7

- i CHAPTER 7. ENERGY USE CHARACTERIZATION

TABLE OF CONTENTS

7.1 INTRODUCTION

................................ ................................ ................................ ........... 7

- 1

7.2 CENTRAL AIR CONDITIO NER AND CENTRAL HEAT PUMP

.............................. 7

- 1

7.2.1 Introduction

................................ ................................ ................................ ...................... 7

- 1

7.2.1.1 General Approach to the Energy Use Analysis

– Residential

....................... 7

- 1

7.2.1.2 General Approach to the Energy Use Analysis

– Commercial

...................... 7

- 4

7.2.1.3 Overview of Energy Use Analysis Inputs

................................ ...................... 7

- 4

7.2.2 Analyzed RECS Households

................................ ................................ ........................... 7

- 5

7.2.3 Baseline Annual Space

- Cooling Energy Use

– Residential

................................ ............ 7

- 6

7.2.3.1 Methodology for Estimating Stock Annual Space

- Cooling Energy Use

....... 7

- 6

7.2.3.2 Stock Annual Space

- Cooling Energy Use

................................ ..................... 7

- 7

7.2.3.3 Stock Space

- Cooling Efficiency (SEER)

................................ ..................... 7

- 10

7.2.3.4 Estimating Baseline Annual Space

- Cooling Energy Use

............................ 7

- 13

7.2.4 Baseline Annual Space

- Heating Energy Use for Central Heat Pump Equipment

— Residential

................................ ................................ ................................ ...................... 7

- 14

7.2.4.1 Estimating Stock Annual Space

- Heating Energy Use

................................ . 7

- 15

7.2.4.2 Stock Space

- Heating Efficiency (HSPF)

................................ ..................... 7

- 17

7.2.4.3 Estimating Baseline Annual Space

- Heating Energy Use

............................ 7

- 19

7.2.5 Baseline Residential Cooling and Heating Res ults

**−** Adjusted for RECS Sample Weights

................................ ................................ ................................ .......................... 7

- 20

7.2.6 Baseline Commercial Central Air Conditioner and Central Heat Pump Energy Use

.... 7

- 21

7.2.7 Overview of Commercial Energy Simulation Approach

................................ ............... 7

- 22

7.2.7.1 Simulation Tool Description

................................ ................................ ........ 7

- 23

7.2.7.2 Efficiency Levels Analyzed and Corresponding Simulation Parameters

.... 7

- 23

7.2.7.3 Small Office Building Characteristics

................................ ......................... 7

- 25

7.2.7.4 Building Operating Characteristics

................................ .............................. 7

- 26

7.2.7.5 Building Mechanical Systems and Equipment

................................ ............ 7

- 29

7.2.8 Baseline Commercial Ener gy Distribution

................................ ................................ .... 7

- 30

7.2.9 Commercial Energy for Intermediate Efficiency Levels

................................ ............... 7

- 3 1

7.2.10 Market Baseline Space

- Cooling and Space

- Heating Efficiencies

................................ . 7

- 32

7.2.11 Determining Installed E quipment Capacity for RECS Observations

............................ 7

- 32

7.2.12 Regional Energy Consumption by Efficiency Level

................................ ..................... 7

- 35

7.2.13 Off Mode Energy Consumption

................................ ................................ ..................... 7

- 40

7.3 FURNACES

................................ ................................ ................................ ................... 7

- 43

7.3.1 Introduction

................................ ................................ ................................ .................... 7

- 43

7.3.2 Household Sample

................................ ................................ ................................ ......... 7

- 43

7.3.3 An nual Heating Load Calculation

................................ ................................ ................. 7

- 45

7.3.3.1 Overview of Heating Load Estimates

................................ .......................... 7

- 47

7.3.4 Annual Energy Consumption

................................ ................................ ......................... 7

- 47

7.3.4.1 Fuel Consumption

................................ ................................ ........................ 7

- 47

7.3.4. 2 Electricity Consumption

................................ ................................ .............. 7

- 48

7.3.5 Assigning Furnace Equipment Characteristics to Sampled Households

....................... 7

- 50

7.3.5.1 Input Capacity of Existing and New Equipment

................................ ......... 7

- 517

- ii 7.3.5.2 Airflow Size of Existing Equipment

................................ ............................ 7

- 52

7.3.5.3 AFUE of Existing Equipment

................................ ................................ ...... 7

- 54

7.3.6 Furnace Energy Consumption Results

................................ ................................ ........... 7

- 57

7.3.7 Standby Energy Consumption Results for Furnaces

................................ ..................... 7

- 58

LIST OF TABLES

Table 7.2.1 The 30

- Year Average CDD (1978

– 2007) and the 2005 CDD for the Nine Census Divisions and the Four Large States

................................ ........................ 7

- 8 Table 7.2.2 Weather Adjusted Space

- Cooling Energy Consumption Statistics at Regional Level (CAC

- Stock)

................................ ................................ .............................. 7

- 9 Table 7.2.3 Weather Adjusted Space

- Cooling Energy Consumption Statistics at Regional Level (CHP

- Stock)

................................ ................................ ............................... 7

- 9 Table 7.2.4 Number of Observations for Each Age Group

................................ ....................... 7

- 11 Table 7.2.5 Average Annual Shipped Space

- Cooling Efficiency

................................ .............. 7

- 13 Table 7.2.6 Baseline (13 SEER) Space

- Cooli ng Energy Consumption Statistics at Regional Level (Central Air Conditioner)

................................ ......................... 7

- 14 Table 7.2.7 Baseline (13 SEER) Space

- Cooling Energ y Consumption Statistics at Regional Level (Central Heat Pump)

................................ ................................ . 7

- 14
Table 7.2.8 30

- Year Average HDD (1978

– 2007) and the 2005 HDD for the Nine Census Divisions and the Four Large States

................................ ................................ .. 7

- 16
Table 7.2.9 Statistics of Annual Weather

- Adjusted Space

- Heating Energy Consumption

for RECS

- Stock Central Heat Pump at Regional Level

................................ .... 7

- 16 Table 7.2.10 Average Annual Heating Pump HSPF

................................ ................................ . 7

- 19 Table 7.2.11 Baseline (13 SEER) Space

- Heating Energy Consumption Statistics at Regional Level (Central Heat Pumps)

................................ ............................... 7

- 20 Table 7.2.12 Central Air Conditioner and Central Heat Pump Blower and Characteristics Used in Modeling Commercial Cooling and Heating Energy Use

.................... 7

- 25 Table 7.2.13 Development of Market Weights for 2

- , 3

- , and 5

- Ton Air Conditioners and Heat Pumps

................................ ................................ ................................ ........ 7

- 34 Table 7.2.14 Fraction of Shipments by Product Class to Each Region

................................ ..... 7

- 36 Table 7.2.15 Split

- System Air Conditioners: Average Annual Energy Use by Efficiency

Level

**−** Coil

- Only Systems

................................ ................................ ................ 7

- 37 Table 7.2.16 Split

- System Air Conditioners: Average Annual Energy Use by Efficiency

Level

**−** Blower

- Coil Systems

................................ ................................ ............ 7

- 37 Table 7.2.17 Split

- System Heat Pumps: Average Annual Energy Use by Efficiency Level

.... 7

- 38 Table 7.2.18 Single

- Package Air Conditioners: Average Annual Energy Use by Efficiency Level

................................ ................................ ................................ . 7

- 39 Table 7.2.19 Single

- Package Heat Pumps: Average Annual Energy Use by Efficiency
Level

................................ ................................ ................................ .................. 7

- 39 Table 7.2.20 Small Duct High Velocity Air Conditioners: Average Annual Energy Use by Efficiency Level

................................ ................................ ............................ 7

- 40 Table 7.2.21 Split

- System Blower Coil, Packaged, Space Constrained, and SDHV Air Conditioner Off Mode Energy Use

................................ ................................ .... 7

- 41 Table 7.2.22 Split

- System Coil

- Only Air Conditioner Off Mode Energy Use

......................... 7

- 427

- iii Table 7.2.23 Split

- System, Packaged, and Space Constrained Heat Pump Off Mode Energy Use

................................ ................................ ................................ ......... 7

- 42 Table 7.2.24 National Average Off Mode Energy Use

................................ ............................. 7

- 42
Table 7.3.1 Selection of RECS 2005 Records for Furnaces

................................ ...................... 7

- 44 Table 7.3.2 Heating Degree Day Adjustment Factors

................................ ............................... 7

- 46 Table 7.3.3 Range of Annual Heating Load for Each Furnace Product Class by Region

......... 7

- 47 Table 7.3.4 Values for Delay and Ignition Times

................................ ................................ ...... 7

- 49
Table 7.3.5 Fraction of Non

- Weatherized Gas Furnace Fan Motors and Controls Shipments by Furnace Efficiency and Construction Type in 2016

................... 7

- 50 Table 7.3.6 Standby Power Consumption by Efficiency Level and Furnace Type

................... 7

- 50
Table 7.3.7 Distributi on of Input Capacity for Non

- Weatherized Gas Furnaces

...................... 7

- 52
Table 7.3.8 Distribution of Airflow for Furnaces

................................ ................................ ...... 7

- 53 Table 7.3.9 Common Furnace Input Capacity and Airflow combinations

................................ 7

- 54 Table 7.3.10 Number of Observations for Each Age Group

................................ ..................... 7

- 54

Ta ble 7.3.11 Historical Fraction of Regional Gas Furnace Shipments by AFUE Bins

............. 7

- 55
Table 7.3.12 Historical Fraction of Manufactured Home Gas Furnace Shipments by
AFUE Bins

................................ ................................ ................................ ......... 7

- 56
Table 7.3.13 Historical Fraction of Oil

- Fired Furnace Shipments by AFUE Bins

.................... 7

- 56 Table 7.3.14 Annual Energy Consumption for Non

- Weatherized Gas Furnaces

...................... 7

- 57 Table 7.3.15 Annual Energy Consumption for Manufactured Home Gas Furnaces

................. 7

- 58 Table 7.3.16 Annual Energy Consumption for Oil

- Fired Furnaces

................................ ........... 7

- 58 Table 7.3.17 Annual Standby Energy Consu mption for Furnaces

................................ ............ 7

- 59

LIST OF FIGURES

Figure 7.2.1 Flow Diagram for the Determination of Annual Space

- Cooling Energy for Residential Central Air Conditioners and Central Heat Pumps

........................... 7

- 3 Figure 7.2.2 Flow Diagram for the Determin ation of Annual Space

- Heating Energy for Residential Central Heat Pumps

................................ ................................ .......... 7

- 3 Figure 7.2.3 Flow Diagram for the Determination of Annua l Space

- Cooling and Space

Heating Energy for Central Air Conditioners and Central Heat Pumps in
Commercial Use

................................ ................................ ................................ ... 7

- 4 Figure 7.2.4 Nat ional Distribution of Weather

- Adjusted Space

- Cooling Energy for CAC

RECS Observations

................................ ................................ ........................... 7

- 10 Figure 7.2.5 National Distribution of Weather

- Adjusted Space

- Cooling Energy for CHP

RECS Observations

................................ ................................ ........................... 7

- 10 Figure 7.2.6 The Distribution of Central Air Conditioners in the RECS Sample by Age Groups

................................ ................................ ................................ ................ 7

- 11 Figure 7.2.7 The Distribution of Central Heat Pumps in the RECS Sample by Age Groups

.... 7

- 12 Figure 7.2.8 Distribution of RECS Households with Central Air Conditioners by Vintage

..... 7

- 12 Figure 7.2.9 National Distribution of Weather

- Adjusted Space

- Heating Energy for RECS
Observations

................................ ................................ ................................ ...... 7

- 17 Figure 7.2.10 Distribution of Households with Heating

- Performance Heat Pumps by Age Groups

................................ ................................ ................................ ................ 7

- 18 Figure 7.2.11 Distribution of RECS Households with Central Heat Pumps by Vintage

.......... 7

- 197

- iv Figure 7.2.12 Baseline National Cooling Energy Distribution for Central Air
Conditioners

– Residential

................................ ................................ ................. 7

- 20 Figure 7.2 .13 Baseline National Cooling Energy Distribution for Central Heat Pumps

– Residential

................................ ................................ ................................ .......... 7

- 21 Figure 7.2.14 Baseline National Heating Energy Distribution for Central Heat Pumps

– Residential

................................ ................................ ................................ .......... 7

- 21 Figure 7.2.15 Small Office Building Design View

................................ ................................ .... 7

- 26 Figure 7.2.16 Small Office Building HVAC Zoning

................................ ................................ . 7

- 26 Figure 7.2.17 Small Office Lighting Schedule

................................ ................................ .......... 7

- 27 Figure 7.2.18 Small Office Plug Load Schedule

................................ ................................ ....... 7

- 28 Figure 7.2.19 Small Office Occupancy Schedule

................................ ................................ ...... 7

- 28 Figure 7.2.20 Small Office Thermostat Set Point Schedule

................................ ...................... 7

- 29 Figure 7.2.21 Baseline National Cooling Energy Distribution for Central Air

Conditioners

**−** Commercial, Weighted

................................ ............................. 7

- 30 Figure 7.2.22 Baseline National Cooling Energy Distribution for CHC

**−** Commercial, Weighted

................................ ................................ ................................ ............ 7

- 31 Figure 7.2.23 Baseline N ational Heating Energy Distribution for Central Heat Pumps

**−** Commercial, Weighted

................................ ................................ ...................... 7

- 31 Figure 7.2.24 Shipments of Residential Air Conditioners and Heat Pumps by Capacity for

2009

................................ ................................ ................................ .................... 7

- 347

- 1 CHAPTER 7. ENERGY USE CHARACTER IZATION

7.1 INTRODUCTION

The life

- cycle cost (LCC) and payback period (PBP) analyses described in chapter 8 require determination of the savings in operating cost consum ers would realize from more energy efficient products. Energy costs are the most significant component of consumer operating costs. The U.S. Department of Energy (DOE or the Department) uses annual energy use, along with energy prices, to establish energy costs at various energy efficiency levels. This chapter describes the determination of annual energy use of central air conditioner (CAC) and central heat pump equipment (CHP) as well as residential non

- weatherized gas furnaces, manufactured home furnaces, and oil

- fired furnaces. I n contrast to the DOE test procedure

, which uses typical operating conditions in a laboratory setting, the energy use characterization seeks to estimate the range of energy consumption of the products in the field

.

7.2 CENTRAL AIR CONDITIONER AND CENT RAL HEAT PUMP

7.2.1 Introduction

The energy use analysis provides estimates of the distribution of annual energy consumption for central air conditioner and central heat pump equipment at the base case ( i.e.

, with no new standards) and at eff iciency standard levels considered in the analysis. This
information is used in the subsequent LCC and PBP analyses (chapter 8 of the technical support document [TSD] ) , which in turn provide the raw energy use data for the national impact analys i s (chapter 1 0 of the TSD ). Energy use in both the residential sector and the commercial sector is
considered . In the energy use analysis, DOE developed energy consumption estimates for four equipment classes analyzed in the engineering analysis (chapt er 5 of the TSD ) . For one equipment class, split

- system air conditioners, two installation methods were separately
considered: coil

- only, where the indoor furnace fan is not replaced for higher efficiency equipment, and blower

- coil, where the indoor blower is replaced, c orresponding to the similar breakdown of this equipment class in the engineering analysis.

7.2.1.1 General Approach to the Energy Use Analysis

– Residential

The general approach adopted in the energy use analysis for central air conditioner equipment used in resid ences was to utilize energy use and household characteristics data from
the DOE Energy Information Agency (EIA) 2005 Residential Energy Consumption Survey (RECS) 1 to provide estimates of cooling and heating energy for the sample households using central air conditioner and central heat pump equipment. This estimate reflects household stock equipment efficiency levels. The energy use estimates are then adjusted to r eflect long

- term average weather conditions and then further adjusted to reflect equipment energy consumption for the same households assuming that they were purchasing and using new equipment at the same rates as they would be in 2016 under a base

- case st andards scenario.

A sample population of U.S. homes using central air conditioner and central heat pump equipment was first identified from the RECS households surveyed. This sample provided a7

- 2 dataset of cooling and heating energy use for 2005 for househo lds with central air conditioners and central heat pumps. These data were then adjusted (weather

- normalized) to reflect corresponding energy

- use figures for typical long

- term climate conditions. Historical efficiency data for shipped central air conditione r and central heat pump equipment was used in conjunction with equipment age information from RECS to estimate the stock equipment efficiency for each household record used in the analysis. The annual energy consumption for equipment in the 2016
base case ( i.e. , no new standards) was then calculated for each equipment class by multiplying
the weather

- adjusted cooling or heating energy use for each RECS household by the ratio of the efficiency ( e.g. , seasonal energy efficiency ratio or SEER) of the stock uni t in that household to that of an assumed efficiency for that household in the 2016 base case. This 2016 base case provides an efficiency level likely to be purchased for each household, assuming a new central air conditioner or central heat pump purchase in that year. The 2016 efficiency levels used for each household were randomly selected from a distribution of shipped efficiencies developed for each product class for the year 2016. This base case is also referred to as the market baseline
case because t he efficiency distribution is representative of the market for central air conditioner and central heat pump equipment in that year in the absence of new standards.

