Field Evaluation of 

Programmable Thermostats

O. Sachs, V. Tiefenbeck, C. Duvier, A. Qin, 

K. Cheney, C. Akers, and K. Roth

Fraunhofer CSE

December 2012
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iii

Field Evaluation of Programmable Thermostats

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:

O. Sachs, V. Tiefenbeck, C. Duvier, A. Qin, K. Cheney, C. Akers, and K. Roth

Fraunhofer Center for Sustainable Energy Systems CSE

25 First Street, Suite 102

Cambridge, MA 02141

NREL Technical Monitor: Chuck Booten

Prepared under Subcontract No. KNDJ-0-40345-00

December 2012
iv

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v 

Contents

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

List of Tables ............................................................................................................................................. vii

Definitions................................................................................................................................................. viii

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

Acknowledgments ...................................................................................................................................... x

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

1.1 Background ..........................................................................................................................1

1.2 Thermostat Usability ............................................................................................................2

1.3 Relevance to Building America’s Goals ..............................................................................3

2 Experiment ............................................................................................................................................ 4

2.1 Research Question ...............................................................................................................4

2.2 Experimental Design ............................................................................................................4

2.2.1 Thermostat Models Used .........................................................................................4

2.2.2 Default Schedule and Possibilities for User Interventions ......................................4

2.2.3 Design ......................................................................................................................5

2.3 Field Deployment.................................................................................................................6

2.4 Data Acquisition ..................................................................................................................8

3 Results................................................................................................................................................. 12

3.1 Setback Analysis ................................................................................................................12

3.2 Nighttime Setbacks ............................................................................................................15

3.3 Daytime Setbacks ...............................................................................................................19

3.4 Permanent Hold Events ......................................................................................................22

3.5 Regular Reprogramming Patterns ......................................................................................25

3.6 Satisfaction with Thermostats and Comfort Ratings .........................................................27

3.7 Gas Consumption ...............................................................................................................31

3.8 Data Validity Checks .........................................................................................................32

4 Conclusions ........................................................................................................................................ 34

4.1 Results and Discussion ......................................................................................................34

4.2 Limitations .........................................................................................................................36

References................................................................................................................................................. 37

Appendix A: Central Department of Energy Institutional Review Board Letter................................. 38

Appendix B: Participant Survey 1 ........................................................................................................... 40

Appendix C: Participant Survey 2 ........................................................................................................... 42

Appendix D: Thermal Simulation Results .............................................................................................. 45
vi 

List of Figures

Figure 1. Default settings for the Honeywell VisionPro TH8000 and the Honeywell RTH221B .......... 5

Figure 2. 250 Broadway Tower, Revere, Massachusetts ........................................................................ 6

Figure 3. Floor 5 of the building where thermostats and data acquisition devices were installed. 

The two condo units were left out of the study................................................................................. 7

Figure 4. Participation and attrition rate in the study ............................................................................. 9

Figure 5. Simplified example of joint sensor data sets......................................................................... 10

Figure 6. Example of apartment dataset and interpretation: Furnace activity (green lines) and 

temperature (black curve).................................................................................................................. 11

Figure 7. Illustration of setback analysis process. In Step 5 the data were clustered by date (timeseries analysis) or by apartment (cross-sectional analysis). ........................................................ 13

Figure 8. Outdoor temperature during the study. The shaded area marks the period after the 

heating season.................................................................................................................................... 14

Figure 9. Hourly group mean temperature over the course of the day (averaged over all nights 

between January 12 and March 6).................................................................................................... 15

Figure 10. Minimum apartment temperature at night averaged by thermostat condition................. 16

Figure 11. Mean apartment temperature at night averaged by thermostat condition ....................... 16

Figure 12. Mean apartment temperature for the nights below freezing point .................................... 17

Figure 13. Minimum apartment temperature for the nights below the freezing point ....................... 17

Figure 14. Percentage of households who reported nighttime setbacks ........................................... 18

Figure 15. Minimum apartment temperature during the day, averaged by thermostat condition.... 19

Figure 16. Mean apartment temperature during the day, averaged by thermostat condition .......... 20

Figure 17. Mean apartment temperature for the days below the freezing point................................. 20

Figure 18. Minimum apartment temperature for the days below the freezing point.......................... 21

Figure 19. Percentage of households who reported daytime setbacks.............................................. 22

Figure 20. Duration of the longest hold event per apartment .............................................................. 23

Figure 21. Total time on hold by apartment ........................................................................................... 23

Figure 22. Temperature and duration of individual hold events. All hold events of the apartments 

within the same group were pooled. ................................................................................................ 24

Figure 23. Percentage of households who reported vacation setbacks/holds .................................. 25

Figure 24. Percentage of households who reported adjustment of thermostats before and at the 

end of the study.................................................................................................................................. 27

Figure 25. Survey 2: Self-reported opinions about new thermostats.................................................. 28

Figure 26. Survey 2: Self-reported satisfaction with high and low usability thermostats ................ 28

Figure 27. Self-reported comfort levels with high and low usability thermostats in the morning ... 29

Figure 28. Self-reported comfort levels with high and low usability thermostats in the afternoon . 30

Figure 29. Self-reported comfort levels with high and low usability thermostats in the evening.... 30

Figure 30. Self-reported comfort levels with high and low usability thermostats at night ............... 31

Figure 31. Weekly gas consumption in high usability (VP) and low usability (BA) apartments....... 32

Figure 32. Correlation of furnace activity and gas consumption ........................................................ 32

Figure 33. Measured outdoor air temperature from Boston Logan Airport........................................ 45

Figure 34. Simulated interior surface temperatures of exterior southwest wall ................................ 47

Figure 35. Simulated air temperatures in the conditioned zone and measured air temperatures in 

two apartments in the experimental building.................................................................................. 47

Unless otherwise noted, all figures were created by Fraunhofer. 
vii 

List of Tables

Table 1. Equipment Deployed for Sensing and Data Acquisition.......................................................... 8

Table 2. Demographics of Survey Participants ....................................................................................... 9

Table 3. Setback Analysis Criteria .......................................................................................................... 14

Table 4. Mean and Minimum Temperature Maintained in Low and High Usability Apartments at 

Nighttime (Midnight to 4:00 a.m.)...................................................................................................... 18

Table 5. Percentage of Apartments in Low and High Usability Conditions That Used the Default 

Nighttime Temperature (62°F)........................................................................................................... 18

Table 6. Mean and Minimum Temperature Maintained in Low and High Usability Apartments during 

the Day (10:00 a.m. to 4:00 p.m.)....................................................................................................... 21

Table 7. Percentage of Apartments in Low and High Usability Conditions That Used the Default 

Daytime Temperature (62°F).............................................................................................................. 21

Table 8. Overview of Permanent Hold Results....................................................................................... 24

Table 9. List of Apartments in Which Residents Reprogrammed Their Thermostats Based on 

Algorithm Data and Visual Inspection.............................................................................................. 26

Table 10. Summary of Main Results........................................................................................................ 34

Table 11. Summary of Key Modeling Parameters.................................................................................. 46

Unless otherwise noted, all tables were created by Fraunhofer.
viii

Definitions

BA Basic (low usability thermostat)

CSE Center for Sustainable Energy Systems

DOE U.S. Department of Energy

EIA DOE Energy Information Administration 

LBNL Lawrence Berkeley National Laboratory

RH Relative humidity

VP VisionPro (high usability thermostat)
ix

Executive Summary

In this study, a team from Fraunhofer Center for Sustainable Energy Systems (CSE) evaluated a 

low-cost and scalable way to reduce heating energy consumption using the energy-saving 

features of programmable thermostats (i.e., automatic daytime and nighttime setbacks). Even 

though these functions are available in most programmable thermostats, previous research at the 

Lawrence Berkeley National Laboratory (Meier et al. 2011) suggests that poor usability features 

of this product class could prevent their effective use, leaving their energy savings potential 

unrealized. We hypothesized that home occupants with high usability thermostats are more likely 

to use them to save energy than people with a basic thermostat.

To test this hypothesis, we collected field data from 77 apartments in an affordable housing 

complex in Revere, Massachusetts, and applied a novel data analysis approach to infer occupant 

interaction with thermostats from nonintrusive temperature and furnace on–off state sensors. Our 

analysis of the data collected from January through March 2012 focused on four types of 

occupant interactions with thermostats that can lead to energy savings: nighttime setbacks, 

daytime setbacks, vacation holds, and reprogramming. Surprisingly, usability did not influence 

the energy saving behaviors of study participants. We found no significant difference in 

temperature maintained in apartments that had either high or low usability thermostats. The 

minimum and mean nighttime and daytime setback temperature was 70°F–71°F in both 

thermostat conditions—considerably higher than the energy saving default of 62°F. 

We also found that the proportion of households that used thermostat-enabled energy-saving 

settings was very low. Only 3% of households used default nighttime setbacks, regardless of the 

thermostat usability. No households with high usability thermostats and only 3% of households 

with low usability thermostats used daytime setbacks. Although many households used the 

permanent hold feature, it was used to maintain a high temperature and not to keep it at a 

constant low level when the apartment was unoccupied. The few cases of reprogramming that we 

found seem incidental and do not involve any meaningful lowering of the temperature to save 

energy. 

