## APPENDIX 7C. PROPOSED REGIONS FOR CAC REGIONAL STANDARDS AND MAPPING OF RECS OBSERVATIONS

TABLE OF CONTENTS

7C.1 Introduction .................................................................................................................. .... 7C-1 7C.1.1 Cooling Load Hours and their use to establish Hot Regions................................7C-1 7C.2 Cooling Degree Days and Relative Humidity and their use to verify Hot-Humid and Hot-Dry Regions ....................................................................................................... 7C-3 7C.3 Mapping of RECS observations to Regions .................................................................... 7C-4 7C.4 Use of Population Weights and TMY-Base d Temperature Bin Data to Develop Cooling Load Profiles for SEER HD 

## Metric ...................................................................... 7C-8 7C.4.1 State by State Analysis..........................................................................................7C-8 7C.5 Conclusion .................................................................................................................... . 7C-22

LIST OF TABLES

Table 7C.1. TMY2 Weights for Arizona ...................................................................................7C-9 Table 7C.2. TMY2 Weights for California................................................................................7C-9 Table 7C.3. TMY2 Weights for New Mexi co .........................................................................7C-10 Table 7C.4. TMY2 Weights for Nevada..................................................................................7C-10 Table 7C.5. Distribution of Fractional Hours within Cooling Season Temperature Bins—Arizona Climates ....................................................................................7C-11 Table 7C.6. Distribution of Fractional Hours within Cooling Season Temperature Bins— California Climates ...............................................................................7C-11 Table 7C.7. Distribution of Fractional Hours within Cooling Season Temperature Bins— New Mexico Climates ...........................................................................7C-12 Table 7C.8. Distribution of Fractional Hours within Cooling Season Temperature Bins— Nevada Climates ...................................................................................7C-12 Table 7C.9. Design Load Conditi ons for TMY3 locations ......................................................7C-13 Table 7C.10. Distribution of Cooling Load across Cooling Season Temperature Bins—Arizona Climates ....................................................................................7C-14 Table 7C.11. Distribution of Cooling Load across Cooling Season Temperature Bins—California Climates ................................................................................7C-15 Table 7C.12. Distribution of Cooling Load across Cooling Season Temperature Bins—New Mexico Climates ............................................................................7C-15 Table 7C.13. Distribution of Cooling Load across Cooling Season Temperature Bins—New Nevada Climates ............................................................................7C-16 Table 7C.14. Distribution of Cooling Load ac ross Temperature Bins, by State .....................7C-16

LIST OF FIGURES

Figure 7C.1.1. Dist ribution of Cooling Load Ho urs throughout the US....................................7C-2 Figure 7C.2.1. Scatter Plot of the Average Relative Humidity (RH) During Cooling Hours vs. CDD by Region ...................................................................................7C-3

7C-i

## Figure 7C.3.1. U.S. Census Divisions with RECS Defined Hot-Humid and Hot-Dry Regions—Over Allocated States Indicated with Blue-Dashed Areas.................7C-5 Figure 7C.4.1. Calculat ed Cooling Load Profiles for Ca ndidate Hot-Dry States ....................7C-18 Figure 7C.4.2. Cooling-Load -Weighted Average Outdoor Air Temperatures for Selected TMY Locations and Averag e Values for Four Candidate Hot-Dry States...................................................................................................7C-19 Figure 7C.4.3. Comparison of Temperature Bin Data-CAC Test Procedure and Proposed SEERHD ............................................................................................7C-21 Figure 7C.4.4. Comparison of Cooling Load Profile-Current SEER Test Procedure and Proposed SEER HD 

## .......................................................................................7C-22

7C-ii

## APPENDIX C. PROPOSED REGIONS FOR CAC REGIONAL STANDARDS AND MAPPING OF RECS OBSERVATIONS

7C.1 Introduction

Under EPACT, regional effi ciency standards may be defined for up to two regions consisting of contiguous groups of states. Fo r cooling equipment performance, the most important regional variations in climate are the annual numbe r of operating hours, and the relative humidity during the hours when cooling is used. Currently DOE is considering using two variants of the SEER rating to establish perf ormance standards: the current definition and a modified definition term ed “SEER hot-dry” (SEER HD 

## ). SEER is establishe d based on test data measured at various operating conditions, whic h are weighted and summed into a single numerical rating using a temperature profile that is representative of an average U.S. cooling climate. The SEER HD 

## rating can be developed using the same test data, using a different set of weightings that represent a hot, dry climate.

A national standard may be established using SEER. To account for higher operating hours in the south, a higher SEER level ma y be considered for this region. The SEER HD 

## rating is expected to be appropriate for the arid west. Whether these regional standards are appropriate will be determined by the LCC and other analyses. However, to carry out these analyses requires a definition of the regions within which the differe nt standard levels may apply. In this memo, a method is proposed for establishing these regions. Region definitions are based on three metrics: cooling load hours (CLH) as defined by the DOE CAC test procedure, annual cooling degree days (CDD), defined as usual using a base temp erature of 65 degrees Fahrenheit (°F), and the average relative humidity (RH) during those hours in which the temperature is greater than 65 °F.

The two proposed regions are defined as follows:

• Region 1 is defined as the hot-humid area with high CLH, high CDD, and high RH; in this region a higher SEER level may apply.

• Region 2 is defined as the hot-dry area with high CLH, high CDD, and low RH; in this region the SEER HD 

## standard may apply.

The remainder of the continental U.S. is termed Region 0 and has low CLH and low CDD, with a range on RH.

7C.1.1 Cooling Load Hours and their use to establish Hot Regions

Cooling load hours (CLH) are the operational hours of an air conditioner to meet the cooling load in a building. The current DOE test procedure includes a CLH map of the U.S. (Figure 1). The CLH map was determined base d on research conducted in the 1970s and, in simple terms, was developed by simulating th e performance of a prototypical residence throughout the US. 1,2



### 7C-1

## Figure 7C.1.1. Distribution of Cooling Load Hours throughout the US

CLHs can be used to estimate hot regions of the US, irrespective of whether the regions are humid or dry. As established by the DOE te st procedure, 1000 CLHs are estimated to be the U.S. average. As depicted in Figure 1, 1000 CL Hs roughly separates the U.S. into two equal regions (by area)—a Northern region and Southern region. Because 1000 CLHs define average climatic conditions for air-conditioning, it is reasonable to assume that CLHs above 1000 generally represent a hotter than average region. The area below the 1200 CLH line in Figure 7C.1.1 can reas onably be assumed to represent a hot region of the U.S. Because the 1200 CLH line cuts through several States, any State with an appreciable fraction of the population in areas with CLHs of 1200 and greater are considered to lie in the hot region. Based on general knowledge of the climatic conditions in this hot region, the area can be reasonably divided into the following hot-humid and hot-dry regions:

• Region 1 (Hot-Humid) : Alabama, Arkansas, Florida, Georgia, North Carolina, Louisiana, Mississippi, Oklahoma, Sout h Carolina, Tennessee, and Texas.

