CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CO NTENTS

9.1 INTRODUCTION ........................................................................................................... 9-1

9.2 METHODOLOGY .......................................................................................................... 9-1

9.2.2 Method Overview ................................................................................................ 9-2

9.2.3 Stock Equations ................................................................................................... 9-4

9.2.4 Shipments for New Installations .......................................................................... 9-6

9.3 CUSTOMER CHOICE MODE L ..................................................................................... 9-7

9.3.1 Efficiency Bins

................................ ................................ ..................................... 9-7

9.3.2 Shipments Partition .............................................................................................. 9-8

9.3.3 Calculation of Market Share Probabilities ........................................................... 9-9

9.4 REPAIR/REPLACE DECIS ION MODEL

................................ ...................................... 9-9

9.5 DATA INPUTS ............................................................................................................. 9-10

9.5.1 Historical Shipments .......................................................................................... 9-10

9.5.1.2 Historical Shipped Tonnage ..................................................................... 9-12

9.5.2 Commercial Floor Space and Market Saturations ............................................. 9-13

9.5.2.1 Market Saturations ................................................................................... 9-13

9.5.2.2 Commercial Floor Sp ace Projections ....................................................... 9-16

9.5.3 Historical Data (1999 -2001) for Development of Parameters ........................... 9-17

9.5.3.1 Efficiency Market Share ........................................................................... 9-17

9.5.3.2 Equipment Price ....................................................................................... 9-18

9.5.3.3 Installation Costs ...................................................................................... 9-21

9.5.3.4 Electricity Costs ....................................................................................... 9-23

9.5.3.5 Maintenance Costs ................................................................................... 9-24

9.5.3.6 Repair Costs ............................................................................................. 9-25

9.5.4 Data for Efficiency Level Distribution Projections ........................................... 9-28

9.5.4.2 Equipment Price ....................................................................................... 9-28

9.5.4.3 Installation Costs ...................................................................................... 9-31

9.5.4.4 Electricity Costs ....................................................................................... 9-31

9.5.4.5 Maintenance Costs ................................................................................... 9-34

9.5.4.6 Repair Costs ............................................................................................. 9-34

9.5.5 Equipment Lifetime ........................................................................................... 9-35

9.6 MODEL CALIBRATION ............................................................................................. 9-36

9.6.1 Estimating Customer Choice Model Parameters ............................................... 9-36

9.6.2 Estimating Repair/Replace Model Parameters .................................................. 9-38

9.7 RESULTS ...................................................................................................................... 9-39

9-iLIST OF TABLES

Table 9.2.1 Equipment capacities for CUAC and CUHP equipment classes ......................... 9-1
Table 9.5.1 CUAC Market Saturations ................................................................................. 9-14
Table 9.5.2 CUHP Market Saturations ................................................................................. 9-14
Table 9.5.3 Efficiency Market Share for CUACs (1999 -2001) ............................................ 9-18
Table 9.5.4 Average Equipment Price Per Unit for CUACs (2001$) (2001) ....................... 9-20
Table 9.5.5 Average Eq uipment Price Per Unit for CUACs (2013$) (1999-2001) .............. 9-20
Table 9.5.6 Unit, Ton, and Square Footage Relationships ................................................... 9-21
Table 9.5.7 Average Equipment Price (Per SF) for CUACs (2013$) (1999-2001) .............. 9-21
Table 9.5.8 Weight to EER Relationship .............................................................................. 9-22
Table 9.5.9 Average Installation Cost Per Unit for CUACs (2013$) (1999-2001) .............. 9-22
Table 9.5.10 Average Installation Cost Per SF for CUACs (2013$) (1999-2001) ................. 9-22
Table 9.5.11 UEC Relationship to EER .................................................................................. 9-23
Table 9.5.12 Average UEC Per Unit for CUACs (kWh/year) (1999-2001) ........................... 9-23
Table 9.5.13 Average UEC Per SF for CUACs (kWh/year) (1999-2001) ............................. 9-24
Table 9.5.14 Compressor Costs by Ton (2013$) .................................................................... 9-26
Table 9.5.15 RS Means Material Cost Adjustments (2013$) ................................................. 9-26
Table 9.5.16 Repair Costs for CUAC Units Based on RS Means (2013$)............................. 9-26
Table 9.5.17 Average Repair Cost Per Unit for CUACs (2013$) (1999-2001)...................... 9-27
Table 9.5.18 Average Repair Cost Per SF for CUACs (2013$) (1999-2001) ........................ 9-27
Table 9.5.19 CUHP Efficiency Levels

................................ .................................................... 9-28
Table 9.5.20 Average Customer Price for CUACs (2013$) ................................................... 9-29
Table 9.5.21 CUHP Baseline Manufacturing Cost (2013$) ................................................... 9-29
Table 9.5.22 Average Customer Price (P er Unit) for CUHPs (2013$) ................................... 9-30
Table 9.5.23 Average Customer Price (per SF) for CUACs and CUHPs (2013$) ................. 9-30
Table 9.5.24 Average Installation Cost per Unit for CUACs and CUHPs (2013$) ............... 9-31
Table 9.5.25 Average Installation Cost per SF for CUACs and CUHPs (2012$) .................. 9-31
Table 9.5.26 CUAC UECs ...................................................................................................... 9-32
Table 9.5.27 CUHP Cooling UECs ........................................................................................ 9-32
Table 9.5.28 CUHP Heating UECs ......................................................................................... 9-33
Table 9.5.29 Total UECs by SF for CUAC and CUHPs ........................................................ 9-33
Table 9. 5.30 Average Maintenance Cost per SF for CUACs and CUHPs (2013$) ............... 9-34
Table 9.5.31 Average Repair Cost Per Unit for CUACs and CUHPs (2013$)....................... 9-35
Table 9.5.32 Average Repair Cost Per SF for CUACs and CUHPs (2013$) ......................... 9-35
Table 9.6.1 Consumer choice model calibration parameters ................................................ 9-37
Table 9.6.2 Repair/Replace decision model constraints and calibration parameters ............ 9-39

LIST OF FIGURES

Figure 9.5.1 Historical Shipments Data (Estimated and Actual) ........................................... 9-12 Figure 9.5.2 Historical stock saturations and model fit (orange triangles) for small and large CUACs. ..................................................................................................... 9-15 Figure 9.5.3 S mall CUAC Equipment Price to EER Relationship ........................................ 9-19 Figure 9.5.4 Large CUAC Equipment Price to EER Relationship ........................................ 9-19

9- iiFigure 9.5.5 AEO Historic Price Index .................................................................................. 9-24 Figure 9.5.6 Compressor Costs by Ton (Drillspot, Gra inger) ................................................ 9-25 Figure 9.5.7 Repair Cost by CUAC Capacity ........................................................................ 9-34 Figure 9.7.1 Base case shipment for all CUAC and CUHP equipment classes. ................... 9-40 Figure 9.7.2 Model shipments for small CUACs for the base case and standards cases. ..... 9-41 Figure 9.7.3 Model shipments for large CUACs for the base case and standards cases. ...... 9-42 Figure 9.7.4 Model shipments for very large CUACs for the base case and standards

cases. .................................................................................................................. 9-43 Figure 9.7.5 Total model shipments for all CUAC equipment classes for the base case
and standards cases. ........................................................................................... 9-44 Figure 9.7.6 Shipments- weighted IEER shipments for small CUACs for the base case
and standards cases. ........................................................................................... 9-45 Figure 9.7.7 Shipments-weighted IEER shipments for large CUA Cs for the base case
and standards cases. ........................................................................................... 9-46 Figure 9.7.8 Shipments-weighted IEER shipments for very large CUACs for the base

c ase and standards cases. ................................................................................... 9-47 Figure 9.7.9 Number of units repaired and replaced for small CUACs for the base case
and standards cases. ........................................................................................... 9-48 Figure 9.7.10 Number of units repaired and replaced for large CUACs for the base case
and standards cases. ........................................................................................... 9-49 Figure 9.7.11 Number of units repaired and replaced for very large CUACs for the base
case and standards cases. ................................................................................... 9-50

9- iiiCHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

The U.S. Department of Energy (DOE) analyzes shipments of affected equipment as part of every rulemaking regarding new or amended energy efficiency standards for appliances or commercial and industrial equipment. Estimates of equipmen t shipments are a necessary input
for calculati ng national energy savings (NES) and net present value (NPV) of the investment, which are required to justify potential new or amended energy efficiency standards . Shipments also are a necessary input to the manufacturer impact analysis

. This chapter describes DOE’s method and results of projecting annual shipments for small, large, and very large air -cooled
CUAC and CUHPs

.

To project shipments of small, large, and very large air- cooled CUAC and CUHP, DOE considered specific market segments, the results for which are then aggregated to estimate total shipments. DOE accounted for three market segments: (1) shipments to new construction, (2) replacement shipments going into existing buildings, and (3) existing buildings acquiring new equipment for the first time.

The ship ments models are prepared as Microsoft Excel spreadsheets that are accessible on the Internet ( www.eere.energy.gov/buildings/appliance_standards/ ). The rest of this chapter explains the shipments model in more detail. Section 9.2 describes the methodology that underlies development of the model ; section 9.3 describes the c usto mer choice model used to predict market share probabilities as a function of e ffi ciency; section 9.4 describes the repair/replace decision model that is used to calculate the average number of repairs of broken equipment in a given year; section 9.5 describes the input data requirements and mode l development; section 9.6 brief y discusses the approach used to calib rate the shipments model
and section 9.7 discusses the results in terms of base- case and standards

- case shipments.

9.2 METHODOLOGY

This section outlines the methodology used to develop the shipments model that provides projections of shipments for CUAC and CU HP equipment classes defined in Table 9.2.1

. First, a brief summary of the basic framework is provided in the following section.

Table 9.2.1 Equipment capacities for CUAC and CUHP equipment classes Description Equipment capacity (KBtu/hr) Tonnage (Tonnes/unit)
Small 65

- 135 7.5 Large 135

- 240 15 Very Large 240

- 760 30

9-19.2.2 Method Overview

The basic shipments model is used to calculate the number of units shipped, the stock of functioning (never repaired) equipment, and the the stock of extended- lifetime (repaired once) equipment. Shipments forecasts are calculated for three market segments namely (a) new buildings, (b) existing buildings acquiring equipment for the first time and (c) existing building replacing broken units.

For existing buildings replacing broken equipment, the shipments model uses a stock accounting framework. Given the equipment entering the stock in each year and a retirement function based on the lifetime distribution developed in the LCC analysis, the model predicts how many units reach the end of their lifetime in each year. DOE typically refers to new shipments inte nded to replace retired units as replacement shipments. Such shipments are usually the largest part of total shipments.

For CUAC and CUHP, end of lifetime is generally associated with compressor failure. The customer has the choice of repairing the existing unit by installing a new compressor, thereby extending its life considerably, or purchasing a new unit. A new unit is more expensive than compressor replacement, but it may be more energy-efficient than the existing unit, which means it would have lower operating costs. If standards increase the cost of new equipment, one would expect that the repair option would become more attractive.

To model the repair vs. replacement decision, for the small and large CUAC and CUHP equipment classes DOE used a consumer choice model that estimates customer sensitivity to total installation cost. A sensitivity parameter was calculated using efficiency market share data for years 1999−2001, along with estimates of equipment prices and installation costs by
efficie ncy level. DOE applied this sensitivity to the difference between the total installed cost of a new unit and the repair cost of the existing unit. For the very large CUAC and CUHP equipment classes, at the first failure DOE assumed that all customers repai r the unit because the total installed cost of a new unit is so high. (If a very large unit encounters a second failure in the shipments model, DOE assumed that the customer replaces the unit with a new one.)