Although our results are limited to the specific study sample that we used, they demonstrate that 

thermal comfort is much more important to people than energy efficiency. This is particularly 

striking for affordable housing residents who pay their own heating bills. It implies that only 

people with a strong motivation to save energy or money or both can benefit from energy saving 

features of programmable thermostats. The rest of the population is likely to use them to 

maintain a comfortable temperature in their houses.

The results of this project support previous research by Nevius and Pigg (2000), showing that 

installation of programmable thermostats alone does not lead to reliable energy savings. 

Effective use of energy saving features enabled by programmable thermostats depends on many 

factors besides usability. Our study demonstrates that home occupants strive to achieve thermal 

comfort in their homes regardless of what thermostat model they have. Without motivation to 

save energy, high usability alone is not enough to facilitate the use of energy saving features in 

programmable thermostats.
x 

Acknowledgments

We would like to thank all the people and organizations that contributed to this project: 

• Alan Meier (Lawrence Berkeley National Laboratory) and Marco Pritoni (University of 

California, Davis) for inspiration, thermostat usability research insights, and advice on 

selecting the thermostats for the study

• Darien Crimmin and Eli Herman from Winn Residential for finding and making the 

Broadway Towers property available for the study, supporting the deployment process, 

and supplying all relevant information about the building for the data analysis 

• Marisa Cummings from the Broadway Towers for supporting the project daily and 

resolving any site and resident logistical issues

• Fraunhofer CSE interns and employees Tony Fontanini and Linda Mayer, who 

contributed to parts of the project 

• Bryan Urban and Diana Elliott of Fraunhofer CSE for the building envelope analysis. 

Finally, we are grateful to all residents of Broadway Towers for participating in the project, 

using their new thermostats, filling out surveys, and sharing their life philosophies and poetry 

with us on occasion. 
1 

1 Introduction

1.1 Background

Programmable thermostats have a high potential for saving energy. First, unlike several new 

categories of home energy management technologies, thermostats are already available to the 

majority of U.S. households. For example, 48% of U.S. households use a manual thermostat and 

37% use a programmable thermostat for heating (Energy Information Administration [EIA] 

2009). Second, programmable thermostats have been on the market for decades and have 

reached considerable technical maturity, which makes them a low-cost and low-risk investment 

for energy efficiency. Finally, and most important, building performance models developed in 

1970s and 1980s suggest that each degree of reduction in daily nighttime temperature setback 

can result in approximately a 3% reduction in heating energy use, making a convincing case for 

the energy savings potential of programmable thermostats (Nelson and MacArthur 1978). 

In 1995 the EPA established the ENERGY STAR programmable thermostat program to promote 

these devices as a way to save energy. EPA suggested that homeowners could save as much as 

$180 a year with a programmable thermostat that had default energy saving and comfort set 

point settings among its required features (EPA 2009). The mere availability of energy saving 

features, however, is not sufficient to achieve estimated energy savings. These are only possible 

if homeowners actively program thermostats and select settings that result in energy savings 

(e.g., daytime and nighttime setbacks). First anecdotal and then empirical evidence demonstrated 

that programming a thermostat is not a trivial task (Meier et al. 2011). A typical programmable 

thermostat has schedules for weekdays, weekends, and vacations, in addition to a hold or 

override option. Programming complexity is further exacerbated by buttons and fonts that are too 

small, abbreviations and terminology that are hard to understand, and lights and symbols that are 

confusing, as well as by illogical positioning of interface elements (Meier et al. 2011). On a more 

general conceptual level, people have many misconceptions about energy and thermostats. They 

may believe, for example, that heating all the time is more efficient than turning the heat off, that 

a thermostat is simply an on/off switch, that a thermostat is a dimmer switch for heat, or some 

combination (see, for example, Kempton 1986). Energy savings, then, ultimately depend on 

occupant behavior and whether home occupants are motivated to program their thermostats and 

capable of doing it when necessary. 

Empirical studies of energy savings associated with programmable thermostats revealed 

conflicting results: some showed savings in heating energy consumption resulting from an 

upgrade from a manual to a programmable thermostat; others found no such savings, or even 

increases in energy consumption in homes that relied on programmable thermostats. On one 

hand, a survey and a gas bill analysis of 7,000 households that installed ENERGY STAR-rated 

programmable thermostats found a 6% reduction in total household annual gas consumption 

(RLW Analytics 2007). On the other hand, a study of 299 households in Wisconsin showed that 

installing programmable thermostats alone did not lead to energy savings (Nevius and Pigg 

2000). Residents who practiced regular setbacks did it regardless of thermostat type—

programmable or manual. Residents who did not use their manual thermostat for saving energy 

did not start doing so once their manual thermostat was replaced with a programmable model. 

This study has made a convincing argument that behavioral factors play a decisive role in 

effectiveness of thermostats for saving energy. Combined with the work of Meier and colleagues 
2 

(2011), all these findings suggest that people find programmable thermostats difficult to 

understand and use, calling into question the effectiveness of thermostats for saving energy. 

1.2 Thermostat Usability

Since the end of 2009, the research focus has shifted toward usability of programmable 

thermostats. The ENERGY STAR Program started developing new specifications, focused on 

usability, for climate control devices. Program administrators began operating under one main 

assumption—that improved usability will facilitate energy saving behavior, enabling people to 

use the energy saving features of thermostats (EPA 2011). 

Alan Meier and his colleagues at Lawrence Berkeley National Laboratory (LBNL), the 

University of California, Davis, and the University of California, Berkeley, have led the research 

to develop a reliable methodology to measure thermostat usability and to understand the 

variability in ease of use among currently available thermostats. In their laboratory, these 

researchers developed a testing protocol for evaluating the usability of thermostat interfaces and 

tested five thermostat models in a series of six tasks (Meier et al. 2011): 

• Task 1: Turn the thermostat from “off” to “heat.” 

• Task 2: Set the correct time on the thermostat clock.

• Task 3: Identify the temperature the device is set to reach.

• Task 4: Identify what temperature the thermostat is set to reach on Thursday at 9:00 p.m.

• Task 5: Put the thermostat in “hold” or “vacation” mode to keep the same temperature during a 

human absence.

• Task 6: Program a schedule and temperature preferences for Monday through Friday.

Twenty-nine participants representing varied occupations and backgrounds (e.g., construction 

workers, business managers, nonprofit staffers, maintenance workers, and students) were asked 

to complete the tasks without any previous training. Notably, the majority of participants had 

“low” to “moderate” previous experience with programmable thermostats. Participants were 

videotaped and their behavior was measured using the following usability metrics: 

• Success or failure in accomplishing the task

• Elapsed time to accomplish the task

• Number of times buttons were pushed (or other actions)

• Sequence of actions

• Hesitations and comments of users.

The results of the study revealed a wide range in the usability of tested programmable 

thermostats. All of the metrics used consistently produced sufficient ranges in results to 

demonstrate the robustness of the task-based approach to measuring usability. A wi-fi enabled 

thermostat with a Web interface and a touch-screen thermostat were clearly superior to other 

tested thermostats on Tasks 1, 3, and 4, from Meier et al. 2011. 
3 

Results of this research demonstrate that usability is an important factor affecting user 

experience with programmable thermostats (Meier et al. 2011). Significantly, it influences an 

individual’s ability to operate the functions that are essential for achieving energy savings, 

suggesting that usability might play a key role in determining the effectiveness of programmable 

thermostats for saving energy. No existing research, though, has focused on testing this 

hypothesis and collecting field data to evaluate the impact of thermostat usability on encouraging 

energy saving behaviors of thermostat users. Closing this critical gap in existing knowledge is 

the main objective of this project. 

1.3 Relevance to Building America’s Goals

Evaluation of energy savings from high usability programmable thermostats is highly relevant to 

the goals of the U.S. Department of Energy’s Building America Program. Energy savings 

resulting from automatic setbacks (~6%; RLW Analytics 2007) can substantially contribute to 

the program’s overall 30%–50% energy reduction goal. Results of this project will contribute to 

development of usability specifications for programmable thermostats for the ENERGY STAR

certification of this product class. More than 33 million of U.S. households in all climate zones 

have a programmable thermostat. Survey results suggest that 14.5 million of these households do 

not currently use their thermostat for daytime setbacks and 11.6 million do not use nighttime 

setbacks (DOE/EIA 2009). The successful outcome of this project, then, could be scaled quickly 

to millions of homes in the United States.
4 

2 Experiment 

2.1 Research Question

We designed this project to answer the following research question: 

Are people with a high usability thermostat more likely to use energy saving 

features than people with a low usability thermostat?

We have defined the following energy saving features in a programmable thermostat that can be 

used to reduce heating energy consumption: nighttime setbacks, daytime setbacks, permanent 

vacation holds, and reprogramming involving lowering of the temperature. In this project, we 

focused mainly on identifying how owners of high and low usability thermostats used these 

features. 

In addition to investigating the impact of usability on these metrics, we explored users’ 

subjective satisfaction with high and low usability thermostats to evaluate whether satisfaction is 

related to effective use of energy saving features.

