A Persuasive GPS-Controlled Thermostat System
by
Manu Gupta
M.S., Interactive Systems Engineering
Royal Institute of Technology
Stockholm, Sweden
June 2006
B.E., Instrumentation and Control Engineering
Pune Institute of Computer Technology
University of Pune, India
June 2003
Submitted to the Program in Media Arts & Sciences, School of Archiitecture and
Planning, in partial fulfillment of the requirements for the deg ree of
Master of Science in Media Arts and Sciences
MASSACHUSETTS INSTITUTE
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September 2008
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Program in Me Arts and Sciences
August 19, 2008
C ertified by ..............................
Kent Larson
Principal Research Scientist
MIT Department of Architecture
Thesis Supervisor
.. ... ................
Deb Roy
in
Meia
AProf.
Program
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Chair, Program in Me la Arts and Sciences
Accepted by ................ ................
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ARCHIVES
A Persuasive GPS-Controlled Thermostat System
by
Manu Gupta
Submitted to the Program of Media Arts and Sciences, School of Architecture and Planning, on
August 19, 2008, in partial fulfillment of the requirement for the degree of Master of Science in
Media Arts and Sciences
Abstract
Using 10 weeks of data from a couple living in an instrumented home, we report on the potential
of context-aware power management for energy saving. We identify an opportunity to save on
heating and cooling using a system we propose for just-in-time heating and cooling based on
travel distance computation from GPS-enabled mobile phones. Analyzing GPS travel data from
eight participants (8-12 weeks each) and heating and cooling characteristics from 4 homes, we
report results of running computer simulations estimating potential energy savings of a system
that could augment existing manual and programmable thermostats. Although manual and
programmable thermostats can save large amounts of energy when used properly, studies have
shown that well over 40% of U.S. homes may not use energy-saving setbacks when homes are
unoccupied. We estimate that using a GPS-enabled thermostat might lead to savings of as much
as 7% for some households that do not regularly use the setback features afforded by manual and
setback thermostats, without requiring any change in behavior. For some end users, providing
one extra bit of information each day could lead to savings as high as 9%, and additional
persuasive strategies could save up to 15%. We then report on the implementation of a prototype
system and pilot testing of the user interface. We propose five design guidelines for a just-intime heating and cooling system.
Thesis Supervisor: Kent Larson
Title: Principal Research Scientist, MIT Department of Architecture
A Persuasive GPS-Controlled Thermostat System
by
Manu Gupta
The following individuals graciously served as readers for this thesis:
Thesis Reader:
Dr. Stephen Intille
Technology Director
House_n Consortium
MIT Department of Architecture
Thesis Reader:
Prof. William J. Mitchell
Professor of Architecture and Media Arts and Sciences
Director MIT Design Laboratory
MIT Media Laboratory
Thesis Reader:
Prof. Pattie Maes
Associate Professor of Media Technology
MIT Media Laboratory
Acknowledgements
This thesis would not have been possible without the contributions, support, and help of Kent
and Stephen who guided me patiently throughout my masters. I am grateful for their time,
patience, support, encouragement, wisdom and inspiration.
Special thanks to my lab mates Jennifer, Aydin and Fahd, I am indebted to you for the help,
encouragement, suggestions and valuable ideas that you have contributed during the
development of this work. Thanks to Selene, Emmanuel and Clay for helping me with their
expert knowledge input at different points of my thesis. Thanks to Pilar for volunteering her
house for experiments. Thanks to Hemant, who took out his precious time and drove me in and
around Boston to conduct my user studies. It would not have been possible for me to conduct my
user studies without his help. Thanks to Bob for his help in the last study of my thesis.
I would like to dedicate my thesis to my family: Mom, Dad, my brothers Suvrat and Sunny.
Thank you for all the love, encouragement, and support throughout my life and my education.
You have always supported my dreams, aspirations, and shown me that nothing is impossible if
you really wish it. I am especially grateful for your financial support during all those difficult
times.
Finally thanks to all my friends at MIT for such a wonderful time and helping me through all
those difficult times and for making my stay at MIT memorable.
Table of Contents
1
Introduction .....
2
Residential Context-Aware Power Management: The Potential........................................ 11
...................................................................................................
...............
9
2.1
Pilot Experiment: Overview...................................................................................................12
2.2
Data Analysis: Obtaining Occupancy Patterns .....................................................................
12
2.3
Data Analysis: Estimating Energy Wastage ..........................................................................
14
2.4
Best-Case Results .....................................................................................................
14
3
Thermostat Types
...................................................................................................
17
3.1
Thermostat Usage Behavior ........................................................................................................
18
3.2
Thermostat Essential Components ..........................................................................................
19
4
Opportunity............................................................................................................................
20
5
Measuring Potential Savings from a GPS-Controlled Thermostat..............................
22
5.1
Data Collection .... ..................................................................................................
.............
22
5.2
Evaluation ........
.............
23
5.3
Results with Common Travel Patterns for Daily Workers ........................................................ 25
5.4
Energy-Saving Persuasive Strategies ...................................................................................
5.5
Real Travel Time Data............................................................................................................29
.....................................................................................................
6
Measuring Potential Savings and Pilot Usability of a Persuasive GPS-Controlled
Thermostat: Cooling .....................................................................................................
28
36
6.1
System Design ...............................................................................................
36
6.2
Recruiting .......................................................................................................................
40
6.3
Experimental Setup ..........................................................................................................
40
6.4
Evaluation.........................................................................................................................
41
6.5
Energy Saving Estimations..................................................................................................41
6.6
Interface Design Observations ............................................................................................
42
7
Discussion and Future Work .........................................................................................
45
8
Conclusion ...............................................................................................................
48
Appendix A: PlaceLab Sensor Location Pictures ................................................................... 49
Appendix B: CAPM Detailed Results .....................................................................................
50
Appendix C: Simulation of GPS Thermostats for Various Scenarios ....................................
51
Appendix D: Simulation Interface Screenshot .......................................................................... 56
Appendix E: Thermostat Installation and Wiring Diagrams .....................................................
57
Appendix F: Laptop Interface Screenshots for Different Scenarios ............................................. 60
Appendix G: GPS Thermostat System ...................................................................................... 64
Appendix H: GPS Thermostat System Setup Instructions ........................................................ 65
Appendix I: Interview Questions for Heating Study ............................................................
67
Appendix J: Interview Questions for Cooling Study..........................................................
68
Appendix K: Consent Form for Collecting Data to Inform the Design of a.............................. 69
G PS Therm ostat System ............................................................
. ............................................ 69
Appendix L: Consent Form for Study to Evaluate a GPS Thermostat System ........................ 77
Appendix M: Flyer for Recruiting Participants for Heating Study............................................ 86
Appendix N: Flyer for Recruiting Participants for Cooling Study ............................. 87
Bibliography ......
......................................................
.... .... ................................................
88
Table of Figures
Figure 1. (a) Total power consumption distribution by category. (b) Overall category wastage
broken dow n by category.............................................................................................................. 15
Figure 2. (a) Heat gain curves at an outside temperature of 25 °F (-3.8 0 C) for a highly insulated
and poorly insulated house. (b) Heat loss curves at an outside temperature of 25 OF (-3.8 0 C) for a
highly insulated and poorly insulated house ...........................................................................
. 25
Figure 3. This figure shows a typical day with 90 min simulated travel time and simulated
results with different thermostat types. (a) Results with GPS thermostat. (b) Results with
programmable thermostat. (c) Results with manual thermostat. See the text for an explanation. 26
Figure 4. (a) Target and estimated indoor temperature for travel time of 26 min with a GPS
thermostat. (b) Target temperature for return at lower temperature strategy. (c) Target
temperature for return at a specified time strategy. (d) Target temperature for return at lower
temperature and at a specified time strategy ......................................................................... 29
Figure 5. National Travel Times - Travel Time Index (TTI) Chart by all hours of each day of the
w eek. C ourtesy IN R IX ................................................................. . ......................................... 30
Figure 6. One week of real travel time data of a participant and GPS thermostat simulation
resu lts ......... .........................
.........................................................................................................
30
Figure 7. Travel patterns for entire duration of the study (a) House#1 travel patterns of one
person. (b) House#2 travel times of male and female and the minimum of the travel times of
male and female. (c) House#3 travel times of male and female and the minimum of the travel
times of male and female. (d) House#4 travel times of male and female and the minimum of the
travel tim es of male and fem ale.............................................................................................
31
Figure 8. GPS thermostat simulation for a vacation period of participant of house #2............... 32
Figure 9. GPS-Controlled thermostat system: design of functional prototype used for pilot
testing ............. .........................................................
................................................... . 37
Figure 10. Motorola 9Qh with an intervention question displayed on the screen. ...................... 37
Figure 11. Intervention questions on the mobile phone. (a) Return temperature. (b) Return time.
(c) Maintain lower temperature. (d) Rewarding message..................................................... 39
Figure 12. Laptop interface screenshots. ..........................................................................
. 40
Figure 13. A week of travel pattern of the participant using the GPS thermostat prototype for 14
days. ......................................................
............................................................. 42
Figure 14. Screenshot of a status message on the laptop interface, which the participant found
confusing...............................
. ........
... ........................... ..............................................
44
Figure A 1. View from infrared and color cameras at PlaceLab .............................................. 49
Figure A 2. A view of PlaceLab and sensor locations ........................................................... 49
Figure B 1.Break down of power wasted out of total wastage by different categories ............... 50
Figure B 2.Projected usage and savings for each element in all the categories at PlaceLab....... 50
Figure C 1. Simulated travel time of 90 min with GPS thermostat ............................................ 51
Figure C 2.Temperature regulation by programmable thermostat ............................. 51
Figure C 3.Temperature regulation by manual thermostat .....................................
..............
52
Figure C 4. Simulated travel time of 26 min with GPS thermostat ........................
....
52
Figure C 5. Simulated travel time of 15 min with GPS thermostat............................................. 53
Figure C 6. A five days business trip to California .............................................................. 53
Figure C 7. Return at lower temperature ............................................................................. 54
Figure C 8. Specify a return tim e........................................................................................
54
Figure C 9. Specify a return time and return at lower temperature ...................................
. 55
Figure C 10. Various temperature regulation scenarios for PlaceLab....................
............... 55
Figure D 1. GPS thermostat regulating the target temperature based on the travel time. If there is
a cleaning service entering the house or the user arrives home to a warm house, they will see this
message ........... ........................................................................................................... .. 60
Figure D 2. If earlier during the day user decided to come back at a specified time and showed
up earlier, he will see this message .................. .................................................................. 60
Figure D 3. When home temperature is less than the target temperature system will display this
message ............ ............................................................................................................ . 6 1
Figure D 4. When home and target temperature are equal, system will display this message .... 61
Figure D 5. When user arrives to warmer house, system will display this message ................... 62
Figure D 6. When user arrives to warmer house with high humidity in the house and high
outside temperature, system will display this message ......................................................... 62
Figure D 7. Whenever user changes temperature settings the system will provide feedback..... 63
Figure G 1. Picture of GPS thermostat system .................................................................... 64
Table of Tables
Table 1. Thermostat usage statistics in the U.S (summarized from[4])...................................... 9
Table 2. Participants house details .......................................................................................
22
Table 3. Simulation results on savings of different thermostat for different (A) travel times with
PlaceLab as control house and (B) intervention strategies with travel time of 26 min. and
PlaceLab as control house............................................
................................................ 27
Table 4. Simulated energy and monetary savings for the entire duration of the study for all the
participating houses . ..................................................
................................................ . 33
Table 5. Simulated energy and money savings for general population using compliance rates for
m anual and program m able therm ostat.......................................................................................
34
Table 6. Results of augmenting manual and programmable thermostats with GPS thermostat.. 35
1
Introduction
With only 5% of the world's population, the U.S. uses 25% of the world's energy [1]. The U.S
residential sector is responsible for 21% of the total U.S energy consumption, and residential
heating and cooling accounts for 46% of the total energy consumed in the U.S residential
buildings which amounts to 9% of the total U.S energy consumption [2, 3]. Forty-nine percent
of the U.S homes are unoccupied during the day [4], 30% of the U.S homes have programmable
thermostats [4], and 56% have manual thermostats [4], but 53% of the U.S homes do not lower
their temperature during the daytime when no one is at home in winters [4]. In addition, 46% of
U.S homes do not set higher temperature settings during daytime when no one is at home in
summers [4].
A significant amount of energy is probably being wasted in heating and cooling unoccupied
environments, mostly because available thermostat solutions do not adapt to variable occupancy
schedules and people have difficulty setting and optimizing a programmable thermostat[5]. This
creates an opportunity for ubiquitous and mobile computing, which can be exploited to create
systems that can adapt to variable schedules and motivate people towards energy conservation.
Table 1 shows the estimated usage of programmable and manual thermostats in the U.S. As
many as 55 million households - some with manual and some with programmable thermostats may not change their temperature settings when there is no one in the house. The goal of this
research is to develop a system that can augment standard programmable thermostats so that if
they are not actively programmed, the thermostat will automatically change the temperature
(without any user interaction) when no one is in the house to save energy.
Despite these savings,
the house will always be at a comfortable temperature when the user returns.
Table 1. Thermostat usage statistics in the U.S (summarized from[4]).
Estimated no. of
Estimated no. of
Total homes in homes that use
homes not using
In millions
the U.S
setback when away
setback when away
Manual Thermostat
62.16
21.7
40.46
Programmable Thermostat
33.3
18.7
14.6
Total
95.46
40.4
In this thesis, we describe a concept for a just-in-time heating and cooling system that is
controlled using travel time information that could be gathered from GPS-enabled mobile
phones. Although existing thermostats can lead to large energy savings [6], we show, via a set of
simulations using real travel data and home heating and cooling characteristics, that the proposed
just-in-time system might augment existing thermostat systems, providing energy savings for the
substantial number of people who do not use manual or programmable thermostats optimally.
The system we propose does not require users to program the just-in-time heating system for
occupancy schedules, and no change in behavior on the part of the home occupants from what
they do now is required. Moreover, we estimate that as GPS-enabled phones become the norm,
the savings generated from the use of the system for many households might cover the cost of
the system hardware in as little as 24 -36 months of system use.
