Comfort in High-Performance 

Homes in a Hot-Humid Climate 

A. Poerschke and R. Beach

IBACOS, Inc.

January 2016
NOTICE

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iii

Comfort in High-Performance Homes in a Hot-Humid Climate

Prepared for: 

The National Renewable Energy Laboratory

On behalf of the U.S. Department of Energy’s Building America Program

Office of Energy Efficiency and Renewable Energy

15013 Denver West Parkway

Golden, CO 80401

NREL Contract No. DE-AC36-08GO28308

Prepared by: 

A. Poerschke and R. Beach

IBACOS, Inc.

2214 Liberty Avenue

Pittsburgh, PA 15222

NREL Technical Monitor: Stacey Rothgeb

Prepared under Subcontract No. KNDJ-0-40341-05

January 2016
iv

The work presented in this report does not represent 

performance of any product relative to regulated 

minimum efficiency requirements.

The laboratory and/or field sites used for this work are 

not certified rating test facilities. The conditions and 

methods under which products were characterized for 

this work differ from standard rating conditions, as 

described.

Because the methods and conditions differ, the reported 

results are not comparable to rated product performance 

and should only be used to estimate performance under 

the measured conditions.
v

Contents

List of Figures ............................................................................................................................................ vi

List of Tables .............................................................................................................................................. vi

Definitions.................................................................................................................................................. vii

Acknowledgments ................................................................................................................................... viii

Executive Summary................................................................................................................................... ix

1 Introduction and Background ............................................................................................................. 1

1.1 Large-Scale Thermal Comfort Research .............................................................................2

1.2 Comfort Metrics...................................................................................................................2

1.3 Research Questions..............................................................................................................4

2 Mathematical and Modeling Methods................................................................................................. 5

2.1 Data Analysis Methods........................................................................................................5

2.2 System Runtime Determination...........................................................................................5

2.3 Uncertainty Analysis............................................................................................................6

3 Research and Experimental Methods ................................................................................................ 8

3.1 Home Identification and Selection.......................................................................................8

3.2 Sensor Selection...................................................................................................................8

3.3 Sensor Installation and Survey Data Collection ..................................................................9

4 Results and Discussion..................................................................................................................... 11

4.1 Summary of Data ...............................................................................................................11

4.2 Indoor Temperature Variability with Time........................................................................14

4.3 Indoor Temperature Variability with Outdoor Temperature .............................................18

4.4 Thermostat Temperature....................................................................................................19

4.5 System Run Impact on Uniformity....................................................................................24

4.6 Psychrometrics...................................................................................................................26

4.7 Builder Metrics..................................................................................................................29

5 Conclusions ........................................................................................................................................ 31

5.1 Future Work.......................................................................................................................32

References................................................................................................................................................. 34

Appendix: Specifications for the Homes in the Study and the Homeowner Survey Results ........... 35
vi

List of Figures

Figure 1. Hypothetical probability of callback function.......................................................................... 3

Figure 2. Illustration of the system runtime calculation method ........................................................... 6

Figure 3. Probability of drawing an incorrect conclusion in the ACCA analysis for sensors 

with an error of 0.38°F.......................................................................................................................... 7

Figure 4. A logger placed on a supply register...................................................................................... 10

Figure 5. Heat map showing the period of data collection for each sensor....................................... 11

Figure 6. Heat map showing humidity measurements for each sensor.............................................. 12

Figure 7. Cumulative summation of room-to-room temperature differences for all homes 

based on 1-min data........................................................................................................................... 13

Figure 8. Cumulative summation of room-to-room temperature variability by hour of the day....... 14

Figure 9. Average daily room-to-room temperature variability by city ............................................... 15

Figure 10. Average daily room-to-room temperature variability by number of stories ..................... 15

Figure 11. Average daily room-to-room temperature variability by occupant employment ............. 16

Figure 12. Average daily room-to-room temperature variability by number of thermostats ............ 17

Figure 13. Average daily room-to-room temperature variability by use of programmable 

thermostat ........................................................................................................................................... 17

Figure 14. Room-to-room temperature variability by number of stories ............................................ 18

Figure 15. Room-to-room temperature variability by city where the houses were located .............. 19

Figure 16. Average thermostat temperature over 1 week of data with programmable 

thermostats used................................................................................................................................ 20

Figure 17. Aggregated median thermostat temperature with programmable thermostat used ....... 21

Figure 18. Thermostat trend, House 28 .................................................................................................. 22

Figure 19. Thermostat trend, House 24 .................................................................................................. 22

Figure 20. Thermostat trend, House 26 .................................................................................................. 23

Figure 21. Thermostat trend, House 23 .................................................................................................. 23

Figure 22. Thermostat trend, House 21 .................................................................................................. 24

Figure 23. HVAC system off and on periods, House 24........................................................................ 25

Figure 24. HVAC system off and on periods, House 25........................................................................ 25

Figure 25. HVAC system off and on periods, House 26........................................................................ 26

Figure 26. Psychrometric chart, House 24 ............................................................................................. 27

Figure 27. Psychrometric chart, House 25 ............................................................................................. 27

Figure 28. Psychrometric chart, House 26 ............................................................................................. 28

Figure 29. Psychrometric plot of data from all measured rooms in all houses ................................. 28

Unless otherwise noted, all figures and photos were created by IBACOS.

List of Tables

Table 1. Home Location Summary ............................................................................................................ 8

Table 2. Sensor Measurement Capacity and Accuracy .......................................................................... 9

Table 3. Observed Thermostat Behavior................................................................................................ 31

Unless otherwise noted, all tables were created by IBACOS.
vii

Definitions

ACCA Air Conditioning Contractors of America

CLO Clothing (insulation)

HVAC Heating, Ventilating, and Air Conditioning

MET Metabolic (rate)

IRB Institutional Review Board
viii

Acknowledgments

The authors acknowledge Armin Rudd of ABT Systems, LLC for his technical guidance in 

planning and executing the study. 
ix

Executive Summary

Although space-conditioning systems are required to meet standards such as those in the Air 

Conditioning Contractors of America (ACCA) Manual RS (Rutkowski 1997) and the ASHRAE 

Standard 55 (ASHRAE 2013) comfort criteria, little is known about how space-conditioning 

systems are actually operating in the field and if these systems are meeting those standards. 

Unconventional space-conditioning systems may be necessary for low-load homes in the future. 

It is important to know how current systems are functioning in existing homes and to what 

temperatures occupants typically set the thermostats in their homes. 

The U.S. Department of Energy’s Building America research team IBACOS set out to find some 

answers. By using low-cost data loggers and occupant surveys to monitor 37 single-family 

homes that have standard heating, ventilating, and air-conditioning (HVAC) equipment, the team

gained insight into how sensitive occupants are to comfort in their homes relative to the ACCA 

and ASHRAE standards. This study focused on one climate zone (hot-humid); however, future 

studies may find differences among occupants’ ideas of thermal comfort in other climate zones. 

Data were collected from installed sensors during a 2-month period from late August 2014 

through late October 2014. The final aspect of this project was to review the business metrics 

associated with builders involved in a comfort and performance guarantee program.

The results of this study—taken as an aggregate—show that the homes provided room-to-room

temperature differences less than 6°F 95% of the time. Temperature differences were less than 

4°F 80% of the time. Some homes showed better or worse performance. On average, two-story 

homes had a 3.3°F temperature difference between rooms; single-story homes had an average of

a 2.2°F temperature difference between rooms. Occupants who did not use programmable 

thermostats had an average set point of 75°F. Homes in which the thermostats were programmed 

with a setback schedule showed a median baseline temperature value of 74°F and had a median

afternoon setback of 75°F.

System runtime was analyzed and showed that the room-to-room temperature uniformity in some 

homes worsened during an on cycle, whereas the uniformity improved in other homes. That is, 

when the system was running, some rooms were receiving too much or too little air relative to 

the rest of the house, and the temperatures in those rooms were moving away from the 

temperatures of the other rooms in the house.

