### CHAPTER 7. ENERGY USE ANALYSIS

TABLE OF CONTENTS

7.1 INTRODUCTION ........................................................................................................... 7-1

7.2 CUAC EQUIPMENT ENERGY SAVINGS DETERMINAT ION ................................. 7-1

7.2.1 Building Loads ..................................................................................................... 7-2

7.2.2 Modeling Equipment Energy Use ........................................................................ 7-6

7.2.2.1 Modeling of 7.5-Ton CUAC Equipment .................................................... 7-7

7.2.2.2 Modeling of 15-Ton CUAC Equipment ..................................................... 7-8

7.2.2.3 Modeling of 30-Ton CUAC Equipment ................................................... 7-10

7.2.2.4 Energy Consumption ................................................................................ 7-12

7.2.3 Energy Decrements ............................................................................................ 7-12

7.2.4 Results ................................................................................................................ 7-13

7.3 GENERALIZED BUILDING SAMPLE ....................................................................... 7-17

7.3.1 Data Sources ...................................................................................................... 7-17

7.3.2 Commercial Building Characteristics ................................................................ 7-17

7.3.3 Interpolation of Weights across Building Characteristics ................................. 7-21

7.3.4 Assigning Electricity Load Profiles to Generalized Buildings .......................... 7-22

7.3.5 CUAC Energy Use Results ................................................................................ 7-24

REFERENCES .......................................................................................................................... 7-26

LIST OF TABLES

Table 7.2.1 Description of Equipment and Calculation Methods for 7.5-ton Equipment at Various ELs ............................................................................. 7-7

Table 7.2.2 Calculated Equations for 7.5-Ton Equipment ............................................... 7-8

Table 7.2.3 Description of Equipment and Calculation Methods for 15-ton Equipment at Various ELs ............................................................................. 7-9

Table 7.2.4 Calculated Equati ons for 15-Ton Equipment .............................................. 7-10

Table 7.2.5 Description of Equipment and Calculation Methods for 30-ton Equipment at Various ELs ........................................................................... 7-11

Table 7.2.6 Calculated Equations for 30-Ton Equipment .............................................. 7-12

Table 7.3.1 Characteristics and Associated Values Used to Define the Generalized Building Sample

................................ ........................................................... 7-18

Table 7.3.2 Floor Space by Region ................................................................................. 7-19

Table 7.3.3 Floor Space by Building Activity ................................................................ 7-19

Table 7.3.4 Floor Space by Size (Annual Energy Consumption) ................................... 7-20

Table 7.3.5 Floor Space Distribution by Size and Region .............................................. 7-20

Table 7.3.6 Floor Space by Vintage ................................................................................ 7-20

Table 7.3.7 Required capacity in tons per square foot for different regions and representative unit capacities ....................................................................... 7-22

Table 7.3.8 Factors used to represent relative changes in required cooling energy for vintage category 3. ................................................................................. 7-23

7-i

### Table 7.3.9 7.5-ton CUAC Equipment: Annual Energy Use Results by Efficiency

Level ............................................................................................................ 7-24

Table 7.3.10 15-ton CUAC Equipment: Annual Energy Use Results by Efficiency

Level ............................................................................................................ 7-25

Table 7.3.11 30-ton CUAC Equipment: Annual Energy Use Results by Efficiency

Level ............................................................................................................ 7-25

LIST OF FIGURES

Figure 7.2.1 Capacity Ratio as a Function of Outdoor Dry-Bulb Temperature ................. 7-3 Figure 7.2.2 COP Ratio as a Function of Outdoor Dry-Bulb Temperature ....................... 7-3 Figure 7.2.3 Cumulative Fraction of Rated Capacity by Compressor Operating

Hours .............................................................................................................. 7-5 Figure 7.2.4 Cumulative Fractio n of Hour of Fan Run Time by Hours of Year ................ 7-6 Figure 7.2.5 Annual Cooling Energy Use by Efficiency Level for 7.5-ton Equipment .................................................................................................... 7-14 Figure 7.2.6 Annual Fan Energy Use by Efficiency Level for 7.5 -ton Equipment.......... 7-14 Figure 7.2.7 Annual Cooling Energy Use by Efficiency Level for 15-ton Equipment .... 7-15 Figure 7.2.8 Annual Fan Energy Use by Efficiency Level for 15 -ton Equipment........... 7-15 Figure 7.2.9 Annual Cooling Energy Use by Efficiency Level for 30-ton Equipment .... 7-16 Figure 7.2.10 Annual Fan Energy Use by Efficiency Level for 30 -ton Equipment........... 7-16 Figure 7.3.1 Distribution of Floor Space in 2011 by PBA, Region, and Vintage ............ 7-21 Figure 7.3.2 Comparison of cooling degree days by state for TMY3 and TMY2 ........... 7-23

7- ii

### CHAPTER 7. ENERGY USE ANALYSIS

7.1 INTRODUCTION

The building energy use analysis produced energy use estimates and end -use load shapes for commercial unitary air

- cooled air conditioners (CUAC). The energy use estimates enabled evaluation of energy savings from the operation of small, large, and very large air-cooled CUAC equipment at various efficiency levels, while th e end -use load shapes allowed evaluation of the impact on electricity demand from the operation of CUAC equipment. The analysis produced results from a variety of building types in multiple climate locations in order to capture some of the diversity of use, and performance, of CUAC equipment.

DOE developed energy consumption estimates only for the CUAC equipment classes that have no heating or electric resistance heating. For equipment classes with all other types of heating, the incremental change in each efficiency level is identical to the incremental change in each efficiency level for the equipment classes with no heating or electric resistance heating. Therefore, DOE estimated that the energy savings for any efficiency level relative to the baseline would be identical for both sets of equipment classes. In turn, the energy savings estimates for the efficiency levels associated with the equipment classes that have no heating or electric resistance heating were used by DOE in the LCC and PBP analysis an d the NIA to represent both sets of equipment classes.

The energy use analysis described in this chapter consists of two related parts. In the first

part (Section 7.2) , DOE calculated energy savings for small, large, and very large air -cooled

CUAC at the c onsidered efficiency levels based on modifications to the energy use simulations conducted for the 2004 Advanced Notice of Proposed Rulemaking (ANOPR). 1 

### These building

simula tion data are based on the 1995 Commercial Building Energy Consumption Survey (CBECS). 2 

### Because the simulation data reflects the building stock in 1995 that uses air-cooled CUAC equipment, in the second part (Section 7.3), DOE developed a “generalized building sample” to represent the current installation conditions for the equipment covered in this rulemaking. This part involved making adjustments to update the building simulation data and the building weights to reflec t the building stock that uses air -cooled CUAC equipment in 2011.