• Region 2 (Hot-Dry) : Arizona, California, Ne vada, and New Mexico.

As noted above, the States identified as bein g in the hot-humid region correspond to Region 1, and the States identified as being in the hot-dry region correspond to Region 2. Note that in the four states identified for Region 2, a large frac tion of the geographic area of the states falls in regions of the country north of the 1000 CLH line in Figure 1. However, the majority of the population in each of these states lies below th e 1000 CLH line. The remaining States, also referred to as Rest-of-Country, have been termed Region 0 for purposes of discussion. The following section analyzes CDD and RH data for each State to verify whether the proposed regional breakdowns based on CLH data alone are reasonable.

7C-2

## 7C.2 Cooling Degree Days and Relative Humidity and their use to verify Hot-Humid and Hot-Dry Regions

The National Climatic Data Center (NCDC) provides typical meteorological data (TMY) data, which the most recent version is TMY3. Th e TMY3 data set can be used to calculate the CDD and cooling RH by State. TMY data consists of measured values that typify the weather at a given location over a long period of time. The TMY3 data set has been developed at NREL from data for the period 1976-2005. 3 

## Annual CDD and the average RH for hours for which the temperature is greater than 65 °F are calculated for each station in the data set.

Figure 2 shows a scatter plot of the calculated values of CDD and RH for the TMY3 stations, sorted into the three regions defined above in Section 2. Region 1 (hot-humid) is dark blue, Region 2 (hot-dry) is purple, and Region 0 (remainder of US) is yellow. Hawaii is called out separately in light blue.

region 0 region 1 region 2 Hawaii

90) 80Hours

70ling

60(Coo

50idity

40Hum

30

20Average Relative

10

0
0 1000 2000 3000 4000 5000 6000

CDD base 65



## Figure 7C.2.1. Scatter Plot of the Average Rela tive Humidity (RH) During Cooling Hours vs. CDD by Region

Figure 7C.1.1 demonstrates that, in general, Regions 1 and 0 reflect the assumed trends in climatic variables, i.e., high CDD and high RH for Region 1 and low CDD for Region 0. Specifically, Region 1 (hot-humid) generally cons ists of stations with CDD > 2000 and RH > 60% and Region 0 (remainder of the US) generally consists of stations with CDD < 1500.

As shown in Figure 7C.1.1, several stations for Region 2 (hot-dry) exhibit the assumed climatic trend of high CDD a nd low RH, i.e., CDD > 2000 and RH < 40%. But there are two

7C-3

## other groupings within Region 2 which do not exhibit the assumed trend: one grouping has low CDD and high RH and a second grouping has low CDD and low RH. This outcome is a result of the climatic diversity within Region 2. The Region 2 grouping with high RH generally consists of stations on the coast of California with ma rine climates while the grouping with low RH generally consists of stations in the northern or mountainous areas of California, Nevada, and New Mexico. There is clearly significant varia tion in climates in Region 2, but because EISA 2007 requires that regions for which higher regional standards are set be comprised of contiguous States; areas of Region 2 without a ch aracteristic hot-dry clima te would be included.

7C.3 Mapping of RECS observations to Regions As proposed in the Framework Document, th e Residential Energy Consumption Survey (RECS) 4 

## is to serve as the basis for conducting th e LCC analysis. In order to conduct the analysis for Region 1 (hot-humid) and Region 2 (hot-dry), RECS must be able to specify the geographic location of each household record so that it can be properly assigned to one of the three regions.

RECS specifies the geographic location of each household record down to the Census Division and Large State level. Figure 7C.3.1 show s the nine Census Divisions. The four large States are California, Florida, New York, and Te xas. Additionally, because RECS specifies the CDD and Heating Degree Days (HDD) of each household record, the observations in the Mountain Census Division can be readily split into northern, cold states (Idaho, Montana, Wyoming) and southern warmer states (Arizona, Colorado, Nevada, New Mexico, Utah). Therefore, the RECS observations can be most readily be allocated th e Census Divisions and Large States to the following three geographic areas:

• RECS Hot-Humid : South Atlantic Census Division ( Florida, Delaware, Georgia, Maryland, North Carolina, South Carolina, Virginia, West Virginia ), East South Central Census Division ( Alabama, Kentucky, Mississippi, Tennessee ), and West South Central Census Division ( Arkansas, Louisiana, Oklahoma, Texas ).

• RECS Hot-Dry : California, Southern Mountain Census Division ( Arizona, Colorado, Nevada, New Mexico, Utah ).

• RECS Remainder of U.S. : New England Census Divisi on, Middle Atlantic Census Division, East North Central Census Divi sion, West North Central Census Division, Northern Mountain Census Division ( Idaho, Montana, Wyoming ), Pacific Census Division minus California

. The RECS geographic areas above are similar th e proposed climate regions specified using CLH, CDD, and RH data. But RECS does over allocate a number of States based on its limited geographic data (as indicated in Figure 7C.3.1 by the three areas bordered with blue dashed lines). The RECS hot-humid area defined by th e three census divisions allocates five more States than it should—Delaware, Kentucky, Mary land, Virginia, and West Virginia. The RECS Hot-Dry geographic designation above allocates two more States than it should—Colorado and Utah. As a result of this over allocation, it was necessary to de velop for excluding these overallocated States.

7C-4Hot-Dr y Re g ion

Hot-Humid Region



## Figure 7C.3.1. U.S. Census Divisions with RECS Defined Hot-Humid and Hot-Dry Regions—Over Allocated States Indicated with Blue-Dashed Areas

Analysis of energy consumption of the CA C and CHP equipment in the proposed climate regions for which separate efficiency standard s could be applicable required that the RECS records located in the following census divisions first be partitioned as described below:

• South Atlantic: RECS records for the Sout h Atlantic census division are separated in two sets i.e., one set for the northern st ates of the census division viz. Virginia, West Virginia, Maryland, District of Co lumbia and Delaware , and the second set for the southern states of the division viz. North Carolina, South Carolina and Georgia.

7C-5

## • Mountain: RECS records for the Mountai n census division are separated in two sets i.e., one set for the southern states of the division viz., Nevada, Arizona and New Mexico and a second set comprising the remaining states.

• East South Central: RECS records for Kentucky, the state at the northern extremity of the East South Central cen sus division was separated out from other states of the division.