Finally, analysis of the thermostat data shows a wide degree of variability among homes in the 

study. Interpretation of the measured data suggests that 21 occupants made no regular 

adjustments to the thermostats in their homes, seven had regular setbacks, seven showed varying 

setbacks, and 10 appeared to follow no pattern (with random adjustments).

Ultimately, the data show that current systems are maintaining expected levels of comfort. As 

expected, it is more challenging to maintain a uniform temperature in all rooms of two-story 

homes, and future systems should address this issue. Furthermore, the data show that the 

majority of homes maintained relative humidity levels below 60% without any supplemental 

dehumidification.
1

1 Introduction and Background

Questionnaires conducted over the last few years by the Best Practices Research Alliance1 reveal 

that providing satisfactory comfort in new homes is one of the top priorities of production 

homebuilders. Providing comfort in low-load homes is a challenge that is well documented by 

U.S. Department of Energy Building America research teams such as IBACOS. Previous 

research has identified three main systematic challenges that production builders must overcome

to provide consistent comfort: (1) floor-to-floor stratification, (2) isolated hot or cold rooms, and 

(3) humidity control.

The issues around floor-to-floor stratification have been documented in numerous reports. Ueno 

and Loomis (2014) found that in new-construction high-performance test houses (in International 

Energy Conservation Code climate zone 5) that are heated and cooled with mini-split heat 

pumps, at least one ductless head unit was needed per floor to provide adequate comfort. In 

research conducted by Herk et al. (2015), new-construction two-story homes in Denver, 

Colorado, showed some degree of floor-to-floor stratification. These homes had highperformance enclosures but used traditional heating and cooling air-delivery systems, which

suggests a need for alternative space-conditioning systems. Rittelmann (2008) conducted a study 

of head-to-toe and floor-to-floor stratification. He found that floor-to-floor stratification could be 

kept within 4°F under most external conditions in a high-performance test house with a welldesigned air-delivery system.

Temperature excursions beyond the recommended guidelines of Air Conditioning Contractors of 

America (ACCA) Manual RS (Rutkowski 1997) in specific rooms can be caused by a number of 

factors. These factors include: system problems from insufficient airflow resulting from poor 

duct design or balancing, enclosure, and other design issues; significant solar heat gains relative 

to the rest of the house; greater surface area associated with rooms that are isolated from the 

remainder of the house or adjacent to semiconditioned spaces such as garages; and occupant 

issues such as significant internal gains caused by occupant-installed equipment.

Humidity control is a growing concern in hot-humid climates as improved building enclosures 

reduce the sensible cooling demand and duration. This problem was well documented by Rudd

(2014). As the duration of central system runtime is reduced, interior humidity can exceed 60%

relative humidity, which leads to concerns about comfort, health, and building durability. 

Alternative strategies for latent load control are needed—such as stand-alone or central 

dehumidification systems. However, these systems can add to costs and can increase energy 

consumption. Rightsizing central cooling equipment can reduce humidity levels, does not add to 

system cost, and reduces energy consumption; however, systems with low thermal capacity may 

not be available from manufacturers. Furthermore, heating, ventilating, and air-conditioning 

(HVAC) contractors often are reluctant to rightsize equipment because of industry stigma and 

the perception that peak loads will not be met.

 1 Best Practices Research Alliance. www.theresearchalliance.org/.
2

1.1 Large-Scale Thermal Comfort Research

Most research about thermal comfort focuses on commercial buildings and controlled laboratory 

experiments. Some large-scale studies of residential dwellings have been conducted; however,

many such studies were conducted in multifamily dwellings or among a sample with inconsistent 

building stock (Arena et al. 2010). Few studies have looked at the indoor environment of new

high-performance single-family residences in a specific climate zone. This study will help to fill 

the knowledge gap by providing data about how these new single-family residences are 

performing.

1.2 Comfort Metrics

Research about the concept of thermal comfort has been conducted for many generations. 

Several important milestones have been discussed by de Dear et al. (2013). Perhaps the most 

significant models to gain wide acceptance are the Predicted Mean Vote Index2 and the Predicted 

Percentage Dissatisfied Index3 as defined by Fanger (1970). Adaptive thermal comfort concepts 

best characterize Predicted Mean Vote and Predicted Percentage Dissatisfied. ASHRAE 

Standard 55 (ASHRAE 2013) has adopted these factors and today is one of the de facto

standards for thermal comfort.

ASHRAE Standard 55 (ASHRAE 2013) specifies six primary factors that impact the thermal 

comfort of an occupant:

1. Metabolic (MET) rate

2. Clothing (CLO) insulation

3. Air temperature

4. Radiant temperature

5. Air speed

6. Humidity.

To fully define comfort, the values of all these factors must be known. Although all these factors 

can be controlled in a laboratory setting, measuring and controlling each factor for a long-term 

study in occupied homes is not feasible. Therefore, some assumptions had to be made.

The two simplest factors to measure are air temperature and humidity. The other environmentspecific factors include radiant temperature and air speed. Air speed measurements must be 

specific and localized to be accurate and are difficult to gather in an occupied test house. Radiant 

temperature asymmetry can have a significant impact on an occupant’s perceived comfort. 

Accurately measuring the mean radiant temperature requires a specialized device that mimics the 

direction human surfaces face. This device must be positioned exactly where the occupant is 

located. For this study, such an installation was impossible. Furthermore, asymmetric radiant 

effects due to wall temperature in a highly insulated house are less significant in the cooling 

season. 

 2 The Predicted Mean Vote Index predicts the mean response of a larger group of people according to an ASHRAE 

thermal sensation scale. 

3 The Predicted Percentage Dissatisfied Index is a quantitative measure of the thermal comfort of a group of people 

at a particular thermal environment.
3

The nature of the project involved human subjects who were under scrutiny; therefore, approval 

from the Institutional Review Board (IRB) first was necessary before the project could begin. 

IBACOS submitted pertinent information about the project to the IRB, and ultimately an IRB 

committee decided that the project was not classified as Human Subject Research.

For this project, occupants in their own houses were assumed to have greater control over MET 

rate and CLO insulation than they would in a typical office environment. Generally speaking, 

occupants wear a comfortable ensemble in their own houses. An occupant can balance comfort 

and energy efficiency by modifying clothing choice and indoor set point. For the analysis in this 

report, the team assumed the occupants had a CLO value of 0.5, which is typical for someone in 

casual attire. Although the MET rate of an occupant may increase for periods (e.g., exercising in 

a home gym or carrying a heavy load of groceries), these periods are usually brief and should not 

have a major impact on the analysis.

Investigation of comfort can go from the smallest granularity (i.e., finite element models that 

simulate every component of the human experience) to the simplest question: Does a builder or 

contractor receive a comfort complaint from the homeowner? The results of this study land 

somewhere in the middle of those two extremes.

An ideal study to identify the situations in which a homebuilder is at risk for comfort complaints 

would involve installing sensors in hundreds of homes—unknown to the occupants—and then 

carefully tracking the instances of comfort callbacks. A model then could be created that would 

estimate the probability of a comfort callback based on measured thermal metrics. Figure 1

illustrates the concept of this type of model in hypothetical numbers. The greater the average 

room-to-thermostat deviation, the greater the likelihood a homeowner will request a comfort 

callback. At some temperature difference, nearly every homeowner will feel uncomfortable and 

will make a comfort callback. An HVAC designer and homebuilder then could decide on the 

acceptable callback level and could design a system to meet such thermal requirements. 

Figure 1. Hypothetical probability of callback function

0

10

20

30

40

50

60

70

80

90

100

Probability of Comfort Callback

Average Room-to-Thermostat Temperature Difference
4

Unfortunately, a complete ideal data set that is necessary to create a callback probability model is 

not available. However, for this report the room-to-room and room-to-thermostat variabilities 

were measured in 37 homes. Thus, assuming that the homeowners are comfortable in their 

homes and that they live in homes with a comfort guarantee (i.e., if they are uncomfortable they 

will make a callback), then some idea of acceptable comfort limits can be discussed. The comfort 

guarantee is critical in making the assumption that occupants are comfortable.