Once the base

- case cooling energy use was established for a selected RECS household
record , the corresponding annual cooling energy consumption for higher efficiencies under new standard scenarios was calculated by multiplying the cooling energy consumption for each household in the base case by the ratio of the SEER in the base case to that of the SEER in the
standards case. In the case of heating energy use for heat pumps

, the corresponding household annual heat pump heating energy consumption under new standard scenarios was calculated by multiplying the heat pump heating energy consumption f or each household in the base case by the ratio of the heating seasonal performance factor (HSPF) in the base case to the HSPF in the
standards case , and then further multiplied by a heat pump HSPF improvement degradation factor to account for the impact o f climate on relative heating efficiency improvement. Figure

7.2

. 1 and Figure 7.2

. 2 provide flow diagrams for the calculation of cooling and heating energy for the residential energy use analysis.7

- 3 Figure 7.2

. 1 Flow Diagram for the Determination of Annual Space

- Cooling Energy for

Residential Central Air Conditioners and Central Heat Pumps

Figure 7.2

. 2 Flow Diagram for the Determination of Annual Space

- Heating Energy for

Residential Central Heat Pump s RECS
Stock Household
Annual Heat ing Energy (Weather Normalized)

RECS Age of Household

CHP Standard Level

HSPF Market Baseline

HSPF Distribution Baseline SEER ( HSPF 7.7 )

AHRI Shipment Average HSPF by Vintage

CHP Baseline Annual Heating Energy Use Market Baseline Annual Heating Energy Use
Estimated

HSPF of Stock

CHP Standard Level Annual Heating Energy Use RECS
Stock Household Annual Cooling Energy (Weather Normalized)

RECS Age of Household

CAC or CHP Standard Level

SEER Market Baseline

SEER

Distribution Baseline SEER ( SEER 13)

AHRI Shipment Average

SEER
CAC and CHP Baseline Annual Cooling Energy Use Market Baseline Annual Cooling Energy Use Standard Level Annual Cooling Energy Use Estimated S EER
of Sto ck

CAC or CHP7

- 4 7.2.1.2 General Approach to the Energy Use Analysis

– Commercial

DOE also developed estimates for the annual energy use for central air conditioner and central heat pump equipment sold into the commercial building market. The commercial market is estimated to use approximately 7 percent of all central air conditioner an d central heat pump equipment. For the commercial analysis, DOE relied on the use of the EnergyPlus building simulation software to estimate the energy consumption of this equipment at four specific efficiency levels

— from baseline to the max

- tech level

— for 237 climates around the United States for a typical commercial application: a small office building. DOE extracted equipment sizing, supply fan, condenser, and heater energy use from these simulations. DOE then used these results to develop estimates of a nnual energy use for central air conditioner and central heat pump equipment at other efficiency levels defined in the engineering analysis via interpolation between the efficiency levels simulated. Figure 7.2

. 3 provides a flow diagram for the calculation of central air conditioner and central heat pump cooling and heating energy for the commercial energy use analysis.

Figure 7.2

. 3 Flow Diagram for the Determination of Annual Space

- Cooling and Space

Heating Energy for Central Air Conditioners and Central Heat Pumps in Commercial Use

7.2.1.3 O verview of Energy Use Analysis Inputs

The following are the principal inputs to the residential energy use analysis:

**•** Household cooling and heating energy from 2005 RECS

**•** Central air conditioner and central heat pump equipment age information for the

RECS households

**•** Historical state average monthly cooling and heating degree

- day data (1978

– 2007) Small Office Building Model EnergyPlus Simul ation Tool

CAC or CHP
Characteristics for
Four Selected Efficiency Levels TMY Weather

Data Bldg. Annual Cooling, Heating, Fan Energy Use Zone Peak Equipment Sizes

Annual Cooling, Heating, Fan Energy Use for Selected Efficiency Levels (Normalized to 3

- Ton Unit) Market Baseline Annual Cooling, Heating, and Fan Energy Use Market Baseline

SEER /HSPF

Distribution Standard Level
SEER/HSPF

Standard Level Annual Cooling, Heating, and Fan Energy Use7

- 5 **•** Historical average cooling and heating efficiency data for central air conditioner and central heat pump equipment from the Air

- Conditioning, Heating and Refrigerat ion Institute (AHRI)

**•** Base

- case efficiency distributions for 2016 developed for this analysis

**•** Engineering efficiency levels to be analyzed

The following are the principal inputs to the commercial energy use analysis:

**•** Engineering efficiency levels to be anal yzed

**•** Location

- specific weather file data for use with building simulation tool

**•** Population

- based weighting factors for each weather file

7.2.2 Analyzed RECS Households

The 2005 RECS includes data for 4,382 housing units , of which 2

, 3 17 were reported to
have “CAC only” cooling systems. These include 4 16 housing units reporting their central air conditioner unit also being a cooling

- performance heat pump

. The number s of households actually included in the LCC and PBP analyses were 1,854 for the central air condition er systems
and 339 for heat pumps

. Of the 1,901 (2,317

**−** 416) households that could have been considered for analysis of central air conditioner energy consumption, 47 were not included in the analysis for the following reasons:

**•** T he central air conditioner system was reported as not being used (40 households).

**•** The household reported a “heat pump” being the main heating equipment, contradicting a second response that the central air conditioner system is not a heat pump (7 households).

**•** For the seven househo lds with conflicting responses, it was considered that the response regarding the main heating equipment being a heat pump should override the secondary response, and these records were included in the set for analyzing energy consumption for heat pumps.

For the heat pumps, out of the 423 (416 + 7) candidate households, DOE removed 84 for the following reasons:

**•** T he system was reportedly not being used (6 households).

**•** The survey responses reported non

- furnace heating equipment ( i.e. , heating stove, fireplace, built

- in wall furnaces, etc.) as the main heating equipment, thus implying that the heating energy use reported for the household should not be primarily attributed to the heat pump (17 households).

**•** Some households indicated a warm air furnace a s the main heating equipment and no electrical energy use for space heating, implying that the heat pump function is not used7

- 6 at all or that the RECS may be reporting the “heat pump” energy consumption or presence in error (36 households).

**•** Some households reported a warm air furnace as well as a heat pump, but questions on usage of any complementary fuel were skipped. This led to ambiguity about whether the heating energy use reported from the conditional demand model was representative of a heat pump or a n electric furnace. Barring more detail on the analysis of these records by EIA, these households were removed from consideration in DOE’s analysis (25 households).

7.2.3 B aseline Annual Space

- Cooling Energy Use

– Res idential

In order to estimate the energy sav ings for higher standards, DOE first established estimates of cooling and heating energy consumption for baseline equipment based on energy consumption in the current stock and product efficiency assumptions appropriate to the stock equipment and the effic iency of baseline products. The energy consumption of central air conditioners and central heat pumps in the equipment stock is related to the capacity and efficiency of these products, the climate, and individual household use patterns. The efficiency of the equipment in the building stock also varies depending on the vintage. The energy consumption of residential air conditioners and heat pumps in the current stock and estimates of the efficiency in the current stock are used in conjunction with the effic iencies of baseline equipment to provide an estimate of energy consumption of baseline equipment. The baseline annual space

- cooling energy consumption for residential air conditioners and cooling

performance heat pumps was estimated for each RECS observati on using equation 7.2.1.

base

stock res

c stock res c base res SEER SEER UEC UEC

_

_ _ _ _ **× =**  Eq. 7.2

. 1

Where :

UEC res_ base _c = the baseline annual unit energy consumption for space

- cooling

, UEC res_stock_c = the annual unit energy consumption space

- cooling associated with the
stock equipment

, SEER base = the space

- cooling efficiency a ssociated with the baseline equipment

, and

SEER res_stock_c = the space

- cooling efficiency associated with the stock equipment

.

Thus, once the annual space

- cooling energy consumption associated with the stock equipment, UEC res_stock_c , and the space

- cooling efficiency, SEER res_stock_c , are determined, the baseline annual space

- cooling energy consumption is estimated. In the next se ction, the methodology for obtaining UEC res_stock_c and SEER res_stock is discussed.

7.2.3.1 Methodology for Estimating Stock Annual Space

- Cooling Energy Use

The stock annual space

- cooling energy consumption was estimated based on data from the 2005 RECS. This survey contains data on household characteristics, end

- use energy7

- 7 consumption, general location in the United States (limited to location within census d ivision and four large states: New York, Florida, Texas

, and California ), and approximate heating and cooling degree

- days for each sample location for the year of the survey. The latter two statistics are purposefully kept general or sufficiently masked by EIA to preclude identification of the surveyed residences.

The household energy consumption for the individual surveyed households in RECS is
obtained by EIA from the energy suppliers via monthly energy bill records. EIA uses a conditional demand analysi s (CDA) 1 model to further break down the household energy use int o end

- use energy consumption estimates ( e.g. , cooling, heating, lighting, and hot water energy), which it reports as annual end

- use energy in the survey results. The cooling energy use for the
selected households with central air conditioner or central hea t pump products represents the cooling energy consumption associated with the stock equipment for 2005. Because 2005 may not be representative of typical weather for that location, the cooling energy consumption associated with the stock equipment in the R ECS households is first adjusted (weather

normalized) using 30

- year average (1978

– 2007) cooling degree

- day (CDD) data (base 65 °F) and equation 7.2.2:

2005

_ _ _

_ 30

_ _ _ _ _ stock res avg yr adj non c stock res c stock res CDD CDD UEC UEC **× =** 

**−** Eq. 7.2

. 2

Where:

UEC res_stock_c _non

- adj = annual space

- cooling energy consumption associated with the stock equipment, extracted directly from RECS household data,

UEC res_stock_c = annual space

- cooling consumption associated with the stock equipment, adjusted by using 30

- year average CDD data for the
RECS location

, CDD res_stock _2005 = CDD in 2005 for the specific census division or state where the housing unit is located, and

CDD 30

_ yr_avg = 30

- year average CDD (1978

– 2007) for the specific census division or state where the housing u nit is located.

7.2.3.2 Stock Annual Space

- Cooling Energy Use

DOE first weather

- normalized the cooling energy use data for the selected RECS households to better reflect long

- term average weather conditions for each location. DOE
obtained CDD data for each state o n a monthly basis for 1978

– 2007 from the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data C enter (NCDC). 2 DOE aggregated the monthly data for each year to produce annual CDD data for each state. These were then further aggregated geographically to the specific census divisions or four large state geographic regions used by RECS. The states inclu ded in each of these geographic regions are
shown in Table 7.2

. 1

.

In estimating annual CDD for each census division, the annual CDD for the states in each
division are weighted according to the population in the component states for the specific year.7

- 8 Population data from 1978 to 2007 were developed based on 1980, 1990, and 2000 U.S. Census

Bureau data

, 3 with estimates for other years obtained by linear interpolation or extrapolation as necessary. The resulting 30

- year average CDD and the 2005 CDD for the regions are shown in
Table 7.2

. 1

.

Table 7.2

. 1 The 30

- Year Average CDD (1978

– 2007) and the 2005 CDD for the Nine C ensus D ivisions and the F our Large States

Di vision States 30

- Year Average CDD 2005 CDD

1: New England Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont 462 579

2: Middle Atlantic New Jersey, Pennsylvania 737 991

3: East North Central Indiana, Illinois, Michigan, Ohio, Wisconsin 758 983

4: West North Central Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota 989 1

, 171

5: South Atlantic Delaware, District of Columbia, Georgia, Maryland, North Carolina, South Carolina, Virginia, West Virginia 1

, 382 1

, 486

6: East South Central Alabama, Kentucky, Mississippi, Tennessee 1

, 620 1

, 747

7: West South Central Arkansas, Louisiana, Oklahoma 2

, 215 2

, 402

8: Mountain Arizona, Colorado, Idaho, New Mexico, Montana, Utah, Nevada, Wyoming 1

, 318 1

, 498

9: Pacific Alaska, Hawaii, Oregon, Washington 226 226

Four L arge S tates New York 654 943

California 949 837

Texas 2

, 711 2

, 908

Florida 3

, 505 3

, 436

Based on the 2005 RECS annual space

- cooling energy consumption data and the CDD data, the weather

- adjusted annual space

- cooling energy consumption for each household was obtained by using equation 7.2.2. Statistics showing the weather

- adjusted annual space

- cooling energy consumption for the households that have air conditioners are shown in Table 7.2

. 2

. Statistics showing the weather

- adjusted annual space

- cooling energy consumption for the households that have heat pumps are shown in Table 7.2

. 3 . The frequency distribution of the
weather

- adjusted annual household energy consumption for space

- cooling for central air conditioner and central heat pump equipment for the RECS observations is shown in Figure 7.2

. 4
and Figure 7.2

. 5

.7

- 9 Table 7.2

. 2 W eather Adjusted Space

- Cooling Energy Consumption Statistics a t Regional

Level (CAC

- Stock)

Division or State Number of

Observations Min kWh/yr * Max kWh/yr Average kWh/yr New England 50 56 4

, 186 1

, 229

Middle Atlantic 127 289 5

, 420 1

, 650

East North Central 346 130 9

, 459 1

, 477

West North Central 224 207 9

, 882 2

, 067

South Atlantic 233 152 8

, 628 2

, 952

East South Central 173 331 11

, 573 3

, 653

West South Central 79 647 12

, 359 4

, 070

Mountain 130 65 26

, 078 3

, 167

Pacific 23 89 3

, 610 1

, 273

New York 34 333 5

, 361 1

, 295

California 160 36 17

, 335 1

, 835

Texas 181 44 29

, 326 5

, 780

Florida 94 1

, 508 14

, 688 6

, 104

National 1

, 854 36 29

, 326 2

, 851

* kilowatt

- hours per year

Table 7.2

. 3 W eather Adjusted Space

- Cooling Energy Consumption Statistics a t Regional

Level (CHP

- Stock)

Division or State Number of

Observations Min kWh/yr Max kWh/yr Average kWh/yr New England 1 432 432 432

Middle Atlantic 10 214 2

, 182 1

, 460

East North Central 20 455 6

, 938 2

, 315

West North Central 12 234 5

, 634 2

, 413

South Atlantic 108 445 11

, 059 3

, 442

East South Central 54 1

, 157 11

, 586 3

, 968

West South Central 6 1

, 246 9

, 573 4

, 545

Mountain 23 3

, 220 12

, 893 7

, 194

Pacific 13 78 3

, 221 1

, 060

New York 1 761 761 761

California 11 312 6

, 449 2

, 492

Texas 15 2

, 166 11

, 363 6

, 423

Florida 65 1

, 805 17

, 380 6

, 738

National 339 78 17

, 380 4

, 2647

- 10 Figure 7.2

. 4 National Distribution o f Weather

- Adjusted Space

- Cooling Energy for CAC

RECS Observations

Figure 7.2

. 5 National Distribution o f Weather

- Adjusted Space

- Cooling Energy for CHP

RECS Observations

7.2.3.3 Stock Space

- Cooling Efficiency (SEER)

To estimate annual space

- cooling energy consumption data at the baseline and higher efficiency levels, DOE relies on the cooling and heating energy calculated for the stock
households and the historic space

- cooling efficiency levels of the stock equipment

, SEER res_stock

. The space

- cooling efficiency of stock equipment is related to the vintage of the equipment. I n the 2005 RECS database, the age of the equipment is reported in terms of age groups and not the 0% 2% 4% 6% 8% 10% 12% 14% 16% 18%Percentage of Obersavtions

Weather

- Adjusted CAC Cooling Energy from RECS (kWh/yr)

0% 2% 4% 6% 8% 10% 12% 14%Percentage of Obersavtions

Weather

- Adjusted CHP Cooling Energy from RECS (kWh/yr)7

- 11 specific vintage year . The five age groups are “less than 2 years old

, ” “2 to 4 years old

, ” “5 to

9 years old

, ” “ 10 to 19 years old

, ” and “20 years or older

. ” The data also include one additional age category: “as old as the home.” In RECS the years of construction of each residence for older homes are also reported in age bands, though the specific year of construct ion is indicated for the
newer homes. DOE assumed that the age of the central air conditioner system, w ithin a given age

group in the general population, would be approximately uniformly distributed throughout the range of the age band . For example, for the central air conditioner systems in the “less than 2 years old” age group, it was assume d that 50 percent of heat pumps we re 1 year old and the other

50 percent we re 2 years old. A similar technique was used in ascertaining the probab le vintage year of the home and the equipment when the equipment was reported to be the age of the home. The resulting age distribution of the central air conditioners and central heat pumps in the RECS households identified for analysis is listed in Table 7.2

. 4 and shown in Figure 7.2

. 6 and Figure

7.2

. 7 , respectively.