Our simulations also suggest that an additional 4% monthly savings might be achievable for
some households willing to occasionally answer one or more questions about their travel
behavior and thermal comfort level on their mobile phones. In addition, our pilot study provides
promising evidence that persuasive strategies, such as encouraging occupants to try slightly
altered comfort temperatures combined with just-in-time information about the effect of this
decision, could lead to additional energy savings of up to 15%. A fully-functional version of the
just-in-time heating system was built to gather further insight on the usability of such a system.
Based on qualitative feedback obtained from one subject and the investigative team after using
the system, five design guidelines are proposed.
We begin by describing a pilot experiment that we conducted to evaluate where ubiquitous
computing technology could impact residential energy conservation. This motivates a discussion
on recent work and opportunities in ubiquitous computing systems for promoting energy
conservation. Finally, we describe the concept of the just-in-time heating system and how we
estimated the savings such a system might afford and gathered information that could inform
design decisions in future versions of the system.
2
Residential Context-Aware Power Management: The Potential
Harris et al. [7] identified an opportunity: using ubiquitous computing technology to conserve
energy. They write,
"Context information (e.g., user location) likely to be available in pervasive computing
environments could enable highly effective device power management. The objective of such
context-aware power management (CAPM) is to minimize the overall electricity consumption
of a building while maintaining acceptable user-perceived device performance".
Harris et al. proposed a CAPM framework to predict user behavior patterns from multi modal
sensor data for effective computer power management. They used sensors ranging from idle time
based on keyboard/mouse input to user presence based on proximity to a Bluetooth beacon [8].
They computed the "optimal" electricity usage assuming users shut down their PC when out of
the office or put it in standby when they are not using it for periods greater than break-even time
of the computer. They calculated the energy consumed by the sensor infrastructure and
calculated the potential savings for heavy computer users and light computer users. They
concluded that it is possible to get within 6% of the "optimal" policy, but the policy performance
is very dependent on user behavior. They expressed a need for sensors that predict what someone
is about to do in addition to what the person may currently be doing.
Others have tried to optimize HVAC systems using sensors as well [9, 10] and developed
algorithms to predict a user's behavior patterns, but these projects only considered one aspect of
home consumption.
We start by asking this question: Assuming that there is a ubiquitous computing system in place
that can determine or predict behavior and that consumes negligible energy, what is the
maximum energy savings that an automation system might be able to achieve overall for the
home? We designed a pilot study using data of home behavior as recorded in an instrumented
apartment to evaluate in what areas of home energy consumption ubiquitous computing systems
might be able to make the greatest impact.
2.1 Pilot Experiment: Overview
Data on home energy use and occupant behavior were obtained from an experiment where a
married couple was recruited to live in a highly instrumented 93 sq. m one-bedroom
condominium, the PlaceLab[11, 12], for 10 weeks from August to October 2006. The
participants, ages 31 and 29 and without advanced knowledge of computer science or sensor
technology, responded to an ad on an electronic mailing list for teachers announcing that subjects
were needed to study how to make technology easier to use in the home. They both worked full
time. Energy or resource conservation was not mentioned as a topic of interest. The participants
moved into a new environment but were encouraged to maintain as normal a routine as possible.
They went to work, had visitors, cooked meals, slept, attended to other personal needs, and
worked on projects and engaged in leisure activities according to their own preferences. There
researchers have no reasons to believe that the electrical power consumption behavior of the
participants is atypical or that they would have been particularly concerned about it while living
in the instrumented home.
Data were collected from 37 electrical current sensors indicating when electricity consuming
devices were switched on and off, for how long they were used, and whether they were in
standby mode. Data from twenty object usage sensors[13] and a comprehensive audio-visual
record of all behavior in the home were also used by researchers to infer whether the device was
actively used or not.
2.2 Data Analysis: Obtaining Occupancy Patterns
To evaluate just how much energy may be wasted, it is necessary to know whether the occupants
were at home, what room they were in, and the device usage pattern to infer if they were actively
using appliances, lights, and HVAC devices.
The instrumented home does not directly sense the room location of each person, although it
does collect data on a person's interaction with objects and systems and stores a complete audiovisual record of activity in most spaces. To manually annotate the full 71 days of data would
have taken months of painstaking work at a standard annotation ratio of 2-3 hours of annotation
for every one hour of real time. Therefore, we used sensor data from the instrumented lab to
estimate home and room occupancy automatically. Over 100 sensors of different types (object
usage, water flow, RFID, motion, electrical current and switch) were distributed throughout the
instrumented home. Nearly any movement or activity by the participants tended to activate one
or more of these sensors, which could be assigned a fixed location. Based on the number of
sensors triggered at a location, the location is assigned a weight between 0 (no sensor activated)
and 1 (all the sensors activated). Every 8 seconds the algorithm outputs the location of the
participants in the house. If there are two participants in the house and they are at two different
locations, those locations will have some weight depending on the number of sensors triggered;
the other locations will not have any weight. Until there are new weights for different locations,
the participants are considered to be at the same location. The algorithm may get confused if
within the 8 seconds interval the participants make several transitions to different locations or
object usage and switch sensors falsely trigger.
To validate the algorithm, it was run on three randomly-selected days of activity that were
observed and annotated in their entirety using the recorded video. From this, occupant location
and energy wastage was determined based on careful annotation of activity. The locations
predicted by the algorithm were compared to the locations observed in the three days of
annotated video. The algorithm prediction is considered correct only if the locations of both the
participants match with the locations observed in the video. The algorithm's in-house movement
prediction accuracy over these days was 91%, and prediction whether the house was occupied
was 100%. The daily mean wastage predicted by the location-detection algorithm on the three
manually annotated days was 9.2KWh, which is 800 watts more than daily mean wastage of
8.4KWh calculated manually on the same three days.
Using the location-prediction algorithm, occupancy data were gathered on 65 days. Six days of
data were removed from 71 days of data because they were corrupted due to sensor network
failure or power failure in the instrumented home.
2.3 Data Analysis: Estimating Energy Wastage
Given home and room occupancy data, we consider the following situations to be wasting
energy: (1) Lights - on when a space is unoccupied, (2) Computer - on and inactive (i.e.,
consuming only standby power) when no one is in the room, (3) Appliances other than computer
and refrigerator - on when a space is unoccupied or not actively used (i.e., in standby mode)
when the space is occupied, (4) HVAC - running with nobody home. (5) Oven, microwave,
washer, dryer and dishwasher - in standby mode, irrespective of space occupancy. The
Refrigerator and the hot water heater were never considered as wasting energy.
There are situations where each of these assumptions about energy wastage is invalid. For
example, the couple could have left a bathroom light on to provide night light or they may not
have wanted to turn off the HVAC to avoid returning to a somewhat uncomfortable temperature.
It is possible that someone started the blender and left the kitchen for a while, or that someone
turned on the TV and was listening to it across the home despite being unable to see it.
Nonetheless, we believe most of the situations described b y the rules above are times when
energy is generally being wasted.
2.4 Best-Case Results
The question then becomes this: assuming we had ubiquitous computing technologies that could
detect occupancy and automatically shut down and turn on devices properly, and that the energy
consumed by such a control system was minimall, what is the best-case energy savings we
might expect for the participant couple over the ten week period? Although we could never
achieve these "optimal" results, we can get some sense of how energy is being wasted and where
the opportunities for new technologies may be.
1 We realize this is a design challenge in itself. We are aware of three prior papers that explicitly address this cost in their work
[7,9,10]. Here we assumed the cost is minimal. We also assume that devices such as clocks could be designed to cleverly and
properly save and recover state quickly.
For the results presented below, appliances and devices in the home are categorized as follows:
lights (in all the nine residential spaces), major appliances (dishwasher, microwave, washer,
dryer and stove), minor appliances (toaster, bottle opener, garbage disposer, answer machine,
laptop charger), entertainment system (TV, cable box, home theatre, DVD player), study/office
appliances (computer, printer, fax, boom box) and HVAC system.
Total power consumption for the 65 day study was 1326 KWh with a daily mean of 20.4 KWh
and standard deviation of 6.4 KWh. Figure 1(a) shows overall consumption by category. The
average monthly consumption over the 65 days was 530 KWh, which is below the average
monthly electricity consumption in Massachusetts of 626 KWh in 2006 [2]. This was most likely
due to the fact that all the appliances in the instrumented home are new Energy Star devices.
Using the heuristics listed in Section 2.3, the total estimated wasted energy was 597 KWh, with a
daily mean of 9.2KWh and standard deviation of 1.4KWh. Figure 1(b) shows the overall
electricity wastage of 45% broken down by category. This is in line with what Meier et al. [14]
and Williams et al. [15] found in their studies.
Electricity Bill Distribution
Consumption
Vs
Figure 1. (a) Total power consumption distribution by category. (b) Overall category
wastage broken down by category
The data were gathered at a mild time of year with average outdoor daily high/low temperatures
ranging from 98oF (36oC)/78oF (25oC) at the start of the study to 64oF (17oC)/54.5oF (12oC) at
the end, and a mean high/low temperature of 88F (31 0 C) /53'F (11C). Nonetheless, HVAC
contributed 42% of the potential electricity waste. This number is likely to have been larger at
other times of the year. Although ubiquitous computing could be used to reduce the extensive
waste we observed from lighting use and vampire power consumption, the pilot study strongly
suggests that HVAC optimization offers the greatest potential for savings, especially if those
savings could be achieved without an elaborate energy-consuming sensor infrastructure.
3
Thermostat Types
A thermostat is a device for regulating the temperature of a house so that it is maintained at a
desired comfort level. Apart from maintaining a comfortable temperature, thermostats can also
help to save energy and money by allowing home occupants to switch the HVAC system off
when no one is in the house. There are three types of thermostats in common use; manual,
programmable, and "intelligent" thermostats [16].
Manual thermostats simply allow manual temperature control. To set the temperature differently
when away from the home, the home occupant must remember to change the thermostat upon
leaving and change it back upon return. The home may be uncomfortable when the home
occupant returns.
With programmable thermostats, one can program a daily working schedule and different
temperature preferences for different periods of a day (and different days of the week) and the
thermostat will automatically regulate the temperature according to the scheduled program [17,
18]. If schedules change, the user must remember to re-program the system. Programmable
thermostats do not adapt to variable occupancy schedules.
So-called intelligent thermostats have "adaptive recovery control" that learns the house's
temperature profile and starts the system early enough to reach the setpoint at the start of
program period. These thermostats may also learn the temperature preferences of the user for
different contexts [19]. Some of them also use occupancy sensors to infer occupancy patterns
[20]; others use light levels to change the temperature settings in the house [21]. Some even
control the air velocity and direction to attain a user-preferred thermal comfort [22] "The
intelligent thermostat", [19], "NIGHTVENT"[23] and light level controlled thermostat [21] are
research systems and have not been extensively tested at time of this writing. Thermostats that
use occupancy sensors are suitable for commercial properties like hotels or office buildings
where building-wide sensor infrastructures can be installed.
The information provided by different thermostats varies significantly. In manual thermostats,
occupants can only see the current temperature and target temperature settings. Many
programmable thermostats provide current temperature settings, current room temperature, and
mode of the system (i.e. heating or cooling). The "Intelligent Thermostat" [19] provides
suggestions in collaborative dialog with the user to manage temperature settings while reducing
energy savings. "NIGHTVENT" [23] has advanced thermostat controls, for example, where the
user can select the desired minimum temperature to produce desired maximum afternoon thermal
comfort; feedback on the system status is provided whenever the user changes temperature
settings.
The "Intelligent Thermostat" [19] and "NIGHTVENT" systems [23] also have some persuasive
elements. For example, they inform users about the minimum temperature settings that can
produce the desired maximum thermal comfort.
3.1 Thermostat Usage Behavior
According to Energy Information Administration data, although 86% of the U.S households have
either manual or programmable thermostats [4], more than 50% [4] of those households do not
set lower temperature settings during the day when no one is at home in the winter. Similarly,
46% [4] of the U.S homes with air conditioner do not set higher temperature settings during
daytime when no one is at home in the summer. A common way of using a thermostat is like an
on/off switch rather than a temperature controller; this method of operation is estimated to be
used by 25% to 50% of the U.S households [24]. Another common misconception is that the
thermostat operates as an automobile gas pedal, where, the more you press the pedal, the faster
the automobile will go [25, 26]. But in HVAC systems, setting extreme temperatures does not
speed up heating or cooling of the house. Instead, more energy is consumed as the temperature
oscillates above or below comfort levels.
The programmable thermostats have compliance rates of only 56% [4], the primary reason being
poorly designed interfaces that many people do not know how to use successfully [5, 27]. Also,
it has been shown that programmable thermostats do not save as much energy as predicted [5,
28, 29]. We have not found any study evaluating the usage behavior of intelligent thermostats,
probably because they are relatively new in the market and can be seen as an extension of
programmable thermostats.
Each of these thermostats provides a promising solution for temperature control in homes, but
surveys suggest that they are often not used consistently.
3.2 Thermostat Essential Components
The purpose of a conventional thermostat is to regulate temperature. We believe that an ideal
thermostat should also provide relevant information about what the system is doing to reach the
setpoint. In addition, it should provide feedback to the users whenever they change the
temperature settings about how the system is going to react to a change, and whether this
decision will save them energy and money without sacrificing their comfort. We propose a
thermostat that integrates the following components:
1. Automation: If a home occupant is not using manual or programmed setback, the system
should automatically regulate the house temperature as a function of the travel time of the
nearest occupant so that the comfort temperature is reached as the occupant arrives home without requiring occupant programming.
2. Information: The system should provide information to the user about the accumulated
savings, the current house temperature, and how the system is saving energy or maintaining
comfort temperature. If the user changes temperature settings, the system should provide
feedback on how this decision is going to affect his or her savings.
3. Persuasion: The system should use strategies to persuade the user to save energy (and
money), such as suggesting a more energy-efficient comfort temperature, and presenting the
positive impact of that decision. It should motivate the user towards gradual behavior change
to save energy and money.
