yr

_ avg 

### UEC

res

_ stock

_ h 

### = UEC

res

_ stock

_ h

_ non

− adj 

### ⋅ HDD 

### Eq. 7.4.2

res

_ stock



### Where:

UEC res_stock_h 

### = annual space-heating energy consumption associated with the stock equipment after weather-adjustment,
UEC res_stock_h_non-adj 

### = annual space-heating energy c onsumption, obtained directly from RECS database,

HDD 30_yr_avg 

### = 30-year (1978-2007) average heating degree day (HDD, base 65 °F) for the census division or st ate where the household is located,
and

HDD res-stock 

### = HDD in 2005 (base 65 °F) for the census division or state where
the household is located.

State-wise monthly HDD data from 1978 to 2007 were obtained from National Oceanic and Atmospheric Administra tion (NOAA) database center. 2 

### DOE aggregated the monthly data for each year to produce annual HDD for each st ate. The state annual HDD data were used to derive annual HDD for the regions identified in RECS (census divisions and separately four large states). In estimating divisional annual HDD, the annual HDD values for states within the regions were weighted by the state population in that year as described in section 7.3.1. The 30­ year average HDD and the 2005 HDD for the nine cen sus divisions and the four large states are
shown in Table 7.4.1

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### Table 7.4.1. 30-Year Average HDD (1978–2007) a nd the 2005 HDD for the Nine Census Divisions and the Four Large States

Division or State 30-Year HDD Average 2005 HDD

New England 6,571 6,453

Middle Atlantic 5,602 5,612

East North Central 6,103 6,323

West North Central 5,989 6,506

South Atlantic 3,665 3,720

East South Central 3,309 3,496

West South Central 2,415 2,723

Mountain 4,666 5,142

Pacific 5,133 5,193

New York 5,912 5,929

California 2,450 2,488

Texas 1,686 1,944

Florida 647 657



### By using equation (7.3.2), the weather-a djusted annual space-heating energy consumption was estimated for the equipment in each household. Statistics for the weatheradjusted space-heating energy consumption at both divisional and nationa l level are shown in Table 7.4.2. The frequency distribution of the RE CS sample households in different heating energy consumption bands is shown in Figure 7.4.1.

Table 7.4.2. Statistics of Annual Weather-Ad justed Space-Heating Energy Consumption

for RECS-Stock CHP at Regional Level

Division or State Number of
Observations Min kWh/yr Max kWh/yr Mean kWh/yr New England 1 2,399 2,399 2,399

Middle Atlantic 10 581 5,177 2,871

East North Central 20 1,911 10,037 4,237

West North Central 12 926 4,778 2,702

South Atlantic 108 0 6,412 2,692

East South Central 54 0 5,667 2,468

West South Central 6 354 4,434 2,386

Mountain 23 424 1,630 891

Pacific 13 2,171 5,251 3,621

New York 1 3,409 3,409 3,409

California 11 324 3,850 1,391

Texas 15 998 3,788 1,671

Florida 65 200 1,470 625

National 339 0 10,037 2,179

7-150% 5% 10% 15% 20% 25%Percentage of Obersavtions

Weather-Adjusted CHP Heating Energy from RECS (kWh/yr)



### Figure 7.4.1. National Distribution of Weathe r-Adjusted Space-Heat ing Energy for RECS
Observations

7.4.2 Stock Space-Heating Efficiency (HSPF)

The space-heating efficiency of CHP equi pment has improved over time in response to both technology improvement and minimum efficien cy standards. DOE related the space-heating efficiency of heat pumps in the stock to the e quipment vintage, much as was done for the cooling efficiency. In the 2005 RECS database, the age groups of the equipment and not their specific vintage is given. The five age groups are “less than 2 years old,” “2 to 4 years old,” “5 to 9 years old,” “10 to 19 years old,” and “20 years or olde r.” The data reported also include one additional category: “as old as the home.” The years of construction of the home for older homes are also reported in age bands. It was a ssumed that the age of a heat pump, within a given age group was uniformly distributed over the ra nge of the age group. For exampl e, for the heat pumps in the “less than 2 years old” age group, it was assumed that 50 percent of heat pumps were one year old and the other 50 percent were two years old. Similar technique was used in ascertaining the probable vintage year of the home and the equipm ent when both were reported to have the same age. The distribution of households with h eating-performance heat pumps by age groups is shown in Figure 7.4.2 and the distribution heat pump vintages are shown in Figure 7.4.3.

AHRI does not publish data on the historical average HSPF for CHP equipment. DOE developed historical HSPF es timates from two data sources. For the years from 1973 to 1993, DOE used data reported in the Technica l Support Document for the 2001 Rulemaking. 5 

### For the period from 1994 to 2005, DOE examined the 1994 ARI directory of Ce rtified Products, and developed a regression equation which relates th e SEER of residential split system heat pump DOE used the 1994 ARI Directory of Certified Equipment Database 6 

### to generate regressions between SEER and HSPF for CHP equipment on the market in 1994. For split systems, DOE used a regression based on datafor all CHP characterized by the AHRI designations HRCU-ACB and HRCU-A-C. For packaged heat pumps, DOE used a regression for all CHP equipment with an HSP-A designation. Only single phase eq uipment with ratings listed as “Active”, “not

7-16

### intended for export only”, were included. This resulted in two regressi on equations, one for the split system heat pump equipment class and one fo r single package heat pump equipment class. The historical average HSPF for each equipment class was calc ulated using historical annual average SEER values by equipment class from AHRI 7 

### and the appropriate regression equation. The annual average HSPF for all CHP equipment was then developed by weighting the annual average HSPF values by class using historical shipment data by equipment class. Table 7.4.3 shows the resulting historical HSPF values by year.