7.2 CUAC EQUIPMENT ENERGY SAVINGS DETERMINATION

This section describes the determination of energy savings for small, large, and very large

air

- cooled CUAC at the considered efficiency levels . Whole-building simulations are an excellent source of energy use and equipment operation data, so DOE determined

, from the 2004

ANOPR’s energy use simulations, hourly building cooling load profiles for each of the 1,033 commercial buildings . Section 7.2.1 d escribes this process and appendix 7A further describes the

simulations and updates made to the simulation methods used for the 2004 ANOPR . DOE then modeled the equipment introduced in Chapter 5 and simulated equipment energy use that would

result from mee t ing the building cooling loads. Section 7.2.2 highlights this methodology.

7-1

### Energy decrements can then be determined by comparing the equipment energy use at the baseline level and at a considered efficiency level, as described in Section 7.2.3. The decrements

are shown in Section 7.2.4.

7.2.1 Building Loads

DOE calculated hourly building cooling loads and hourly fan operation based on the compressor and fan energy consumptions derived from simulations of equipment at an energy efficiency rating (EER) of 11. The following expressions demonstrate how the conversion was conducted in order to arrive at hourly total cooling load for each building.

First, the total input power for the CUACs serving the building was determined with the following equation for each hour:

POWER CUAC 

### ( hr ) = SYS

_ SIZE CUAC 

### × CapRatio

EER CUAC 

### × COPratio Eq. 7.1

w here:

POWER CUAC 

### ( hr ) = total input power to the CUAC s in an hour (in Watts),

SYS SIZE CUAC 

### = system size of all CUACs serving the building (in Btu/hr),

EER CUAC 

### = e fficiency of the CUAC s (11 EER)

.

CapRatio and COPratio are the capacity ratio and COP ratio, respectively. They are found for

each hour using the hourly weather file from the building simulations and their relationships to dry bulb temperature, which are provided by the cooling system component of the building

simulations . These relationships depicted in Figure 7.2.1 and Figure 7.2.2

.

7-2

### Figure 7.2.1 Capacity Ratio as a Function of Outdoor Dry- Bulb Temperature

Figure 7.2.2 COP Ratio a s a Function of Outdoor Dry

- Bulb Temperature

7-3

### Second, compressor input power for the CUAC s serving the building was determined with the following equation for each hour:

����� ����

− ���� 

### ( �� ) = ����� ���� 

### ( �� )

− ����� ��� �

− ��� 

### ( �� ) Eq. 7.2

w here:

POWER CUAC

− COMP 

### ( hr ) = compressor input power to the CUAC s in an hour (in Watts),

POWER CUAC

− FAN 

### ( hr ) = ventilation fan input power of the CUAC s in an hour (in Watts).

In the above equation, the ventilation fan is assumed to run whenever the compressor is running

and runs constant regardless of load or outdoor dry-bulb temperature.

Third, the fraction of the hour that the CUAC compressors run was de termined with the following equation for each hour:

FRA C HOUR CUAC

− COMP 

### ( hr ) = ENERGY CUAC

− COMP 

### ( hr )

POWER CUAC

− COMP 

### ( hr ) Eq. 7.3

where :

FRA C HOUR CUAC

− COMP 

### ( hr ) = fraction of the hour the compressor runs each hour,

ENERGY CUAC

− COMP 

### ( hr ) = energy used by the CUAC compressors.

In the above equation, the compressor hourly energy is obtained from the building simulations.

Finally, hourly building loads were determined with the following expression:

Bld g Load ( hr )



### = SY S SIZE CUAC 

### × CapRatio × FRA C HOUR CUAC

− COMP 

### ( hr )

− POWER CUAC

− FAN 

### ( hr )

× ConvFactor Eq. 7.4

where :

Bld g Load ( hr ) 

### = building load in each hour,

ConvFactor = factor to convert watts into Btu/hr.

For most hours in all buildings, the compressor needs to supply only a fraction of its rated cooling capacity to meet the cooling load . Cooling systems for each building are sized to meet the expected peak load of the building. As a result, the system is oversized for almost all hours of the year. For each building, the number of CUAC units was calculated by dividing the modeled eq uipment’s rated capacity at 95 °F with the system size used in the building

simulations. The method for determining the system size for each building for use in the simulation modeling is described in the 2004 ANOPR. 2 

### Figure 7.2.3 shows the cumulative

fraction of rated capacity by hours of compressor run time in a simulated year for each of the 1,033 commercial buildings.

7-4

### Figure 7.2.3 Cumulative Fraction of Rated Capacity by Compressor Operating Hours

Because the 11EER equipment modeled in the building simulations utilized a singlespeed fan, the fraction of each hour that the fan wa s on in the simulation is the ratio of that hour’s fan energy consumption to the maximum hourly fan energy consumption. The heat from

the indoor fan was added to the cooling load created by the building. The heat created by the indoor fan for each hour was calculated by multiplying three variables: the power of the indoor fan, the fracti on of the hour the fan operated in the building simulation, and the number of

CUAC units in that building

. The cooling load attributable solely to the building was calculated by subtracting the hourly fan energy consumption from the total hourly cooling provided by the compressor. In addition to distributing cooling from the CUAC equipment, the fans are run to provide ventilation for building occupants. Figure 7.2.4 shows the cumulative fraction of hour of

fan run t ime for every hour of the year for all 1,033 commercial buildings. In all simulated buildings the fans run for the full hour for at least 2,700 hours per year. In some buildings the fans run continuously all year long.

7-5

### Figure 7.2.4 Cumulative Fraction of Hour of Fan Run Time by Hours of Year

7.2.2 Modeling Equipment Energy Use

DOE applied the hourly building cooling loads and hourly fan operating hours to the baseline equipment and to equipment at the various ELs. The energy consumption of equipment at baseline efficiency (EL 0) and at four higher efficiency levels (E L1 through EL4 ) was determined for each hour of the year for the 1,033 commercial buildings. The energy consumption was calculated based on the req uired building cooling load and the fraction of fan

run time for each hour.

DOE used data specified per the AHRI rating standard to calculate the energy consumption of equipment at each efficiency level. 3 

### Data per t he AHRI rating standard identifies the net capacity, compressor power, condenser fan power, indoor fan power, control power, and EER at outdoor temperatures of 95, 81.5, 68, and 65 °F. DOE then developed equations to define the relationships between outdoor dry bulb temperature and the various component s’ power. The CUAC equipment for which these relationships were developed are described in Tables 7 -1, 7-3, and 7-5 for 7.5-, 15-, and 30-ton equipment, respectively. The relationships as a function of efficiency level are shown in Tables 7 -2, 7-4, and 7-6 for 7.5-, 15-, and 30-ton equipment, respectively.

7-6

### As noted above, for each building, the number of CUAC units was calculated by dividing the modeled equipment’s rated capacity at 95 °F with t he system size used in the building simulations. For calculation purposes, the result was not necessarily an integer.