U.S. Department of Energy Building America teams have identified the thermostat set point as 

the most sensitive factor in residential energy modeling (Metzger and Norton 2014). An 

additional benefit of the current study is that the data that were collected can be used in future 

studies by combining them with data from many climate zones to analyze the indoor temperature 

set point across the United States. This report will discuss system runtime as a variable that 

strongly influences total energy consumption; however, the report will not provide a detailed 

analysis of HVAC energy consumption.

1.3 Research Questions

This work attempts to understand the current state of comfort in modern high-performance

production homes that are located in hot-humid climates. Specifically, the variability of comfort 

was analyzed; the goal was to understand general trends across many homes. Another goal of 

this research was to further identify what conditions might be causing comfort-related risks for 

builders. By monitoring homes that have a comfort guarantee, researchers can assume occupants 

are comfortable in their houses if those occupants have not made callbacks—thus removing a 

critical amount of uncertainty. A byproduct of this report will be an analysis of system runtime, 

which can be used to understand the sizing of each system relative to the actual load on each 

home.

This project addresses the following research questions:

1. How much do temperatures and relative humidity vary from room to room in houses 

constructed in the last 7 years to meet comfort guarantee program requirements—

assuming the occupants consider their homes to be comfortable?

2. How do occupants use their space-conditioning equipment controls for scheduled 

setbacks and nonscheduled adjustments?

3. How do builders feel that participating in a comfort and performance guarantee program 

has impacted their business? What business metrics are associated with their involvement 

in terms of cost, marketing, and performance?
5

2 Mathematical and Modeling Methods

2.1 Data Analysis Methods

Upon the completion of measurement, data from each Onset HOBO logger4were downloaded 

and were stored as a comma-separated file for each logger, using the logger serial number as the 

file name. Two key files then were created: one that contained the house and room associated 

with each serial number, and another containing the house survey results from each home. Given 

this input data structure, the team used the scripting language Python5 for easy data analysis and 

plot and table output. This framework allowed the team to continuously update input parameters 

as needed and to rapidly modify the nature of each phase of analysis. The team also used the data 

package pandas6 to store and manipulate values and then used matplotlib7 to create high-quality 

raster graphics.

Because of inconsistencies with the exact timestamp for the HOBO data, all room air 

temperature measurements were resampled from their 10-min intervals to 1-min intervals. All

calculations presented in this report use the resampled 1-min data.

2.2 System Runtime Determination

To minimize the time and expense associated with each monitoring installation, the team decided 

to measure the supply air temperature as a surrogate for system runtime. Installing electric 

sensors on an HVAC system is time consuming, and electric sensors are more expensive than 

simple temperature and humidity data loggers. Onset HOBO UX-100-M sensors were installed 

on a supply register near the central air handling unit of each home in the study, such that airflow 

was directed across the sensor element.

The research team then used Python to analyze the data collected from each supply register. 

Through the trial of several algorithms, the team decided on the following method to determine 

HVAC system runtime.

The average temperature change over the next 5 min was calculated for each time step, such that 

single-minute fluctuations would not affect the analysis. Local maxima and minima were 

identified, which correspond to the largest changes in the signal slope. Each local maxima was 

then considered an on cycle if the average temperature change over the next 5 min also was 

greater than a threshold. Because of variations in sensor signal, the threshold was determined for 

each house using the standard deviation. Calculating runtime in this fashion filtered highfrequency noise but kept the start and end points of each cycle as close to the actual values as 

possible.

This method is illustrated in Figure 2; the sensor signal is plotted in blue and the forward rolling 

average of the signal’s slope is shown in green. (This number has been offset by +50 to show on 

the graph.) The local maximum and minimum of the rolling average then are identified, and if 

these values are higher or lower than the shown thresholds, an on or off cycle is flagged. Each 

 4 Onset HOBO UX100 loggers. Onset Computer Corporation, Bourne, MA. www.onsetcomp.com/. 5 Python scripting language. Python Software Foundation. www.python.org/. 6 pandas data package. http://pandas.pydata.org/. 7 matplotlib. http://matplotlib.org/.
6

identified on cycle is shown in gray. The calculation of the rolling average can be expressed by 

the following function:

f(x) = 1

n�xt − xt−1

n

t=0

where

n = time periods to average over (a 5-min interval was used)

xt = sensor reading at time t

Figure 2. Illustration of the system runtime calculation method

2.3 Uncertainty Analysis

According to ACCA Manual RS, the room-to-thermostat temperature variation in occupied space 

may not exceed 2°F during the heating season and 3°F during the cooling season (Rutkowski 

1997). Additionally, the room-to-room temperature difference should be less than 4°F (2°F 

average) in the heating season and less than 6°F (4°F average) in the cooling season. 

The team performed a propagation of error analysis to determine the likelihood of drawing an 

incorrect conclusion about whether a room passed the ACCA standard because of sensor error. 

This analysis was highly dependent on the room-to-thermostat temperature difference 

distribution, in that the closer and more frequent a measurement was to the ±2°F threshold, the 

greater the likelihood of drawing an incorrect conclusion. To perform the error analysis, the team 

employed a Monte Carlo method for determining the likelihood of drawing an incorrect 

conclusion. For this method, IBACOS made the following assumptions:
7

• Sensor error is random and normally distributed, with listed inaccuracies indicating the 

95% interval (two standard deviations).

• Measurement uncertainty is independent between sensors.

• Room air temperature to thermostat temperature is normally distributed, with the room 

air temperature centered at the thermostat temperature.

An analysis of previously measured data from a new-construction unoccupied test house in 

Pittsburgh, Pennsylvania, has indicated that the typical room-to-thermostat temperature 

difference followed a normal distribution, with standard deviations ranging from 1.7 to 2.4°F. 

Using R software,

8 the team took 100,000 normally distributed samples of possible sensor 

measurements and errors and then calculated the average failure rate in correctly determining the 

pass rate of a room according to the ACCA Manual RS standard (Rutkowski 1997). Figure 3

indicates these probabilities for the HOBO sensor chosen with an error of 0.38°F. Based 

on Figure 3, the team concluded that there is close to a 95% confidence in correctly determining 

failure rates with the HOBO data logger. To create this figure, the team ran the simulation for a 

number of discrete values for room-to-thermostat standard deviation and created a curve based 

on these results.

Figure 3. Probability of drawing an incorrect conclusion in the ACCA analysis for sensors with an 

error of 0.38°F

 8 R software. The R Foundation. www.r-project.org/.

0 1 2 3 4

0.00 0.01 0.02 0.03 0.04 0.05

Room to Thermostat Temperature Difference Standard Devia

Probability of an Incorrect Comfort Conclusio
8

3 Research and Experimental Methods

3.1 Home Identification and Selection

Initially, the project team planned to exclusively identify homes for this study through builder 

participation in the Environments For Living program. Environments For Living is a highperformance home program that, among other things, offers a home comfort and performance 

guarantee to homeowners. Difficulties in finding a sufficient number of volunteers meant the 

team broadened the search criteria to other acceptable high-performance homebuilders in the 

southeastern region of the United States. Homebuilders requested volunteers through several 

methods, including direct requests to employees living in builder homes, mass emails, and 

posting to community social media pages. The majority of homes were built within 2 years of the 

study period (one home was 7 years old). Table 1 contains a summary of the homes in the study. 

In total, 37 homes were monitored from three production homebuilders. The thermostat 

measurement device from one of the homes fell from the wall; therefore, data from only 36 

homes were reported.

Table 1. Home Location Summary

State City Number of Homes Number of Builders

Texas San Antonio 11 2

Texas Houston 7 1

Florida Tampa 10 1

Florida Orlando 9 1

IBACOS also solicited input from several homebuilders that were constructing high-performance

homes that included comfort guarantees. Once the homebuilders were identified, questions were 

emailed directly to the homebuilder contacts. IBACOS then compiled the responses. 

The Appendix contains a detailed list of the specifications of each home in the study as well as 

homeowner questionnaire results.

3.2 Sensor Selection

Low-cost, easy-to-use temperature and relative humidity data loggers were required for this 

project. IBACOS conducted a search for possible options. Based on price, accuracy, battery life, 

logging rate, data recovery, risk, and appearance, sensors from several manufacturers were rated. 