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0%

Less than 2 years

old 2 to 4 years old 5 to 9 years old 10 to 19 years old 20 years
or older

Age of Heat Pump SystemsPercent Of households



### Figure 7.4.2. Distribution of Households with Heating-Performance Heat Pumps by Age Groups

0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%

1 ,

9 7 6

1 ,97

9 1,982

1 , 9 8 5

1 , 9 8 8 1,991 1,994

1 , 9 9 7 2,00 0 2,003

Vintage Year of the Heat Pump SystemsPercent of Households



### Figure 7.4.3. Distribution of RECS Households with Central Heat Pumps by Vintage

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### Table 7.4.3. Average Annual Heating Pump HSPF

Year Average HSPF Year Average HSPF

1973 5.66 1990 6.98

1974 5.66 1991 7.08

1975 5.66 1992 7.19

1976 5.66 1993 7.30

1977 5.74 1994 7.38

1978 5.83 1995 7.39

1979 5.91 1996 7.40

1980 6.00 1997 7.39

1981 6.09 1998 7.29

1982 6.18 1999 7.47

1983 6.28 2000 7.46

1984 6.37 2001 7.49

1985 6.47 2002 7.49

1986 6.56 2003 7.53

1987 6.66 2004 7.56

1988 6.81 2005 7.58

1989 6.87



### 7.4.3 Estimating Baseline Annual Space-Heating Energy Use

For each class of equipment, the baseline annual space-heating energy consumption for CHP was estimated by using equation 7.4.2. The key inputs are the weather-adjusted annual space-heating energy consumption derived fro m the 2005 RECS data, the stock space-heating efficiency estimates for CHP shown in Tabl e 7.3.5, and the baseline heating efficiency,
7.7 HSPF.

Statistics showing the baseline space-heating energy consumption calculated for RECS households with CHP equipment at regional and national level are shown in Table 7.4.4.

Table 7.4.4. Baseline (13 SEER) Space-Heating Ener gy Consumption Statistics at Regional Level(CHP)

Division Number of
Observations Min kWh/yr Max kWh/yr Average kWh/yr New England 50 1,869 1,869 1,869
Middle Atlantic 127 443 5,030 2,778
East North Central 346 1,869 9,855 4,000
West North Central 224 789 4,585 2,546
South Atlantic 233 0 6,105 2,554
East South Central 173 0 5,068 2,323
West South Central 79 340 3,956 2,240
Mountain 130 346 1,329 824
Pacific 23 2,132 4,542 3,407
New York 34 3,356 3,356 3,356
California 160 287 3,745 1,305

Texas 181 982 3,684 1,580
Florida 94 170 1,351 586
National 1,854 0 9,855 2,058

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### 7.5 BASELINE RESIDENTIAL COOLING AND HEATING RESULTS-ADJUSTED
FOR RECS SAMPLE WEIGHTS

Table 7.3.6 , Table 7.3.7, and Table 7.4.4 direct ly reflect the RECS household sample data. These tabulated results do not take into account the sample weights assigned by RECS for the different households. Figure 7.5.1, Figure 7.5.2 , and Figure 7.5.3 show the distribution of calculated baseline cooling and heating energy for CAC and CHP products used in residences taking into account the RECS household samples weights.

0% 5% 10% 15% 20% 25%Percentage of Households

Baseline CAC Cooling Energy (kWh/yr)



### Figure 7.5.1. Baseline National Cooling En ergy Distribution for CAC – Residential

0% 2% 4% 6% 8% 10% 12% 14%Percentage of Households

Baseline CHP Cooling Energy (kWh/yr)



### Figure 7.5.2. Baseline National Cooling Energy Distribution for CHP – Residential

7-190% 5% 10% 15% 20% 25%Percentage of Households

Baseline CHP Heating Energy (kWh/yr)



### Figure 7.5.3. Baseline National Heating Energy Distribution for CHP – Residential

7.6 BASELINE COMMERCIAL CAC AND CHP ENERGY USE

Seven percent of re sidential-type ( i.e. , single-phase) CAC and CHP applications are assumed to be in commercial buildings. While potentially the variety of buildings to which this equipment could be utilized is wide, DOE believes that the vast majority of the shipments are utilized in small buildings, primarily small retail and commercial buildings. This is primarily because CAC and CHP equipment use single-phase electricity which limits the application to smaller buildings.

Cooling and heating energy use in commercial buildings can be distinctly different from residential buildings even for the same equipment. This is predominantly due to commercial buildings being operated differently, often havi ng defined operating hours when the equipment is used to condition the space, thermostatic setback during other hours, induced ventilation into the building structure to meet occupant needs, and typically continuous fan operation during occupied hours. Space heat gains in commercial bu ildings can be significantly different than in residential buildings due primarily to high internal thermal loads from lighting and electrical equipment during the occupied periods.

In addition multiple cooling and heating units are commonly used. A similar data source to the RECS data discussed earlier is the EIA Commercial Building Energy Consumption Survey (CBECS) 8 

### which provides estimates of end-use energy consumption in commercial buildings. Because multiple pieces of cooling and heatin g equipment are the norm in most commercial buildings, and the number of units by equipment type is not captured in the survey, it is not possible to readily determine the energy use of individual pieces of CAC and CHP equipment using CBECS. In addition, CBECS does not clearly distinguish equipment types by electrical phase. Hence, identification of buildings that are predominantly served by CAC and CHP equipment is difficult. Given these difficulties, it was determined that CBECS could not be used to estimate the cooling and heating energy data for CAC and CHP equipment used in

7-20

### commercial buildings. Instead, DOE relied on a building simulation approach to estimate the electrical energy usage of CAC and CHP equipment in commercial buildings.

7.7 OVERVIEW OF COMMERCIAL ENERGY SIMULATION APPROACH

In order to develop energy use statistics that reflect the use of CAC and CHP equipment in commercial buildings, DOE performed an analysis similar to that described in the Framework
for Central Air Conditioners. 9 

### DOE used the EnergyPlus 10 

### whole-building energy simulation software to estimate the energy consumption of CAC and CHP used to satisfy the cooling, supply fan, and in the case of CHP, the electri cal heating energy required in a representative small office building. Simulations were carried out for 237 locations around the United States. The building model used was an existing single story, 5,500 ft 2 

### office building model developed by the DOE for energy benchmarking purposes fo r this analysis. This benchmark building model is one of 16 commercial building benchmark models de veloped in support of the DOE Net Zero Energy Commercial Building Initiative. 11,12