Once these partitions were identified, an approach for mapping specific RECS records to the states of the census division partition was developed. Attempts to identify the city or county location of a given RECS household by matching the reported CDD and HDD with the specific year CDD and HDD data obtained fr om historical meteorological records for specific locations (e.g. urban areas) are problematic as the CDD and the HDD figures reported in RECS are masked by addition of small randomization components (noise). An approaching using RECS household weight da ta in conjunction with available population data and climate statistics was developed. This approach is outlined as follows (using a South Atlantic Census Division example):

• For the reference census divisions, the st ate wise population-we ighted CDD and HDD data for the year 2005 was obtained from Nati onal Climatic Da ta Center (NCDC). This was used in conjunction with the census data on state wise population to compute the weighted average CDD and the HDD for the partitioned regions of the census divisions outlined above. The weighted average CDD a nd the HDD values obtained are then used as target metrics which are compared with the similar weighted average values of any disaggregated trial set of RECS observations.

• The base weights assigned to each RECS r ecord represents the number of housing units in the total stock of reside ntial housing units. The RECS sampling plan is based on multistage area-probability cluster-sample design in which the sample housing units are geographically clustered to reduce survey expenses 5 

## . This particular sampling design offers the opportunity of identifying large ur ban clusters as all the sampled housing units in a sampling cluster have identical sample weights. Consequently in a given census division, the records with identical sampling weights are grouped together and it is inferred that they are from a single geographical area e.g., a specific city or a county. The reported CDD and HDD figures for the grouped RECS records are examined to confirm whether these values for the grouped records indeed vary within narrow ranges.

• For comparison and matching of the climate characteristic of RECS households with that of a known geographical area, direct comp arison of the CDD and HDD values was inconvenient due to the masking of the repo rted values. Consequently the HDD and the CDD values were instead represented by a polar coordinate form. For a given RECS record, the CDD was considered as the value of the abscissa and HDD as the value of ordinate corresponding to a point defining th e climate characteris tic in the Cartesian plane. In this approach, the polar values of this coordinate i.e., th e polar radius and the polar angle defines the climate characteristic of the RECS record. This polar characterization was found more convenient for matching of the climate characteristic of the record clusters with that of known geographical areas.

7C-6

## • The standard deviations and the average values of the polar radius and the polar angle of the climate characteristic points of the RECS records in a cluster were computed. It was observed that, in most cases, the standard de viation of a cluster of observation in a known census division and with the same sample weights were within 5% of their mean value for the cluster. The CDD and HDD data repor ted in RECS for a given household relates to the values recorded in the nearest NOAA weather station, but with a small perturbation for masking. For some such clusters, particularly with fewer records, it was noticed that the standard deviations are higher than 5%. It was concluded that these are primarily from counties where there are multiple NOAA weather stations and which have significant divergent climate characteristic, for example due to altitude variations.

• The mean values of the polar distances and the polar angles of the RECS record trial clusters were subsequently compared with the NOAA data for urban areas in the census division and an approximate urban cluster is matched. For example in the South Atlantic census division, the largest record cluster is a group with 17 records having identical sampling weights of 29248.47633 with a mean c limate characteristic polar distance of 3637 degree days and a polar angle of 59°. Since Atlanta in Georgia is the largest metropolitan area in this census division, identi cal climate metrics i.e., the polar distance and the polar angle for the 2005 CDD and HDD for Atlanta were computed and the values obtained for the polar distance was f ound to be 3356 and the polar angle value was 57° which matches approximately with the similar parameters for these 17 RECS records. Similarly a cluster of 12 RECS records each having an identical sampling weight of 31433.23266 were identified with c limate characteristic of Ch arlotte, NC. More records for these multi-record clusters with hotter climate characteristic were identified and added to the subset. Some single records with ‘hotter’ climate characteristic were also added to this subset and a tally of 263 RE CS records was obtained. These were then identified as likely in the target zone of th e ‘hot and humid’ region of the South Atlantic Census division comprising th e three states of Georgia, North Carolina, and South Carolina.

• For the partitioning of the set of RECS records for the South Atlantic census division (excluding Texas) into a subset corresponding to target ‘hot and humid’ southern part comprising of the three states of Georgia and Carolina and the remainder region, three key metrics were computed for this region using the population and 2005 climate data for all the states of the South Atlantic censu s division. These were compared with the identical metrics for the subset of RECS records identified as above. These three metrics were the population ratio (defined as the ratio of the target ‘hot-humid’ states to that of the entire census division) and the ratios of the population weighted CDD and HDD for the target ‘hot-humid’ states to that of the entire census division. From the data, the population ratio for the target ‘hot and humid’ region was established at 55% of the South Atlantic census division. The target ratios fo r the population weighted HDD and the CDD were found to be 82% and 117% of the respectiv e averages for the entire census division.

• Out of the 483 RECS records assigned to this census division excluding Texas, 263 identified RECS records formed the subset for which the climate characteristics approximately matched with the same for the counties and large urban centers of the

7C-7

## target ‘hot and humid’ region. The population weighted HDD ratio for these subset of records was 79% and the corresponding C DD metric was 120%. The population ratio, computed using the RECS sampling weights assigned to these records was found at 55%. It was concluded that the key characteristics for these subset of RECS records identified were well matched with that for the target region. Consequently these records were assigned to the target ‘hot and humid’ zone.

RECS records for the colder state of Kentucky from the East South Central Census Division were isolated in similar fashion. A subset of 84 RECS records from a total of 344 RECS records in that census division were obtained for which the key metrics describe d above approximately matched with the climate and the population ratio parameters for Kentucky.

Similarly, a subset of 165 RECS records out of the 344 assigned to the Mountain census division were identified to belong to the hotter states of Arizona and Nevada and these records were included in the Hot-Dry region.

Finally, two RECS records in the Pacific Cens us Division were identif ied with Hawaii based on the reported CDD data and high electricity co sts. These records were grouped into Region 1.

7C.4 Use of Population Weights and TMY-Based Temperature Bin Data to Develop Cooling Load Profiles for SEER HD 

## Metric The candidate hot-dry region consisted of Arizona, California, New Me xico, and Nevada, all with significant fractions of the geographic area, the population, or both, in areas with greater than 1200 CLH. Because of the need to establish regions based on whole states however, DOE undertook to examine how a SEER HD 

## rating could be developed ba sed on available temperature bin data for climates within each state and using population base d-weighting of those specific climates.

7C.4.1 State by State Analysis Through previous work, PNNL had develope d population-based weightings for TMY2 weather locations. A detailed disc ussion of this mapping process can be found in Appendix 7-B. For the purpose of this analysis, DOE only relie d on the mapping as it applied to the four candidate hot-dry states. Table 7C.1 throug h Table 7C.4 shows the resulting population based weights for each of 237 TMY2 locations found within these four states, in order of weight. For simplicity within the analysis, DOE examined data for the TMY2 locations whose weight comprised ~98% of the total popul ation weighting within a given state. These locations are shown in Bold. Note that in some instances, due to local climate mapping, the best representative TMY2 climate for a given popul ation falls outside of the state boundary.