4. Control: The system should provide convenient control for the user to change settings or to
override automation control.
4
Opportunity
We describe a new type of thermostat designed to substantially reduce the HVAC wastage that
we saw in our pilot experiment while at the same time addressing issues of usability, function,
and comfort that were discussed in the previous section. The key idea is to control heating and
cooling using travel time, as determined automatically via GPS-enabled mobile phones that will
become commonplace in the near future. Such a just-in-time thermostat system communicates
with the GPS-enabled mobile phones of the residents2 via an Internet connection. Based on the
location of the resident as determined by the resident's mobile phone and free geo-location
mapping services, travel time to home is continuously estimated. The travel time is adjusted
using time of day to account for rush hour traffic. The thermostat uses travel time of the home
occupants, inside and outside temperature, and heating/cooling characteristics of the home to
dynamically control the thermostat so that energy savings are maximized without sacrificing
comfort. By setting temperature as a function of the fastest possible return time of the closest
resident (and the other factors mentioned above), the system ensures that the home will always
be comfortable when the residents return.
The system provides the following advantages over the existing thermostats: (1) it requires no
thermostat programming from the user, (2) it adapts to irregular schedules, (3) the user always
returns to a comfortable house, and (4) it creates opportunities for motivating additional savings
using context-sensitive prompting and persuasive strategies. That said, the idea is to layer the
GPS thermostat capability on top of existing thermostats so that the GPS system is used when
users have demonstrated that they are not using manual or programmable thermostat setbacks.
Further, if we assume that (1) mobile phone adoption trends continue [30] so that everyone in a
family will have a phone, (2) that nearly all new phones will be GPS capable within a few years
[31], and (3) that most homes will have Internet access [32], the only new hardware required to
create the system is a thermostat with an Internet connection and programmable LCD display.
2 In this work we use "GPS," but future phones may also use other methods, such as cell tower
triangulation and proximity to
wireless networks or beacons to determine position.
We believe that if commercially developed, the system could consume little additional power
and
cost
only
slightly
more
than
a
conventional
programmable
thermostat.
5 Measuring Potential Savings from a GPS-Controlled Thermostat
In this section we estimate the potential energy savings that might be achieved using the just-intime GSP-controlled thermostat system relative to other thermostats in use today, using actual
travel patterns and heating data from real homes.
5.1 Data Collection
In response to mailing lists, flyers, internet advertisement and word of mouth, we recruited eight
people living in four different homes in the Greater Boston Metropolitan Area. Each person
worked outside of his or her home and had a separate car that was used as the main mode of
transport. Each house had an accessible heating system that was not shared with any neighboring
residences. Table 2Error! Not a valid bookmark self-reference. provides additional detail on
the participating homes. The study was approved by our human subjects review board.
Table 2. Participants house details
House 1
House 2
House 3
House 4
Type of fuel
Natural gas
Natural gas
Oil ($4.5 per
Oil ($4.5 per
used in the
($1 per therm) ($1 per therm) gallon)
gallon)
Forced air
heating
system
(cost$)
Type of
Hot water
Hot water
Hot water
radiator
radiator
radiator
100,000
130,000
132,880
153,861
Btu/hour
Btu/hour
Btu/hour
Btu/hour
Medium
Medium
High
heating
system
Heating
capacity of
the system
Insulation of Low
house
Type of
Programmable Programmable Programmable Programmable
thermostat
Average
35 min-50 min 10 min-15 min 7 min-10 min
20 min-35 min
travel time/
travel
pattern
Days of data
gathered
Number of
75
75
63
63
1
2
2
2
2
4
2
2
cars
Number of
members in
the house
Number of
0/no
2/no
0/no
0/no
kids/ stay
home during
the day
To gather realistic data on travel patterns, between March and June, 2008 a TrackStick Pro GPS
logger [33] was installed on the dashboard of each vehicle of each member of each house. The
logger was plugged into the cigarette lighter socket. These loggers were left for up to three
months, recording position of the vehicle when operating. The sampling rate of the logger was
one minute. Though the logger can store data for several months, data was downloaded every
four weeks during the study.
To measure the heating and cooling properties of the homes, two temperature and humidity
loggers (EL-USB-2 and EL-USB-1) [34] and Logit LCV electrical current loggers [35] were
installed in each house for three days of measurement. One temperature and humidity logger
was placed near the thermostat and another outside of the house on the north facing wall or
window, where the sensor was not in contact with direct sunlight. The current logger was
attached to the current-carrying wire from the thermostat to the HVAC system to gather
information about when the heating system was activated. During the three days that the sensors
were installed, each home had its programmable thermostat reprogrammed by the investigators
as follows: set to 50F (10'C) from 9 AM to 4:30 PM and set to a comfortable temperature,
typically between 67-69oF (19.4-20.5oC), all other times.
In addition to the participant homes, the instrumented home (PlaceLab) was used as a baseline or
control house at a time when no one was living there in December, 2007.
It is a newly
constructed and well insulated (R-25) environment. A typical day, where a person leaves at 7AM
for work and remains in the office from 9 AM to 6 PM and arrives home at 7 PM, was scheduled
on the programmable thermostat for three weeks, with a comfort temperature of 69oF (20.5oC)
and a setback temperature of 45oF (7.2oC); these are Energy Star recommended settings [36].
5.2 Evaluation
Software was written to simulate the functionality of three different types of thermostats
(manual, automatic, and GPS-controlled) using the data collected under typical conditions.
The simulator, which uses the same algorithm later described when discussing a real-time, fullyfunctional prototype in Section 6, requires the following for input at each point throughout the
day, at 1 minute intervals: (1) indoor temperature, (2) outdoor temperature, (3) latitude/longitude
coordinate for each occupant's phone (if available), and (4) heating/cooling tables for the home.
Additional information can be provided to the simulator when modeling various conditions (e.g.,
minimum allowable temperature in the home, time schedule and temperature setting for
programmable and manual thermostat, and type of heating system).
Heating/cooling tables were created for each house using the temperature profile data collected
over three days. A heating table was created for heat gain (i.e. the time it takes for the house to
heat up by one degree given an outdoor temperature with the heating system running at full
capacity) and a cooling table was created for heat loss (i.e. the time it takes for the house to lose
one degree given an outdoor temperature with the heating system off). The 3 days of data cannot
cover the entire range of outdoor temperature for winters (OoF (-17.7oC) to 60oF (15.5 oC)).
Therefore, we made an approximation based on the energy transfer equation for a building,
which states that the amount of energy that gets into a building is proportional to the differential
of indoor and outdoor temperature [37].
Qin = U - (Toutside - Tinside)
(1)
In equation (1), Qin is the amount of energy the heating system has to add to the house per hour
per square foot and U is the constant of insulation and is a function of the construction material
of the house. Therefore as the outdoor temperature decreases the system has to pump in more
heat to maintain the indoor temperature. Similarly, as the outdoor temperature decreases it will
take more time to raise the indoor temperature by 1oF. Although other factors like wind speed,
sunshine, and house construction also impact heating/cooling, we have assumed that the house
acts as an isolated system, and we considered only outside and indoor temperature for the
purpose of creating the heating/cooling tables. For example Figure 2 (a) and (b) shows the heat
gain and heat loss curves for a highly insulated and poorly insulated house at an outside
temperature of 250 F (-3.8 0 C). The highly insulated house heats up quickly whereas a poorly
insulated house takes a long time to heat up. Conversely, a highly insulated house loses heat
slowly as compared to a poorly insulated house.
so5
' .........
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0
3o
t. .
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m
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atd.
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........................................................
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1
2
3
4
5
6
7
8
TemperaturedegreehaW
9
10
.
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....
.
11
12
1
2
3
4
5
6
7
3
9
Temrperature
degree danle
.
10
11
12
Figure 2. (a) Heat gain curves at an outside temperature of 25 F (-3.8 0 C) for a highly insulated and poorly insulated house. (b)
Heat loss curves at an outside temperature of 25 F (-3.8°C) for a highly insulated and poorly insulated house.
At each point in time throughout the day when a longitude and latitude coordinate is available,
the simulation software sends the coordinates and the occupant's home coordinate to the
MapQuest web service [38] and obtains estimated travel time and distance from the phone
location to the home of the participant. The MapQuest web service [38] uses a proprietary
algorithm for calculating travel time and distance. The travel time computation appears to use
road type, speed limit, and distance but not local traffic conditions. An outdoor temperature file
was created for the greater Boston area for the entire duration of the study by accessing online
weather archive databases provided by National Oceanic and Atmospheric Administration [39]
and Weather Underground [40].
At every minute the simulator outputs the target temperature based on the travel time and
distance of the participant's phone from his or her house, estimated indoor temperature, and
HVAC on/off cycle duration. It also outputs the simulated indoor temperature and the HVAC
cycle state (on/off).
5.3 Results with Common Travel Patterns for Daily Workers
First we discuss simulator results using the instrumented home (PlaceLab) and simulated travel
patterns where people commute every weekday with average commute times of 15 min., 26 min.
(Boston's average commute time) [32], and 90 min. We assume the comfort temperature of the
home is set to 69oF (20.5oC), the recommended settings by Energy Star [36] . We compare four
scenarios. The first is the baseline, where the thermostat is set to the comfort temperature at all
times of day. As indicated in Section 1, many people [4] with both programmable and manual
thermostats do not use setbacks at all. The second is manual setback, where the temperature is
lowered manually upon leaving the house and raised manually upon returning. The third is
programmable setback, where schedules are programmedfor lowering and raising the setpoint
each day based on standard work patterns. In programmable mode, we assume that the system
starts heating 30 min. before the return time and maintains a target temperature (setback) of 60oF
(15.5oC) during the day when the home is unoccupied. Finally, the last case is the GPS-
thermostat, where the target temperature is set as a function of travel time. All of the savings
reported are calculated with respect to the baseline condition.
S/Baseline
HVAC
ON/OFF
Cycle
Temperature
Target
2
co
IndoorTemperature
Estimated
=(
Cycle
GPS
G
Thermostat
HVACON/OFF
o.....
.....
...................
. E,
90mln
Traveltlme
) /
OutdoorTemperatore
S......
..
e
ome
ReturnHom
30 min difference in starting of
Sprogrammable and manual
. .
..
rot S
~
*0
thermostat
(a)
Figure 3. This figure shows a typical day with 90 min simulated travel time and simulated
results with different thermostat types. (a) Results with GPS thermostat. (b) Results with
programmable thermostat. (c) Results with manual thermostat. See the text for an
explanation.
Figure 3(a) shows the simulation results for a 24 hour period assuming a long travel time to and
from work of 90 min. each way. The line labeled A shows when the simulation software
estimates that the heater will cycle on and off, with up being an on-cycle. The line labeled B is
the baseline temperature, showing how the target temperature will oscillate around 69 0F
(20.50 C), assuming the thermostat triggers when the temperature drops 0.3 OF (0.17 oC) below the
target (at 68.7F (20.4 0 C)) and runs until the temperature exceeds the target by 0.5 OF (0.280 C)
above the target (or 69.5 0F (20.8 0 C)). Line C shows the 90 minute travel time, leaving at 7AM
and returning at 7PM, assuming a standard eight hour day at work. Line D shows the outdoor
temperature fluctuation, which does influence cycle times (a small change can be seen in line D
from morning to mid-day in cycle length). Line E is the target temperature determined based on
travel distance. This value is determined using the estimated heating/cooling parameters of the
home at various indoor and outdoor temperatures. It drops as the travel time increases and
plateaus at the lowest temperature that will allow the home to heat back up in time to achieve the
comfort temperature given the travel time. In Figure 3(b), line F shows the target temperature for
the programmable thermostat simulation. In Figure 3(c), line G shows the target temperature for
the manual thermostat simulation. Figure 4(a) shows the results of running the simulation for 24
hours with a shorter travel time of 26 min with the GPS thermostat.
Maximal savings can be achieved by simply turning off a heating or cooling system whenever
someone leaves home, but this sacrifices comfort. Good savings can be achieved if
programmable thermostats are used properly and users have regular schedules, but this may also
not be the case. Therefore, we present results in terms of "expected energy savings" and
"expected monetary savings". Expected savings is equal to the maximum possible savings
multiplied by expected compliance. The compliance of a manual thermostat is defined as
whether the user reduces the temperature settings in winter while leaving the house. The Energy
Information Administration's (EIA) survey has shown that the compliance for manual
thermostats is 35% [4]. The compliance for a programmable thermostat is defined as whether the
user programs the thermostat according to his or her home occupancy schedule. EIA's survey
has shown that the programmable thermostats have a compliance of 56% [4] . The GPS
thermostat, however, does not require any programming and it automatically changes the
temperature according to the travel time. We assume that 10% of the time the GPS thermostat
user may forget to take the phone, lack a GPS connection, be out of mobile phone coverage, or
discharge the phone battery, and therefore estimate the GPS thermostat to have a relatively high
compliance of 90%.
Table 3 part A shows the simulation results for three different thermostats for different travel
times. The savings using the GPS thermostat increases as the travel time increases, whereas the
savings from programmable and manual thermostat are constant and independent of travel time.
The savings from the GPS thermostat begin to exceed the savings from the programmable and
manual thermostat when travel time reaches 60+ min.
Table 3. Simulation results on savings of different thermostat for different (A) travel times with PlaceLab as control house and
(B) intervention strategies with travel time of 26 min. and PlaceLab as control house
B
~
wr
Daycompilane
4C
GPS+Retu...
Tr.
GPS+Return
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Tlme
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GPSReturnTime
GPS+Return
atLowrTempersture
6
A
67
4C
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67
67
6e
%DaycomplianceSavings (9%)Savings(S)
ManualThermostat
35
8.6
0.8
ProgrammableThermostat
56.2
12.2
1.1
GPSThermostatTraveltime 15
96
4.5
0.3
Traveltme 26
0.95
9C
Traveltime 45
10.6
0.8
96
Traveltime 60
96
12.4
0.9
Traveltime 90
90
15.3
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675
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5.4 Energy-Saving Persuasive Strategies
One of the benefits of the GPS thermostat concept is that unlike existing technologies, it adapts
to changes in behavior without requiring any behavior change on the part of the end user. For the
over 50%[4] of households not performing manual setbacks or using programmable thermostats
properly, the GPS thermostat provides a low-burden energy-saving option. However, additional
savings may be possible if the user provides occasional just-in-time feedback to the system.
