NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Improved Modeling of Residential 

Air Conditioners and Heat Pumps 

for Energy Calculations

D. Cutler, J. Winkler, N. Kruis, and C. Christensen

National Renewable Energy Laboratory

M. Brandemuehl

University of Colorado

Technical Report

NREL/TP-5500-56354

January 2013
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

1617 Cole Boulevard

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Improved Modeling of Residential 

Air Conditioners and Heat Pumps 

for Energy Calculations 

D. Cutler, J. Winkler, N. Kruis, and C. Christensen

National Renewable Energy Laboratory

M. Brandemuehl

University of Colorado

Prepared under Task No. BE12.0103

Technical Report

NREL/TP-5500-56354

January 2013
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iii

Executive Summary

This report presents improved air conditioner (AC) and heat pump (HP) modeling methods in the 

context of whole-building simulation tools. Its goal is to enable more accurate evaluation of costeffective equipment upgrade opportunities and efficiency improvements in residential buildings. 

EnergyPlus and DOE-2 AC and HP model algorithms and required inputs were investigated (see

Section 2). This was accompanied by a survey of the available AC and HP performance data. 

Manufacturer-expanded performance tables were initially deemed the best option for generating 

model inputs, and an effort was undertaken to compile these tables into a usable format. This 

effort resulted in a database that contained performance data on 460 AC and HP systems. Section 

3 describes the data evaluation process and highlights several issues. The database was used to 

generate the necessary model inputs for each system.

Annual simulation results using unit-specific inputs were not as consistent as expected (see 

Section 4). These differences included wide ranges of predicted efficiency for a given seasonal 

energy efficiency rating (SEER) value (both within a single manufacturer’s product lines and 

across manufacturers), and significant SEER overlap (lower SEER families outperformed higher 

SEER families). Current shortcomings in consistency and completeness of manufacturer data are 

presented.

These inconsistencies prompted an analysis of the performance data used to generate model 

inputs. Section 5 presents an analysis of the sensitivity to the modeling inputs that revealed little 

sensitivity to the selected performance curves used to predict off-rated performance. Using unitspecific performance curves did not have a significant impact on predicted efficiency. Therefore, 

annual simulations in Houston, Phoenix, Atlanta, and Chicago were run at three airflow rates to 

select and test a standardized set of performance curves. The results supported initial

observations that the impact of unit-specific curves was limited. Using a standardized set of

performance curves greatly reduces the complexity of generating model inputs. 

Following the performance curve sensitivity study, the impacts of the rated performance values 

on the annual simulation results were evaluated. The study showed that the rated values were 

driving the variation and inconsistency observed in initial simulations. It was therefore necessary 

to evaluate the quality of the rated value modeling inputs generated from manufacturer data (see 

Section 6). This was accomplished by comparing manufacturer data to the Air Conditioning, 

Heating, and Refrigeration Institute (AHRI) Directory of Certified Product Performance (AHRI 

2012). Its data are based on well-regulated testing procedures; AHRI is the most consistent data 

source available. These comparisons showed significant differences between AHRI and 

manufacturer data. 

When simulating ACs and HPs in the context of a whole-building simulation tool, the standard 

set of performance curves presented in this report should be used with unit-specific rated 

performance values obtained from AHRI. Section 7 presents a library of AC and HP modeling 

inputs with a set of standardized performance curves to facilitate energy and cost comparisons. 

This modeling approach simultaneously reduces the complexity of the model inputs and 

increases the agreement with the SEER and heating seasonal performance factor rating

procedures. 
iv

Figure ES–1 highlights the benefit of using a standard AC modeling library over unit-specific 

inputs (when comparing AC energy use across SEER families). The figure displays a simulated 

annual average efficiency for the unit-specific model inputs (shown as dots) and the standardized 

set of model inputs (shown as solid lines). The unexpected and inconsistent trends associated 

with using unit-specific inputs limit their usefulness when making energy and cost comparisons 

across varying efficiency levels. 

Figure ES–1. Simulation results comparing annual average efficiency of a standard AC component 

library (shown in black lines) to specific AC units for five leading manufacturers

(1.5-, 2-, 2.5-, 3-, 3.5-, 4-, and 5-ton sizes shown for each SEER family, when available) 

This report describes how the standard AC library was developed by: 

1. Collecting and evaluating AC and HP performance data (Sections 3 and 4) 

2. Determining model sensitivity to the required input parameters (Sections 5 and 6) 

3. Developing a library of appropriate model inputs (Section 7). 

This work can inform future efforts to improve data reporting by manufacturers and AHRI and 

improve current modeling efforts. 

2

3

4

5

6

7

Manufacturer A Manufacturer D

Manufacturer B Manufacturer E

Manufacturer C

SEER 13 SEER 14 SEER 15 SEER 16 SEER 17 SEER 18 SEER 21
v 

Definitions

AC air conditioner

ADP apparatus dew point

AHRI Air-Conditioning, Heating, and Refrigeration Institute

BEopt Building Energy Optimization

BF bypass factor

cfm cubic feet per minute

COP coefficient of performance

DX direct expansion

EER energy efficiency ratio (Btu/Wh)

EIR energy input ratio (1/COP)

EPT expanded performance table

EWB entering wet-bulb temperature

FF flow fraction

HP heat pump

HSPF heating seasonal performance factor (Btu/Wh)

ODB Outdoor dry-bulb temperature

P power (W, Btu/h)

Q cooling or heating capacity (W, Btu/h)

PLF part load fraction

PLR part load ratio

RTF runtime fraction

SEER seasonal energy efficiency ratio (Btu/Wh)

SHR sensible heat ratio

T temperature

v′ fan air flow rate per unit of capacity (m3

/s/W, cfm/ton)

V air volumetric flow rate (m3

/s, cfm)

η fan fan efficacy (W/m3

/s, W/cfm)
vi 

Contents

Executive Summary................................................................................................................................... iii

Definitions.................................................................................................................................................... v

List of Figures ........................................................................................................................................... vii

List of Tables ............................................................................................................................................ viii

1 Introduction........................................................................................................................................... 1

2 Background........................................................................................................................................... 3

2.1 Definitions............................................................................................................................3

2.1.1 Standard Rating Conditions .....................................................................................3

2.1.2 Net Versus Gross Performance Values ....................................................................3

2.2 Split System Models ............................................................................................................5

2.2.1 EnergyPlus Direct Expansion Cooling Model Overview ........................................5

2.2.2 EnergyPlus Direct Expansion Heating Model Overview ........................................7

2.2.3 Summary of Model Inputs .......................................................................................8

2.3 Performance Data and Generating Model Inputs .................................................................8

3 Manufacturer Performance Data Collection and Evaluation.......................................................... 12

3.1 Data Visual Inspection .......................................................................................................12

3.2 Bypass Factor and Apparatus Dew Point Calculation and Verification ............................12

3.3 Apparatus Dew Point/Bypass Modeling Algorithm Comparison ......................................13

3.4 Performance Curve Visual Inspection ...............................................................................13

4 Simulation of Specific Air Conditioner Units................................................................................... 16

4.1 House Description ..............................................................................................................16

4.2 Modeling Assumptions ......................................................................................................16

4.3 Performance/Comparison Metric .......................................................................................16

4.4 Air Conditioner Simulation Results ...................................................................................17

5 Performance Curve Sensitivity Study .............................................................................................. 19

5.1 Air Conditioner Simulation Results ...................................................................................19

6 Importance of Rated Value Inputs .................................................................................................... 22

6.1 Single-Stage Rated Values .................................................................................................22

6.2 Two-Stage Rated Values....................................................................................................24

6.3 System Tonnage Impact on High SEER Air Conditioners ................................................26

7 Standard Library Development ......................................................................................................... 28

7.1 Air Conditioner Library Inputs ..........................................................................................28

7.2 Air Conditioner Performance Curve Coefficients .............................................................29

7.3 Heat Pump Library Inputs ..................................................................................................30

7.4 Heat Pump Performance Curve Coefficients .....................................................................31

7.5 Air Conditioner Library Simulation Results ......................................................................32

8 Conclusions ........................................................................................................................................ 34

References................................................................................................................................................. 35

Appendix A—Performance Coefficients in SI Units .............................................................................. 36
vii 

List of Figures

Figure ES–1. Simulation results comparing annual average efficiency of a standard AC component 

library (shown in black lines) to specific AC units for five leading manufacturers ..................... iv

Figure 1. Net versus gross capacity for a blow-through fan configuration.......................................... 4

Figure 2. Comparison of total capacity performance curve to manufacturer EPT values ................ 14

Figure 3. Comparison of total capacity curve to manufacturer data with an adjusted rating point 

(shown in red) ..................................................................................................................................... 15

Figure 4. Simulation results for ACs evaluated in Houston (individual capacities of 1.5, 2, 2.5, 3, 

3.5, 4, and 5 tons shown in ascending order within each SEER family, when available)........... 18

Figure 5. Variations in simulation results attributed to using a fixed set of performance curves 

(individual capacities of 1.5, 2, 3, 4, and 5 tons shown in ascending order within SEER values)20

Figure 6. Comparison of AHRI-certified EER to manufacturer EPT data for single-stage ACs........ 23