The team decided that sensors with Internet connectivity and the ability to send real-time data to 

the researchers were too expensive with uncertain logistics and may not significantly reduce the 

risk of losing data.

After evaluating the choices, IBACOS determined that the Onset HOBO UX100 would be the 

most appropriate data logger to use for this project, because it offers a reliable and cost-effective 

solution and provides highly accurate data. Metzger and Norton (2014) also recommend using 

the UX100-011 HOBO data logger at 1-h intervals. The preferred log interval for this study was

1 min to best characterize the maximum and minimum temperatures in each 15-min interval and 

to characterize system runtime. Because the capacity of the UX100-011 was limited to 
9

84,000 measurements, that data logger would not allow for 1 year of data to be collected without 

replacing the logger during that time. 

To provide a balance of cost, availability, and accuracy, the team decided to use a combination 

of three types of HOBO loggers. The HOBO U10-003 loggers collected temperature and 

humidity measurements from the perimeter rooms in each house at 10-min intervals. Highcapacity HOBO UX-100-M loggers (346,795-measurement capacity) collected the temperature 

and humidity data at the supply register and thermostat of each house at 1-min intervals. The 

high-capacity HOBO UX-100-M loggers were supplemented with 20 HOBO UX100-011 

loggers because of availability. IBACOS owned the HOBO UX100-011 loggers; all other 

loggers were rented for the project. Table 2 gives details about the sensors chosen for this 

project.

Table 2. Sensor Measurement Capacity and Accuracy

Sample 

Capacity

Temperature 

Accuracy

Humidity 

Accuracy

Number

UX100-011 84,650 ±0.38°F ±2.5% 20

UX100-M 346,795 ±0.38°F ±3.5% 80

U10-003 52,000 ±0.95°F ±3.5% 300

3.3 Sensor Installation and Survey Data Collection

IBACOS initially intended to find local contractors to install the monitoring equipment in test 

houses. Because of the close geographic location of groups of test houses and the shortened 

timeline due to slow partner involvement, IBACOS installed the equipment and collected survey 

data directly from the homeowners.

Sensors were installed with removable adhesive mounting strips in the subject houses at 

locations that were acceptable to the homeowners. These locations were 24–48 in. above the 

floor and closely followed the recommendations in Metzger and Norton (2014). Wall-mounted 

sensors were placed in each living space, away from the direct flow of HVAC equipment, and on 

interior walls. Each runtime logger was installed on the external face of a supply register close to 

the central air handling unit. Airflow direction was optimized across the sensor probe. Figure 4

shows an example of a logger placed on a supply register. Finally, a logger was mounted directly 

next to the thermostat.
10

Figure 4. A logger placed on a supply register
11

4 Results and Discussion

4.1 Summary of Data

As a starting point for this analysis, the team plotted all measured temperature data in a heat map

(Figure 5). This figure shows the period of data collection by each sensor. Blacked-out regions 

on the left and right of the graph show sensor installation and removal times. Each measured data 

point is represented as a cell on the map; sensors are organized by house on the y axis and by 

time on the x axis. Individual sensors are not labeled; however, they are sorted such that the 

supply temperatures and then the thermostat temperatures are at the top of each home’s strip. The 

color of each cell represents the temperature measured by the sensor. From this graphic, diurnal 

trends become apparent. Some homes have an obvious setback; others show an almost constant 

temperature for the duration of the study. Several vacation periods also are apparent, during 

which the indoor temperature rose for a period of several days. Supply temperature 

measurements appear at the top of each house as a dark purple band.

Figure 5. Heat map showing the period of data collection for each sensor
12

The team plotted humidity measurements in a similar fashion (Figure 6). Relative humidity 

values higher than 60% are shown in magenta. Supply temperature measurements again are 

shown as the first horizontal line for each house. Only two houses (House 02 and House 36) 

show humidity levels that are consistently higher than 60%. Other homes show brief periods of 

elevated humidity. According to industry standards such as ASHRAE Standard 55 (ASHRAE 

2013), humidity levels should remain lower than 60% to provide conditions for a comfortable 

and healthy environment. Industry experts agree that the 60% humidity level presents a risk for 

comfort and health-related problems (Rudd 2014). The cause of these two houses having 

consistently elevated humidity is unknown; the houses were built and are occupied similarly to 

the other houses in the study. As listed in the Appendix, House 02 had a gas stove, whereas 

House 01, House 03, and House 04 had electric stoves. The excess moisture generated from 

combustion may have impacted the humidity levels.

Figure 6. Heat map showing humidity measurements for each sensor
13

To understand the average room-to-room temperature variability among all houses, the team 

created a cumulative summation plot (Figure 7). For this plot, the maximum room-to-room

temperature difference was calculated at each time step for each house. (All data were resampled 

to 1-min intervals.) Using 6°F as the maximum allowable temperature difference, it is apparent 

that more than 95% of the data are lower than this threshold, meaning that for 95% of the time, 

the room-to-room temperature variability in all homes was within an acceptable limit.

Furthermore, the room-to-room temperature variability is less than 4°F for 80% of the time.

Figure 7. Cumulative summation of room-to-room temperature differences for all homes based on 

1-min data

To further break out the data, the cumulative summation has been subset by hour of the day 

(Figure 8). The early morning hours (6–10 a.m.) show the best room-to-room uniformity, 

whereas the evening hours (4–6 p.m.) show the worst uniformity. This may be explained by 

several factors. The morning hours typically are associated with the lowest outdoor temperature 

and the lowest cooling demand on the house. Outdoor temperatures typically are still elevated in 

the evening hours, and at this time, solar heat gains can be at their worst because of lower sun 

angles. The evening also is associated with many occupants returning home and setting the 

thermostats in their homes to a lower temperature after a setback. As shown in later figures, 

system runtime may increase room-to-room temperature variations in some houses.
14

Figure 8. Cumulative summation of room-to-room temperature variability by hour of the day

4.2 Indoor Temperature Variability with Time

To better understand the room-to-room temperature variability and its potential causes, the team 

created a number of colored line plots (Figure 9 through Figure 13). In each figure, the average 

daily room-to-room temperature variability9 is plotted against time for each house. To help 

understand trends in uniformity, the color of each line is based on one of several parameters.

According to ACCA Manual RS (Rutkowski 1997), each room of the house should be within 

6°F of every other room during the cooling season. Based on daily averages, all except one house

in the study maintained this level of uniformity. This single house showed a period of elevated 

temperature difference between September 18, 2014, and September 30, 2014, which likely was 

the result of a vacation setback.

As a starting point, Figure 9 represents with a different color the city in which each house was 

located. This figure shows no apparent trend based on city.

 9 The average daily room-to-room temperature variability was calculated as follows: For each time step, the 

temperature of the coolest room was subtracted from the temperature of the warmest room. This calculation always 

yields a positive number because all sensors never read the exact same number. These values then were averaged for 

the entire day.
15

Figure 9. Average daily room-to-room temperature variability by city

The team found that the strongest trend in determining uniformity was based on the number of 

stories of construction. As expected, single-story homes provided a more uniform temperature 

(Figure 10). This trend was caused by thermal stratification between floors. Based on the data in 

Figure 10, the average temperature difference between rooms for the two-story houses was 

3.3°F, whereas the average temperature difference between rooms for single-story houses was 

2.2°F.

Figure 10. Average daily room-to-room temperature variability by number of stories
16

Occupant behavior can have a significant influence on temperature variations. One measure of 

occupant behavior—job status—was collected as part of the study. Data in Figure 11 show that 

retired homeowners generally had more uniform temperatures within their homes, which may be 

the result of their preference for single-story homes and a constant thermostat set point. On the 

other hand, occupants with full-time job schedules, and thus the greatest opportunity for a 

thermostat setback, tended to show the most variability.