7.2.2.1 Modeling of 7.5-Ton CUAC Equipment

Table 7.2.1 describes the baseline and four efficiency levels and the calculation method s used in the analysis of 7.5-ton CUAC equipment. At each EL, the description of the components of the equipment and the modeling methodology for the components are shown in the table below. Detailed descriptions of the CUAC equipment at each EL are provided in Chapter 5.

Table 7.2.1 Description of Equipment and Calculation Methods for 7.5

- ton Equipment at

Various ELs

Efficiency

Level Description Calculation Method

EL 0 (baseline) Single

- stage c ompressor;

constant- speed indoor fan Linear fit of net capacity (NetCap) and compressor power (Pc) to ambient temperature. Apply load fraction (LF) and cyclic degradation (CD).

EL 1 Single

- stage compressor;

constant- speed indoor fan Linear fit of net cap acity (NetCap) and compressor power (Pc) to ambient temperature. Apply load fraction (LF) and cyclic degradation (CD).

EL2 Two equal- sized fixedspeed compressors;

constant- speed indoor fan Linear fit of net capacity (NetCap) and compressor power (Pc) to ambient temperature. Apply load fraction (LF) and cyclic degradation (CD) to stage 1. If stage 1 is unable to meet load, stage 2 is added. Only LF, not CD, is applied to stage 2.

EL3 Two equal- sized fixedspeed compressors;

variable

- speed indoor fan Linea r fit of net capacity (NetCap) and compressor power (Pc) to ambient temperature. Apply load fraction (LF) and cyclic degradation (CD) to stage 1. If stage 1 is unable to meet load, stage 2 is added. Only LF, not CD, is applied to stage 2.

EL4 Two unequal

- sized compressors; 3

- stage operation: first compressor followed by second compressor, then both

compressors; variable

speed indoor fan Linear fit of net capacity (NetCap) and compressor power (Pc) to ambient temperature. Assume the same linear fit

for each independently operating compressor. Apply load fraction (LF) and cyclic degradation (CD) when only 1 compressor is on. Only LF is applied when both

compressors are on.



### The load fraction (LF) is the building load for one hour multiplied by the ratio of t he net capacity at 95 °F to the net capacity at the actual ambient temperature at that hour. In other words, the LF is the fraction o f the hour the last- stage compressor needs to run to cool the building and compensate for the fan energy. The coefficient of degradation (CD) equals -0.13 x

LF + 1.13.

7-7

### Table 7.2.2 shows the calculated equations as functions of outdoor dry-bulb temperature (T DB 

### ) for the parameters of the 7.5-ton CUAC equipment as applied to the simulated loads. The equipment for EL2, EL3, and EL4 has multiple stages. The equations differ depending on the number of stages that are operating.

Table 7.2.2 Calculated Equations for 7.5- Ton Equipment

Efficiency

Level Net Capac ity (BTU/h) Compressor Power (W) Condenser

Fan Power (W) Indoor Fan Power (W) Control

Power

(W)

EL 0 122985.509

- 343.683 × T DB 927.645 + 63.197 × T DB 775.1 827.4 17

EL1 144651.652

- 527.517 × T DB 905.559 + 61.059 × T DB 667 775.875 17

EL2 .1 59581.333

- 112.833 × T DB 596.667 + 21.667 × T DB 608.17 642.5 20

EL2 .2 110692.333

- 225.667 × T DB 1763.333 + 43.333 × T DB 587.2 642.5 24

EL3 .1 92224.174

- 642.353 × T DB 27.522 + 32.940 × T DB 352.0 If ( T DB <68, 207,

- 225.123 + 6.807 × T DB ) 20

EL3 .2 206316.161

- 1284.707 × T DB -669.516 + 65.879 × T DB 702.7 If ( T DB <68, 207,

- 225.123 + 6.807 × T DB ) 24

EL4 .1 127876.333

- 1311.667 × T DB -1565.000 + 45.000 × T DB 177 68 55

EL4 .2 248966.504

- 2184.080 × T DB -5814.561 + 113.393 × T DB 297 -289.270 + 5.448 × T DB 55

EL4 .3 423122.915

- 3495.746 × T DB -8562.371 + 158.393 × T DB 443 -289.270 + 5.448 × T DB 55



### 7.2.2.2 Modeling of 15-Ton CUAC Equipment

Table 7.2.3 and Table 7.2.4 describe the ELs and equations for 15 -ton CUAC equipment. 15-ton equipment involves two or three compressor stages at every EL.

7-8

### Table 7.2.3 Description of Equipment and Calculation Methods for 15

- ton Equipment at

Various ELs

Efficiency

Level Description Calculati on Method

E L0 (baseline) Two equal

- sized, fixed

speed compressors;

constant- speed indoor fan ;

constant- speed condenser

fan Linear fit of net capacity (NetCap) and compressor power (Pc) to ambient temperature. Apply load fraction (LF) and cyclic degradation (CD) to stage 1. If stage 1 is unable to meet load, stage 2 is added. Only LF, not CD, is applied to stage 2.

EL 1 Two equal

- sized, fixed

speed compressors;

constant- speed indoor fan ;

constant- speed condenser

fan Linear fit of net capacity (NetCap) and c ompressor power (Pc) to ambient temperature. Apply load fraction (LF) and cyclic degradation (CD) to stage 1. If stage 1 is unable to meet load, stage 2 is added. Only LF, not CD, is applied to stage 2.

EL 2 Two equal-sized, fixedspeed compressors; two

co nstant- speed indoor fans ;

constant- speed condenser

fan Linear fit of net capacity (NetCap) and compressor power (Pc) to ambient temperature. If stage 1 is unable to meet load, stage 2 is added. Apply load fraction (LF) and cyclic degradation (CD) to stage 1 ; o nly LF, not CD, is applied to stage 2. Indoor fans run simultaneously. Condenser fans run simultaneously.

EL3 Two equal -sized, fixedspeed compressors; two

variable

- speed indoor fans;

two

- stage condenser fan Linear fit of net capacity (NetCap) and com pressor power (Pc) to ambient temperature. If stage 1 is unable to meet load, stage 2 is added. Apply load fraction (LF) and cyclic degradation (CD) to stage 1 ; o nly LF, not CD, is applied to stage 2. Indoor fan power (Pif)

constant below Tdb = 68 °F. Linear fit above that

temperature. Indoor fans run simultaneously. Condenser fans run simultaneously in two stages.

EL4 Single

- circuit, tandem

, unequal- sized compressors; two variable

- speed indoor fans; three

- stage condenser

fan Linear fit of net capacity (Ne tCap) and compressor power (Pc) to ambient temperature. If stage 1 small

compressor is unable to meet load, stage 2 large compressor is run . If stage 2 is unable to meet load, stage 3 is set by running both compressors simultaneously . Apply load fraction ( LF) and cyclic degradation (CD) to stages 1 and 2 ; o nly LF, not CD, is applied to stage 3. Indoor fan power (Pif) constant below Tdb = 68 °F. Linear fit above that temperature. Indoor fans run simultaneously. Condenser fans run simultaneously in three stag es.



