CHAPTER 6. SHIPMENTS ANALYSIS

TABLE OF CONTENTS

6.1 INTRODUCTION ........................................................................................................... 6­1

6.2 FORMULATION OF SHIPMENTS MODEL................................................................ 6­2

6.2.1 Stock Events...................................................................................................... 6­3

6.2.1.1 Replacing Equipment...................................................................... 6­3

6.2.1.2 New Equipment .............................................................................. 6­3

6.3 DATA INPUTS ............................................................................................................... 6­4

6.3.1 Historical Shipments......................................................................................... 6­4

6.3.2 Equipment Class Breakdown............................................................................ 6­4

6.3.3 Equipment Life and Survival Function............................................................. 6­4

6.3.3.1 Building Growth Forecasts............................................................. 6­5

6.4 RESULTS ........................................................................................................................ 6­6

REFERENCES ............................................................................................................................ 6­7

LIST OF TABLES

Table 6.3.1 AEO 2008 Forecast of Total Building Floor Space............................................. 6­5

Table 6.4.1 Forecasted Shipments of New and Replacement Commercial Boiler

Equipment, 2012 Through 2042 .......................................................................... 6­6

LIST OF FIGURES

Figure 6.1.1 Flow Chart Showing Inputs to the Shipments Model.......................................... 6­1

Figure 6.3.1 Graph of Weibull Function .................................................................................. 6­5

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CHAPTER 6. SHIPMENTS ANALYSIS

6.1 INTRODUCTION

Estimates of commercial boiler equipment (CBE) shipments are a necessary input to

national energy savings (NES) and net present value (NPV) calculations. This chapter describes

the U.S. Department of Energy’s (DOE’s) methodology for projecting annual shipments of CBE

and presents results.

The Shipments Model results are driven primarily by base year (2007) shipment data for

the 10 classes of commercial boiler equipment under consideration (see TSD chapter 4). The

flow chart presented in Figure 6.1.1 outlines the structure of the Shipments Model, which

assumes that, in each year, the existing commercial boiler equipment stock either ages by 1 year

or breaks. Broken equipment is replaced and new equipment can be shipped into new

commercial floor space.

Figure 6.1.1 Flow Chart Showing Inputs to the Shipments Model

The Shipments Model is in a Microsoft Excel spreadsheet format embedded in the

National Impacts Model spreadsheet that is accessible on the DOE website at

http://www1.eere.energy.gov/buildings/appliance_standards/commercial/ashrae_products_docs_

meeting.html.

This chapter explains the Shipments Model and its derivation in detail. Section 6.2

discusses the formulation of the model, section 6.3 describes the data input to the model, and

section 6.4 presents the results for the base­case energy conservation standard level scenario and

discusses the development of higher standard level scenarios. The energy conservation standards

for commercial boiler equipment will set the maximum allowable rated energy consumption for

all equipment within an equipment class.

Shipment forecasts were determined for the baseline level and all higher efficiency levels

(reflecting potential higher standard levels) for which NES and NPV are required. DOE is

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considering up to four efficiency levels plus one baseline efficiency level for each of the

10 equipment classes.

6.2 FORMULATION OF SHIPMENTS MODEL

The Shipments Model is a description of commercial boiler equipment stock flows as a

function of year and equipment age in the stock. While there are 10 equipment classes, no

coupling is assumed between them, so the equations describe each type of equipment

independently.

This section begins with a definition of the different commercial boiler equipment stock

categories. DOE formulates the equations as updates of the distribution of stock in year t as a

function of age a to year t+1.

DOE used industry­provided data of base­year (2007) shipments to calculate current

shipments by equipment class. It used the lifetime of the equipment and a corresponding

survival function to estimate a replacement rate for existing stock. DOE then used this

information combined with estimates from the DOE Energy Information Administration’s

1 (EIA’s) Annual Energy Outlook 2008 (hereafter referred to as AEO 2008) for surviving and new

commercial floor space in 2007, along with an assumption of constant boiler saturation rates

across sin the stock and in new construction, to determine the percentage of shipments to existing

buildings and to new construction. Then, DOE forecasted new sales as a function of new

construction by multiplying new building floor space by the share of new shipments (1­

replacement ratio). Sales of new and replacement equipment are recorded by the year sold, and

each annual vintage is depreciated over the estimated life of the equipment. At the beginning of a

new year, DOE determines the fraction of stock that has failed based on the survival function

(see section 6.3.3) and uses an assumed replacement rate to find replacement sales for that year.

Total sales for that year are replacement sales plus new stock. Sales in each year are allocated to

the 10 equipment classes in proportion to their relative base­year shipments. DOE assumed that

all units experience a normal rate of repairs and that this is included in the average lifetime of the

equipment.