### Two variations were considered for the simulations: buildings were either entirely served by CAC equipment with gas furnace backup or we re entirely served by CHP equipment. The simulated building utilized five individual CAC (or CHP units). For each equipment type, DOE simulated the equipment at four different efficiency levels starting from the baseline level. For CAC, these levels were: 13 SEER, 14 SEER, 16 S EER, and a max tech level of SEER 22 based on blower-coil ratings. For CHP these levels were: 13 SEER, 14 SEER , 16 SEER, and a max tech level of SEER 19.5 based on blower-coil ratings. Equipment parameters used in the simulation were adjusted to reflect engineering de signs corresponding to each efficiency level. Simulations were carried out using Typical Metrological Weather Files (TMY2) for the 237 U.S.
locations 13



### .
DOE extracted the annual energy consumption for cooling and fan energy use and heating from each modeled CAC or CHP unit as well as the equipment cooling capacity for each unit from each simulation run and aggregated these to the whole building level. Using this whole building cooling and heating data as well as the equipment capacities, DOE normalized the CAC and CHP energy consumption for each location and efficiency level to that of an average 3-ton CAC or CHP unit. These normalized energy use statistics were then used to represent the commercial energy use for a 3-ton CAC or CHP product for the LCC analysis.

To estimate the energy consumption for intermediate efficiency levels between those simulated, DOE linearly interpolated the energy use estimates for cooling energy, fan energy and heating energy based on the results at the four simulated efficiency levels, the SEER or HSPF rating as the independent variable, and using the nearest lower and upper SEER or HSPF levels
simulated.

7.7.1 Simulation Tool Description

DOE chose to use EnergyPlus version 3.0 for th e energy analysis. EnergyPlus is the most recent of several large scale software programs developed for whole-building energy simulation analysis and has been validated by comparing its results with thermal and energy use measurements on actual buildings and with othe r software tools and cal culations. EnergyPlus

7-21

### predicts the hourly energy use of a building gi ven hourly weather information and a description of the building, the operation of the building, and its heating, ventilation and air-conditioning (HVAC) equipment. Energy use statistics for the building can be readily extracted for different time steps and at detailed sub-component levels if necessary. En ergyPlus is an integrated software tool, where both the building loads and how those loads are served by equipment are solved iteratively and has a separa te, but robust ground heat transfer model, which is particularly useful in the simulation of smaller buildings. DOE is currently using EnergyPlus as the basis for most of its building energy simulation research and the tool and its ongoing development are actively supported within DOE.

7.7.2 Efficiency Levels Analyzed and Corresponding Simulation Parameters

DOE simulated four efficiency levels for CA C equipment and four efficiency levels for CHP equipment, corresponding to particular effi ciency levels identified in the engineering analysis. In order to simulate the equipment within the confines of the simulation software, the SEER and HSPF rating from the engineering analysis must be converted to parameters used by the simulation software. These parameters are th e cooling COP, and for CHP the heating COP, of the condensing unit at rate d conditions, and the fan power . The CAC and CHP condensing unit parameters are in turn calculated from 95 °F EER (EER) and 47 °F COP (COP 47 °F 

### ) ratings for the equipment and the fan power at rated conditions. DOE first estimated these from the SEER and HSPF rating.

The EER rating reflects the cooling performan ce for CAC equipment at a single-point rating condition defined by 95 °F outdoor temperat ure, 80 °F entering air dry bulb temperature and a 67 °F entering air wet bulb temperature. The COP 47 °F 

### rating reflects the heating performance of the heat pump at a single poi nt rating condition defined by 47 °F outdoor temperature, 70 °F entering air dry bulb temper ature and a maximum 60 °F entering air wet bulb temperature. The EER and COP 47 °F 

### ratings for equipment at di fferent SEER levels were estimated using best fit curves for residen tial air conditioning equipment and heat pump equipment established by Wassmer and Brandemuehl. 14 

### These curve fits are expressed in equation 7.7.1 and 7.7.2.

EER = − 0.0182 × SEER 2 

### + 1.1088 × SEER 

### Eq. 7.7.1

COP 47 F 

### = − 0.0255 × HSPF 2 

### + 0.6239 × HSPF 

### Eq. 7.7.2

For 13 SEER equipment, the fan power at rating conditions was established assuming a blower motor efficiency of 0.65, a fan efficiency of 0.58, and a constant total static pressure of
1.105 in H 2 

### O for the furnace/air handler. The same characteristics for fan power were also assumed to apply to the heat pumps at ratin g condition. The blower motor efficiency was assumed to be 0.65 for the 13 and 14 SEER ratings , consistent with that of a permanent split capacitor (PSC) blower motor. The blower moto r efficiency was assumed to be 0.80 for the 16 SEER and higher SEER ratings consistent w ith that of an electronically commutated motor (ECM) blower motor in this size range. A tran sition to ECM blower motors between the 14 and 16 SEER ratings reflects the use of that technology in the CAC/CHP market from the engineering analysis where ECM motors a ppear to be commonplace at 16 SEER, but not common at 14 SEER. These assumptions result in a blower power under rating conditions of

7-22

### 138 W/ton for 13 and 14 SEER equipment, and a lo wer fan power of 112 W/ton for higher SEER equipment at rated conditions. For comparis on, using the test procedure default 365 W/1,000 CFM default for a coil-only rating would provide the same power levels assumed for 13 and 14 SEER equipment at an ai rflow rate of 377 CFM/ton.

The cooling and heating condens ing unit COP values that are entered in the EnergyPlus
software reflect the EER and COP 47 °F 

### ratings but are adjusted for the impact of the blower power. The blower power is directly incorporated into the total system power for these ratings, but because of the effect of fan heat, the incorporating the blower reduces the cooling output and raises the heating output. Equations 7.73 and 7.74 provide cooling and he ating COP ratings for the condensing unit alone after adjus ting for the fan power and fan heat.


# ( EER / 3.413 + R 

# ) COP CUCool ,95 ° F 

### = 

### Eq. 7.7.3 1

− R



### COP

− R ⎛

47 ° F 

### ⎞ COP CUHeat ,47 ° F 

### = ⎜ ⎟ Eq. 7.7.4 ⎝ 1

− R ⎠

Where:

R = ratio of supply fan power to total equipment power at the AHRI rating condition,
COP CUCool,95 °F 

### = condensing unit cooling COP, and
COP CUHeat,47 °F 

### = condensing unit heating COP.

Table 7.7.1 shows the resulting equipment pe rformance statistics for the commercial building models.