Using the GPS data, it may be possible to create a system that benefits from modest user
feedback without requiring the user to proactively remember to change the thermostat or predict
schedules far in advance.
To estimate the savings of using just-in-time intervention strategies, three additional (winter
time) scenarios were simulated:
1. Return at lower temperature: A user agrees to return at a temperature slightly lower than his
or her comfort temperature. On the user's return, the home will be at a lower temperature, but
the house will continue to heat up until it reaches the comfort temperature (see Figure 4(b)).
In this scenario the system will have a lower target temperature and more energy savings
during the day as compared to the temperature set automatically by the GPS thermostat.
2. Specify a time to return home: If the user decides to return back at a specific time, the GPS
thermostat will ignore the GPS data and operate like an intelligent thermostat that calculates
the lowest possible target temperature (setback) and heating start time so that when the user
returns, the house is at comfort temperature (see Figure 4(c)). This will result in an even
lower target temperature and more energy savings during the day.
3.
Return at lower temperature and specify a return time: If the user agrees to return back at a
lower temperature and also specifies a return time, maximal savings are achieved (see Figure
4(d)).
A properly-programmed manual thermostat may achieve similar savings, but due to the burden
of programming schedules (and remembering to change them in advance), they are often not
used. Occasional questions presented on a mobile phone may be perceived as a less burdensome
way to gather the same information.
urat mp~ratatsThermosEat
Stmted
ttid Indoorrmmporatr
Indoor
Tamper
t
(a)
I.~. .........
. ..
...
(b)
re TartemperatureReaningat6pm
(c)
..........
(d)
Figure 4. (a) Target and estimated indoor temperature for travel time of 26 min with a GPS
thermostat. (b) Target temperature for return at lower temperature strategy. (c) Target
temperature for return at a specified time strategy. (d) Target temperature for return at lower
temperature and at a specified time strategy.
To evaluate potential savings from a small amount of user input, in the simulations we report
expected savings results for two cases: 40% or 60% compliance for the intervention questions
that are asked on the mobile phone as soon as the user leaves his home, as detected by the system
monitoring position. This means that the user answers questions on only 2 (40%) or 3 (60%)
workdays in a 5 day work week.
Table 3 part B shows the simulated results for the scenarios described above with a travel time
each way of 26 minutes. Using the intervention strategies like coming home to a 30 F lower house
or specifying a return time can outperform the savings from programmable and manual
thermostats. The maximum savings are achieved if the user agrees to come back at a lower
temperature and also specifies a return time, which is similar to the case of using manual setback
5.5 Real Travel Time Data
The simulator took the GPS data downloaded from each house and used MapQuest's webservice to calculate the drive time and distance between two the GPS coordinates. Since each
house had two participants, the simulator always takes the minimum drive/travel time of the two.
The drive time given by the MapQuest server does not take into account the traffic congestions
and delays in commute time during the different hours of the day. Therefore we created a travel
time index3 (TTI) [41] lookup table for Boston using the national travel time index that is
available on INRIX [41] . The national TTI is 1.133. Boston's TTI is 1.18. INRIX only provides
national TTI chart for all the hours of each day of a week. One has to pay a license fee to get
similar charts for a particular city. Therefore to approximate the TTI chart for all the hours of
each of a week for Boston, we used the difference between national TTI and Boston's TTI. We
applied this factor to the national TTI chart to create a TTI chart for Boston.
National Travel Time Index
by Hour/Day
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Figure 5. National Travel Times - Travel Time Index (TTI) Chart by all hours of each day of the week. Courtesy INRIX.
Figure 6.
shows the travel data and GPS thermostat simulation for a typical week of one
participant. The travel pattern of the participant is regular throughout the week. He took a small
trip during a weekend. The dip highlighted in the figure results from the delay in travel time due
to congestion during rush hours.
B
TargetTemierature GPS
Estimated
IndoorTempeortu
.eoinTemp
atureC cle
Outdor T~m: atur
GPSTh4nostatHVACN
.
FCychI
e
tme
Trave
Dipin
travlimeduring
noncongostlnhours
Figure 6. One week of real travel time data of a participant and GPS thermostat simulation results
3
Travel Time Index (TTI) is the ratio of actual to uncongested travel time. A ratio of 1.10 means 10% additional trip time due
to
congestion.
House 2.TraveltimePm
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2
4
6
8
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6
8
10
12
Weeks
(d)
Figure 7. Travel patterns for entire duration of the study (a) House#1 travel patterns of one person. (b)
House#2 travel times of male and female and the minimum of the travel times of male and female. (c)
House#3 travel times of male and female and the minimum of the travel times of male and female. (d)
House#4 travel times of male and female and the minimum of the travel times of male and female.
Figure 7 (a) shows the travelling patterns of the participants in House#l; both members of the
household used one car and traveled together. Figure 7 (b) (c) and (d) shows Male (Pm)
travelling time, female (Pf) travelling time and the minimum of the travel time of male and
female participants for each house (Min(Pm,Pf)). The simulation algorithm takes the minimum
of the travel time of both the participants because we made an assumption that the person with
the shortest travel time will arrive home earlier. The results in Table 4 are governed by the
minimum of the travel times. For example, Figure 7(c) clearly shows that for that home the
female participant's average travel time of seven minutes puts an upper bound on savings. The
figure areas that are marked with "V" in Figure 7 are the durations where participants went for a
vacation. If only one person in a house went for the vacation, the travelling time of the person
staying at the home is considered. Travel time during vacations was manually entered into the
dataset in cases where participants left their cars at home. For example, participants in house#2
went to London which is 8 hours by air plane. Therefore we manually entered 8 hours of travel
time for the duration of the vacation (see Figure 8 ).
:
Baselie
HVAC
O /OFFC e
ravelime
75
vtation
Wr"~'mi
iaseline temperature
g4S
spo a
1arget
tempwature
f
panp
M/ay
P'
P
Figure 8. GPS thermostat simulation for a vacation period of participant of house #2
Table 4 shows the simulation results for the entire study duration for all the four participating
houses for programmable and GPS thermostats. The house details are given in In response to
mailing lists, flyers, internet advertisement and word of mouth, we recruited eight people living
in four different homes in the Greater Boston Metropolitan Area. Each person worked outside of
his or her home and had a separate car that was used as the main mode of transport. Each house
had an accessible heating system that was not shared with any neighboring residences. Table
2Error! Not a valid bookmark self-reference. provides additional detail on the participating
homes. The study was approved by our human subjects review board.
Table 2. The baseline for calculating the energy and monetary savings is the cost of maintaining
the comfort temperature throughout the study using no setbacks. In the simulation the
programmable thermostat for all the participants is scheduled from 7AM to 6:30PM. This was
selected because all of the four participants leave and arrive home at about this time. The target
temperature (setback) for the programmable thermostat was set to 600 F. The additional savings
from the "return at lower temperature" scenario are also simulated with the temperature setting
of 2 degrees less than the comfort temperature of the participant. The compliance of a GPS
thermostat system may reasonably be assumed to be more than 90% because the system requires
almost no user interaction other than keeping a mobile phone charged.
Table 5 shows the savings that can be expected for the general population. We have used the
U.S national estimated compliance rates of 56.2% [4] for a programmable thermostat and 35%
[4] for a manual thermostat to calculate expected savings for each type of thermostat assuming
our four households are somewhat representative of dual commuter households4. Given expected
patterns of usage, we wanted to determine how a system like the GPS thermostat would be
expected to perform relative to the existing systems.
In the case of the programmable thermostat, the participant may or may not return to a
comfortable home. But in case of the GPS thermostat, the occupant will always return to a
comfortable home. The energy savings from a GPS thermostat depends upon the travel time and
the insulation of house; therefore, it varies significantly among the participants. The monetary
savings not only depend on travel time but also on the type of the fuel and the capacity of the
heating system. By using just-in-time questioning like asking the participant if he or she is
willing to return home at 2 degrees cooler house could increase the energy and monetary savings.
Table 4. Simulated energy and monetary savings for the entire duration of the study for all the participating houses.
House#1 - 75 days
Comfort Temp = 67
Lower Temp =65
Programmable Thermostat
GPS Thermostat
GPS+Return at Lower Tamp
House#2 - 75 days
Comfort Temp 69
Lower Temp = 67
House#3 -60 days
Comfort Temp =69
Lower TSmp = 67
House#4 - 60 days
Comfort Temp = 69
Lower Tamp =67
Savings (%) Savings($) Savingls (%) Savlnlgs($) Savings (%) Savings($) Savings(%) Svings($
19.4%
18.0
14.1%
102.8
17.1%
217.8
7.6%
137.3
2.9%
25.5
7.1%
49.7
0.3%
7.5
0.8%
15.5
3.5%
63..:
7.0%r
61.7
9.0%
62.2
4.2%1
97.2
In each of the homes we studied, the occupants currently use a programmable thermostat.
Therefore, during the study period, continuing their current practices, they would have saved the
following over the baseline: House#l: 19.4%, House#2: 14.1%, House#3: 17.2% and House#4:
7.6%. In the general population, however, savings from installation of programmable thermostats
for similar homes and commuter patterns would be much less, as reported in the Table 5, due to
the estimated 43.8% of people who do not use the setback capabilities of the devices.
Despite high estimated compliance, using only the GPS thermostat generally provides lower
savings than the programmable thermostat. This is because the GPS thermostat changes the
target temperature based on the travel time and if the travel time is short, the target temperature
(setback) will be low, even if someone is gone from the home all day.
4 The households selected all have dual commuters and therefore the results represent savings that might be achievable for only
that type of household. We fully expect, for example, that savings in households with stay-at-home parents and young children
might be substantially less, because setbacks are often not appropriate. The GPS thermostat might actually be most convenient
in those homes, however, where occupants have highly variable travel time schedules that are rarely known in advance and
where occupants may be less willing to tolerate a house that is uncomfortable upon return.
33
The performance of the GPS system is highly dependent on travel patterns. Once commute times
reach 60 min., the GPS thermostat starts to outperform the programmable system. As shown in
the Figure 7(b), the expected minimum travel time of the participants of House#3 is only 5-10
min.
The GPS thermostat, therefore, performs only 0.3% better than the baseline and a
programmable thermostat would be more effective. House#2 has larger savings because the
house occupants had 2 vacations during the study totaling 12 days when the GPS thermostat
automatically selects a very low target temperature that is sustained for the duration of the trips.
The insulation of the house plays a major role in determining potential savings. The dynamic
target temperature regulation by the GPS thermostat, which is dependent on travel time, is
governed by the insulation of the house. If the house is poorly insulated the target temperature
for a given travel time will be lower as compared as to a highly insulated house. But, it will take
longer to heat the house back to the comfort temperature as compared to a highly insulated house
(see Figure 2). Therefore for the some duration of travel time and home vacancy, the HVAC
system in the highly insulated house will run for less time as compared to a poorly insulated
house.
Table 5. Simulated energy and money savings for general population using compliance rates for manual and programmable
thermostat.
House#1 - 75 days
Comfort Temp =67
Lower Tamp =65
% Day
compliance Savings(%)
Manual Thermostat
ProgrammableThermostat
GPS Thermostat
35
56.2
GPS+Raturn at Lower Temp
90
90
6.a3%
10.9%
2.9%
7.0%
House#2- 75 days
Comfort Temp = 69
Lower Temp =67
House#3 - 60 days
Comfort Tamp =69
Lower Tamp = 67
Savings($)Savings (%) Savings($)
58.1
4.8%
34.0
House#4- 60 days
Comfort Temp =69
Lower Temp 67
5.1S
%
116.
217.9
94.4
25.5
7.9%
7.1%
57.
9.6"%
49.7
61.7
9.0%
62.2
0.3%
4.2%
Savings (%) Savings($ )
7.A
97.2
4.4%
79.0
7.6%
137.3
0.89%
15.5
3.5%1
63.3
The GPS thermostat excels in cases where travel patterns may be unpredictable. For example, the
participants in House#2 (see Figure 7(c)) had 2 vacations during the study totaling 12 days.
During these days the savings achieved by the GPS thermostat was 42%.
Although above we compare the performance of the GPS system to a programmable system, the
GPS system is not intended to replace the programmable system but instead augment it in the
high percentage of households that do not use manual or programmable setbacks. Therefore, the
expected savings in Table 6 are computed as follows. Savings are estimated using the
programmable thermostat model for the 56.2% of the population that use a programmable
thermostat, but for the remaining 43.8% who do not program it, we assume that the thermostat
defaults to the GPS model. The expected savings for a programmable thermostat which defaults
to GPS thermostat can be over 20%. Similarly, we assume that 35% of households that use the
manual thermostats set them back but that the remaining 65% would default to the GPS system.
In this case, an expected savings of about 20% can be achieved. The GPS thermostat alone may
or may not outperform the savings from a programmable or a manual thermostat, but in
combination it can increase expected average performance without requiring complicated
programming or sacrificing comfort.
Table 6. Results of augmenting manual and programmable thermostats with GPS thermostat.
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12
6
Measuring Potential Savings and Pilot Usability of a Persuasive GPSControlled Thermostat: Cooling
The GPS thermostat system described in Section 5 was implemented for controlling air
conditioning and was installed for pilot usability and feasibility evaluation in a participant's
house for duration of 2 weeks. The study was approved by our human subjects review board.
6.1 System Design
The back-end of the GPS thermostat system is a client server model (see Figure 9) based on
Transmission Control Protocol (TCP). The client is the GPS-enabled mobile phone (Motorola
9Qh [42]) and the server is a laptop computer that was placed at the participant's house near the
location of the thermostat. The server receives the GPS coordinates from the client once per
minute (1) and contacts MapQuest web service (2) to get the travel time and distance from the
home of the participant. The server then contacts Yahoo weather web service [43] (3) to get the
outside temperature of that area. The algorithm calculates the target temperature based on the
travel time and outside temperature using the heating/cooling tables (4). Finally the server sends
the new target temperature over a serial connection to the computer controlled thermostat (RCS
TR40 [17]) and in reply gets the confirmation and the current room temperature (5). The client,
in reply to sending the GPS coordinates, receives the travel time, distance, current home
temperature, and energy saving information related to the intervention questions.