7C-8

## Table 7C.1. TMY2 Weights for Arizona

Arizona WT. CUM. WT PHOENIX SKY HARBOR INTL AP TUCSON INTERNATIONAL AP PRESCOTT MUNICIPAL AP SAN DIEGO LINDBERGH MUNI AP FLAGSTAFF PULLIAM ARPT 70.4% 18.5%

4.6% 2.4% 2.1% 70.4% 88.9% 93.5% 95.9% 97.9% LAS VEGAS MCCARRAN INTL AP ALBUQUERQUE INTL ARPT CEDAR CITY MUNICIPAL AP GRAND JUNCTION WALKER FIELD DAGGETT BARSTOW-DAGGETT AP EL PASO INTERNATIONAL AP 1.5% 0.4% 0.1% 0.0% 0.0% 0.0% 99.4% 99.9% 100.0% 100.0% 100.0% 100.0%



## Table 7C.2. TMY2 Weights for California

California WT. CUM. WT LOS ANGELES MUNICIPAL ARPT LONG BEACH DAUGHERTY FLD SAN FRANCISCO INTL AP SACRAMENTO EXECUTIVE ARPT SAN DIEGO LINDBERGH MUNI AP DAGGETT BARSTOW-DAGGETT AP FRESNO AIR TERMINAL BAKERSFIELD MEADOWS FIELD 24.0% 21.5% 17.0% 14.0%

9.9% 5.6% 4.0% 1.6% 24.0% 45.5% 62.5% 76.5% 86.5% 92.0% 96.0% 97.7% SANTA MARIA PUBLIC ARPT 1.2% 98.9% ARCATA AIRPORT 0.7% 99.6% RENO CANNON INTL AP 0.2% 99.8% MEDFORD ROGUE VALLEY INTL AP 0.1% 99.9% LAS VEGAS MCCARRAN INTL AP 0.1% 100.0% TONOPAH AIRPORT 0.0% 100.0% WINNEMUCCA MUNICIPAL ARPT 0.0% 100.0% PHOENIX SKY HARBOR INTL AP 0.0% 100.0% PRESCOTT MUNICIPAL AP 0.0% 100.0%

7C-9

## Table 7C.3. TMY2 Weights for New Mexico

New Mexico WT. CUM. WT ALBUQUERQUE INTL ARPT EL PASO INTERNATIONAL AP MIDLAND REGIONAL AIR TERMINAL TUCUMCARI FAA AP GRAND JUNCTION WALKER FIELD 61.2% 13.5% 11.6%

6.3% 5.5% 61.2% 74.7% 86.3% 92.7% 98.2% PUEBLO MEMORIAL AP 0.8% 98.9% ALAMOSA SAN LUIS VALLEY RGNL A 0.5% 99.5% TUCSON INTERNATIONAL AP 0.3% 99.7% AMARILLO INTERNATIONAL AP 0.1% 99.8% LUBBOCK WEST TEXAS AIR TERMINA 0.1% 99.9% COLORADO SPRNGS MUNI 0.1% 100.0% PRESCOTT MUNICIPAL AP 0.0% 100.0% FLAGSTAFF PULLIAM ARPT 0.0% 100.0% PHOENIX SKY HARBOR INTL AP 0.0% 100.0%


## Table 7C.4. TMY2 Weights for Nevada

Nevada WT. CUM. WT LAS VEGAS MCCARRAN INTL AP RENO CANNON INTL AP ELKO MUNICIPAL ARPT WINNEMUCCA MUNICIPAL ARPT 61.7% 32.8%

2.7% 1.3% 61.7% 94.4% 97.1% 98.4% ELY YELLAND FIELD TONOPAH AIRPORT SALT LAKE CITY MUNICIPAL ARPT CEDAR CITY MUNICIPAL AP BURNS MUNICIPAL ARPT DAGGETT BARSTOW-DAGGETT AP 0.7% 0.5% 0.3% 0.1% 0.0% 0.0% 99.1% 99.6% 99.9% 100.0% 100.0% 100.0%



## DOE developed temperature bin data for each of the locations identified in bold in Table 7C.1 through 7C.4. While the mapping of weights had been previously done using TMY2 weather locations, DOE opted to use the more recent TMY3 weather data for each site for calculation of typical bin data as TMY3 data was available for each lo cation identified above. Five degree temperature bins were defined as are used in the current CAC test procedure for the development of the load profile and the total number of hours in the TMY3 file belonging to each temperature bin was accumulated. For example, the 65-69 oF temperature bin would be allocated all the hours with an outside temperature greater than or equal to 64.5 oF, but less than 69.5 oF. This binning method appears to be most similar to what would have been used for the development of the current central air conditioning test procedure temperature bins 6 

## . A representative bin temperature is defined for each bin as the midway point in the bin. So, for the 65-69oF bin, the representative temperature is 67 oF , again in accordance with the current test procedure. Because of the wider range of temperature in several of the hot and dry climates, additional high temperature bins beyond the 8 bi ns provided in the CAC test procedure were used.

7C-10

## The CAC test procedure is structured to consider all hours with outdoor air temperature above 65oF as hours with a define d cooling load. Table 16 of th e CAC test procedure provides the fraction of the total temperature bin hours ab ove 65oF as determined for the average U.S. Climates used for the SEER rating. Similarly, for each of the TMY3 locations, DOE calculated the fraction of total temperatur e bin hours above 65oF. Table 7C .5 through 7C.8 shows this data for each of the 20 TMY3 data files used in the analysis.

Table 7C.5. Distribution of Fractional Hours within Cooling Season Temperature Bins— Arizona Climates

Temperature Bin DOE PT.430 PHOENIX SKY HARBOR INTL AP TUCSON INTERNATIO

NAL AP PRESCOTT MUNICIPAL AP SAN DIEGO LINDBERGH

MUNI AP FLAGSTAFF PULLIAM

ARPT
65 – 69 0.214 0.123 0.175 0.260 0.597 0.339

70 – 74 0.231 0.100 0.147 0.200 0.269 0.239
75 – 79 0.216 0.116 0.174 0.199 0.107 0.246

80 – 84 0.161 0.177 0.175 0.181 0.026 0.153
85 – 89 0.104 0.161 0.116 0.117 0.000 0.024

90 – 94 0.052 0.109 0.084 0.041 0.000 0.000
95 – 99 0.018 0.116 0.082 0.003 0.000 0.000

100 - 104 0.004 0.071 0.041 0.000 0.000 0.000
105 - 109 0 0.024 0.005 0.000 0.000 0.000