Table 7.7.1. CAC and CHP Blower and Charact eristics Used in Modeling Commercial Cooling and Heating Energy Use
Equipment
Class SEER HSPF EER 47 °F
COP Blower
Motor Energy Efficiency Blower
Power (w/ton)* Fan Power to
Total Power (Rated Conditions) Cool
COP 95°F (Condensin g Unit Only) Heating
COP 47°F (Condensing Unit Only)
CAC 13 NA 11.339 NA 0.65 138 0.13 3.966 NA

14 NA 11.956 NA 0.65 138 0.135 4.208 NA
16 NA 13.802 NA 0.80 112 0.123 4.511 NA

22 NA 15.585 NA 0.80 112 0.14 5.471 NA
CHP 13 7.7 11.339 3.292 0.65 138 0.13 3.966 3.626

14 8 11.956 3.359 0.65 138 0.135 4.208 3.703
16 8.6 13.802 3.480 0.80 112 0.123 4.511 3.842

22 10.3 15.585 3.721 0.80 112 0.14 5.471 4.118

* Calculated for rating conditions.



### 7.7.3 Small Office Building Characteristics

The small office building prototype model is a theoretical building modeled with characteristics typical of buildings of this size and use. The building is a 5,500 ft 2 

### single-story

7-23

### building. The building is rectangular shaped, 52 ft by 105 ft with an aspect ratio of 1.5. The building has a window-area-fraction of 21 percent w ith a 10 ft floor-to-ceiling height. The small office building is assumed to have slab-on-grade construction for the floor and an attic. A picture of the building structure is shown in Figure 7.7.1. The building is divided into five HVAC zones, four perimeter zones and a core zone as illustrated in Figure 7.7.2, with a single CAC or CHP unit assumed for each HVAC zone. The perimeter z one depth in the center of each zone is 16 ft.

Figure 7.7.1. Small Office Bu ilding Design View

Figure 7.7.2. Small Office Building HVAC Zoning

The building model presumes wood frame construction and punch-out type windows. Where applicable the building components for this model were assumed to “just meet” the minimum prescriptive requirements of the ASHRAE 90.1-2004 standard. Components not regulated by Standard 90.1 are a ssumed to be designed as is considered standard practice for a small office building. Standard practice is deter mined from various sources including a review of the Commercial Buildings Energy Consumption Su rvey (CBECS 2003) and the input of various design and construction industry professiona ls including members of the ASHRAE 90.1 Standing Standard Project Committee. The following sections provide a review of the baseline building and how the baseline building is simulated in EnergyPlus, including characteristics of the building envelope, building internal loads (people, lighting, miscellaneous equipment, and infiltration), HVAC equipment, and service water heating.

7.7.4 Building Operating Characteristics

The building is assumed to follow typical small office occupancy patterns with peak occupancy occurring from 8 AM to 5 PM weekdays with limited occupancy beginning at 6 AM and extending until midnight for janitorial functi ons. The building is assumed to be unoccupied Saturday, Sunday and holidays. Schedules fo r lighting and miscellaneous equipment were matched to occupancy schedules with lower usage during unoccupied times. HVAC system

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### schedules were matched to the occupancy schedules, and allow for earlier startup times to bring the space to the desired temperature at the beginning of normal occupancy. Figure 7.7.3, Figure 7.7.4, Figure 7.7.5, and Figure 7.7.5 show the EnergyPlus schedules for lighting, plug loads, occupancy, and thermostat set point. Detailed schedules are show n in appendix 7-A for these as well as for HVAC fans, infiltra tion, and service hot water. Thes e schedules represent multipliers to defined daily “peak” conditions. In the case of ventilation, it is assumed that ventilation occurs whenever the fan system is operational. In the ca se of infiltration, a max imum infiltration rate for each hour is calculated hourly using wind speed and multiplied by the value shown in the hourly
infiltration schedule. That valu e is 1.0 for hours when the ventilation is off and the building is assumed to be unpressurized. Th e schedule multiplier is assumed to be 0.25 during hours when the fan system is on and the building is pressurized.

The HVAC systems are scheduled to be on from 6 a.m. to 7 p.m. weekdays (13 hours/day) and our schedule off weekends a nd holidays. Building thermostats are set to 70 °F degree heating, 75 °F degree cooling during the occupied period. Thermostats are setback to 60 °F heating, 85 °F cooling during the unoccupied period. Between 6 a.m and 7 a.m weekday mornings the thermostats are set to 65 °F and 80 °F cooling heating set points to provide a onehour thermostat ramp between the setback and oc cupied set points to prevent equipment sizing and temperature balance problems.

For the occupancy, lighting, plug use, ve ntilation, the schedules shown in Appendix 7-B are multipliers to defined peak densities. Ligh ting density was defined using the ASHRAE 90.1­ 2004 maximum LPD requirement of 1.0 w/ft 2 

### . Plug load density ( e.g. , computers and other miscellaneous loads) was 0.63 w/ft 2 

### . Occupancy peak density was defined as 31 persons for the building (5.6 persons per thousand square foot ). Ventilation was base d on ASHRAE Standard
62-1999 and set to 0.1 cfm/ft 2 

### (cubic feet per minute per square ft) of floor area for each conditioned zone. The peak infiltration for th e building was based on hourly wind speed as discussed previously and averages to 0.2016 cfm/ft 2 

### of total exterior c onditioned zone wall area at a wind velocity of 10 miles per hour.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24PercentofPeakPowerDensity

Weekday

Sat/Sun/Hol



### Figure 7.7.3. Small Office Lighting Schedule

7-25

### Figure 7.7.4. Small Office Plug Load Schedule

Figure 7.7.5. Small Office Occupancy Schedule

7-260 10 20 30 40 50 60 70 80 90 100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24ThermostatSetpoint(°F)

Heat

‐ Weekday

Heat

‐ Sat/Sun/Hol

Cool

‐ Weekday

Cool

‐ Sat/Sun/Hol



### Figure 7.7.6. Small Office Thermostat Set Point Schedule

7.7.5 Building Mechanical Systems and Equipment

As discussed, the building used either five CAC units or five CHP units, one for each conditioned zone depending on which equipment class was analyzed. Equipment performance characteristics were developed using perform ance curves based on existing equipment. Performance curves and model input s are documented in appendix 7-A.