2.2 Experimental Design

2.2.1 Thermostat Models Used

Selecting the thermostats to be tested was a key component of the experimental design. Based on 

LBNL thermostat usability research (Meier and Aragon 2010), we selected two programmable 

thermostats for testing. The high usability thermostat that we chose is the VisionPRO TH8000 by 

Honeywell; the low usability thermostat is the RTH221B – a basic programmable model by the 

same manufacturer. Both thermostats control the gas furnace and the central air conditioner, have 

identical program default settings and similar aesthetic qualities. 

In this report, we refer to the high usability thermostat as VP (VisionPro) and the low usability 

thermostat as BA (basic).

2.2.2 Default Schedule and Possibilities for User Interventions

Importantly, both thermostats had identical default energy saving schedules (see Figure 1). New 

thermostats are shipped with these factory settings. During the heating season, the temperature 

set point is 70°F from 6:00 a.m. to 8:00 a.m. and from 6:00 to 10:00 p.m. During the other times 

of the day, the default set point is 62°F. This means that during those times, the furnace was 

active only when the temperature in the apartment fell below 62°F—unless it was manually 

overridden by the users.
5 

Time of Day

Figure 1. Default settings for the Honeywell VisionPro TH8000 and the Honeywell RTH221B 

Users have several ways to make changes to this default schedule. These adjustments can be put 

into three categories:

• Manual override (one-time). The thermostat holds the temperature that the user puts in 

until the next set point change is scheduled. For example, for the default schedule, if a 

user manually sets the thermostat to 72° at 1:00 p.m., this temperature will be maintained 

until 6:00 p.m. when the set point changes to 70°F according to the default schedule.

• Permanent hold (“cruise control”). The current temperature set point is maintained until 

the permanent hold mode is suspended by pressing “Run” button. 

• Reprogramming. Most users’ daily schedules do not exactly match the preset times of 

the default program. Users can modify the automated schedule of the device to adjust set 

point temperatures and times to meet their daily schedule and comfort preferences. For 

instance, a household could program the thermostat to keep the apartment at 72°F at night 

between 5:00 p.m. and midnight or to set back the temperature to 64°F between 11:00

p.m. and 7:00 a.m. every day. 

2.2.3 Design

In the study, households were randomly assigned to the high and low usability thermostat 

conditions. This ensured that any differences found in occupants’ interaction with thermostats 

were attributable to the main factor—usability. The main dependent variable measured in the 

study was the percentage of households in each usability condition that used energy saving 

features of thermostats. Other dependent variables used were weekly gas consumption data and 

self-reported survey data. The surveys asked about home occupants’ demographics, satisfaction 

with the previous thermostats, thermostat usage patterns, and comfort levels in their apartments. 
6 

2.3 Field Deployment

2.3.1 Test Site and Recruitment

We recruited households to participate in the study from a 96-unit multifamily building in 

Revere, Massachusetts (see Figure 2). Figure 3 shows one of the floors on the building plan. The 

building is heated with natural gas and residents pay for their heating consumption. 

The project and all project materials were approved by the Central Department of Energy 

Institutional Review Board for human subject participation under the expedited review category 

10 CFR 745.110(b)(1). Appendix A contains the approval letter.

We used an opt-out scheme to recruit residents for the study starting in October. All but ten 

residents agreed to participate in the study. Most residents in the building have low incomes and 

no Internet access (90% of residents without Internet). This precluded any possibility of using a 

wi-fi thermostat model.

Figure 2. 250 Broadway Tower, Revere, Massachusetts
7 

Figure 3. Floor 5 of the building where thermostats and data acquisition devices were installed.

The two condo units were left out of the study.

2.3.2 Deployment Process

Thermostat installation began on December 15, 2011, and was completed on January 11, 2012. 

The data collection period ended in the last week of March 2012. The thermostats and data 

acquisition devices were installed at the same time as other energy efficiency upgrades

(furnace/air-conditioner replacement and the insulation of the back wall in the utility closets). 

The Fraunhofer deployment team worked in parallel with the weatherization company team as 

they went into each apartment to complete their work. Weatherization work shifted the residents’ 

attention away from thermostat installation, making it unlikely that thermostat behavior changes 

were observed just because something new was installed in the units. 

The property managers gave access to each apartment; in most cases, the thermostat installations 

were coupled with other work undertaken in the apartments to minimize disruption to residents. 

Where residents were not present, all additional materials, namely the survey, the thermostat 

manual, and an informational flyer, were left in a highly visible place; otherwise residents 

received additional material personally. To collect energy consumption data, we read gas meters 

for all apartments once a week. 

In addition to installing thermostats and the data acquisition devices, we collected subjective data 

via a standard survey instrument (see Appendix B). At the end of the study another survey was 

conducted that assessed self-reported use patterns, as well as residents’ satisfaction with the 

thermostats (see Appendix C). 
8 

2.4 Data Acquisition

2.4.1 Data Acquisition Equipment Deployed

Besides thermostats, two types of data loggers were installed in every participating apartment: 

one to collect temperature and relative humidity (RH) data at 10-min intervals, and one to track 

the change of the furnace state (0 = on, 1 = off). Table 1 gives more details about the equipment 

used. 

Table 1. Equipment Deployed for Sensing and Data Acquisition

Measurement Equipment Used

First Zone 

Temperature

HOBO U12-011 Temp/RH: A two-channel electronic device that 

measures and logs temperature and RH. It features high-accuracy internal 

sensors and 12-bit resolution, and is capable of storing 43,000 samples. 

We programmed it to start at midnight on the day of installation and set 

the rate of sampling to 10 min.

HVAC State 

Data Logging

CSV-A8 Current Switch: A split-core inductance-based alternating 

current sensor with adjustable threshold switch. The switch outputs a 

signal when current flowing through the equipment reaches the desired 

threshold. The switch also outputs a different signal when the current 

flowing through the equipment falls below the desired threshold.

HOBO U9 State Data Logger: A single-channel recording device 

that detects and logs on/off changes in the desired appliance. The device

can store up to 43,000 state changes. The state data logger is attached to 

the current switch, and detects state changes from the current switch with 

1-s resolution. We programmed it to start at midnight on the installation 

date of the thermostats for each apartment.

2.4.2 Participation and Attrition

Of the 96 apartments in the building, five were owner-occupied and did not participate in the 

study. Eight apartments opted out before the deployment started. One thermostat was used in the 

property management’s office; three more apartments were empty during the study period. 

Residents of one apartment asked to have the old thermostat back and in another unit the old 

thermostat was never replaced. Fourteen apartments had sensor issues, with either the state data 

logger or the temperature data logger not working correctly (see Figure 4). 
9 

Figure 4. Participation and attrition rate in the study

We asked residents of all apartments where new thermostats were deployed to participate in a 

pen-and-paper survey. All those who filled out a survey took part in a raffle for gift cards at local 

supermarkets. There were two rounds of surveys. The first round took place when thermostats 

were first installed (December–January) and the second round occurred when thermostats had 

been in place for at least 3 months (March–April). In the first round, 33 residents filled out a 

survey, in the second round, 19 completed one. Of those, 13 residents filled out both surveys. 

Both surveys consisted of demographic items, a five-point comfort scale (from very 

uncomfortable to very comfortable), and a short five-point behavioral scale (from never to very 

often). The second survey had additional yes/no format questions about the use of the thermostat, 

additional heating methods used, and an open-response segment asking about work and life 

schedule questions. Out of study participants who responded to the survey, 26 respondents were 

female and 7 were male. The average educational attainment of the sample was high school, with 

46% of the sample having a high school education. Eighteen percent had received some 

schooling, and 36% had received professional certifications or higher education. Table 2 gives 

the rest of the demographic data. 

Table 2. Demographics of Survey Participants

Mean Median Range

Age 44 42 22–75

People in the 

Household

2 2 1–5

Children in the 

Household

0.6 0 1–5

People Older Than 60 0.2 0 0–2

To establish the comfort temperature range of the respondents, we asked them to indicate the 

range of temperature at which they feel the most comfortable in winter. Responses were elicited 
10 

using an open-response format. Respondents from the first survey reported feeling comfortable 

at an average of 72°F (95% Confidence Interval = ±2.03). Respondents from the second survey 

indicated an average comfort level at 73°F (95% Confidence Interval = ±1.82). 

2.4.3 Sensor Data Set Description

Two data sets were thus available from each participating apartment: 10-min data on temperature 

and RH from the HOBO U12-011 Temp/RH data logger (with time stamp), and event-based data 

from the state data logger that logged an entry (with time stamp) every time the furnace switched 

on or off. 

In the first step of the analysis, we joined these two data sets. Figure 5 illustrates the results of 

this step in simplified form. 

Figure 5. Simplified example of joint sensor data sets 

Figure 6 shows an actual apartment’s combined data set. We can identify periods where the 

furnace is cycling to maintain a certain temperature level (orange and red circles around 

temperature data in black, underlain by green vertical lines where the furnace switches on and 

off). We can also detect set point changes, where the temperature falls (no furnace activity) until 

the new set point is reached (light blue circles for examples of set point changes toward the 

default temperature of 62°F). 