The replacement cost at each efficiency level is the total installed cost derived in the LCC analysis. For repair cost, DOE developed its own estimates of the material costs for compressors. (DOE examined RS Means material costs for compressors and concluded that they were inaccurate for all size classes, as several of the estimates exceeded the costs for an entire new unit.) For labor and non- compressor material costs, DOE used data in RS Means Facilities Maintenance & Repair Cost Data, 2013 . Within each equipment class, DOE used repair costs
that in crease in direct proportion with the price of the equipment and with IEER level.

DOE recognizes that the repair or replace decision is not solely a function of the difference between the total installed cost of a new unit and the repair cost of the exist ing unit. The difference in operating costs may also play a role, as may general economic conditions and other factors. DOE did not have sufficient information to incorporate these factors explicitly into

9-2its model, so it developed an alternative approach that assumes that the factors influencing the repair or replace decision will be similar in the future as they were in the past. DOE estimated an historical average repair rate by minimizing the difference between actual historical shipments
and model- pred icted shipments in a “no- repairs” scenario. DOE developed a series for historical shipments using data provided by AHRI in 2001 for the small and large CUAC and CUHP equipment classes for the years 1980 to 2001, combined with Census data on manufacturer
sh ipments as the basis for shipments in earlier and later years, and for very large CUAC and

CUHP.

The repair/replace model is a binary choice model with two parameters, “alpha” and “gamma.” "Alpha" represents customer sensitivity to the efficiency

- weighte d average cost difference between total installed cost of replacement and repair costs. DOE assumed that the "alpha" is equal to the parameter used to represent customer sensitivity to total installed cost in the customer choice model. Since repair costs a re generally lower than total installed costs of equipment, "gamma" is a scenario parameter that limits the number of repairs and can be thought of as representing "unknown replacements." Since "alpha" is assumed to be known, DOE estimated "gamma" by minim izing the difference between the historical average repair rate and the repair probability predicted by the repair/replace model. This approach ensures that the estimated repair rate in each forecast year in the base case is close to the historical average rate. In the standards cases, which have higher installed cost, the repair rate is higher.

If a unit is repaired, its life is extended by another lifetime, based on the retirement function. If a unit encounters its second failure, the model assumes that the customer replaces the
unit with a new one.

For existing buildings acquiring new equipment for the first time, DOE first estimated saturation values (percentages of total floor space served by different cooling capacities or types of equipment) for the stock. CBECS provides overall CUAC and CUHP saturation values. To derive percentages of floor space served by different cooling capacities or types of equipment, DOE used shipments data from the Census. DOE derived the approximate historical floor space saturations for each of the CUAC and CUHP equipment classes by multiplying the CUAC and CUHP saturation values from CBECS by the shipment shares from the Census. DOE used a logistic regression procedure to fit the CBECS historical stock saturations to get a smooth time series of saturation estimates for the analysis period.

Shipments for existing buildings acquiring new equipment for the first time in each future year are estimated by multiplying the difference in projected stock saturation values between the future year and the previous year with the estimated floor space without CUAC and CUHP equipment in the previous year. In other words, the shipments account for the incremental
increase in stock saturation.

For new commercial buildings acquiring equi pment, shipments are estimated by multiplying new construction floor space in each future year by saturation values (percentages of new floor space served by different cooling capacities or types of equipment). The shipments

9-3model relies on AEO 2013 for fo recasts of new construction floor space. It assumes that the saturation value in new commercial buildings is the same as the stock

- average saturation for each

year.

The methods described above provide total shipments in each equipment class for each

year. To estimate the market shares of the considered efficiency levels in future shipments, DOE developed a customer choice model. The model was calibrated by estimating values for two parameters, representing customer sensitivity to total installation cost an d annual operating cost. To calibrate the model, DOE used data on market shares by EER for small and large CUAC equipment classes provided by AHRI for the previous rulemaking. These market shares are for 1999−2001. DOE used the equipment prices by EER level from the 2004 ANOPR to assign equipment prices to each EER bin. The same installation costs and maintenance costs developed for the NOPR were the basis for the installation costs in 1999−2001. DOE derived unit energy consumption (UEC) values for each of the EER bins using the UEC to EER relationships presented in the 2004 ANOPR TSD, and then applied historic electricity prices to calculate annual energy costs.

To estimate values for the parameters, DOE used a non -linear regression approach that
minimize d the sum of the squared difference between historical market shares and the model predicted values at each efficiency level. Starting in 2013, application of the parameters, along with data on the installed cost and operating cost at each efficiency level considering this rulemaking, determines the market shares of each efficiency level.

9.2.3 Stock Equations

The stock equations for either equipment type are used to describe changes in the distribution of stock from year y to year y +1. The total stock U ( y ) in a given year can be separated into functioning stock 

### U F 

### ( y
, v ) , carried over from the previous year and stock whose life has been extended through repairs 

### U X 

### ( y
, v
, y b 

### ) . i.e.

U ( y ) = ∑ U F y

v ( y
, v ) + ∑ ∑ 

### U X � 

### y
, v
, y b � y
y b >v y

v (1)

where:

• v is the vintage of the equipment and corresponds to the year in which a unit was shipped. By definition, the functioning stock of vintage v < y corresponds to all surviving stock from the previous year.

• The age a of stock of vintage v in year y is a = y − v

.

• y b is the year in which the unit breaks. The age of a broken unit a b is then defined as

a b = y b − v . We assume that a b >0 , i.e. stock cannot break down in the same year it was

shipped.

9-4The surviving stock in year y corresponds to functioning stock carried over from the previous year and is given by the expression,



### U

F 

### ( y
, v ) = η F 

### ( y − v ) U F 

### ( y − 1, v )

= η F 

### ( a ) U F 

### ( y − 1, v ) (2)

where:

• 

### η F 

### ( a ) is the “survival function” for functioning stock and represents the fraction of stock of age a that doesn’t break. The “non

- survival” function for functioning stock 

### U F 

### ( y
, v ) is



### 1 − η F 

### ( a ) ; this is the fraction of stock of age 

### a that does break.

• The probability that an equipment will survive up to an age a is given by a Weibull cumulative distribution function,

η ( a ; θ

, λ ) = 1 − e −� a
λ � θ (3)

where θ and λ are shape and scale parameters respectively, that are estimated using th e calibration procedure described in section 9.6.2.

The functioning stock of vintage v = y are just the shipments S ( y ) in year y ,
U

F ( y
, y ) = S ( y ) (4)

where:

• S ( y ) = S N ( y ) + S E ( y ) + S Rep ( y ) ; S N ( y ) corresponds to shipments that go into new floor space, S E ( y ) pertains to shipments that go into existing floor space that did not have the equipment before and S Rep ( y ) are shipments that go to replace broken stock.

To update extended life stock U X ( y
, v
, y b ) we consider several components. The stock of vintage

v (< y ) that breaks in year y is given by � 1

− η F ( a ) �U F ( y − 1, v )

. Broken stock can either get repaired or be replaced. Repairs are proportional to an average repair

rate ρ ′ ( y ) (defined in Section 9.4) and hence become the extended

- lifetime stock of the current

year

U X ( y
, v
, y b ) = ρ ′ ( y ) � 1

− η F ( a ) �U F ( y − 1, v ) for v < y b = y (5)

and the replacements are given by (1 − ρ ′ ( y ) ) � 1

− η F ( a ) �U F ( y − 1, v ) . These replacements

co ntribute to the determination of S Rep ( y )

. The surviving stock of equipment that have undergone life

- extending repairs carried over from the previous year is given by

U

X ( y
, v
, y b ) = η X ( y − y b ) U X ( y − 1, v
, y b ) for v < y b < y (6)

where:

• η X ( a b ) is the survival function for extended

- life stock U X ( y
, v
, y b ) and its complement

1 − η X ( a b ) represents the non-survival function.

9-5The “non

- surviving" extended stock (1 − η X ( y − y b )) U X ( y − 1, v
, y b ) corresponds to
extended

- life stock that breaks down again and hence are due for replacement. These also

contribute to the determination of 

### S Rep 

### ( y ) . Based on this accounting, shipments for replacements in year y is given by

S

Rep ( y ) = ∑ (1 − ρ ′ ( y ) ) � 1

− η F ( a ) �U F ( y − 1, v ) + v<y

v ∑ ∑ 

### (1 − η X 

### ( y − y b 

### )) U X � 

### y − 1, v
, y b � y b <y

y b v<y

v (7 )

9.2.4 Shipments for N ew Installations

In order to calculate total shipments S ( y ) in year y , we need to determine shipments to new installations that we define as shipments to new floorspace ( S N

( y ) ) plus shipments to

existing floorspace that did not previously have the equipment ( S E (

y ) ). The model equations for

these shipments are based on time series of floorspace and saturations, which are assumed to be
known. In year y , the total floorspace cooled by equipment of capacity k is x k μ( y ) F E (

y ).

Here,

• F E (

y ) is the existing floor space in year y ,

• μ is the CBECS market saturation in year y and represents the percentage of floorspace cooled by all capacities of the equipment, and

• x k is the fraction of the total floor space cooled by the equi pment of capacity k and is

defined as

x k = sf k 

### ∑

k sf k . (8)

where

sf k

= n k c k

r

k (9)

is the total number of square feet associated with each equipment class k . In the above equation, n k is the average number of units shipped per year, c k is the capacity in tons and

r

k

is the required tons per square foot of capacity for equipment of capacity k

. With regard to the above definitions, the number of units shipped to new floorspace is equal to

S

N

( y ) = x k r

k

c k μ( y ) F N

( y ) (10)

9-6where F N

( y ) corresponds to new floor space added in year y . The number of units shipped to

existing floor space that did not have the equipment before is determined by considering the growth in saturation. From year y −1 to year y , the increase in floorspace is

F

E (

y )= F

E (

y −1)+ F

N

( y )− F

D ( y ) (11)

where F D ( y ) is floor space demolished in year y . The stock in existing buildings is proportional

to μ( y ) F E (

y ); equivalently

μ(

y ) F E (

y ) = μ( y ) F E (

y −1) +μ ( y ) F N

( y ) −μ ( y ) F D ( y )

= {μ( y −1) F E (

y −1) +[μ ( y ) −μ ( y −1)] F E (

y −1)}

Error! Bookmark not defined. + μ( y ) F N

( y ) −μ ( y ) F D ( y )

(12)

The last term represents units taken out of the stock with demolished buildings and can be ignored. The middle term represents shipments to new buildings which are already accounted for in equation (10). The first term has two parts, the first representing stock carried over from the previous year and the second an increment due to the increase in saturation. This increment is
what we define as S E (

y ) ; hence

S

E (

y ) = x k r

k

c k [μ ( y ) −μ ( y −1)] F E (

y −1) (13)

9.3 CUSTOMER CHOICE MODEL

The Consumer Choice model developed by the Department comprises of a logit model that is used to generate market share probabilities corresponding to predefined efficiency bins. The probabilities are then used to partition the total shipments in a given year into these efficiency bins whose boundaries represent minimum allowed e fficiencies for that particular equipment type and class. This section outlines the steps involved in the calculation of these probabilities.

9.3.1 Efficiency Bins

For years y ≥ y std , the shipments are partitioned into efficiency bins labelled j = 0... M (curr ently M =4). The bin boundaries represent the efficiency levels used in the analysis; these are hypothetical minimum allowed efficiencies. The fraction of shipments that fall into each bin is determined by a consumer choice model, described below. However b efore we get into the details of the consumer choice model, a short summary on notation is provided below first.

9-7• EL j is the efficiency level j for j = 0... M . The base case EL 0 corresponds to the current

market minimum efficiency.