Table 7.2

. 4 Number of O bservations for E ach A ge G roup Equipment Type Less than 2

Years Old 2 to 4

Years Old 5 to 9

Years Old 10 to 19

Years Old 20 Years

or Older Total

Central A ir

C onditioners 243 321 472 502 316 1854

13.1% 17.3% 25.5% 27.1% 17.0% 100% Cooling

- P erformance

Heat P ump 57 61 104 89 28 339

16.8% 18.0% 30.7% 26.3% 8.3% 100%

Figure 7.2

. 6 The D istribution of Central Air Conditioners in the RECS Sample by A ge G roups

Once the age group into which the household equipment falls was established, DOE estimated the vintage of the equipment for each household in each age group by random assignment using the uniform age distribution assumption for each age group and the known 2005 survey year. For the 20

- year and older age group, all equipment was assumed to be 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%

Less than 2 years old 2 to 4 years

old 5 to 9 years

old 10 to 19 years old 20 years or

older

Age of CAC SystemsPercent Of households7

- 12 between 20 and 29 years old with the actual vintage assign ed using a uniform distribution. The resulting vintage distribution of the residential air conditioners and heat pumps in the overall sample is shown in Figure 7.2

. 8

.

DOE estimated the stock cooling SEER for each household using equipment vintage and average shipped efficiency for each vintage year . The latter was developed from AHRI data 4 and

is shown in Table 7.2

. 5

.

Figure 7.2

. 7 The D istribution of Central Heat Pump s in the RECS S ample by A ge G roups

Figure 7.2

. 8 D istribution of RECS H ouseholds with Central Air Conditioners by Vintage 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0%

Less than 2 years

old 2 to 4 years old 5 to 9 years old 10 to 19 years old 20 years

or older

Age of Heat Pump SystemsPercent Of households

0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0%1,9761,9781,9801,9821,9841,9861,9881,9901,9921,9941,9961,9982,0002,0022,004

Vintage year of CAC EquipmentPercent of Households7

- 13 Table 7.2

. 5 Average Annual Shipped S pace

- C ooling E fficiency

Year Central A/C

SEER Heat Pump

SEER

1976 7.15 6.87

1977 7.18 6.89

1978 7.41 7.24

1979 7.45 7.34

1980 7.51 7.51

1981 7.73 7.70

1982 8.30 7.79

1983 8.44 8.23

1984 8.70 8.45

1985 8.84 8.56

1986 8.87 8.70

1987 8.95 8.93

1988 9.11 9.13

1989 9.23 9.26

1990 9.25 9.41

1991 9.44 9.73

1992 10.43 10.56

1993 10.52 10.82

1994 10.58 10.89

1995 10.64 10.93

1996 10.63 10.95

1997 10.62 10.93

1998 10.23 10.52

1999 10.86 11.24

2000 10.95 11.23

2001 11.07 11.31

2002 11.07 11.32

2003 11.22 11.48

2004 11.32 11.58

2005 11.33 11.69

7.2.3.4 Estimating Baseline Annual Space

- Cooling Energy Use

For each class of equipment, the baseline annual space

- cooling energy consumption for each equipment type (air conditioner or heat pump) was estimated by using equation 7.2.2. The key inputs are the weather

- adjusted annual space

- cooling energy consumption derived from the 2005 RECS data; the stock s pace

- cooling efficiency estimates for air conditioners and heat pumps from Table 7.2

. 5 ; and the baseline cooling efficiency, 13 SEER.

Stati stics for the baseline space

- cooling energy consumption for RECS households at regional and national level are shown in Table 7.2

. 6 for air conditioners and Table 7.2

. 7 for heat

pumps.7

- 14 Table 7.2

. 6 Baseline (13 SEER) Space

- Cooling Energy Consumption Statistics at Regional Level ( Central Air Conditioner )

Division or State Number of

Observations Min kWh/yr Max kWh/yr Average kWh/yr New England 50 45 3

, 526 987

Middle Atlantic 127 246 4

, 550 1

, 270

East North Central 346 104 7

, 967 1

, 148

West North Central 224 145 8

, 324 1

, 593

South Atlantic 233 119 7

, 513 2

, 314

East South Central 173 247 10

, 078 2

, 864

West South Central 79 433 10

, 115 3

, 082

Mountain 130 53 21

, 344 2

, 536

Pacific 23 78 3074 1

, 029

New York 34 229 3645 1

, 016

California 160 24 15

, 095 1

, 440

Texas 181 30 25

, 536 4

, 520

Florida 94 1

, 234 12

, 372 4

, 860

National 1

, 854 24 25

, 536 2

, 231

Table 7.2

. 7 Baseline (13 SEER) Space

- Cooling Energy Consumption Statistics a t Regional Level ( Central Heat Pump )

Division or State Number of

Observations Min kWh/yr Max kWh/yr Average kWh/yr New England 1 249 249 249

Middle Atlantic 10 118 1

, 823 1

, 236

East North Central 20 323 5

, 566 1

, 882

West North Central 12 190 4

, 911 1

, 962

South Atlantic 108 320 9

, 417 2

, 779

East South Central 54 909 9

, 465 3

, 203

West South Central 6 973 6

, 797 3

, 568

Mountain 23 2

, 424 8

, 876 5

, 616

Pacific 13 68 2

, 339 842

New York 1 663 663 663

California 11 263 5

, 492 1

, 960

Texas 15 1

, 473 9

, 191 5

, 278

Florida 65 1

, 311 13

, 677 5

, 339

National 339 68 13

, 677 3

, 412

7.2.4 B aseline Annual Space

- Heating Energy Use for Central Heat Pump Equipment

— R esidential

The baseline annual space

- heating energy consumption for the central heat pump system is estimated following procedures similar to that for estimating the baseline annual s pace

- cooling energy consumption. The baseline annual space

- heating energy consumption for residential heat pumps is estimated by using the following generic equation:

DF HSPF HSPF UEC UEC

base stock res
h stock res h base res **× ×**  **=** _

_ _ _ _ Eq. 7.2

. 37

- 15 Where:

UEC res_ base

_ h = the baseline annual space

- heating energy consumption,

UEC res_stock_ h = the annual space

- heating energy consumption associated with the stock heat pump,

HSPF res_stock_ h = the space

- heating efficiency associated with the stock equipment,

HSPF base = the space

- heating efficiency associated with baseline equipment, and

DF= HSPF improvement d egradation f acto r. a

The DF term is a factor used to adjust the HSPF ratio to make it reflect the improvement

in heating seasonal performance for a pa rticular climate region . See appendix 7

- A for a discussion of the derivation of the HPSF improvement degradation factor.

7.2.4.1 Estimating Stock Annual Space

- Heating Energy Use

The annual space

- heating energy consumption for the central heat pump systems in the

s tock was obtained from the 2005 RECS database for the heat pump sample identified. To account for the variation in weather conditions for the year 2005 from statistical normals, the annual space

- heating energy consumption in 2005 was adjusted by using the following equation:

stock res avg

yr adj non h stock res h stock res HDD HDD UEC UEC

_ _ _ 30

_ _ _ _ _ **⋅ =** 

**−** Eq. 7.2

. 4

Where:

UEC res_stock_ h = annual space

- heating energy consumption associated with the stock equipment after weather

- adjustment,

UEC res_stock_ h_non

- adj = annual space

- heating energy consumption, obtained directly from RECS database,

HDD 30

_ yr_avg = 30

- year (1978

**−** 2007) average heating degree

- day (HDD, base 65 °F) for the census division or state where the household is located,
and

HDD

res

- stock = HDD in 2 005 (base 65 °F) for the census division or state where
the household is located.

State

- wise monthly HDD data from 1978 to 2007 were obtained from the NOAA
database center. 2 DOE aggregated the monthly data for each year to produce annual HDD for
each stat e. The state annual HDD data were used to derive annual HDD for the regions identified in RECS (census divisions and separately four large states). In estimating divisional annual HDD, the annual HDD values for states within the regions were weighted by th e state population in that year as described in section 7.2.1 . The 30

- year average HDD and the 2005 HDD for the nine census divisions and the four large states are shown in Table 7.2

. 8

.

a HSPF improvement degradation factors were assumed to be 1.0 for the calculation of baseline heating energy.7

- 16 Table 7.2

. 8 30

- Y ear A verage HDD (1978

– 2007) and the 200 5 H DD for the N ine C ensus D ivisions and the F our Large S tates

Division or State 30

- Year HDD Average 2005 HDD

New England 6

, 571 6

, 453

Middle Atlantic 5

, 602 5

, 612

East North Central 6

, 103 6

, 323

West North Central 5

, 989 6

, 506

South Atlantic 3

, 665 3

, 720

East South Central 3

, 309 3

, 496

West South Central 2

, 415 2

, 723

Mountain 4

, 666 5

, 142

Pacific 5

, 133 5

, 193

New York 5

, 912 5

, 929

California 2

, 450 2

, 488

Texas 1

, 686 1

, 944

Florida 647 657

By using equation 7.2.4, the weather

- adjusted annual space

- heating energy consumption was estimated for the equipment in each household. Statistics for the weather

- adjusted space

heating energy consumption at both divisional and national level are shown in Table 7.2

. 9 . The frequency distribution of the RECS sample households in different heating energy consumption
bands is shown in Table 7.2

. 9

.

Table 7.2

. 9 S tatistics of A nnual W eather

- A djusted S pace

- H eating E nergy C onsumption for

RECS

- Stock Central Heat Pump at Regional L evel

Division or State Number of

Observations Min kWh/yr Max kWh/yr Mean kWh/yr New England 1 2,399 2,399 2,399

Middle Atlantic 10 581 5,177 2,871

East North Central 20 1,911 10,037 4,237

West North Central 12 926 4,778 2,702

South Atlantic 108 0 6,412 2,692

East South Central 54 0 5,667 2,468

West South Central 6 354 4,434 2,386

Mountain 23 424 1,630 891

Pacific 13 2,171 5,251 3,621

New York 1 3,409 3,409 3,409

California 11 324 3,850 1,391

Texas 15 998 3,788 1,671

Florida 65 200 1,470 625

National 339 0 10,037 2,1797

- 17 Figure 7.2

. 9 National Distribution of Weather

- Adjusted Space

- Heat ing Energy for RECS

Observations

7.2.4.2 Stock Space

- Heating Efficiency (HSPF)

The space

- heating efficiency of central heat pump equipment has improved over time in response to both technology improvement and minimum efficiency standards. DOE related the

space

- heating efficiency of heat pumps in the stock to the equipment vintage, much as was done for the cooling efficiency. I n the 2005 RECS database, the age groups of the equipment and not their specific vintage is given. The five age groups are “less than 2 years old

, ” “2 to 4 years old

, ” “5 to 9 years old

, ” “10 to 19 years old

, ” and “20 years or older

. ” The data reported also inc lude one additional category: “as old as the home.” The years of construction of the home for older homes are also reported in age bands. It was assume d that the age of a heat pump

, w ithin a given

age group , was uniformly distributed over the range of the age group . For example, for the heat pumps in the “less than 2 years old” age group, it was assume d that 50 percent of heat pumps

we re 1 year old and the other 50 percent we re 2 years old. A similar technique was used in ascertaining the probable vintage y ear of the home and the equipment when both were reported to have the same age. The distribution of households with heating

- performance heat pumps by age groups is shown in Figure 7.2

. 10 and the distribution of heat pump vintages is shown in Figure 7.2

. 11

.

AHRI does not publish data on the historical average HSPF for central heat p ump equipment. DOE developed historical HSPF estimates from two data sources. For the years from 1973 to 1993, DOE used data reported in the TSD for the 2001 CAC Rulemaking. 5 For the period from 1994 to 2005, DOE examined the 1994 Air

- Conditioning & Refrigeration Institute (ARI) directory of Certified Products and developed a regression equation that relates the SEER of residential split

- system heat pumps. DOE used the 1994 ARI Directory of Certified Equipment Database 6 to generate regressions between SEER and HSPF for central heat pump equipment on the market in 1994. For split systems, DOE used a regression based on data for all 0% 5% 10% 15% 20% 25%Percentage of Obersavtions

Weather

- Adjusted CHP Heating Energy from RECS (kWh/yr)7

- 18 central heat pump equipment characterized by the AHRI designations H RCU

- A

- CB and HRCU

A

- C. For packaged heat pumps, DOE used a regression for all central heat pump equipment with

an HSP

- A designation. Only single

- phase equipment with ratings listed as “Active,” “ not intended for export only ,” w ere included

. This resulted in two regression equations: one for the split

- system heat pump equipm ent class and one for the single

- package heat pump equipment class. The historical average HSPF for each equipment class was calculated using historical annual average SEER values by equipment class from AHRI 7 and the appropriate regression equation. The annual average HSPF for all central heat pump equipment was then developed by weigh ting the annual average HSPF values by class using historical shipment data by equipment
class. Table 7.2

. 10 shows the resulting historical HSPF values by vintage.

Figure 7.2

. 10 D istribution of H ouseholds with H eating

- P erformance H eat P umps by A ge G roups 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0%

Less than 2 years

old 2 to 4 years old 5 to 9 years old 10 to 19 years old 20 years

or older

Age of Heat Pump SystemsPercent Of households7

- 19 Figure 7.2

. 11 D istribution of RECS H ouseholds with C entral Heat Pumps by Vintage

Table 7.2

. 10 Average Annual Heating Pump HSPF

Year Average HSPF Year Average HSPF

1973 5.66 1990 6.98

1974 5.66 1991 7.08

1975 5.66 1992 7.19

1976 5.66 1993 7.30

1977 5.74 1994 7.38

1978 5.83 1995 7.39

1979 5.91 1996 7.40

1980 6.00 1997 7.39

1981 6.09 1998 7.29

1982 6.18 1999 7.47

1983 6.28 2000 7.46

1984 6.37 2001 7.49

1985 6.47 2002 7.49

1986 6.56 2003 7.53

1987 6.66 2004 7.56

1988 6.81 2005 7.58

1989 6.87

7.2.4.3 Estimating Baseline Annual Space

- Heating Energy Use

For each class of equipment, the baseline annual space

- heating energy consumption for central heat pump equipment was estimated by using equation 7. 2.4 . The key inputs are the
weather

- adjusted annual space

- heating e nergy consumption derived from the 2005 RECS data ; the stock space

- heating efficiency estimates for central heat pump equipment shown in Table

7.2

. 10 ; and the b aseline heating efficiency, 7.7 HSPF. 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0%

1,976 1,979 1,982 1,985 1,988 1,991 1,994 1,997 2,000 2,003

Vintage Year of the Heat Pump SystemsPercent of Households7

- 20 Statistics showing the baseline space

- heating energy consumption calculated for RECS
households with central heat pump equipment at regional and national level are shown in Table

7.2

. 11

.

Table 7.2

. 11 Baseline (13 SEER) Space

- Heat ing Energy Consumption Statistics a t Regional Level ( Central Heat Pumps )

Division Number of

Observations Min kWh/yr Max kWh/yr Average kWh/yr New England 50 1

, 869 1

, 869 1

, 869

Middle Atlantic 127 443 5

, 030 2

, 778

East North Central 346 1

, 869 9

, 855 4

, 000

West North Central 224 789 4

, 585 2

, 546

South Atlantic 233 0 6

, 105 2

, 554

East South Central 173 0 5

, 068 2

, 323

West South Central 79 340 3

, 956 2

, 240

Mountain 130 346 1

, 329 824

Pacific 23 2

, 132 4

, 542 3

, 407

New York 34 3

, 356 3

, 356 3

, 356

California 160 287 3

, 745 1

, 305

Texas 181 982 3

, 684 1

, 580

Florida 94 170 1

, 351 586

National 1

, 854 0 9

, 855 2

, 058

7.2.5 B aseline Residential Cooling and Heating Results

**−** Adjusted for RECS Sample Weight s

Table 7.2

. 6

, Table 7.2

. 7 , and Table 7.2

. 11 directly reflect the RECS household sample data. These tabulated results do not take into account the sample weights assigned by RECS for
the different households. Figure 7.2

. 12

, Figure 7.2

. 13 , and Figure 7.2

. 14 show the distribution of calculated baseline cooling and heating energy for central air conditioner and central heat pump products used in residences, taking into account the RECS household samples weights.

Figure 7.2

. 12 Baseline National Cooling Energy Distribution for Central Air Conditioners

– Residential 0% 5% 10% 15% 20% 25%Percentage of Households

Baseline CAC Cooling Energy (kWh/yr)7

- 21 Figure 7.2

. 13 Baseline National Cooling Energy Distribution for Central Heat Pumps

– Residential

Figure 7.2

. 14 Baseline National Heating Energy Distribution for Central Heat Pumps

– Residential

7.2.6 Baseline Commercial Central Air Conditioner and Central Heat Pump Energy Use

Seven percent of r esidential

- type ( i.e. , single

- phase) central air conditioner and central heat pump applications are assumed to be in commercial buildings. While this equipment could
be used in a wide variety of buildings, DOE believes that the vast majority is u sed in small buildings

, in particular small retail and commercial buildings. This is primarily because central
air conditioner and central heat pump equipment use s single

- phase electric ity, which limits the

app lication to smaller buildings. 0% 2% 4% 6% 8% 10% 12% 14%Percentage of Households

Baseline CHP Cooling Energy (kWh/yr)

0% 5% 10% 15% 20% 25%Percentage of Households

Baseline CHP Heating Energy (kWh/yr)7

- 22 Cooling and h eati ng energy use in commercial buildings can be distinctly different from
such use in residential buildings

, even for the same equipment. This is predominantly because commercial buildings are operated differently, often having defined operating hours when th e equipment is used to condition the space, thermostatic setback during other hours, induced ventilation into the building structure to meet occupant needs, and

, typically

, continuous fan operation during occupied hours. Space heat gains in commercial buil dings can be significantly different than in residential buildings due primarily to high internal thermal loads from lighting and electrical equipment during the occupied periods.