### 7-9

### Table 7.2.4 Calculated Equations for 15- Ton Equipment

Efficiency

Level Net Capacity (BTU/h) Compressor Power (W) Condenser

Fan Power (W) Indoor Fan Power (W) Control

Power

(W)

EL0 .1 117432.152

- 345.372 × T DB 1445.449 + 51.932 × T DB 1494.100 1271.025 16.047

EL0 .2 237731.514

- 690.743 × T DB 3774.821 + 103.864 × T DB 1494.100 1271.025 20.700

EL1 .1 117963.541

- 333.994 × T DB 1376.236 + 51.227 × T DB 1116.000 1172.125 16.047

EL1 .2 237859.169

- 667.989× T DB 3679.607 + 102.454 × T DB 1116.000 1172.125 20.700

EL2 .1 143521.939

- 584.719 × T DB 1557.668 + 51.225 × T DB 839.563 1103.890 16.047

EL2 .2 288512.667

- 1169.439 × T DB 3152.188 + 102.450 × T DB 839.563 1139.320 20.700

EL3 .1 98877.313

- 23.722 × T DB 1115.2 86 + 58.329 × T DB 685.0 If ( T DB <68, 507,

9.120 + 7.530 × T DB ) 16.047

EL3 .2 181813.159

- 47.445 × T DB 1505.092 + 116.658 × T DB 914.000 If ( T DB <68, 507,

9.120 + 7.530 × T DB ) 20.700

EL4 .1 98757.855

- 278.582 × T DB minimum of (

- 23.180 +

36.587 × T DB , 0) 494.000 If ( T DB <65, 376,

- 670.077 + 16.519 * T DB ) 84.0

EL4 .2 161042.867

- 437.377 × T DB -1297.863 + 90.547 × T DB 742.000 If ( T DB <65, 376,

- 670.077 + 16.519 * T DB ) 84.0

EL4 .3 246466.937 +

- 715.959 × T DB - 855.427 + 127.135 × T DB 989.000 If ( T DB <65, 376,

- 670 .077 + 16.519 * T DB ) 84.0



### 7.2.2.3 Modeling of 30-Ton CUAC Equipment

All the ELs for the 30-ton CUAC equipment have four scroll compressors on two circuits. EL0, EL1, and EL2 are two

- stage systems associated with constant-speed indoor fans.

EL0 has one single -speed, single-stage outdoor fan. EL1 has two single -speed outdoor fans operated in two stages. EL2 and EL3 have four stages and a variable -speed indoor fan. EL2 and

EL3 have four single-speed outdoor fans operated in two stages. EL4 has six single

- speed outdoor fans operated in four stages.

Table 7.2.5 and Table 7.2.6 describe the ELs and equations for 30-ton CUAC equipment. 30-ton equipment involves two or three compressor stages at every EL.

7-10

### Table 7.2.5 Description of Equipment and Calculation Methods for 30

- ton Equipment at

Various ELs

Efficiency

Level Description Calculation Method

EL0 (baseline) Four equal-sized, fixed-speed compressors operated in two

stag es; two constant- speed indoor fans; two constant-speed

outdoor fans Linear fit of net capacity (NetCap) and compressor power (Pc) to ambient temperature. If stage 1 is unable to meet load, stage 2 is added. Apply load fraction (LF) and cyclic degradation ( CD) to stage 1 ; only LF, not CD, is applied to stage 2. Indoor fans run simultaneously. Condenser fans run simultaneously in two stages.

EL1 Four equal-sized, fixed-speed compressors operated in two

stages; two constant

- speed indoor fans; two constant-speed

outdoor fans Linear fit of net capacity (NetCap) and compressor power (Pc) to ambient temperature. If stage 1 is unable to meet load, stage 2 is added. Apply load fraction (LF) and cyclic degradation (CD) to stage 1 ; only LF, not CD, is applied to stage 2. Indoor fans run simultaneously. Condenser fans run simultaneously in two stages.

EL 2 Four equal-sized, fixed-speed compressors operated in two

stages; two constant

- speedindoor fans; four constantspeed outdoor fans Linear fit of net capacity (NetCap) and compressor power (Pc) to ambient temperature. If stage 1 is unable to meet load, stage 2 is added. Apply load fraction (LF) and cyclic degradation (CD) to stage 1 ; only LF, not CD, is applied to stage 2. Indoor fans run simultaneously. Condenser fans run simultaneously in two stages.

EL3 Based on EL2 plus a variable

speed indoor blower; compressor operates in four

stages Linear fit of net capacity (NetCap) and compressor power (Pc) to ambient temperature. Indoor fan power (Pif) constant for first stage; linear fit with temperature above that. If stage 1 is unable to meet load, subsequent stages are added. Apply load fraction (LF) and cyclic degradation (CD) to stage 1 ; only LF, not CD, is applied beyond stage 1. Condenser fans run simultaneously in tw o stages.

EL4 Based on EL3 but with six outdoor fans operating in four

stages Linear fit of net capacity (NetCap) and compressor power (Pc) to ambient temperature. Indoor fan power (Pif) constant for first stage; linear fit with temperature above that. If stage 1 is unable to meet load, subsequent stages are added. Apply load fraction (LF) and cyclic degradation (CD) to stage 1 ; only LF, not CD, is applied beyond stage 1. Condenser fans run simultaneously in four stages.



### 7-11

### Table 7.2.6 Calculated Equations for 30- Ton Equipment

Efficiency

Level Net Capacity (BTU/h) Compressor Power (W) Condenser

Fan Power (W) Indoor Fan Power (W) Control

Power

(W)