Figure 11. Average daily room-to-room temperature variability by occupant employment

The number of thermostats, or the number of thermal zones, also can have a significant impact 

on comfort in a house. Having more thermostats can allow occupants to have more uniform 

comfort in their house or to selectively condition spaces based on occupancy. In Figure 12, the 

widest degree of variability, as measured by room-to-room temperature difference, is apparent 

for homes with a single thermostat.
17

Figure 12. Average daily room-to-room temperature variability by number of thermostats

Another factor impacting the comfort of each house is the choice of a thermostat setback 

schedule. In this study, 26 respondents stated that they did not program their thermostats with a 

regular schedule, whereas 11 respondents stated that they used their programmable thermostats 

for a regular setback. The data shown in Figure 13 display no generalized comfort improvement 

between occupants who program their thermostats and those who do not.

Figure 13. Average daily room-to-room temperature variability by use of programmable thermostat
18

4.3 Indoor Temperature Variability with Outdoor Temperature

Figure 14 and Figure 15 were created to determine if room-to-room temperature variability and 

outdoor temperature correlated. In these plots, the daily average temperature difference versus 

the daily average outdoor temperature are plotted for all homes for data between September 10, 

2014, and September 15, 2014. The data in Figure 14 are colored based on the number of stories 

in each house and continue to show the trend that multistory houses have greater temperature 

variability. Outdoor temperature data were obtained from Weather Analytics’ historical 

database10 for each city.

Figure 14. Room-to-room temperature variability by number of stories

The calculated trend line shows no correlation between outdoor temperature and room-to-room

temperature difference for these houses. Many more factors are influencing the temperature 

uniformity but are not captured using a simplistic linear model. Visually it appears as if there 

may be a slight trend; however, this is likely due to the concentration of samples at warmer 

temperatures. Because there were many more measured occurrences at the warmer temperatures, 

the probability of randomly sampling from the extremes of the room-to-room temperature 

difference was higher.

The data in Figure 15 are colored by the location of the homes. Data for Tampa and Orlando 

show the smallest variation in outdoor temperature, whereas Houston and San Antonio show 

wider variations in temperature. The indoor temperature difference appears to be greatest in 

Orlando; however, this may be the result of a tendency toward two-story houses in that location.

 10 Weather Analytics. Bethesda, MD. http://www.weatheranalytics.com/wa/.
19

Figure 15. Room-to-room temperature variability by city where the houses were located

4.4 Thermostat Temperature

An additional benefit of this study to the homebuilding industry is a better understanding of the 

set point used in each house. The National Renewable Energy Laboratory is interested in these

data to improve thermostat set point modeling in simulation programs such as its Building 

Energy Optimization software.

11 The Building America House Simulation Protocol assumes a 

76°F indoor set point during the cooling season, with no setback (Metzger and Norton 2014).

The thermostat temperature for each house was averaged by the hour of day for each day in a 5-

day work week (Monday through Friday) period (September 15, 2014 through September 20, 

2014). This provides an average indoor thermostat profile for each house. These profiles are 

shown in Figure 16, as well as whether the homeowners indicated they use a programmed 

thermostat setback. Almost all homes with a setback showed a warmer thermostat temperature 

during the daytime hours; however, several homes with nonprogrammed setbacks showed cooler 

temperatures during the day. This may result from atypical occupancy patterns, such as the 

growing trend of individuals working from home.

 11 Building Energy Optimization software. Golden, CO: National Renewable Energy Laboratory. 

https://beopt.nrel.gov/.
20

Figure 16. Average thermostat temperature over 1 week of data with programmable thermostats

used

Data for occupants who did or did not claim to use a programmed setback then were aggregated 

into two typical profiles. The profiles were calculated by determining the median set point value 

for each hour of the day and then calculating the 10th and 90th quantiles to provide a 

determination of the range in set points. The two profiles are apparent in Figure 17; the setback 

population shows increased indoor temperatures through the afternoon. The median change in 

thermostat setting was from 74° to 75°F during evening and morning hours. To provide an idea 

of the setback for individual houses, additional data are presented in the next section. An 

interesting takeaway is that for the houses without a setback, their median set point was a 

continuous 75°F. It may be that the occupants use the setback to achieve better comfort relative 

to the baseline of 75°F.
21

Figure 17. Aggregated median thermostat temperature with programmable thermostat used

Raw thermostat measurements from several interesting and comparable houses are presented in 

Figure 18 through Figure 22. The team plotted the thermostat measurements for the entire 

monitoring period by the hour of day, along with the average profile for weekdays and 

weekends. Differences in occupant behavior can be inferred from these graphics.

Figure 18 shows the thermostat profile of the occupant of House 28; this occupant appeared to 

make no set point changes throughout the duration of the monitoring period. There is a slight 

hump in the middle of the day, possibly caused by a few factors. In the middle of the day, during 

periods of highest heat gain, the thermostat tends to spend more time at the upper end of the dead

band. Through the night, the opposite is true. 
22

Figure 18. Thermostat trend, House 28

Data for House 24 and House 26 (Figure 19 and Figure 20, respectively) show a uniform setback 

period. The occupants in both of these houses reported that they did not use a programmed 

thermostat setback. However, it is apparent that they used a manual setback ranging from 3°F to 

6°F. These occupants had very predictable behavior.

Figure 19. Thermostat trend, House 24
23

Figure 20. Thermostat trend, House 26

The data for House 23 (Figure 21) show three distinct set point periods. Analysis of the data 

indicates these different set points may be entirely arbitrary and do not follow any external 

factor, such as ambient air temperature. This kind of occupant behavior is unpredictable, based 

on the information available.

Figure 21. Thermostat trend, House 23

Figure 22 is shown as an interesting case in which the occupant of House 21 reported a 

programmed thermostat setback; however, the actual behavior, especially the weekday response, 

shows the users may have frequently intervened. Regardless, the return from setback (76°F to 

74°F) follows a very regular time (hour 21), whereas the return from setback for House 24 and 
24

House 26 shows a deviation of several hours, supporting the idea that the setback is user

controlled.

Figure 22. Thermostat trend, House 21

4.5 System Run Impact on Uniformity

The team used the runtime calculation algorithm outlined in Section 2 to create a plot to show 

the change in indoor temperature uniformity during system run. This “hair plot” shows each 

system off and on cycle for a period of several days between September 15, 2014, and 

September 18, 2014. System off periods are represented by lines leading up to the 0-min marker. 

System on periods then are shown as minutes increasing from 0. The starting and ending 

temperatures for each period are marked with a small vertical line. For periods longer than the x 

axis boundary, the line has been truncated. Lines showing a positive slope represent periods with 

worsening room-to-room uniformity, whereas periods of negative slope represent periods of 

improving room-to-room uniformity. The color of each line represents the change in starting and 

ending uniformity. Magenta lines show periods of large decline in room-to-room uniformity; 

light blue lines show an improvement in uniformity.

All homes represented in Figure 23 through Figure 25 are located in the same neighborhood and 

on the same street, with similar house geometries and orientations. All respondents indicated that 

they do not use programmable thermostats. Three very different behaviors are apparent. House

24 and House 26 appear to have a manual setback, with long run and drift periods. House 25 

does not appear to have any adjustments to the thermostat set point, with very regular and 

relatively short on/off cycles. Of note is the apparent behavior that, during long periods when the 

system is off, the temperatures of rooms in the house tend to float away from each other; during 

shorter off periods, the temperatures of rooms tend to drift closer. When the system cycles on 

after a long off period, the room-to-room uniformity tends to improve, whereas the system on 

cycles after short system off cycles tend to show worsening room-to-room uniformity. This trend 

is most apparent in House 24 (Figure 23). A possible explanation for this behavior is the 

temporal nature of manual system overrides. Typically, the system will be off for longer periods 
25

during the day, when the house is unoccupied, and certain rooms will be subject to unbalanced 

solar heat gains. Houses that show worsening thermal uniformity during system on periods may 

have poorly balanced register airflows.

Figure 23. HVAC system off and on periods, House 24

Figure 24. HVAC system off and on periods, House 25
26

Figure 25. HVAC system off and on periods, House 26

4.6 Psychrometrics

Data for the period of September 7, 2014, through September 9, 2014, for House 24 through

House 26 as examples have been plotted on psychrometric charts in Figure 26 through Figure 28, 

respectively. Each zone is plotted in a different color and with a 10% opacity value to begin 

showing relative density. In addition to the measured data, the ASHRAE Standard 55 comfort 

box for an occupant with 0.5 CLO and 1.0 MET has been drawn for reference (ASHRAE 2013). 