Figure 7. Back-calculated CD values for single-stage ACs using AHRI SEER values and 

manufacturer EPT data ...................................................................................................................... 24

Figure 8. Comparison of AHRI-certified EER to manufacturer EPT data for two-stage ACs ............ 25

Figure 9. SEER calculated using manufacturer data compared to AHRI reported SEER values for 

two-stage ACs (letters A, B, C, and E denote manufacturer; capacities of 2, 3, 4, and 5 tons 

shown in ascending order within a product line)............................................................................ 26

Figure 10. Simulation results for the AC library (shown in solid black lines) compared to 

individual AC units (individual capacities of 1.5, 2, 3, 4, and 5 tons shown in ascending order 

within SEER values)........................................................................................................................... 33
viii

List of Tables

Table 1. Standard Rating Conditions for Cooling and Heating.............................................................. 3

Table 2. Summary of EnergyPlus DX Cooling and Heating Model Inputs ............................................ 8

Table 3. Common Independent and Dependent Variables Found in Manufacturer EPTs ................... 9

Table 4. Example Manufacturer EPT (Subset of Data Displayed)........................................................ 11

Table 5. Calculated Error in Annual Average COP Attributed to Using Fixed Curves ...................... 21

Table 6. BEopt AC Rated Value Inputs ................................................................................................... 29

Table 7. AC Total Capacity Coefficients as a Function of Operating Temperatures (°F) .................. 29

Table 8. AC EIR Coefficients as a Function of Operating Temperatures (°F)..................................... 30

Table 9. AC Total Capacity Coefficients as a Function of Flow Fraction............................................ 30

Table 10. AC EIR Performance Curve Coefficients as Function of a Flow Fraction .......................... 30

Table 11. BEopt HP Rated Value Inputs.................................................................................................. 31

Table 12. HP Total Capacity Coefficients as a Function of Operating Temperatures (°F) ................ 31

Table 13. HP EIR Coefficients as a Function of Operating Temperatures (°F) ................................... 32

Table 14. HP Total Capacity Coefficients as a Function of Flow Fraction.......................................... 32

Table 15. HP EIR Performance Curve Coefficients as a Function of Flow Fraction .......................... 32

Table 16. AC Total Capacity Coefficients as a Function of Operating Temperatures (°C)................ 36

Table 17. AC EIR Coefficients as a Function of Operating Temperatures (°C)................................... 36

Table 18. HP Total Capacity Coefficients as a Function of Operating Temperatures (°C)................ 36

Table 19. HP EIR Coefficients as a Function of Operating Temperatures (°C)................................... 37
1 

1 Introduction

Air conditioning is practically ubiquitous across the United States and is becoming commonplace 

in most developed countries. The vapor compression cycle (the most common way of providing 

chilled air to an indoor environment) is run off electricity, making air conditioning especially 

energy intensive on a source energy basis. Residential air conditioning is also a major contributor 

to summer peak demand. 

In the U.S. residential sector, air conditioning is now found in approximately 90% of new singlefamily homes (up from 75% in 1991). Heat pumps (HPs) have also been on a steady rise over the 

last 20 years. HPs as the primary heating system in new homes rose from 22% in 1991 to 37% in 

2009 (EIA 2009). In 2008, residential air conditioning accounted for 9% of the site energy 

consumed and 14% of the source energy in the U.S. residential housing stock (EIA 2009). 

The energy consumption of the equipment and the relative efficiencies of different pieces of 

equipment must be accurately quantified to recommend efficient designs and reduce the energy 

consumption of air conditioners (ACs) and HPs. The principal metrics used to quantify the 

energy efficiency of ACs and HPs are the seasonal energy efficiency ratio (SEER) and heating 

seasonal performance factor (HSPF), respectively. These metrics consist of a single value used to 

quantify the seasonal energy efficiency of a unit and do not vary with respect to climate, building 

type, or specific unit characteristics. The consensus from the research community is that these 

are convenient, yet inaccurate metrics for quantifying annual energy use (Courtney 2006, Fairey

et al. 2004, Kavanaugh 2002). SEER and HSPF provide only very rough estimates of energy 

consumption and are meant only as ranking methods for different levels of efficiency (though 

even these rankings are inaccurate for different climates and different units (Courtney 2006, 

James J. Hirsch & Associates 2005). 

An alternative approach to quantifying annual energy consumption is through whole-building 

annual simulation software. Programs such as EnergyPlus and DOE-2 can capture complex 

interactions between buildings, climate, and occupants. The simulation engines enable users to 

evaluate energy use trends across different equipment lines, buildings, and climates. Though 

manufacturers and the marketplace will not likely discard the SEER/HSPF metrics in lieu of

annual simulation, the use of annual simulation tools can inform designers, policy makers, and 

future improvements to the SEER and HSPF rating procedures. 

The Building Energy Optimization Tool (BEopt) is a residential-specific modeling program that 

provides a graphical user interface for EnergyPlus and DOE-2, an integrated cost database, and 

cost-based optimization capabilities for annual simulations. It enables users to model potential 

energy-saving technologies and to select the optimal package of energy efficiency measures 

based on either cost savings or energy savings. 

BEopt enables users to evaluate energy consumed by ACs and HPs and to consider the complex 

interactions of all building/climate variables. The incumbent modeling approach in BEopt had 

two issues with AC and HP simulations: 

• The “shipped” options (prepackaged options representing the SEER/HSPF families that 

were included in BEopt) did not always generate monotonically decreasing energy 
2 

consumption for increasing SEER/HSPF. This begged the question of whether installing 

a SEER 14 over a SEER 15 (for example) should be recommended in certain climates. 

• To effectively model a specific AC or HP using manufacturer data, large sets of 

complicated inputs (more than 35 inputs, including biquadratic curve fits of various 

performance metrics that are very difficult to compute) had to be generated. 

Consistent results that are representative of the equipment in the marketplace are necessary to 

make informed recommendations on efficiency measures. This enables evaluation of the current 

equipment and its impacts on energy use across various climates and building types, and can help 

inform future efficiency and rating procedure improvements. It is also necessary to enable the 

simulation of units outside the shipped options in BEopt. This gives the energy modeler 

flexibility in an analysis.

The research described in this report takes an in-depth look at the data available to inform 

models used in simulation engines. These data are evaluated for consistency and reliability. The 

results of the extensive data evaluation are then used to propose a new method for modeling ACs

and HPs that takes advantage of simplified inputs to provide more flexibility in modeling, while 

retaining or improving overall accuracy in simulations. 
3 

2 Background

When simulating AC (or HP) performance in the context of a whole building, several parameters 

should be accurately captured. For ACs, power, capacity, sensible heat ratio (SHR), and runtime 

must be accurately predicted as functions of outdoor dry-bulb temperature, indoor wet-bulb 

temperature, indoor air mass flow rate, and part load ratio (PLR) (cycling). For HPs, power and 

total capacity predictions are functions of outdoor dry-bulb temperature, indoor dry-bulb 

temperature, indoor air mass flow rate, and PLR. 

Section 2.1 establishes several definitions that are necessary to understand the algorithms used to 

simulate AC and HP performance. 

2.1 Definitions

2.1.1 Standard Rating Conditions

Air Conditioning, Heating, and Refrigeration Institute (AHRI) Standard 210/240 defines 

Standard Rating Condition as rating conditions used as the basis of comparison for performance 

characteristics (ANSI/AHRI 2008). Several standard rating conditions are used to evaluate the 

energy efficiency ratio (EER) and coefficient of performance (COP), which are used to calculate 

seasonal energy efficiency ratio (SEER) and/or heating seasonal performance factor (HSPF). 

However, industry has accepted two standard rating conditions (one for cooling and one for 

heating) for comparing product capacity and efficiency (see Table 1). 

Table 1. Standard Rating Conditions for Cooling and Heating

AHRI Standard 

Rating Condition

Air Entering Indoor Unit Air Entering Outdoor Unit

Dry-Bulb Wet-Bulb Dry-Bulb Wet-Bulb

(°F) (°C) (°F) (°C) (°F) (°C) (°F) (°C)

Cooling (A-Test) 80.0 26.7 67.0 19.4 95.0 35.0 75.01 23.91

Heating (H1 Test) 70.0 21.1 60.0max 15.6max 47.0 8.33 43.0 6.11

1 Applies only if the unit rejects condensate to the outdoor coil

Source: AHRI 2008 

For two-stage and variable-speed equipment, these conditions are tested only at the high stage 

and speed and are two of several operating conditions required to calculate the SEER and HSPF. 

However, manufacturers typically report performance at the standard operating condition for low 

stage and speed operation. 

The conditions shown in Table 1 will be called the rated operating point and the performance at 

these conditions will be called the rated performance. 

2.1.2 Net Versus Gross Performance Values

Typically the net capacity is reported and readily available. Net capacity takes into account heat 

produced by the fan motor in the airstream. Gross capacity is delivered by the refrigerant, which 

is equivalent to the airside capacity measured from the cooling coil inlet to the cooling coil outlet 

(fan heat not accounted for). The relationship between net and gross capacity is described in Eq. 

1 as
4 

if in heating mode 

if mode coolingin 









+

− =

gross fan

gross fan

net PQ

PQ Q  

   (1) 

Similarly, 

net gross PPP fan +=  (2) 

where the gross power ( Pgross

 ) is equivalent to the power of the outdoor unit (compressor and 

outdoor fan). 