0

0.2

0.4

0.6

0.8

1

1.2

66

67

68

69

70

71

72

73

74

0:00 0:20 0:40 1:00 1:20 1:40 2:00 2:20 2:40 3:00 3:20 3:40 4:00

Furnace state [0=off, 1=on]

Temperature [°F]

Time (date and daytime)

Temperature

Furnace state change
11 

Figure 6. Example of apartment dataset and interpretation: 

Furnace activity (green lines) and temperature (black curve) 

2.4.4 Sensor Data Retrieval

To download data from each individual HOBO sensor, we used the Hoboware Pro software from

Onset Computer Corporation. After that, all further analyses were carried out in Matlab. We used 

three main files, with each calling several functions. The first main file generated a matrix for 

each apartment that contained information for each time stamp, furnace state, and temperature. 

These were saved into separate mat-files. 

Cycling (manual) 

Setback

Cycling (schedule) 
12 

3 Results 

3.1 Setback Analysis

One of the main arguments for using programmable thermostats is that they can meet occupant 

comfort preferences while they are present and awake, yet allow at the same time for energy 

savings while they are gone or asleep. By default, the thermostats were set up to allow colder 

temperatures (62°F) during night hours (10:00 p.m. to 6:00 a.m.) and during the day (8:00 a.m. 

to 6:00 p.m.). In the following analyses we define setback periods as the times when the 

temperature was at or below 62°F. We chose this definition to be consistent with the EPArecommended factory settings for programmable thermostats (most programmable thermostats 

are sold with such settings). 

3.1.1 Methodology

We decided to narrow the time windows for nighttime setback analysis to midnight to 4:00 a.m., 

and for daytime setback to 10:00 a.m. to 2:00 p.m. These hours are most representative for 

nighttime and daytime behavior. We left a 2-h buffer for a nighttime setback and a larger buffer 

(4 h) for a daytime setback because daytime schedules are more variable. We needed the buffer 

because VP thermostats show anticipation behavior: in heating mode (winter), they start to heat 

the space in advance to ensure that it has reached the desired higher temperature level at the time 

indicated for a higher temperature set point (in contrast, BA thermostats start heating at the time 

of the scheduled set point change). In addition, data between 10:00 p.m. and midnight can be 

affected by occupants staying up late; temperatures after midnight are more likely to be 

representative of nighttime settings. We applied the same 2-h buffer to the time window for 

daytime setback analysis. 

Figure 7 illustrates the methodology used for setback analysis. The raw 10-min data from the 

temperature/RH files were filtered for the dates and buffered setback time windows (Step 2). For 

each apartment, we calculated the minimum and the mean temperature of this time window for 

every night (Step 3) and put these values into a list (Step 4). Thereafter, these mean and 

minimum values by apartment and by night could be averaged either by apartment for crosssection analyses or by dates for time-series analyses (Step 5). 
13 

Figure 7. Illustration of setback analysis process. In Step 5 the data were clustered by date (timeseries analysis) or by apartment (cross-sectional analysis).

Because outdoor temperatures increased considerably after March 6 (see Figure 8), we limited 

the data set to the period from January 12 to March 6, 2012. Temperatures were relatively mild 

for this time of the year in the Boston area. Consequently, on many days many apartments were 

able to maintain temperatures above 68°F or 70°F all night without any furnace activity. This, 

however, makes setback analyses more challenging because it is not possible to determine the 

temperature set point of these apartments for those nights without furnace activity. 

As a result, more meaningful results can be obtained from nights/days where the mean 

temperature was below the freezing point (32°F). For the study period starting on January 12, 

this was the case for 22 nights. We calculated the minimum and mean apartment temperature for 

each of these nights (between midnight and 4:00 a.m.). Then we took the 22 minimum and mean 

values of each apartment and calculated the mean of these minimum and mean values, 

respectively, for each apartment. 
14 

Figure 8. Outdoor temperature during the study. 

The shaded area marks the period after the heating season. 

As a first step in the analysis, we investigated how many apartments used thermostats effectively 

and had temperature setbacks similar to the default schedule and what percentage did not. We 

distinguished four different cases for this (Table 3). 

Table 3. Setback Analysis Criteria

No Furnace Activity All Day and Night Disregarded (“vacation”)

Furnace on During Designated Hours, 

T>T

threshold (62°F)

No Setback

Furnace on During Designated Hours, 

T<T

threshold (62°F)

Setback

Furnace off During Setback Hours, but on 

at Other Times of the Day

Uncertain

We only analyzed cases where the furnace was active at least at some point during the day, to 

avoid the days when residents were gone. 

Figure 9 shows the evolution of the temperature in the apartments over the course of the day. 

Data from all nights between January 12 and March 6 (the end of the heating period) were 

averaged on an hourly basis per apartment. Based on these hourly averages per apartment, we 

calculated the hourly group mean temperature. The graph also shows the confidence interval for 

each group (a standard deviation of the mean). The group mean temperature profile is very flat 

and the two groups show a very similar temperature profile, especially when compared with the 

default temperature profile. 

0

10

20

30

40

50

60

70

80

90

Temperatue [

°F]

Date

Outdoor temperature

0
15 

Figure 9. Hourly group mean temperature over the course of the day 

(averaged over all nights between January 12 and March 6)

3.2 Nighttime Setbacks

This section focuses on the results for nighttime temperature setbacks: what temperature was 

maintained in apartments overnight, how close it was to the energy saving default temperature, 

and what proportion of households used nighttime setbacks during the study. Nighttime setbacks 

represent the easiest opportunity to save energy because people would likely either be asleep or 

working a nighttime shift during that period. 

3.2.1 Temperature: Time-Series Analysis

The goal of this analysis was to understand what temperature, on average, apartments in each 

thermostat condition had at night during the project period. 

We conducted a time series analysis on the daily group averages of the minimum and mean 

apartment nighttime temperature (between midnight and 4:00 a.m.) over the study duration 

(Figure 10 and Figure 11, respectively). In contrast to the large oscillations of the outdoor 

temperature (Figure 9), we can see that the group means stay quite stable during heating season 

(ending around March 6) for both the minimum and the mean temperature. Both groups show 

very similar behaviors and trends and the mean temperature of both groups is clearly above 62°F 

on a day-by-day analysis. 

61

63

65

67

69

71

73

75

77

0h 2h 4h 6h 8h 10h 12h 14h 16h 18h 20h 22h

Temperature [°F]

Time of day

BA

VP

Default
16 

Figure 10. Minimum apartment temperature at night averaged by thermostat condition

Figure 11. Mean apartment temperature at night averaged by thermostat condition 

3.2.2 Temperature Cross-Sectional Analysis

The goal of this analysis was to understand how many apartments in each thermostat condition 

lowered the temperature for the night. As stated earlier, this analysis was limited to the 22 

coldest nights of the heating season when the outside nighttime temperature fell below the 

freezing point. Figure 12 shows the percentage of all apartments that retained a low setback 

temperature on cold nights (as their average across all cold nights). It also shows what 

temperatures were kept by other apartments and illustrates the most common nighttime 

temperature. Figure 13 does the same for the minimum temperatures recorded inside the 

apartments on the coldest nights. 
17 

Figure 12. Mean apartment temperature for the nights below freezing point 

Figure 13. Minimum apartment temperature for the nights below the freezing point 

Figures 12 and 13 illustrate two main findings: (1) there is no significant difference between the 

two thermostat conditions and (2) the vast majority of apartments did not use the default setback 

temperature.

Table 4 shows the mean and minimum apartment temperature during the nights below freezing 

point in two thermostat conditions. Both thermostat groups have similar values well above the 

default setback value of 62°F. 
18 

Table 4. Mean and Minimum Temperature Maintained in Low and High 

Usability Apartments at Nighttime (Midnight to 4:00 a.m.) 

Low Usability 

(BA)

High Usability 

(VP)

Tmean (°F) 71.6 71.2

Tmin (°F) 69.4 69.5

Table 5 shows the compliance rate with the default settings. Only 3.1% of the high usability 

thermostat apartments had an average nighttime setback temperature at or below 62°F. This 

number was higher—6.25%—when only minimum nighttime temperatures were averaged across 

all cold nights. For the low usability group, the proportion of households who kept their 

apartments at or below 62°F remained the same—3.2%—whether the average or minimum 

temperature was considered.

Table 5. Percentage of Apartments in Low and High Usability Conditions 

That Used the Default Nighttime Temperature (62°F)

Low Usability 

(BA)

High Usability 

(VP)

Tmean (°F) 3.2% (1 of 31) 3.1% (1 of 32)

Tmin (°F) 3.2% (1 of 31) 6.25% (2 of 32)

3.2.3 Self-Reported Nighttime Setbacks

The observable data on the percentage of households who practiced nighttime setback are in 

stark contrast to self-reported data. In both in the initial and follow-up survey, more than 50% of 

survey respondents indicated that they practice nighttime setbacks occasionally, often, or very 

often (see Figure 14).

Figure 14. Percentage of households who reported nighttime setbacks 

0

5

10

15

20

25

30

35

Never Seldom Occasionally Often Very Often

Percentage of Responses

Survey Response Options

Survey I (n = 33)

Survey II (n = 19)
19 

3.3 Daytime Setbacks

We also analyzed setback behavior during the daytime (10:00 a.m. to 2:00 p.m.) for the entire 

study period and for the coldest days (below the freezing point). The goal was to identify both 

the temperature that was maintained in the apartments during the setback period and how many 

households used daytime setbacks during the study. In this period the average outdoor air 

temperature was 35.8°F, or 5.5°F warmer than the typical Boston average of 30.2°F. 