110 - 114 0 0.002 0.000 0.000 0.000 0.000
115 - 119 0 0.000 0.000 0.000 0.000 0.000

120 - 124 0 0.000 0.000 0.000 0.000 0.000
125 - 129 0 0.000 0.000 0.000 0.000 0.000



## Table 7C.6. Distribution of Fractional Hours within Cooling Season Temperature Bins— California Climates

Temperature Bin DOE PT.430 LOS ANGELE S MUNICI

PAL ARPT LONG BEACH DAUGH

ERTY FLD SAN FRANCI

SCO INTL AP SACRA MENTO EXECUT

IVE ARPT SAN DIEGO LINDBE

RGH MUNI

AP DAGGE TT BARSTOWDAGGE TT AP FRESNO AIR TERMIN

AL BAKERS FIELD MEADO

WS FIELD
65 - 69 0.214 0.689 0.498 0.663 0.248 0.597 0.173 0.231 0.218

70 - 74 0.231 0.243 0.250 0.203 0.162 0.269 0.142 0.157 0.168
75 - 79 0.216 0.057 0.156 0.083 0.169 0.107 0.163 0.167 0.186

80 - 84 0.161 0.010 0.076 0.045 0.182 0.026 0.162 0.160 0.168
85 - 89 0.104 0.001 0.016 0.005 0.115 0.000 0.123 0.101 0.104
90 - 94 0.052 0.000 0.003 0.001 0.064 0.000 0.074 0.076 0.068
95 - 99 0.018 0.000 0.001 0.000 0.040 0.000 0.073 0.069 0.059

100 - 104 0.004 0.000 0.000 0.000 0.016 0.000 0.052 0.035 0.025
105 - 109 0 0.000 0.000 0.000 0.003 0.000 0.033 0.003 0.004

110 - 114 0 0.000 0.000 0.000 0.000 0.000 0.004 0.000 0.000
115 - 119 0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

120 - 124 0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
125 - 129 0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000



### 7C-11

## Table 7C.7. Distribution of Fractional Hours within Cooling Season Temperature Bins— New Mexico Climates

Temperature Bin DOE PT.430 ALBUQUER QUE INTL

ARPT EL PASO INTERNATIO

NAL AP MIDLAND REGIONAL AIR TERMINAL TUCUMCARI FAA AP GRAND JUNCTION WALKER

FIELD
65 – 69 0.214 0.288 0.199 0.224 0.288 0.260

70 – 74 0.231 0.189 0.183 0.198 0.182 0.179
75 – 79 0.216 0.196 0.198 0.184 0.187 0.174

80 – 84 0.161 0.162 0.191 0.176 0.162 0.177
85 – 89 0.104 0.100 0.133 0.102 0.094 0.113

90 – 94 0.052 0.047 0.061 0.071 0.051 0.062
95 – 99 0.018 0.015 0.030 0.037 0.031 0.035

100 - 104 0.004 0.004 0.005 0.007 0.004 0.000
105 - 109 0 0.000 0.000 0.000 0.000 0.000

110 - 114 0 0.000 0.000 0.000 0.000 0.000
115 - 119 0 0.000 0.000 0.000 0.000 0.000

120 - 124 0 0.000 0.000 0.000 0.000 0.000
125 - 129 0 0.000 0.000 0.000 0.000 0.000



## Table 7C.8. Distribution of Fractional Hours within Cooling Season Temperature Bins— Nevada Climates

Temperature Bin DOE PT.430 LAS VEGAS MCCARRAN

INTL AP RENO CANNON INTL AP ELKO MUNICIPAL ARPT WINNEMUCCA MUNICIPAL ARPT
65 – 69 0.214 0.153 0.230 0.250 0.215

70 – 74 0.231 0.116 0.172 0.172 0.174
75 – 79 0.216 0.139 0.191 0.183 0.174

80 - 84 0.161 0.174 0.196 0.193 0.182
85 - 89 0.104 0.133 0.122 0.132 0.129
90 - 94 0.052 0.096 0.060 0.055 0.075
95 - 99 0.018 0.098 0.028 0.017 0.042

100 - 104 0.004 0.069 0.000 0.000 0.009
105 - 109 0 0.020 0.000 0.000 0.000

110 - 114 0 0.001 0.000 0.000 0.000
115 - 119 0 0.000 0.000 0.000 0.000

120 - 124 0 0.000 0.000 0.000 0.000
125 - 129 0 0.000 0.000 0.000 0.000



## In addition to the distribution-of-fractiona l bin hours data from the TMY3 files, DOE define a peak sizing temperature. As shown in equation 4.1-2 of the CAC test procedure, the building load in any bin is calculated as

k = 2 

# ( T

j 

## − 65 

# ) Q

c 

## (95) BL ( T j 

## ) = • 

## Eq. 7C- 1 95

− 65 1.1

7C-12

## Where BL is the building load

T

j 

## is the representative temperature for bin j Q c 

## (95) is the equipment cooling capacity at 95 oF

Implicitly, the building load varies linearly with outdoor air temperature from 65oF to 95oF where it is essentially equal to the equipment cap acity (at the 95oF condition) divided by 1.1. We presume that the best interpretation of this equa tion is that the equipment is sized for the 95oF condition, with a presumed oversizing factor of 10%. Using equation 7C-1, the building load does not reach the equipment capacity until the representative temperature T equals 98oF. Equation 1 is used to establish a building load profile based on the repres entative bin temperature T

j 

## and the fractional bin hour ratio for bin j.

To establish realistic building load profiles for hot and dry climates where the peak design temperatures may be significantly higher, the 95o F temperature in equation 1 was replaced with the ASHRAE 1% cooling dry bulb temperatur e design condition for each TMY3 location 7



## . Those design conditions are shown in Table 7C.9.

Table 7C.9. Design Load Condi tions for TMY3 locations

TMY 3 Location 1% DB Design Temp
PHOENIX SKY HARBOR INTL AP 108.1
TUCSON INTERNATIONAL AP 102.9
PRESCOTT MUNICIPAL AP 91.5
SAN DIEGO LINDBERGH MUNI AP 81.5
FLAGSTAFF PULLIAM ARPT 83.2
LOS ANGELES MUNICIPAL ARPT 80.4
LONG BEACH DAUGHERTY FLD 87.7
SAN FRANCISCO INTL AP 78.0
SACRAMENTO EXECUTIVE ARPT 97.8
SAN DIEGO LINDBERGH MUNI AP 81.5
DAGGETT BARSTOW-DAGGETT AP 106.2
FRESNO AIR TERMINAL 101.0
BAKERSFIELD MEADOWS FIELD 101.0 ALBUQUERQUE INTL ARPT 93.5
EL PASO INTERNATIONAL AP 98.6
MIDLAND REGIONAL AIR TERMINAL 97.8
TUCUMCARI FAA AP 95.7
GRAND JUNCTION WALKER FIELD 94.4
LAS VEGAS MCCARRAN INTL AP 106.2
RENO CANNON INTL AP 92.5
ELKO MUNICIPAL ARPT 92.3
WINNEMUCCA MUNICIPAL ARPT 94.9