• z is the efficiency of a given piece of equipment.

• Bin j includes all efficiencies z in the range EL j −1 ≤ z ≤ EL j .

• z j is the average equipment efficie ncy for bin j and L is the efficiency level under

consideration.

• p L

, j (

y ) is the fraction of shipments that fall into efficiency bin j when the minimum

efficiency is set at level L

.

• S L

,

j (

y ) is the total number of shipment s that fall into efficiency bin j when the minimum

efficiency is set at level L

.

9.3.2 Shipments P artition

By definition,

p L

, j (

y ) = d 0 for j < L

, an (14)



### ∑

j p L

, j (

y ) = 1 (15)

The probabilities are assumed to satisfy

p L

, j 1

p L

, j 2 = h ( j 1, j 2) (16)

when both probabilities are non

- zero. This means that the ratio of shipments falling into bins j 1
and j 2 remains the same under all standard levels. Hence, in any given year, under a change in L the probabilities are simply renormalized. The probabilities also evolve over time due to changes in the parameters that go into the consumer choice model.

We assu me that the partition of shipments into efficiency bins is independent of where the shipments are going, i.e. the probability of purchase of higher efficiency equipment is the same for new installations and replacements. For a given efficiency level L , we then partition total shipments S ( y ) as

S L

,

j (

y )= p L

, j (

y ) S ( y ) (17)

9-89.3.3 Calculation of M arket S hare P robabilities

As described earlier, the consumer choice model to estimate market share probabilities

p L

, j (

y ) is essentially a logit model and is defined in terms of a utility function W ( y ) as

p L

, j (

y ) = e W j (

y )



### ∑

j ≤ L [ e W j (

y ) ] (18)

where

W j ( y ) = α TIC j ( y ) + β OC j ( y )

. (19)

Here

• W j (

y ) is the utility function in year y corresponding to bin j

• TIC j (

y ) is the total installed cost in year y averaged over bin j

• OC j (

y ) is the annual operating cost in year y averaged over bin j

• α is a sensitivity parameter for installed cost (up front costs)

• β is a sensitivity parameter for operating costs (continuing costs).

The parameters α and β are determined by a fit to available market share data as described in
Section 9.6.

9.4 REPAIR/REPLACE DECISION MODEL

The repair/replace decision model is a binary logit model that accounts for decisions to extend the life of a broken unit through repairs rather than replacing it. The model provides an estimate of the probability that a broken unit wi ll be repaired ( ρ ) by accounting for the total installed costs and repair costs of the equipment under consideration as well as its lifetime. Specifically for a binary choice, ρ is based on a repair utility function R ( y ) and takes the following form



### ρ ( y ) = e R ( y )

1+e R ( y ) (20)

The repair utility R ( y ) is given by

R ( y ) = γ + κ Δ ( y ) (21)

where :

9-9• Δ( y ) is the efficiency weighted average difference between total installed cost in the
current year y and the cost associated with repairing an older unit based on its lifetime.

• κ is a sensitivity parameter to the cost differential

• γ is a scaling parameter that represents “unobserved replacements” and can be thought to minimize repairs.

Since con sumers decisions regarding purchase of such equipment are primarily first cost drive,

we assume κ = α and treat γ as a scenario variable that limits the total number of repairs by accounting for “unknown” replacements. The procedure used to obtain an es timate of γ is
described in Section 9.6. The above expression for ρ does not take into account the age of the unit when it breaks, a b . In

reality, we expect the repair probability to be higher for relatively newer broken units or lower

values of a b and lower for older units or higher values of a b . To model this effect, we multiply ρ
by the function

f ( a b ) = 1

− ( a b − a 1 )

( a 2 − a 1 ) , a 1 ≤ a b ≤ a 2 (22)

with f ( a b ) =1 for a b < a 1 and f ( a b ) =0 for a b > a 2 . For both CUACs and CUHPs we set a 1 =3 years,

while a 2 depends on the lifetimes of either equipment type and is set equal to 18 years for

CUACs and 15 years for CUHPs. The repair probability is then equal to the product

ρ ′ ( y
, a b ) = f ( a b ) ρ ( y ) (23)

9.5 DATA INPUT S

9.5.1 H istorical Shipments

Historical shipments are critical to the development of the Shipments Model, since DOE used the historical data to calibrate the model. For the years 1980 through 2001 for small and large CUAC and CUHP, DOE used shipments data provided by the Air-Conditioning and Refrigeration Institute (ARI) in 2005. 1 For the remainder of years (1969-1979 and 2002-2010) for small and large CUAC and CUHP and all years for very large equipment , DOE relied upon
the U.S. Census Bureau’s Current Industrial Reports on Refriger ation, Air Conditioning, and Warm Air Heating Equipment

. 2

For the three CUAC equipment classes of interest to this analysis small, large, and very large , the Census Bureau provides shipments data in the following equipment categories (as defined by the Census Bureau): (1) single package air conditioners, with or without evaporator fans, including refrigeration chassis and remote- condenser types; (2) year -round air conditioners,

9-10single package and remote-condenser type (except heat pumps); and (3) split- system air

conditioning condensing units. Within each of the above equipment categories, the shipments data are disaggregated into cooling capacity ranges. The capacity ranges and equipment categories are defined in a way that allowed DOE to aggregate the Census Bureau shipments data into the three equipment classes of interest, with three notable exceptions: (1) the Census Bureau specified a capacity range o f ≥ 135,000 Btu/h to <250,000 Btu/h, rather than the desired range of ≥ 135,000 Btu/h to <240,000 Btu/h (large units) ; (2) the Census Bureau does not specify a high -end of capacity, so all of the highest capacity disaggregations may include units that surpass 760,000 Btu/h (the limit for very large units) ; and (3) the Census Bureau combined

water -cooled and air

- cooled equipment.

With respect to the first issue, DOE used the Census Bureau shipments data in the ≥135,000 Btu/h to <250,000 Btu/h capacity range to represent shipments for the large ( ≥135,000 Btu/h to <240,000 Btu/h) equipment class. With respect to the second issue, DOE assumed that all units in the highest capacity disaggregations (≥ 380,000 Btu/h for single package airconditioners, and ≥640,000 Btu/h for year-round and split system air conditioners) fall into the very large ( ≥240,000 Btu/h to <760,000 Btu/h) equipment class. With respect to the third issue, DOE used the ARI shipments data for 1980-2001 to extract the appropriate percentage of w ater

cooled equipment from the aggregated water-cooled and air-cooled shipments for all other years (i.e. , 98.5% of small units considered air

- cooled and 98% of large units considered air -cooled; DOE also applied the latter to very large units) . DOE also f urther scaled all Census data for the small and large equipment classes downward to account for the difference between ARI and
Census data in the years 1980 through 2001 (resulting in scaling of 90.5% and 84.9%, respectively , for small and large equipment classes)

.

For the three CU HP equipment classes of interest to this analysis (small, large, and very large ), the Census Bureau provides shipments data in the following equipment categories (as defined by the Census Bureau): (1) air source heat pumps (excl uding room air-conditioners), single package; and (2) air source heat pumps (excluding room air-conditioners) , split system. However, for both of these equipment categories, the only capacity range relevant to this rulemaking is 65,000 Btu/h and over. In o rder to disaggregate this category into the three CUHP equipment classes, DOE calculated the average percent of CUHP attributed to each equipment class (capacity range ) for all years available with ARI data . The result wa s 66% small, 12% large, and 21% very large. However, the entire very large class in the years with ARI data was estimated based on the difference between census shipments and ARI reported shipments for
CUHP between 65 ,000 and 250,000 Btu/h. Based on a comparison of this percentage to the

pe rcentage of CUAC units in the very large equipment class, DOE determined that this estimate
was unrealistic . As a result, for all years, DOE calculated shipments to very large CUHP based on shipments of large CUHP times the ratio of CUAC very large to CUAC large shipments.

In some intermediate years with no data (1971-1973, 1976-1979, 1981-1982), DOE used linear interpolation to estimate shipments. The most recent year for which the census reports shipments is 2010. Figure 9.5.1 shows the historical shipments data (estimated and actual) for all CUAC and CUHP equipment classes

.

9-11Figure 9.5.1 Historical Shipments Data (Estimated and Actual)

9.5.1.2 Historical Shipped Tonnage

Historica l shipped tonnage represents the annual amount of cooling capacity (in tons) shipped and is an alternative way to express shipments data. DOE determined the actual shipped tonnage for any given year by multiplying each unit shipped by its associated coolin g capacity or tonnage, and then summing all the tonnage values.

DOE converted shipped tonnage to percent of shipped tonnage for use in the Shipments
Model to establish the CUAC and CU HP market shares attributed to each equipment class. The
market share calculation and its use in projec ting shipments are explained in section 9.5.2.1

. DOE calculated the percent of shipped tonnage for CUAC by dividing the shipped tonnage for each equipment class by the total shipped tonnage for all CUAC equipment. DOE calculated the percent of shipped tonnage for CUHP by dividing the shipped tonnage for each equipment class by the total shipped tonnage for all CUHP equipment.

DOE used shipments data from the U.S. Census Bureau’s Current Industrial Reports on Refrigeration, Air Conditioning, and Warm Air Heating Equipment to derive shipped tonnages - 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000Shipments (Units)

Year

Small CUAC Large CUAC Very Large CUAC

Small CUHP Large CUHP Very Large CUHP

9-12for each of the six CUAC and CU HP equipment classes. The Census Bureau classifies the range of cooling capacities for the entire set of commercial unitary air conditioners from less than 22,000 Btu/h to greater than 640,000 Btu/h. Across this span of cooling capacities, the Census Bureau disaggregated shipments into as many as 16 smaller capacity ranges. For each capacity range, DOE used the mid

- point of the range as the representative capacity for the range a

. Then

DOE determined the shipped tonnage for eac h capacity range by multiplying the representative capacity by the number of shipments in the range. Because the Census Bureau disaggregated shipments into several capacity ranges, DOE was able to establish the annual shipped tonnage
for each of the six CU AC and CU HP equipment classes and the entire set of commercial unitary air conditioners and heat pumps b

.

The ARI shipments data were instrumental in separating water

- cooled air -conditioning and heat pump equipment from air-cooled CUAC and HP equipment. Fu rthermore, the ARI shipments data identified which air conditioners and heat pumps with cooling capacities less than 65,000 Btu/h were commercial equipment and which were residential products. Although DOE
derived CUAC data for the years back to 1969, it did not obtain the necessary shipments data to derive shipped tonnage for the following years: 1971–1973, 1976–1979, and 1981–1982. For CUHP equipment, available data only goes back to 1980, and also excludes 1995-1996. The most recent year for which shipme nts data are reported is 20 10. T he percent of shipped tonnage
for each CUAC and CUHP equipment class does not vary greatly from year to year. As a result, DOE used average fixed values for inputs to the Shipments Model, as shown in section 9.5.2.1

.

9.5.2 Commercial Floor Space and Market Saturations

9.5.2.1 Market Saturations

DOE defined market saturation, MKT_SAT(y) , for each class of commercial unitary equipment as the ratio of existing cooled floor space, by class, to all existing floor space, EFS(y)

. It is one of the constraints DOE used to calibrate the Shipments Model.