In addition

, multiple cooling and heating units are commonly used. A similar data source
to the RECS data discussed earlier is the EIA Commercial Building Energy Consumption Survey (CBECS)

, 8 which provides estimates of end

- use energy consumption in commercial buildings. Because multiple pieces of cooling and heating equipment are the norm in most commercial building s, and the number of units by equipment type is not captured in the survey, it is not possible to readily determine the energy use of individual pieces of central air conditioner and central heat pump equipment using CBECS. In addition, CBECS does not clea rly distinguish equipment types by electrical phase. Hence

, it is difficult to identif y buildings that are predominantly served by central air conditioner and central heat pump equipment. Given these difficulties, DOE determined that CBECS could not be use d to estimate the cooling and heating energy data for central air conditioner and central heat pump equipment used in commercial buildings. Instead, DOE relied on a building simulation approach to estimate the electrical energy usage of central air conditi oner and central heat pump equipment in commercial buildings.

7.2.7 Overview of Commercial Energy S imulation Approach

In order to develop energy use statistics that reflect the use of central air conditioner and central heat pump equipment in commercial building s, DOE performed an analysis similar to

that described in the Framework for Central Air Conditioners

. 9 DOE used the EnergyPlus 10

whole

- building energy simulation software to estimate the energy consumption of central air
conditioner and central heat pump used to satis fy the cooling, supply fan, and

, in the case of central heat pump , the electrical heating energy required in a representative small office building

. Simulations were carried out for 237 locations around the United States. The building model
used was an exi sting single

- story, 5, 5 00 ft 2 off ice building model developed by DOE for energy benchmarking purposes for this analysis. This commercial reference building model is 1 of 16
commercial reference building models developed in support of DOE ’s Commercial Build ing

Initiative program. 11

Two variations were considered for the simulations: buildings were either entirely served by central air conditioner equipment with a gas furnace backup or were entirely served by central heat pump equipment. The simulated building used five individual central air conditioner (or central heat pump) units. F or each equipment type, DOE simulated the equipment at four different efficiency levels , starting from the baseline level

. For central air conditioner equip ment,

t hese levels were 13 SEER, 14 SEER, 16 SEER, and a max

- tech level of SEER 24.5 based on
blower

- coil ratings . For central heat pump equipment, these levels were 13 SEER, 14 SEER, 16 SEER, and a max

- tech level of SEER 22 based on blower

- coil ratings

. E quipment parameters7

- 23 used in the simulation were adjusted to reflect engineering designs corresponding to each efficiency level. Simulations were carried out using Typical Metrological Weather Files (TMY2)

for the 237 U.S. locations. 12

DOE extracted the annual energy consumption for cooling and fan energy use and heating from each modeled central air conditioner or central heat pump unit

, as wel l as the equipment cooling capacity for each unit from each simulation run

, an d aggregated these to the
whole

- building level. Using th e s e whole

- building cooling and heating data , as well as t he equipment capacities , DOE normalized the central air conditioner and central heat pump energy consumption for each location and efficiency level to that of an average 3

- ton central air
conditioner or central heat pump unit . These normalized energy use s tatistics were then further scaled linearly, as necessary, to reflect the energy use for a 2

- , 3

- , or 5

- ton central air conditioner or central heat pump product, depending on the size of unit modeled in the LCC analysis.

To estimate the energy consumption for intermediate efficiency levels between those simulated, DOE linearly interpolated the energy use estimates for cooling energy, fan energy

, and heating energy based on the results at the f our simulated efficiency levels, the SEER or HSPF rating as the independent variable, and using the nearest lower and upper SEER or HSPF
levels simulated.

7.2.7.1 Simulation Tool Description

DOE chose to use EnergyPlus version 3.0 for the energy analysis. EnergyPlus is the most recent of several large

- scale software programs d eveloped for whole

- building energy simulation analysis and has been validated by comparing its results with thermal and energy use measurements on actual buildings and with other software tools and calculations . EnergyPlus predicts the hourly energy use of a building given : hourly weather information an d a description of the building; the operation of the building; and its heating, ventilation and air

- conditioning ( HVAC ) equipment . Energy use statistics for the building can be readily extracted for differen t time steps and at detailed sub

- component levels if necessary. EnergyPlus is an integrated software tool, where both the building loads and how those loads are served by equipment are solved iteratively. EnergyPlus has a separate, but robust, ground heat transfer model, which is particularly useful in the simulation of smaller buildings. DOE is currently using EnergyPlus as the basis for most of its building energy simulation research, and the tool and its ongoing development are actively supported within DOE.

7.2.7.2 Efficiency Levels Analyzed and Corresponding Simulation Parameters

DOE simulated four efficiency levels for central air conditioner equipment and four efficiency levels for central heat pump equipment, corresponding to particular efficiency levels
ide ntified in the engineering analysis. In order to simulate the equipment within the confines of the simulation software, the SEER and HSPF rating from the engineering analysis must be converted to parameters used by the simulation software. These parameters are the cooling coefficient of performance (COP)

— for central heat pump the heating COP

— of the condensing unit at rated conditions and the fan power. The central air conditioner and central heat pump condensing unit parameters are in turn calculated from 9 5 ° F energy efficiency ratio (EER) and7

- 24 47 ° F COP (COP 47 °F ) ratings for the equipment and the fan power at rated conditions. DOE first estimated these from the SEER and HSPF rating.

The EER rating reflects the cooling performance for central air condition er equipment at a single

- point rating condition defined by 95 ° F outdoor temperature, 80 ° F entering air dry bulb temperature, and a 67 ° F entering air wet bulb temperature. The COP 47 °F rating reflects the heating performance of the heat pump at a single

- point rating condition defined by 47 ° F outdoor temperature, 70 ° F entering air dry bulb temperature, and a maximum 60 ° F entering air wet bulb temperature. The EER values for each SEER level modeled were established based on the median EER corresponding to that SEER level in the AHRI Certified Directory as established for equipment designated as “not for export only.” b

HSPF HSPF COP F **× +**  **×**

**− =**  6239

. 0 0255

. 0 2

47 HSPF levels were established based on the engineering analysis HSPF v alues corresponding to the SEER modeled for split

- system heat pumps. Becau se COP 47 °F ratings are not provided in the AHRI database, DOE relied on a relationship between HSPF and COP 47 °F established in the literature. 13 This curve fit is shown in equation 7.2.5

.

Eq. 7.2

. 5

In addi tion, DOE modeled the highest efficiency equipment as a multistage condenser system with two stages of operation. To properly account for two stages of operation, DOE used the EnergyPlus multi

- speed unitary heat pump model for both air conditioners and heat pumps, disabling the heat pump heating capacity when modeling the unit as an air conditioner and relying on a gas furnace for backup heating.

For 13 SEER equipment, the fan power at rating co nditions was established assuming a blower motor efficiency of 0.65, a fan efficiency of 0.58, and a constant total static pressure of 1.105 inches of water (in. H 2 O) for the furnace/air handler. The same characteristics for fan power were also assumed to apply to the heat pumps at rating condition. The blower motor efficiency was assumed to be 0.65 for the 13 and 14 SEER ratings, consistent with that of a permanent split capacitor blower motor. T he blower motor efficiency was assumed to be 0.80 for
the 16 SEER and higher SEER ratings, consistent with that of an electronically commutated motor (ECM) blower motor in this size range. A transition to ECM blower motors between the 14 and 16 SEER rating s reflects the use of that technology in the central air conditioner/central heat pump market as determined in the engineering analysis, where ECM motors appear to be commonplace at 16 SEER, but not common at 14 SEER. These assumptions result in a blower

p ower of 138 W/ton at rated conditions for 13 and 14 SEER equipment, and a lower fan power of 112 W/ton for higher SEER equipment at rated conditions. For comparison, using the test procedure default 365 W/1,000 cubic feet per minute (cfm) for a coil

- only r ating would provide the same power levels assumed for 13 and 14 SEER equipment at an airflow rate of 377 cfm/ton. The cooling and heating condensing unit COP values that are entered in the EnergyPlus software
reflect the EER and COP 47 °F ratings, but are a djusted for the impact of the blower power. The blower power is directly incorporated into the total system power for these ratings but, because

b This refers to the AHRI Certified Directory information as of July 17, 2010, for air conditioning and heat pump

units.7

- 25 of the effect of fan heat, incorporating the blower reduces the cooling output and raises the heating output. E quations 7.2.6 and 7.2.7 provide cooling and heating COP ratings for the condensing unit alone after adjusting for the fan power and fan heat.



## ( **)**

R R EER COP F CUCool

**− +** 

**= °**  1 413

. 3 /

95

, Eq. 7.2

. 6

** **  ** **  ** ** 

**− −** 

**= °** 

**°** R R COP COP F

F CUHeat 1 47

47

, Eq. 7.2

. 7

Where:

R = ratio of supply fan power to total equipment power at t he AHRI rating condition,

COP CUCool ,95 °F = condensing unit cooling COP, and

COP CUHeat ,47 °F = condensing unit heating COP.

Table 7.2

. 12 shows the resulting equipment performance statistics for the commercial building models.

Table 7.2

. 12 Cent ral Air Conditioner and Central Heat Pump Blower and Characteristics Used in Modeling Commercial Cooling and Heating Energy Use
Equipment

Class SEER HSPF EER 47 ° F

COP Blower

Motor Energy Efficiency Blower

Power

W/ton * Fan Power to

Total Power (Rated Conditions) Cool COP 95°F (Condensing Unit Only) Heating

COP 47°F (Condensing Unit Only)

CAC 13 NA 11. 07 NA 0.65 138 0.1 27 3. 8 6 NA

14 NA 11. 8 NA 0.65 138 0.135 4. 15 NA

16 NA 1 2.53 NA 0.80 112 0.1 17 4. 29 NA

2 4.5 NA 15. 0 NA 0.80 112 0.14 0 5. 27 NA

CHP 13 8 11. 2 3.29 0.65 138 0.1 28 3.9 1 3. 71

14 8 2 11. 8 3.36 0.65 138 0.135 4. 15 3.7 5

16 8. 7 1 2.4 3.48 0.80 112 0.1 16 4. 24 3.8 7

22 10. 1 1 4 3.72 0.80 112 0.1 30 4.87 4.1 0

* Calculated for rating conditions.

7.2.7.3 Small Office Building Characteristics

The small office building prototype model is a theoretical building modeled with characteristics typical of buildings of this size and use. The building is 5,500 ft 2 , single

- story, rectangular, 52 ft by 105 ft, with an aspect ratio of 1.5. The building has a window

- area ratio of 21 percent with a 10

- ft floor

- to

- ceiling height. The small office building is assumed to have slab

on

- grade construction for the floor and an attic. A picture of the building structure is shown in Figure 7.2

. 15

. The building is divided into five HVAC zones, four perimeter zones

, and a core zone as illustrated in Figure 7.2

. 16 , with a single central air conditioner or central heat pump unit
assumed for each HVAC zone . The perimeter zone depth in the center of each zone is 16 ft.7

- 26 Figure 7.2

. 15 Small Office Building Design View

Figure 7.2

. 16 Small Office Building HVAC Zoning

The building model presumes wood frame construction and punch

- out type windows. Where applicable , the building components for this model were assumed to “just meet” the minimum prescriptive requirements of American Society of Heating, Refrigerating and Air

Conditioning Engineers (ASHRAE) Standard 90.1

- 2004. Components not regulated by Standard

90.1 a re assumed to be designed as is considered standard practice for a small office building. Standard practice is determined from various sources, including a review of the 2003 CBECS and the input of various design and construction industry professionals, in cluding members of the ASHRAE 90.1 Standing Standard Project Committee. The following sections provide a review of the baseline building and how the baseline building is simulated in EnergyPlus, including characteristics of the building envelope, building internal loads (people, lighting, miscellaneous equipment, and infiltration), HVAC equipment, and service water heating.

7.2.7.4 Building Operating Characteristics

The building is assumed to follow typical small office occupancy patterns

, with peak

occupancy occur ring from 8 a.m. to 5 p.m. weekdays and limited occupancy from 6 a.m. until 8 a.m. and from 5 p.m. extending until midnight for janitorial functions. The building is assumed to be unoccupied on weekends and holidays. Schedules for lighting and miscellaneou s equipment were matched to occupancy schedules, with lower usage during unoccupied times. HVAC system schedules were matched to the occupancy schedules and allow for earlier startup times to bring the space to the desired temperature at the beginning of n ormal occupancy. Figure

7.2

. 17

, Figure 7.2

. 18

, Figure 7.2

. 19 , and Figure 7.2

. 20 show the EnergyPlus schedules for lighting, plug loads, occupancy, and thermostat set point. Detailed schedules are shown in append ix 7

- B for these as well as for HVAC fans, infiltration, and service hot water. These schedules represent multipliers to defined daily “peak” conditions. In the case of ventilation, it is7

- 27 assumed that ventilation occurs whenever the fan system is operation al. In the case of infiltration, a maximum infiltration rate for each hour is calculated using wind speed and multiplied by the value shown in the hourly infiltration schedule. That value is 1.0 for hours when the ventilation is off and the building is ass umed to be unpressurized. The schedule multiplier is assumed to be 0.25 during hours when the fan system is on and the building is pressurized.

The HVAC systems are scheduled to be on from 6 a.m. to 7 p.m. weekdays (13 hours/day) and are scheduled to be o ff on weekends and holidays. Building thermostats are

set to 70 °F heating and 75 °F cooling during the occupied period. Thermostats are set back to 60 °F heating and 85 °F cooling during the unoccupied period. Between 6 a.m. and 7 a.m. weekday mornings, t he thermostats are set to 65 °F cooling and 80 °F heating set points to provide a

1

- hour thermostat ramp between the setback and occupied set points to prevent equipment sizing and temperature balance problems.

For the occupancy, lighting, plug use, and ve ntilation, the schedules shown in appendix

7

- B are multipliers to defined peak densities. Lighting density was defined using the ASHRAE

90.1

- 2004 maximum lighting power density requirement of 1.0 W/ft 2 . Plug load density ( e.g. , computers and other miscella neous loads) was 0.63 W/ft 2 . Occupancy peak density was defined as 31 persons for the building (5.6 persons per 1,000 ft 2 ). Total outdoor air exchange during the occupied period (effective ventilation) was based on ASHRAE Standard 62

- 1999 and set to 0.1

cf

m/ft 2 of floor area for each conditioned zone, corresponding to 0.6 air changes per hour. Ventilation was introduced directly into the zone and not through a mixed air box associated with the cooling system. The peak infiltration for the building was based on hourly wind speed, as discussed previously, averaging to 0.2016 cfm/ft 2 of total exterior conditioned zone wall area at a wind velocity of 10 miles per hour.

Figure 7.2

. 17 Small Office Lighting Schedule 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Percent of Peak Power Density

Weekday

Sat/Sun/Hol7

- 28 Figure 7.2

. 18 Small Office Plug Load Schedule

Figure 7.2

. 19 Small Office Occupancy Schedule7

- 29 Figure 7.2

. 20 Small Office Thermostat Set Point Schedule

7.2.7.5 Building Mechanical Systems and Equipment

As discussed, the building used either five central air conditioner units or five central heat

pump units, one for each conditioned zone

, depending on which equipment cla ss was analyzed. Equipment performance characteristics were developed using performance curves based on existing equipment. Performance curves and model inputs are documented in a ppendix 7

- B

.

Blower energy for the building model used the characteristics fo r blower motor and fan power efficiency discussed in section 7.2.7.2 . However, the total fan static pressure was 1.45 in

. H 2 O based on the assumptio n used in the benchmark prototype, compared to 1.105 in

. H 2 O total static pressure used to characterize the equipment performance at rating conditions. DOE was not able to identify a specific source of information regarding the use of continuous air circul ation for residential (single

- phase) hea t pumps in commercial buildings. A California study of 215 small air conditioners in commercial buildings found intermittent (cycling) ventilation operation during the occupied period in 38 percent of cases examined

. 14 To emulate this, the commercial analysis had 40 perce nt (two out of five) of the HVAC zones operate in intermittent circulation model during the occupied period.