### 7C-13

## Using the 1% design condition to replace the 95oF sizing temperature in equation 1, and using the number of hours in each bin and the building load calculated for each bin, DOE developed cooling load profiles for each TM Y location. In addition, by weighting therepresentative bin temperatures by the relative load in each bin, DOE generated a cooling-loadweighted average outdoor air temperature (CLW T) for each TMY location. This temperature represents the most representative temperature for the climate in terms of calculating the cooling load served by the equipment, and thus the most representative temperature that might be used for calculation of an annualized efficiency (assuming cooling efficiency is generally linear with outdoor air temperature) for that climate. The resulting cooling load profiles and the coolingload-weighted average outdoor air temperatur e are shown in Table 7C.10 through Table 7C.13 along with the CAC test procedure load profile. It is noted that the cooling load weighted average outdoor temperature from the current SEER test procedure is 82oF. The performance (in terms of cooling output and power) at 82oF is cu rrently used in an optional rating methodology for establishing SEER in the current test procedure.

Using the population based weighting fact ors for each TMY location; DOE calculated population-averaged load profiles for each state. Note, because the sites for a given state only represented approximately 98% of the stat e population, the weighting factors were all normalized to 100% prior to weighting across clim ates. The resulting average load profiles by state are shown in Table 7C.14.

Table 7C.10. Distribution of Cooling Load across Cooling Season Temperature Bins— Arizona Climates

Temperature Bin (°F) DOE PT.430 PHOENIX SKY HARBOR INTL AP TUCSON INTERNATIO

NAL AP PRESCOTT MUNICIPAL AP SAN DIEGO LINDBERGH

MUNI AP FLAGSTAFF PULLIAM

ARPT
65 – 69 0.036 0.013 0.022 0.047 0.248 0.080

70 – 74 0.137 0.036 0.066 0.125 0.391 0.198
75 – 79 0.220 0.072 0.134 0.214 0.267 0.350

80 – 84 0.232 0.156 0.190 0.276 0.093 0.309
85 – 89 0.194 0.184 0.163 0.230 0.001 0.063
90 – 94 0.119 0.153 0.145 0.100 0.000 0.000
95 – 99 0.049 0.192 0.168 0.009 0.000 0.000

100 – 104 0.013 0.137 0.098 0.000 0.000 0.000
105 – 109 0.000 0.052 0.014 0.000 0.000 0.000

110 – 114 0.000 0.004 0.000 0.000 0.000 0.000
115 – 119 0.000 0.000 0.000 0.000 0.000 0.000

120 – 124 0.000 0.000 0.000 0.000 0.000 0.000

CLWT (°F) 82.1 90.6 87.4 81.3 73.0 77.4



### 7C-14

## Table 7C.11. Distribution of Cooling Load across Cooling Season Temperature Bins— California Climates

Temperature

Bin (°F) DOE PT.430 LOS ANGELE S MUNICI

PAL ARPT LONG BEACH DAUGH

ERTY FLD SAN FRANCI

SCO INTL AP SACRA MENTO EXECUT

IVE ARPT SAN DIEGO LINDBE

RGH MUNI

AP DAGGE TT BARSTOWDAGGE TT AP FRESNO AIR TERMIN

AL BAKERS FIELD MEADO

WS FIELD
65 – 69 0.036 0.348 0.157 0.285 0.038 0.248 0.021 0.033 0.032

70 – 74 0.137 0.430 0.275 0.306 0.087 0.391 0.060 0.077 0.085
75 - 79 0.220 0.172 0.295 0.215 0.156 0.267 0.118 0.141 0.162

80 - 84 0.232 0.045 0.203 0.163 0.238 0.093 0.166 0.192 0.207
85 - 89 0.194 0.004 0.055 0.024 0.195 0.001 0.163 0.157 0.165
90 - 94 0.119 0.000 0.013 0.006 0.134 0.000 0.121 0.145 0.134
95 - 99 0.049 0.000 0.003 0.000 0.098 0.000 0.141 0.155 0.138

100 - 104 0.013 0.000 0.000 0.000 0.045 0.000 0.117 0.091 0.067
105 - 109 0.000 0.000 0.000 0.000 0.009 0.000 0.083 0.010 0.011

110 - 114 0.000 0.000 0.000 0.000 0.000 0.000 0.011 0.000 0.000
115 - 119 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.000

120 - 124 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 CLWT (°F) 82.1 71.6 75.9 73.8 84.7 73.0 89.4 86.7 85.7



## Table 7C.12. Distribution of Cooling Load ac ross Cooling Season Temperature Bins—New Mexico Climates

Temperature Bin (°F) DOE PT.430 ALBUQUER QUE INTL

ARPT EL PASO INTERNATIO

NAL AP MIDLAND REGIONAL AIR TERMINAL TUCUMCARI FAA AP GRAND JUNCTION WALKER

FIELD 65 – 69 0.036 0.052 0.030 0.035 0.050 0.043

70 – 74 0.137 0.119 0.098 0.109 0.111 0.103
75 – 79 0.220 0.212 0.183 0.174 0.195 0.172

80 – 84 0.232 0.249 0.250 0.236 0.240 0.248
85 – 89 0.194 0.198 0.225 0.177 0.181 0.204

90 – 94 0.119 0.114 0.127 0.152 0.121 0.138
95 – 99 0.049 0.044 0.073 0.094 0.088 0.091

100 - 104 0.013 0.012 0.014 0.021 0.013 0.000
105 - 109 0.000 0.000 0.000 0.000 0.000 0.000

110 - 114 0.000 0.000 0.000 0.000 0.000 0.000
115 - 119 0.000 0.000 0.000 0.000 0.000 0.000

120 - 124 0.000 0.000 0.000 0.000 0.000 0.000

CLWT (°F) 82.1 82.0 83.4 83.8 82.8 83.2



### 7C-15

## Table 7C.13. Distribution of Cooling Load ac ross Cooling Season Temperature Bins—New Nevada Climates

Temperature Bin (°F) DOE PT.430 LAS VEGAS MCCARRAN

INTL AP RENO CANNON INTL AP ELKO MUNICIPAL ARPT WINNEMUCCA MUNICIPAL ARPT
65 – 69 0.036 0.017 0.037 0.041 0.032

70 – 74 0.137 0.046 0.096 0.100 0.091

75 – 79 0.220 0.094 0.184 0.181 0.156

80 – 84 0.232 0.166 0.266 0.271 0.231

85 – 89 0.194 0.163 0.215 0.240 0.213
90 – 94 0.119 0.145 0.130 0.123 0.150
95 – 99 0.049 0.176 0.071 0.044 0.100