To calculate market saturation for a particular class of unitary equipmen t, DOE first calculated the market saturation for all CUAC equipment and a market saturation for all CUHP equipment. DOE used CBECS data from 1992, 1995, 1999, and 2003 to determine the commercial floor space cooled by CUAC, CUACFS(y) , and space cooled by CUHP, CUHPFS(y) , as well as the total amount of existing floor space, EFS(y)

. 3

a For capacity ranges defined as less than a given capacity value (e.g., less than 22,000 Btu/h), DOE used 90 percent of the capacity as the representative capacity (e.g., 22,000 Btu/h @ 90% = 19,800 Btu/h). For capacity ranges defined as greater than a given capacity value (e.g., 640,000 Btu/h and over), DOE used 110 percent of the capacity as the representative capacity (e.g., 640,000 Btu/h @ 110% = 704,000 Btu/h).

b The entire set of commercial unitary air conditioner and heat pumps represents both air

- co oled and water

- cooled equipment and all cooling capacity ranges, from less than 22,000 Btu/h to greater than 640,000 Btu/h.

9-13Then, to estimate the MKT_SAT for a particular class of commercial unitary equipment, DOE used the market share, or percent, of unitary equipment of a particular class. DOE us ed the above shipments data to estimate the market share, MKTSHARE(y) , of each equipment class. Because the percent of shipments in each class is relatively constant over time and the lifetime of
each class is the same, the ratio of shipments to a class is indicative of the market share for that
class. DOE estimated the MKTSHARE for a particular class to be a constant value equal to the average of the yearly percent of shipments to that class (from available years 1 969 through 2010). From these data , DOE calculated the MKT_SAT(y) for each commercial unitary air conditioning class using the following formula.

MK T SAT ( y ) = CUACFS ( y )

EFS ( y ) ∗ MKTSHARE

DOE used a corresponding equation to find MKT_SAT(y) for each CUHP class using CUHPFS(y) in place of CUACFS(y)

.

Table 9.5.1 summarizes the CUAC market saturations for 1992, 1995, 1999, and 2003,
which are the years for which CUACFS data are available in CBECS. Table 9.5.2 summarizes
the CUHP market saturations for the same years

.

Table 9.5.1 CUAC Market Saturations

Year CUAC

Market
Saturation (CBECS) Small CommercialPackaged AC (AirCooled) - ≥65,000 Btu/h

and < 135,000 Btu/h Large CommercialPackaged AC (AirCooled) - ≥135,000 Btu/h and < 240,000
Btu/h Very Large Commercial Packaged AC (Air

- Cooled) - ≥240,000 Btu/h and <
760 ,000 Btu/h

Market

Share MKT_SAT Market
Share MKT_SAT Market
Share MKT_SAT

1992 21% 24% 4.9% 16% 3.4% 16% 3.3%

1995 24% 24% 5.6% 16% 3.9% 16% 3.8%

1999 32% 24% 7.4% 16% 5.2% 16% 5.0%

2003 51% 24% 12.0% 16% 8.2% 16% 8.1%

Table 9.5.2 CUHP Market Saturations

Year CUHP

Market
Saturation (CBECS) Small Commercial Packaged HP (Air

Cooled) - ≥65,000 Btu/h

and < 135,000 Btu/h Large Commercial Packaged HP (Air

Cooled) - ≥135,000 Btu/h and < 240,000
Btu/h Very Large Commercial Packaged HP (Air

- Cooled) - ≥240,000 Btu/h and < 760,000 Btu/h

Market

Share MKT_SAT Market
Share MKT_SAT Market
Share MKT_SAT

1992 5.1% 3.4% 0.15% 1.2% 0.05% 1.0% 0.04%

1995 6.0% 3.4% 0.18% 1.2% 0.06% 1.0% 0.05%

1999 5.6% 3.4% 0.16% 1.2% 0.06% 1.0% 0.05%

2003 7.2% 3.4% 0.24% 1.2% 0.08% 1.0% 0.07%

9-14Since projections of shipments going into new and existing buildings re quire a near

continuous series of market saturations for each projection year, a time series of floor space saturations was developed for each equipment class by fitting the historical saturations described
in the above tables us ing a logistic regression model based on the utility functions in the previo us rulemaking. Figure 9.5.2 shows the fits to the historical saturations for the small and large CUAC equipment categories. A similar fit was derived for all categories of CUHP equipment as well

.

(a)

(b) Figure 9.5.2 Historical stock saturations and model fit (orange triangles) for small and large CUACs.

9-159.5.2.2 Commercial Floor Space Projections

The amount of commercial floor space is the main driver for CUAC and CUHP equipment shipments and is appropriately one of the basic inputs into the Shipments Model. T he model divides commercial floor space into three components: floor space from new cons truction ( F

N (y) ), existing floor space ( F E (y) ), , and demolished floor space ( F

D (y) ),

.

For this analysis, commercial floor space refers to non- residential buildings that are used primarily for purposes other than industrial or manufacturing.



### Floor Space – New Construction (F N



### (y) )

DOE used new construction floor space prior to 1999 from the previous rulemaking (ANOPR TSD)

. 4 These values are based on U.S. Census Bureau data fo r available years and linear interpolations for the additional years.

For this analysis, DOE obtained new construction floor space for the years 1999-2010 from the Census Bureau publication: Statistical Abstract of the United States: 2012, Table 966. 5

DOE took the projected floor space after the year 20 10 from the National Energy Modeling System (NEMS) projection published in the Annual Energy Outlook 2013 ( AEO20 13 ). 6 AEO20 13 lists the projections for years through 2040. Beyond 2040 , DOE extrapolated NFS from the ten

year trend of NEMS- projected data (2030-2040). The NEMS value in 2003 shows approximately 50 percent more floor space than the historical Census Bureau data in 2003. To match the Census
Bureau data with the NEMS projections, DOE calculated the rate of change in NFS for each NEMS year and applied it to the Census Bureau data, starting in 2010 and stepping forward each

year.



### Floor Space – Existing Stock (F E 

### (y) )

DOE used existing floor space in 1999 and prior from the previous rulemaking. In that analysis, DOE used CBECS data for available years along with linear interpolations for the missing years.

For this rulemaking, DOE obtained the latest CBECS data, for 2003. DOE then developed floor space for the years 2000-2002 by interpolating between the 2003 and 1999 CBECS values. DOE took the projected floor space after the year 2003 from the NEMS projection published in AEO2013 . The projections are listed through 2040. Beyond 2040, DOE extrapolated EFS from the 10 -year trend of NEMS projected data (2030-2040). There is a small
difference between the NEMS data for 2003 and CBECS data in 2003. To match the CBECS data with the NEMS projections, DOE calculated the rate of change in EFS for each NEMS year. DOE then applied this rate to the CBECS data, starting in 2003 and stepping forward each year.



### Floor Space – Demolished (F D 

### (y) )

9-16DOE used the demolition rate through 1999 from the previous rulemaking. This value was approximated as 0.0108 for all years.

For years after 1999, DOE established the demolished floor space from NEMS projections. In addition to predicting 

### F N 

### (y) and 

### F N 

### (y)

, NEMS also predicts surviving square feet ( F

S (y) ) (i.e., square feet in the building stock that are not demolished). The following formula gives the 

### F D 

### (y) for any given NEMS year.



### F

D 

### (y) = F E 

### (y) + F S 

### (y- 1) (25)

After DOE determined the 

### F D 

### (y) for NEMS years (through 2040), it calculated the


### F

D 

### (y) rate per year, 

### F D 

### (y) / 

### F E 

### (y) . For the years 2003 through 2040, DOE used the calculated value for each year , w hich are all in the range of 0.011 to 0.012. For the years 2000-2002, and 2041 through 2048, DOE used the average 

### F D 

### (y) rate for 2003 through 2040, or 0.011.

9.5.3 Historical Data (1999-2001) for Development of Parameters

DOE developed the parameters of the customer choice model using data only for small and large CUAC equipment classes. The parameters developed for the small CUAC equipment class were also used for the small CUHP equipment class. The parameters developed for the large CUAC equipment class wer e also used for the very large CUAC, large CUHP, and very large CUHP equipment classes.

9.5.3.1 Efficiency Market Share

The only efficiency market share data available were provided by AHRI in the previous rulemakingfor the years 1999-2001. DOE used these market shares, shown in Table 9.5.3 , to develop the customer choice model parameters

.

9-17Table 9.5.3 Efficiency Market Share for CUACs (1999-2001)

Equipment
Class Year EER Bin

9.9 &

Under 10

- 10.9 11

- 11.9 12

- 12.9 13

- 13.9 14

- 14.9 15 &
Over
Small
Commercial Packaged AC (Air

Cooled) - ≥65,000
Btu/h and < 135,000
Btu/h 1999 58.5% 34.5% 6.6% 0.3% 0.0% 0.0% 0.0%

2000 53.1% 36.7% 9.9% 0.4% 0.0% 0.0% 0.0%

2001 37.3% 49.1% 13.0% 0.6% 0.0% 0.0% 0.0%

Large
Commercial Packaged AC (Air

Cooled) - ≥135,000
Btu/h and < 240,000
Btu/h 1999 77.4% 15.7% 6.3% 0.6% 0.0% 0.0% 0.0%

2000 72.7% 19.2% 8.1% 0.1% 0.0% 0.0% 0.0%

2001 59.8% 29.4% 10.7% 0.0% 0.0% 0.0% 0.0%

9.5.3.2 Equipment Price

For each of the efficiency bins for which DOE had market share data, DOE had to develop associated costs, including equipment price. DOE used the equipment prices provided by efficiency level in the ANOPR TSD to develop relationships between equipm ent price and

EER ( Figure 9.5.3 and Figure 9.5.4 ). DOE then used those relationships to calculate equipment prices for the EERs assigned to each bin of available market share data ( Table 9.5.4 ).

9-18Figure 9.5.3 Small CUAC Equi pment Price to EER Relationship

Figure 9.5.4 Large CUAC Equipment Price to EER Relationship y = 209.35x 2
- 4191.3x + 25840
R2 = 0.9984

$4,800 $4,900 $5,000 $5,100 $5,200 $5,300 $5,400 $5,500 $5,600 $5,700 $5,800

10 10.5 11 11.5 12 12.5Equipment Price

EER

y = 301.86x 2
- 5851.7x + 37540
R2 = 0.995

$9,000 $9,200 $9,400 $9,600 $9,800 $10,000 $10,200 $10,400 $10,600 $10,800 $11,000

8 9 10 11 12 13Equipment Price

EER

9-19Table 9.5.4 Average Equipment Price Per Unit for CUACs (2001$) (2001)

Equipment Class EER

9.9 &

Under 10

- 10.9 11

- 11.9 12

- 12.9 13

- 13.9 14

- 14.9 15 &
Over

9.25 10.25 11.25 12.25 13.25 14.25 15.25
Small Commercial Packaged AC (Air

Cooled) - ≥65,000 Btu/h and < 135,000
Btu/h $4,860* $4,874 $5,184 $5,912 $7,059 $8,625 $10,610

Large Commercial Packaged AC (Air

Cooled) - ≥135,000 Btu/h and < 240,000
Btu/h $9,240 $9,274 $9,913 $11,155 $13,000 $15,450 $18,503

*Note this value was adjusted from the regression

- predicted value of $4

, 983 because the regression erroneously created an upward trend below 10.0 EER.

DOE then adjusted the equipment prices from 2001$ to 2013$ and to represent y ears 1999 and 2000 in addition to 2001. ( DOE assumed the ANOPR prices represented the year 2001.) For 2001, DOE divided the price in 2001$ by the GDP deflator to arrive at the price in 2013$. 7 For the years 1999 and 2000, DOE also adjusted the equipment price using the deflated

PPI. 8 (See chapter 8 for a discussion of the PPI used for this rulemaking.) Table 9.5.5 shows the resulting average equipment price s by year.