EL0 .1 265285.091

- 931.394 × T DB 1865.195 + 93.691 × T DB 4052.667 4579.1 199

EL0 .2 488939.755

- 1862.788 × T DB 8645.451 + 187.382 × T DB 3820.000 4579.1 67

EL1 .1 263309.540

- 865.718 × T DB 1996.483 + 94.282 × T DB 1765.0 4443.0 199

EL1 .2 493406.060

- 1731.435 × T DB 8428.729 + 188.564 × T DB 3313.0 4443.0 67

EL2 .1 275458. 577

- 955.357 × T DB 2026.693 + 91.105 × T DB 1119.810 4443.0 199

EL2 .2 512342.957 +

- 1910.714 × T DB 8956.395 + 182.209 × T DB 2331.630 4443.0 67

EL3 .1 144563.535

- 531.824× T DB minimum of (0, 1260.079 + 43.300

× T DB ) 1118.925 1839.210 250

EL3 .2 276119.033

- 983.238 × T DB 1897.460 + 94.396 × T DB 1118.925 - 3818.060 +

86.565 × T DB 250

EL3 .3 372354.679

- 1278.137 × T DB 1676.031 + 174.565 × T DB 2331.630 - 3818.060 +

86.565 × T DB 250

EL3 .4 482780.770

- 1599.533 × T DB - 250.892 + 279.128 × T DB 2331.630 - 3818.060 +

86. 565 × T DB 250

EL4 .1 146357.810

- 536.064 × T DB minimum of (0, 1209.806 + 43.485

× T DB ) 872.076 1042.246 250

EL4 .2 268626.142 +

- 952.497 × T DB 1768.857 + 93.981 × T DB 1003.000 - 5806.649 +

100.741× T DB 250

EL4 .3 376527.857

- 1281.478 × T DB 1189.700 + 169.438 × T DB 1602.105 - 5806.649 +

100.741× T DB 250

EL4 .4 473718.131

- 1563.307 × T DB - 576.808 + 264.766 × T DB 2466.602 - 5806.649 +

100.741× T DB 250



### 7.2.2.4 Energy Consumption

The hourly energy consumption was calculated for each hour as the number of CUAC units multip lied by the power consumption derived from the equations for the components listed in the preceding tables. Powers for the compressor and condenser fan (where appropriate) were multiplied by LF and CD, much like the process used in the AHRI rating standard, to account for loading and cycling effects. The controls power was assumed to be constant for each stage. The indoor fan power was multiplied by the fraction of an hour of fan operation but not by LF or CD.

7.2.3 Energy Decrements

DOE calculated the energy r eduction provided by operating higher- efficiency CUAC equipment relative to baseline equipment as the difference in energy consumption for each hour.

7-12

### The savings in cooling energy represent the combined energy savings of the compressor and the

condenser fa n. The fan and controls energy decrements are the difference between the energy use of the efficiency level and the baseline. The calculated hourly energy decrements are used in the

calculations of operating costs , as described in chapter 8

.

7.2.4 Results

Provided here are results for the baseline and four ELs analyzed for the 7.5-, 15-, and 30- ton CUAC equipment. Results are presented for the both the annual energy required for cooling and the annual energy use required for the ventilation or supply fan.

Figure 7.2.5 and Figure 7.2.6 show the annual cooling and fan energy use results, respectively, for 7.5-ton CUAC equipment. Because results for all 1,033 buildings are being

re ported, buildings are ordered from highest to lowest annual energy use. Energy use scales with building size, so very large annual energy use corresponds to large building floor area. As the range of building size and annual energy use is very high, a log arithmic scale is used for the y

axis in each figure to report energy use. The primary purpose of the figures is to demonstrate that as EL increases, the annual energy use for both cooling and fan operation decrease as well. In the case of EL4, a 3

- stage compressor system and a variable-speed fan are used to improve efficiency. The EL4 design has a more pronounced impact on improving part- load performance than the other ELs. As a result, the impact of EL4 on reducing annual energy use is more dependent on how often the CUAC equipment operates in part-load. As a result, the reduction in the annual energy use is more variable than the other ELs.

Figure 7.2.7 and Figure 7.2.8 show the annual cooling and fan energy use results, respectively, for 15 -ton CUAC equipment. Figure 7.2.9 and Figure 7.2.10 show the same for 30- ton CUAC equipment.

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### Figure 7.2.5 Annual Cooling Energy Use by Efficiency Level for 7.5

- ton Equipment

Figure 7.2.6 Annual Fan Energy Use by Efficiency Level for 7.5

- ton Equipment 100 1000 10000 100000 1000000 10000000Building Energy Use for Cooling (kWh/yr)

Building ID (from highest to lowest energy use) Baseline

EL 1

EL 2

EL 3

EL 4

100 1000 10000 100000 1000000 10000000Building Fan Energy Use (kWh/yr)

Building ID (from highest to lowest energy use) Baseline

EL 1

EL 2

EL 3

EL 4



### 7-14

### Figure 7.2.7 Annual Cooling Energy Use by Efficiency Level for 15- ton Equipment

Figure 7.2.8 Annual Fan Energy Use by Efficiency Level for 15- ton Eq uipment 1000 10000 100000 1000000 10000000Building Energy Use for Cooling (kWh/yr)

Building ID (from highest to lowest energy use) Baseline

EL 1

EL 2

EL 3

EL 4

1000 10000 100000 1000000 10000000Building Fan Energy Use (kWh/yr)

Building ID (from highest to lowest energy use) Baseline

EL 1

EL 2

EL 3

EL 4



### 7-15

### Figure 7.2.9 Annual Cooling Energy Use by Efficiency Level for 30- ton Equipment

Figure 7.2.10 Annual Fan Energy Use by Efficiency Level for 30- ton Equipment 1000 10000 100000 1000000 10000000Building Energy Use for Cooling (kWh/yr)

Building ID (from highest to lowest energy use) Baseline

EL 1

EL 2

EL 3

EL 4

1000 10000 100000 1000000 10000000Buidling Fan Energy Use (kWh/yr)

Building ID (from highest to lowest energy use) Baseline

EL 1

EL 2

EL 3

EL 4



### 7-16

### 7.3 GENERALIZED BUILDING SAMPLE

DOE constructed a “generalized building sample” to represent the installation conditions for the equipment covered in this rulemaking. Only floor space cooled by the covered equipment

is incorporated into the sample. Conceptually, the main difference between the generalized building sample (GBS) and the sample of specific commercial buildings compiled in CBECS is that the GBS aggregates all building floor space associated with a particular set of building

charact eristics into a single category.

7.3.1 Data Sources

The generalized building sample was developed based on data from the U.S. Energy Information Administration’s (EIA’s) 2003 C ommercial Buildings Energy Consumption Survey (CBECS) and from the Commercial Demand Module of the National Energy Modeling System (NEMS) version distributed with EIA’s Annual E nergy Outlook 2013. 4

, 5

, 6



### The amount of floor space allocated to each category for buildings built in or before 2003

was taken from the 2003 CBECS. CUAC

- cooled commercial floor space was estimated by taking the CBECS entries with main cooling equipment identif ied as “packaged cooling.” To update the building floor space to 2013, the commercial building data included with the 2013 version of NEMS were used. This dataset includes a historical component, starting in 2004, and provides both existing floor space and new floor space additions by year, census division and building activity.

CBECS and NEMS differ somewhat in their estimate of the total building stock. In order to have a consistent baseline, DOE adjusted the NEMS floor space estimates to coincide with the CBECS estimate of total building stock. DOE projected 2004 floor space by Census division and building activity through linear regression of previous CBECS floor space data (1979, 1983, 1986, 1989, 1992, 1995, 1999, 2003). By comparing the CBECS- bas ed projections of 2004 floor

space and historical 2004 NEMS floor space, DOE defined correction factors as a function of Census division and building activity. The cross product of the Census division and building activity correction factors were applied to NEMS data for 2004 – 2013, so the combined data set

uses CBECS as a baseline.