3.3.1 Temperature: Time-Series Analysis

As in the nighttime setback section, the goal of this analysis was to understand what temperature, 

on average, apartments in each thermostat condition had during the day throughout the project 

period. Again, the vast majority of apartments do not use default temperature settings.

Figure 15 shows the daily group means of the minimum temperature between 10:00 a.m. and 

4:00 p.m. in each apartment over time. Figure 16 shows the group means of the average 

apartment temperature. Similar to nighttime behavior, temperatures are quite stable. Both group 

means are well above the 62°F default setback temperature. 

Figure 15. Minimum apartment temperature during the day, averaged by thermostat condition 
20 

Figure 16. Mean apartment temperature during the day, averaged by thermostat condition 

3.3.2 Temperature Cross-Sectional Analysis

The goal of this analysis was to understand how many apartments in each thermostat condition 

lowered the temperature during the day. Figure 17 shows the percentage of all apartments that 

retained a low setback temperature on days below the freezing point (as their average across all 

cold days). It also shows what temperatures were kept by other apartments, along with the most 

common daytime temperature. Figure 18 does the same for the minimum temperatures recorded 

inside the apartments on the coldest days. 

Figure 17. Mean apartment temperature for the days below the freezing point 
21 

Figure 18. Minimum apartment temperature for the days below the freezing point

The group average of the mean daytime temperature (10:00 a.m. to 4:00 p.m.) during those days 

was very similar for both groups and far above the default setback temperature: 71.8°F for low 

usability apartments and 71.6°F for high usability apartments (Table 6). The use of the default 

settings in the course of the day was even lower than with default night setbacks (Table 7).

Table 6. Mean and Minimum Temperature Maintained in Low and 

High Usability Apartments during the Day (10:00 a.m. to 4:00 p.m.) 

Low Usability 

(BA)

High Usability 

(VP)

Tmean (°F) 71.8 71.6

Tmin (°F) 70.1 69.7

Table 7. Percentage of Apartments in Low and High Usability Conditions 

That Used the Default Daytime Temperature (62°F)

Low Usability 

(BA)

High Usability 

(VP)

Tmean (°F) 3.2% (1 of 31) 0%

Tmin (°F) 3.2% (1 of 31) 3.1% (1 of 32)

3.3.3 Self-Reported Daytime Setbacks

As with nighttime setbacks, there is a gap between observable and self-reported data.

More than 60% of respondents to the first survey indicated that they set back the temperature 

during the day either often or very often. This pattern changed slightly in the second survey, 

where just over 50% of respondents indicated that they use daytime setbacks, with more 

respondents reporting that they do so often instead of very often (see Figure 19). 
22 

Figure 19. Percentage of households who reported daytime setbacks

3.4 Permanent Hold Events

The permanent hold function is another way users can override default settings. The intended use 

for this mode is to maintain a certain minimum temperature in the apartment over longer periods 

of absence (e.g., vacation). This mode, however, can also be used as a “set and forget” feature 

that disables temperature setbacks permanently (until the hold mode is manually canceled).

Forty-nine percent of BA apartments and 25% of VP apartments used the hold functionality at 

some point. Most hold events were of rather short duration: 87% of BA hold events and 75% of 

hold events in the VP group were up to 2 days and a single apartment in the BA group 

maintained hold events of 1 week or more (duration of 7 or 10 days, respectively). Summing the 

time on hold of all hold events by apartment, we found that two apartments in the BA group and 

five apartments in the VP group had the temperature on hold for a week or more. These findings 

are illustrated in Figure 20, Figure 21, and Figure 22. 

Figure 20 shows the duration of the longest hold event per apartment by group. With the 

exception of one apartment (in the BA group) that maintained the temperature on hold for 10 

consecutive days, all other apartments limited the duration of individual hold events to less than 

6 days. 

0

10

20

30

40

50

60

Never Seldom Occasionally Often Very Often

Percentage of Responses

Survey Response Options

Survey I (n = 33)

Survey II (n = 19)
23 

Figure 20. Duration of the longest hold event per apartment 

Figure 21 shows the total time on hold by apartment for all apartments that had used the hold 

functionality at some point. Although more apartments in the BA group used the hold 

functionality at all, among those who used it, more VP apartments kept the temperature on hold 

for more than 1 week during the study. 

Figure 21. Total time on hold by apartment 

Figure 22 shows at what temperature and for how long the temperature was kept on hold in the 

two groups. The five events with the longest duration in the BA group (≥4 days) all come from 

the same apartment. In both groups, we can see the general tendency to use the hold function for 

increased comfort, not to maintain the apartment at a minimum temperature while occupants are 

on vacation or otherwise gone as foreseen by thermostat manufacturers. Instead, most apartments 

used this feature as a set-and-forget autopilot that keeps the temperature at a level perceived as 

0

5

10

15

20

25

none

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

6.5

7

7.5

8

8.5

9

9.5

10

Number of apartments

Duration of longest hold event [days]

BA

VP

0

1

2

3

4

123456789

101112131415161718192021

Number of apartments

Total time on hold [days]

BA

VP
24 

comfortable all day long, saving them the hassle of repetitive overrides of default setbacks or 

reprogramming. 

Figure 22. Temperature and duration of individual hold events. 

All hold events of the apartments within the same group were pooled.

Table 8 presents an overview of these findings. 

Table 8. Overview of Permanent Hold Results

Low Usability 

(BA)

High Usability 

(VP)

% of Apartments Using Hold Feature 49 25

Average Hold Temperature (°F) 75.3 74.4

Average Duration per Hold Event 1.8 days 1.9 days

Mean of Maximum Hold Event 

Durationa

2.1 days 2.9 days

a Among all apartment who used the hold functionality in each group

3.4.1 Self-Reported Permanent Hold Events

Even though a high percentage of households used a permanent hold function, they did not do so 

to reduce the temperature while they were gone, but to retain high temperature for longer periods 

of time. It is particularly striking that the self-reported vacation setbacks deviate so much from 

observable data.

In the first survey, more than 70% of respondents indicated that they set back the temperature 

either very often or often when they are on vacation, and nobody reports that they do not set back 

the temperature when they are on vacation. In the second survey, more than 20% of respondents 

indicated that they never set back the temperature when they go on vacation, and just over 50% 

60

65

70

75

80

85

90

0 2 4 6 8 10 12

Temperature (F°) 

Duration of hold events (days)

BA

VP
25 

reported that they set back the temperature either often or very often when they go on vacation 

(see Figure 23). A potential confound in these data is that when talking about vacation, survey 

respondents could be thinking about an occasional event that may or may not have coincided 

with the test period, and they might overestimate their behavior related to that event.

Figure 23. Percentage of households who reported vacation setbacks/holds 

3.5 Regular Reprogramming Patterns 

3.5.1 Methodology: Development of the Algorithm

We used Matlab to analyze the data for reprogrammed schedules versus manual overrides. As we

mentioned in the data retrieval section, it consisted of two main files. The first one created a 

matrix for each apartment (saved as a mat-file), which was the starting point for all further 

analyses. Based on this matrix, classification procedures could be easily rerun with different 

parameters and thresholds (e.g., to test the sensitivity of the results on the exact temperature cutoff values). 

3.5.2 Reprogramming Results

Very few apartments showed patterns that fit reprogramming criteria. After additional visual 

analysis of the eight reprogramming candidates, we determined that the four apartments 

described in Table 9 actually reprogrammed their thermostats. 

0

10

20

30

40

50

60

70

80

Never Seldom Occasionally Often Very Often

Percentage of Responses

Survey Response Options

Survey I (n = 33)

Survey II (n = 19)
26 

Table 9. List of Apartments in Which Residents Reprogrammed 

Their Thermostats Based on Algorithm Data and Visual Inspection

Apt Group Temperature and schedule Detection

B02 Low usability 

(BA) 78°F from 7:00 p.m. to 11:30 a.m. Algorithm

B03 Low Usability 

(BA) 

70°F from 6:00 a.m. to noon, 72°F from noon to 

1:30 p.m., (default 70°F from 6:00 p.m. to 10:00 

p.m., 72°F from 11:00 p.m. to 1:00 a.m., 

72°F from 2:00 a.m. to 3:00 a.m.)

Algorithm

B04 Low Usability 

(BA) 

70.5°F from 10:00 p.m. to 4:00 a.m., 

73°F from 4:00 a.m. to 6:00 p.m., 

72°F from 6:00 p.m. to 10:00 p.m.

Visual 

inspection 

B26 Low Usability 

(BA)

74°F from 3:00 p.m. to 6:00 a.m., and to 

75°F from 6:00 a.m. to 3:00 p.m.

Visual 

inspection

 

This list is striking for several reasons. First, very few apartment occupants reprogrammed their 

thermostats. Somewhat surprisingly, all four apartments that do show reprogramming patterns 

had a BA thermostat. None of the VP apartments successfully reprogrammed their thermostats. 

Also, none of the four apartments that show reprogramming patterns allowed the temperature to 

fall back anywhere close to the default setback temperature. Although one apartment actually set 

a schedule that held 78°F for 16.5 hours of the day, the three remaining apartments chose set 

points between 70°F and 75°F. The set points programmed by these three apartments are very 

close to each other. Overall, just as the manual override data show, most residents seem to have a 

preference for much higher temperature in the winter than the default schedule of current 

programmable thermostats.