Figure 1 depicts net and gross capacities. Net capacity is measured between points A and C; 

whereas gross capacity is measured between B and C.

Figure 1. Net versus gross capacity for a blow-through fan configuration

The simulation engine used for this study modeled the cooling coil and fan separately. Thus, 

gross performance values were required. 

2.1.2.1 Converting From Net to Gross

Net performance values often need to be converted to the corresponding gross value (published 

performance tables typically display net values and simulation engines generally require gross 

performance inputs). Unfortunately, the required fan performance is not often reported and 

assumptions often need to be made. 

In cooling mode, starting with the following
5 

gross fan gross

gross fan gross

net

net

net cooling QvP

QvQ

P

Q COP  

 





+ ′

− ′ == ηη

, (3) 

results in

( ) fan net cooling

net cooling

gross cooling v COP

COP

COP

,

,

, − ′ 11 + = η

(4) 

where ηfan is the fan efficacy and v’ the indoor volumetric flow rate per unit of gross cooling 

capacity. Several manufacturers state the assumptions used to determine net performance values. 

An assumed fan efficacy of 0.365 W/cfm and flow rate of 400 cfm/ton were used in Equation 4

when the necessary information could not be found (0.365 W/cfm is the AHRI default value for 

fan efficacy). Equation 4 can be manipulated to convert from a net EER to a gross EER if careful 

attention is paid to the units used in the equation. 

A similar approach is used to determine the net COP in heating mode. Starting with

gross fan gross

gross fan gross

net

net

net heating QvP

QvQ

P

Q COP  

 





+ ′

+ ′ == η

η

, (5) 

results in

( ) fan net heating

net heating

gross heating v COP

COP

COP

,

,

, + ′ 11 − = η

(6) 

2.2 Split System Models

EnergyPlus and DOE-2 were both considered for this study. EnergyPlus was selected as the 

principal simulation tool for the following reasons: 

• Simpler and more theoretically sound split direct expansion (DX) model 

• Fewer and simpler inputs 

• Improved documentation 

• Position as the most current and advanced simulation engine with ongoing development. 

Cutler (2012) provides an in-depth discussion of the EnergyPlus DX cooling coil and model; 

Kruis (2010) discusses the differences between the approaches used by DOE-2 and EnergyPlus 

to model air-conditioning equipment. Thus, only a brief overview of the EnergyPlus DX cooling 

coil model is provided here. 

2.2.1 EnergyPlus Direct Expansion Cooling Model Overview

EnergyPlus uses a set of performance curves that scale rated performance values based on 

current operating conditions to simulate DX equipment. The required rated performance values 

include (DOE 2011): 
6 

• Total capacity

• SHR

• COP

• Indoor coil airflow rate.

Rated total capacity and COP inputs are gross values. The model uses the following set of 

normalized performance curves to scale the rated performance values for off-rated operating 

conditions: 

• Total capacity—function of entering wet-bulb temperature (EWB) and outdoor dry-bulb 

temperature (ODB)

• Energy input ratio (EIR = 1/COP)—function of EWB and ODB

• Total capacity—function of flow fraction (flow rate/rated flow rate)

• EIR—function of flow fraction (flow rate/rated flow rate)

• Part load fraction (PLF)—function of PLR. 

The total capacity and EIR curves that are a function of EWB and ODB are both biquadratic 

curve fits. Each takes the form

ewb ewb odb odb ewb TTfTeTdTcTbay odb ⋅⋅+⋅+⋅+⋅+⋅+= 2 2 (7) 

where ewb is EWB and odb is OWB. The total capacity and EIR curves as functions of flow 

fraction (FF, defined as mm rated  ) are both quadratic curves and take the form

2 bay FF c ⋅+⋅+= FF (8) 

The PLF as a function of PLR curve is a quadratic curve of the form 

2 PLF ba PLR c ⋅+⋅+= PLR (9) 

PLR is defined as the ratio of sensible cooling load to total sensible capacity for the current time 

step in the simulation. The runtime fraction (RTF) is calculated as 

PLF RTF = PLR (10) 

Thus, if the output of the PLF curve is <1, it causes an increase in the runtime for that time step. 

This adjustment accounts for the unit’s cycling losses. All the capacity and power calculations 

are assumed to be at steady state; therefore, part load is accounted for by extending the unit’s

total runtime (at steady state).

The power consumption for the time step is calculated as
7 

 QEIRP 

total ⋅⋅= RTF (11) 

where EIR and Qtotal

 are calculated as

EIREIR rated ( ) ( ) Tf EIREIR f FF ⋅⋅= (12) 

total ratedtotal ( ) ( ) QQQQ fTf FF  , ⋅⋅= (13) 

In Equations 12 and 13, EIR ( ) Tf and Q ( ) Tf  are curves for normalized EIR and total capacity as a 

function of EWB and ODB; and EIRf ( ) FF and Qf ( ) FF  are curves for normalized EIR and total 

capacity as functions of flow fraction. 

The preceding discussion describes how EnergyPlus evaluates total capacity and power. The 

sensible and latent performances of the unit are also evaluated for each time step. The rated SHR 

and the apparatus dew point (ADP)/bypass factor (BF) model are used to calculate the sensible 

and latent portions (DOE 2011). The EnergyPlus Engineering Reference provides additional 

information on the ADP/BF model. 

Calculations for two-stage and multispeed ACs are quite similar to the single-speed model. In 

addition to specifying the number of speeds, the above set of inputs is required for each stage

andspeed. Simply put, the two-stage and multispeed DX cooling coil models interpolate between 

speeds to calculate the performance of the given building’s sensible load and operating 

conditions. 

2.2.2 EnergyPlus Direct Expansion Heating Model Overview

The DX heating model is quite similar to the DX cooling model; albeit slightly simpler because 

the DX heating model needs to calculate only the sensible heat output. The model includes a set 

of algorithms to account for defrost operation. Those algorithms were not investigated during 

this study, but details about the defrost calculations can be found in DOE (2011). 

Similar to the DX cooling model, the required rated performance values include: 

• Capacity

• COP

• Indoor coil airflow rate.

The model uses the following set of normalized performance curves to scale the rated 

performance values for off-rated operating conditions: 

• Capacity—function of entering dry-bulb and outdoor ODB

• Energy input ratio (EIR = 1/COP)—function of entering dry-bulb and ODB temperatures

• Capacity—function of flow fraction (flow rate/rated flow rate)

• EIR—function of flow fraction (flow rate/rated flow rate)
8 

• PLF—function of PLR. 

2.2.3 Summary of Model Inputs

The key required inputs for the EnergyPlus DX cooling and heating models are summarized in 

Table 2. 

Table 2. Summary of EnergyPlus DX Cooling and Heating Model Inputs

Cooling Heating

Rated 

values

Q ,ratedtotal  Q ,ratedtotal 

COPrated COPrated

rated SHR Vrated



Vrated



Performance 

curves 

Q Tf )(  Q Tf )( 

EIR Tf )( EIR Tf )(

Qf FF )(  Qf FF )( 

EIRf FF )( EIRf FF )(

PLFf PLR)( PLFf PLR)(

2.3 Performance Data and Generating Model Inputs

The input parameters included in Table 2 must be acquired to simulate specific AC and HP units 

in the context of EnergyPlus. Published performance data are often the source for determining 

model inputs (direct testing is cost prohibitive) and three main data sources are available:

• AHRI Directory of Certified Product Performance (AHRI 2012) 

• Manufacturer product data tables

• Manufacturer expanded performance tables (EPTs). 

The AHRI Directory of Certified Product Performance is a source of accurate data. It has welldefined testing procedures and third-party oversight of the testing; however, only a small fraction 

of the required model inputs can be obtained from it. The directory contains the rated capacity, 

EER, and SEER, but does not include the rated SHR, cycling degradation coefficient (CD—a 

metric describing part load degradation caused by cycling), flow rate, or sufficient data to 

generate the set of performance curves. 

Manufacturer product data tables typically contain information about the rated performance of a 

particular unit, whereas manufacturer EPTs include performance data at various operating 

conditions and indoor airflow rates. EPTs are produced and made available for unit selection and 

sizing purposes, but also allow for the development of performance curves required for 
9 

simulation. Table 3 shows common independent and dependent variables found in manufacturer 

EPTs.

Table 3. Common Independent and Dependent Variables Found in Manufacturer EPTs

Independent

Variables

Dependent

Variables

Cooling 

ODB Total Capacity

Indoor Dry-Bulb Sensible Capacity

Indoor Wet-Bulb Total Power

Airflow Rate

Heating

ODB Total Capacity

Indoor Dry-Bulb Integrated Capacity

Airflow Rate Total Power

EPTs are not without shortcomings. EPTs contain performance data at many operating 

conditions, yet there is no governing standard that dictates how the data are made available 

(PDF, spreadsheets, HTML, etc.) and what ranges and format are reported. This lack of 

standardization leads to a wide variety in reporting formats. It also leads to wide variations in the 

selected dependent variables and range of independent variables. 