In view of industry practice, available information suggests that the average lifetime of a

unit is 30 years (see TSD chapters 2 and 5). The age of base­year stock for each equipment class

was developed by application of a survival function (based on a Weibull distribution with a mean

life of 30 years) to an assumed constant level of shipments into the building population over a

45­year period. This results in a steady­state long­term distribution of the vintages of the

equipment in the building population for 2007. The total stock of age a in a given year t is

represented by

U(t,a) = U0(t,a) Eq. 6.1

and the average age of the stock in year y is defined as

StockAge(t) = ∑aU( t, a) x a / ∑(U( t,a)) Eq. 6.2

where

a = age of stock,

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t = year,

U(t,a) = total stock of age a in a given year y,

U0(t,a) = stock of existing units.

The shipments of new stock in a given year are Uship(t). By definition, the age of the

equipment is zero in the year that it is shipped, so that Uship(t) = U(t,0).

6.2.1 Stock Events

In the transition from year t to year t+1, two things could happen to the stock of

commercial boiler equipment:

1. existing equipment could fail and be replaced, or

2. the stock could simply age by 1 year.

In the model, early replacements (i.e., existing equipment that is replaced before the

expected end of service life) are not considered, and all failed equipment is assumed to be

replaced by a similar replacement boiler (e.g., no change of equipment is assumed). The model is

capable of forecasting total shipments with replacement rates different than 100 percent, but this

was not implemented because of lack of data. The following sections present the equations used

to represent each possible event.

6.2.1.1 Replacing Equipment

DOE determines the probability that commercial boiler equipment of age a from stock U0

will break or will be replaced using a Weibull survival distribution function PB1(a), with a scale

parameter of 33.1 and a shape parameter of 4 such that PB1(a)=W(4,33.1) results in a 30­year

average lifetime with a minimum lifetime of about 8 years, and a maximum lifetime of about

50 years (consistent with industry comments). Similarly, the probability that equipment of age a

from stock U1 will fail is given by the same function PB1(a). These probabilities do not depend

on the model year t. DOE defines the quantities of replaced equipment as

UB(t, a) = PB0(a) x UB0(t,a) Eq. 6.3

where

UB0(t,a) = stock of existing units,

PB0(a) = probability that stock of existing units will fail, or will be replaced,

U0(t,a) = stock of existing units,

a = age of stock,

t = year.

6.2.1.2 New Equipment

New commercial boiler equipment will be purchased to replace the units described

above. Available information suggests that the purchase of new equipment that would go into

new buildings is driven by total building floor space. DOE uses AEO 2008 projections through

2030 to create a building floor space index. DOE uses these floor space projections to create an

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index that is used to estimate future boiler sales by inflating base­year sales. The total of new

equipment shipments is the estimated total shipments for 2007 multiplied by the index of new

construction of square feet of buildings for each future year.

DOE has no information about the variation in the market saturation of commercial boiler

equipment by building type or over time. Therefore, in the model, the purchase of new

equipment is driven by the construction of new floor space and the replacement of broken or

removed equipment on a one­for­one basis.

6.3 DATA INPUTS

6.3.1 Historical Shipments

Historical shipments are necessary to calibrate a shipments model to the existing market.

DOE has no data for historical shipments of commercial boilers and therefore developed a model

that relies on 2007 shipments to make its predictions. All remaining stock from historical

shipments is assumed to be distributed across vintages based on the survival function and an

assumption of constant boiler shipments over time into the building population.

6.3.2 Equipment Class Breakdown

DOE organized commercial boiler equipment into either gas­ or oil­fired equipment

classes according to the boiler’s input capacity size, output, input, and draft type based on the

ASHRAE 90.1­2007 equipment classes . Following these guidelines, DOE used the following 10

equipment classes:

• Small gas­fired hot water boilers;

• Small gas­fired steam, all except natural draft;

• Small gas­fired steam, natural draft boilers;

• Small oil­fired hot water boilers;

• Small oil­fired steam boilers;

• Large gas­fired hot water boilers;

• Large gas­fired steam all except natural draft boilers;

• Large gas­fired steam natural draft boilers;

• Large oil­fired hot water boilers; and

• Large oil­fired steam boilers.

6.3.3 Equipment Life and Survival Function

DOE determined the average lifetime for commercial boilers to be 30 years. DOE

developed a survival function based on a Weibull distribution (see Figure 6.3.1) that generated a

minimum lifetime of commercial boiler equipments of about 8 years, and a maximum lifetime of

about 50 years. As commercial boiler equipment enters and ages through the total stock, each

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boiler has percentage chances of surviving to the next year or failing (1­survival probability)

based on its failure rate for the general population according to its vintage.