Cooling equipment was sized based on ASHRAE 2.5 percent cooling design

- day sizing for all climates. A sizing run is done before the hourly simulat ions in EnergyPlus and is used to size all HVAC equipment. The design

- day sizes are then used in the annual simulation runs. For the heat pumps, equipment sizing is based on cooling design

- day

, and electric resistance heat is used to make up any remaining heating load. 0 10 20 30 40 50 60 70 80 90 100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Thermostat Setpoint (°F)

Heat

- Weekday

Heat

- Sat/Sun/Hol

Cool

- Weekday

Cool

- Sat/Sun/Hol7

- 30 7.2.8 Baseline Commercial Energy Distribution

To be of use in the central air conditioner analysis, the baseline commercial central air conditioner and central heat pump use developed for the 237 TMY locations must have relative weighting factors associated with each TMY location, much as the residential analysis uses the representative RECS weights. DOE used population

- based weighting factors developed for each
TMY climate based on data from the 2000 U.S. Census to represent the importance of each climate in each state. The development and documentation of these weighting factors is found in appendix 7

- C. Because certain TMY locations represent climates that cover more than one state (for instance, where the TMY location is on the border between tw o states), DOE originally developed weighting factors for a total of 543 TMY

- State combinations. These weighting factors were then used in the subsequent LCC analysis in addressing central air conditioner and central heat pump energy use in commercial buil dings. However, 17 locations in Alaska, which make up approximately 0.2 percent of the national weight, were removed from the energy analysis due to issues with electrical price data. Figure 7.2

. 21

, Figure 7.2

. 22 , and Figure 7.2

. 23 show
distribu tions of commercial central air conditioner cooling and central heat pump cooling and heating energy use in the nation that accounts for the relative weights attached to each of the remaining 526 TMY

- State combinations.

Figure 7.2

. 21 Baseline National Cooling Energy Distribution for Central Air Conditioners

**−** Commercial, Weighted 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%Percentage of SimulatedCommercial Small Offices

Baseline CAC Cooling Energy (kWh/yr)7

- 31 Figure 7.2

. 22 Baseline National Cooling Energy Distribution for C HC

**−** Comme rcial, Weighted

Figure 7.2

. 23 Baseline National Heating Energy Distribution for Central Heat Pumps

**−** Commercial, Weighted

7.2.9 Commercial Energy for Intermediate Efficiency Levels

Calculation of commercial annua l cooling and fan energy for intermediate efficiency levels not simulated was by linear interpolation between SEER levels simulated. Calculation of

c ommercial heating energy use for heat pumps for intermediate levels not simulat ed was by interpolation betw een HSPF levels simulated. 0% 10% 20% 30% 40% 50% 60%Percentage of SimulatedCommercial Small Offices

Baseline CHP Cooling Energy (kWh/yr)

0% 5% 10% 15% 20% 25% 30% 35% 40% 45%Percentage of SimulatedCommercial Small Offices

Baseline CHP Heating Energy (kWh/yr)7

- 32 7.2.10 Market Baseline S pace

- Cooling and S pace

- Heating Efficiencies

The analysis results presented in this chapter to this point present the development of the baseline space

- cooling and space

- heating energy use for central air conditi oner and central heat pump equipment. “Baseline” in this discussion refers to the energy consumption if all equipment was brought to the current standard levels of SEER 13 and HSPF 7.7 for packaged and split

system central air conditioner and central heat pump product classes. Not all equipment is sold at the baseline efficiency levels. Much equipment exceeds the baseline level in the current market and is expected to continue to do so in the absence of new standards. In order to account for this
within the LCC analysis discussed in the next chapter, DOE defined a base

- case efficiency distribution for 2016, also referred to as the market baseline. The market baseline efficiency distribution was defined to represent the range of efficiencies likely sold into the market in 2016 in the absence of new standards. The market baseline energy consumption is calculated as the energy estimated to be consumed by each individual entity (as characterized by RECS household observation or commercial building TMY

- State combi nation).

DOE estimated this energy use in the same manner used to estimate the baseline energy consumption. For RECS household records, DOE first randomly selected a 2016 efficiency level from the market baseline distribution. Then equations 7.2.1 and 7.2 .3 were used to calculate the cooling and heating consumption for the RECS observations in the 2016 base case, by replacing the baseline efficiency (cooling or heating) with the selected efficiency for each observation in
the base case. In the case of heat pump heating, HSPF improvement degradation factors were used as developed in appendix 7

- A to adjust the HSPF improvement to reflect different climate regions used in DOE’s analysis. For commercial TMY

- State observations, DOE first randomly
selected a 2016 efficiency level from the market baseline distributions and calculated the corresponding cooling fan and heating energy use for that observation in 2016 by linear interpolation between the efficiency levels simulated in the commercial energy analysis. See chapter 8 for the market distribution of efficiency levels by product class.

7.2.11 Determining Installed Equipment Capacity for RECS Observations

The notice of proposed rulemaking analysis was done using cost curves developed for three different sizes of spli t

- system air conditioners and three different sizes of split

- system heat pumps in order to capture variability in the costs to improve the efficiency of the equipment. In order to better analyze this within the market as a whole, DOE developed a process to assign a particular system size ( 2

- , 3

- , or 5

- ton ) to each RECS observation to support the LCC analysis discussed in chapter 8. DOE selected an appropriate capacity to assign to each RECS observation, taking into account the size of the residence and the geographic location of the
residence as it influences the climate and sizing of equipment. The assignment of a cooling capacity is done to establish the first cost of equipment provided to the residence. The energy consumption is based on the RECS data, as adjusted for the baseline efficiency or other efficiency scenario.

The starting point for this process was to allocate all air conditioner and heat pump shipments to the capacities analyzed in the engineering analysis. The AHRI website provides a breakdown of monthly combined shipments of air conditioners and heat pumps by cooling7

- 33 capacity bins

. 15 Th e s e data are not shown independently for the product c lasses, but are assumed

to be representative for split systems

, which are the vast majority of shipments.

For the residential equipment in question, eight cooling capacity bins encompassing capacities from 0 to 64.9 kilo British thermal units per hour ( k Btu/h ) are available. The SEER bins and unit shipments for 2009 are shown i n the first two columns of Table 7.2.13 under AHRI

Data. The data show that there are approximately equal values for combined air conditioner and heat pump shipments in the 22.0

**−** 22. 9 kBtu/h and 33.0

**−** 38.0 k Btu/h bins, each corresponding to nominally sized 2

- and 3

- ton air conditioners

, respectively. There are also significant shipments
in the intermediate bin between these two. There are relatively fewer, but still significant, shipme nts above and below these bins. Because air conditioning equipment is typically sold in nominally one

- half cooling ton increments, the estimated average cooling capacity for each bin
was also estimated and is shown in Table 7.2

. 13

. The distribution of s hipments at these nominal
sizes is shown in Figure 7.2

. 24

. The average cooling capacity shipped was estimated from the AHRI data at 34.4 kBtu/h, or very close to a 3

- ton unit.

DOE assigned the shipments in each bin to each of the cooling capacities identified in the engineering analysis (2

- , 3

- , or 5

- ton). Most bins were wholly assigned to one of the engineering capacities. In the case of nominally 30 kBtu/h units (27.0

**−** 32.9 kBtu/h bin), shipments were equally divided between 2

- and 3

- ton sizes. In the case of the 48 kBtu/h units ( 44

**−** 53.9 kBtu/h bin), shipments were equally divided between 3

- and 5

- ton sizes. The resulting fraction of the market assigned to each of the engineering capacities is shown in Table 7.2

. 13 . Also shown is the weighted average cooling capacity based on the overall actual shipments and corresponding nominal sizes allocated to each engineering capacity. For example, the engineering 2

- ton uni ts represent 40.1 percent of all shipments and, on average, those shipments had an actual capacity
of 23.2 kBtu/h. T he 3

- ton un i ts represent 44.5 percent of s hipments, and the engineering 5

- ton units are assumed to represent 14.5 percent of shipments. The average capacity of the shipments assigned to 2

- and 3

- ton units is very close to the nominal 2

- and 3

- ton engineering capacities. The weighted average of shipments assigned to the 5

- ton engineering capacities is approximately midway between 4

- and 5

- ton nominal sizes.

Applying the market percentages assigned to each of the nominal 2

- , 3

- , and 5

- ton engineering sizes yields an average market size of 34.9 kBtu/h. This corresponds closely with the 34.4 kBtu/h average capacity shipped estimated directly from the AHRI data

, which suggest s that the percentage weights assigned to the engineering sizes result in a good representation of the average total cooling capacity shipped.7

- 34 Table 7.2

. 13 Development of Market We ights for 2

- , 3

- , and 5

- T on Air Conditioners and Heat Pumps

AHRI Data Mapping to Engineering Sizes

SEER Bin

kBtu/h Units (2009 Shipped) Est. Avg

. Cooling Capacity

kBtu/h Assigned to Engineering

Size

tons Weighted Average Cooling Capacity Assigned

kBtu/h Fraction

of

Market
% Nominal Engr. Cooling Capacity

kBtu/h

0

- 16.4 155,396 12 2

23.195 0.401 2 16.5

- 21.9 380,779 18 2

22.0

- 26.9 1,006,905 24 2

27.0

- 32.9 854,454 30 50% 2

- ton
50% 3

- ton

36.053 0.445 3 33.0

- 38.9 1,039,122 36 3

39.0

- 43.9 428,931 40 3

44.0

- 53.9 591,554 48 50% 3

- ton
50% 5

- ton 55.314 0.154 5 54.0

- 64.9 461,598 60 5

Total 4,918,739
Average Cooling Capacity (kBtu/h) 34.4 34.9

Figure 7.2

. 24 Shipments of Residential Air Conditioners and Heat Pumps by Capacity for

2009 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000

12 18 24 30 36 40 48 602009 Shipments (Units)

Nominal Cooling Capacity (kBtu/h)7

- 35 In order to size the RECS households, DOE presumed that the distribution of shipment
sizes described previously represented the distribution within the RECS sample after taking into account the household weights. For each household

, DOE extracted all relevant household size data, including the cooled square footage of the home. DOE also used ASHRAE design data to develop estimates of the average 1 percent design dry bulb temperat ure in each of the three analysis regions. For each region

, DOE then developed a scaling factor to be applied to the home square footage and equal to :

SF design,r = ( T design, r

– 65) / (95

- 65) Eq. 7.2

. 8

Wh ere :

SF design,r = d esign s caling f actor , and

T

design, r = a verage 1 percent ASHRAE design dry bulb temperature for region (°F)

.

This scaling factor was then multiplied by the reported cooled area for each household. A

± 20 percent uncertainty factor was then applied to the cooled area to reflect some uncertainty in the sizing of the equipment to that observation, which provided some randomization in the size assignment to the RECS households. The RECS observations were then sorted by this new scaled cooled household area, and the central air conditioner equipment sizes (2

- , 3

- , and 5

- ton) were assigned from smallest to largest area and according to each central air conditioner size ’ s share in the overall market. As a check on the re sults, the estimated cooling full load operating
hours c of the equipment (calculated based on the RECS reported cooling energy use

, RECS
estimated SEER

, and the selected equipment size) were examined. In a few instances where the full load operating hours calculated were very high ( e.g. , >4

, 000) and did not appear to represent reasonable estimates for the regions in question , DOE modified the assignment of equipment size by choosing the next larger size central air conditioner available from the engineering analysis

.

It is noted that the design scaling factor is used as a proxy to represent lower cooling loads for the same household area in cooler climates and supports the allocation of the sizes across observations, but that the total relative allocation of sizes is unaffected. This end result was a distribution of sizes assigned to the weighted RECS samples that matches the distribution of sizes for shipments of residential air conditioners and heat pumps.

7.2.12 Regional Energy Consumption by Efficiency Level

Under the Energy Policy and Conservation Act, regional efficiency standards for central air conditioners may be defined for up to two regions consisting of contiguous groups of states. These regional standards are in addition to a base national standard ef ficiency that represents the minimum efficiency that a manufacturer could produce or that could be imported into the United States. As discussed in chapter 2, DOE defined two regions for regional standards: a hot

- humid region and a hot

- dry region. In addit ion, DOE defined the remainder of the states as “rest

- of

country.” For each equipment class, DOE developed energy use statistics for the market baseline

c Cooling full load opera ting hours are a measure of how many hours in a year the equipment would run at full capacity to provide the total estimated cooling load met by the equipment.7

- 36 and for higher efficiency levels by using a Monte Carlo random selection process to select 10,000 obser vations in each region and 10,000 for the nation as a whole. Of those observations, 93 percent were residential and 7 percent were commercial instances of the use of central air conditioner (or central heat pump) equipment.

In addition, at the national le vel, energy use statistics for each equipment class are further influenced by relative shipments between each region, not captured in the RECS data. Shipments by region for each air conditioner class were provided by AHRI for the years 2008
and 2009. d

Table 7.2

. 14 Th ese were converted to relative fractions of shipments for each product class by year. These relative shipment fractions by year are shown in chapter 9. DOE averaged the relative shipment fraction for each region across both years to provide product

- speci fic regional weighting factors and used these to further adjust the weighting factors developed for each observation. For small duct high velocity equipment, AHRI did not provide a weighting factor. DOE used the pr oduct

- specific regional weighting factor developed for split system air conditioners also for small duct high velocity equipment. These product

- specific regional weighting factors are shown in

.

Table 7.2

. 14 Fraction of S hipments by P roduct C lass to E ach R egion
Region

Product Class Hot

- Dry Hot

- Humid Rest of U.S. Split System CAC 0.374 0.515 0.112 Split System HP 0.194 0.736 0.070 Single Package CAC 0.174 0.578 0.248 Single Package HP 0.033 0.719 0.248 Small Duct High Velocity

AC 0.374 0.515 0.112

Source : AHRI

In the Monte Carlo simulations for each pro duct class, the relative weighting factors for each residential household observation were multiplied by the product

- specific regional weighting factors for that region that household was in. Similarly, the observation weights developed for each commercial observation ( TMY

- State combination ) were multiplied by the product specific regional weighting factors for that region . Thus all the individual observation weighting factors in a given region were scaled by the same value. This scaling has no impact

on the relative probability of selection for the observations within a given region, and thus no impact on the calculated results from the Monte Carlo simulations within a given region. However, when developing national results for each product class these p roduct

- specific regional weighting factors affect the relative probability of selecting an observation between regions.
Further d etails for the Monte Carlo analysis procedure are found in chapter 8. Table 7.2

. 15 to
Table 7.2

. 20 show the resulting baseline, market baseline, and higher efficiency annual energy

sta tistics developed for each region for each product class analyzed.

d Shipments for hot

- humid, hot

- dry, and rest

- of

- country regions provided by AHRI for 2008 and 2009 in shipments data provided to DOE (Aug 2010).7

- 37 Table 7.2

. 15 Split

- System Air Conditioners: Average Annual Energy Use by Efficiency

Level

**−** Coil

- Only Systems
Efficiency Level Annual Energy Consumption kWh/yr

National Hot

- Humid Hot

- Dry Rest of Country

# SEER HSPF * Cooling Heating Cooling Heating Cooling Heating Cooling Heating

1 13.0 N/A 2,470 0 3,411 0 2,226 0 1,334 0

Market

Baseline Market

Baseline N/A 2,340 0 3,226 0 2,111 0 1,266 0

2 13.5 N/A 2,382 0 3,288 0 2,147 0 1,289 0

3 14.0 N/A 2,301 0 3,174 0 2,074 0 1,247 0

4 14.5 N/A 2,222 0 3,066 0 2,003 0 1,204 0

5 15.0 N/A 2,147 0 2,964 0 1,936 0 1,163 0

6 15.5 N/A 2,078 0 2,868 0 1,873 0 1,124 0

7 16.0 N/A 2,012 0 2,778 0 1,814 0 1,087 0

8 16.5 N/A 1,966 0 2,714 0 1,772 0 1,066 0

9 17 N/A 1,924 0 2,653 0 1,733 0 1,045 0

10 18 N/A 1,892 0 2,606 0 1,707 0 1,032 0
* N/A means not applicable.