The EPTs generally report net capacity and power values, yet most manufacturers do not report 

the fan efficacy associated with the data. The energy model inputs require gross values, because

the fan is modeled independently of the AC or HP unit. Therefore, fan power consumption must 

often be assumed to convert the EPT net values to the gross values required by EnergyPlus. 

The EPTs are not representative of experimental test data, but are typically generated by 

manufacturers’ in-house computer models. These models use test data from the AHRI test points 

and then predict performance at off-rated conditions. Therefore, although these are the best 

available data, they are only as good as the models produced by individual manufacturers. 

When model inputs are generated, the temperature-based performance curves should be 

generated using data corresponding to the rated airflow rate only. These curves account for 

performance variations at various EWBs and ODBs; the flow fraction curves are responsible for 

adjusting for off-rated flow rates. Similarly, the flow fraction performance curves should be 

generated using data for the rated temperatures in Table 1. 

An example EPT for a leading AC manufacturer is included in Table 4. Only a subset of outdoor 

temperatures is listed. Manufacturers typically include outdoor temperatures from approximately 

75°F to 115°F in 10°F increments. The following process was used to develop a set of model 

inputs using these data. However, the process is quite similar for EPTs using different formats 

and containing different dependent variables. 

1. Calculate net EIRs using total capacity and total system power. 

2. Convert net total capacity and net EIR to gross values using Equations 1 and 4. 
10 

3. Select the rated airflow rate (this is often 400 cfm/ton, yet can also correlate to the middle 

fan jumper if tested with an indoor fan). 

4. Normalize the data to the rated values by dividing by the capacity or EIR values at the 

rated conditions (95/80/67 in the case of the temperature cures, and the rated airflow rate 

as selected above for the flow fraction curves).

5. Perform linear least square regressions on the data corresponding to the rated airflow rate 

to generate temperature-based performance curves (Q ( ) Tf  and EIR ( ) Tf ) using Equation 7. 

6. Perform linear least square regressions on the data corresponding to standard rating 

temperatures listed in Table 1 to generate flow fraction performance curves (Qf ( ) FF  and 

EIRf ( ) FF ) using Equation 8. 
11 

Table 4. Example Manufacturer EPT (Subset of Data Displayed)

Evaporator Air Condenser Air °F (°C)

75 (23.9) 95 (35) 105 (40.6)

cfm EWB

°F (°C)

Capacity kBtu/h Total

Sys

kW3

Capacity kBtu/h Total

Sys

kW3

Capacity kBtu/h Total

Sys

kW Total 3 1 Sens1

,

2 Total1 Sens2 Total1 Sens2

875

72 (22) 34.32 17.27 1.96 31.24 16.13 2.44 29.59 15.54 2.71

67 (19) 31.45 21.21 1.96 28.59 20.05 2.43 27.04 19.44 2.71

63 (17) 29.35 20.58 1.96 26.66 19.40 2.43 25.19 18.78 2.70

62 (17) 28.82 25.13 1.95 26.24 23.94 2.43 24.86 23.29 2.70

57 (14) 28.00 28.00 1.95 25.89 25.89 2.43 24.74 24.74 2.70

1000

72 (22) 34.88 18.05 2.01 31.66 16.90 2.48 29.96 16.30 2.76

67 (19) 31.98 22.49 2.01 29.00 21.31 2.48 27.40 20.68 2.75

63 (17) 29.88 21.78 2.00 27.07 20.58 2.48 25.55 19.95 2.75

62 (17) 29.44 26.90 2.00 26.81 26.81 2.48 25.62 25.62 2.75

57 (14) 29.10 29.10 2.00 26.85 26.85 2.48 25.62 25.62 2.75

1125

72 (22) 35.27 18.78 2.06 17.61 17.61 2.53 30.22 17.07 2.81

67 (19) 32.36 23.68 2.05 22.50 22.50 2.53 27.66 21.88 2.80

63 (17) 30.25 22.90 2.05 21.70 21.70 2.52 25.82 21.07 2.80

62 (17) 30.02 28.49 2.05 27.62 27.62 2.52 26.32 26.32 2.80

57 (14) 29.99 29.99 2.05 27.62 27.62 2.52 26.32 26.32 2.80

1 Total and sensible capacities are net capacities. Blower motor heat has been subtracted.

2 Sensible capacities shown are based on 80°F (27°C) entering air at the indoor coil. For sensible capacities at other 

than 80°F (27°C), deduct 835 Btu/h (245 W) per 1000 cfm (480 L/S) of indoor coil air for each degree below 80°F 

(27°C), or add 835 Btu/h (245 W) per 1000 cfm (480 L/s) of indoor coil air per degree above 80°F (27°C).

3 System kilowatt is the total of indoor and outdoor unit kilowatts.
12 

3 Manufacturer Performance Data Collection and 

Evaluation

Manufacturer EPTs were used to develop the simulation inputs for this study. The assortment of 

publishing formats and chosen dependent variables across the leading equipment manufacturers 

made the collection and processing procedure quite challenging. Data were collected from seven

leading U.S. manufacturers responsible for 97% of the AC market (EIA 2009). The data 

provided by two of the manufacturers were not sufficient for full model input generation and 

were not incorporated into the analysis. The final dataset used for the study included data from 

five leading manufacturers that are responsible for 76% of the AC market. 

Thousands of indoor and outdoor unit combinations are available. Collecting performance data 

for every mix-and-match combination from a given manufacturer was not feasible and data were

collected for only the most common system combinations sold. As a result, data were collected 

for 260 ACs and 200 HPs. Performance and system data were read into a common format and 

stored in a SQLite database. SEER values for the collection ranged from 13–21 and nominal 

system capacities ranged from 1.5–5 tons. This database brought together performance data from 

each manufacturer into one usable format. The SQLite database format also used SQL-style 

queries to make the data fully searchable. The data and resulting simulation inputs were 

evaluated for quality and consistency before the generated sets of model inputs were used. 

Several methods were used to evaluate the data:

1. Visual inspection for data entry errors

2. Calculation of BF and ADP for each set of reported conditions 

3. Comparison of ADP/BF algorithm predictions directly with manufacturer EPT data 

4. Performance curve visualization and evaluation across manufacturers and 

models/capacities. 

3.1 Data Visual Inspection

Visual inspection was limited because of the large quantity of data collected. It was used briefly 

during the creation of the database to ensure the data were processed and transferred to the 

database correctly. A certain number of errors were expected to be generated through the data 

parsing, but did not occur. The data transfer process proved to be quite robust and accurate. 

Data inspection was most often precipitated by unexpected or suspicious results while model 

inputs or simulation results were being evaluated. These unexpected results required inspection 

of the database and the EPTs, and most often traced back to the EPTs. The errors encountered 

included decimal placement errors and identical fan speeds indicated for low and high speed data 

(for two-stage units). This type of data error was rare (only 5–7 cases in all the EPTs processed). 

The data were unusable for three units, which were discarded from the study. 

3.2 Bypass Factor and Apparatus Dew Point Calculation and 

Verification
13 

The BF was calculated for each indoor/outdoor condition listed in the EPTs. The BF is used in 

the EnergyPlus simulation algorithm to determine the sensible capacity of the unit at each time 

step. The sensible capacity determines the runtime of the unit, because ACs and HPs are 

generally controlled with thermostats, which are sensible-only devices (humidity control was not 

considered). The ADP/BF model played a central role in determining performance and energy 

consumption, so the BF was calculated for each point reported by the manufacturer.

Calculation of the BF led to some unexpected results. Many points could not be calculated, 

because the predicted exiting conditions were beyond the saturation curve on the psychrometric 

chart. This led to the calculation of a negative BF. The percent of total data points with a 

calculated negative BF ranged from 5% to 25% across the five manufacturers. These calculated 

exiting states are physically impossible, because supersaturated air states are not attainable at 

normal atmospheric pressures.

Discussion with one manufacturer indicated that the sensible capacities received limited attention 

during the manufacturer’s model calibration because: 

• EPTs are produced to aid in unit sizing and selection. 

• SHR is important in unit selection in different climates, yet precision is not crucial. 

Therefore, from the manufacturer’s standpoint, the sensible capacities can be a few percentage 

points off and still be sufficiently accurate for system sizing. A discrepancy of a few percentage 

points in the sensible capacity can change the slope of the SHR line on the psychrometric chart 

enough to produce some of the physically impossible results that were observed. These results 

raised concerns about the overall accuracy of the manufacturer’s models.

3.3 Apparatus Dew Point/Bypass Modeling Algorithm Comparison 

For whole-building modeling, the BF is a function of airflow rate only in the EnergyPlus 

algorithm. It thus limits the impact of this data variability on the model. Therefore, the data 

points that were calculated as having a negative BF were retained in the dataset and the 

EnergyPlus BF factor algorithm was investigated. 

Sensible capacities from the constant BF algorithm (used in Energy Plus) were compared with 

the reported sensible capacities listed in the EPTs. The predicted values were very close to EPT 

values (≈1-3% difference) for most units. 