100 - 104 0.013 0.144 0.001 0.000 0.026

105 - 109 0.000 0.048 0.000 0.000 0.000

110 - 114 0.000 0.002 0.000 0.000 0.000

115 - 119 0.000 0.000 0.000 0.000 0.000

120 - 124 0.000 0.000 0.000 0.000 0.000

CLWT (°F) 82.1 89.9 83.0 82.6 84.4



## Table 7C.14. Distribution of Cooling Load across Temperature Bins, by State

Temperature Bin (°F) DOE PT.430 Arizona California New Mexico Nevada
65 – 69 0.036 0.023 0.203 0.046 0.025
70 – 74 0.137 0.058 0.280 0.114 0.065
75 – 79 0.220 0.101 0.209 0.200 0.127
80 – 84 0.232 0.170 0.149 0.247 0.203 85 – 89 0.194 0.175 0.064 0.198 0.183
90 – 94 0.119 0.142 0.038 0.122 0.139
95 – 99 0.049 0.170 0.031 0.059 0.136
100 - 104 0.013 0.117 0.018 0.013 0.091
105 - 109 0.000 0.040 0.007 0.000 0.030
110 - 114 0.000 0.003 0.001 0.000 0.001
115 – 119 0.000 0.000 0.000 0.000 0.000
120 – 124 0.000 0.000 0.000 0.000 0.000
CLWT (°F) 82.1 88.8 76.8 82.5 87.3



## Based on this process, only two states clear ly showed average load profiles that were significantly hotter, in terms of CLWT than that used in the DOE test procedure. During the course of review of this analysis, it was noted that for California, which showed an average cooling load-weighted outdoor temperature of 77oF, the wide variation in climates very close to the coast compared with climates only miles inland made it difficult to ensure characterization of the climates experienced by the population with the TMY sites that are near the coast (Los Angeles Municipal Airport, Long Beach Daughert y Field, San Francisco Intl. Airport, San Diego Lindbergh Municipal Airport). This is largely a question of the mapping and weighting process for the TMY locations in that state. To examine this, DOE calculated the CDD65 for the eight

7C-16

## California TMY locations and app lied the weighting factors deve loped for those TMY locations directly to those CDD65 to see how close this calculated value was to other estimates of the average CDD65 for California. The weighting of the TMY3 data resulted in a state-wide CDD65 of 763. To calculate a more representative aver age CDD, Census Population Data available by county was used to weight county-average CDD values developed directly from the PPL and NOAA sites without the mapping to TMY. This result and two other estimates are as follows:

• Based on using the county CDD65 values dire ctly and using county population data from the 2000 census data, the average CDD65 va lue calculated for California was 1134.

• Based on using the county CDD65 values and using the 1990 to 2000 census data for population growth by county (a possible surrogate for new construction only), the average CDD65 value for California was 1217.

• Based on using CDD65 data for each of the 16 climate zones identified by the California Energy Commission 8 

## in conjunction with and 2000 census data for housing units by zip code within the state (mapped using CEC data for each zip code 9 

## ), the average CDD65 value for California was 1065.

The results from examining the calculations based on the county CDD65 values indicated that a value of CDD65 near 1100 was probabl y most reasonable for the state based on 2000 census data for population. With that in mind, DOE did a sensitivity analysis by adjusting the TMY weights for the California locations to be tter reflect an average CDD65 value of ~1100. This was achieved by multiplying the calculated TMY weights for the four coastal TMY sites by an adjustment factor of 0.635 and the four inla nd TMY sites by an adjustment factor of 2.05. These factors were chosen so that they would correctly result in a combined total weight for the state identical to that calculated originally and preserve the relative weight between the four states. Using these revised weights, the we ighted-average CDD65 from the TMY sites for California was 1100. These revised weights were also then used to recalculate the average cooling load profile for the state, as shown in Table 7C.15 and shown graphically in Figure 7C.4.1. The revised CLWT for California was 80.2°F, s till the lowest of the four candidate hotdry states and below that of the DOE test proce dure load profile. These temperatures are shown graphically in Figure 7C.4.2, along with s imilar figures calculated for other locations in the U.S using the same methodology. In addition, Tabl e 7C.15 shows the fraction of the cooling load calculated for binned temperatures above 85°F.

7C-17

## Table 7C.15. Distribution of Cooling Load with in Temperature Bins by State –Revised Weights Temperature Bin ( 

### oF) DOE PT.430 Arizona California New Mexico Nevada
65 - 69 0.036 0.023 0.141 0.046 0.025
70 - 74 0.137 0.058 0.207 0.114 0.065
75 - 79 0.220 0.101 0.186 0.200 0.127
80 - 84 0.232 0.170 0.172 0.247 0.203 85 - 89 0.194 0.175 0.106 0.198 0.183
90 - 94 0.119 0.142 0.073 0.122 0.139
95 - 99 0.049 0.170 0.064 0.059 0.136
100 - 104 0.013 0.117 0.037 0.013 0.091
105 - 109 0.000 0.040 0.014 0.000 0.030
110 - 114 0.000 0.003 0.001 0.000 0.001
115 – 119 0.000 0.000 0.000 0.000 0.000
120 – 124 0.000 0.000 0.000 0.000 0.000
CLWT (°F) 82.1 88.8 80.2 82.5 87.3 Fraction of cooling load above 85 

### oF 0.375 0.648 0.294 0.393 0.581

Weighted Average Load Profile (Hot Dry States) (Based on Population Wts for Selected TMY Sites and TMY3 Data)Fraction of Cooling Load(Calculated using Bin Temp Data and CFR 430 Method) 0.400

0.350

0.300

0.250

0.200

0.150

0.100

0.050

0.000
65-70 70-75 75-80 80-85 85-90 90-95 95-100 100-105 105-110 110-115 115-120 120-125 125-130 Temperature Bin 10CFR Pt. 430 California
New Mexico
Nevada Arizona



## Figure 7C.4.1. Calculated Cooling Load Profiles for Candidate Hot-Dry States

7C-181 0 C F R 4 3 0

P h o e n i x

T

u c so n

P r e s co t t

S a n D i e g o

F l a g s t a f f

L o s A n g e l e s

L o n g B e a c h

S a n F r a n c i s co

S a cr a m e n t o

S a n D i e g o

D

a g g e t t

F

r e s n o

B a ke r sf i e l d

A l b u q u e r q u e

E

l P a s o

M i d l a n d

T u cu m c a r i

G r a n d Ju n c t i o n

L a s V e g a s R e n o E l k o

W i n n e m u c c a

C h i ca g o
Y

u m a

O r l a n d o

A

t l a n t a

B a l t i m o r e

K a n sa s C i t y 

### Cooling-Load-Weighted Average Outdoor Air Temperature (Tj)

110

105

100

95

90

85

80

75

70

65

60

55

50

45 82.1 90.6

87.4

81.3

73.0 77.4

71.6 75.9 73.8 84.7

73.0 89.4
86.7 85.7

82.0 83.4 83.8 82.8 83.2 89.9

83.0 82.6 84.4

79.1 90.6

80.0 80.6 80.0 81.0 ASHRAE 1% Design Condition 120

100

80

60

40

20

0Temperature (F)