The GPS was on continuously on the phone and was sampling the coordinates every 1 min. GPS
coordinates were sent to the server via GPRS each minute. If the participant answered an
intervention question, the responses were sent as well.
Figure 9. GPS-Controlled thermostat system: design of functional prototype used for pilot testing.
The front-end of the GPS thermostat system is divided into two interfaces: one on the laptop
located in the house and connected to the computer controlled thermostat and the second on the
mobile phone. The phone interface uses strategies to persuade the participant to save energy (see
Figure 10). There are three types of persuasive questions that are triggered based on whether the
participant is leaving or returning back home, which is decided by travel time thresholds. These
strategies were based on: Suggestion[44] , Interruption [45] and Just-in-time motivation [46].
Figure 10. Motorola 9Qh with an intervention question displayed on the screen.
1. The first intervention question triggers as soon as the participant leaves the house and is two
minutes away. The query remains on the display until the user has a chance to respond typically on arrival at the workplace. The intervention tries to persuade the participant to
return home to a 1-20F warmer house. It provides the user with the information on the savings
achieved in each case and how much time it will take the warmer house to reach the desired
comfort temperature upon return home (see Figure 11(a)). The 2 min. threshold is selected
because it takes about 2- 5 min. for GPS to get a fix. And the sooner this question is answered
more savings can be achieved. If the user does not answer the question, it will remain on the
phone's screen and the user can opt to answer it anytime.
2. The second question asks the participant his or her approximate return time for the day (see
Figure 11(b)). It is very unlikely that every time the user returns home the GPS will have the
exact house coordinates. Therefore this question is triggered when the user is within a 2 min.
radius of the house.
3. The second question is triggered immediately after the response to the first question, and asks
the participant for his or her approximate return time for the day (see Figure 11(b)). For
shorter commutes, an answer to this question can significantly increase energy savings.
4. The last intervention question is triggered if the participant responded that they would like to
return to a slightly warmer home. It is triggered when the participant is returning home and is
two minutes away. The query remains on the display until the user has a chance to respond typically on arrival at home. The intervention asks whether the participant would like to try a
degree or two warmer temperature for the rest of the day until bedtime, giving him extra
energy savings (see Figure 11(c)).
All the above questions are asked only once in a day. If the participant did not answer a question,
it will remain on the screen and whenever he uses the phone again, he will see the question. The
participant is rewarded for making energy saving decisions with encouraging positive feedback
about energy savings and additional energy saving tips (see Figure 11l(d)). The interface never
interferes or forces the participant to answer the questions.
Return Temperature
Want to save
on cooling?
Temperature when you arrive
home
..
63 Ad~
I wont be home before...
know
] Don't
LI Ask melater
No Savings: Fully Cooled
[ 9%Savings: Wait 47 min to Fully Cool
E 15%Savings: Wait 90 min to Fully Cool
(a)
(b)
Want to save more on cooling?
Temperature when the home isoccupied
(before bed time)
No savings: Normal
10% Savings: Tad hi her (IDegree)
16% savings: Alittle more (2 Degree)
(c)
R At this time...
Great! You have made 15energy saving
you started using this system
decision since
Do You Know?
"Replacing old model air conditioners with
Energy Starunits cancut cooling bills by 209
or more.
(d)
Figure 11. Intervention questions on the mobile phone. (a) Return temperature.
(b) Return time. (c) Maintain lower temperature. (d) Rewarding message.
The laptop interface is intended to be used as a replacement for the thermostat wall interface and
simulates a small LCD screen. It provides the user with system status and working information
and full manual control. It also uses persuasive strategies to attempt to motivate additional
savings (see Figure 12). The interface displays the messages on the current operation of the
system. Unlike most current thermostats, it always displays what the system is currently trying to
do and the reasons why it is doing that. The interface provides the energy and monetary savings
since the system was installed (Figure 12 (F)). It also provides control to manually change the
comfort settings (Figure 12(L)). It rewards the participant for making energy saving decisions.
Whenever the participant manually changes the temperature settings, the system shows a pop-up
window showing how much money the participant may save or lose depending on the decision
(Figure 12 (K)) and how the system is going to react to the change (Figure 12 (I&J)). There is a
"COOL" button that can override the GPS control and resume the manual control of the cooling
system (Figure 12 (H)).
Figure 12
shows a typical scenario that may occur when the
participant returns to a warmer home and system is not able to track his or her mobile phone. The
system displays (A) the current home temperature, (B & C) the current state of the system, (D)
why that is the state, and (E) what the user should do if he or she wants to change the
temperature settings.
Figure 12. Laptop interface screenshots.
6.2 Recruiting
To evaluate the GPS-controlled thermostat, a single working professional living in a single
family house in Stoughton, Massachusetts with a central cooling system was recruited. He lived
alone with a pet. He used his car as the main mode of transport during the 14 days that he used
the system.
The participant had a 4800 sq.ft. (446 sq.m) single house with a cooling area of 3000 sq.ft (278.7
sq.m). At the time of the experiment, the house was approximately 40 years old and the cooling
system was approximately 23 years old. The cooling capacity of the compressor was 60,000btuh
and the whole system was controlled by one thermostat. The participant had a pet but he
mentioned that he left his pet in the basement whenever he was away from the house, and the
basement was not included in the area cooled by the air conditioning system. The comfort
temperature of the participant was 74oF (23.3oC). The travel pattern of the participant was
irregular as he also works from home (see Figure 13).
6.3 Experimental Setup
The same experimental setup as described in the section 5 was used with the following changes:
1. The participant was asked to set the temperature to his comfort temperature when he was in
the house and to 90F (32.2oC) when he was not in the house.
2. The GPS logger was installed in his car for 30 days. The actual system was installed for the
last 14 days of the study.
3. A professional electrician installed the computer controlled thermostat (RCS TR40) in the
participant's home. A laptop computer (server) was connected to the thermostat and was kept
underneath it on a table with the screen clearly visible. The participant had a broadband
wireless internet connection from Verizon, which was configured so that the client (Motorola
9Qh mobile phone) and server could communicate. The participant was given a GPS-enabled
mobile phone (Motorola 9Qh) and was asked to use it as his personal phone, for which he
had to activate an unlimited data plan for 2 weeks on his phone contract and swap out the
SIM card.
6.4 Evaluation
After the completion of the study, a qualitative evaluation of the system was done by
interviewing the participant, where he was asked to provide his experience of using the system
on the basis of usability and user friendliness of the GPS thermostat system.
To estimate the savings that were achieved using the GPS thermostat for cooling, the baseline
was the comfort temperature (74oF (23.3oC)) of the participant. The lookup tables for the
participant's house were created using the three day procedure as described in Section 5.2. The
outside temperature file was created using online databases for weather archives [39, 40].
6.5 Energy Saving Estimations
The energy and monetary savings that were obtained during the two weeks when the GPS
thermostat system was installed in the house were 3.4% and $2.70 respectively. Since the
participant's work schedule and travelling patterns were irregular (see Figure 13), the GPS
thermostat system proved to be successful in achieving comfort on arrival, saved energy, and
persuaded the participant to alter his comfort temperature settings to save energy. The GPS
thermostat application on the phone was running all the time in the background. If the phone was
restarted for any reason, the GPS thermostat application restarted automatically. The participant
was told to recharge the phone every night. According to the participant, the phone was restarted
only twice, since he was punctual in recharging the phone every night. During the 14 day study,
the participant left his home and activated the GPS control eight times during the study. He opted
to save energy eight times out of possible 24 times during the study, by selecting energy saving
choices in the intervention questions. During three of his trips he opted to select energy saving
option for "return at warmer temperature" and "specify return time" question. During two of his
trips he opted to select the energy saving option for "return at warmer temperature" and for
"maintain warmer temperature after return." For 3 of his trips, he either did not select an energy
saving option or forgot to answer the questions.
Figure 13. A week of travel pattern of the participant using the GPS thermostat prototype for 14 days.
6.6 Interface Design Observations
After the completion of the study the participant was interviewed about his experience using
the system. The study was of short duration (2 weeks) because of time constraints. The main
reason for the study was to check the robustness and working of the system in a real home
environment and to gather pilot usability and feasibility feedback.
When asked to explain what he thinks about the GPS thermostat system the participant said
that "the idea is interesting and practical." He further mentioned that his work travelling
patterns are irregular as sometimes he works from home as well, so the system is ideal for him.
He said "I don't have to remember to change my temperaturesettings before leaving". He also
suggested that he would like to see how such a system could be extended to control certain
lights which are relevant when he is returning home in the evening like the main door light, the
driveway light and his living room lights.
In answer to the question about his experience of using the laptop interface (Appendix 9,
question 5) he said that "the laptop interface gave me information about what the system was
doing and why, which I cannot see on my thermostat." About the popup window feature,
which gave him information about savings that could be affected by making a particular
temperature change, he said "it made me feel good if I make energy saving decisions andfeel
bad if I did not." The messages that were displayed when the temperature in the house was at
the comfort temperature confused the participant (see Figure 14 for an example). For example
if the room temperature was 740 F and the target temperature was set to 76 0 F the status
message displayed by the system was "Now feels like 74oF Maintaining temperature to 76 0F."
In actuality the system was off until the room temperature was greater than the target
temperature. But the message gave an impression that it was running, and he said that "I was
wondering why it is cycling even though the house is already cooled." The button for
overriding the GPS control was named "COOL" but was never used by the participant because
he was not sure what its purpose was (see Figure 12 (H)). When asked why he never used this
button, he said that "I did not know what it will do so I was afraid of touching it". The
"COOL" button was visible all the time on the system display. But the explanation about it
was only shown along with a particular status message. The message was triggered if the
participant arrived home and the system was unable to track his phone. In this case the
participant could override the system, but during the study this scenario never occurred.
Figure 14. Screenshot of a status message on the laptop interface, which the participant found confusing.
The participant did not find the phone interface to be user friendly because he said that "it was
difficult to figure out how to make calls and receive calls. It took some time to learn it." He
also was not able to see the current and target temperatures of his house, which he would
prefer to because he wanted to see what this system is doing and whether it is working or not.
There was an icon which can be clicked to get the system status and current temperature of the
house on the window's phone desktop, but he could not find it. When asked about the mobile
phone interface (Appendix 9, question 4) he said that "I did notfind the GPS thermostat phone
interface informative as it was not showing me the temperature of my house." He further added
that the main features that he wanted on the phone interface were the information about
current temperature, target temperature and energy savings, and the ability to change the
temperature or return time decision at any point of time. But when asked about the mobile
phone intervention questions (Appendix 9, question 9) he said that "I liked the persuasive
status messages, because they were showing how much I can save". The rewarding message
which was shown if he makes an energy saving decision was not appreciated by him. He
thought that "the message was adding extra information to an already cluttered day". About
the frequency of these questions (Appendix 9 question 10) he said that "since my work
schedule is irregular,I would want to have these messages whenever I go outfor work". When
asked, about how much he would pay for such a system, the participant responded $300
(Appendix 9, question 15).
7 Discussion and Future Work
The GPS thermostat system uses just-in-time temperature control to save energy. The work
presented here suggests that the GPS system is unlikely to outperform a properly-programmed,
consistently used programmable thermostat for most people. A GPS thermostat system that
augments a programmable thermostat, however, could lead to saving of up to 12.2% for the
over 50%[4] of homes where programmable thermostats are improperly set or controlled
manually. These savings are achieved without requiring complicated programming or change
in behavior and ensure that the house temperature is always comfortable upon arrival even for
people with irregular schedules.
The system prototype as described here might be significantly improved by having an
algorithm which dynamically updates the lookup tables for the temperature profile of the
house based on the current weather conditions like outside temperature, humidity, wind
velocity, and sunshine. These factors significantly affect the heat gain and heat loss rates of a
house that may ultimately affect the energy savings. These tables could be automatically
learned over time and tuned for specific homes.
The prototype might also be improved by taking into account not only outside temperature but
also forecasted outdoor temperature. A sudden change in weather condition may increase heat
loss rate and decrease the heat gain rate and create a situation where the GPS thermostat
cannot catch up in time to ensure the home is at a comfortable temperature when the occupants
return. On days when drastic temperature changes are forecast, the algorithm could be
designed to adjust accordingly.
The GPS thermostat system can be augmented with a programmable thermostat. Pattern
recognition can be used to understand the usage patterns of a programmable thermostat and an
inference algorithm could be used to decide when to operate in programmable mode vs. GPS
mode. This will increase the energy savings.
The persuasive phone interface still needs to be tested for an extended time to see if it genuinely
motivates and sustains behavior change. We propose five design improvements to the phone
interface:
1.
It should show the current room temperature and the status of the GPS thermostat system.
2. It should allow the user to change their answer to the intervention questions at any time.
3.
It should have a manual thermostat control and whenever the user changes the temperature
settings, it should provide feedback on how their actions can affect the working of the
system and the energy savings from the system.
4. It should learn the temperature preferences and travelling patterns over time.
5. It should not interrupt the user while driving. Rather it should wait until it detects that user's
vehicle is not in motion.
Overall system performance will improve as location-determination technology is improved in
phones. We found in our lab testing that the built-in GPS in the phone did not always get a rapid
fix on satellites, depending on the location and weather conditions. Urban locations with tall
buildings require more time to acquire satellites that an open highway. Similarly, on a cloudy
day it can take up to 15 minutes for the GPS to get a fix. On a sunny day with the sky in view it
takes about 2-5 minutes for GPS to get a fix. The GPS system does not work inside buildings.
These problems may well be addressed through the use of assisted-GPS supplements that use cell
tower location information [47].