7.3.2 Commercial Building Characteristics

The set of characteristics that are used to define a category include all building features that are expected to influence either the cooling load and energy use, or the energy costs. The set of building characteristics, and the specific values these characteristics can take, are listed in

Table 7.3.1

.

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### Table 7.3.1 Characteristics and Associated Values Used to Define the Generalized Building Sample

Characteristic Number of Values Range of Values

Region 10 9 C ensus divisions with Pacific sub

- divided into North

and S outh

Building Activity 7 A ssembly, E ducation, F ood S erv ice, Small Office, Large

O ffice, M ercantile, W arehouse

Size (based on annual energy consumption) 3 S mall: < 100,000 kWh/yr Medium: 100,000 to 1,000,000 kWh/yr

L arge: > 1,000,000 kwh/yr

Vintage (year of construction) 3 C ategory 1: before 1950 Category 2: 1950-1979

C ategory 3: 1980 and later



### The region in which the building is located affects both the cooling loads (through the weather) and the cost of electricity. The building activity affects building schedules and occupancy, which in turn influence the demand for cooling and internal loads. The building activity categories used here are those defined in the NEMS commercial building energy demand module, limited to those building types that use the equipment covered in this rule. The building size influences the cost of electricity, because larger facilities tend to have lower marginal prices. The building vintage may influence shell characteristics that can affect the cooling loads. The combination of 10 regions, 7 building types, 3 sizes and 3 vintages leads to a set of 630 independent categories in the GBS. Tables 7.3.2 -7.3.5 summarize various characteristics of

CUAC

- cooled commercial floor space in each building category

.

Table 7.3.2 summarizes commercial f loor space by Census division. The Pacific division is separated into North (WA and OR) and South (CA) sub-divisions based on climate zone, asdefined in 2003 CBECS Floor space in climate zones 1, 2, and 3 are assigned to the North subdivision, and climate zones 4 and 5 are assigned to the South sub-division. The CBECS data set

does not include HI and AK.

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### Table 7.3.2 Floor Space by Region

Census Division or

Region Number Region Floor Space (million sq.ft. ) Percent of Floor Space

1 New England 872 3%

2 Middle Atlantic 4,769 16%

3 East North Central 5,126 18%

4 West North Central 2,567 9%

5 South Atlantic 5,391 19%

6 East South Central 1,537 5%

7 West South Central 3,283 11%

8 Mountain 1,603 5%

9.1 Pacific (North) 1,034 0%

9.2 Pacific (South) 2,738 4%

Source: CBECS 2003



### Table 7.3.3 summarizes commercial floor space by primary building activity (PBA)

. The

numerical labels associated with PBA are taken from the NEMS data s et and differ from the numerical labels used in CBECS. Floor space is aggregated based on AEO 2013 building categories.

Table 7.3.3 Floor Space by Building Activity

PBA

Number Primary Building Activity Floo r Space (million sq.ft.) Percent of Total Floor Space * Percent of Package-Cooled

Floor Space *

1 Assembly 3,450 11% 12%

2 Education 4,217 14% 15%

4 Food Service 988 2% 3%

7 Large Office 2,921 8% 10%

8 Small Office 3,361 10% 12%

9 Mercantile 4,190 21% 14%

10 Warehouse 4,232 8% 15%

* Will not sum to 100% of floor space because there are other building activities that were excluded from analysis because they are not expected to use CUAC.

Source: CBECS 2003



### Table 7.3.4 summarizes commercial floor space by annual energy consu mption size bin

and Table 7.3.5 shows the distribution of floor space by size and by region. Size bins were constructed based on annual energy consumption data included in 2003 CBECS. This definition of building size is chosen to be consistent with the way that utility companies assign consumers to tariffs, which is typically based on annual peak electricity demand. Annual electricity

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### consumptions correlates very strongly with annual peak demand, and as only consumption is

included with the AE O commercial floor space data, it is used to segregate buildings by size.

Table 7.3.4 Floor Space by Size (Annual Energy Consumpti on)

Size Bin Annual Energy Consumption (kWh/yr) Floor Space (million sq

. ft

. ) Percent of Floor Space

Small < 100,000 18,856 28%

Medium 100,000 to 1,000,000 24,981 38%

Large > 1,000,000 22,595 34%

Source: CBECS 2003



### Table 7.3.5 Floor Space Distribution by Size and Region

Region Large Medium Small

1 29% 34% 37%

2 34% 35% 30%

3 36% 41% 24%

4 23% 40% 37%

5 39% 35% 26%

6 28% 40% 32%

7 33% 39% 28%

8 33% 40% 27%

9.1 31% 44% 25%

9.2 38% 33% 29%



### Table 7.3.6 summarizes commercial floor space by construction vintage. These values were estimated by combining CBECS 2003 and AEO 2013 data as described above. Additionally, demolition since 2003 is accounted for in the older building stock bins. DOE estimated the rate of demolition based on the floor space attrition parameters published in the

NEMS Commercial Demand Module documentation.

Table 7.3.6 Floor Space by Vintage

Vintage Floor Space (million sq

. ft

. ) Percent of Floor Space

Pre

– 1950 4,490 13 %

1950

- 1979 11,356 32 %

1980

- 20 11 19,878 56 % *

* 1980

– 2011 vintage bin is constructed using both CBECS and scaled AEO data.

Source: CBECS 2003, AEO 2013



### The distribution of floor space by building activity, region and vintage is graphically represented in Figure 7.3.1 . The figure columns show floor space in millions of square feet by vintage category and region. The distribution by PBA is repr esented by the different color

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### stacked bars in each column. Large (> 50,000 square feet) and small office spaces are shown separately. The health care category includes only inpatient facilities; outpatient health care floor space is aggregated into the office category. Warehouse floor space is non- refrigerated only.

Figure 7.3.1 Distribution of Floor Space in 2011 by PBA, Region, and Vintage

7.3.3 Interpolation of Weights across Building Characteristics

Th e weighting used in the life

- cycle cost and payback period analysis (chapter 8) is based on the relative probability that a piece of equipment of given capacity ( a "unit" ) would see the specific operating conditions and energy prices associated with the si ze, region, building activity and vintage of a member of the generalized building sample. These probabilities are determined by estimating the total required cooling capacity for each of the generalized buildings in the sample. This total required capacity is equal to the required capacity per square foot times the

total cooled floor space. The required capacity per square foot is an output of the simulation s of the 1033 buildings that use the covered equipment. This value is calculated for the sample as a whole, and varies somewhat with equipment capacity, as shown in Table 7.3.7 . The table also shows the capacity per square foot separately for warm and cool climates . Here the warm climate

is defined as regions 5 (South Atlantic), 6 (ESC), 7 (WSC) and 8 (Mountain). Although geographically region 8 includes several cool northern states, the population is located primarily

in the warmer southern states.