Blower energy for the building model used the characteristics for blower motor and fan power efficiency discussed in section 7.7.2. Howeve r, the total fan static pressure was 1.45 in

H 2 

### 0 based on the assumption used in the benchmark prototype, compared to 1.105 in H 2 

### 0 total static pressure used to characterize the equipment performance at rating conditions.

Cooling equipment was sized based on ASHRAE 2.5 percent cooling design day sizing for all climates. A sizing run is done prior to the hourly simulations in EnergyPlus and is used to size all HVAC equipment. The design-day sizes ar e then used in the annual simulation runs. For the heat pumps, equipment sizing is based on coo ling design-day and electric resistance heat is used to make up any remaining heating load.

7.8 BASELINE COMMERCIAL ENERGY DISTRIBUTION

To be of use in the CAC analysis, the ba seline commercial CAC a nd CHP use developed for the 237 TMY locations must have relative weighting factors associated with each TMY location much as the residential analysis uses the representative RECS weights. DOE used population-based weighting fact ors developed for each TMY c limate based on data from the 2000 U.S. Census data to represent the impor tance of each climate in each state. The development and documentation of these weightin g factors is found in Appendix 7-B. Because certain TMY locations represent climates that cover more than one state ( e.g. , for instance where
the TMY location is on the border between two st ates), DOE originally developed weighting
factors for a total of 543 TMY-State combinations. These weighting factors were then used in the

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### subsequent LCC analysis in addressing CAC and CHP energy use in commercial buildings. Seventeen Alaskan locations, which make up appr oximately 0.2 percent of the national weight, were however removed from the energy analysis due to issues with electrical price data. Figure 7.8.1, Figure 7.8.2, and Figure 7.8.3 shows a distribution of commercial CAC cooling and CHP cooling and heating energy use in the nation which accounts for the relative weights attached to each of the remaining 526 TMY-State combinations.

0% 10% 20% 30% 40% 50% 60% 70%Percentage of SimulatedCommercial Small Offices

Baseline CAC Cooling Energy (kWh/yr)



### Figure 7.8.1. Baseline National Cooling En ergy Distribution for CAC-Commercial, Weighted

0% 10% 20% 30% 40% 50% 60% 70%Percentage of SimulatedCommercial Small Offices

Baseline CHP Cooling Energy (kWh/yr)



### Figure 7.8.2. Baseline National Cooling En ergy Distribution for CHP-Commercial, Weighted

7-280% 10% 20% 30% 40% 50% 60%Percentage of SimulatedCommercial Small Offices

Baseline CHP Cooling Energy (kWh/yr)



### Figure 7.8.3. Baseline National Heating En ergy Distribution for CHP-Commercial, Weighted

7.9 COMMERCIAL ENERGY FOR INTERMEDIATE EFFICIENCY LEVELS

Calculation of commerc ial annual cooling, and fan energy for intermediate efficiency levels not simulated was by linear interpolation between SEER levels simulated. Calculation of commercial heating energy use for heat pumps for intermediate levels not simulation was by interpolation between HSPF levels simulated.

7.10 MARKET BASELINE SPACE-COOLING AND SPACE-HEATING
EFFICIENCIES

The analysis results presented in this chapter up to this point presents the development of the baseline space-cooling and space-heating energy use for CAC and CHP equipment. Baseline in this discussion refers to the energy consump tion if all equipment was brought to the current
standard levels of SEER 13 and HSPF 7.7 for packaged and split system CAC and CHP product classes. Not all equipment is sold at the baseline efficiency levels. Much equipment exceeds the baseline level in the current market and is expect ed to continue to do so in the absence of new
standards. In order to account for this within the LCC analysis discussed in the next chapter, DOE defined a base case efficiency distribution for 2016, also referred to as the market baseline. The market baseline efficiency distribution was defined to represent the range of efficiencies likely sold into the market in 2016 in the absence of new standards. The market baseline energy consumption is calculated as the energy estimated to be consumed by each individual entity (as characterized by RECS household observation or commercial building TMY-State combination)

DOE estimated this energy use in the same manner as was used in estimating the baseline energy consumption. For RECS household r ecords, DOE first randomly selected a 2016 efficiency level from the market baseline di stribution and then equations 7.3.1 and 7.4.1 were utilized to calculate the cooling and heating c onsumption for the RECS observations in the 2016 base case, by replacing the baseline efficiency (cooling or heating) with the selected efficiency
for each observation in the base case. For commercial TMY-State observations, DOE first

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### randomly selected a 2016 efficiency level from the market baseline distributions and calculated the correspond cooling fan, and heating energy use for that observation in 2016 by linear interpolation between the efficiency levels simulated in the commercial energy analysis.

DOE did not have access to sales data describing the actual distribution of efficiencies in current sales, nor was such information provi ded by industry for this rulemaking. As a consequence, DOE developed estimates of the distribution of SEER levels for four classes of product: split system CAC, single package CAC, split system CHP, and single package CHP. The development of these distributions wa s based on the following key data inputs:

From AHRI data, the average SEER of equi pment sold in each product class in 2006 as well as the relative shipments by product class for the market as a whole.

An industry trade press article which indicat ed approximate sales segmentation by SEER for the CAC/CHP industry in 2007. 15



### AHRI Directory of Certified Product Performance 16 

### which allowed review of specific
efficiencies sold into the ma rket for each product class.

According to the trade press article, the di stribution in 2007 had approximately 9 percent of sales at 14 SEER, 3 percent of sales at 15 SEER, 2 percent at 16 SEER, and 2 percent for
17 SEER. The remainder of the product sales was assume d to fall below 14 SEER.