The phone data transfer scheme, described in section 6.1, resulted in a battery life of only 10-12
hours on a charge. To improve the battery life, we subsequently devised a simple scheme for
pilot testing where the GPS switches on every three minutes and remains on for two minutes to
get a fix. If the GPS gets a fix, the GPRS connection will be opened and the coordinates will be
sent to the server. The GPRS connection is then immediately closed. One of the investigating
team members tested this scheme by carrying the phone for seven days; we found that using this
scheme the phone battery was able to last for 24h. The inherent problem with this approach is
that if user's travel time is only 15 min. and GPS does not get a quick satellite fix, it will miss
much of the journey of a short commute. This scheme can further be improved by developing a
dynamic GPS coordinate sampling algorithm. The algorithm can understand when the user is
moving and will start the GPS and sample the coordinates at a higher frequency. Whereas, when
the user is at office or home, it will reduce the sampling frequency of the coordinates and the on
time of the GPS. This will reduce the time for which GPS and GPRS is on and may increase the
battery life significantly.
The question-prompting system can be easily modified for multiple participants in a house, but
there is a need to develop a clever algorithm that can decide who should be queried by the
system and when. Moreover, the system must then convey to the other house occupants what
decisions other family members have made about heating and cooling.
We estimated the current cost of a GPS thermostat not including a GPS-enabled phone to be
about $300. The maximum average monthly savings that were achieved in this study by the GPS
thermostat alone during winter was about $16, and during summer is $5. This assumes 5 months
of winter and 4 months of summer, with the remaining 3 months of a pleasant when people do
not use their HVAC system. The GPS-thermostat could save up to $100 in 12 months. Therefore
the payback time for a GPS thermostat system is estimated to be 24-36 months.
According to a recent article in Boston Globe [48], in 2009 annual average heating cost in
Massachusetts may go as high as $3000. In our study a programmable thermostat augmented by
a GPS thermostat could save as high as 12.2%. A system like this may lead to an annual savings
of $360.
We have not considered the night setback temperature settings in our simulations and the
prototype system. Incorporating night setback temperature setting will lead to more energy
savings. The GPS system could be extended to control lights and hot water heating systems as
well as the HVAC. Estimating the potential savings of this approach is left for future work.
8 Conclusion
We have prototyped, tested and evaluated a GPS controlled thermostat system. We have
shown through simulations that such a system is capable of savings as high as 7%. But when
augmented with a programmable and manual thermostat it may save up to 12.2%. We have
demonstrated feasibility of implementation with a pilot study where a fully-functional
prototype was able to control temperature based on travel patterns without requiring user
interaction and without sacrificing the comfort of the user. Such a system has the potential to
save energy for the more than 50% [4] of the U.S homes that do not change their temperature
settings when there is no one in the house. The evaluation described here is based on
simulation and a14 day live test. More testing with a functional system is recommended to
prove viability.
Appendix A: PlaceLab Sensor Location Pictures
Figure A 1. View from infrared and color cameras at PlaceLab
at qual
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Appendix B: CAPM Detailed Results
PowerWastage Distribution
Figure B 1.Break down of power wasted out of total wastage by different categories
Savings
Projected usage and savings for each element of all the categories
IS
Figure B 2.Projected usage and savings for each element in all the categories at PlaceLab
Appendix C: Simulation of GPS Thermostats for Various Scenarios
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Appendix D: Simulation Interface Screenshot
Appendix E: Thermostat Installation and Wiring Diagrams
OLD
Heating &
Cooling
System
__
Thermostat
4, 5 or 6 wires
18Ga thermostat wiring
Insert Control Unit into
the thermostat wiring
Replace
Thermostat with
Wall Display Unit
NEW
4 Wires
TR40
Wall Display
TR 40
Con trol
Unit
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Cooling
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as original
installation
TR40 Wiring Diagram
TR40 CONTROL UNIT
iypicai I nermostat wiring colors.
TR40 Control Unit
FUSE
SWi
I
STD
S2
IMPORTANT! For typical central heating/cooling systems,
leave jumper JP1 installed. If you have separate Heating
and Cooling system transformers, CUT JUMPER JP1 Wire
RC to the Cooling system transformer and RH to the
Heating system transformer.
HP
Standard HVAC System
OFF ON
JP1
Note: Set SW1-S1 to STD
L U V,t
RC&RH
I
THERMOSTAT CONNECTION
S C 24VAC Common
E
R 24VAC Return
Wre R to either RH or RC forcommon
transformer HVACsystems
e
W1 Heat Stage 1
e
W2 Heat Stage 2
5G Fan
e
Y1 Comp Stage 1
e
Y2 Comp Stage 2
Typical thermostat wiring color codes
Control Unit to WDU wiring
Connect the WDU to the Control Unit with existing thermostat wiring in retrofit installations. For new
installs, 22/24 GA twisted pair, typically Cat3 or Cat 5 wiring is preferred.
nCntrnl I init
TS40
WDU
GND
+12VDC
CLOCK
DATA
J1
iysllm Cheekeut
It is strongly recommended that you hook-up and run a simple bench test before installing the TR40. Not
only will this save you time in system checkout but will also familiarize you with the thermostat's operation.
THERMOSTAT BENCH TEST
1. Connect the Wall Display Unit to the Control Unit with a short (12-24 inch) 4 wire cable.
2. Before power up, set the Control Unit dipswitch, SW1, to ALL OFF
3. Connect power to the Control Unit.
4. Verify Control Unit Status LED is blinking.
5. Verify the WDU display comes on and shows the current temperature.
a. If no display and backlights are not on, check wiring and power at the Control Unit.
b. If a "CF" display is shown on the WDU, double check your wiring to the Control Unit.
d. Do not proceed until the current temperature is displayed on the WDU.
6. With the current temperature displayed on the WDU, we have verified communication between it and
the Control Unit is OK. Any communication problems will result in a "CF" (Communications Failure)
display on the WDU and must be fixed before proceeding. If all is OK with the WDU proceed to the next
step.
7. Press the Fan button on the WDU. The Control Unit's Fan LED and relay should turn on.
8. Press the Fan button again. The Fan LED and relay should turn off.
9. Press the Mode button until the WDU is showing "H" for Heat Mode.
10. Press the Setpoint Up button until the setpoint is above the current temperature. The Heat LED and
relay should come on. (If they don't, make sure the Status LED is not flashing twice indicating minimum off
delay...wait until LED stops flashing twice before proceeding).
11. Press the Mode button until the WDU is showing "0" for OFF. The Heat LED and relay will turn OFF.
12. Wait 6 minutes for the minimum off delay to expire. The Status LED will stop flashing twice.
13. Press the Mode button until the WDU is showing "C" for Cool Mode.
14. Press the Setpoint Down button until the setpoint is below the current temperature.
15. The Compressor and Fan LEDs and relays should turn on.
16. Press the mode button until the WDU is showing "0" for OFF Mode.
17. All LEDs and relays should turn off.
18. When you have successfully completed all these tests, you have verified that the TR40 is working
correctly.
SERIAL COMMUNICATIONS TEST
Connect the TR40 to a PC via a RS232/485 converter to a PC serial COM port.
Note: Any RS232 to RS485 adapter used with RCS serial thermostats must have AUTOMATIC
SEND DATA CONTROL. All RCS converters have this feature, if you use another type of converter,
be sure it has this capability.
Using Windows HyperTerminal.
*
*
*
*
*
Set up a direct connection session to the COM port you will be using on the PC.
Configure Port Settings to 9600 baud, 8 bit, No parity, 1 stop bit and no flow control.
Under Settings tab go to ASCII Setup...
Set the ASCII sending to echo characters and no delays.
Set ASCII receiving to Append line feeds and Wrap lines.
NOTE: RCS serial ASCII commands are CASE sensitive!
Verify Serial communications by typing the following command sequence:
A=1 R=1 <cr>
Thermostat should reply with the R1 status message. A=00 0=1 T=xx SP=xx SPC=xx SPH=xx M=x FM=x
If you have problems communicating, check addresses and port settings.
You can also use the RCS Thermostat Control Program to check out serial communications to the
TR40.
Appendix F: Laptop Interface Screenshots for Different Scenarios
Figure D 1. GPS thermostat regulating the target temperature based on the travel time. If there is a
cleaning service entering the house or the user arrives home to a warm house, they will see this message.
Figure D 2. If earlier during the day user decided to come back at a specified time and showed
up earlier,
he will see this message
---------------------------
Figure D 3. When home temperature is less than the target temperature system will display this message
Figure D 4. When home and target temperature are equal, system will display this message
Figure D 5. When user arrives to warmer house, system will display this message
Figure D 6. When user arrives to warmer house with high humidity in the house and high outside
temperature, system will display this message
Figure D 7. Whenever user changes temperature settings the system will provide feedback
Appendix G: GPS Thermostat System
Figure G 1. Picture of GPS thermostat system
Appendix H: GPS Thermostat System Setup Instructions
1. Make sure that TR40 wall display unit is connected to the TR40 control unit as shown in
appendix 4.
2. Connect the DB9 serial connector of the TR40 control to the laptop serial port.
3. Connect the 1-wire humidity sensor to the USB port of the laptop.
4. If using the GPS thermostat for demo, connect the 24VAC power supply to TR40 control
unit.
5. Power the TR40 thermostat; you will see blue screen lighting up on the TR40 display unit.
6. Start the GPSRealTimeThermostat.exe application on the laptop.
7. Press "Start" to start the GPS thermostat system.
8. Start the GPSThermstat.exe application on the phone.
Starting the GPSThermostatRealTime.exe from MIT network
The GPSThermostatRealTime.exe application on the laptop starts a server by opening a TCP
port 13000. To make the laptop as server you need to have a static IP address. Dynamic DNS
service provided by www.dyndns.org can be used to assign a host name to the laptop. This host
name is assigned the IP address and will automatically track the IP address changes using a
"dyndns" tracker application and keep on updating. In this way you can have server running even
with a dynamic IP address. Make sure to change the server (host) name on the phone client.
Starting the GPSThermostatRealTime.exe from outside MIT network
If you want to run the GPS thermostat application from a home which is having a internet
connection provided by a service provider like Comcast, Verizon etc. You have to make the
following changes on the internet router.
1. Assign a local static IP to the laptop running the GPS thermostat.
2. Enable port forwarding to TCP port 13000 from any port.
3. Disable the "dyndns" tracker if running on the laptop.
4. Enable the dynamic DNS service on the router and give the host name you have created for
the laptop.
5. Make sure to change the server (host) name on the phone client.
To access these setting on the router, please go to http://www.portforward.com/help.htm to see
the instructions for your router.
Appendix I: Interview Questions for Heating Study
1. Is there anything that would make it easier for you to save money on your heating or air
conditioning?
2. How did you feel about having the GPS logger in your car logging your traveling patterns
and distance from your home?
3. How much on an average do you spend on heating your house each month during the
winter?
4. How much of your bill, in terms of dollars, do you think goes toward heating your
home when no one is at home?
5. [After describing a location-aware thermostat] How much would you pay for a system
like a location-aware thermostat that can control your heating and cooling without
sacrificing your comfort?
6. Given your travel patterns and family activity, do you think a location-based thermostat
would work for you? Why or why not?
7. How much money do you think you might be able to save with a location-aware
thermostat?
8. How much money would you need to save to make buying and installing a locationaware thermostat system worthwhile?
9. How much on an average do you spend on cooling your house each month during the
summer?
Questions on travel data
1. Where is your office?
2. What's the fastest and shortest time to reach your office from your home and vice versa?
3. I s this your typical route for the day/week/month?
4. Do you change the route or take the same route every day?
5. Do you run errand, traveling to or from work?
6. What parts of the town would you be driving too?
7. When do you leave house and when do you return on a typical day?
Appendix J: Interview Questions for Cooling Study
1. What would make it easier for you to save money on your air conditioning?
2. What do you think about the GPS thermostat idea and the system?
3. How did you feel about having the GPS-enabled mobile phone and a GPS device in your
car communicating your traveling patterns and distance from your home?
4. How was your experience with mobile phone graphical interface for thermostat control?
5. Was the mobile phone interface intuitive or Laptop interface?
6. How do you feel about the status messages on the laptop?
7. How do you feel about the energy saving messages that you got when you changed
temperature settings?
8. How do you feel about controlling your cooling system by your mobile phone?
9. How do you feel about the messages generated by the mobile phone informing you about
how much energy you can save
10. What do you think about the frequency of the messages?
11. Did you know any instance where the system did not worked as you expected?
12. How did you feel about having the GPS logger in your car logging your traveling patterns
and distance from your home?
13. How much on an average do you spend on cooling your house each month during the
summer?
14. How much electricity or money do you think is wasted by cooling your home while you
are away?
15. How much would you pay for a system like a location-aware thermostat that can control
your heating and cooling without sacrificing your comfort?
16. How much fuel or money do you think you might be able to save with a location-aware
thermostat?
17. How much fuel or money would you need to save to make buying and installing a
location-aware thermostat system worthwhile?
Appendix K: Consent Form for Collecting Data to Inform the Design of a
GPS Thermostat System
CONSENT TO PARTICIPATE IN
NON-BIOMEDICAL RESEARCH
Collecting Data to Inform the Design of a Location-Aware System
You are asked to participate in a research study conducted by the following researchers from the
Department of Architecture and the MIT Media Laboratory at the Massachusetts Institute of
Technology (M.I.T.):
Kent Larson, Principal Research Scientist, Architecture
Dr. Stephen Intille, Research Scientist, Architecture
Manu Gupta, Research Assistant, Media Arts and Sciences
Jennifer Beaudin, Research Scientist, Architecture
Aydin Oztoprak, Visiting Researcher, Architecture
You were selected as a possible participant in this study because:
o You are at least 18 years of age,
o You live in an apartment or home,
o Your heating system is not connected to another home or apartment,
o You live within the Greater Boston area at a location easily accessible via public
transportation,
o There are no more than 2 persons including yourself, resting or staying in your household
and each has agreed to participate, and
o You own a car, which you use as your main mode of transport.
You should read the information below, and ask questions about anything you do not understand,
before deciding whether or not to participate.