3.5.3 Self-Reported Adjustments of Thermostats

About 3 months after the new thermostats were installed, we asked respondents a range of 

questions about the use of the new devices. These questions were designed to find out whether 

respondents relied primarily on the set schedule or engaged in frequent manual interaction. We 

found that 56% of households indicated that they adjust their thermostats two or more than two 

times a day. In 13% of households, the thermostat was adjusted once a day. These responses 

show a lack of understanding of a programmable schedule that should theoretically be adjusted 

rarely. Thirty-one percent of respondents reported that they adjust their thermostats either very 

rarely or not at all. Compared to the results of the first survey, where we asked respondents to 

indicate whether they adjust their thermostat either daily, weekly, or monthly, there is no large 

difference. In the first survey, 68% of respondents indicated that they adjusted their thermostats 

at least daily. 
27 

Figure 24. Percentage of households who reported adjustment of thermostats 

before and at the end of the study 

3.6 Satisfaction with Thermostats and Comfort Ratings

When the thermostats were installed, the residents were given a user manual. In the second 

survey, we asked respondents if they had read the manual, and 83% reported that they had. We 

also questioned which household occupant primarily adjusted the thermostat. We found that 61% 

of those filling out the survey were the only ones in the household who adjusted the thermostat, 

39% also had a family member or other person apart from the respondent who adjusted the 

thermostat. 

Finally, respondents to the second survey were asked to indicate their satisfaction with the 

thermostat—whether they found it easy to use, liked the look, enjoyed using it, would 

recommend it to others, and thought the thermostat helped to save money. Figure 25 shows the 

responses to all five items, with satisfaction, dissatisfaction, and “do not care” answers. The 

majority of respondents are overall satisfied with the new thermostat, with over 70% positive 

responses for each item. We further analyzed these results by comparing satisfaction with all five 

items for respondents with high usability thermostats to those with low usability thermostats (see 

Figure 26). Results show a trend towards higher satisfaction with the high usability thermostats 

in terms of aesthetics, enjoyment of using it, recommending it to others, and its ability to save 

money. Respondents with low usability thermostats were much less convinced that the 

thermostat helps them to save money than respondents with high usability thermostats. 

0

10

20

30

40

50

60

70

80

Adjust rarely to every few days Adjust at least daily

Percentage of responses

Survey I (n = 33)

Survey II (n = 19)
28 

Figure 25. Survey 2: Self-reported opinions about new thermostats 

Figure 26. Survey 2: Self-reported satisfaction with high and low usability thermostats 

Perceived thermal comfort in the apartment is another self-report measure that we used in the 

survey. This measure can indicate whether high and low usability thermostats had a different 

impact on occupant satisfaction. 

0

10

20

30

40

50

60

70

80

90

100

Easy to use Like the look Like using it Would

recommend to

others

Helps save

money

Percentage of responses

Yes

No

Don't care

0

10

20

30

40

50

60

70

80

90

100

Easy to use Like the look Like using it Would

recommend to

others

Helps save

money

Percentage of responses

High usability T

Low usability T
29 

We surveyed comfort levels in the morning, afternoon, and evening to see if there were 

noticeable dips in comfort during setback periods. Few respondents indicated that they were 

uncomfortable or very uncomfortable at any part of the day, with the majority falling in the 

neutral or comfortable category. One trend that is obvious for all time periods is that the low 

usability thermostat received more “regular” or neutral ratings, whereas the high usability 

thermostat received more ratings on the extreme sides of the scale—“very uncomfortable” or 

“very comfortable.” 

Because of a low sample size (19 respondents to the second survey), we could not run any

meaningful statistical analyses on the difference in comfort levels between high and low 

usability thermostats. We can, however, discuss the trends for comfort in the morning, afternoon, 

evening, and nighttime. In the morning, similar numbers of respondents were comfortable with 

both high and low usability thermostats (see Figure 27). 

Figure 27. Self-reported comfort levels with high and low usability thermostats in the morning 

In the afternoon, more high usability thermostat owners were more comfortable than owners of 

low usability thermostats (see Figure 28). Similar trends were also observed for the evening and 

nighttime periods (see Figure 29 and Figure 30, respectively).

0

20

40

60

80

100

Percentage of Responses

Comfort Rating Scale

Low Usability (BA)

High Usability (VP)
30 

Figure 28. Self-reported comfort levels with high and low usability thermostats in the afternoon 

Figure 29. Self-reported comfort levels with high and low usability thermostats in the evening 

0

20

40

60

80

100

Percentage of Responses

Comfort Rating Scale

Low Usability (BA)

High Usability (VP)

0

20

40

60

80

100

Percentage of Responses

Comfort Rating Scale

Low Usability (BA)

High Usability (VP)
31 

Figure 30. Self-reported comfort levels with high and low usability thermostats at night 

These results should be used with caution, considering the small sample size and self-report 

biases that tend to skew results to be more positive and socially acceptable.

3.7 Gas Consumption

Throughout the study period we conducted weekly gas meter readings for all participating 

apartments to see if gas consumption differed depending on the usability of thermostats. This 

apartment building uses gas for space and water heating. 

Gas consumption is metered for every apartment individually and all apartments have the same 

heating system. We found that apartments with a high usability thermostat consumed the same 

amount of gas as apartments with a low usability thermostat (p = 0.73; see Figure 31 for the 

weekly gas consumption plot).

0

20

40

60

80

100

Percentage of Responses

Comfort Rating Scale

Low Usability (BA)

High Usability (VP)
32 

Figure 31. Weekly gas consumption in high usability (VP) and low usability (BA) apartments 

3.8 Data Validity Checks

Gas consumption data can also be used to check if our sensor data are valid. For that purpose, we 

compared apartment gas consumption and furnace “on” time for a given period (January 12–

March 13, 2012). Apart from differences in hot water consumption, furnace activity should be 

linearly correlated with gas consumption over a given period. As Figure 32 shows, there is a very 

good correlation between gas usage and the time that the furnace was on. 

Figure 32. Correlation of furnace activity and gas consumption 

In several apartments, we noticed that the indoor air temperature did not fall below the lower set

point temperature every night. This could occur because of mild weather, high performance 

0

2

4

6

8

10

12

13/Jan20/Jan27/Jan

3/Feb

10/Feb17/Feb24/Feb

2/Mar9/Mar

16/Mar23/Mar30/Mar

6/Apr

Gas consumption [100 cf]

Week (first day)

Low Usability

(BA) mean

High Usability

(VP) mean

0

50

100

150

200

250

300

0.0 2.0 4.0 6.0 8.0 10.0

Gas consumption [100 cf] (Jan 12-

Mar13)

Average of furnace activity [hours/day]

Furnace activity and gas consumption
33 

building envelope construction, and/or occupant behavior. To find out what causes are most 

likely, we collected information about the outdoor air temperature and building envelope and 

used the data to perform thermal simulations using the EnergyPlus whole-building energy 

simulation program. We used these simulations to predict expected temperature variation of 

indoor air temperature and exterior wall surface temperature. Our analysis shows that the 

building construction and mild winter were not primarily responsible for units whose air 

temperature does not decay below the set point temperature. Instead, it appears that manual 

thermostat overrides are the most likely explanation for why some apartments’ temperatures 

rarely fell below the default set point temperatures (see Appendix D for details). 
34 

4 Conclusions

4.1 Results and Discussion

The main objective of this project was to evaluate the impact of thermostat usability on 

facilitating energy saving behaviors of thermostat users. Specifically, are home occupants with a 

high usability thermostats more likely to use them to save energy than people with a basic 

thermostat? To answer this question, we collected field data from 63 apartments in an affordable 

housing complex and used an analytical approach to infer occupant interaction with thermostats 

from nonintrusive temparature and furnace on–off state sensor data. Our analysis focused on four 

types of occupant interaction with thermostats that can lead to energy savings: nighttime 

setbacks, daytime setbacks, vacation holds, and reprogramming. Table 10 summarizes the 

project’s main findings related to these types of occupant interaction with their thermostats.

Table 10. Summary of Main Results

Low Usability 

(BA)

High Usability 

(VP)

Number of Apartments per Group With 

Valid Datasets

31 32

NIGHTTIME SETBACKS

Minimum Nighttime Temperature 

(2:00 a.m. to 4:00 a.m.)

69.4 69.5

Mean Nighttime Temperature 

(2:00 a.m. to 4:00 a.m.)

71.6 71.2

Nighttime Setback (% of Apartments), 

T≤62°F

3.2% 3.1%

DAYTIME SETBACKS

Minimum Daytime Temperature 

(10:00 a.m. to 4:00 p.m.)

70.1 69.7

Mean Daytime Temperature 

(10:00 a.m. to 4:00 p.m.)

71.8 71.6

Daytime Setback (% of Apartments), 

T≤62°F

3.2% 0%

PERMANENT HOLD ANALYSIS

% of Apartments With Hold Events 49% 25%

Temperature of Hold Events 75.3 74.4

Average Duration per Hold Event 1.8 days 1.9 days

REPROGRAMMING ANALYSIS

# of Apartments With Reprogrammed 

Schedule

4 (13%) 0 (0%)

Group Average of Rescheduled Temperature 

(Average Weighted by Hours per Day)

73.8°F NA
35 

We found that usability did not influence energy saving behaviors of study participants. We 

found no significant difference in temperature maintained in apartments that had either high or 

low usability thermostats. The minimum and mean nighttime and daytime setback temperature 

was 70°F–71°F in both conditions—considerably higher than the energy saving default of 62°F. 

