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-Based Temperature Bin Data to Develop 

Cooling Load Profiles for SEERHD 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 Mexico .........................................................................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 Conditions 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 across Temperature Bins, by State .....................7C-16
 

LIST OF FIGURES 

Figure 7C.1.1. Distribution of Cooling Load Hours 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. Calculated Cooling Load Profiles for Candidate Hot-Dry States ....................7C-18 

Figure 7C.4.2. Cooling-Load-Weighted Average Outdoor Air Temperatures for 

Selected TMY Locations and Average 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 SEERHD.......................................................................................7C-22
 

7C-ii 
 

 

 

APPENDIX C. PROPOSED REGIONS FOR CAC REGIONAL STANDARDS AND 

MAPPING OF RECS OBSERVATIONS 

7C.1 Introduction 

Under EPACT, regional efficiency standards may be defined for up to two regions 

consisting of contiguous groups of states. For cooling equipment performance, the most 

important regional variations in climate are the annual number 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 performance standards: the current definition and a 

modified definition termed “SEER hot-dry” (SEERHD). SEER is established based on test data 

measured at various operating conditions, which are weighted and summed into a single 

numerical rating using a temperature profile that is representative of an average U.S. cooling 

climate. The SEERHD 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 may be considered for this region. The SEERHD 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 different 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 temperature 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 SEERHD 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 based on research conducted in the 1970s and, in 

simple terms, was developed by simulating the 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 test procedure, 1000 CLHs are estimated to be the 

U.S. average. As depicted in Figure 1, 1000 CLHs 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 reasonably 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, South Carolina, Tennessee, and Texas. 

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

As noted above, the States identified as being 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 fraction 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 the 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. The 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 

) 

80

Hours

70

ling 

60

(Coo

50

idity 

40

Hum

30 

20

Average Relative 

10 

0 

0 1000 2000 3000 4000 5000 6000 

CDD base 65 

Figure 7C.2.1. Scatter Plot of the Average Relative 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 consists 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 and 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 marine 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 variation 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 characteristic hot-dry climate would be included. 

7C.3 Mapping of RECS observations to Regions 

As proposed in the Framework Document, the Residential Energy Consumption Survey 

(RECS)4 is to serve as the basis for conducting the 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 shows the nine Census Divisions. The four large 

States are California, Florida, New York, and Texas. 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 the 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 Division, Middle Atlantic Census 

Division, East North Central Census Division, West North Central Census Division, 

Northern Mountain Census Division (Idaho, Montana, Wyoming), Pacific Census 

Division minus California. 

The RECS geographic areas above are similar the 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 the three census divisions allocates five more 

States than it should—Delaware, Kentucky, Maryland, 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 develop for excluding these overallocated States. 

7C-4 
 

Hot-Dry Region 

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 CAC and CHP equipment in the proposed climate 

regions for which separate efficiency standards 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 South Atlantic census division are separated 

in two sets i.e., one set for the northern states of the census division viz. Virginia, 

West Virginia, Maryland, District of Columbia 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 Mountain 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 census 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 from 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 data 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 state wise population-weighted CDD and HDD 

data for the year 2005 was obtained from National Climatic Data 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 and 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 record represents the number of housing units 

in the total stock of residential 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 expenses5

. This particular sampling design 

offers the opportunity of identifying large urban 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 comparison of the CDD and HDD values was 

inconvenient due to the masking of the reported 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 the climate characteristic in the Cartesian 

plane. In this approach, the polar values of this coordinate i.e., the 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 deviation 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 reported 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 climate 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, identical 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 found 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 climate characteristic of Charlotte, 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 RECS records was obtained. These were then 

identified as likely in the target zone of the ‘hot and humid’ region of the South Atlantic 

Census division comprising the 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 census 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 for the population weighted HDD and the CDD 

were found to be 82% and 117% of the respective 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 CDD 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 described 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 Census Division were identified with Hawaii based 

on the reported CDD data and high electricity costs. 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 SEERHD Metric 

The candidate hot-dry region consisted of Arizona, California, New Mexico, 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 SEERHD rating could be developed based on available temperature 

bin data for climates within each state and using population based-weighting of those specific 

climates. 