A value of 0.5 CLO is typical of a person wearing an ensemble of a shirt, pants, and shoes. An

MET rate of 1.0 is typical of a person sitting still—for example, watching TV. Conditions within 

the box are thought to satisfy 80% of people. The percentage of data points that fall inside the 

box has been calculated and is shown on each chart for easy reference.

Figure 26 shows the highest compliance with the comfort zone; Figure 27 shows the highest 

number of occurrences outside the comfort zone. Figure 28 shows the greatest variation of the 

three houses, which corresponds to the conclusion of the hair plot shown in Figure 25. Further 

analysis could be done to determine the optimum CLO value for each house, which maximizes 

the amount of time each house spends within the comfort box.
27

Figure 26. Psychrometric chart, House 24

Figure 27. Psychrometric chart, House 25
28

Figure 28. Psychrometric chart, House 26

As a final summary of all the measured temperature and humidity data, a density psychrometric 

chart is shown in Figure 29, containing more than 2 million data points. In this chart, data from 

each room in each house (excluding the supply registers) have been tabulated into a twodimensional density plot. Contour lines show areas of more frequent occurrence. An extreme 

peak in the data is visible around 75°F and 50% relative humidity. One house is seen as an 

“island” with temperatures that consistently exceed 80°F.

Figure 29. Psychrometric plot of data from all measured rooms in all houses
29

In Figure 29, the assumed CLO value of 0.5 may not be an indication of actual clothing worn in 

a house. The results suggest that a CLO value closer to 0.75 may be more representative of the 

average residential occupant in hot-humid climates. Another factor that may influence the 

comfort band is activity level. The comfort box assumes a 1.0 MET rate; however, occupants 

may have higher MET rates for various household activities such as cooking or exercising. 

4.7 Builder Metrics

Summaries of the homebuilders’ responses to each survey question are listed below. At times,

some disagreement arose about the impact of providing a comfort guarantee on the specific 

business metric, and this is discussed.

Do you offer any comfort guarantee, either an internal warranty or an external program such as 

Masco Environments For Living?

Comfort guarantees can be perceived by buyers as valuable in higher market groups such 

as the move-up market or active adult market. How the guarantee is presented can have a 

direct impact on the buyer’s perception of the value. Some respondents noted that 

misunderstandings have occurred about how the guarantee was calculated, leading to 

confrontation. All builders have at least explored the idea of offering a comfort 

guarantee.

Since implementing the comfort guarantee, have you seen a change in the rate of comfort 

callbacks? 

Respondents noted fewer callbacks related to comfort in the homes that were built to 

receive a comfort guarantee and a generally higher level of customer satisfaction; 

however, they acknowledged that real data numbers to support the general perception of 

higher customer satisfaction are not available.

What impact has the comfort guarantee had on the HVAC design and installation process and 

costs?

Higher costs have been associated with HVAC and with insulation and air sealing. A

higher level of attention has also been given to the installation quality. 

What impact has the comfort guarantee had on referral sales?

No data are available to back up the opinion that the comfort guarantee benefits sales. 

However, the consensus is that comfortable homeowners are referring new buyers and 

that the program is another component of market differentiation. 

What impact has the comfort guarantee had on market share?

The comfort guarantee has helped maintain market share through the market downturn, 

but the specific impact is difficult to measure.
30

What impact has the comfort guarantee had on sales velocity? 

The comfort guarantee is one part of the overall high-quality product that helps maintain 

sales velocity.

What impact has the comfort guarantee had on warranty service? 

Again, although difficult to measure, the impact of the comfort guarantee has played a 

role, along with all other quality items, in reducing service tickets. Mechanical 

contractors, and not builders, often see the direct impact from warranty service costs;

however, the perception is that the comfort guarantee indeed has lowered the number of 

warranty claims.
31

5 Conclusions

The following research questions were addressed in this report:

1. How much do temperatures and relative humidity vary from room to room in houses 

constructed in the last 7 years to meet comfort guarantee program requirements—

assuming the occupants consider their homes to be comfortable?

Measured data indicate that on average the temperature uniformity in these homes is 

within the 6°F boundary specified by ACCA Manual RS (Rutkowski 1997). The 

aggregate of all homes in this study showed that 95% of the time, the room-to-room

temperature difference was less than 6°F. Some homes showed higher levels of room-toroom variability. A temporal analysis of these data shows that room-to-room temperature 

uniformity was worse during the late afternoon and evening hours. This is when solar 

heat gains tend to have the most impact on southwestern- and western-facing glazing and 

when most systems were returning from setback. Uniformity was best in the early 

morning before the sun had risen and after most of the HVAC systems had been in an 

unadjusted state for many hours.

2. How do occupants use their space-conditioning equipment controls for scheduled 

setbacks and nonscheduled adjustments?

The team plotted the average thermostat temperatures over a day for each thermostat in 

the 37 homes; the team focused on average patterns over the weekdays and weekends 

separately. After analyzing the data for each thermostat, the team determined 

approximate occupant behavior as defined by each thermostat. Each thermostat was 

classified in one of the following five categories: no adjustments were made, a regular 

setback was used, a sporadic setback behavior was observed, a random behavior was 

observed with no pattern, or it was a Nest thermostat.

12 The results are presented in Table 

3, which shows that almost half the thermostats were not adjusted. Note that 10 of the 

houses had two thermostats; there were a total of 47 thermostats in 37 houses. The 

average room-to-room temperature difference was 2.8°F for homes with a setback and 

2.8°F for homes without a setback. So based on the data collected in this study, a setback 

had no observed impact on thermal uniformity.

Table 3. Observed Thermostat Behavior

No 

Adjustment Setback Sporadic Setback Random Nest

21 7 7 10 2

3. How do builders feel that participating in a comfort and performance guarantee program 

has impacted their business? What business metrics are associated with their involvement 

in terms of cost, marketing, and performance?

Through communication with builders in Texas and Florida who participate or have 

participated in programs offering a comfort guarantee, the research team gained the 

 12 Nest. Palo Alto: CA. https://nest.com/thermostat/life-with-nest-thermostat/.
32

following insights. No builders surveyed keep numeric records or were willing to share 

specific values relating to the impact that a comfort guarantee has had on their 

businesses. In higher market groups such as the move-up market or active adult market,

comfort guarantees can be perceived by buyers as valuable. How the guarantee is 

presented can have a direct impact on the buyer’s perception of the value. 

Some respondents noted that misunderstandings occurred over how the guarantee was 

calculated, which led to undesirable confrontation. Higher costs have been associated 

with HVAC and with insulation and air sealing. Also, a higher level of attention has been 

given to installation quality. Again, no data are available to back up the opinion that there 

is a benefit to sales, but the consensus is that comfortable homeowners are referring new 

buyers and that the program is another component of market differentiation. Comfort 

guarantees helped maintain market share through the downturn. Although it is difficult to 

measure the impact, the comfort guarantee has played a role—along with all other quality 

items—in reducing service tickets.

Some additional and interesting conclusions were reached in this study. 

Homes that did not use programmable thermostats had a median cooling set point of 75°F. 

Homes that used programmable thermostats had a median baseline temperature of 74°F and a 

setback temperature of 75°F. These values suggest that the assumed 76°F cooling set point in the

Building America House Simulation Protocols (Wilson et al. 2014) may be high for newconstruction homes in a hot-humid climate.

Relative humidity can be maintained at or lower than 60% by operating the air conditioner alone. 

Additional dehumidification was not required in any of these homes. This observation should be 

taken with a grain of salt, however, because the data collection period did not extend through the 

entire shoulder season. A complete year’s worth of data may show elevated humidity during 

cooler months when the air conditioning is not cycling as often.

5.1 Future Work

Given the nature of this data set, many follow-up questions could be considered with additional 

analysis. Collecting data for a longer period also would allow for additional conclusions to be 

drawn. Ideally, a whole year of data could be collected to understand peak conditions and the 

impact of humidity during shoulder seasons.