Table 9.5.5 Average Equipment Price Per Unit for CUACs (2013$) (1999-2001)

Equipment
Class Year EER Bin

9.9 &

Under 10

- 10.9 11

- 11.9 12

- 12.9 13

- 13.9 14

- 14.9 15 &
Over
Small
Commercial Packaged AC (Air -Cooled) - ≥65,000 Btu/h and < 135,000
Btu/h 1999 $6,718 $6,738 $7,166 $8,173 $9,758 $11,923 $14,666

2000 $6,432 $6,450 $6,860 $7,824 $9,342 $11,414 $14,040

2001 $6,284 $6,302 $6,703 $7,644 $9,128 $11,152 $13,718

Large
Commercial Packaged AC (Air -Cooled) - ≥135,000
Btu/h and < 240,000 Btu/h 1999 $12,772 $12,820 $13,703 $15,419 $17,971 $21,357 $25,577

2000 $12,227 $12,273 $13,118 $14,762 $17,204 $20,446 $24,486

2001 $11,947 $11,992 $12,817 $14,423 $16,809 $19,976 $23,924

In the shipments model, parameters such as equipment price are used based as a function of square footage rather than unit. DOE developed a tons of capacity per square foot value as

9-20part of its energy use analysis (see chapter 7) . This allowed DOE to calculate a cooled square footage per unit value for each CUAC equipment class ( Table 9.5.6 ). DOE then divided the equipment prices per unit by the cooled square foot per unit values to arrive at equipment prices per square foot ( Table 9.5.7 ).

Table 9.5.6 Unit, Ton, and Square Footage Relationships Small Commercial Packaged AC (Air -Cooled) - ≥65,000 Btu/h and < 135,000 Btu/h Large Commercial Packaged AC (Air -Cooled) - ≥135,000 Btu/h and < 240,000 Btu/h

Tons/unit 7.5 15

Tons/SF 0.00260 0.00273

Cooled SF/Unit 2885 5495

Table 9.5.7 Average Equipment Price (Per SF) for CUACs (2013$) (1999-2001)

Equip ment
Class Year EER Bin

9.9 &

Under 10

- 10.9 11

- 11.9 12

- 12.9 13

- 13.9 14

- 14.9 15 &
Over
Small
Commercial Packaged AC (Air -Cooled) - ≥65,000 Btu/h and < 135,000
Btu/h 1999 $2.33 $2.34 $2.48 $2.83 $3.38 $4.13 $5.08

2000 $2.23 $2.24 $2.38 $2.71 $3.24 $3.96 $4.87

2001 $2.18 $2.18 $2.32 $2.65 $3.16 $3.87 $4.76

Large
Commercial Packaged AC (Air -Cooled) - ≥135,000
Btu/h and < 240,000 Btu/h 1999 $2.32 $2.33 $2.49 $2.81 $3.27 $3.89 $4.66

2000 $2.23 $2.23 $2.39 $2.69 $3.13 $3.72 $4.46

2001 $2.17 $2.18 $2.33 $2.62 $3.06 $3.64 $4.35

9.5.3.3 Installation Costs

The same installation costs developed for the this rulemaking were the basis for the
installation costs estimated for 1999−2001. The base values used, shown in Table 8.2.5 in chapter 8, were $2 ,476 for small CUAC and $3,346 for large CUAC. DOE assigned these values to the 11-11.9 EER bin, as approximately representative of the baseline equipment on the market today. For the other EER bins, DOE varied the install ation cost in direct proportion to the weight of the equipment. DOE estimated the weight for equipment in each bin based on a relationship of weight to EER presented in the ANOPR TSD (Chapter 8, Figure 8.2.1; shown here in Table

9.5.8 ). Table 9.5.9 shows the resul ting average installation cost per unit for CUACs. DOE then divided the installation costs per unit by the cooled square foot per unit values shown previously

9-21to arrive at installation costs per square foot ( Table 9 .5.10 ). DOE used the same installation costs for all years 1999-2001.

Table 9.5.8 Weight to EER Relationship Equipment Class Slope Intercept Small Commercial Packaged AC (Air

- Cooled)

- ≥65,000 Btu/h and < 135,000 Btu/h 42.1 498.1

Large Commercial Packaged AC (Air

- Cooled)

- ≥135,000 Btu/h and < 240,000 Btu/h 171.8 194.6

Table 9.5.9 Average Installation Cost Per Unit for CUACs (2013$) (1999-2001)

Equip ment
Class EER

9.9 &

Under 10

- 10.9 11

- 11.9 12

- 12.9 13

- 13.9 14

- 14.9 15 &
Over

9.25 10.25 11.25 12.25 13.25 14.25 15.25
Small
Commercial Packaged AC (Air -Cooled) - ≥65,000 Btu/h and < 135,000
Btu/h Weight 888 930 972 1,014 1,056 1,098 1,141

Install

Cost $2,261 $2,368 $2,476 $2,583 $2,690 $2,798 $2,905

Large
Commercial Packaged AC (Air -Cooled) - ≥135,000
Btu/h and < 240,000 Btu/h Weight 1,784 1,956 2,127 2,299 2,471 2,643 2,815

Install

Cost $2,805 $3,076 $3,346 $3,616 $3,886 $4,156 $4,427

Table 9.5.10 Average Installation Cost Per SF for CUACs (2013$) (1999-2001)

Equip ment Class EER Bin

9.9 &

Under 10

- 10.9 11

- 11.9 12

- 12.9 13

- 13.9 14

- 14.9 15 &
Over
Small Commercial Packaged AC (Air

Cooled) - ≥65,000 Btu/h and < 135,000
Btu/h $0.78 $0.82 $0.86 $0.90 $0.93 $0.97 $1.01

Large Commercial Packaged AC (Air

Cooled) - ≥135,000 Btu/h and < 240,000
Btu/h $0.51 $0.56 $0.61 $0.66 $0.71 $0.76 $0.81

9-229.5.3.4 Electricity Costs

DOE derived unit energy consumption ( UEC ) values for each of the EER bins and then applied historic electricity prices to calculate annual ener gy costs.

The ANOPR TSD provided a relationship between EER and UEC: UEC =A* EER +B ( Table 9.5.11 ). Using this relationship, DOE calculated a UEC for each of the EER bins provided by AHRI, using the representative EER value for each bin ( Table 9.5.12 ). DOE used the cooled square foot per unit values shown previously to calculate the UEC per square foot for the shipments model ( Table 9.5.13 ). DOE used the same UECs for all years 1999-2001.

Table 9.5.11 UEC Relationship to EER

Equipment Class A B Small Commercial Packaged AC (Air

- Cooled)

- ≥65,000 Btu/h and < 135,000 Btu/h -1,168.8 27,835

Large Commercial Packaged AC (Air

- Cooled)

- ≥135,000 Btu/h and < 240,000 Btu/h -2,488.9 57,386

Table 9.5.12 Average UEC Per Unit for CUACs (kWh/year) (1999-2001)

Equip ment Class EER Bin (with Representative EER)

9.9 &

Under 10

- 10.9 11

- 11.9 12

- 12.9 13

- 13.9 14

- 14.9 15 &
Over

9.25 10.25 11.25 12.25 13.25 14.25 15.25
Small Commercial Packaged AC (Air

Cooled) - ≥65,000 Btu/h and < 135,000
Btu/h 17,024 15,855 14,686 13,517 12,348 11,180 10,011

Large Commercial Packaged AC (Air

Cooled) - ≥135,000 Btu/h and < 240,000
Btu/h 34,364 31,875 29,386 26,897 24,408 21,919 19,430

9-23Table 9.5.13 Average UEC Per SF for CUACs (kWh/year) (1999-2001)

Equip ment Class EER Bin (with Representative EER)

9.9 &

Under 10

- 10.9 11

- 11.9 12

- 12.9 13

- 13.9 14

- 14.9 15 &
Over

9.25 10.25 11.25 12.25 13.25 14.25 15.25

Small Commercial Packaged AC (Air

Cooled) - ≥65,000 Btu/h and < 135,000
Btu/h 5.90 5.50 5.09 4.69 4.28 3.88 3.47

Large Commercial Packaged AC (Air

Cooled) - ≥135,000 Btu/h and < 240,000
Btu/h 6.25 5.80 5.35 4.90 4.44 3.99 3.54

DOE developed a historic price index based on AEO data to calculate annual energy

costs in the past. 9 Figure 9.5.5 shows the price index. This trend was applied to the marginal

e lectricity price s (MEP) that were developed separately for CUACs and CUHPs as disc ussed in
Section 8.2.2.2.

Figure 9.5.5 AEO Historic Price Index

9.5.3.5 Maintenance Costs

The same maintenance costs developed for this NOPR were the basis for the maintenance costs in 1999−2001. These costs are $297 for both Small and Large CUAC. See Chapter 8 for

more details. DOE divided the maintenance cost by the cooled square foot per unit values shown previously to arrive at the total maintenance cost per square foot used in the shipments model, or $0.10 for small CUAC and $0.05 for large CUAC. 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015Historic Price Index (2013=1)

Year

9-249.5.3.6 Repair Costs

DOE as sumed that any routine or minor repairs are included in the annualized maintenance costs. As a result, repair costs we re not explicitly modeled in the LCC and PBP analysis. Instead, DOE incorporated a one- time cost for major repair (compressor replacement) as a primary input to the repair/replace customer choice model in the shipments analysis.

RS Means provides repair costs for various types of heat pumps and compressors. The
costs include both labor and material costs. DOE reviewed these costs and note d that the material costs were inaccurate for all size classes, as several of the estimates exceeded the costs for an
entire new unit. For labor and non- compressor material costs, DOE used data in RS Means Facilities Maintenance & Repair Cost Data, 2013

. 10 DOE substituted compressor costs developed from a review of Grainger and Drillspot websites. 11

, 12

DOE reviewed compressor costs for 316 units from 3.2 tons to 21.1 tons on the Grainger and Drillspot websites. DOE calculated the average prices for 5, 7.5, 10, and 15 ton units, sometimes including compressors in a small ton range surrounding those points. DOE also used the full data range to develop a relationship of compressor cost to tons (Figure 9.5.6). DOE used this relationship to estimate compressor co sts for 25 ton and 60 ton units ( Table 9.5.14 ).

Figure 9.5.6 Compressor Costs by Ton (Drillspot, Grainger) y = 311.11x - 191.54
R2 = 0.9205

0 1000 2000 3000 4000 5000 6000 7000

0.0 5.0 10.0 15.0 20.0 25.0Compressor Cost (2013$)

Tons

9-25Table 9.5.14 Compressor Costs by Ton (2013$)

Tons Avg Price Predicted Price

5 $ 1061 $ 1,36 4

7.5 $ 2,60 8 $ 2,14 2

10 $ 266 1 $ 2,9 20

15 $ 5,08 3 $ 4,475 1

25 N/A $ 7,586 1

60 N/A $ 18,47 5

DOE used these compressors costs - average if available, and predicted otherwise - to revise the material costs found in RS Means ( Table 9.5.15 ). Table 9.5.16 shows the R S Means costs, combined with the revised material costs, used to arrive at the final total repair cost with O&P. To calculate the Material Costs with O&P, DOE assumed the revised compressor cost already contained O&P as it was based on end

- user (website) p ricing. Therefore DOE only added the typical RS Means O&P multiplier of 1.25 to the non- compressor material costs.

Table 9.5.15 RS Means Material Cost Adjustments (2013$)

Tons * RS Means Material
Cost RS Means Compressor Cost Revised Compressor
Cost Revised Material
Cost

5 $ 90 5 $ 470 $ 1 ,061 $ 1 ,496

7.5 $ 1

, 536 $ 1

, 050 $ 2 ,608 $ 3 ,094

10 $ 17

, 76 1 $ 17

, 219 $ 2 ,661 $ 3 ,202

15 $ 26

, 41 1 $ 25

, 700 $ 5 ,083 $ 5 ,793

25 $ 34

, 26 5 $ 33

, 375 $ 7 ,586 $ 8 ,476

60 $ 38

, 56 2 $ 38

, 200 $ 18 ,475 $ 18 ,837 *DOE used systems described as single zone rooftop units for this analysis.