For building vintages earlier than 2003, the total cooled floor space as a function of region, size, PBA and vintage was estimated in a two-step process. First, the distribution of all

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### floor space as a function of these four variables was calculated by multiplying the total floor space times the percentage factors show in Table 7.3.2 though Table 7.3.6 . Second, the amount of floor space cooled by packaged equipment was estimated by multiplying total floor space times a percentage factor that varies with building activity, shown in t he last column of table

Table 7.3.2 . For vintages after 2004 which use the AEO data, the distribution of all floor space by region, vintage, size and PBA is determined in the same way. To estimate the floor space cooled by package d equipment, the factors defined from the CBECS 2003 data and listed in

Table 7.3.3 were used, as no comparable data were available from the AEO. In developing these floor space estimates, any buildings for which the required capacity was less than 90% of the capacity of the representative unit were removed from the sample.

Table 7.3.7 Required capacity in tons per square foot for different regions and representative unit capacities

Region 7.5 ton 15 ton 30 ton

US 0.00260 0.00273 0.00268

warm 0.00286 0.00298 0.00276

cool 0.00236 0.00252 0.00262



### 7.3.4 Assigning Electricity Load Profiles to Generalized Buildings

Load profiles for each of the 630 generalized buildings were developed from the simulation data described above in Section 7.2. For each capacity category, a subset of the 1,033 buildings was used to develop the cooling energy use profiles. The subset was defined by excluding the buildings with a capacity requirement that is signi ficantly smaller than the capacity of the representative unit. The building capacity lower bound is 5 tons for the 7.5 ton sample, 12.5 tons for the 15-ton sample, and 27 tons for the 30-ton sample. For each GBS type, a weighted average energy use profile was compiled from the simulated building subs et, with the weights defined as the required capacity for the building multiplied by the CBECS building weight . The average was taken over all buildings in the subset that have the same region, building type, size and vintage category as the GBS category. For some of the GBS categories, no simulation data were available. In these cases the weighted

- average energy use profile for the same building type and a nearby region or vintage were used.

Updating the sam ple to 2013 required some additional adjustments to the energy use data. The 1,033 building simulations used TMY2 weather data. 7 

### The TMY2 weather data files were updated to TMY3 in 2008. 8 

### A comparison of the two datasets showed that total annual cooling degree

- days (CDD) have increased by 5% at all locations used in this analysis , as shown in Figure 7.3.2 . The figure shows the cooling degree days (CDD) relative to 65 deg F computed from the TMY3 data vs. the CDD for TMY2. The calculation is done separat ely for day time (8 am to 8pm) and night time hours Each point corresponds to a state; in cases where there are multiple stations in a single state the data are av eraged. As the TMY3 data are considered to be more representative of current weather, the increase in CDD is accounted for by increasing the energy use (for all efficiency levels) by 5% at all locations

. The maximum daily temperatures, which may affect equ ipment sizing, were also compared between TMY2 and TMY3. In this case

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### there is less than a 1% increase in the TMY3 data over TMY2, so no adjustments to equipment sizing were made.

Figure 7.3.2 Comparison of cooling degree days by state for TMY3 and TMY2

Changes to building shell characteristics and internal loads in recent construction can lead to a change in the energy required to meet a given cooling load. The NEMS commercial demand

module accounts for t hese trends by adjusting the cooling energy use by a factor that is a function of region and building activity. These factors can be either less than one indicating an relative decrease in energy consumption for cooling, or positive, indicating an increase. In the generalized building sample, these same factors are used to adjust the cooling energy use for floor space in the generalized buildings sample constructed after 1999 included in vintage category 3 ( Table 7.3.8 ). These factors represent an average expected increase or decrease in required cooling energy, relative to the assumptions encoded in the 1033 simulation sample which was based on CBECS 1995 building characteristics

.

Table 7.3.8 Factors used to represent relative changes in required cooling energy for vintage category 3.

PBA Region

1 2 3 4 5 6 7 8 9.1 9.2

1 0.966 0.889 0.978 0.926 0.888 0.865 0.783 0.91 0.768 0.768

2 0.96 0.603 0.963 0.911 0.873 0.748 0.805 0.915 0.77 0.77

4 1.047 1.029 1.036 1.009 1.018 1.016 1.014 1.03 1.024 1.024

7 0.98 0.913 0.989 0.966 0.883 0.883 0.849 0.945 0.786 0.786

8 0.985 0.934 0.982 0.952 0.896 0.848 0.871 0.92 0.857 0.857

9 1.048 0.952 0.998 1.035 0.953 0.907 0.905 0.952 0.795 0.795

10 0.807 0.872 0.816 0.851 0.852 0.864 0.809 0.762 0.622 0.622



### 7-23

### 7.3.5 CUAC Energy Use Results

Table 7.3.9

- Table 7.3. 11 show the annual energy use results for 7.5-, 15-, and 30-ton CUAC equipment, respective ly, by efficiency level

. Each set of results are based on the GBS that was developed for each CUAC tonnage category. Because each GBS consists of 630 independent categories or records, the energy use being reported for cooling, fan operation, and

the tot al are weighted

- averages across the entire GBS sample. The weighted

- average calculation was performed based on the total capacity assigned to each GBS category. The energy use results are provided by cooling capacity (i.e., ton), square foot, and unit.

The energy use for cooling and fan operation for each of the 630 independent categories in the GBSs are provided in the Microsoft Excel spreadsheet model developed for the life- cycle cost (LCC) and payback period (PBP) analysis. In the spreadsheet model , there are worksheets labeled “7.5 ton,” “15 ton,” and “30 ton” which contain the GBS for each CUAC equipment size. The Microsoft Excel spreadsheet is accessible over the Internet (http://www1.eere.energy.gov/buildings/appliance_standards/buildings/appliance_standards/prod uct.aspx/productid/77).