3.4 Performance Curve Visual Inspection

The final data evaluation was visualization of the performance curves that were generated from 

the data and comparison with the EPT data points. An example of the total capacity curves as a 

function of ODB (x-axis) and EWB (different series) is shown in Figure 2. 
14 

Figure 2. Comparison of total capacity performance curve to manufacturer EPT values

The only significant error encountered during this evaluation was a particular manufacturer’s

EPT data modified the rated point to maintain consistency with the AHRI test results. All other 

listed data points remained at the values predicted by the manufacturer’s in-house model. An 

example of this adjustment is shown in Figure 3. The larger dot highlighted in the figure should 

be in line with the 67 EWB series, yet it has been shifted out of line with that series to attain the 

tested rated value at the A test point. The least squares regression generally ignores this point and 

fits to the remaining data. 
15 

Figure 3. Comparison of total capacity curve to manufacturer data with an adjusted rating point 

(shown in red)

This section has described the EPT data evaluation process. The database was used to generate 

full sets of EnergyPlus inputs for each unit in the database

The data issues described in this section raise the following important questions: 

• Which inputs are the most sensitive to errors and variations? 

• How will the errors in the available data affect the predicted performance of AC and HP

units? 

• What would be the best approach to limit the effects of data variability and capture the 

crucial aspects of the unit’s performance?

• Which set of model inputs will best predict unit performance?

The answers to these questions are addressed in the following sections. 
16 

4 Simulation of Specific Air Conditioner Units

4.1 House Description

The house simulated for this study was a two-story, 2,500-ft2 design compliant with International 

Energy Conservation Code 2009 building codes (ICC 2009). The house was simulated in four 

cities (Houston, Phoenix, Atlanta, and Chicago), representing hot-humid, hot-dry, mixed-humid, 

and cold Building America climate regions (PNNL and ORNL 2007) in accordance to the 

Building America House Simulation Protocols (Hendron and Engebrecht 2010). The chosen 

foundation type was based on common construction practice for the given region—slab-on-grade 

for Houston and Phoenix, crawlspace for Atlanta, and unfinished basement for Chicago. The 

equipment (AC or HP) was sized according to Manual J for each climate (Rutkowski 2006). 

4.2 Modeling Assumptions

Air leakage and conduction losses associated with ductwork were neglected to prevent the 

selected duct characteristics from impacting modeled equipment performance. The purpose was 

to improve AC and HP models through a comparative study. The complicated interaction 

between ducts and equipment would have made the comparison more difficult without adding 

insight. 

Part load effects were neglected by setting the part load curves to unity, effectively modeling 

steady-state performance only. This was done for two principal reasons: 

• The cycling performance data are not published for individual units. This was the 

most significant reason. Any part load performance curves introduced into the 

simulations would thus be based on estimates. This was not desirable, because it would 

mask some impacts of the inputs generated from manufacturer data on the results. 

• To eliminate the impact of the sizing routine on the annual simulation results.

Without part load losses, the simulated size of the unit would not have an impact on the 

performance. The goal of these runs was to evaluate performance variation as influenced 

by the inputs generated for specific AC and HP units. 

Fan efficiencies are not reported, so values had to be assumed. Most single-stage units use 

permanent split capacitor motors, whereas two-stage units contain brushless permanent magnet 

motors. Thus, a fan efficiency of 0.365 W/cfm was assumed for single-stage ACs and 0.14 

W/cfm was used for two-stage units.

4.3 Performance/Comparison Metric

A metric was developed to evaluate the performance of the units in the various climates 

simulated. An annual average, steady-state, net COP calculated using Equations 14 and 15 for 

cooling and heating, respectively, was used to compare units. The metric is steady state because

part load effects were eliminated. 
17 

( )

( )

0: 8760 ,

1

, ,

8760

1

, ,

,, >∀

+

−

=

∑

∑

=

=

ncool

n

noutdoor fan n

n

ncool fan n

net ss c Qn

PP

PQ

COP 



 

(14) 

( )

( )

0: 8760 ,

1

, ,

8760

1

, ,

,, >∀

+

+

=

∑

∑

=

=

nheat

n

noutdoor fan n

n

nheat fan n

net ss h Qn

PP

PQ

COP 



 

(15) 

This metric allows for clear comparison across climates and nominal capacities without the 

confusion that an annual energy metric might introduce. Some variation is expected between 

climates because of different sensible and latent load profiles and different ambient conditions, 

but with significantly less discrepancy compared to an absolute number. 

4.4 Air Conditioner Simulation Results

Figure 4 shows simulation results for all 260 AC units in Houston. Results from the other cities 

were similar. Results have been grouped by the nominal (product-line) SEER value. Within a 

nominal SEER grouping, units are plotted from left to right in order of increasing nominal 

capacity (starting with 1.5 tons and increasing to 5 tons in 0.5-ton increments). The Manufacturer 

E, SEER 15 product line and SEER families 16 and above (for all manufacturers) are two-stage

ACs. In several instances, multiple product lines from a single manufacturer are plotted. 

However, for clarity, only a single point is displayed if different product lines yielded similar 

results (resulting in fewer than 260 individual points). 
18 

Figure 4. Simulation results for ACs evaluated in Houston (individual capacities of 1.5, 2, 2.5, 3, 

3.5, 4, and 5 tons shown in ascending order within each SEER family, when available) 

Several interesting trends and issues were immediately apparent after this initial set of runs:

• Variation in COPnet ss,, c within a model line without a consistent trend (see Manufacturer 

C, SEER 14)

• Significant outliers for most manufacturers (see minimum value for Manufacturer B, 

SEER 14)

• Differences in COPnet ss,, c for the same nominal SEER rating for a given manufacturer 

(see range for Manufacturer E, SEER 13)

• Differences in COPnet ss,, c for the same nominal SEER rating across manufacturers (see 

range for SEER 14 units) 

• Minimal increase in COPnet ss,, c for increasing nominal SEER values for a given 

manufacturer (see Manufacturer A, SEER 14 versus SEER 15)

• Lack of consistent trend COPnet ss,, c with nominal SEER value

• DecreasingCOPnet ss,, c as nominal tonnage increases for SEER 18 and SEER 21 product 

lines. 

2

3

4

5

6

7

Manufacturer A Manufacturer D

Manufacturer B Manufacturer E

Manufacturer C

SEER 13 SEER 14 SEER 15 SEER 16 SEER 17 SEER 18 SEER 21
19 

5 Performance Curve Sensitivity Study

An initial two-part question was: Can we provide more accurate modeling through a full 

database of inputs that represent all current makes and models? Or are we better served by using 

the database to inform a model with reduced complexity and better overall accuracy? The data 

issues that were outlined in Section 3 brought this question to the forefront. To answer it, a 

sensitivity study had to be performed on the EnergyPlus model inputs to determine whether a 

generalized approach was possible. 

The rated values (especially COP) had the largest impact on the variations noted in Figure 4. 

Therefore, a sensitivity study was performed on the curve inputs. This process is discussed in 

detail in Cutler (2012) and will not be repeated here. The results showed the difference between 

using unit-specific performance curves and selected standard curves were very small. The 

standard set of curves was selected from the manufacturer that had consistently shown the 

highest quality data and, when applied across all units, the average error was ±2%–5%. The 

selection process used for choosing the standardized curves, and the results from using the 

selected curves, are presented in Section 5.1. 

5.1 Air Conditioner Simulation Results

The curve sensitivity study showed that a single set of curves could be used for all single-stage 

AC units. Two sets of curves could be used for the two-stage units (one for low-stage and one for 

high-stage operation). This was also demonstrated for HPs (Cutler 2012). The set of curves was 

selected by minimizing the difference between the unit-specific COPnet ss,, c and the average of the 

COPnet ss,, c across all simulated units. This process resulted in a standard set of curves that 

represented the average performance at off-rated conditions across all units and introduced the 

least discrepancy (from the unit-specific curves) into the simulations. 

The results for all manufacturers using the standard set of curves are shown in Figure 5. The 

error bar denotes the difference between using specific unit performance curves (indicated by the 

dot) and fixed, representative curves. 
20 

Figure 5. Variations in simulation results attributed to using a fixed set of performance curves

(individual capacities of 1.5, 2, 3, 4, and 5 tons shown in ascending order within SEER values)

Most units showed very little variation. The noticeable exception was Manufacturer E, which 

had significant error in the EPT data throughout the study; its simulation inputs were not 

considered reliable.

High SEER, two-stage units had noticeably more discrepancy with a standardized set of 

performance curves than did single-stage units. This trend likely results from the uniqueness of 

two-stage units, whereas single-stage units are very common across manufacturers. Nevertheless, 

the assumption of using a standardized curve set was appropriate for comparing energy use 

across SEER families. 

The full set of simulations was run in four climates at four fan speeds to bound the possible 

inaccuracy that could be generated from using a standard set of curves. These simulations 

exercised the temperature-related and the flow fraction curves and bound the total expected

errors. 

Table 5 shows results for a fan speed of 400 cfm/ton for all four cities. Other fan speeds yielded 

similar results. Although this variation is denoted “error” here (because it is a deviation from the 

manufacturer’s data), it is not necessarily a less accurate answer, because some of the 

manufacturer’s data are variable (see Section 3).