Table 7.2

. 16 Split

- System Air Conditioners: Average Annual Energy Use by Efficiency

Level

**−** Blower

- Coil Systems

Efficiency Level Annual Energy Consumption kWh/yr

National Hot

- Humid Hot

- Dry Rest of Country

# SEER HSPF* Cooling Heating Cooling Heating Cooling Heating Cooling Heating

1 13.0 N/A 2,468 0 3,409 0 2,227 0 1,333 0

Market
Baseline Market
Baseline N/A 2,200 0 3,025 0 1,986 0 1,190 0

2 13.5 N/A 2,381 0 3,286 0 2,148 0 1,288 0

3 14.0 N/A 2,299 0 3,172 0 2,074 0 1,246 0

4 14.5 N/A 2,220 0 3,063 0 2,003 0 1,203 0

5 15.0 N/A 2,146 0 2,962 0 1,937 0 1,162 0

6 15.5 N/A 2,076 0 2,866 0 1,874 0 1,123 0

7 16.0 N/A 2,011 0 2,776 0 1,815 0 1,087 0

8 16.5 N/A 1,952 0 2,695 0 1,762 0 1,057 0

9 17.0 N/A 1,898 0 2,618 0 1,713 0 1,028 0

10 18.0 N/A 1,797 0 2,478 0 1,622 0 976 0

11 19.0 N/A 1,726 0 2,377 0 1,556 0 941 0

12 20.0 N/A 1,662 0 2,286 0 1,497 0 910 0

13 21.0 N/A 1,604 0 2,204 0 1,444 0 882 0

14 22.0 N/A 1,550 0 2,129 0 1,394 0 856 0

1,531 0 2,100 0 1,378 0 848 0

1,504 0 2,061 0 1,356 0 836 0

* N/A means not applicable.7

- 38 Table 7.2

. 17 Split

- System Heat Pumps: Average Annual Energy Use by Efficiency Level

Efficiency Level Annual Energy Consumption kWh/yr

National Hot

- Humid Hot

- Dry Rest of Country

# SEER HSPF Cooling Heating Cooling Heating Cooling Heating Cooling Heating

1 13.0 8.00 3,265 1,645 3,664 1,452 4,037 809 1,547 2,738

Market

Baseline Market

Baseline Market

Baseline 3,030 1,594 3,401 1,407 3,747 784 1,439 2,656

2 13.5 8.1 3,149 1,629 3,532 1,437 3,891 801 1,494 2,713

3 14 8.2 3,040 1,606 3,410 1,416 3,756 788 1,445 2,680

4 14.5 8.3 2,936 1,588 3,293 1,400 3,627 780 1,395 2,653

5 15 8.5 2,838 1,567 3,184 1,380 3,507 762 1,348 2,648

6 15.5 8.6 2,747 1,554 3,082 1,367 3,394 755 1,303 2,635

7 16 8.7 2,661 1,538 2,985 1,353 3,287 747 1,261 2,613

8 16.5 8.8 2,583 1,526 2,898 1,342 3,192 741 1,226 2,594

9 17 8.9 2,510 1,508 2,815 1,326 3,102 731 1,193 2,569

10 18 9.1 2,378 1,487 2,667 1,307 2,940 719 1,133 2,538

11 19 9.4 2,289 1,466 2,567 1,289 2,811 707 1,099 2,506

12 20 9.6 2,207 1,445 2,477 1,269 2,695 694 1,068 2,476

13 21 9.8 2,134 1,429 2,395 1,255 2,589 685 1,040 2,454

14 22 10.1 2,109 1,421 2,367 1,248 2,557 679 1,029 2,4377

- 39 Table 7.2

. 18 Single

- Package Air Conditioners: Average Annual Energy Use by Efficiency

Level
Efficiency Level Annual Energy Consumption kWh/yr

National

# SEER HSPF * Cooling Heating

1 13 N/A 2,703 0

Market

Baseline Market

Baseline N/A 2,648 0

2 13.5 N/A 2,606 0

3 14 N/A 2,517 0

4 14.5 N/A 2,430 0

5 15 N/A 2,349 0

6 15.5 N/A 2,273 0

7 16 N/A 2,202 0

8 16.5 N/A 2,138 0

Table 7.2

. 19 Single

- Package Heat Pumps: Average Annual Ene rgy Use by Efficiency Level

Efficiency Level Annual Energy Consumption kWh/yr

National

# SEER HSPF Cooling Heating

1 13 8 3,831 1,526

Market

Baseline Market

Baseline Market

Baseline 3,682 1,484

2 13.5 7.9 3,694 1,495

3 14 8.1 3,566 1,466

4 14.5 8.3 3,444 1,438

5 15 8.4 3,329 1,425

6 15.5 8.6 3,222 1,398

7 16 8.8 3,121 1,376

8 16.5 9 3,029 1,3547

- 40 Table 7.2

. 20 Small Duct High Velocity Air Conditioners: Average Annual Energy Use by Efficiency Level

Efficiency Level Annual Energy Consumption kWh/yr

National Hot

- Humid Hot

- Dry Rest of Country

# SEER HSPF * Cooling Heating Cooling Heating Cooling Heating Cooling Heating

1 13 N/A 2,469 0 3,409 0 2,227 0 1,334 0

Market

Baseline Market

Baseline N/A 2,469 0 3,409 0 2,227 0 1,334 0

2 13.5 N/A 2,381 0 3,287 0 2,148 0 1,289 0

3 14 N/A 2,300 0 3,173 0 2,075 0 1,247 0

4 14.5 N/A 2,203 0 3,046 0 1,986 0 1,185 0
* N/A means not applicable.

7.2.13 Off Mode Energy Consumption

DOE is considering national standards for off mode energy consumption, but does not intend to set regional standards for off mode energy consumption. DOE established annual off mode energy consumption estimates for each off mode technology option identifie d in the engineering analysis for air conditioners and for heat pumps. DOE estimated annual off mode energy consumption for air conditioners based on the shoulder season off mode power consumption (P1) and heating season off mode power consumption (P2) def ined for each design option multiplied by the representative shoulder season rating hours (739 hours) and heating season rating hours (5,216 hours) established in the test procedure. DOE estimated annual energy consumption for heat pumps based only on the shoulder season off mode power consumption multiplied by the representative shoulder season rating hours (739 hours) established in the test procedure because heat pumps operate in active mode during the heating season. These seasonal
hours are consistent with the rating hours used in the SEER and HSPF ratings for air conditioners and heat pumps.

DOE is considering national standards for off mode energy consumption, but does not intend to set regional standards for off mode energy consumption. It is recog nized that there will be some variation in off mode hours depending on location and individual household usage, but DOE believes that the defined off mode hours in the test procedure represent a reasonable basis for calculation of energy savings from off m ode energy conservation standards. DOE does not include in the off mode period the time during the cooling season when a unit cycles off because energy use during this period is captured in the seasonal SEER rating of the equipment. Similarly, DOE does not include in the off mode period the time during the heating season when a heat pump cycles off because energy use during this period is captured in the seasonal HSPF rating of the equipment. To avoid double counting the benefits of design options which red uce energy consumption during the heating or cooling off

- cycles as opposed to the defined off mode time period, DOE relied on the representative off mode hours from the test procedure to estimate national average off mode period for the energy analysis.7

- 41 T he component that uses the most power during off mode is the crankcase heater, but it is not found in all products. Off mode energy use estimates for air conditioners and heat pumps
were estimated for units with and without crankcase heaters.

DOE was not able to identify a data source establishing the fraction of central air conditioner or heat pump products in the U.S. market which would be tested with crankcase heaters or would be expected to have crankcase heaters installed in the field. A 2004 study of the
Australian market estimated that one in six air conditioners in that market utilized crankcase

heaters. 16 However, changes in compressor technology since 2004, in particular market growth in the use of scroll compressors, are expected to have resulted in a lower fraction of the air
condition er market with crankcase heaters in the U.S. today. DOE assumed that 10 percent of air conditioners within each air conditioner product class would utilize crankcase heaters. Crankcase heaters are more commonly used in heat pumps, which must be able to cyc le on in cold weather. Based on discussions with manufacturers, DOE assumed that approximately two

- thirds of heat pumps would utilize crankcase heaters in each heat pump product class.

The engineering technology options examined for off mode energy use d o not impact blower energy consumption in off mode. For this reason, the energy savings from employing these options is identical for equipment utilizing either ECM or PSC blower motors. Annual off mode energy use for each technology above the baseline are shown in Table 7.2

. 21 to Table

7.2

. 23 for equipment with ECM motors and bot h with and without a crankcase heater.

While both ECM and PSC motors employing these technologies will have the same energy savings, DOE recognized that the wattage savings from the toroidal transformer design option was very small and that separate stand ard levels based on these design options that would be set at levels for which equipment with ECM blower motors could meet would not require the use of the toroidal transformer design option when PSC blower motors are used. Because of this
certain efficien cy levels options only apply to equipment with ECM motors. See chapter 8 for a
detailed discussion.

Table 7.2

. 21 Split

- System Blower Coil, Packaged , Space Constrained , and SDHV Air

Conditioner Off Mode Energy Use
Annual Off Mode Energy Consumption with an ECM Motor

and Crankcase Heater kWh/yr Annual Off Mode Energy Consumption with an ECM Motor

and No Crankcase Heater kWh/yr

Baseline 286 66 Efficiency Level 1 212 61 Efficiency Level 2 180 NA Efficiency Level 3 175 NA7

- 42 Table 7.2

. 22 Split

- System Coil

- Only Air Conditioner Off Mode Energy Use
Annual Off Mode Energy Consumption with an ECM Motor

and Crankcase Heater kWh/yr Annual Off Mode Energy Consumption wit h an ECM Motor

and No Crankcase Heater kWh/yr

Baseline 220 0 Efficiency Level 1 146 NA Efficiency Level 2 115 NA

Table 7.2

. 23 Split

- System, Packaged , and Space Constrained Heat Pump Off Mode Energy

Use
Annual Off Mode Energy Consumption with an ECM Motor

and Crankcase Heater kWh/yr Annual Off Mode Energy Consumption with an ECM Motor

and No Crankcase Heater kWh/yr

Baseline 38 10 Efficiency Level 1 24 10 Efficiency Level 2 23 NA

Not all equipment will be at the baseline efficiency level. DOE established a 2016 base case distribution of off mode energy use by efficiency level for each product class. This base case took into account that an estimated 10 percent of the split system a ir conditioner market are blower coil equipment and 90 percent are coil only equipment. It also took into account that approximately 27 percent of the current shipping blower coil split system and packaged air conditioner and the split system and packaged heat pump markets use ECM motors. See chapter 8 for discussion of the base case assumptions.

DOE determined energy savings for each technology option as compared to the base case in the LCC analysis using a special purpose off mode LCC spreadsheet. See c hapter 8 for discussion of the off mode LCC analysis. For each equipment class, DOE developed energy use
statistics for the base case and for each off mode efficiency level by using a Monte Carlo random selection process to select 10,000 observations using the base case off

- mode efficiency distributions. The resulting national average energy use estimates from this process are shown in
Table 7.2

. 24 by equipment class.

Table 7.2

. 24 National Average Off Mode Energy Use
Efficiency Level Average Annual Off Mode Energy Consumption kWh/yr Split Air

Conditioners

Coil Only Split Air

Conditioners

Blower Coil

and SDHV Split System Heat Pumps Single Package

Air

Conditioners Single Package Heat Pumps

Baseline 22 88 28 88 29

2016 Base Case 19 84 24 84 24 Efficiency Level 1 15 80 19 80 20 Efficiency Level 2 11 77 19 77 20 Efficiency Level 3 11 77 NA 77 NA7

- 43 7.3 FURNACES

The following sections describe the factors that determine the amount of heat load and energy that furnaces use , the characteristics of furnace energy efficiency

, and furnace operating
conditions

.

7.3.1 Introduction

A furnace utilizes gas or oil fuel for heating and electric energy to power a blower, a draft inducer, an ignitor, and sometimes auxiliary equipment. DOE estimated the energy consumption of furnaces in actual housing units, as represented by the sample dev eloped from RECS 2005. 17
This sample is further described in section 7.3 .2.

DOE calculated the energy use of residential furnaces in each of the three product
classes : non

- weatherized gas furnaces

, manufactured home furnaces, and oil

- fired furnaces

. For each household in the sample, DOE used RECS 2005 reported heating energy consumption (based on the existing heating system) to calculate the heating load of each household. The heating load represents the amount of heating required to keep a housing un it comfortable throughout an average year. DOE assigned the energy efficiency of existing systems based on a regional historical distribution of energy efficiencies for furnaces and RECS data on the age of the existing furnace . The estimation of heating lo ads also required calculating the electricity consumption of the blower (when applicable), because heat from the blower contributes to heating the housing unit. In addition, DOE made adjustments based on historical weather data, projections of shell effici ency and building square footage, and for homes that had secondary heating equipment that used the same fuel as the furnace. To complete the analysis, DOE calculated the energy consumption of alternative (more energy

- efficient) products, if they replaced e xisting systems in each housing unit.

7.3.2 Household Sample

DOE’s calculation of the annual energy use of residential furnaces relied on data from the

RECS 2005 17 . RECS collects energy

- related data for occupied primary housing units in the

United States. The RECS 2005 included data from 4,381 housing units that represent almost
111.1 million households.

The subset of RECS 200 5 records used to study furnaces met all of the following criteria:

**•** used a furnace as the main or secondary source of heat;

**•** used a heat ing fuel that is natural gas, liquefied petroleum gas (LPG), or fuel oil;

**•** heated only one housing unit; and

**•** had an energy consumption greater than zero.

DOE divided the furnace subset into three further subsets designed to include households
that use one of the three furnace product classes ( Table 7.3

. 1 ). Appendix 7

- D presents the
variables included and their definitions.7

- 44 Table 7.3

. 1 Selection of RECS 2005 Records for Furnaces

Product Class Algorithm Region No. of

Records RECS

2005 DOE 2016

Number

of Houses

million Number of

Furnaces

million

Non

- Weatherized

Gas Furnace Central heating equipment = furnace Heating fuel = gas Home type = single or multi

- family
Number of Housing Units Heated = 1 National 1726 45.5 45.2

North 990 25.9 26.9

South 736 19.6 18.3

Manufactured

Home Gas Furnace Central heating equipment = furnace Heating fuel = gas Home type = manufactured home
Number of Housing Units Heated = 1 National 109 2.5 2.5

North 58 1.2 1.2

South 51 1.2 1.3

Oil

- Fired Furnace Central heating equipment = furnace Heating fuel = fuel oil Home type = manufactured home
Number of Housing Units Heated = 1 National 150 2.9 3.0

DOE made some adjustments to EIA’s weightings for each RECS 200 5 household , in order to create a furnace population weight for 2016. The RECS 2005 we ighting indicate s how commonly each household configuration occurs in the general population in 2005 . The first adjustment was to compensate for the fact that the RECS 2005 sample does not distinguish

between weatherized and non

- weatherized gas furnaces. Therefore, to acco unt for non

weatherized gas furnaces, DOE assumed that a fraction of the households in the south with both a central air conditioner and gas furnace were using weatherized furnaces. Based on AHRI shipment data for weatherized and non

- weatherized furnaces ( which shows that about 10 percent of total furnace shipments are weatherized furnaces), DOE multiplied the RECS 2005 weight for households in the south region with both a central air conditioner and gas furnace by 0.80.

DOE also took into account the gr owth in population by region from 2005 to 2016 based on U.S. census population projections. For all product classes, the number of houses in the north was multiplied by a factor of 0.96 and the number of houses in the south by a factor of 1.04.

Finally, DOE adjusted the weightings to account for households with multiple furnaces. According to the 2008 American Comfort Survey, 7 percent of households in the United States have multiple gas furnaces. 18 For simplicity DOE assumed that these households had two furnaces. Therefore, for 7 percent of the households, the weighting was doubled, so it could be
representat ive of the furnace weight. DOE believes that the household records, along with their adjusted weightings, are representative of housing nationwide in 2016 (see a ppendix 7

- D for details).7

- 45 7.3.3 Annual Heating Load Calculation

The annual house

- heating load (HHL) is the total amount of heat output from the furnace that the house needs during the heating season. This includes heat from the burner and heat from
the blower and the blower motor.

The Department determined HHL for each sampled housing unit, based on the burner operating hours ( BOH ) and the characteristics of the assigned existing furnace, using the following calculations:



### Factor Adj t t N BOH BE AFUE Q HHL

ex ex RECS YR



### _ 3600 412

. 3

, 

### × ** **  ** **  ** **  ** **  ** **  ** **  ** **  ** **  ** **  ** **  ** ** 

**− ×**  **+ ×**  **× +**  **× =**  **− +**  Eq. 7.3

. 1

Where:

Q YR,RECS = annual fuel consumption for heating based on RECS 2005 (kBtu/yr),

AFUE

ex = AFUE of the existing furnace (see section 7.3.5.4 ),

3.412 = constant to convert kW to kBtu/hr,

BE

ex = power consumption of the blower motor of the existing furnace (kW),

BOH

ex = as defined below (hr/yr),

N = number of cycles per hour (set equal to 5 for furnaces),

HLH = heating load hours (hr) (see appendix 7

- E for the derivation),

t + = off delay (seconds),

t - = on delay (seconds), and Adj_Factor = adjustment factor.

Burner operating hours ( BOH

ex ), the number of hours the existing furnace burner is on during a year, is a key variable in the calculation of HHL. The Department calculate d BOH for the existing furnace as:

ex IN

RECS YR

ex Q Q BOH

, , **=** Eq. 7.3

. 2

Where:

BOH

ex = burner operating hours of existing household (hr/yr), Q YR,RECS = as defined above (kBtu/yr), Q

IN

, ex = input capacity of the existing furnace (see section 7.3.5.1) (kBtu/hr).

The power consumption of the blower motor depends on the steady

- state operating conditions (the pressure and airflow) for the furnace. This calculation is explained in appendix 7

- F

.7

- 46 The Department made adjustments to reflect the expectation that newly built housing units in 2016 will have a somewhat different heating load than the housing units in the RECS
2005 sub

- sample. The adjustment involves multiplying the calculated HHL for each RECS 2005

hou sing unit by a building shell efficiency index derived from the NEMS simulation performed

for EIA’s AEO 2010

. 19 The building shell efficiency index sets the heating load value at 1.00 for

an average home in 2005 (by type) in each c ensus division. The values listed represe nt the change in heating load based on the difference in phy sical size and shell attribute s for homes in the future (which takes into account physical size difference and efficiency gains from better
insulation and windows )

. This factor differs for new construction and replacement households.
The value for househol ds in 2016 is 0.87 for replacements and 0.77 for new construction, which means that the average new home in 2016 will require less heat energy to maintain indoor comfort. To add variability, the factor is varied by plus or minus 15 percent.