*
PARTICIPATION AND WITHDRAWAL
Your participation in this study is completely voluntary and you are free to choose whether to be
in it or not. If you choose to be in this study, you may subsequently withdraw from it at any time
without penalty or consequences of any kind. The investigator may withdraw you from this
research if circumstances arise which warrant doing so.
*
PURPOSE OF THE STUDY
The U.S., with 5 percent of the world's population, uses 25 percent of the world's energy. The
U.S residential sector is responsible for 21% of the total U.S energy consumption, and residential
heating and cooling accounts for 9% of total U.S energy consumed. Our previous research at
MIT suggests that as much as 18% or more of the residential power consumption may be wasted
by heating or cooling unoccupied spaces. If this is typical for all the houses in the U.S., then it
amounts to 1.6% of all U.S. energy consumption.
Significant savings in space heating and cooling can be achieved, in principle, by using
programmable thermostats. Studies show, however, that these are rarely used, mainly because
they require the occupants to program a regular schedule that does not reflect the complex and
unpredictable nature of modern life.
In this study we are collecting data to inform the design of a "location-aware" thermostat system
that will communicate with GPS-enabled mobile phones and will use computer algorithms to
model travel and occupancy patterns to optimize the control of the home heating and cooling
system. The goal is to develop an easy-to-use, affordable, and non-invasive system that will
reduce energy waste based on real-time detection of home occupancy. We will use data collected
from real homes and from participants' travel patterns to simulate the working of this system and
estimate the potential energy savings if the technology were fully installed.
* PROCEDURES
If you volunteer to participate in this study, we would ask you to do the following things: 1)
permit the researchers to install three non-invasive temperature and electrical current loggers in
your house for 3 days of measurement, (2) follow the instructions to adjust your thermostat for
the 3 days of measurement, and (3) carry a GPS logger that we provide in your vehicle for 3
months that will record the position of your car and distance from your home.
1. Data Collection in Your Home
1. A researcher will schedule a mutually convenient time to vist your home and and setup
the data loggers. At least one participating occupant will need to remain at home during
this session, which in total is expected to take up to of 3 hours.
2. The researcher will ask you to show him or her your heating and cooling systems. The
researcher will only interact with to the easy-to-access components of these systems, such
as the thermostats and meters. Nothing will be modified or disassembled.
3. The researcher will place one temperature logger near your thermostat and another
temperature logger outside of your house on the north facing wall or window.
4. After examining your heating and cooling system, the researcher will attach a "current
logger" that will provide information about how often your heating system is triggered.
5. After the installation the reseacher will ask you to follow these instructions to control the
heating in your house for 3 days:
If you have a programmable thermostat, the researcher will program it with the following
temperatures:
a. Set to 50 degrees from 9 am to 4:30 pm.
b. Set to a comfortable temperature, typically between 67 degrees to 70 degrees all
other times
If you do not have a programmable thermostat, the researcher will instruct you how to set
it as follows:
a. Set to 50 degrees at either 9 am or when you leave for work, whichever is earlier
and record the time on a logsheet you will be given.
b. Set to a comfortable temperature, typically between 67 degrees to 70 degrees at
4:30 pm or whenever you return from work, whichever is later and record the
time on a logsheet you will be given.
The researcher will call you about 30 minutes before the time you would typically leave
the house and about 30 minutes after the time when you would typically return, as a
reminder.
6. During the 3 days, you should not adjust the thermostat settings in any way other than
what is described above. If you must adjust the thermostat for some reason, you should
call the researcher at the provided number.
7. The researcher will come back after 3 days at a mutually convenient time to collect the
data loggers; this will take no more than 30 minutes.
The heating system will be returned to its original settings after the data collection session.
2. Travel-Routine Logging
1. The researcher will ask you to place a portable GPS logging device (TrackStick pro) in
your vehicle for the duration of the study period, up to 3 months. The device will
continuously log information about your vehicle's location change, including the time
and longitude and latitude. These data will be used to estimate the distance and travel
time to your home throughout the day.
2. Data will be stored locally on the GPS logging device. Every 3 weeks, a researcher will
arrange a mutually convenient time and place to download the data from the logging
device (this can be your home). This procedure takes about ten minutes. After
downloading the data, the researcher will place the operational GPS logging device back
in your vehicle.
3. The researcher will ask you to always keep the GPS logger plugged into your vehicle's
charger.
4. The researcher will stick the velcro either on the dash-board or on the wind screen and
will stick the GPS logger on it. When velcro is removed after the study it will not leave
any mark on the object it was placed on.
The data collected using the above procedure will be used by the researchers to estimate the
energy savings that might have been achieved for your home over 3 months if a location-aware
thermostat were installed. At the end of the study you will be interviewed about the study. The
interview will be audio recorded.
*
POTENTIAL RISKS AND DISCOMFORTS
Possible sources of discomfort may include:
1. Physical reactions to changes in the heating and cooling during the home data collection
session.
2. Concerns about wasting fuel during the home data collection session
3. Privacy concerns related to the GPS travel data collection
4. Concerns about allowing a researcher into your home for a day
5. Concerns about availability for periodic data collection from GPS
Possible procedures to prevent potentional discomfort may include:
1. You will be asked for a convenient time to conduct the measurements for the one day
data collection and will be advised about the changes in temperature. You may choose to
wear layers of clothing to compensate for the temperature fluctuations.
2. On average, in peak usage during wintertime, 12-14 therms are consumed for heating per
day. In our one day measurement process we will not exceed 20 therms. The price of 1
therm is about $1 and therefore the session may result in up to $8 extra in heating costs.
Your compensation will cover this expense.
3. During the course of this study, you might at times feel uncomfortable about having your
traveling behavior recorded. You will be shown how to turn off the GPS logger if you
wish to immediately stop participating in the study. You may request to see your data.
During all stages of the research project, the investigators will inform you that any data
collected in this study will be subject to stringent security procedures, and that personal
identifiers will not be associated with your data in any way. Further, vehicle position data
will not be shared between individuals of the same household. You must sign an
informed consent agreement indicating that you understand and agree to these conditions.
All research personnel involved in this study must have passed the MIT training course
on Research Involving Human Subjects. Additional training on procedures for handling
data and maintaining subject confidentiality will be conducted by the investigators.
4. In addition to (1), researcher will sit quietly and work during the procedure.
5. Every 3 weeks, a researcher will arrange a mutually convenient time and place to
download the data from the logging device (this can be your home). This procedure takes
about ten minutes. After downloading the data, the researcher will place the operational
GPS logging device back in your vehicle.
During all stages of the research project, any data collected in this study will be subject to
stringent security procedures. Personal identifiers will not be associated with your data in any
way, and only the reseachers listed above will have access to the data and your identity. If you
wish to discontinue data collection altogether, you may withdraw from the study at any time.
You will be given contact information for the research team and will be instructed to contact the
investigators at any time during the study if you wish to discontinue recording or to terminate
your participation in the study.
*
POTENTIAL BENEFITS
You will have the opportunity to learn about new home technologies for saving energy and
improving comfort.
The data collected during this study will inform the design of a new system for reducing the
energy consumption from heating and cooling of unoccupied homes. If the developed system is
successful, families may be able to save money and help save energy for the greater good.
*
PAYMENT FOR PARTICIPATION
This compensation will be provided in the following ways:
* After the completion of the three day temperature and heating system measurement
procedure in your home, your household will receive $30.
* Each day each person in your household uses the GPS logger your household will receive
$1, for a maximum of 90 days, or $90. This will be awarded at the completion of the
study.
* After 3 months of GPS logging, your household will receive an additional $20.
* After completion of a 45 minutes interview at the end of the study with yourself and other
members of your household, your household will receive $10.
* Each time a researcher collects GPS data from your household's vehicles, roughly once
every three weeks, you will receive an additional $3 gift certificate, for up to three times
during the study.
If you must drop out of the study because the investigator asks you to or because you have
decided on your own to withdraw, you will still receive the compensation for all the days
completed.
The compensation will be given per household and not per participant. If one participant in a
household withdraws or is withdrawn, the other participant from the household will be
withdrawn.
If you choose, you may also participate in the study without compensation, as a volunteer; please
indicate your decision to do so by initialing one of the following blanks:
Paid _
Unpaid
*
CONFIDENTIALITY
Any information that is obtained in connection with this study and that can be identified with you
will remain confidential and will be disclosed only with your permission or as required by law.
Your responses and GPS data will be referenced by an ID number in order to protect your
identity. A study enrollment log will be kept that will include participants' unique identification
numbers, names, telephone numbers and enrollment data. This log will be stored in a locked
cabinet in the investigators' office and will be destroyed 1 year after the completion of the study.
When the results of the research are published or discussed in conferences, no information will
be included that would reveal your identity. Your name will not in any way be associated your
data, including photographs and audio recordings. Once your data is anonymized, it may be
shared with other researchers for future studies.
Investigators may take still photographs of your home environment, including the locations
where they will install the loggers. With your permission, they may audio-record section of the
one day procedure installation and the post-study interview. If you do not wish to be audio taped
or have pictures taken of you or your home environment, you may still participate in this study
without prejudice. You can ask the investigator at any time to stop the audio recording. You may
review any photographs and audio-recordings taken during the experiment and request that tapes
are erased or digital images be deleted.
The audiotapes and photographs will only be used by the investigators to document that work in
academic publications. The media will be under the sole control of the investigators and will be
stored in a location accessible only to the investigators. After the investigators have analyzed the
results, prior to showing any images or audio in academic and peer-reviewed papers, or
anywhere else, they will use standard methods to manipulate the media to protect your identity,
such as blurring the face. Any media that cannot be appropriately anonymized will be destroyed
within one year after completion of the study.
Please indicate your consent to have still photography and audio recording during the one day
procedure and/or post-study interview by initialing below:
Audio/ Photo Consent for one day procedure and final interview:
Accept:
Decline:
*
IDENTIFICATION OF INVESTIGATORS
If you have any questions or concerns about the research, please feel free to contact
Kent Larson
Principal Research Scientist, MIT Dept. of Architecture
617-253-9396, kll@mit.edu
Stephen Intille, Ph.D.
Research Scientist, MIT Dept. of Architecture
617-452-2346, intille@mit.edu
Manu Gupta
Research Assistant, MIT Media Laboratory
617-452-5640, manug@mit.edu
Jennifer Beaudin
Research Scientist, MIT Dept. of Architecture
617-452-5677, jbeaudin @mit.edu
Aydin Oztoprak
Visiting Researcher, MIT House_n, Architecture
617-452-5640, oztoprak@mit.edu
*
EMERGENCY CARE AND COMPENSATION FOR INJURY
"In the unlikely event of physical injury resulting from participation in this research you may
receive medical treatment from the M.I.T. Medical Department, including emergency treatment
and follow-up care as needed. Your insurance carrier may be billed for the cost of such
treatment. M.I.T. does not provide any other form of compensation for injury. Moreover, neither
the offer to provide medical assistance nor the actual provision of medical services shall be
construed as an admission of negligence or acceptance of liability. Questions regarding this
policy may be directed to M.I.T's Insurance Office, (617) 253-2823."
*
RIGHTS OF RESEARCH SUBJECTS
You are not waiving any legal claims, rights or remedies because of your participation in this
research study. If you feel you have been treated unfairly, or you have questions regarding your
rights as a research subject, you may contact the Chairman of the Committee on the Use of
Humans as Experimental Subjects, M.I.T., Room E25-143b, 77 Massachusetts Ave, Cambridge,
MA 02139, phone 1-617-253 6787.
SIGNATURE OF RESEARCH SUBJECT OR LEGAL REPRESENTATIVE
I understand the procedures described above. My questions have been answered to my
satisfaction, and I agree to participate in this study. I have been given a copy of this form.
Name of Subject
Name of Legal Representative (if applicable)
Signature of Subject or Legal Representative
Date
SIGNATURE OF INVESTIGATOR
In my judgment the subject is voluntarily and knowingly giving informed consent and possesses
the legal capacity to give informed consent to participate in this research study.
Signature of Investigator
Date
Appendix L: Consent Form for Study to Evaluate a GPS Thermostat System
CONSENT TO PARTICIPATE IN
NON-BIOMEDICAL RESEARCH
A Study to Evaluate a GPS Thermostat System
You are asked to participate in a research study conducted by the following researchers from the
Department of Architecture and the MIT Media Laboratory at the Massachusetts Institute of
Technology (M.I.T.):
Kent Larson, Principal Research Scientist, Architecture
Dr. Stephen Intille, Research Scientist, Architecture
Manu Gupta, Research Assistant, Media Arts and Sciences
Jennifer Beaudin, Research Scientist, Architecture
Aydin Oztoprak, Visiting Researcher, Architecture
You were selected as a possible participant in this study because you and any other members of
your household meet these criteria:
o You are at least 18 years of age,
o You live in an apartment or home that you own or co-own,
o Your cooling system is not connected to another home or apartment,
o You live within the Greater Boston area,
o There are no more than 2 adults including yourself, resting or staying in your household
daily and each has agreed to participate,
o Each person in your household has a mobile phone contract with a network provider that
uses GSM (T-Mobile or AT&T/Cingular) and you do not plan to switch carriers for at
least 4 months,
o You have a broadband Internet connection in your home (cable modem or DSL),
o You own a car, which you use as your main mode of transport, and any adult you live
with also owns a car, used as his or her main mode of transport.
o You have a central cooling system, either furnace or electric heat pump based. And your
thermostat is easily accessible and replaceable.
You should read the information below, and ask questions about anything you do not understand,
before deciding whether or not to participate.
*
PARTICIPATION AND WITHDRAWAL
Your participation in this study is completely voluntary and you are free to choose whether to be
in it or not. If you choose to be in this study, you may subsequently withdraw from it at any time
without penalty or consequences of any kind. The investigator may withdraw you from this
research if circumstances arise which warrant doing so.
*
PURPOSE OF THE STUDY
The U.S., with 5 percent of the world's population, uses 25 percent of the world's energy. The
U.S residential sector is responsible for 21% of the total U.S energy consumption, and residential
heating and cooling accounts for 9% of total U.S energy consumed. Our previous research at
MIT suggests that as much as 18% or more of the residential power consumption may be wasted
by heating or cooling unoccupied spaces. If this is typical for all the houses in the U.S., then it
amounts to 1.6% of all U.S. energy consumption.