2

3

4

5

6

7

Manufacturer A Manufacturer D

Manufacturer B Manufacturer E

Manufacturer C

SEER 13 SEER 13 SEER 14 SEER 14 SEER 15 SEER 15 SEER 16 SEER 16 SEER 17 SEER 17 SEER 18 SEER 18 SEER 21 SEER 21
21 

Table 5. Calculated Error in Annual Average COP Attributed to Using Fixed Curves

Manufacturer A B C D E

Houston

Average error –0.3% –5.1% –3.6% 3.3% 3.2%

95% confidence interval 

(± around average 1.8% 2.2% 2.1% 3.2% 12.8%

Atlanta

Average error –0.3% –5.5% –3.8% 0.6% 3.4%

95% confidence interval 

(± around average

1.9% 2.3% 2.3% 3.7% 13.1%

Phoenix

Average error 0.9% 5.8% –2.3% 0.6% 1.5%

95% confidence interval 

(± around average 1.6% 2.5% 1.7% 3.7% 13.6%

Chicago

Average error 0.4% –8.3% –2.2% 4.3% 2.7%

95% confidence interval 

(± around average 2.5% 2.2% 3.0% 5.0% 13.5%

A similar study on HPs (operating in cooling and heating modes) was conducted and presented 

in Cutler (2012). HP data provided by the manufacturers were very limited in scope. Only three 

manufacturers provided data at multiple airflow rates (necessary to generate the flow fraction 

curves) and only two provided data on both entering dry-bulb temperature and ODB (for the 

biquadratic curves). The lack of these curves would not allow EnergyPlus to evaluate the HP

performance at various airflow rates or indoor temperatures. All possible comparisons (given the 

scarcity of data) were performed for HPs. The results indicated very limited error because of the 

curves that were evaluated (similar to the AC analysis presented earlier). Of the two 

manufacturers providing sufficient data to generate all inputs, one was Manufacturer E, which 

had the most questionable data evaluated in this research. Therefore, a single set of curves was 

selected from the remaining manufacturer that provided sufficient, high-quality data. 
22 

6 Importance of Rated Value Inputs

The results presented in Section 5 did not resolve all issues observed in Figure 4. The observed 

variation when modeling individual units was not resolved by the selection of representative 

performance curves; these just simplified the problem and indicated the real source of the 

variation—the rated values. Therefore, the rated values from the database of EPT data were 

evaluated. 

AHRI (2012) was used to evaluate the validity of the rated values that were generated from the 

database. The AHRI data were used during this section of the analysis because: 

• They represent laboratory test data (not the manufacturer model generated data that are in 

the EPTs). 

• AHRI reports data that facilitate the comparison with manufacturers’ rated values. 

Data published by AHRI are representative of laboratory test data, where the test procedures are

clearly defined and monitored by AHRI (ANSI/AHRI 2008). This allows the values gathered 

from the manufacturer’s performance tables to be validated against certified data. This would be 

a relatively simple evaluation if AHRI published the results for all the tests required in the SEER 

rating process; however, the directory includes only results of the A-test point. Publishing the 

unrounded SEER values along with the A-test point (second bullet) would facilitate the inference 

of some other testing points for further comparison. 

To compare the EPT data to the AHRI data, it was necessary to quantify the uncertainty in AHRI 

data and its testing procedures. ANSI/AHRI (2008) describes the acceptable tolerance in Section 

6.5 as follows: “To comply with this standard, measured test results shall not be less than 95% of 

Published Ratings for performance ratios and capacities.” This indicates that AHRI accepts error 

of <5% in both the performance ratios (SEER and rated EER values) and rated capacities. 

Therefore, agreement to ≈95% of the AHRI data is expected. Conclusions drawn about results 

with <5% error are within the uncertainty of the data and cannot be conclusive. This margin of 

error also further supports the selection of the single set of curves described in the previous 

section where average error was <5% for all but one manufacturer, which had >6% error in only 

one location tested. 

6.1 Single-Stage Rated Values

The single-stage rated values were evaluated in two ways: (1) direct comparison of rated EER 

values reported in the certified AHRI data and EPT values; and (2) evaluation of off-rated EER 

(EERB) and cycling degradation coefficient (CD) values using the SEER calculation procedure 

and the AHRI-reported SEER values.

Figure 6 compares the AHRI-certified rated EER to the rated EER determined using 

manufacturer EPT data. The figure clearly shows a significant discrepancy. Based on these

results, the manufacturer-reported EPTs are not a reliable data source for rated input values and 

the AHRI-certified data should be used when possible. 
23 

Figure 6. Comparison of AHRI-certified EER to manufacturer EPT data for single-stage ACs

The equation to calculate SEER for single-stage equipment is

PLF EERB SEER )5.0( ⋅= (16) 

where PLF CD 5.01)5.0( ⋅−= . To evaluate the rated values from the database of manufacturer 

EPT data, CD values were back-calculated using Equation 16 with EERB values generated from 

manufacturer data, and certified SEER values obtained from AHRI. The back-calculated CD

values for each single-stage air-conditioning unit, by manufacturer, are shown in Figure 7. The 

EERB values used in calculating CD are net values that were evaluated by interpolating between 

listed values in the EPTs (EPTs do not generally contain data at 82°F outdoor dry-bulb). The fact 

that these are net values obviates any need for assumptions about fan power. This evaluation 

resulted in a large number of units with negative CD values or CD values >0.25 (the upper limit 

default defined by ANSI/AHRI [2008]). Both values are impossible. This cast further doubt on 

the validity of acquiring the rated values from manufacturer’s data. Refer to Cutler (2012) for 

further discussion of the CD evaluation process. 
24 

Figure 7. Back-calculated CD values for single-stage ACs using AHRI SEER values and 

manufacturer EPT data

6.2 Two-Stage Rated Values

Similar to the single-stage case, a direct comparison was made between the rated efficiency from 

the AHRI data and that in the EPTs. This metric was able to show only half the picture for twostage ACs, because AHRI publishes only high stage-rated performance. Figure 8 shows the 

comparison between the AHRI-rated efficiencies and efficiencies listed in manufacturer EPT 

data for two-stage ACs. 
25 

Figure 8. Comparison of AHRI-certified EER to manufacturer EPT data for two-stage ACs 

For a more complete picture of the other rated values for two-stage units, comparisons were 

made between the SEER values obtained from AHRI (2012) and those calculated using data 

from the manufacturer EPTs. This comparison provided a clearer picture of the discrepancies 

between the two data sources, and a picture of how the different sets of rated values would 

theoretically compare (on an annual energy basis). 

The two-stage SEER calculation requires knowledge of four operating points (A2, B2, B1, F1) 

and the CD value. The regression curves were evaluated to determine the performance at all four 

operating points. Because of the complexity inherent in the two-stage SEER calculation, it was 

necessary to assume a CD when using manufacturer data to calculate a SEER value. The CD was 

assumed to be 0.1—considered a conservative estimate for two-stage ACs. The impact of CD on 

the calculated SEER values is shown in Figure 9 by the shaded band around the calculated SEER 

values representing calculated SEER at CD equal to 0.01 and 0.25 (upper and lower bounds 

given by AHRI on the CD value). A sensitivity to fan efficiency was not necessary, because net 

values are used in the SEER calculation procedure and are provided in the EPTs. This is similar 

to the calculated CD values shown in Figure 7; the capacity and efficiency values used in 

calculating the SEER values shown here are essentially interpolations in the EPT tables. 

The results are shown in Figure 9, presented in blocks of three to five units. Each block 

represents a particular manufacturer’s specific model line (labeled A–E); each point represents a 

different size unit within the model line (in ascending order of capacity, i.e. 2–5 tons). The figure 

has three series: (1) SEERAHRI (SEER values listed in AHRI [2012]); (2) SEERCALC (calculated 
26 

from manufacturer EPTs assuming a CD of 0.1); and (3) the nominal/family SEER value for the 

particular model line. 

Figure 9. SEER calculated using manufacturer data compared to AHRI reported SEER values for 

two-stage ACs (letters A, B, C, and E denote manufacturer; capacities of 2, 3, 4, and 5 tons shown 

in ascending order within a product line)

These results showed interesting issues with the two-stage models: 

• The large discrepancies between SEERCALC and SEERAHRI for most units evaluated were 

most significant. The differences ranged from 0 to >3.5 SEER points. In some cases the 

trend in SEERCALC did not match the trend in SEERAHRI values. 

These results demonstrate the significance of the various rating points when evaluating 

the annual efficiency of two-stage units. Figure 8 demonstrates improved agreement 

(over the single-speed results) for the A2 rating points, yet evaluation of the other rating 

points (B2, A1, and F1) through the SEER rating comparison demonstrated a significant 

discrepancy between manufacturer data and the data gathered from the AHRI directory. 

• Figure 9 identifies a correlation between SEER and capacity for the highest SEER model 

lines. Both the SEERCALC and the SEERAHRI demonstrated significant degradation of the 

SEER value for large capacities in the SEER 18 and SEER 21 model lines. The 2-ton 

capacities for the high (nominal) SEER lines had SEER values that were 3–5 points 

higher than the 5-ton unit of that same model line. 