The team noted wide variability in the supply air temperature. Some of this may have been the 

result of sensor placement; however, duct length and insulation are likely to have played a 

significant role. Additional analysis could look at the impact of duct insulation and attic type on 

supply temperature.

Based on the system-runtime algorithm, an analysis of cycle duration compared to outdoor 

temperature would help researchers to better understand the appropriate sizing of each HVAC 

system. Observed behavior indicated short cycling for some homes. The impact of short cycling 

on dehumidification also could be analyzed. Despite the short cycling, the measured results 

indicate that the indoor air humidity was maintained at or lower than 60% most of the time.
33

The measured data were specific to a hot-humid climate region. These data could be compared to 

measured data from homes in other climate regions, and regional assessment of preferred 

thermostat settings may be observed.
34

References

Arena, L., P. Mantha, and K. Achilles. 2010. Monitoring of Internal Moisture Loads in 

Residential Buildings. Prepared for the U.S. Department of Housing and Urban Development, 

Washington, DC. Steven Winter Associates and Oak Ridge National Laboratory, December 

2010.

ASHRAE. 2013. ANSI/ASHRAE Standard 55-2010, Thermal Environmental Conditions for 

Human Occupancy. Atlanta, GA: ASHRAE.

de Dear, R.J., T. Akimoto, E. Arens, G. Brager, C. Candido, K.W. Cheong, B. Li, N. Nishihara, 

S.C. Sekhar, S. Tanabe, J. Toftum, H. Zhang, and Y. Zhu. 2013. “Progress in Thermal Comfort 

Research over the Last Twenty Years.” Indoor Environmental Quality Series. Berkeley, CA: 

University of California at Berkeley. https://escholarship.org/uc/item/92z5q2qb#page-4.

Fanger, P.O. 1970. Thermal Comfort. Copenhagen, Denmark: Danish Technical Press.

Herk, A., A. Poerschke, and R. Beach. 2015. Brookfield Homes Passive House Performance 

Evaluation. Golden, CO: National Renewable Energy Laboratory (in publication).

Metzger, C., and P. Norton. 2014. The Building America Indoor Temperature and Humidity 

Measurement Protocol. Golden, CO: National Renewable Energy 

Laboratory. http://apps1.eere.energy.gov/buildings/publications/pdfs/building_america/indoor_te

mp_humidity_protocol.pdf.

Rittelmann, W. 2008. “Thermal Comfort Performance—Field Investigation of a Residential 

Forced-Air Heating and Cooling System with High Sidewall Supply Air Outlets.” Presented at 

the BEST1 Conference, Minneapolis, MN, June 1–12,

2008. http://best1.thebestconference.org/pdfs/045.pdf. 

Rudd, A. 2014. Measure Guideline: Supplemental Dehumidification in Warm-Humid Climates. 

Golden, CO: National Renewable Energy 

Laboratory. http://apps1.eere.energy.gov/buildings/publications/pdfs/building_america/measure_

guideline_dehumidification_warmhumid.pdf.

Rutkowski, H. 1997. Manual RS—Comfort, Air Quality, and Efficiency by Design. Arlington, 

VA: Air Conditioning Contractors of America. www.acca.org/technical-manual/manual-rs/.

Ueno, K. and H. Loomis. 2014. Long-Term Monitoring of Mini-Split Ductless Heat Pumps in the 

Northeast. Golden, CO: National Renewable Energy Laboratory, NREL/SR-5500-

63079. www.nrel.gov/docs/fy15osti/63079.pdf. Revised 2015.

Wilson, E., C. Engebrecht-Metzger, S. Horowitz, and R. Hendron. 2014. Building America 

House Simulation Protocols (Technical Report, NREL/TP-5500-60988). Golden, CO: National 

Renewable Energy 

Laboratory. http://energy.gov/sites/prod/files/2014/03/f13/house_simulation_protocols_2014.pdf

.
35

Appendix: Specifications for the Homes in the Study and the 

Homeowner Survey Results
36

State City Occupants Adults Children

Home 

During 

Weekday

Employment

1 Texas San Antonio 4 2 2 1 full time

2 Texas San Antonio 2 2 0 1 retired

3 Texas San Antonio 4 2 2 0 full time

4 Texas San Antonio 1 1 0 1 retired

5 Texas San Antonio 2 2 0 1 full time

6 Texas San Antonio 5 2 3 0 full time

7 Texas San Antonio 2 2 0 1 full time

8 Texas San Antonio 2 2 0 1 full time

9 Texas San Antonio 4 2 2 0 full time

10 Texas San Antonio 3 2 1 0 full time

11 Texas San Antonio 4 2 2 0 full time

12 Florida Tampa 3 2 1 1 0

13 Florida Tampa 2 2 0 1 part time

14 Florida Tampa 2 2 0 1 0

15 Florida Tampa 3 2 1 1 0

16 Florida Tampa 2 2 0 1 part time

17 Florida Tampa 2 2 0 1 0

18 Florida Tampa 2 2 0 1 0

19 Florida Tampa 2 2 0 0 0

20 Florida Tampa 2 2 0 1 0

21 Florida Tampa 4 2 2 1 0

22 Florida Orlando 3 2 0 1 0

23 Florida Orlando 4 1 3 0 0

24 Florida Orlando 5 2 3 1 0

25 Florida Orlando 2 2 0 0 0

26 Florida Orlando 4 2 2 1 0

27 Florida Orlando 4 2 2 1 0

28 Florida Orlando 2 2 0 0 0

29 Florida Orlando 3 2 1 1 0

30 Texas Houston 3 2 1 1 0

31 Texas Houston 3 2 1 1 0

32 Texas Houston 4 2 2 1 0

33 Texas Houston 4 2 2 1 0

34 Texas Houston 2 2 0 0 0

35 Texas Houston 2 2 0 1 0

36 Texas Houston 3 1 2 0 0
37 

Year of 

Constr.

No. of 

Stories

Front 

Door 

Orient.

No. of 

Tstats. 

Prog. 

Tstat. 

Used

House 

Changes

Cond.

Square 

Footage

Foundation 

Type

1 2014 2 E 2 yes 0 3,656 slab

2 2014 2 SW 2 no 0 3,420 slab

3 2013 2 SW 2 yes 0 3,070 slab

4 2009 1 S 1 yes 0 1,532 slab

5 2013 2 SW 2 yes 0 3,656 slab

6 2013 2 E 3 yes 0 3,120 slab

7 2013 1 S 1 yes 0 1,921 slab

8 1 1 no 0

9 2 2 no 0

10 2 2 yes 0

11 2 2 no 0

12 2013 1 W 1 no 0 2,681 slab

13 2014 1 W 1 no insulation 2,154 slab

14 2014 1 E 1 no 0 2,154 slab

15 2014 1 W 1 no 0 1,818 slab

16 2013 1 S 1 yes 0 1,710 slab

17 2013 1 N 1 no 0 1,573 slab

18 2013 1 S 1 no ceiling fans 1,953 slab

19 2013 1 E 1 no pool; 

ceiling fans 2,300 slab

20 2013 2 W 1 no ceiling fans 3,326 slab

21 2014 2 SE 1 yes ceiling fans 2,681 slab

22 2014 2 S 1 no ceiling fans 2,318 slab

23 2 1 no ceiling fans slab

24 2014 2 W 1 no ceiling fans 2,638

25 2014 2 W 1 no ceiling fans 1,707

26 2014 2 E 1 no ceiling fans 2,318

27 2013 2 W 1 no ceiling fans; 