Perhaps the reason why usability had no impact on the use of energy saving settings was the low 

proportion of households that ended up using thermostat-enabled energy saving settings. Only 

3% of households used default nighttime setbacks, regardless of the thermostat usability. No 

household in the high usability condition used daytime setbacks and only 3% used them in the 

low usability condition. Although many households used the permanent hold feature, it was used 

to maintain a high temperature and not to keep it at a constant low level for the time when the 

apartment was unoccupied. More households in the low usability condition used the hold 

function than in the high usability condition, possibly because the button was more salient than a 

touch screen tab. Correspondingly, the few cases of reprogramming that we found in the low 

usability condition seem rather incidental and do not involve any meaningful lowering of the 

temperature.

These results could be explained by one of the following theories. First, our results could mean 

that the high usability thermostat was not sufficiently easy to use. A better thermostat could 

make programming or maintaining an energy saving schedule easier and therefore would have 

been more likely to be used by home occupants. 

Second, study participants did not understand that there is a relationship between thermostat 

setting and their heating bill. An educational intervention that made such a relationship clearer, 

as well as an explanation of the advantages of using a setback schedule, might have produced 

very different results. 

A third theory is that study participants were able to use energy saving settings on their 

thermostats but chose not to do so. This interpretation of results is realistic considering how 

much and how often study participants adjusted their thermostats to override the default 

schedule. Regardless of the thermostat model, they managed to keep the temperature at the level 

that ensured their comfort and negated any energy saving features. Why this happened is the 

question that warrants careful consideration and future research. If we consider that the three 

main factors that are essential for any behavior to take place are motivation, ability, and trigger 

(Fogg 2009), increasing the usability of programmable thermostats is driving only one 

component of the behavior change triad—ability. It does little, however, to increase motivation

or set up situational triggers that make behavior happen. 

Further development of climate control technology is likely to improve usability and make it 

easier for anybody to use thermostats. This means that motivation is a key variable that is 

missing from any attempts to make energy saving via thermostats more predictable and reliable. 

As part of our survey, we asked study participants if they used setbacks to save money or energy. 

The majority of respondents chose and agreed with both motivations. If we look at the actual 

behavior of study participants, though, it is driven by one dominating factor—thermal comfort. 

To achieve comfortable temperature study participants made regular adjustments to their

thermostat settings and overrode any program that would enable saving energy and money. 
36 

If we take it as a given that thermal comfort has a much higher priority in individual decision 

making than saving energy and money, energy saving features enabled by thermostats must 

preserve individual comfort levels. Analysis of temperature data from our sample is 

unambiguous in terms of what temperature study participants prefer in their homes even when 

they sleep—it is well above currently used default settings (62°F) and even above the 70°F used 

in nonsetback periods. 

A further insight gained from our study is a poor relationship between self-reported survey data 

and observed measurable behavior. Participants have drastically overestimated their use of 

energy saving features such as daytime and nighttime setbacks and vacation holds. The only 

measure where there was consensus between self-reported and observable data was apartment 

temperature that was comfortable for study participants. Both temperature sensors and survey 

respondents have agreed on 72°F.

4.2 Limitations

One limitation of this study stems from the fact that it was carried out in an affordable housing 

building where residents differ from average U.S. demographics. Our survey data indicate that 

most participants in the study had a low education level and few if any children or elderly family 

members. Most units in the building are reserved for low-income tenants. Thus, our results 

cannot necessarily be directly generalized to an average U.S. family. Our findings are, however, 

valuable for the affordable housing market, in which reducing utility costs is one of its top 

priorities. Moreover, our study is a strong test of the hypothesis that people are only interested in 

saving energy to save money. If people who have a restricted income are not interested in saving 

energy to save money, it is unlikely that people with an average or above average income will be 

compelled by financial incentives to change their heating behavior. 

Another limitation of the study results from the specific thermostat models we used. Our study 

was carried out with only two types of thermostats. One had a high usability score and the other 

one was sold as a more basic model. Although thermostat usability was evaluated using the same 

usability metrics, we cannot guarantee that our results were not affected by the specifics of these 

two models and to what extent they can be generalized to other models available on the market.
37 

References 

EIA (2009). Residential Energy Consumption Survey: Preliminary Housing Characteristics 

Tables. Space Heating in U.S. Homes, by Housing Unit Type, 2009. Washington, DC: EIA. 

EPA (2009). Programmable Thermostats. Accessed November 5, 

2012: http://www.energystar.gov/index.cfm?c=products.pr_save_energy_at_home 

EPA (2011). Draft 2 Version 1.0 Specification for Climate Controls Webinar. February 23, 2011. 

Accessed November 5, 2012: 

http://energystar.gov/products/specs/node/161

Fogg, B.J. (2009). “Creating Persuasive Technologies: An Eight-Step Design Process.” 

Presented at Persuasive’09, Claremont, California, April 26–29. 

Kempton, W. (1986). “Two Theories of Home Heat Control.” Cognitive Science (10:1); pp.75–

90. 

Meier, A.; Aragon, C. (2010). “Quantifying Usability in Programmable Thermostats.” Paper 

presented at Behavior, Energy and Climate Change Conference, Sacramento, 

California, November 14–17. 

Meier, A.; Aragon, C.; Peffer, T.; Perry, D.; Pritoni, M. (2011). “Usability of Residential 

Thermostats: Preliminary Investigations.” Building and Environment (46); pp. 1891–1898. 

Nelson, L.W.; MacArthur, J.W. (1978). “Energy Savings through Thermostat Setbacks.” 

ASHRAE Transactions (1978:83); pp. 319–333. 

Nevius, M.; Pigg, S. (2000). “Programmable Thermostats That Go Berserk: Taking A Social 

Perspective on Space Heating in Wisconsin.” Proceedings of the 2000 ACEEE Summer Study on 

Energy Efficiency in Buildings (2000:8); pp. 233–244. 

Peffer, T.; Pritoni, M.; Meier, A.; Aragon, C.; Perry, D. (2011). “How People Use Thermostats in 

Homes: A Review.” Building and Environment (46); 2529–2541. 

RLW Analytics (2007). Validating the Impact of Programmable Thermostats. Middletown, CT: 

RLW Analytics. 

Walker, I.S.; Meier, A. (2008). Residential Thermostats: Comfort Controls in California Homes. 

LBNL-938E. Berkeley, CA: LBNL.

Weather Underground (2012). Historical Boston Weather Data. Accessed November 5, 

2012: http://www.wunderground.com/history/airport/KBOS. 
38 

Appendix A: Central Department of Energy Institutional Review 

Board Letter
39 
40 

Appendix B: Participant Survey 1 

Dear Broadway Tower resident, 

Thank you very much for taking your time to answer our questions. Your time and your opinion 

are highly valuable to us. On the next two pages you will find a few questions concerning your 

household and your old thermostat. This applies to the thermostat you had installed previous to 

the new one. Please answer all questions as they apply to your previous thermostat. 

Your data will be treated completely confidential and anonymously.

 It should take around 10 minutes to answer all questions. After you finish answering the 

questions, drop the survey off at the office on the first floor at your earliest convenience (last 

drop-off date: Friday, December 16). 

Remember: By taking part in this survey you are eligible for our raffle worth of 500 dollars 

in Target gift cards! You will get your ticket from the office on the first floor when you 

drop off this survey.

 

1. Are you:  male  female 2. Please indicate your year of birth:

3. How many members are in your household? 

4. How many children under age 12 live with you? 

5. How many people over age 60 live in your apartment? 

6. How long have you lived in this apartment? Years Months 

7. How long do you think you will stay in this apartment? Another years

8. Please indicate your highest level of education/schooling: 

 Some school  High school diploma  some college  Associate’s 

degree 

 Bachelor’s degree  Professional certification  Master’s degree  Doctorate

Below are some questions about your comfort with the indoor temperature at home. 

The temperature in my home is... Very 

uncomfort

able

A little

uncomfort

able

Regular Comfort

able

Very 

comfortable

Not 

applic

able 

...on weekdays in the morning.      

...on weekdays in the afternoon (leave 

blank if working)      

...on weekdays in the evening.      

...on weekdays during the night.      

...on weekends when I am home.      

...on weekends when I come back from      

RAFFLE
41 

having been away for more than two hours.

...on weekends during the night.      

...when I come back from a vacation.      

At which temperature to do you feel the 

most comfortable at home? 

Winter: Between ____ °F and ____ °F

Summer: Between ____ °F and ____ °F

Below are a few questions around your use of the thermostat. 

never seldom occasion

ally

often very 

often

n.a

I regulate the temperature according to how 

comfortable I (my family) feel.      

I regulate the temperature so I don’t have to 

wear a sweater.      

I turn down the thermostat when I leave the 

house.      

I turn down the thermostat when I go to bed.      

I lower the temperature when I go on vacation.      

I lower the temperature of the thermostat to save 

money.      

I lower the temperature of the thermostat to save 

energy.