6.3 System Tonnage Impact on High SEER Air Conditioners

Figure 9 displays a clear trend in the SEER values for high SEER (SEER 18 and 21 product 

lines) ACs—the SEER decreases as the nominal system tonnage increases. This trend is apparent 

in the simulation results, the calculated SEER results, and the SEER values listed in AHRI 

(2012). 
27 

This decrease in efficiency needs to be accounted for when simulating high SEER ACs in wholebuilding simulation tools. For example, when evaluating the potential energy savings of a SEER 

21 AC in a large home, the unit cannot be modeled as SEER 21 equipment. The SEER for this 

unit (as reported by AHRI) is approximately 16 and must be modeled as such. The procedure 

used to take this trend into account is explained in Section 8. 

The decreasing trend in ratings for larger capacity ACs can be explained by the extremely large

outdoor heat exchanger areas, which would be required to achieve these high SEER ratings for 

large-capacity ACs. At least three of the four product lines plotted in Figure 9 use the same 

outdoor heat exchanger design across all or several system capacities. Thus, it is not surprising 

that the efficiency drops as the compressor and indoor heat exchanger increase in size to achieve 

the rated nominal capacity, while the outdoor heat exchanger size remains constant. 
28 

7 Standard Library Development

The principal goal of this research was to improve energy use predictions for ACs and HPs 

through accurate energy modeling with whole-building simulation tools. Section 6 showed that 

unit-specific performance curves have a minimal impact on predicted annual efficiency and 

selected standard performance curves for each type of DX unit (single- and two-stage ACs and 

HPs) achieved acceptable accuracy. Almost all the unexpected variations seen in unit-specific 

simulation results were due to variations in the rated value inputs. Section 6 evaluated the rated 

values in manufacturer EPTs through comparison with AHRI (2012). This comparison 

demonstrated limited agreement between the certified rating values in the AHRI directory and 

the rated values gathered from the EPTs.

The results from these two sections are combined in this section to provide consistent and 

accurate modeling in the residential modeling software BEopt (providing simulation capability in 

EnergyPlus or DOE-2). This same methodology can be implemented directly into EnergyPlus, if 

desired. It was desirable to have representative models for each SEER and HSPF efficiency 

family in the BEopt library. This allows for effective cost and energy comparisons between 

SEER and HSPF values. 

This section presents the rated value inputs for the BEopt library and the curve coefficients for 

the standard curves (discussed in Section 5) for ACs and HPs. 

The use of the standard set of performance curves significantly reduced the number of required 

modeling inputs. Remaining inputs were total rated capacity, rated EER, rated SHR, rated 

airflow rate, and a part load curve. Rated total capacity is a function of the building and is 

determined by BEopt using Manual J procedures (Rutkowski 2006). The rated airflow rate (the 

value the flow rate curves are normalized to) is inherently tied to the performance curves and 

cannot be changed. The remaining inputs (rated COP, rated SHR, and the degradation 

coefficient) need to be specified in the built-in library or defined by the user. 

This modeling approach provides the shipped options in the standard BEopt library that are 

presented in this section. It also allows for more customized use. The user who wants to model a 

specific make or model can find the unrounded SEER value in the AHRI directory (and at least 

one of the rated EER values), and then select the rated values that will result in that exact SEER 

value. This allows the advanced user to model ACs and HPs that are representative of the 

officially tested data inherent in the data published by AHRI. The required SHR can be obtained 

from the EPTs on that model. 

7.1 Air Conditioner Library Inputs

Table 6 shows the model inputs for the eight AC options included in BEopt. Rated EER values 

were determined using the nominal (rounded) SEER value and CD values provided by a 

particular manufacturer. Two-stage units require the rated values inputs for each stage and the 

capacity ratio (defined as the rated total capacity of the low stage divided by the rated total 

capacity of the high stage). Manual J calculations are used to define the high stage capacity of 

the unit; the capacity ratio is used to calculate the lower stage. The selected capacity factors were 

representative of the two-stage units studied. Derate factors scale the rated EER to capture the 

efficiency degradation for larger units (described in Section 6.3).
29 

Table 6. BEopt AC Rated Value Inputs 

SEER Rated EER

(low, high) CD

Rated SHR

(low, high) 

Capacity

Ratio1

EER Derate 

Multiplier

(2, 3, 4, 5 tons)

13 11.1 0.05 0.73 – –

14 12.0 0.05 0.73 – –

15 13.0 0.07 0.73 – –

16 (1) 14.0 0.12 0.73 – –

16 (2) 13.5,12.4 0.10 0.71,0.73 0.72 – 

17 14.4,13.2 0.10 0.71,0.73 0.72 – 

18 15.2,14.0 0.10 0.71,0.73 0.72 1, 1, 0.94, 0.88

21 17.7,15.3 0.07 0.71,0.73 0.72 1, 0.95, 0.82, 0.76

1 The rated capacity of the low stage divided by the rated capacity of the high stage

7.2 Air Conditioner Performance Curve Coefficients

Coefficients for the representative performance curves are included in Tables 7–10. Appendix A 

includes corresponding coefficients as a function of operating temperature in SI units, which can 

be directly input into EnergyPlus. Total capacity and EIR performance curves that modify 

performance for off-rated flow rates are nondimensional. Thus, these flow fraction coefficients 

can be used in EnergyPlus without modification. 

Table 7. AC Total Capacity Coefficients as a Function of Operating Temperatures (°F)

Single-Stage 

Units

Two-Stage/Speed Units

Low High

a 3.670270705 3.940185508 3.109456535

b –0.098652414 –0.104723455 –0.085520461

c 0.000955906 0.001019298 0.000863238

d 0.006552414 0.006471171 0.00863049

e –0.0000156 –0.00000953 –0.0000210

f –0.000131877 –0.000161658 –0.000140186
30 

Table 8. AC EIR Coefficients as a Function of Operating Temperatures (°F)

Single-Stage 

Units

Two-Stage/Speed Units

Low High

a –3.302695861 –3.877526888 –1.990708931

b 0.137871531 0.164566276 0.093969249

c –0.001056996 –0.001272755 –0.00073335

d –0.012573945 –0.019956043 –0.009062553

e 0.000214638 0.000256512 0.000165099

f –0.000145054 –0.000133539 –0.0000997

Table 9. AC Total Capacity Coefficients as a Function of Flow Fraction

Single-Stage 

Units

Two-Stage/Speed Units

Low High

a 0.718605468 0.65673024 0.690334551

b 0.410099989 0.516470835 0.464383753

c –0.128705457 –0.172887149 –0.154507638

Table 10. AC EIR Performance Curve Coefficients as Function of a Flow Fraction

Single-Stage 

Units

Two-Stage/Speed Units

Low High

a 1.32299905 1.562945114 1.31565404

b –0.477711207 –0.791859997 –0.482467162

c 0.154712157 0.230030877 0.166239001

7.3 Heat Pump Library Inputs

Table 11 shows the model inputs for the seven HP options in the BEopt library. These are very 

similar to the AC inputs, except both rated EER (for cooling) and COP (for heating) values are 

required. A separate CD is also required for heating mode. 
31 

Table 11. BEopt HP Rated Value Inputs 

SEER, HSPF Rated EER

(low, high) 

Rated COP

(low, high) 

CD

(cool, heat)

Rated SHR

(low, high) 

Capacity

Ratio1

EER Derate 

Multiplier

(2, 3, 4, 5 tons)

13, 7.7 11.4 3.05 0.08, 0.10 0.73 – –

14, 8.2 12.2 3.35 0.07, 0.10 0.73 – –

15, 8.5 12.7 3.5 0.01, 0.10 0.73 – –

16, 8.6 13.1, 11.7 3.6, 3.2 0.12, 0.10 0.71, 0.72 0.72 – 

17, 8.7 13.9, 12.8 3.6, 3.3 0.13, 0.10 0.71,0.72 0.72 – 

18, 9.3 14.5, 13.3 4.0, 3.5 0.09, 0.10 0.71,0.72 0.72 1, 1, 0.93, 0.90

19, 9.5 15.5, 13.8 4.1, 3.6 0.10, 0.10 0.71,0.72 0.72 1, 0.95, 0.88, 0.81

1 The rated capacity of the low stage divided by the rated capacity of the high stage

7.4 Heat Pump Performance Curve Coefficients

Coefficients for the representative HP curves are included in Tables 12–15. Cooling and heating 

coefficients are listed. Appendix A includes coefficients for the corresponding curves using SI 

units. 