Location 

## Figure 7C.4.2. Cooling-Load-Weighted Average Outdoor Air Temperatures for Selected TMY Locations and Average Values for Four Candidate Hot-Dry States Using population based weights two of these ca ndidate states, Arizona and Nevada, stand out as having CLWT and fraction of cooling hours greater than 85°F that are distinctly higher than that of the DOE test procedure bins. While other western states, in cluding New Mexico and California may indeed be considered “dry” as co mpared to the country as a whole, they do not appear necessarily hotter, when considering the state as a whole, than the climate characterized by the test procedure. DOE used the relative populations of the four states (Arizona-6.5 million people, California- 36.5 million people, Nevada-2.6 million people, New Mexico- 2.0 million people) to derive a weighted cooling load profile and design day rating that would be characteristic of that experienced by customers in these state. This design day temperature was 94.3oF. As this was very close to the test pro cedures current value of 95oF, DOE did not propose a modification of the design day value for th e Hot-Dry region. DOE then back calculated a cooling bin distribution for the region that, when combined with the design day rating, would generate the region-average cooling load profile . These profiles are shown in Table 7C-16 and Figure 7C.4.3 and Figure 7C.4.4.

7C-19

## Table 7C.16. Weighted Cooling Bin Data and Corresponding Load Profile for Proposed for Hot-Dry Rating

Temperature Bin (oF) Fraction of Hours in Cooling Bin (Bin > 65oF) Resulting Cooling Load Profile DOE PT.430 SEER HD 

### DOE PT.430 SEER HD 

### 65 - 69 0.214 0.477 0.036 0.115 70 - 74 0.231 0.208 0.137 0.175 75 - 79 0.216 0.119 0.220 0.172 80 - 84 0.161 0.086 0.232 0.176 85 - 89 0.104 0.047 0.194 0.124 90 - 94 0.052 0.027 0.119 0.088 95 - 99 0.018 0.021 0.049 0.082
100 - 104 0.004 0.011 0.013 0.050 105 - 109 0.000 0.004 0.000 0.018
110 - 114 0.000 0.000 0.000 0.001 115 – 119 0.000 0.000 0.000 0.000 120 – 124 0.000 0.000 0.000 0.000 Design Temp (oF) 95 95 94.3
CLWTemp (oF) 82.1 82.1 81.9

Fraction of cooling load above 85oF - - 0.375 0.362

7C-200.00 0.10 0.20 0.30 0.40 0.50 0.60

65 - 69 70 - 74 75 - 79 80 - 84 85 - 89 90 - 94 95 - 99 100 - 104 105 - 109 110 - 114Fraction of Cooling Hours in Bin Profile

Outdoor Temperature Bin (oF) DOE PT.430

SEERHD



## Figure 7C.4.3. Comparison of Temperature Bin Data-CAC Test Procedure and Proposed SEERHD

7C-210.00 0.05 0.10 0.15 0.20 0.25

65 - 69 70 - 74 75 - 79 80 - 84 85 - 89 90 - 94 95 - 99 100 - 104 105 - 109 110 - 114Fraction of Cooling Load

Outdoor Temperature Bin (oF) DOE PT.430

SEERHD



## Figure 7C.4.4. Comparison of Cooling Load Profile-Current SEER Test Procedure and Proposed SEER HD



## 7C.5 Conclusion The analysis presented provides a methodolog y for defining two additional regions, and Hot-Dry and a Hot-Humid region proposed for use in setting regional air conditioning standards in the U.S. for the CAC rulemaking. In addition, the analysis presents a methodology for establishing bin temperature data for use in a new SEER HD 

## rating for the proposed four-state Hot-Dry Region, with emphasis on preserving a we ighted-average cooling load profile for the region assuming that cooling equipment is sized to meet the design day conditions for each climate. The resulting temperature distribution of bin hours needed to obtain this cooling load profile is derived and compared w ith that of the current test procedure, with the result that a greater fraction of bin hours exist at lower temper ature bins in the hot dry region, but also at the very high temperature bins above 95°F. Since th e population-weighted average of the design day temperature data for all locations in the Hot-Dry region was very close to 95°F, this analysis did not propose changing the cooling load line desc ribed by equation 4.1-2 of the current test method. The significantly higher cooling load cal culated at the high temperature bins and the higher bin hours in those bins results in gr eater emphasis on cooling performance at high temperatures (above 95°F) and at low temperatur es (below 75°F compared to the current test method.

7C-22

## 1. L.W. Nelson and J.R. Tobias. “Energy Savings in Residentia l Buildings,” 1974. ASHRAE Journal . pp. 34–45. February.

2. R.R. McConnell, J.R. Tobias, and L.W. Ne lson. “Reducing Energy Consumption During the Cooling Season,” 1976. ASHRAE Journal . pp. 61–65. June.

3. S. Wilcox and W. Marion, Users Manual for TMY3 Data Sets , NREL/TP-581-43156, May 2008.

4. U.S. Department of Energy-E nergy Information Administration. Residential Energy Consumption Survey, 2005 Public Use Data Files , 2005. Washington, DC. http://www.eia.doe.gov/emeu/recs/recspubuse05/pubuse05.html (Accessed Apr, 2009)

5. U.S. Department of Energy-E nergy Information Administration. Residential Energy Consumption Survey, 2001 Survey Methods . Washington, DC. http://www.eia.doe.gov/emeu/r ecs/recs2001/append_a.html (Accessed Apr, 2009)

6. 10CFR430 Appendix M, Subpart B. Uniform Test Method for Measuring the Energy Consumption of Central Air Conditioners and Heat Pumps

7. ASHRAE. 2005 Handbook of Fundamentals , American Society of Heating, Refrigerating, and Air Conditioning Engineers. Atlanta, GA

8. PGE. Guide to California Climate Zones . Available at http://www.pge.com/mybusiness/edusafety/training/pec/toolbox/arch/climate/index.shtml (Accessed Feb, 2009)

9. CEC. Climate Zones by Zip Code List. MS Excel File available at http://www.energy.ca.gov/maps/building_climate_zones.html (Accessed Feb, 2009)

7C-23