DOE also mad e adjustments to the HHL calculated using RECS 2005 data to reflect historical average climate conditions. Table 7.3

. 2 shows the 1895 to 2009 average heating degree days (HDD) as well as the 2005 average HDD for the nine census divisions. The adjustment factors are calculated using the equation below and are almost all positive, which means that 2005 had warmer temperatures compared to the 115

- year average.

2005

_ _ _

_ 115

lim

_ _ stock res avg yr

ate c average HDD HDD Factor Adj **=** Eq. 7.3

. 3

Where:

HDD res_stock _2005 = HDD in 2005 for the specific census division or state where the housing unit is located, and

HDD 115

_ yr_avg = 115

- year average HDD (189 5

– 2009) for the specific census division where the housing unit is located.

Table 7.3

. 2 Heating Degree Day Adjustment Factors

Census Division Average HDD Adjustment Factor

1895

- 2009 2005

1 New England 6,559 6,567 1.00

2 Middle Atlantic 5,822 5,752 1.01

3 East North Central 6,307 6,102 1.03

4 West North Central 6,582 5,988 1.10

5 South Atlantic 2,756 2,757 1.00

6 East South Central 3,418 3,303 1.03

7 West South Central 2,175 1,928 1.13

8 Mountain 5,105 4,742 1.08

9 Pacific 3,217 3,031 1.06

Nation 4,416 4,246 1.04

For households in which it is clear that the fuel use for heating is associated solely with the use of furnace equipment as the primary or secondary heating equipment, DOE used the7

- 47 annual fuel consumption for heating the housing unit from RECS 2005. D OE ad just ed the house heating load for households that used a both a furnace (either as the primary or secondary heating equipment) and other heating equipment using the same fuel. RECS 2005 reports the percentage of heating energy consumption attributable to s econdary products. DOE derived the house heating load applicable to the furnace by subtracting the estimated amount of heat provided by
the other heating system

. In the case when a household was determined to have multiple furnaces, the house heating load was divided by the number of furnaces. Details are presented in appendix 7

- F.

7.3.3.1 Overview of Heating Load Estimates

DOE calculated that the national average annual heating load to be 32.0 for non

weatherized gas furnaces, 27.7 for manufactured home gas furnaces, and 60.7 for oil

- fired furnaces. The variations between product types primarily reflect differences in the di stribution of

furnaces. These results are smaller than previous studies, primarily due to improvements in building shell efficiency and heating equipment efficiency

. Table 7.3

. 3 shows the range in heating load among sample households.

Table 7.3

. 3 Range of Annual Heating Load for Each Furnace Product Class by Region

Regi on Min Max Average Percentiles

5% 25% 50% 75% 95%

Non

- Weatherized Gas Furnaces

National 0.3 190.5 32.0 5.7 16.3 28.7 43.0 69.8

North 1.5 190.5 40.5 14.9 26.9 37.1 50.2 76.3

South 0.3 133.2 19.1 3.6 9.0 15.5 25.1 46.5

Manufactured Home Gas Furnaces

National 3.5 104.7 27.7 7.2 17.5 23.7 36.1 57.5

North 3.8 104.7 34.1 13.9 22.0 32.1 42.6 68.4

South 3.5 76.1 21.7 6.1 12.4 20.2 26.4 45.8

Oil

- f ired Furnaces

National 0.0 220.9 60.7 27.7 41.2 60.3 76.2 99.9

7.3.4 Annual Energy Consumption

Once the heating load of each sample housing unit is known, it is possible to estimate what the energy consumption would be if more efficient equipment, rather than the baseline equipment, were used in each housing unit.

7.3.4.1 Fuel Consumption

The Department calculated the fuel consumption ( FuelUse ) for each furnace using the
foll owing formulas from the current American Society of Heating, Refrigerating and Air

Conditioning Engineers (ASHRAE) test procedure SPC 103

- 2007 section C:7

- 48 IN SS 

### Q BOH FuelUse **× =**  , e , f for single

- stage furnace Eq. 7.3

. 4

Where:

BOH SS = steady

- state burner operating hours (hr), and Q

IN = input capacity of existing furnace (kBtu/hr).

The details for calculating energy consumption appear in appendix 7

- F.

7.3.4.2 Electricity Consumption

The Department calculated furnace electricity consumption for the blower, the draft inducer, and the ignitor. g The blower moves heated air through the house whenever the furnace burner is on (adjusted for delay times betwe en burner and blower operation). It also operates in the cooling season (summer) if the house is air conditioned, but the Department only considered use during the heating season. DOE also took into account the electricity consumption of such auxiliary equ ipment as condensate pumps and heat tape, which are sometimes installed with higher efficiency equipment. The Department calculated the electricity consumption as:



# ( **)**
**( )**  HTE OH CPE BOH StbyE BOH PE y PE y BE y BOH ElecUse

HT SS SS ig ig p SS



### × **+ ×**  **+ ×** 

**− +**  **× +**  **× +**  **× ×**  **=**

8760 , h

Eq. 7.3

. 5 for single

- stage furnace

,

Where:

BOH SS = as defined above,

y = ratio of blower on

- time to burner on

- time,

BE = power consumption of the blower motor (kW),

y

P = ratio of induced

- draft blower on

- time to burner on

- time,

PE = power consumption of the draft

- inducer blower

- motor (kW),

y

IG = ratio of ignitor on

- time to burner on

- time,

PE IG = power consumption of the ignitor (kW), StbyE = power consumption during standby (kW),

CPE = power consumption of condensate pump (kW),

OH HT = operating hours of heat tape (hours), and

HTE = power consumption of heat tape (kW).

e For natural draft equipment this formula is modified to include the pilot light consumption.

f For modulating equipment this formula includes parameters for the operation at full, modulating, and reduced load.

g The DOE and ASHRAE test procedures do not count the electricity used by controls when the furnace is not firing.

h For modulating equipment this formula includes parameters for the operation at full, modulating, and reduced load. See appendix 7

- G

.7

- 49 The ratio of blower on

- time to burner on

- time and the ratio of induced draft blower on

time to burner on

- time are from the c urrent ASHRAE test procedure SPC 1 03

- 2007 20 using delay times (pre

- p urge, post

- purge, on

- delay, and off

- delay) derived from DOE’s 2007 Furnace

and Boiler Final Rule

. 21 The ratio of ignitor on

- time to burner on

- time comes from the DOE test procedur e and the ignition time derived from the 2007 final rule. The delay times are defined as follows: pre

- purge and post

- purge times are the lengths of time the draft inducer operates before and after a firing cycle. On

- delay is the amount of time the blower w aits to begin operating after the burner starts firing. Off

- delay is the time the blower keeps operating after the burner turns off. Ignition time is the length of time the hot surface ignitor is on before gas is sent to the
burner. The values for the dela y and ignition times are shown in Table 7.3

. 4

.

Table 7.3

. 4 Values for Delay and Ignition Times

Pre

- Purge Post

- Purge On

- Delay Off

- Delay Ignition

15 seconds 5 seconds 30 seconds 120 seconds 37 seconds

A common value for the power consumption of the draft inducer, PE , for basic non

condensing model furnaces is 75 W, and the average value is about 75 W, so DOE selected 75 W
for all the non

- condensing models. The Department found no correlation between the PE and input capacity or between PE and airflow capacity. For condensing furnaces, the Department used a PE of 90 W, which closely matches the mean for that group. Details on the derivation of these values can be found in appendix 7

- H

.

The standby power c onsumption ( StbyE) values are dependent on the furnace motor blower type. DOE considered two motor types: PSC and ECM. Table 7.3

. 5 shows the fractions
of installation s with different motor types that DOE used based on a 2004 Canadian study of

furnace fan market in Vancouver for non

- weatherized gas furnaces. 22 Overall ECM furnace fans would represent 29 percent of the market by 2016. For manufactured home gas furnaces, oil

fired furnaces , and electric furnaces , DOE assumed that ECM furnace fans would represent 10 percent of the market in 2016. DOE believes that the furnace fan market in this study is a good proxy for the 2016 U.S. non

- weatherized gas furnace market, based on the growing market share

of ECM furn ace fan motors. Table 7.3.6 summarizes the power consumption at each standby efficiency level. For the furnace energy use calculations only the baseline standby efficiency values are used. See appendix 7

- G for more details.7

- 50 Table 7.3

. 5 Fraction of Non

- Weatherized Gas Furnace Fan Motors and Controls Shipments by Furnace Efficiency and Construction Type in 2016

Furnace Technology and Efficiency

AFUE PSC ECM Single

- Stage Two

- Stage Single

- Stage Two

- Stage

Replacement Market

Non

- Condensing (80% AFUE) 50% 40% 0% 10%

Condensing (90%+ AFUE) 40% 10% 0% 50%

New Construction Market

Non

- Condensing (80% AFUE) 85% 10% 0% 5%

Condensing (90%+ AFUE) 70% 5% 0% 25%

Table 7.3

. 6 Standby Power Consumption by Efficiency Level and Furnace Type

Product Class Energy Efficiency

Level PSC

Watts ECM

Watts

Non

- Weatherized

Gas Furnaces 0 (baseline) 8 11

1 8 9.8

2 8 9

Manufactured Home

Gas Furnaces 0 (baseline) 8 11

1 8 9.8

2 8 9

Oil

- F ired Furnaces 0 (baseline) 9 12

1 9 10.8

2 9 10

Electric Furnaces 0 (baseline) 8 11

1 8 9.8

2 8 9

Some higher efficiency installations require the use of auxiliary equipment such as condensate pumps and heat tape. The electricity consumption of this equipment is calculated by DOE and added to the total electricity consumption. If a household required a condensate pump,

D OE assumed that it consumed 60 watts and operated at the same time as the burner. If a household required heat tape to prevent the condensate pipe from freezing, DOE assumed that it
consumed 3 watts per square foot (on average 45 watts total) and operated only when the average monthly outside temperature dropped below 45 **°** F. Details of how DOE determined whether a household required a condensate pump or heat tape can be found in chapter 8.

Details for the calculation of the blower motor energy consumption can be found in appendix 7

- F

.

The details for calculating electricity consumption appear in appendix 7

- G

.

7.3.5 Assigning Furnace Equipment Characteristics to Sampled Households

To estimate the heating load of each sample housing unit, DOE represented the existing furnace by assigning an input capacity, airflow capacity, and AFUE to the furn ace in the RECS sample housing units.7

- 51 7.3.5.1 Input Capacity of Existing and New Equipment

The Department assigned an input capacity for the existing furnace of each housing unit based on an algorithm that correlates the housing unit size and outdoor design temp erature with the distribution of input capacity of furnaces. DOE assumed that, for the new furnace installation, the input capacity would remain the same. For manufactured home and oil

- fired furnaces, DOE used a single fixed input capacity value of 80 kBtu /h and 105 kBtu/h, respectively. The following steps describe the assignment process for non

- weatherized gas

furnaces:

1) The Department ranked all the RECS housing units in ascending order by size (heating square foot) multiplied by a scaling factor to acco unt for the outdoor design temperature (see equation 7.3.6 ) and calculated the percentile rank of each housing unit using the statistical weight of each of the sample records.

2) The Department constructed percentile tables by input capacity of furnaces based on the historical shipment information and number of models in AHRI Directory (see Table

7.3

. 7 ).

3) After selecting a housing unit from the RECS database during each Mon te Carlo iteration, DOE noted the size of the selected housing unit and determined the percentile rank from Step 1.

4) To avoid a one

- to

- one deterministic relation between the housing unit size and input capacity, DOE added a random term to the percentile ide ntified in Step 3 so that the correlation was not perfect. The Department used a normal distribution to characterize
the random term. The random term has a mean of zero and a standard deviation of 8
percent.

5) Using the percentile from Step 4, DOE looked up the input capacity from the input capacity percentile table in Step 2.

DOE used ASHRAE design data to develop estimates of the average 1 percent design dry bulb temperature for each household (see appendix 7

- E for more details). Using this data, DOE
then developed a scaling factor to be applied to the home heating square footage and equal to:

SF design,h = (65

- T design, h ) / (65

- 42) Eq. 7.3

. 6

Where:

SF design,h = heating design scaling factor, and

T

design, h = average 1 percent ASHRAE design dry bulb temperature (°F) for heating

.

It is noted that the design scaling factor is used as a proxy to represent lower heating
loads for the same household area in cooler climates and supports the allocation of the s izes across observations, but that the total relative allocation of sizes is unaffected. Th e end result was a distribution of sizes assigned to the weighted RECS samples that matches the distribution of sizes for shipments of residential furnaces by input capacity

. Table 7.3

. 7 shows the distribu tion7

- 52 of input capacities for the most commonly available input capacity bins based on the June 2010

AHRI Residential Furnace Di rectory and AHRI shipment data

. 23

Table 7.3

. 7 Distribution of Input Capacity for Non

- Weatherized Gas Furnaces
Input Capacity

kBtu/h AHRI 2001 Shipments
% 2010 AHRI Directory

Fraction of

Models
% Shipment Weighted

Fraction of

Models
% Cumulative

Fraction of

Models
%

45 9.4 10.2 6.8 6.8

50 3.8 2.6 9.4

60 8.6 16.5 8.6 18.0

70 24.8 1.7 5.0 23.0

75 6.6 19.8 42.8

80 13.7 17.2 13.7 56.4

90 23.2 4.6 4.8 61.3

100 17.4 18.4 79.6

110

20.4 5.3 4.9 84.5

120 6.2 5.7 90.2

125 7.7 7.1 97.4

140 2.8 2.6 100.0

7.3.5.2 Airflow Size of Existing Equipment

The Department classified furnaces by nominal maximum airflow in cfm at 0.5 in. w.g

. of external static pressure. The Department assigned the airflow capacity of existing furnaces for housing units that had air conditioners in a manner similar to how it assigned furnace input capacity. Larger air conditioners go to larger housing units, according to the distribution of sizes of air conditioners sold the year the air conditioner was installed in that housing unit. The Department used the air conditioner nominal size of two, three, four, or five tons to set the airflow capacity with a ratio of 400 cfm per ton of cooling. The steps were:

1) The Department ranked all the RECS housing units in ascending order by size (cooling square foot) multiplied by a scaling factor to account for the outdoor design temperature (see equation 7.3.7 ) and calcula ted the percentile rank of each housing unit using the statistical weight of each of the sample records.

2) Based on historical shipment information of residential central air conditioners by capacity, DOE constructed the airflow capacity percentiles table fo r air conditioners. (See Table 7.3.8). The Department restricted the airflow sizes to two, three, four, or five

tons

— the equivalent of 800, 1,200, 1,600, or 2,000 cfm at 0.5 in. w.g. static pressure. Since there are no available shipment data on the airflo w capacity of furnaces, the Department used the airflow capacity of residential central air conditioners as a proxy.

3) After selecting a housing unit from the RECS database during each Monte Carlo iteration, DOE noted the size of the selected housing unit an d determined the percentile rank from Step 1.7

- 53 4) To avoid a one

- to

- one deterministic relation between the housing unit size and input capacity, DOE added a random term to the percentile identified in Step 3 so that the correlation was not perfect. The Departm ent used a normal distribution to characterize
the random term. The random term has a mean of zero and a standard deviation of 8
percent.

5) Using the percentile from Step 4, DOE looked up the airflow from the airflow percentile table in Step 2. The Departmen t selected an input capacity and airflow combination with the identified airflow capacity, based on commonly available models (see Table 7.3

. 9 ). If no input capacity and airflow combination with the identified airflow capacity was available, the Department selected the input capacity and airflow combination with the same input capacity and the closest airflow capacity as a substitute.

DOE used ASHRAE design data to dev elop estimates of the average 1 percent design dry bulb temperature for each household (see appendix 7

- E for more details). Using these data, DOE then developed a scaling factor to be applied to the home cooling square footage and equal to:

SF design, c = ( T design, c – 65) / (95

- 65) Eq. 7.3

. 7

Where:

SF design, c = cooling design scaling factor, and

T

design, c = average 1 percent ASHRAE design dry bulb temperature (°F) for cooling

.