     

I adjust the thermostat: Please circle: Daily Weekly Monthly Never

That is all! 

Thank you very much for helping us with our project, your time and effort are very 

valuable to us.

Please drop this survey off at the office on the first floor at your earliest convenience (last 

drop-off day: Friday, December 16). 

You will get your RAFFLE ticket from the office on the first floor. Bring this 

survey with you, and let them know your apartment number. For us to match the raffle 

tickets, please indicate your apartment number below:

I live in apartment number: __________ 
42 

Appendix C: Participant Survey 2

Dear Broadway Tower resident, 

Below you find a few questions about your household regarding energy use and the new 

thermostat.

As a Thank-You for taking this survey, you will enter a raffle for 10 gift cards for each 

worth $50!! Get your raffle ticket from the office on the first floor by filling out and 

handing in your survey. The raffle will be held on Friday, April 20th, so make sure you 

collect your ticket before that day!

Your answers will be treated completely confidential, they are solely for research purposes, 

and the building management does not see your answers.

Thank you very much for your help!!!

How many people live with you? ____________ 

How many people are working day shifts?_________ Normal work 

hours:________________________ 

How many people are working night shifts? _______ Normal work 

hours:________________________ 

What time does the first person get up?___________ What time does the last person get 

up?_________ 

What time does the last person go to bed? _________ 

How many people are usually home during the day on 

weekdays?______________________________________ 

Some questions about the new thermostat in your apartment:

Did anyone in your household read the manual for the new thermostat?  Yes  No

Who adjusts the thermostat in your household? (check all that apply)

you your husband/wife/partner (please circle) your son/daughter (please circle) 

your mother  your father other (please specify) ____________

How many times per day does anyone in your household adjust the thermostat? 

__________________ 

Do you agree? Yes No Don’t Care Comments

It’s easy to use the thermostat.   

I like how the thermostat looks.   

I like using the thermostat.   

I would recommend this thermostat to 

others. 

  

This thermostat helps me save money.   
43 

Do you..... Never Seldom Occasionally Often Very 

Often

...lower the temperature when you leave the house?     

...lower the temperature when you go to bed?     

...lower the temperature when you go on vacation? 

(if applicable)     

...lower the temperature to save money?     

...lower the temperature to save energy?     

What have you and others in your household usually worn in the past 3 months at home? (check all that apply)

 Sweater  Long-sleeves  T-Shirt  Shorts  Pants  Hat

Other:_____________________________________________ 

Heating in your apartment 

Did you use electric space heaters during the winter? Yes  No 

If yes, for which rooms did you use 

it/them?_____________________________________________________________ 

Did you have problems with the furnace during the winter? Yes  No

Do you remember when and for how long? (if not, 

approximate)_____________________________________________ 

If you used another method, other than the thermostat/furnace or electric space heaters to heat your home, 

please specify here what you used:

Other comments:

Please tell us how comfortable you are in your apartment:

The temperature in my home is... Very 

uncomfort

able

A little 

uncomfort

able

Regular Comfort

able

Very 

comfortable

Not 

applic

able 

...on weekdays in the morning.      

...on weekdays in the afternoon (leave 

blank if working)      

...on weekdays in the evening.      

...on weekdays during the night.      

...on weekends when I am home.      

...on weekends when I come back from 

having been away for more than two hours.      

...on weekends during the night.      
44 

...when I come back from a vacation. (if 

applicable)      

At what air temperature to do you feel the 

most comfortable at home? 

Winter: Between ____ °F and ____ °F

Summer: Between ____ °F and ____ °F

Are you male  female 

What year were you born?________________________ 

How many children under age 12 live in this apartment? ____________ 

How many people over 60 live in this apartment? _________________ 

Optional: What is the highest education received by anyone in your household? 

___________________________ 

Are you or anyone in your household in school right now? Yes No 

Your apartment number:_____________ 

Remember: Get your raffle ticket from the office on the first floor by April 20th!!!

Thank you so much for helping us with our project, your time and effort are very valuable 

to us. 

Fraunhofer Center for Sustainable Energy Systems CSE

25 First Street

Cambridge, MA 02141 

Call us: 617-575-5720 

Write to us: hfresearch@fraunhofer.org 
45 

Appendix D: Thermal Simulation Results

During the analysis period from January 12 through March 6, actual winter air temperatures were 

mild in comparison to typical Boston weather, as shown in Figure 33 (Weather Underground 

2012). The average outdoor air temperature was 35.8°F, or 5.5°F warmer than the typical Boston 

average of 30.2°F. These warmer outdoor air temperatures could have reduced the number of 

nights when indoor air temperature remained higher than thermostat set point temperatures. 

Figure 33. Measured outdoor air temperature from Boston Logan Airport 

We estimate that the test building envelope could be slightly more insulating (about 6% or R-1 

higher) than typical brick construction. According to the building project coordinator, the 

building envelope is brick with metal studs and R-13 fiberglass insulation. Many units have large 

glass sliding doors leading to balconies. Metal stud construction might slightly improve the 

overall opaque wall because of a reduced effective framing factor.

1

 The glazing fraction of the 

test building was 23.3%, slightly higher than the 15% assumed in the Building America 

benchmark. The roof was about 35% more insulating than typical construction; however, this 

would likely only affect thermal transfer on upper floors. 

We used the Building Energy Optimization tool to create an EnergyPlus model of a typical 

apartment unit as built, and for comparison, the same apartment unit with standard construction. 

Table 11 shows the key inputs. We modified the typical Boston weather data file by overlaying 

actual outdoor air temperature and wind speed data. With these models, we calculated the 

expected indoor air and indoor wall surface temperature histories over the study period to see 

how the two buildings compared. In both cases, we assumed that the thermostats were set to their 

default settings. 

 

1 Although metal studs have a much higher thermal conductivity than wood studs that increases thermal bridging, 

they also enable a reduced framing factor (8% for metal studs versus about 25% for wood framing) and therefore a 

slightly higher opaque wall R-value. 

0

20

40

60

1/12 1/19 1/26 2/2 2/9 2/16 2/23 3/1

Outdoor Air 

Temp (°F)

Measured

Typical

0

10

20

30

40

1/12 1/19 1/26 2/2 2/9 2/16 2/23 3/1

Wind Speed 

(mph)

Measured
46 

Table 11. Summary of Key Modeling Parameters

Actual Building Benchmark-1 Benchmark-2

Orientation Northeast and Southwest Northeast and Southwest Northeast and Southwest

Window Percentage 23.3% 23.30% 15.0%

Wall Construction 

Brick

Air gap

1⁄2-in. Plywood 

2 × 4 metal studs, 8% framing 

R-13 fiberglass insulation

Gypsum wall board

Brick

Air gap

1⁄2-in. Plywood 

2 × 4 wood studs, 25% framing

R-13 fiberglass insulation

Gypsum wall board

Exterior Finish

1⁄2-in. Plywood 

2 × 4 wood studs, 25% framing

R-13 fiberglass insulation

Gypsum wall board

Opaque Wall R-Value 12.6 11.8 10.5

Window R-Value 0.5 0.5 0.5

Overall Wall R-Value 9.8 9.1 9.0

Roof Construction 

Asphalt

3⁄4-in. Plywood 

4-in. Rigid Insulation

3⁄4-in. Plywood 

35/8-in. Insulation

Gypsum wall board

Asphalt

3⁄4-in. Plywood 

4-in. Rigid Insulation

3⁄4-in. Plywood 

35/8-in. Insulation

Gypsum wall board

Roof membrane

3⁄4-in. Plywood 

R-16 Insulation 

Gypsum wall board 

Roof R-Value 29.4 29.4 18.6

Air Change Rate 10.3 10.3 10.3

All other inputs BEopt Defaults BEopt Defaults BEopt Defaults
47 

Figure 34 shows the simulated interior surface temperatures of the exterior wall of southwestfacing apartments. Northeast surface modeling results were similar, but had faster decay caused 

by reduced solar gain throughout the day. All cases showed that temperature decayed below the 

night setback temperature on most days. As expected, the brick cases take longer to decay than 

the nonbrick case. 

Figure 34. Simulated interior surface temperatures of exterior southwest wall 

Measured air temperatures from two apartments were plotted with the simulation results in 

Figure 35. Both apartments showed a significant variance and rarely dropped below the set point 

temperatures. As with surface temperature, we find that for the cases modeled, temperature 

behavior does not vary significantly. 

Figure 35. Simulated air temperatures in the conditioned zone and measured air temperatures in 

two apartments in the experimental building 

55

60

65

70

75

80

1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22

Surface Temperature (°F)

Test Building - brick + metal studs - SW

BEopt - brick + wood studs - SW

BEopt default - no brick + wood studs - SW

Thermostat Setpoints

60

65

70

75

80

1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22

Air Temperature (°F)

Test Building - brick + metal studs - SW Beopt - brick + wood studs - SW

BEopt default - no brick + wood studs - SW Apartment A

Apartment B
48 

Based on this analysis, it appears that the building construction and mild winter are not primarily 

responsible for units in which air temperature does not decay below the set point temperature. 

Instead, it appears that manual thermostat overrides are the most likely explanation for why some 

apartments’ temperatures rarely fell below the default set point temperatures.
DOE/GO-102012-3804 ▪ December 2012

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