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 .............................................................................. 6­4
 

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............................................................. 6­5
 

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

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

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

LIST OF TABLES

Table 6.3.1	 AEO 2009 Forecast of Total Building Floor Space............................................. 6­6
 

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

(EIA’s) Annual Energy Outlook 20091

. For the results presented in this chapter, DOE used the

AEO 2009 April 2009 reference case including American Recovery and Reinvestment Act

(ARRA) (hereafter referred to as AEO 2009) for surviving and new commercial floor space in

2007, along with an assumption of constant boiler saturation rates across 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 (1­replacement 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

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where
 

a = age of stock,
 

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.

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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 2009 projections through

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

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.

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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

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 R em aining

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 2009. AEO 2009 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.

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Table 6.3.1 AEO 2009 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

80,094

81,069

85,570

91,506

97,488

103,930

1.012%

1.037

1.049

1.107

1.184

1.262

1.345

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)

Units Shipped by Year by Equipment Class

Equipment Class 2012 2015 2020 2025 2030 2035 2040 2042 Cumulative

Small, Gas­Fired, Hot Water 7110 7377 7832 8364 9420 11019 13023 13812 292,525

Small, Gas­Fired, Steam, All

Except Natural Draft 2409 2500 2654 2834 3192 3734 4413 4680 99,120

Small, Gas­Fired, Steam, Natural

Draft 3702 3841 4078 4355 4905 5737 6780 7191 152,306

Small, Oil­Fired, Hot Water 1998 2073 2201 2350 2647 3096 3659 3881 82,197

Small, Oil­Fired, Steam 3349 3475 3690 3940 4438 5191 6135 6506 137,801

Large, Gas­Fired, Hot Water 1146 1189 1262 1348 1518 1776 2099 2226 47,142

Large, Gas­Fired, Steam, All

Except Natural Draft 2086 2164 2298 2454 2764 3233 3821 4052 85,823

Large, Gas­Fired, Steam, Natural

Draft 2674 2774 2945 3145 3542 4144 4897 5194 109,999

Large, Oil­Fired, Hot Water 558 579 615 657 740 865 1022 1084 22,967

Large, Oil­Fired, Steam 4407 4572 4855 5184 5839 6830 8072 8561 181,317

Total 29440 30544 32429 34629 39004 45625 53920 57188 1,211,199

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 39,070 boilers per year. The average annual shipment number for the period from

2012 through 2042 was approximately 8­percent higher than the 2007 shipment estimates.

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REFERENCES
 

1. U.S. Department of Energy/Energy Information Administration. 2009. Annual Energy

Outlook 2009. DOE/EIA­0383(2009), 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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