It is noted that the design scaling factor is used as a proxy to represent lower cooling
loads for the same household area in warmer climates and supports the allocation of the sizes across observations, but that the total relative allocation of sizes is u naffected. This end result was a distribution of sizes assigned to the weighted RECS samples that matches the distribution of sizes for shipments of residential furnaces. Table 7.3

. 8 shows the distribution of input capacities for the representative product classes listed above, based on AHRI shipment data

. 24

Table 7.3

. 8 Distribution of Airflow for Furnaces

Airflow Rating cfm 2010 AHRI Shipments

% Cumulative Fraction

%

800 37.3 37.3

1200 35.0 72.3

1600 16.8 89.0

2000 11.0 100.0

For non

- weatherized gas furnaces, DOE selected 25 combinations (“bins”) of input capacity and maximum airflow. The marked cells in Table 7.3

. 9 reflect the input capacity and nominal maximum airflow for the most common input and nominal maximum airflow capacities

of models in the June 2010 AHRI Direct ory. 2 5 Most basic models on the market fit into the 25 bins of input capacity and airflow capacity. Some models do not exactly match the bins, but their values are close enough that DOE included them in one of the 25 bins. For example, 40 kBtu/h7

- 54 and 45 kBtu/ h models are grouped together into a single 45 kBtu/h bin. Most bins have at least
two actual models.

Table 7.3

. 9 Common Furnace Input Capacity and Airflow combinations
Airflow Sizing in cfm

tons Input Capacity

kBtu/h

45 50 60 70 75 80 90 100 115 120 125 140 800 cfm (2 tons) x x x 1,200 cfm (3 tons) x x x x x x x x 1,600 cfm (4 tons) x x x x x x x x 2,000 cfm (5 tons) x x x x x x

7.3.5.3 AFUE of Existing Equipment

The Department assigned the AFUE of existing furnaces based on the equipment age of the existing furnace as given by RECS and historical shipments by efficiency. The following steps describe this process:

1) After DOE selected a housing unit from the RECS datab ase during each Monte Carlo iteration, the Department randomly assigned a percentile value and extracted the furnace age information from RECS (see Table 7.3

. 10 ). Usi ng the extracted furnace age, DOE assigned an installation year from the installation year range for the applicable RECS equipment age bin.

2) Based on the historical furnace shipment information sorted by AFUE, DOE constructed percentile tables by AFUE shipm ents of furnaces for 2005 and prior years (see Table

7.3

. 11 to Table 7.3

. 13 ). AHRI shipments data for non

- weatherized gas furnaces indicate that housing units in the northern region receive more efficient furnaces. Therefore, DOE developed two historical AFUE shipment distributions

— one for the northern region and
one for the southe rn region

— for non

- weatherized gas furnaces.

3) DOE determined the AFUE by looking it up from the AFUE percentile table from Step (2) corresponding to the age of the existing equipment in the housing unit and whether the housing unit was located in the norther n or southern regions.

Table 7.3

. 10 Number of O bservations for E ach A ge G roup

RECS Bin Less than 2

Years Old 2 to 4

Years Old 5 to 9

Years Old 10 to 19

Years Old 20 Years

or Older Total

Installation Year s 2004

- 2005 2001

- 2005 1996

- 2000 1986

- 1995 1966

- 1985 Equipment Type

Non

- Weatherized

Gas Furnaces 206 229 367 477 302 1,726

13.2% 13.3% 21.3% 27.0% 17.8% 100.0%

Manufactured Home

Gas Furnaces 11 14 25 25 27 109

8.5% 12.5% 24.4% 23.5% 23.3% 100.0%

Oil

- Fired Furnaces 10 21 29 47 39 150

9.0% 14.1% 14.5% 31.7% 29.7% 100.0%7

- 55 Table 7.3

. 11 Historical Fraction of Regional Gas Furnace Shipments by AFUE Bins

Year North Region South Region

>78 AFUE 78 to < 90 >90 AFUE >78 AFUE 78 to <90 >90 AFUE

2005 0.0% 48.8% 51.2% 0.0% 81.6% 18.4%

2004 0.0% 52.2% 47.8% 0.0% 83.6% 16.4%

2003 0.0% 53.7% 46.3% 0.0% 83.6% 16.4%

2002 0.0% 59.0% 41.0% 0.0% 85.2% 14.8%

2001 0.0% 57.7% 42.3% 0.0% 86.0% 14.0%

2000 0.0% 64.6% 35.4% 0.0% 89.6% 10.4%

1999 0.0% 64.9% 35.1% 0.0% 89.5% 10.5%

1998 0.0% 64.6% 35.4% 0.0% 89.8% 10.2%

1997 0.0% 62.9% 37.1% 0.0% 87.8% 12.2%

1996 0.0% 64.8% 35.2% 0.0% 89.8% 10.2%

1995 0.0% 68.3% 31.7% 0.0% 88.9% 11.1%

1994 0.0% 66.6% 33.4% 0.0% 87.6% 12.4%

1993 0.0% 70.2% 27.4% 0.0% 86.8% 10.2%

1992 2.4% 59.3% 34.0% 3.0% 80.2% 10.7%

1991 6.7% 23.9% 29.7% 9.1% 30.9% 9.3%

1990 46.3% 22.3% 25.3% 59.8% 27.4% 7.9%

1989 52.4% 17.4% 24.3% 64.6% 21.2% 7.6%

1988 58.3% 19.6% 25.7% 71.2% 24.2% 8.1%

1987 54.8% 18.3% 23.7% 67.8% 22.2% 7.4%

1986 58.0% 18.7% 10.2% 70.4% 20.1% 3.2%

1985 71.1% 17.3% 16.2% 76.7% 19.6% 5.1%

1984 66.5% 18.2% 17.8% 75.3% 20.9% 5.6%

1983 64.0% 7.8% 22.9% 73.5% 9.4% 7.2%

1982 69.3% 8.2% 19.2% 83.4% 9.6% 6.0%

1981 72.6% 8.6% 15.6% 84.4% 9.7% 4.9%

1980 75.9% 9.0% 11.9% 85.4% 9.8% 3.7%

1979 79.1% 3.9% 5.9% 86.5% 4.1% 1.9%

1978 90.2% 1.4% 0.0% 94.1% 1.4% 0.0%

1977 98.6% 0.9% 0.0% 98.6% 0.9% 0.0%

1976 99.1% 0.5% 0.0% 99.1% 0.5% 0.0%

1975 99.5% 0.0% 0.0% 99.5% 0.0% 0.0%

1966 to 1974 100.0% 0.0% 0.0% 100.0% 0.0% 0.0%7

- 56 Table 7.3

. 12 Historical Fraction of Manufactured Home Gas Furnace Shipments by AFUE

Bins

Year >78 AFUE 78 to <90

AFUE >90 AFUE

2005 15.0% 80.0% 5.0%

2004 15.0% 80.0% 5.0%

2003 15.0% 80.0% 5.0%

2002 15.0% 80.0% 5.0%

2001 15.0% 80.0% 5.0%

2000 15.0% 80.0% 5.0%

1999 15.0% 80.0% 5.0%

1998 15.0% 80.0% 5.0%

1997 7.2% 92.8% 0.0%

1996 7.2% 92.8% 0.0%

1995 7.2% 92.8% 0.0%

1994 7.2% 92.8% 0.0%

1993 7.2% 92.8% 0.0%

1992 7.2% 92.8% 0.0%

1991 65.9% 34.1% 0.0%

1990 70.1% 29.9% 0.0%

1989 75.7% 24.3% 0.0%

1988 72.3% 27.7% 0.0%

1987 74.8% 25.2% 0.0%

1986 77.9% 22.1% 0.0%

1985 78.4% 21.6% 0.0%

1984 77.0% 23.0% 0.0%

1983 89.3% 10.7% 0.0%

1982 89.2% 10.8% 0.0%

1981 89.2% 10.8% 0.0%

1980 89.1% 10.9% 0.0%

1979 94.1% 5.9% 0.0%

1978 98.6% 1.4% 0.0%

1977 99.1% 0.9% 0.0%

1976 99.5% 0.5% 0.0%

1966 to 1975 100.0% 0.0% 0.0%

Table 7.3

. 13 Historical Fraction of Oil

- Fired Furnace Shipments by AFUE Bins

Year >78 AFUE 78 to <90

AFUE >90 AFUE

1992

- 2005 0.0% 100.0% 0.0%

1991 4.0% 96.0% 0.0%

1990 4.0% 96.0% 0.0%

1989 61.0% 39.0% 0.0%

1988 70.0% 30.0% 0.0%

1987 77.5% 22.5% 0.0%

1966

- 1986 85.0% 15.0% 0.0%7

- 57 7.3.6 Furnace Energy Consumption Results

This section presents the average annual energy use and the average energy savings for each considered energy efficiency level compared to the baseline energy efficiency for each furnace product class

. For its LCC and PBP analyses, DOE used the full distribution of energy use values calculated for the sample households.

Table 7.3

. 14 lists the average annual energy use for non

- weather ized gas furnaces and the average energy savings for each considered energy efficiency level compared to the baseline for the nation and the northern and southern regions.

Table 7.3

. 14 Annual Energy Consumption for Non

- Weatherized Gas Furnaces
Energy Efficiency Level

AFUE Average Energy Consumption Average Energy Savings (Gas)

MMBtu/yr Gas MMBtu/yr Electricity kWh/yr

National 80 (baseline) 37.8 312

- 90 33.7 289 4.1

92 33.0 283 4.8

95 32.0 275 5.8

98 30.7 363 7.1

North 80 (baseline) 47.4 369

- 90 42.2 342 5.2

92 41.3 335 6.1

95 40.1 325 7.3

98 38.4 438 8.9

South 80 (baseline) 23.5 228

- 90 20.9 211 2.5

92 20.5 206 3.0

95 19.9 200 3.6

98 19.1 251 4.4

Table 7.3

. 15 lists average annual energy use for manufactured home gas furnaces and average energy savings for each energy efficiency level evaluate d in the LCC analysis, compared
to the baseline furnace.7

- 58 Table 7.3

. 15 Annual Energy Consumption for Manufactured Home Gas Furnaces

Energy Efficiency Level

AFUE Average Energy Consumption Average Energy Savings (Gas)

MMBtu/yr Gas MMBtu/yr Electricity kWh/yr

National 80 (baseline) 43.7 282

- 90 39.0 257 4.7

92 38.1 252 5.6

96 36.6 241 7.1

North 80 (baseline) 53.4 344

- 90 47.6 314 5.8

92 46.6 307 6.8

96 44.7 295 8.7

South

80 (baseline) 34.4 223

- 90 30.7 203 3.7

92 30.0 199 4.4

96 28.8 191 5.6

Table 7.3

. 16 shows the average annual energy use for oil

- fired furnaces and the average energy savings for each energy efficiency level, compared to the baseline

.

Table 7.3

. 16 Annual Energy Consumption for Oil

- Fired Furnaces
Energy Efficiency Level

AFUE Average Energy Consumption Average Energy Savings (Oil)

MMBtu/yr Oil MMBtu/y) Electricity kWh/yr

National 82 (baseline) 67.3 483

- 83 66.5 477 0.8

84 65.7 472 1.6

85 64.9 466 2.3

97 57.1 410 10.1

7.3.7 Standby Energy Consumption Results for Furnaces

Table 7.3.17 shows the average annual standby energy use for furnaces and the average energy savings for each energy efficiency level, compared to the baseline

.7

- 59 Table 7.3

. 17 Annual Standby Energy Consumption for Furnaces
Energy Efficiency Level Average Energy Consumption

Electricity

kWh/yr Average Savings kWh/yr

Non

- Weatherized Gas Furnaces

0 (baseline) 73.6

- 1 70.9 2.7

2 69.1 4.5

Manufactured Home Gas Furnaces
0 (baseline) 69.2

- 1 68.2 0.9

2 67.6 1.6

Oil

- fired Furnaces 0 (baseline) 75.5

- 1 74.6 0.9

2 73.9 1.5

Electric Furnaces
0 (baseline) 79.4

- 1 78.5 1.0

2 77.8 1.67

- 60 REFERENCES

1. U.S. Department of Energy

- Energy Infomation Administration, Residential Energy Consumption Survey: Quality Profile , 1996. Washington, D.C.

2. National Oceanic and Atmospheric Administration, NNDC Climate Data Online, (Last accessed February 2009.) <www7.ncdc.noaa.gov/CDO/CDODivisionalSelect.jsp>

3. Missouri Census Data Center, U.S. Census Data Archive for 1980, 1990, 2000, (Last accessed February, 2009.) < http://mcdc2.missouri.edu/applications/uexplore.shtml#top >

4. Air

- Conditioning Heating and Refrigeration Institute, Air Conditioning and Refrigeration Institute Statistical Profile, 2008 , 2008.

5. U.S. Department of Energy

- Energy Efficiency and Renewable Energy, Technical Support Document: Energy Efficiency Standards for Consumer Products: Residential Central Air Conditioners and Heat Pumps Including: Regulatory Impact Analysis. Chapter 5, Life

Cycl e Cost and Payback Analysis, May, 2002. Washington, D.C.

< http://www1.eere.energy.gov/buildings/appliance_standards/residential/ac_central_1000 _r.htm l >

6. Air

- Conditioning and Refrigeration Institute, Directory of Certified Unitary Equipment
Standards 210/240 270 . Arlington, VA.

7. Air

- Conditioning Heating and Refrigeration Institute, Shipment data and efficiency data by CAC and CHP equipment class supplied to DOE for the current CAC rulemaking

, 2008. Arlington, VA.

8. U.S. Department of Energy

- Energy Infomation Administration, Commercial Building Energy Consumption Survey (CBECS), (Last accessed May, 2009.)

< www.eia.doe.gov/emeu/cbecs/cbecs2003 >

9. U.S. Department of Energy

- Energy Efficiency & Renewable Energy, Rulemaking Framework for Residential Central Air Conditioners and Heat Pumps, June, 2008. Washington, DC. Report No. 1904

- AB4 7.

10. U.S. Department of Energy

- Office of Energy Efficiency and Renewable Energy, EnergyPlus Energy Simulation Software, 2008.

< http://apps1.eere.energy.gov/buildings/energyplus >

11. U. S. Department of Energy

- Office of Energy Efficiency and Renewable Energy, Commercial Building Benchmark Models, 2009.

< http://www1.eere.energy.gov/building s/commercial_initiative/reference_buildings.html

>7

- 61 12. National Renewable Energy Laboratory, User's manual for TMY2s (Typical meteorological years derived from the 1961

- 1990 national solar radiation database)

, 1995. Golden, C.O.

13. Wassmer, M., and M. J. Brandemuehl, Effect of Data Availability on Modeling of Residential Air Conditioners and Heat Pumps for Energy Calculations

. ASHRAE

Transactions . 111(1): pp. 214

- 225

14. Jacobs, P., Small HVAC Problems and Potential Savings Reports , 2003. California

E nergy Commission. Sacramento, CA. Report No. CEC

- 500

- 03

- 082

- A

- 25.

< www.energy.ca.gov/pier/project_reports/500

- 03

- 082.html >

15. Air

- Conditioning Heating and Refrigeration Institute, AHRI Statistical Releases. U.S. Manufacturers’ Shipments of Central Air Conditioners and Air Source Heat Pumps (by Cooling Capacity). Monthly Tables from January 2009 through December 2009, 2008. (Last accessed September, 2010.)

< www.ahrinet.org/Content/MonthlyShipmentReports_375.aspx >

16. Australian Greenhouse Office, Air Conditioners Standby Product Profile 2004/2006, June, 2004. Canberra, Australia.

< http://www.energyrating.gov.au/library/pubs/sb200406

- aircons.pdf >

17. U.S. Department of Energy

- Energy Information Administration, Residential En ergy Consumption Survey: 2005 Public Use Data Files, 2005.

< http://www.eia.doe.gov/emeu/recs/recspubuse05/pubuse05.html >

18. Decision Analysts, 2008 American Home Comfort Study: Online Database Tool , 2009. Arlington, Texas. < http://www.decisionanalyst.com/Syndicated/HomeComfort.dai >

19. Energy Information Administration, Annual Energy Outlook 2010 with Projec tions to
2035 , 2010. Washington, DC. Report No. DOE/EIA

- 0383(2010).

< http://www.eia.doe.gov/oiaf/aeo/ >

20. American Society of Heating Refrigerating and Air

- Conditioning Engineers Inc.,
ASHRAE Standard: Metho d of Testing for Annual Fuel Utilization Efficiency of
Residential Central Furnaces and Boilers , 2007. Report No. ANSI/ASHRAE 103

- 2007.

21. Department Of Energy, 10 CFR Part 430 Energy Conservation Program for Consumer Products: Energy Conservation Stand ards for Residential Furnaces and Boilers; Final
Rule

. Federal Register , 2007. 72 No.222: pp. 65132

- 65170

22. Innes Hood, High Efficiency Furnace Blower Motors: Market Baseline Assessment

, 2004. The Sheltair Group. Vancouver, BC. < http://www.cee1.org/eval/db_pdf/416.pdf >7

- 62 23. GAMA, "GAMA Shipment Data" . 2002.

24. Air

- Conditioning Heating and Refrigeration Institute, AHRI Shipments Data for Air Conditioners by Capacity, 2010. < http://www.ahrinet.org/monthly+shipments.aspx >

25. Air Conditioning Heating and Refrigeration Institute, Consumer's Directory of Certified Efficiency Ratings for Heating and Water Heating Equipment (AHRI Directory June 2010), 2010. (Last accessed June, 2010.)

< http://www.ahridirectory.org/ahridirectory/pages/home.aspx >