According to AHRI data 7 

### , product sales in 2007 were as follows: 63.0 percent split CAC,
5.9 percent single-package CAC, 27.6 percent sp lit CHP, and 3.5 percent single-package CHP.
Product sales in 2008 thr ough September were as follows: 63.0 percent split CAC, 4.8 percent single-package CAC, 28.8 percent split CHP, and 3.4 percent single-package CHP.

AHRI reported average shipped SEER by product class for 2007: split CAC, 13.72; single-package CAC, 12.83; split CHP 13.86, sing le-package CHP 13.16. DOE did not know if the average 12.83 for single-package CAC repr esented continued sa les of pre-SEER 13 equipment or if it included throug h the wall or other classes with lower efficiency. As this data were provided by AHRI for this rulemaking, it was assumed to be represen tative of single phaseequipment only. AHRI reported average shipped SEER for 2008 as: split CAC, 13.77; singlepackage CAC, 13.17; split CHP 13.97, single-package CHP 13.39.

DOE first created 16 SEER bins corresponding roughly to the SEER efficiency levels developed in the engineering analysis with thos e engineering levels serving as a representative efficiency for each bin. These bins are shown in Table 7.10.1. Then through an iterative process which took into account available efficiency leve ls in each product class, DOE developed an efficiency distribution fo r each product class which was consistent with the above data sources in that it provided average efficiency by split CA C, split CHP, and sing le-package CHP product class as reported by AHRI in 2007 as well as provided an overall distribution by SEER that corresponded with the industry trade press artic le cited above. Bins that included 14 and 15
SEER were assumed to be uniform ly distributed effici encies above and below the characteristic efficiency in the bin. It was obviously not possi ble to develop a shipment-weighted efficiency below 13 SEER as was seen for single package CAC using this distribution. Instead DOE chose

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### to match the 2008 shipment weighted average value for this product class. The estimated
distribution for all classes for 2007 is shown in Table 7.10.1.

Table 7.10.1. CAC and CHP Efficiency Distributions Estimated for 2007

SEER Bins SEER Distribution by Equipment Class Industry- Wide
SEER
Distribution (Four Classes)
% Bottom of Bin Top of Bin Characteristic Efficiency in Bin Split
CAC Single Package
CAC
% Split
CHP% Single Package
CHP
%

13 13.25 13 25.00 76.20 15.00 76.20 27.07
13.25 13.75 13.5 56.00 18.80 57.00 18.80 52.77
13.75 14.25 14 6.00 2.00 8.00 2.00 6.18
14.25 14.75 14.5 2.50 2.00 7.50 2.00 3.83
14.75 15.25 15 3.00 1.00 5.00 1.00 3.36
15.25 15.75 15.5 2.00 0.00 2.00 0.00 1.81
15.75 16.5 16 3.10 0.00 2.00 0.00 2.50
16.5 17.5 17 1.00 0.00 1.50 0.00 1.04
17.5 18.5 18 0.60 0.00 1.50 0.00 0.79
18.5 19.5 19 0.30 0.00 0.50 0.00 0.33
19.5 20.5 20 0.20 0.13
20.5 21.5 21 0.20 0.13
21.5 22.5 22 0.10 0.06
22.5 23.5 23
23.5 24.5 24
24.5 25.5 25 Wt. Average SEER 13.71 13.16 13.85 13.16 13.70
13.66 13.78



### DOE was not able to develop a distribu tion using the same methodology for the 2008
data that would be consistent with the industry trade press artic le overall distribution. DOE made slight changes to the relative shipment weights by efficiency bin from 2007 distribution, in order to match the higher average SEERs in 2008 wh ile maintaining the ge neral flavor of the distribution by product class. This overa ll 2008 distribution is shown in Table 7.10.2. DOE
assumed that this distribution was representative of the market baseline distribution in 2016 and used it as such for the energy and subsequent LCC analysis.

DOE did not have similar data on space heating efficiencies for CHP equipment. For analysis of the market baseline HSPF efficienci es, DOE used the HSPF values that corresponded to each CHP SEER level as identified in the engineering analysis. Because these engineering levels represented typical market averages base d on the AHRI data, they were deemed sufficient for characterizing average market HSPF by S EER level and corresponding average CHP heating energy use in the market baseline.

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### Table 7.10.2. CAC and CHP Efficiency Distribut ions Estimated for Market Baseline

SEER Bins SEER Distribution by Equipment Class IndustryWide SEER
Distribution (Four Classes)
% Bottom of
Bin

SEER Top of Bin

SEER Characteristic Efficiency in Bin

SEER Split
CAC

% * SinglePackage

CAC

% Split
CHP

% SinglePackage

CHP

%

13 13.25 13 24.00 75.00 13.00 50.00 24.18
13.25 13.75 13.5 55.00 20.00 52.00 32.00 51.66
13.75 14.25 14 4.00 2.00 10.00 11.00 5.87
14.25 14.75 14.5 5.25 2.00 9.00 5.00 6.17
14.75 15.25 15 3.25 1.00 5.50 2.00 3.75
15.25 15.75 15.5 2.00 0.00 3.50 0.00 2.27
15.75 16.5 16 3.50 0.00 3.00 0.00 3.07
16.5 17.5 17 1.50 0.00 2.00 0.00 1.52
17.5 18.5 18 0.70 0.00 1.50 0.00 0.87
18.5 19.5 19 0.30 0.00 0.50 0.00 0.33
19.5 20.5 20 0.20 0.13
20.5 21.5 21 0.20 0.13
21.5 22.5 22 0.10 0.06
22.5 23.5 23
23.5 24.5 24
24.5 25.5 25 Wt. Average SEER 13.77 13.17 13.97 13.39 13.78
13.72 13.91

* A separate, coil-only split air conditioner distribution was created by truncating the split AC distribution shown at SEER bin 8 (16.5-17.5 bin) since the max tech level for this type of equipment was 16.5. The distribution was then normalized back to 100%. This distribution is shown in chapter 8, Life Cycle Cost and Payback Period Analysis.