Table 12. HP Total Capacity Coefficients as a Function of Operating Temperatures (°F)

Cooling Heating

Single-Stage 

Units

Two-Stage/Speed Units Single-Stage

Units

Two-Stage/Speed Units

Low High Low High

a 3.68637657 3.998418659 3.466810106 0.566333415 0.335690634 0.306358843

b –0.098352478 –0.108728222 –0.091476056 –0.000744164 0.002405123 0.005376987

c 0.000956357 0.001056818 0.000901205 –0.0000103 –0.0000464 –0.0000579

d 0.005838141 0.007512314 0.004163355 0.009414634 0.013498735 0.011645092

e –0.0000127 –0.0000139 –0.00000919 0.0000506 0.0000499 0.0000591

f –0.000131702 –0.000164716 –0.000110829 –0.00000675 –0.00000725 –0.0000203
32 

Table 13. HP EIR Coefficients as a Function of Operating Temperatures (°F)

Cooling Heating

Single-Stage 

Units

Two-Stage/Speed Units Single-Stage

Units

Two-Stage/Speed Units

Low High Low High

a –3.437356399 –4.282911381 –3.557757517 0.718398423 0.36338171 0.981100941

b 0.136656369 0.181023691 0.112737397 0.003498178 0.013523725 –0.005158493

c –0.001049231 –0.001357391 –0.000731381 0.000142202 0.000258872 0.000243416

d –0.0079378 –0.026310378 0.013184877 –0.005724331 –0.009450269 –0.005274352

e 0.000185435 0.000333282 0.000132645 0.00014085 0.000439519 0.000230742

f –0.0001441 –0.000197405 –0.000338716 –0.000215321 –0.000653723 –0.000336954

Table 14. HP Total Capacity Coefficients as a Function of Flow Fraction

Cooling Heating

Single-Stage 

Units

Two-Stage/Speed Units Single-Stage

Units

Two-Stage/Speed Units

Low High Low High

a 0.718664047 0.655239515 0.618281092 0.694045465 0.741466907 0.76634609

b 0.41797409 0.511655216 0.569060264 0.474207981 0.378645444 0.32840943

c –0.136638137 –0.166894731 –0.187341356 –0.168253446 –0.119754733 –0.094701495

Table 15. HP EIR Performance Curve Coefficients as a Function of Flow Fraction

Cooling Heating

Single-Stage 

Units

Two-Stage/Speed Units Single-Stage

Units

Two-Stage/Speed Units

Low High Low High

a 1.143487507 1.639108268 1.570774717 2.185418751 2.153618211 2.001041353

b –0.13943972 –0.998953996 –0.914152018 –1.942827919 –1.737190609 –1.58869128

c –0.004047787 0.359845728 0.343377302 0.757409168 0.584269478 0.587593517

7.5 Air Conditioner Library Simulation Results

The standard BEopt options were simulated and plotted with the set of AC units in the 

manufacturer’s database (see Figure 10). The shipped options attain a clear progression of 

increasing annual average efficiency over increasing SEER families. The efficiency degradation 

for larger units in high SEER families was also captured effectively through the EER derate 

multiplier. 
33 

Figure 10. Simulation results for the AC library (shown in solid black lines) compared to 

individual AC units (individual capacities of 1.5, 2, 3, 4, and 5 tons shown in 

ascending order within SEER values)

The BEopt library represents the intent of the AHRI rating procedure and provides an average 

performing unit at varying efficiency levels. The simplified set of required inputs can be used to 

simulate a specific AC or HP unit to reflect the AHRI data for that unit. 

2

3

4

5

6

7

Manufacturer A Manufacturer D

Manufacturer B Manufacturer E

Manufacturer C

SEER 13 SEER 14 SEER 15 SEER 16 SEER 17 SEER 18 SEER 21
34 

8 Conclusions

When simulating AC and HP energy use in the context of whole-building simulation tools: 

• Unit-specific performance curves do not significantly impact the predicted energy use. 

The standardized set of curves included in this report for single- and two-stage ACs and 

HPs offers comparable accuracy and significantly reduces the number of modeling 

inputs. 

• The assumed rated value inputs are the key drivers in the predicted energy use; the data 

source chosen to determine these inputs is also important. Rated values from the 

manufacturer EPTs did not always match AHRI (2012). Rated values should be selected 

to represent the SEER and HSPF ratings for the specific unit being modeled. 

• A standard AC and HP library containing inputs consistent with SEER and HSPF rating 

procedures is useful for making energy and cost comparisons. This was made possible 

by the combination of rated inputs and curve coefficients presented in Section 7. 

Simulating specific AC and HP units is also facilitated by using the standard curves with 

unit-specific rated values. 

Further improvements to AC and HP modeling would be facilitated by more complete data 

reporting by AHRI and the individual manufacturers. More detailed performance maps are 

required than are currently provided. Of particular use would be the cycling degradation 

coefficient (CD, verified by AHRI in its testing process), and the fan efficacy (W/cfm) value that 

corresponds to the reported net testing data. 

The modeling procedures presented in this report provide improved consistency and agreement 

with the SEER and HSPF ratings and are recommended for current simulations in wholebuilding energy simulation tools. 
35 

References

ANSI/AHRI. (2008). Performance Rating of Unitary Air Conditioning and Air Source Heat 

Pump Equipment. ANSI/AHRI Standard 210/240. Arlington, VA: Air Conditioning, Heating, 

and Refrigeration Institute. 

AHRI. (2012). Directory of Certified Product Performance. 

www.ahridirectory.org/ahridirectory/pages/home.aspx. Accessed January 2012. 

Courtney, A. (2006). Assessing the Effectiveness of SEER as a Predictor of Seasonal Cooling 

Efficiency. Master’s Thesis. Boulder, CO: University of Colorado. 

Cutler, D. (2012). Improved Modeling of Residential Air Conditioners and Heat Pumps for 

Energy Calculations. Master’s Thesis. Boulder, CO: University of Colorado. 

DOE. (2011). EnergyPlus Engineering Reference. Washington, D.C.: U.S. Department of 

Energy. 

EIA. (2009). 2009 Building Energy Data Book. Washington, D.C.: Energy Information Agency. 

http://buildingsdatabook.eere.energy.gov. 

Fairey, P.; Parker, D.; Wilcox, B.; Lombardi, M. (2004). “Climatic Impacts on Heating Seasonal 

Performance Factor and Seasonal Energy Efficiency Ratio for Air-Source Heat Pumps.” 

ASHRAE Transactions 110(2): pp. 178–188. 

Hendron, R.; Engebrecht, C. (2010). Building America House Simulation Protocols. NREL/TP550-49246. Golden, CO: National Renewable Energy Laboratory. 

James J. Hirsch & Associates. (2005). “EER & SEER as Predictors of Residential Seasonal 

Cooling Performance.” Irwindale, CA: Southern California Edison. 

ICC. (2009). International Energy Conservation Code (IECC). International Code Council, Inc. 

Kavanaugh, S. (2002). “Limitations of SEER for Measuring Efficiency.” ASHRAE Journal, July 

2002: pp. 27–29. 

Kruis, N. (2010). Reconciling the Differences between Residential DX Cooling Models in DOE-2 

and EnergyPlus. Proceedings of IBPSA-USA SimBuild 2010. New York, NY: IBPSA.

PNNL and ORNL. (2007). High-Performance Home Technologies: Guide to Determining 

Climate Regions by County. Building America Best Practices Series. Richland, WA: Pacific 

Northwest National Laboratory.

Rutkowski, H. (2006). Manual J: Residential Load Calculation, Ed. 8, version 2. Arlington, VA: 

Air Conditioning Contractors of America. 
Appendix A—Performance Coefficients in SI Units

The coefficients in Tables 16–19 for AC and HP performance can be directly used in 

EnergyPlus. Coefficients for total capacity and EIR as function of operating temperatures are 

displayed below in SI units. Flow fraction curve coefficients can be found in Section 5. 

Table 16. AC Total Capacity Coefficients as a Function of Operating Temperatures (°C)

Single-Stage 

Units

Two-Stage/Speed Units

Low High

a 1.55090 1.66458 1.36788

b –0.07505 –0.08039 –0.06257

c 0.00310 0.00330 0.00280

d 0.00240 0.00124 0.00504

e –0.00005 –0.00003 –0.00007

f –0.00043 –0.00052 –0.00045

Table 17. AC EIR Coefficients as a Function of Operating Temperatures (°C) 

Single-Stage 

Units

Two-Stage/Speed Units

Low High

a –0.30428 –0.42738 0.04232

b 0.11805 0.14191 0.07892

c –0.00342 –0.00412 –0.00238

d –0.00626 –0.01406 –0.00304

e 0.00070 0.00083 0.00053

f –0.00047 –0.00043 –0.00032

Table 18. HP Total Capacity Coefficients as a Function of Operating Temperatures (°C)

Cooling Heating

Single-Stage 

Units

Two-Stage/Speed Units Single-Stage

Units

Two-Stage/Speed Units

Low High Low High

a 1.557360 1.658788 1.472738 0.876825 0.846130 0.818223

b –0.074448 –0.083453 –0.067222 –0.002955 –0.002279 0.001981

c 0.003099 0.003424 0.002920 –0.000058 –0.000047 –0.000203

d 0.001460 0.002433 0.000052 0.025335 0.026703 0.028703

e –0.000041 –0.000045 –0.000030 0.000196 0.000201 0.000207

f –0.000427 –0.000534 –0.000359 –0.000043 –0.000079 –0.000071
37 

Table 19. HP EIR Coefficients as a Function of Operating Temperatures (°C)

Cooling Heating

Single-Stage 

Units

Two-Stage/Speed Units Single-Stage

Units

Two-Stage/Speed Units

Low High Low High

a –0.350448 –0.582916 –0.488196 0.704658 0.551837 0.815840

b 0.116810 0.158101 0.099162 0.008767 0.020380 –0.006150

c –0.003400 –0.004398 –0.002370 0.000625 0.000546 0.001021

d –0.001226 –0.020335 0.019503 –0.009037 –0.009638 –0.001301

e 0.000601 0.001080 0.000430 0.000738 0.000785 0.001083

f –0.000467 –0.000640 –0.001097 –0.001025 –0.001250 –0.001487