Table 9.5.16 Repair Costs for CUAC Units Based on RS Means ( 2013$)

Single

Zone Rooftop
Unit Labor

Hour Bare Costs Total with O&P

Cost per

Labor

Hour Calc

. Labor
Cost Revised

Mat’l

Cost Total
Cost Cost per

Person

Hour Labor
Cost Mat ’l

Cost Total
Cost

5 ton 18.27 $ 56.65 $1,035 $ 1

, 496 $1,9 40 $85.30 $1,559 $1,60 5 $3,163

7.5 ton 21.83 $ 56.65 $1,23 7 $ 3

, 09 4 $2,773 $85.30 $1,862 $3,215 $5,078

10 ton 54.79 $ 56.65 $3,104 $ 3

, 202 $20,86 5 $85.30 $4,67 4 $3,338 $8,012

15 ton 76.72 $ 56.65 $4,346 7 $ 5

, 793 $30,75 7 $85.30 $6,544 $5,971 $12,51 5

25 ton 96.57 $ 52.87 $5,105 7 $ 8

, 47 6 $39,370 $79.60 $7,68 7 $8,698 $16,385

60 ton 298.68 $ 52.87 $15,791 $ 18

, 83 7 $54,353 $79.60 $23,775 $18,92 7 $42,702

DOE took the 7.5 ton and 15 ton costs to be representative of the small and large CUAC equipment classes, respectively. c Within each equipment class, DOE used repair costs that increase in direct proportion with the price of the equipment and with EER level. For historical

c Cost for the other ton units are shown as a predecessor to developing the cost for very large CUAC equipment (30 ton) discussed in section 9.3.4.5

.

9-26repair costs, DOE assumed that the repair costs developed in 2013 were representative of the 11- 11.9 EER bin. DOE first adjusted the repair c osts for the 11-11.9 bin to represent repair costs in1999 using the UAC PPI shown previously. DOE then varied the repair costs for the other bins in direct proportion to the equipment prices developed for 1999-2001 (see Table 9.5.5 ). DOE adjusted the repair prices by EER bin f rom 1999 to 2000 and 2001, also using the PPI. Table

9.5.17 shows the result. DOE also used the cooled square foot per unit values shown previously
to calcula te the repair cost per square foot for the shipments model ( Table 9.5.18 ).

Table 9.5.17 Average Repair Cost Per Unit for CUACs (2013$) (1999-2001)

Equipment
Class Year EER Bin

9.9 &

Under 10

- 10.9 11

- 11.9 12

- 12.9 13

- 13.9 14

- 14.9 15 &
Over
Small
Commercial Packaged AC (Air -Cooled) - ≥65,000 Btu/h and < 135,000
Btu/h 1999 $5,059 $5,073 $5,396 $6,154 $7,348 $8,978 $11,043

2000 $4,843 $4,857 $5,165 $5,891 $7,034 $8,595 $10,572

2001 $4,732 $4,745 $5,046.84 $5,756 $6,873 $8,397 $10,329

Large
Commercial Packaged AC (Air -Cooled) - ≥135,000
Btu/h and < 240,000 Btu/h 1999 $12,396 $12,442 $13,298 $14,965 $17,441 $20,727 $24,823

2000 $11,867 $11,911 $12,731 $14,326 $16,697 $19,843 $23,764

2001 $11,594 $11,638 $12,439 $13,997 $16,313 $19,387 $23,218

Table 9.5.18 Average Repair Cost Per SF for CUACs (2013$) (1999-2001)

Equipment
Class Year EER Bin

9.9 &

Under 10

- 10.9 11

- 11.9 12

- 12.9 13

- 13.9 14

- 14.9 15 &
Over
Small
Commercial Packaged AC (Air -Cooled) - ≥65,000 Btu/h and < 135,000
Btu/h 1999 $1.75 $1.76 $1.87 $2.13 $2.55 $3.11 $3.83

2000 $1.68 $1.68 $1.79 $2.04 $2.44 $2.98 $3.66

2001 $1.64 $1.65 $1.75 $2.00 $2.38 $2.91 $3.58

Large
Commercial Packaged AC (Air -Cooled) - ≥135,000 Btu/h and < 240,000
Btu/h 1999 $2.26 $2.26 $2.42 $2.72 $3.17 $3.77 $4.52

2000 $2.16 $2.17 $2.32 $2.61 $3.04 $3.61 $4.33

2001 $2.11 $2.12 $2.26 $2.55 $2.97 $3.53 $4.23

9-279.5.4 Data for Efficiency Level Distribution Projections

To develop the efficiency level distribution projections, DOE used costs developed for 2013 as the basis for its analysis. Many of these costs for CUAC have been discussed previously in Chapter 8.

DOE did not conduct an engineering or LCC ana lysis for CUHP. For CUHPs, DOE estimated that a significant portion of the market consists of equipment with electric resistance heating. As a result, DOE developed efficiency levels for CUHP based on the difference in
ASHRAE Standard 90.1-2010 levels for CUAC with electric resistance heating and CUHP with electric resistance heating. This difference is 0.2 for small CUHP and 0.5 for large and very large

CUHP. Table 9.5.19 shows the CUHP efficiency levels analyzed.

Table 9.5.19 CUHP Efficiency Levels

Efficiency

Level IEER

Small Commercial Packaged HP (Air

Cooled) - ≥65,000 Btu/h

and < 135,000 Btu/h Large Commercial Packaged HP (Air

Cooled) - ≥135,000 Btu/h

and < 240,000 Btu/h Very Large Commercial Packaged HP (Air

Cooled) - ≥240,000 Btu/h and < 760,000 Btu/h

0 11.2 10.7 10

1 12.7 11.7 11

2 13.8 12.7 12

3 14.6 13.7 13

4 19.7 17.9 15

9.5.4.2 Equipment Price

The CUAC equipment prices developed for the LCC were shown in Chapter 8. The equipment prices used in the shipments model are slightly different, as they are weightedaverage costs based on the regional distribution of buildings in the sample, as outputted by the

LCC ( Table 9.5.20 ). The equipment costs used in the shipments model are approximately 99% of
the national average costs.

9-28Table 9.5.20 Average Customer Price for CUACs (2013$)

Equipment Class Ef ficiency Level

0 1 2 3 4 Small Commercial Packaged AC (Air -Cooled) - ≥65,000 Btu/h and < 135,000 Btu/h $5,840 $6,048 $6,885 $7,252 $8,270

Large Commercial Packaged AC (Air -Cooled) - ≥135,000 Btu/h and < 240,000 Btu/h $10,628 $11,400 $12,358 $13,176 $13,760

Very Large Commercial Packaged AC (Air -Cooled) - ≥240,000 Btu/h and < 760,000 Btu/h $22,591 $23,641 $25,098 $26,139 $28,536

For CUHP, which were not analyzed in the LCC, DOE developed baseline manufacturing
costs in the engineering analysis ( Table 9.5.21 ). DOE then applied the incremental costs developed for CUAC (see Chapter 8) to these baseline CUHP costs to arrive at manufacturi ng costs per equipment class and efficiency level. Finally, DOE applied the same markups developed for CUAC (see Table 8.2.1 in Chapter 8) to the CUHP baseline manufacturing costs to arrive at equipment prices for CUHPs. Table 9.5.22 shows the CUHP equipment prices used in the shipments model. DOE also divided the per unit equipment prices by the cooled square foot per unit values shown previously to arrive at average equipment prices per square foot ( Table 9.5.23 ).

Table 9.5.21 CUHP Baseline Manufacturing Cost (2013$) Equipment Class Baseline Manufacturing Cost

Small Commercial Packaged HP (Air

- Cooled)

- ≥65,000 Btu/h and < 135,000 Btu/h $2,081

Large Commercial Packaged HP (Air

- Cooled)

- ≥135,000 Btu/h and < 240,000 Btu/h $4,002

Very Large Commercial Packaged HP (Air

- Cooled)

- ≥240,000 Btu/h and < 760,000 Btu/h $7,889

9-29Table 9.5.22 Average Customer Price (Per Unit) for CUHPs (2013$)

Equipment Class Efficiency Level

0 1 2 3 4 Small Commercial Packaged HP (Air -Cooled) - ≥65,000 Btu/h and < 135,000 Btu/h $6,287 $6,494 $7,331 $7,699 $8,716

Large Commercial Packaged HP (Air -Cooled) - ≥135,000 Btu/h and < 240,000 Btu/h $11,914 $12,686 $13,643 $14,461 $15,045

Very Large Commercial Packaged HP (Air -Cooled) - ≥240,000 Btu/h and < 760,000
Btu/h $24,817 $25,866 $27,323 $28,364 $30,760

Table 9.5.23 Average Customer Price (per SF) for CUACs and CUHPs (2013$)

Equipment Class Efficiency Level

0 1 2 3 4 Small Commercial Packaged AC (Air -Cooled) - ≥65,000 Btu/h and < 135,000 Btu/h $2.02 $2.10 $2.39 $2.51 $2.87

Large Commercial Packaged AC (Air -Cooled) - ≥135,000 Btu/h and < 240,000 Btu/h $1.93 $2.07 $2.25 $2.40 $2.50

Very Large Commercial Packaged AC (Air -Cooled) - ≥240,000 Btu/h and < 760,000 Btu/h $2.02 $2.11 $2.24 $2.34 $2.55

Small Commercial Packaged HP (Air -Cooled) - ≥65,000 Btu/h and < 135,000 Btu/h $2.18 $2.25 $2.54 $2.67 $3.02

Large Commercial Packaged HP (Air -Cooled) - ≥135,000 Btu/h and < 240,000 Btu/h $2.17 $2.31 $2.48 $2.63 $2.74

Very Large Commercial Packaged HP (Air -Cooled) - ≥240,000 Btu/h and < 760,000 Btu/h $2.22 $2.31 $2.44 $2.53 $2.75

As discussed in chapter 8, DOE examined historical PPI data for “unitary airconditoiners, except heat pumps” as a method to project future prices , but DOE determined that the extent to which the trends of the past years will continue is very uncertain, and as a result, chose to apply a constant price trend. DOE also examined PPI data for heat pumps: “Air source heat pumps, except room air” (2007-2009) and “heat pumps” (2009-2013). 13 However, because of the short length of time available and the inconsistency between the two series, DOE decided to use the AC trend for both AC and HP, and as a result, DOE also used constant re al prices for

CUHP.

9-309.5.4.3 Installation Costs

The installation c osts developed for the LCC were shown in Chapter 8. The installation costs used in the shipments model are slightly different, as they are weighted- average costs based on the regional distribution of buildings in the sample, as outputted by the LCC ( Table 9.5.24 ). The installation costs are approximately 93% of the national average costs. DOE used the same installation costs developed for CUACs for the equivalent CUHP equipment classes. DOE also divided the per unit installation costs by the cooled square foot per unit values shown previously to arrive at average installation costs per square foot ( Table 9.5.25 ).