### 7.11 ESTABLISHMENT OF GEOGRAPHIC REGIONS FOR STANDARDS
ANALYSIS AND CORRESPONDING REGIONAL ENERGY CONSUMPTION
BY EFFICIENCY LEVEL

Under EPCA, regional efficiency standards for CAC may be defined for up to two regions consisting of contiguous groups of states. These regional standards are in addition to a base national standard efficiency that represents the minimum efficiency that a manufacturer could produce or that could be imported into the Un ited States. In order to consider establishing regional standards, DOE needed to identify regions for which such standards might be applicable. For cooling equipment performance, the most important regional variations in
climate are the annual number of operating hours, and the coincident relative humidity during those hours. For the CAC standard analysis, DOE is considering establishing two regions, hothumid and hot-dry, for the establishment of region al standards in addition to a national standard. In addition, DOE considered using two variants of the SEER rating to establish performance
standards: the current SEER definition and a modified definition termed “SEER hot-dry” (SEER HD 

### ). The SEER HD 

### would be calculated using revised temperature bin data and design sizing data as established for the defined hot-dry region.

A national standard and a sta ndard for the proposed hot-humid region may be established using SEER, although potentially at different SE ER efficiencies based on the cooling load and
other information characteristic to the nation and to the hot-humid region respectively. The

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### SEER HD 

### rating would be developed to be more appropriate for hot and dry climates in certain western States where the averag e outdoor air temperature during cooling may be considerably higher than that seen in the remainder of the United States. Whether these regional standards are appropriate will be determined by the LCC and other analyses. For the purpose of DOE’s preliminary analysis, DOE established the two re gions allowed under EPCA as shown in Table
7.11.1. Region 1 refers to the hot-humid region. Region 2 refers to the hot-dry region. For the purpose of characterizing the geographic rema inder of the nation, DOE uses a Region 0 nomenclature. DOE notes that while analysis can be performed for Region 0, DOE does not have authority to set a regional standard for this geographic area. Rather, this area would be covered
under the national standard.

Table 7.11.1. Proposed Climate Regi ons for Regional Standards

Hot-Humid (Region 1) Hot-Dry (Region 2) Rest-of-Country (Region 0)
Alabama Arizona Alaska Nebraska
Arkansas California Colorado New Hampshire
Florida Nevada Connecticut New Jersey Georgia New Mexico Delaware New York
Hawaii District of Columbia North Dakota
Louisiana Idaho Ohio Mississippi Illinois Oregon
North Carolina Indiana Pennsylvania
Oklahoma Iowa Rhode Island
South Carolina Kansas South Dakota

Tennessee Kentucky Utah

Texas Maine Vermont Maryland Virginia
Massachusetts Washington Michigan West Virginia
Minnesota Wisconsin
Missouri Wyoming

Montana



### For the purposes of the preliminary analysis for the CAC rulemaking, DOE did not develop energy consumption character istics in conformance with the SEER HD 

### rating as this rating was not used in the engineering analysis and no mapping between SEER and SEER HD 

### had been developed. However, DOE did develop the en ergy and subsequent LCC analysis at both the
national level and indivi dually for the regions defined in Table 7.11.1. A complete discussion on the development of both the standard regions and the SEER HD 

### parameter are discussed in Appendix 7-C.

For each equipment class, DOE developed energy use statistics for the market baseline and for higher efficiency levels by using a Monte Carlo random selec tion process to select 10,000 observations in each region and 10,000 for the na tion as a whole. Ninety three percent of
those observations were residen tial and 7 percent were commercial instances of the use of CAC (or CHP) equipment. Details for the selection process are found in chapter 8, LCC. However

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### Table 7.11.6 show the resultant baseline, market ba seline, and higher efficiency annual energy statistics developed for each region for each equipment class analyzed.

Table 7.11.2. Split-System Air Conditioners: Av erage Annual Energy Use by Efficiency Level for Coil-Only Systems

Efficiency Level Annual Energy Consumption kWh/yr

National Hot-Humid Hot-Dr y Rest of Countr y
# SEER HSPF * Coolin g Heatin g Coolin g Heatin g Coolin g Heatin g Coolin g Heatin g
1 13.0 N/A 2,407 0 3,564 0 2,176 0 1,378 0

Market
Baseline Market
Baseline N/A 2,291 0 3,393 0 2,071 0 1,310 0

2 13.5 N/A 2,322 0 3,437 0 2,100 0 1,331 0

3 14.0 N/A 2,243 0 3,318 0 2,029 0 1,287 0

4 14.5 N/A 2,168 0 3,205 0 1,960 0 1,244 0

5 15.0 N/A 2,097 0 3,100 0 1,897 0 1,204 0

6 15.5 N/A 2,031 0 3,002 0 1,836 0 1,166 0

7 16.0 N/A 1,968 0 2,909 0 1,780 0 1,130 0

8 16.5 N/A 1,912 0 2,825 0 1,730 0 1,099 0

* N/A means not applicable.



### Table 7.11.3. Split-System Air Conditioners: Av erage Annual Energy Use by Efficiency Level—Blower-Coil Systems

Efficiency Level Annual Energy Consumption kWh/yr

National Hot-Humid Hot-Dry Rest of Country

# SEER HSPF* Cooling Heating Cooling Heating Cooling Heating Cooling Heating

1 13.0 N/A 2,433 0 3,584 0 2,172 0 1,365 0

Market
Baseline Market
Baseline N/A 2,315 0 3,410 0 2,067 0 1,302 0

2 13.5 N/A 2,347 0 3,455 0 2,096 0 1,319 0

3 14.0 N/A 2,267 0 3,336 0 2,025 0 1,275 0

4 14.5 N/A 2,190 0 3,223 0 1,957 0 1,233 0

5 15.0 N/A 2,119 0 3,117 0 1,893 0 1,193 0

6 15.5 N/A 2,052 0 3,018 0 1,833 0 1,155 0

7 16.0 N/A 1,988 0 2,925 0 1,777 0 1,120 0

8 16.5 N/A 1,932 0 2,840 0 1,727 0 1,089 0

9 17.0 N/A 1,878 0 2,760 0 1,679 0 1,060 0

10 18.0 N/A 1,780 0 2,613 0 1,592 0 1,007 0

11 19.0 N/A 1,692 0 2,482 0 1,514 0 960 0

12 20.0 N/A 1,613 0 2,363 0 1,444 0 917 0

13 21.0 N/A 1,541 0 2,256 0 1,380 0 878 0

14 22.0 N/A 1,476 0 2,158 0 1,322 0 842 0

* N/A means not applicable.