Table 7.3.9 7.5- ton CUAC Equipment: Annual Energy Use Results by Efficiency Level

Cooling Fan Total Cooling Fan Total Cooling Fan Total
Region EL IEER kWh/ton kWh/ton kWh/ton kWh/sq.ft. kWh/sq.ft. kWh/sq.ft. kWh/unit kWh/unit kWh/unit

U.S. Base 11.4 1,035 709 1,744 2.7 1.8 4.5 7,764 5,318 13,082

U.S. 1 12.9 886 640 1,526 2.3 1.7 4.0 6,647 4,796 11,443

U.S. 2 14.0 853 565 1,418 2.2 1.5 3.7 6,401 4,235 10,636

U.S. 3 14.8 653 244 898 1.7 0.6 2.3 4,899 1,833 6,732

U.S. 4 19.9 331 137 468 0.9 0.4 1.2 2,481 1,029 3,511

Warm Base 11.4 1,240 695 1,935 3.5 2.0 5.5 9,300 5,216 14,516

Warm 1 12.9 1,073 627 1,701 3.1 1.8 4.9 8,051 4,705 12,756

Warm 2 14.0 1,009 554 1,563 2.9 1.6 4.5 7,567 4,155 11,722

Warm 3 14.8 826 255 1,081 2.4 0.7 3.1 6,193 1,911 8,104

Warm 4 19.9 475 142 616 1.4 0.4 1.8 3,559 1,062 4,621

Cool Base 11.4 867 720 1,587 2.0 1.7 3.7 6,500 5,401 11,902

Cool 1 12.9 732 650 1,382 1.7 1.5 3.3 5,491 4 ,872 10,363

Cool 2 14.0 726 573 1,299 1.7 1.4 3.1 5,442 4,301 9,743

Cool 3 14.8 511 236 747 1.2 0.6 1.8 3,834 1,769 5,603

Cool 4 19.9 213 134 346 0.5 0.3 0.8 1,594 1,003 2,597



### 7-24

### Table 7.3.10 15- ton CUAC Equipment: Annual Energy Use Results by Efficiency Level

Cooling Fan Total Cooling Fan Total Cooling Fan Total
Region EL IEER kWh/ton kWh/ton kWh/ton kWh/sq.ft. kWh/sq.ft. kWh/sq.ft. kWh/unit kWh/unit kWh/unit

U.S. Base 11.2 1,000 523 1,523 2.7 1.4 4 .2 15,005 7,845 22,850

U.S. 1 12.2 919 477 1,396 2.5 1.3 3.8 13,785 7,151 20,936

U.S. 2 13.2 797 443 1,240 2.2 1.2 3.4 11,955 6,645 18,600

U.S. 3 14.2 881 222 1,102 2.4 0.6 3.0 13,212 3,324 16,536

U.S. 4 18.4 594 231 825 1.6 0.6 2.3 8,916 3,463 12,379

Warm Base 11.2 1,186 512 1,698 3.5 1.5 5.1 17,784 7,679 25,463

Warm 1 12.2 1,093 467 1,559 3.3 1.4 4.7 16,389 6,999 23,389

Warm 2 13.2 960 434 1,394 2.9 1.3 4.2 14,402 6,511 20,914

Warm 3 14.2 1,034 223 1,257 3.1 0.7 3.8 15,514 3,346 18,860

Warm 4 18. 4 731 242 972 2.2 0.7 2.9 10,960 3,624 14,584

Cool Base 11.2 848 532 1,380 2.1 1.3 3.5 12,718 7,982 20,700

Cool 1 12.2 776 485 1,261 2.0 1.2 3.2 11,643 7,276 18,918

Cool 2 13.2 663 450 1,113 1.7 1.1 2.8 9,941 6,755 16,696

Cool 3 14.2 755 220 975 1.9 0. 6 2.5 11,318 3,306 14,624

Cool 4 18.4 482 222 704 1.2 0.6 1.8 7,234 3,331 10,565



### Table 7.3.11 30- ton CUAC Equipment: Annual Energy Use Results by Efficiency Level

Cooling Fan Total Cooling Fan Total Coo ling Fan Total
Region EL IEER kWh/ton kWh/ton kWh/ton kWh/sq.ft. kWh/sq.ft. kWh/sq.ft. kWh/unit kWh/unit kWh/unit

U.S. Base 10.1 869 913 1,782 2.3 2.4 4.8 26,067 27,404 53,471

U.S. 1 11.6 734 842 1,576 2.0 2.3 4.2 22,030 25,259 47,290

U.S. 2 12.5 663 8 37 1,500 1.8 2.2 4.0 19,881 25,117 44,997

U.S. 3 13.5 620 443 1,063 1.7 1.2 2.8 18,610 13,281 31,892

U.S. 4 15.5 575 304 879 1.5 0.8 2.4 17,264 9,110 26,373

Warm Base 10.1 1,053 889 1,941 2.9 2.5 5.4 31,577 26,659 58,236

Warm 1 11.6 899 819 1,718 2.5 2 .3 4.7 26,982 24,571 51,552

Warm 2 12.5 817 814 1,632 2.3 2.2 4.5 24,521 24,433 48,953

Warm 3 13.5 768 456 1,224 2.1 1.3 3.4 23,039 13,674 36,713

Warm 4 15.5 712 322 1,034 2.0 0.9 2.9 21,370 9,645 31,015

Cool Base 10.1 718 934 1,652 1.9 2.4 4.3 21,533 28,017 49,550

Cool 1 11.6 599 861 1,459 1.6 2.3 3.8 17,956 25,826 43,782

Cool 2 12.5 535 856 1,391 1.4 2.2 3.6 16,063 25,679 41,742

Cool 3 13.5 499 432 931 1.3 1.1 2.4 14,966 12,958 27,924

Cool 4 15.5 463 289 752 1.2 0.8 2.0 13,885 8,669 22,554



### 7-25

### REFERENCES

1. U.S. Department of Energy, Energy Conservation Program for Commercial and Industrial Equipment: Energy Conservation Standards for Commercial Unitary Air Conditioners and Heat Pumps Advance Notice of Proposed Rulemaking , O ffice of Energy Efficiency and Renewable Energy, Editor. 2004: Washington, D.C. http://www.regulations.gov/#!documentDetail;D=EERE -2006- STD -0103-0003

2. U.S. DEpartment of Energy, Technical Support Document: Energy Efficiency Program for Commercial and Industrial Equipment: Commercial Unitary Air Conditioners and Heat Pumps , Office of Energy Efficiency and Renewable Energy, Editor. 2004: Washington, D.C. http://www.regulations.gov/#!documentDetail;D=EERE-2006- STD

0103-0078

3. AHRI Standard 340/360-2007, < www.ahrinet.org >

4. U.S. Energy Information Agency, Annual Energy Outlook 2013, 2013. (Last accessed August, 2013.) < http://www.eia.gov/forecasts/aeo/ >

5. U.S. Energy Information Agency, Commercial Buildings Energy Consumption Surve y, 2003. (Last accessed August, 2013.)

< http://www.eia.gov/consumption/commercial/data/2003/index.cfm?view=microdata >

6. U.S. Energy Information Agency, Assump tions to the Annual Energy Outlook 2013
Commercial Demand Module, 2013.

< http://www.eia.gov/forecasts/aeo/assumptions/pdf/commercial.pdf >

7. National Renewable Energy Labora tory, National Solar Radiation Data Base 1961-1990: Typical Meterological Year 2, < http://rredc.nrel.gov/solar/old_data/nsrdb/1961- 1990/tmy2/ >

8. National Renewable Energy Labora tory, National Solar Radiation Data Base 1991-2005 Update: Typical Meterological Year 3, < http://rredc.nrel.gov/solar/old_data/nsrdb/1991- 2005/tmy3/ >

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