Survival Function for Boiler Equipment

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

110%

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Equipment Age

Percentage Remaining

Figure 6.3.1 Survival Function for Commercial Packaged Boilers (Weibull­based)

6.3.3.1 Building Growth Forecasts

The amount of commercial floor space is the main driver for commercial boiler

equipment shipments and is appropriately one of the basic inputs into the Shipments Model.

DOE took the projected floor space construction after the year 2003 from the National

Energy Modeling System (NEMS) projection published in the AEO 2008. AEO 2008 lists the

projections for years 2004 to 2030. Beyond 2030, DOE extrapolated a 10­year trend of AEOprojected data between 2020 and 2030 to estimate future growth in commercial building stock

from 2030 through 2042.

The total of new floor space is the estimated total floor space for 2007 multiplied by an

index of new construction of floor space for buildings for each future year (see Table 6.3.1). The

index is based on the estimated growth in each year from 2012 through 2042, divided by the

2007 estimated growth value.

Table 6.3.1 AEO 2008 Forecast of Total Building Floor Space

Year

Total Building Floor Space

(Million Square Feet)

Floor Space Index

(2007 Base Year)

2009

2010

2015

2020

2025

2030

Annual Growth Factor,

2030 through 2042

77,807

78,799

83,913

89,250

94,832

100,756

1.013%

1.026

1.039

1.107

1.177

1.251

1.329

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6.4 RESULTS

Table 6.4.1 shows the forecasted shipments for new and replacement commercial

packaged boilers from 2012 through 2042 as calculated with the Shipments Model.

Table 6.4.1 Forecasted Shipments of New and Replacement Commercial Boiler Equipment,

2012 Through 2042 (Base Case)

Equipment Class

Units Shipped by Year by Equipment Class

2012 2015 2020 2025 2030 2035 2040 2042 Cumulative

Small, Gas­fFired, Hot Water 6,853 7,112 7,494 7,922 8,848 10,343 12,239 12,984 276,874

Small, Gas­Fired, Steam, All

Except Natural Draft

2,322 2,410 2,539 2,684 2,998 3,505 4,147 4,399 93,817

Small, Gas­Fired, Steam, Natural

Draft

3,568 3,703 3,902 4,125 4,607 5,385 6,372 6760 144,158

Small, Oil­Fired, Hot Water 1,926 1,999 2,106 2,226 2,486 2,906 3,439 3,648 77,799

Small, Oil­Fired, Steam 3,228 3,350 3,530 3,732 4,168 4,872 5,765 6,116 130,428

Large, Gas­Fired, Hot Water 1,104 1,146 1,208 1,277 1,426 1,667 1,972 2092 44,620

Large, Gas­Fired, Steam, All

Except Natural Draft 2,011 2,087 2,199 2,324 2,596 3,034 3,591 3,809 81,232

Large, Gas­Fired, Steam, Natural

Draft 2577 2,674 2,818 2,979 3,327 3,889 4,602 4,882 104,114

Large, Oil­Fired, Hot Water 538 558 588 622 695 812 961 1,019 21,738

Large, Oil­Fired, Steam 4,248 4,408 4,645 4,910 5,485 6,411 7,586 8,048 171,616

Total 28,376 29,449 31,030 32,801 36,637 42,824 50,675 53,758 1,146,397

As equipment purchase price increases with lower energy consumption levels, a drop in

shipments could be expected relative to the base case. Although there is a provision in the

spreadsheet for a change in shipments as the efficiency level increases (or energy consumption

level decreases), DOE has no information with which to calibrate such a relationship. Therefore,

for the Notice of Proposed Rulemaking analysis, DOE presumes that the shipments do not

change in response to higher efficiency levels being considered as possible standard levels.

Results from this model provide an indication of future stock growth for the years 2012

through 2042, but, because of the nature of the model and the lack of shipments data, the model

does not retroactively match the shipments in 2007 (approximately 36,000 units, reported in TSD

chapter 2). However, over the period for which shipments were analyzed the average shipments

per year were 36,980 boilers per year. The average shipments over the period from 2012 through

2042 was 2­percent higher than the 2007 shipment estimates.

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REFERENCES

1. U.S. Department of Energy/Energy Information Administration. 2008. Annual Energy

Outlook 2008. DOE/EIA­0383(2008), DOE/EIA, Washington, D.C.

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ACRONYMS AND ABBREVIATIONS

AEO Annual Energy Outlook

CRE commercial boiler equipment

DOE U.S. Department of Energy

EIA Energy Information Administration

NEMS National Energy Modeling System

NES national energy savings

NOPR Notice of Proposed Rulemaking

NPV net present value

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