Table 9.5.24 Average Installation Cost per Unit for CUACs and CUHPs (2013$)

Equipment Class Efficiency Level

0 1 2 3 4 Small Commercial Packaged AC and HP (Air -Cooled) - ≥65,000 Btu/h and < 135,000 Btu/h $2,316 $2,487 $3,038 $3,070 $3,897

Large Commercial Packaged AC and HP (Air -Cooled) - ≥135,000 Btu/h and < 240,000 Btu/h $3,129 $3,535 $4,501 $4,577 $5,215

Very Large Commercial Packaged AC and HP (Air -Cooled) - ≥240,000 Btu/h and < 760,000 Btu/h $5,194 $5,744 $6,640 $6,689 $7,664

Table 9.5.25 Av erage Installation Cost per SF for CUACs and CUHPs (2012$)

Equipment Class Efficiency Level

0 1 2 3 4 Small Commercial Packaged AC and HP (Air -Cooled) - ≥65,000 Btu/h and < 135,000 Btu/h $0.80 $0.86 $1.05 $1.06 $1.35

Large Commercial Packaged AC and HP (Air -Cooled) - ≥135,000 Btu/h and < 240,000 Btu/h $0.57 $0.64 $0.82 $0.83 $0.95

Very Large Commercial Packaged AC and HP (Air -Cooled) - ≥240,000 Btu/h and < 760,000 Btu/h $0.46 $0.51 $0.59 $0.60 $0.68

9.5.4.4 Electricity Costs

Electricity costs are based on the unit energy consumption for cooling in CUACs and both cooling and heating for CUHPs. DOE developed the cooling UECs for CUACs in the energy use analysis ( Table 9.5.26 ). To develop cooling UECs for CUHPs, DOE adjusted the CUAC UECs based on the relationship of the CUAC and CUHP IEER values for each efficiency level. In other words, to obtain the cooling UEC for 7.5 ton units at efficiency level 0, DOE multiplied 13,119 (k Wh /year) by 11.4/11.2 (IEER/IEER). Table 9.5.27 shows the resulting CUHP cooling UE Cs.

9-31Table 9.5.26 CUAC UECs

Efficiency Level Small CommercialPackaged AC (AirCooled) - ≥65,000 Btu/h

and < 135,000 Btu/h Large CommercialPackaged AC (AirCooled) - ≥135,000 Btu/h and < 240,000

Btu/h Very Large Commercial Packaged AC (Air

- Cooled) - ≥240,000 Btu/h and < 760,000 Btu/h

IEER Cooling
UEC IEER Cooling
UEC IEER Cooling
UEC

0 11.4 13

, 119 11.2 22

, 917 10.1 53

, 453

1 12.9 11

, 476 12.2 20

, 998 11.6 47

, 272

2 14.0 10

, 666 13.2 18

, 655 12.5 44

, 976

3 14.8 6

, 760 14.2 16

, 594 13.5 31

, 921

4 19.9 3

, 531 18.4 12

, 425 15.5 26

, 412

Table 9.5.27 CUHP Cooling UECs

Efficiency Level Small Commercial Packaged HP (Air

Cooled) - ≥65,000 Btu/h

and < 135,000 Btu/h Large Commercial Packaged HP (Air

Cooled) - ≥135,000 Btu/h and < 240,000

Btu/h Very Large Commercial Packaged HP (Air

Cooled) - ≥240,000 Btu/h and < 760,000

Btu/h

IEER Cooling
UEC IEER Cooling
UEC IEER Cooling
UEC

0 11.2 13

, 353 10.7 23

, 988 10 56

, 237

1 12.7 11

, 657 11.7 21

, 896 11 49

, 401

2 13.8 10

, 821 12.7 19

, 390 12 46

, 850

3 14.6 6

, 852 13.7 17

, 199 13 33

, 149

4 19.7 3

, 567 17.9 12

, 772 15 27

, 292

To develop heating UECs for CUHPs, DOE first developed a relationship between IEER
and COP based on units in the AHRI database. 14 DOE then developed an annual energy use per square foot based on the CBECS 2003 dataset. This value was 1.37 (kWh/sf), and assumed to be representative of a unit with 2.9 COP. Using the tons/sf values developed for CUACs in the energy use analysis, DOE calculated an annual energy use per ton (kWh/ton) for each equipment class, based on a COP of 2.9. DOE then took the annual energy use per ton for the given equipment class, multiplied it by the representative tons for that class, and adjusted it by the ratio
of 2.9 COP to th e COP for that efficiency level

. Further details are available in Chapter 10 , NIA.
Table 9.5.28 shows the resulting CUHP heating UECs.

9-32Table 9.5.28 CUHP Heating UECs

Efficiency

Level Small Commercial Packaged HP (Air

Cooled) - ≥65,000 Btu/h

and < 135,000 Btu/h Large Commercial Packaged HP (Air

Cooled) - ≥135,000 Btu/h and < 240,000 Btu/h Very Large Commercial Packaged HP (Air

Cooled) - ≥240,000 Btu/h and < 760,000 Btu/h

COP Heating
UEC COP Heating
UEC COP Heating
UEC

0 3.3 3

, 440 3.3 6

, 549 3.2 13

, 898

1 3.4 3

, 379 3.3 6

, 549 3.2 13

, 898

2 3.4 3

, 336 3.3 6

, 550 3.2 13

, 898

3 3.5 3

, 305 3.3 6

, 550 3.2 13

, 898

4 3.7 3

, 121 3.3 6

, 551 3.2 13

, 898

DOE summed the heating and cooling UECs for CUHP and, for both CUAC and CUHPs, divided the UECs by the cooled square foot per unit values shown previously to arrive at the total UEC per square foot used in the shipments model ( Table 9.5.29 ).

Table 9.5.29 Total UECs by SF for CUAC and CUHPs

Equipment Class Efficiency Level

0 1 2 3 4 Small Commercial Packaged AC (Air -Cooled) - ≥65,000 Btu/h and < 135,000 Btu/h 4.5 4.0 3.7 2.3 1.2

Large Commercial Packaged AC (Air -Cooled) - ≥135,000 Btu/h and < 240,000 Btu/h 4.2 3.8 3.4 3.0 2.3

Very Large Commercial Packaged AC (Air -Cooled) - ≥240,000 Btu/h and < 760,000 Btu/h 4.8 4.2 4.0 2.9 2.4

Small Commercial Packaged HP (Air -Cooled) - ≥65,000 Btu/h and < 135,000 Btu/h 5.8 5.2 4.9 3.5 2.3

Large Commercial Packaged HP (Air -Cooled) - ≥135,000 Btu/h and < 240,000 Btu/h 5.6 5.2 4.7 4.3 3.5

Very Large Commercial Packaged HP (Air -Cooled) - ≥240,000 Btu/h and < 760,000 Btu/h 6.3 5.7 5.4 4.2 3.7

DOE multiplied the UECs by marginal electricity prices (MEP) to calculate energy costs. Marginal electr icity price values of 0.1100 $/kWh and 0.1014 $/kWh were used for CUACs and CUHPs respectively

. The AEO 2013 Reference Case forecast of electricity prices was used to project energy costs into the future. (See Chapter 8 for more details.)

9-339.5.4.5 Maintenance Costs

The maintenance costs developed for the LCC were used directly in the shipments model. These costs are $297 for both s mall and large CUAC, and $408 for very large CUAC. Maintenance costs do not vary by eff iciency level. DOE used the same maintenance costs for the equivalent CUHP equipment classes. DOE divided the maintenance cost by the cooled square foot per unit values shown previously to arrive at the total maintenance cost per square foot used in the shipments model ( Table 9.5.30 ).

Table 9.5.30 Average Maintenance Cost per SF for CUACs and CUHPs (2013$)

Equipment Class Maintenance Cost per SF

Small Commercial Packaged AC and HP (Air

- Cooled)

- ≥65,000 Btu/h and < 135,000 Btu/h $0.10

Large Commercial Packaged AC and HP (Air

- Cooled)

- ≥135,000 Btu/h and < 240,000 Btu/h $0.05

Very Large Commercial Packaged AC and HP(Air

- Cooled)

- ≥240,000 Btu/h and < 760,000 Btu/h $0.04

9.5.4.6 Repair Costs

DOE developed repair costs for baseline units in 2013 as discussed in section 9.5.3.6

. The baseline values for small and large CUAC units were $5,078 and $12,515, respectively ( Table

9.5.16 ). Because RS Means did not provide data for 30 ton units, DOE developed a linear relationship between Repair Cost with O&P and CUAC capacity ( Figure 9.5.7 ). This resulted in a value for 30 ton units of $21,390.

Figure 9.5.7 Repair Cost by CUAC Capacity y = 704.06x + 267.98
R2 = 0.9942

$- $5,000 $10,000 $15,000 $20,000 $25,000 $30,000 $35,000 $40,000 $45,000

0 10 20 30 40 50 60 70Repair Costs with O&P (2013$)

CUAC Capacity

9-34In 2013, these costs wer e taken as representative of EL 0. For all other ELs, DOE varied the repair cost in direct proportion to the equipment price, as shown in Table 8.2.4 in Chapter 8. DOE used the same repair costs for both CUAC and CUHP in their equivalent equipment classes ( Table 9.5.31 ). DOE also divided the repair cost by the cooled square foot per unit values shown previously to arrive at the total repair cost per square foot used in the shipments model ( Table

9.5.32 ).

Table 9.5.31 Average Repair Cost Per Unit for CUACs and CUHPs (2013$)

Equipment Class Efficiency Level

0 1 2 3 4
Small Commercial Packaged AC and HP (Air -Cooled) - ≥65,000 Btu/h and < 135,000 Btu/h $5,078 $5,258 $5,985 $6,304 $7,188

Large Commercial Packaged AC and HP (Air -Cooled) - ≥135,000 Btu/h and < 240,000 Btu/h $12,515 $13,423 $14,550 $15,513 $16,200

Very Large Commercial Packaged AC and HP(Air

Cooled) - ≥240,000 Btu/h and < 760,000 Btu/h $21,390 $22,382 $23,761 $24,746 $27,013

Table 9.5.32 Average Repair Cost Per SF for CUACs and CUHPs (2013$)

Equipment Class Efficiency Level

0 1 2 3 4 Small Commercial Packaged
AC and HP (Air -Cooled) - ≥65,000 Btu/h and < 135,000 Btu/h $1.76 $1.82 $2.07 $2.19 $2.49

Large Commercial Packaged AC and HP (Air -Cooled) - ≥135,000 Btu/h and < 240,000 Btu/h $2.28 $2.44 $2.65 $2.82 $2.95

Very Large Commercial Packaged AC and HP(Air

Cooled) - ≥240,000 Btu/h and < 760,000 Btu/h $1.91 $2.00 $2.12 $2.21 $2.41

9.5.5 Equipment Lifetime

The development of the equipment lifetime for CUAC was discussed in Chapter 8. DOE used an updated CUAC lifetime with a median of 18.7 years and a mean of 18.4 years. DOE also reviewed the equipment lifetime for CUHP for this rulemaking. In the ASHRAE database, t he

9-35category "heat pump, air

- to

- air", which has 1296 units with only one retired, shows a median lifetime of 14 years. 15 Therefore, DOE did no t make any major adjustements to the CUHP
lifetime. DOE used a CUHP lifetime with a median of 15.4 years and a mean of 15.2 years. Calibration of the shipments model assisted in arriving at these numbers (see section 9.6).

9.6 MODEL CALIBRATION

The shipments model is used to project future shipments of commercial unitary air

conditioners and heat pumps for the base case with no standards implemented and all standards cases wherein equipment must conform to a certain minimum efficiency level L . Two of the
main components of the shipments model include the consumer choice model, that predicts market shares as a function of efficiency (Section 9.3), and the Repair/Replace decision model (Section 9.4) which provides an average repair probability for broken units. Both models consist of sets of unknown parameters (α, β) and (κ, γ) that reflect sensitivities to various costs that need to be prescribed. The calibration step of the shipments model involves estimating values for these parameters and is described in this section.