7C.4.1 State by State Analysis 

Through previous work, PNNL had developed population-based weightings for TMY2 

weather locations. A detailed discussion of this mapping process can be found in Appendix 7-B. 

For the purpose of this analysis, DOE only relied on the mapping as it applied to the four 

candidate hot-dry states. Table 7C.1 through 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 population 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 population 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 location 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 bins6

. 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 bins 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 defined cooling load. Table 16 of the CAC test procedure provides 

the fraction of the total temperature bin hours above 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 temperature 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 

BARSTO 

WDAGGE 

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-fractional 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 (Tj − 65) Qc (95) BL(Tj ) = • Eq. 7C- 1 95 − 65 1.1 

7C-12 
 

 

 

 

 

 

 

 

 

 

 

Where 

BL is the building load

 Tj is the representative temperature for bin j

 Qc (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 capacity (at the 95oF condition) divided by 1.1. We 

presume that the best interpretation of this equation 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 representative bin temperature 

Tj 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 95oF temperature in equation 1 was replaced with 

the ASHRAE 1% cooling dry bulb temperature design condition for each TMY3 location7. 

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

Table 7C.9. Design Load Conditions 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 TMY location. In addition, by weighting the 

representative bin temperatures by the relative load in each bin, DOE generated a cooling-loadweighted average outdoor air temperature (CLWT) 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 temperature 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 currently used in an optional rating methodology 

for establishing SEER in the current test procedure. 

Using the population based weighting factors 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 state population, the weighting factors were all 

normalized to 100% prior to weighting across climates. 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 

BARSTO 

WDAGGE 

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 across 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 across 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 clearly 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 Daugherty 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 applied the weighting factors developed 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 average 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 directly and using county population data from 

the 2000 census data, the average CDD65 value 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 Commission8 in conjunction with and 2000 census data for housing units by zip 

code within the state (mapped using CEC data for each zip code9

), 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 probably 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 better 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 inland 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 weighted-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, still the lowest of the four candidate hotdry states and below that of the DOE test procedure load profile. These temperatures are shown 

graphically in Figure 7C.4.2, along with similar figures calculated for other locations in the U.S 

using the same methodology. In addition, Table 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 within 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-18 
 

 

 

10CFR430 

Phoenix 

Tucson 

Prescott 

San Diego 

Flagstaff

Los Angeles

Long

Beach

San Francisco

Sacramento 

San Diego 

Daggett 

Fresno 

Bakersfield

Albuquerque 

El Paso 

Midland 

Tucumcari 

Grand

Junction 

Las Vegas 

Reno 

Elko

Winnemucca 

Chicago 

Yuma 

Orlando 

Atlanta 

Baltimore

Kansas City 

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 

0 

Temperature (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 candidate 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, including New Mexico and 

California may indeed be considered “dry” as compared 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 procedures current value of 95oF, DOE did not propose 

a modification of the design day value for the 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 SEERHD 

DOE 

PT.430 SEERHD 

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-20 
0.00 

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 - 114 

Fraction 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-21 
0.00 

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 - 114 

Fraction 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 SEERHD 

7C.5 Conclusion 

The analysis presented provides a methodology 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 SEERHD rating for the proposed four-state 

Hot-Dry Region, with emphasis on preserving a weighted-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 with that of the current test procedure, with the result that a 

greater fraction of bin hours exist at lower temperature bins in the hot dry region, but also at the 

very high temperature bins above 95°F. Since the 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 described by equation 4.1-2 of the current test 

method. The significantly higher cooling load calculated at the high temperature bins and the 

higher bin hours in those bins results in greater emphasis on cooling performance at high 

temperatures (above 95°F) and at low temperatures (below 75°F compared to the current test 

method. 

7C-22 
 

 

1. L.W. Nelson and J.R. Tobias. “Energy Savings in Residential Buildings,” 1974. 

ASHRAE Journal. pp. 34–45. February. 

2. R.R. McConnell, J.R. Tobias, and L.W. Nelson. “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-Energy 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-Energy Information Administration. Residential Energy 

Consumption Survey, 2001 Survey Methods. Washington, DC. 

http://www.eia.doe.gov/emeu/recs/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