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### Table 7.11.4. Split-System Heat Pumps: Average Annual Energy Use by Efficiency Level

Efficiency Level Annual Energy Consumption kWh/yr

National Hot-Humid Hot-Dry Rest of Country

# SEER HSPF Cooling Heating Cooling Heating Cooling Heating Cooling Heating

1 13.0 7.70 3,487 1,749 4,275 1,2 75 4,203 924 1,978 2,968

Market
Baseline Market
Baseline Market
Baseline 3,268 1,687 4,006 1,230 3,937 891 1,857 2,862

2 13.5 7.85 3,363 1,716 4,122 1,2 51 4,053 906 1,909 2,912

3 14.0 8.00 3,248 1,685 3,980 1,2 28 3,913 890 1,846 2,859

4 14.5 8.15 3,138 1,654 3,845 1,2 06 3,780 873 1,784 2,808

5 15.0 8.30 3,035 1,625 3,719 1,1 84 3,656 858 1,726 2,758

6 15.5 8.45 2,939 1,597 3,601 1,1 63 3,539 843 1,671 2,711

7 16.0 8.60 2,848 1,570 3,490 1,1 43 3,430 828 1,620 2,665

8 17.0 9.10 2,688 1,485 3,293 1,0 81 3,235 783 1,531 2,522

9 18.0 9.60 2,545 1,409 3,117 1,0 26 3,062 743 1,452 2,395

10 19.0 10.10 2,417 1,341 2,959 976 2,907 706 1,381 2,279

11 19.5 10.30 2358 1315 2886 957 2835 693 1,348 2,236



### Table 7.11.5. Single-Package Air Conditioners: Average Annual Energy Use by Efficiency

Level

Efficiency Level Annual Energy Consumption kWh/yr

National Hot-Humid Hot-Dry Rest of Country

# SEER HSPF * Cooling Heating Cooling Heating Cooling Heating Cooling Heating

1 13.0 N/A 2,376 0 3,602 0 2,170 0 1,363 0

Market
Baseline Market
Baseline N/A 2,348 0 3,559 0 2,144 0 1,348 0

2 13.5 N/A 2,292 0 3,473 0 2,093 0 1,316 0

3 14.0 N/A 2,214 0 3,353 0 2,022 0 1,273 0

4 14.5 N/A 2,139 0 3,239 0 1,954 0 1,230 0

5 15.0 N/A 2,070 0 3,133 0 1,890 0 1,191 0

6 15.5 N/A 2,004 0 3,033 0 1,831 0 1,153 0

7 16.0 N/A 1,942 0 2,940 0 1,774 0 1,118 0

8 16.6 N/A 1,876 0 2,838 0 1,714 0 1,081 0

* N/A means not applicable.

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### Table 7.11.6. Single-Package Heat Pumps: Average Annual Energy Use by Efficiency Level

Efficiency Level Annual Energy Consumption kWh/yr

National Hot-Humid Hot-Dry Rest of Country

# SEER HSPF Cooling Heating Cooling Heating Cooling Heating Cooling Heating

1 13.0 7.70 3,464 1,720 4,295 1,272 4,211 927 1,96 4 2,953

Market
Baseline Market
Baseline Market
Baseline 3,372 1,698 4,179 1,256 4,098 915 1,91 4 2,916

2 13.5 7.85 3,341 1,688 4,141 1,248 4,060 910 1,89 6 2,898

3 14.0 8.00 3,227 1,657 3,999 1,225 3,920 893 1,833 2,845

4 14.5 8.00 3,117 1,657 3,863 1,225 3,787 893 1,77 2 2,845

5 15.0 8.00 3,015 1,657 3,737 1,225 3,663 893 1,71 4 2,845

6 15.5 8.50 2,920 1,561 3,618 1,154 3,546 841 1,66 0 2,682

7 16.0 9.00 2,829 1,476 3,506 1,091 3,436 795 1,60 9 2,538

8 16.4 9.00 2,763 1,476 3,424 1,091 3,356 795 1,57 2 2,537

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### REFERENCES

1. Residential Energy Consumption Survey: Qu ality Profile, DOE Pub lication No. DOE/EIA0555(96)/1

2. NOAA, 2009. NNDC Climate Data Online. <www7.ncdc.noaa.gov/CDO/CDODi visionalSelect.jsp> (Assessed February, 2009)

3. Missouri Census Data Center . U.S. Census Data Archive for 1980, 1990, 2000 U.S. Census
Data http://mcdc2.missouri.edu/applications/uexplore.shtml#top and (Accessed February 2009)

4. AHRI 2008, Air Conditioning and Refrigeration Institute Statistical Profile, 2008. Efficiency data averaged by shipment weig hts across air conditioner, and for heat pump product classes.

5. DOE, 2001. Central Air Conditioner Rulema king SNOPR Technical Support Document, Chapter 5. Life Cycle Cost and Payback Analysis.

< http://www1.eere.energy.gov/buildings/applia nce_standards/residential/ac_central_1000

_ r.html > (Accessed April, 2009)

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< http://www.eia.doe.gov/emeu/cbecs/cbecs2003 > (Accessed May, 2009).

9. DOE, 2008. Rulemaking Framework for Resi dential Central Air Conditioners and Heat Pumps. Accessed at http://www1.eere.energy.gov/buildings/appliance_standards/residential/pdfs/cac_framewor k.pdf

10. DOE, 2008. EnergyPlus Energy Simulation Soft ware V3.0. U.S. Department of Energy. Washington, D.C. Available at < http://apps1.eere.energy.gov/buildings/energyplus >

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13. NREL, 1995. Typical Meteorological Years Derived from the 1961-1990 National Solar
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### 14. Wassmer M and MJ Brandemuehl. 2006. Effect of Data Availability on Modeling of
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15. ACH&R News, 2007. “Higher SEERs Got Popular”, December 2007. Accessed March
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­ 2006_A_10000000000000222513

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< http://www.ahridirectory.org/ah ridirectory/pages/home.aspx >. Accessed March, 2009.

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