Significant savings in space heating and cooling can be achieved, in principle, by using
programmable thermostats. Studies show, however, that these are rarely used, mainly because
they require the occupants to program a regular schedule that does not reflect the complex and
unpredictable nature of modern life.
In this study we are installing a "location-aware" thermostat system that will communicate with
GPS-enabled mobile phones and will use computer algorithms to model travel and occupancy
patterns to optimize the control of the home heating and cooling system. The goal is to develop
an easy-to-use, affordable, and non-invasive system that will reduce energy waste based on realtime detection of home occupancy. We will evaluate the system on usability, acceptability and
energy savings that can be achieved using this system.
*
PROCEDURES
If you volunteer to participate in this study, we would ask you to do the following things:
1. Permit an electrician to install the location-aware thermostat in your house,
2. Permit the researchers to install three non-invasive temperature and electrical current
loggers in your house for 3 days of measurement,
3. Follow the instructions to adjust your thermostat for the 3 days of measurement
4. Add unlimited data transfer capability to your mobile phone contract temporarily (for
which you will be reimbursed)
5. Use a GPS-enabled mobile phone provided by us as your mobile phone for up to 3
months. The GPS-enabled mobile phone will communicate your location and distance
from your home to the thermostat. Your current phone's SIM card will be placed in this
new phone.
6. Carry a GPS logger provided by us in your vehicle for the duration of the study, which
will record the position of your car and distance from your home.
7. Participate in an interview with the researchers about the usability, acceptability and
interaction with the new thermostat system.
1. System Installation and Data Collection in Your Home
1. Data Collection in Your Home
8. A researcher will schedule a mutually convenient time to vist your home and setup the
data loggers. At least one participating occupant will need to remain at home during this
session, which in total is expected to take up to 3 hours.
9. The researcher will ask you to show him or her your heating and cooling systems. The
researcher will only interact with the easy-to-access components of these systems, such
as the thermostats and meters. Nothing will be modified or disassembled.
10. The researcher will place one temperature logger near your thermostat and another
temperature logger outside of your house on the north facing wall or window.
11. After examining your heating and cooling system, the researcher will attach a "current
logger" that will provide information about how often your cooling system is triggered.
12. After the installation the reseacher will ask you to follow these instructions to control the
cooling in your house for 3 days:
If you have a programmable thermostat, the researcher will program it with the following
temperatures:
a. Set to 90 degrees or the maximum temperature specified on the thermostat from 9
am to 4:30 pm.
b. Set to a comfortable temperature, typically at 70 degrees all other times
If you do not have a programmable thermostat, the researcher will instruct you how to set
it as follows:
c. Set to 90 degrees or the maximum temperature specified on the thermostat at
either 9 am or when you leave for work, whichever is earlier and record the time
on a logsheet you will be given.
d. Set to a comfortable temperature, typically at 70 degrees at 4:30 pm or whenever
you return from work, whichever is later and record the time on a logsheet you
will be given.
The researcher will call you about 30 minutes before the time you would typically
leave the house and about 30 minutes after the time when you would typically
return, as a reminder.
13. During the 3 days, you should not adjust the thermostat settings in any way other than
what is described above. If you must adjust the thermostat for some reason, you should
call the researcher at the provided number.
14. The researcher will come back after 3 days at a mutually convenient time to collect the
data loggers; this will take no more than 30 minutes.
The cooling system will be returned to its original settings after the data collection session.
2. GPS-enabled Mobile Phone
5. The researcher will ask you to use a provided GPS-enabled mobile phone. You will be
asked to insert your existing SIM card in the provided phone. You are required to use the
phone for the duration of the study period, up to 3 months. The mobile phone will
communicate your location including the time and longitude and latitude to the
thermostat installed at your home. The data will be communicated using a GPRS (data)
connection. These data will be used to estimate the distance and travel time to your home
throughout the day. You will be asked to add unlimited data transfer capability to your
mobile phone contract temporarily (for which you will be reimbursed). You will not be
asked to add any GPS related service contract.
6. Data will be stored on the laptop which is connected to the thermostat at your house. The
algorithm running on the laptop will use these data to dynamically update the setback
7.
8.
9.
10.
11.
12.
13.
temperature on the the thermostat. The system will allow the temperature to fluctuate
based on your travel time to home, so that temperature is comfortable upon return, yet
energy consumption is minimized during periods when the home is not occupied.
The system will have a mobile phone-based interface that will provide real-time feedback
on your savings. It will also provide the current temperature settings for your home and a
manual override control if you want to change these settings.
The mobile phone based interface will also log usability data like how often the interface
was used, how often a button was pressed, how many messages were generated and how
often they were attended.
The researcher will ask you to always keep the GPS-enabled mobile phone charged.
The researcher will ask you to place a portable GPS logging device (TrackStick pro) in
your vehicle for the duration of the study period, up to 3 months. The device will
continuously log information about your vehicle's location change, including the time
and longitude and latitude. These data will be used to estimate the distance and travel
time to your home throughout the day.
Data will be stored locally on the GPS logging device. Every 3 weeks, a researcher will
arrange a mutually convenient time and place to download the data from the logging
device (this can be your home). This procedure takes about ten minutes. After
downloading the data, the researcher will place the operational GPS logging device back
in your vehicle.
The researcher will ask you to always keep the GPS logger plugged into your vehicle's
charger.
At the end of the study the you have to return the GPS-enabled mobile phone, laptop and
GPS logger to the researcher.
At the end of the study you will be interviewed about the study. The interview will be audio
recorded. There will also be more short email/phone interviews possible once every ten days.
*
POTENTIAL RISKS AND DISCOMFORTS
Possible sources of discomfort may include:
6. Damage to the existing thermostat system.
7. Improper functioning of the new thermostat system may cause discomfort.
8. Physical reactions to changes in the heating and cooling during the home data collection
session.
9. Concerns about wasting fuel during the home data collection session
10. Privacy concerns related to the GPS travel data collection
Possible procedures to prevent potentional discomfort may include:
6. The installation of new thermostat system will be performed by a professional electrician
and all the care will be taken not to damage any existing heating and cooling system.
7. There will be a manual override function available both on the mobile phone interface
and the thermostat interface.
8. You will be asked for a convenient time to conduct the measurements for the 3 day data
collection and will be advised about the changes in temperature.
9. On average, in peak usage during summer, 25KW are consumed for cooling per day. In
our measurement process we will not exceed 5KW per day. The price of 1 KW is about $
0.2 and therefore the session may result in up to $1 extra per day in cooling costs. Your
compensation will cover this expense.
10. During the course of this study, you might at times feel uncomfortable about having your
traveling behavior recorded. You will be shown how to turn off the GPS function in the
mobile phone if you wish to immediately stop participating in the study. You may request
to see your data. During all stages of the research project, the investigators will inform
you that any data collected in this study will be subject to stringent security procedures,
and that personal identifiers will not be associated with your data in any way. Further,
individual location data will not be shared between individuals of the same household.
You must sign an informed consent agreement indicating that you understand and agree
to these conditions. All research personnel involved in this study must have passed the
MIT training course on Research Involving Human Subjects. Additional training on
procedures for handling data and maintaining subject confidentiality will be conducted by
the investigators.
During all stages of the research project, any data collected in this study will be subject to
stringent security procedures. Personal identifiers will not be associated with your data in any
way, and only the reseachers listed above will have access to the data and your identity. If you
wish to discontinue data collection altogether, you may withdraw from the study at any time.
You will be given contact information for the research team and will be instructed to contact the
investigators at any time during the study if you wish to discontinue recording or to terminate
your participation in the study.
*
POTENTIAL BENEFITS
You will have the opportunity to learn about new home technologies for saving energy and
improving comfort.
The system tested during this study if successful will help in reducing the energy consumption
from heating and cooling of unoccupied homes and families may be able to save money and help
save energy for the greater good.
*
PAYMENT FOR PARTICIPATION
The total compensation for your participation in this study, which will last up to 90 days, is $160
and the installed programmable thermostat used in the study per household.
This compensation will be provided in the following ways:
*
*
*
*
*
*
After the completion of the three day temperature and cooling system measurement
procedure in your home, your household will receive $30.
Each day each person in your household uses the GPS-enabled mobile phone your
household will receive $1, for a maximum of 90 days, or $90. This will be awarded at the
completion of the study.
After each month of using the system, your household will receive an additional $10.
After completion of a 45 minutes interview at the end of the study with yourself and other
members of your household, your household will receive $10.
If there is a malfunctioning of the system and a researcher visits your house to fix that
problem, you will receive an additional $3 gift certificate.
At the end of the study you can keep the new computer controlled programmable
thermostat used in the study but not the GPS-enabled mobile and GPS logger. After the
study the thermostat will no longer be GPS-enabled. The retail price of this thermostat is
$280. A researcher will teach you how to use it.
If you must drop out of the study because the investigator asks you to or because you have
decided on your own to withdraw, you will still receive the compensation for all the days
completed. The thermostat will be provided only after the completion of full study.
Your original thermostat will be re-installed by a professional electrician and all the original
settings will be restored.
The compensation will be given per household and not per participant. If one participant in a
household withdraws or is withdrawn, the other participant from the household will be
withdrawn.
If you choose, you may also participate in the study without compensation, as a volunteer; please
indicate your decision to do so by initialing one of the following blanks:
Paid
Unpaid
*
CONFIDENTIALITY
Any information that is obtained in connection with this study and that can be identified with you
will remain confidential and will be disclosed only with your permission or as required by law.
Your responses and GPS data will be referenced by an ID number in order to protect your
identity. A study enrollment log will be kept that will include participants' unique identification
numbers, names, telephone numbers and enrollment data. This log will be stored in a locked
cabinet in the investigators' office and will be destroyed 1 year after the completion of the study.
When the results of the research are published or discussed in conferences, no information will
be included that would reveal your identity. Your name will not in any way be associated your
data, including photographs and audio recordings. Once your data is anonymized, it may be
shared with other researchers for future studies.
Investigators may take still photographs of your home environment, including the locations
where they will install the loggers. With your permission, they may audio-record section of the
one day procedure installation and the post-study interview. If you do not wish to be audio taped
or have pictures taken of you or your home environment, you may still participate in this study
without prejudice. You can ask the investigator at any time to stop the audio recording. You may
review any photographs and audio-recordings taken during the experiment and request that tapes
are erased or digital images be deleted.
The audiotapes and photographs will only be used by the investigators to document that work in
academic publications. The media will be under the sole control of the investigators and will be
stored in a location accessible only to the investigators. After the investigators have analyzed the
results, prior to showing any images or audio in academic and peer-reviewed papers, or
anywhere else, they will use standard methods to manipulate the media to protect your identity,
such as blurring the face. Any media that cannot be appropriately anonymized will be destroyed
within one year after completion of the study.
Please indicate your consent to have still photography and audio recording during the one day
procedure and/or post-study interview by initialing below:
Audio/ Photo Consent for one day procedure and final interview:
Accept:
Decline:
*
IDENTIFICATION OF INVESTIGATORS
If you have any questions or concerns about the research, please feel free to contact
Kent Larson
Principal Research Scientist, MIT Dept. of Architecture
617-253-9396, kll@mit.edu
Stephen Intille, Ph.D.
Research Scientist, MIT Dept. of Architecture
617-452-2346, intille@mit.edu
Manu Gupta
Research Assistant, MIT Media Laboratory
617-452-5640, manug@mit.edu
Jennifer Beaudin
Research Scientist, MIT Dept. of Architecture
617-452-5677, jbeaudin@mit.edu
Aydin Oztoprak
Visiting Researcher, MIT House_n, Architecture
617-452-5640, oztoprak@mit.edu
* EMERGENCY CARE AND COMPENSATION FOR INJURY
If you feel you have suffered an injury, which may include emotional trauma, as a result of
participating in this study, please contact the person in charge of the study as soon as possible.
In the event you suffer such an injury, M.I.T. may provide itself, or arrange for the provision of,
emergency transport or medical treatment, including emergency treatment and follow-up care, as
needed, or reimbursement for such medical services. M.I.T. does not provide any other form of
compensation for injury. In any case, neither the offer to provide medical assistance, nor the
actual provision of medical services shall be considered an admission of fault or acceptance of
liability. Questions regarding this policy may be directed to MIT's Insurance Office, (617) 2532823. Your insurance carrier may be billed for the cost of emergency transport or medical
treatment, if such services are determined not to be directly related to your participation in this
study.
*
RIGHTS OF RESEARCH SUBJECTS
You are not waiving any legal claims, rights or remedies because of your participation in this
research study. If you feel you have been treated unfairly, or you have questions regarding your
rights as a research subject, you may contact the Chairman of the Committee on the Use of
Humans as Experimental Subjects, M.I.T., Room E25-143b, 77 Massachusetts Ave, Cambridge,
MA 02139, phone 1-617-253 6787.
SIGNATURE OF RESEARCH SUBJECT OR LEGAL REPRESENTATIVE
I understand the procedures described above. My questions have been answered to my
satisfaction, and I agree to participate in this study. I have been given a copy of this form.
Name of Subject
Name of Legal Representative (if applicable)
Signature of Subject or Legal Representative
Date
In my judgment the subject is voluntarily and knowingly giving informed consent and possesses
the legal capacity to give informed consent to participate in this research study.
Signature of Investigator
Date
Appendix M: Flyer for Recruiting Participants for Heating Study
Try Out GPS Logger
and
Help MIT Researchers Test
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MIT researchers are developing technologies that might
help homeowners to save energy and money .
Participants will try out a GPS logger in their cars and
provide feedback about a new idea.
Interested in learning more?
Please contact
Manu at enerqystudy@mit.edu or call 617-452-5640
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How can your commute help the environment?
MIT researchers are developing technologies that might help homeowners to save energy and money.
Participants will try out a GPS thermostat system in their house and provide feedback about a new idea.
If you are interested, please contact us at...
Manu at energvstudy@mit.edu or call 617-452-5640
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