9.6.1 Estimating Custo mer C hoice M odel P arameters

The consumer choice model consists of two parameters: α and β and that reflect sensitivities to total installed cost (up -front costs) and operating costs respectively. For each type of equipment and ca pacity class, these parameters were estimated using three years (1999 -2001)
of his torical input data for CUACs described in section 9.5.3. The procedure to estimate the two parameters of the consumer choice model is described below.

1. First, the utility fun ction of the consumer choice model W ( y ) given by

W

j ( m ) ( y ) = α ( m ) TIC j ( m ) ( y ) + β ( m ) OC j ( m ) ( y ) , (26)

was defined for a particular equipment type for each of the three years (1999

- 2001) using arbitrary values of α ( ͫ ) and β ( ͫ ) . Here,

• TIC j ( m ) ( y ) = EP j ( m ) ( y ) + IC j ( m ) ( y ) is the total installation cost in 2012 $ per sq. ft for
equipment of capacity m ( = small & large) in year y averaged over EER bin j ( j = 0, ... , N and N = 7).
 EP j ( m ) ( y ) and IC j ( m ) (y) are the equipment price and installation cost for the
aforementioned capacities of equipment whose values are listed as a function of EER in Tables 9.5.8 and 9.5.11 respectively.

• OC j ( m ) (y) = MC j ( m ) ( y ) + MEP × UEC j ( m ) ( y ) × AEO

_ ElecPrice ( y ) is the total operating

and maintenance cost in 2012 $ per sq. ft for equipment of capacity m averaged over EER
bin j

. Here,

9-36 MC j ( m ) ( y ) is the total maintenance cost in 201 2$ for equipment of capacity m in

year y . These costs are described in section 9.3.3.5.
 UEC j ( m ) ( y ) is the unit energy consumption in KWh per sq.ft per year for equipment of capacity m in year y , whose values are listed in Table 9.5.14.  MEP corresponds to the marginal price of electricity which is equal to 0.1100 $/KWh for CUACs and 0.1014 $/KWh for CUHPs.  AEO ElecPrice(y) is the historic price trend (see Figure 9.5.4) developed from AEO data. Three price trends were developed for the analysis period based on low, high and a reference case economic growth scenarios. For the shipments model, the price trend corresponding to the reference case was used to calculate the cost of electricity in a given year as part of total operating and maintenance

costs.

2. The Depar tment then estimated values of α ( m ) and β ( m ) for small and large CUAC equipment classes by using a non

- linear least squares o pti mization routine (Excel's Solver) that minimized the sum of the squared diff erence between historical e ffi ciency

level shares q j ( m ) (Table 9.5.3) and market share probabilities p j ( m ) p redicted by the
consumer choice model (see Section 9.3.3) i.e.

Min � ∑ �q j ( m ) ( y ) − p j ( m ) ( y ))2 � 2001 y= 1999 � → �α ( m )

, β ( m ) � (27)

Since DOE did not have the necessary input data to calibrate the model parameters for very large equipment, DOE set these values to those used for the large equipment type. DOE also assumed

that the α 's and β ’s for the CUHPs were that same as those calculated for the CUACs. Table

9.6.1 shows the consumer choice model parameters for different classes of CUACs and CUHPs.

Table 9.6.1 Consumer choice model calibration parameters

CUAC (KBtu /hr) CUHP (KBtu /hr)

Parameters 65

- 135 135

- 240 240

- 760 65

- 135 135

- 240 240

- 760

α

- 12.144 -9 .082 -9 .082

- 12.144 -9 .082 -9 .082 β

- 14.922

- 2.05 5

- 2.05 5

- 14.922

- 2.05 5

- 2.05 5

It is important to note that during the calibration β was highly sensitive to slight changes in the cost trends across the EER bins in any given year. This resulted in positive values for the large equipment class. However, we speculate that since the total operating costs are invariably lower
than the total installed c osts, the model would tend to produce probabilities which are primarily first cost driven irrespective of operating costs. This perhaps explains the large α that we obtain for all equipment types. In other words, the predicted probabilities would not be g reatly affected by resorting to a single parameter model that reflects sensitivity to first cost alone.

9-379.6.2 Estimating R epair/ R eplace M odel P arameters

The repair/replace decision model is a simple binary choice logit model that provides an estimate of the average repair probab ility associated with broken units in a given year. Like the consumer choice model, it consists of two parameters γ and κ

. κ represents customer sensitivity to the average efficiency weighted cost differential between total installed cost and the cost of repairing an older unit in a give year. Since repair costs are generally smaller than total installed costs, γ is a scenario parameter that is thought to represent “unknown" replacements by li miting the number of repairs in a given year. For simplicity, we assume that κ is equal to the cost sensitivity
parameter α used in the consumer choice model. This assumption is reasonable because we expect a repair/replace decision to be first cost driv en. Therefore, the only parameter that needs

to be ascertained is γ . The steps involved in estimating γ are outlined below:

1. For a given equipment type and capacity m , an average historical repair rate � r̅ ( m ) � is

fi rst calculated by using an iterative optimization proce dure that utilizes a combination of
average equipment lifetime � η̅ ( m ) � and an adjustment factor �A

0 ( m ) � for market saturation

to minimize the diff erence between historical shipments and model predicted shipments
with zero repairs. It is a ssumed that the average historical repair rate represents the fraction of model predicted shipments that are larger than historical shipments with repairs set equal to zero . Note that r̅ ( m ) represents a repair fraction averaged across all
effic iencies f or a particular equipment class in a given year.
 A 0 ( m ) represents an adjustment factor that is applied to the fitted stock saturation trends (see Section 9.5.2.1; Figure 9.5.2) in combination with mean lifetime η̅ ( m ) to minimize the sum of the difference between historical shipments and model predicted
shipments with repairs set equal to zero. In other words, A 0 ( m ) and η̅ ( m ) represent two parameters that maximize the likelihood of obtaining r̅ ( m ) by minimizing the
difference between cumu lative historical and model predicted shipments with zero repairs. These parameters are calculated as part of the optimization procedure used to

determine r̅ ( m )

.  The mean lifetime of a particular type of equipment of capacity η̅ ( m ) is given by



### η̅

( m )



### = λ Г � 1 + 1

θ 

### � (28)

where θ and λ are the shape and scale parameters of the equipment lifetime function defined earlier (see equation (3)).  For CUACs, the calibration approach yielded values θ = 10 .0 and λ = 19

. 35 that resulted in mean and median lifetimes of 18.4 and 18.7 years respectively. For CUHPs, these values were θ = 10 .0 and λ = 16 .0 that produced mean and median lifetimes of 15.22 and 15.44 years respectively.

2. Next, the utility function R ( m ) ( y ) is calculated based on the efficiency w eighted average

cost diff erence Δ ( m ) Δ between total installed costs TIC ( m ) ( y ) and repair costs

9-38R ( m ) ( y ) for the given year y . The repair costs associated with small and large CUACs for years 1999- 2001 are described in Table 9.5.19.

3. The scenario parameter γ ( m ) is then obtained by minimizing the sum of the squared

diff erence between r̅ ( m ) and the model predicted value ρ ′ ( m ) using historical cost data from years 1999- 2001 that are used tocalculate R ( y ) for each year y

. 4. Mathematically, this procedure can be expressed as

Min � ∑ � r̅ ( m ) ( y ) − ρ ′ ( m ) ( y )) 2 � 2001 y= 1999 � → �γ ( m ) � (29)

Table 9.6.2 presents a summary of the constraints used in the calibration of the Repair/Replace decision model as well as the calibrated parameters.

Table 9.6.2 Repair/Replace decision model constraints and calibration parameters CUAC (KBtu /hr) CUHP (KBtu /hr)

Parameters 65

- 135 135

- 240 240

- 760 65

- 135 135

- 240 240

- 760

A 0 0.645 0.760 0.760 1.95 2.15 4.28

r̅ 0.176 0.204 0.278 0.096 0.156 0.259

γ

- 14

. 431

- 3

. 450

- 5.065

- 14

. 551

- 5.940

- 5.665

9.7 RESULTS

Figures 9.7.1 shows the base case shipments for all CUAC and CUHP equipment classes considered. Figures 9.7.2-9.7. 3 compare model shipments between the base case and all standards cases for small and large CUACs. The varia bility in the model shipments stem primarily from shipments for replacements that depend upon the number of units getting repaired and hence the repair rate predicted by the repair/replace model in each of the projection years. Since we do not apply any equipment price learning, the repair probabilities predicted by the repair/replace model remain constant for all projection years in the analysis period. For the base case, the repair probability is close to the historical average repair rate described in Section 9.6.2. For the standards cases, e ven though the repair costs increase in proportion to the installation costs, they are still generally smaller than the installation costs and this difference is most significant at the highest standard level EL4. Consequently, this results in a large r number of units getting repaired and hence a significant drop in shipments for the standards cases (see Figures 9.7.2- 3)

.

Figures 9.7.9-10 illustrate the number of units getting repaired for the base case and all the standards cases for small and large CUACs based on the repair rate predicted by the repair/replace decision model. The number of repairs are close for standards cases EL2 and EL3 because the cost differential between efficiency weighted average total installed cost and repair costs are relatively close for these two levels.

9-39For very large equipment (240-760 KBtu/hr), the repair rate is equal to the historical average repair rate till year 2013 after which DOE assume d that all units get repaired at first
failure resulting in a huge drop in shipments as seen in Figure 9.7.4. Since broken units get another lifetime after repairs, these units are replaced at second failure and cause a spike in the number of shipments in future years.

Figure 9.7.1 Base case shipment for all CUAC and CUHP equipment classes.

9-40Figure 9.7.2 Model shipments for small CUACs for the base case

and standards cases.

9-41Figure 9.7.3 Model shipments for large CUACs for the base case

and standards cases.

9-42Figure 9.7.4 Model shipments for very large CUACs for the base

cas e and standards cases.

9-43Figure 9.7.5 Total model shipments for all CUAC equipment

classes for the base case and standards cases.

9-44Figure 9.7.6 Shipments

- weighted IEER shipments for small
CUACs for the base case and standards cases.

9-45Figure 9.7.7 Shipments

- weighted IEER shipments for large
CUACs for the base case and standards cases.

9-46Figure 9.7.8 Shipments

- weighted IEER shipments for very large
CUACs for the base case and standards cases.

9-47Figure 9.7.9 Number of units repaired and replaced for small
CUACs for the base case and standards cases.

9-48Figure 9.7.10 Number of units repaired and replaced for large
CUACs for the base case and standards cases

.

9-49Figure 9.7.11 Number of units repaired and replaced for very large CUACs for the base case an d standards cases.

9-50REFERENCES

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2 U.S.Census Bureau. Current Industrial Reports for Refrigeration, Air Conditioning, and Warm Air Heating Equipment, MA333M

. http://www.census.gov/manufacturing/cir/historical_data/ma333m/index.html

3 U.S. Department of Energy- Energy Information Administration, Commercial Buildings Energy Consumption Survey. 1992, 1995, 1999, and 2003. http://www.eia.gov/consumption/commercial/index.cfm

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Go vernment, Fiscal Year 2013

. http://www.gpo.gov/fdsys/pkg/BUDGET-2013-TAB/contentdetail.html Table 10.1- Gross Domestic Product and Deflators Used in Historical Tables.

Acces sed: 6/6/2013.

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.

10 RS Means Facilities Maintenance & Repair Cost Data 2013. www.rsmeans.com

.

11 Grainger, http://www.grainger.com/ . Accessed: 6/11/2013.

9-5112 Drillspot, http://www.drillspot.com/ . Accessed: 6/11/2013.

13 U.S. Department of Labor-Bureau of Labor Statistics. Series ID PCU333415333415F and Series ID PCU333415333415H; http://www.b ls.gov/ppi/Accessed: 6/6/2013.

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