insulation 2,443 

28 2014 1 S 1 no 0 1,847

29 2013 2 S 1 no ceiling fan 3,070

30 1 1 no ceiling fan

31 1 1 yes ceiling fans

32 1 1 no 0

33 2 2 no 0

34 1 SW 1 no 0

35 2 N 2 no ceiling fans

36 2 1 no ceiling fans
38 

Bedrooms Bathrooms Master 

Bdrm. Door

Bdrm. 2 

Door

Window 

Vent. Zones Fireplace

1 4 3.5 closed - night closed -

night no 1 no 

2 4 3.5 open open no 1 yes

3 4 2.5 open open no 1 no

4 3 2 open open yes 1 no

5 4 3.5 open open no 1 yes

6 4 3.5 open open yes 1 yes

7 3 2.5 open closed no 1 no

8 open closed no

9 open

closed -

night no 

10 open open no

11 open open no

12 4 2 open open no 1 no

13 3 2 open open winter 1 no

14 3 2 open open winter 1 no

15 3 2 closed 0 no 1 no

16 3 2 open 0 no 1 no

17 3 2 open 0 winter 1 no

18 3 2 open 0 Octobersummer

1 no 

19 3 2 open 0 winter 1 no

20 4 4 open 0 winter 2 no

21 4 2 closed - night closed -

night

late October, 

onward 1 no 

22 4 2.5 open

closed -

night winter 1 no 

23 0 0 no no

24 4 2.5 closed - night open winter 1 no

25 3 2.5 open 0 no 1 no

26 4 2.5 open 0 winter 1 no

27 5 3 open 0 no 1 no

28 3 2 open 0 no 1 no

29 4 2.5 open 0 no 1 no

30 closed - night 0 no

31 open 0 no

32 open open no

33 open closed no

34 closed 0 no

35 closed closed no

36 open 0 no
39 

Enclosure Insulation Type Wall R-Value

Ceiling 

RValue

Solar 

Panels/Capacity

1 Wood frame

OC foam+0.5" cont. 

XPS 13+3 22 yes/1.94 kw

2 Wood frame

OC foam+0.5" cont. 

XPS 13+3 22 yes/1.94 kw

3 Wood frame

OC foam+0.5" cont. 

XPS 13+3 22 yes/1.94 kw

4 Wood frame

OC foam+0.5" cont. 

XPS 13 22 yes/unknown

5 Wood frame

OC foam+0.5" cont. 

XPS 13+3 22 yes/1.94 kw

6 Wood frame

OC foam+0.5" cont. 

XPS 13+3 22 yes/1.94 kw

7 Wood frame

Cellulose+0.5" cont. 

XPS 13+3 22 yes/1.94 kw

8

9

10

11

12 CMU blown 4.1 R-38 no

13 CMU blown 4.1 R-38 no

14 CMU blown 4.1 R-38 no

15 CMU blown 4.1 R-38 no

16 CMU blown 4.1 R-38 no

17 CMU blown 4.1 R-38 no

18 CMU blown 4.1 R-38 no

19 CMU blown 4.1 R-38 no

20 1-CMU,2-wood batt & blown up-11;down4.1 R-38 no 

21 1-CMU,2-wood batt & blown up-11;down4.1 R-38 no 

22 1-CMU,2-wood batt & blown up-11;down4.1 R-30 no 

23 no

24 1-CMU,2-wood batt & blown up-11;down4.1 R-30 no 

25 1-CMU,2-wood batt & blown up-11;down4.1 R-30 no 

26 1-CMU,2-wood batt & blown up-11;down4.1 R-30 no 
40

27 1-CMU,2-wood batt & blown up-11;down4.1 R-30 no

28 CMU batt & blown 4.1 R-30 no

29 1-CMU,2-wood batt & blown up-11;down4.1 R-30 no

30

31

32

33

34

35

36
41

Window 

Type Infiltration Duct Leakage

Ductwork 

Location

Ductwork 

Insulation

1 dbl. – vinyl 1,581 112 sealed attic & midfloor R-6

2 dbl. – vinyl 1,264 87 sealed attic & midfloor R-6

3 dbl. – vinyl 1,290 97 sealed attic & midfloor R-6

4 dbl. – vinyl 830 unknown sealed attic R-6

5 dbl. – vinyl 1,396 94 sealed attic & midfloor R-6

6 dbl. – vinyl 980 172 sealed attic & midfloor R-6

7 dbl. – vinyl 1,133 73 sealed attic & midfloor R-6

8

9

10

11

12 dbl. - alum 5.0936 0.026

13 dbl. - alum 5.0588

14 dbl. - alum 5.9275 0.019

15 dbl. - alum 5.0742 0.046

16 dbl. - alum 5.1277 0.041

17 dbl. - alum 4.1742 0.037

18 dbl. - alum 5.457 0.034

19 dbl. - alum 4.9984 0.03

20 dbl. - alum 4.5761 0.032

21 dbl. - alum 4.4901 0.018

22 dbl. - alum 4.2438 0.022

23

24 dbl. - alum 4.2429 0.022

25 dbl. - alum 4.572 0.016

26 dbl. - alum 4.3178 0.016

27 dbl. - alum 4.1816 0.011

28 dbl. - vinyl 3.5793 0.016

29 dbl. - vinyl 3.65 0.021

30

31

32

33

34

35

36
42

AHU 

Location

Fuel 

Source

Fuel 

Source 

Range

Fuel 

Source 

Oven

Fuel 

Source 

Heating

Adtnl.

Htg/

Clg

Space 

Heater

Humidifier/

Dehumidifier

1 sealed attic both electric electric gas no 0 0

2 sealed attic both gas electric gas no 0 0

3 sealed attic both electric electric gas no 0 0

4 sealed attic both electric electric gas yes 2 2

5 sealed attic both gas gas gas no 0 0

6 sealed attic both electric gas gas no 0 0

7 sealed attic both gas electric gas no 0 0

8 no 0 0

9 no 0 0

10 no 0 0

11 no 0 0

12 closet electric electric electric electric no 0 0

13 closet electric electric electric electric no 0 0

14 closet electric electric electric electric no 0 0

15 closet electric electric electric electric no 0 0

16 closet electric electric electric electric no 0 0

17 closet electric electric electric electric no 0 0

18 closet electric electric electric electric no 0 0

19 closet both electric electric electric yes 0 0

20 closet both gas gas gas no 0 0

21 closet electric electric electric electric no 0 0

22 closet electric electric electric electric no 0 0

23 closet no 0 0

24 closet electric electric electric electric no 0 0

25 closet electric electric electric electric no 0 0

26 closet electric electric electric electric no 0 0

27 closet electric electric electric electric no 0 0

28 closet electric electric electric electric no 0 0

29 closet electric electric electric electric no 0 0

30 no 0 0

31 no 0 0

32 no 0 0

33 no 0 0

34 no 0 0

35 no 0 0

36 no 0 0
43

Heating 

Equipment

Heating 

Capacity

Heating 

Distribution

Cooling 

Equipment

Cooling 

Capacity

1 gas furnace 112 kBtuh forced air AC 46 kBtuh

2 gas furnace 76 kBtuh forced air AC 44.5 kBtuh

3 gas furnace 57 kBtuh forced air AC 41 kBtuh

4 gas furnace 56 kBtuh forced air AC 29 kBtuh

5 gas furnace 92 kBtuh forced air AC 45 kBtuh

6 gas furnace 92 kBtuh forced air AC 40.5 kBtuh

7 gas furnace 57 kBtuh forced air AC 29 kBtuh

8

9

10

11

12 heat pump 46 Btu forced air AC 46

13 heat pump 26.2 forced air AC 28.6

14 heat pump 26.2 forced air AC 28.6

15 heat pump 26.2 forced air AC 28.6

16 heat pump 26.2 forced air AC 28.6

17 heat pump 26.2 forced air AC 28.6

18 heat pump 26.2 forced air AC 28.6

19 gas 28.8 forced air AC 28.8

20 gas & pump 28.8 & 35 forced air AC 28.8 & 35

21 heat pump 46 forced air AC 46

22 heat pump 8.0 HSPF forced air AC 14 SEER

23 forced air AC

24 heat pump 8.0 HSPF forced air AC 14 SEER

25 heat pump 8.0 HSPF forced air AC 14 SEER

26 heat pump 8.0 HSPF forced air AC 14 SEER

27 heat pump 8.0 HSPF forced air AC 14 SEER

28 heat pump 8.3 HSPF forced air AC 15 SEER

29 heat pump 8.3 HSPF forced air AC 15 SEER

30

31

32

33

34

35

36
DOE/GO-102016-4762 ▪ January 2016

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