Rising power demands resulting from technological advancements is an increasingly important global issue. One way to tackle this problem is to motivate individual behavior change, for which the ubiquity of mobile phones o er an ideal platform to in uence consumption behavior of users. In this paper we explore the possibilities for using timely recommendations, goal-setting, immediate feedback, and visualization of past consumption behavior in order to motivate people to reduce power consumption resulting from heating/cooling devices. We describe a mobile application which gives the user direct access to the thermostat and provides feedback everyday on how sustainable the user was on the previous day. In addition to this feedback, it gives recommendations to improve the behavior and also o ers a behavior overview. The contributions of this paper include a working system for remote control over the thermostat and a goal-setting, recommendation, and feedback application designed to in uence a user's behavior.
H5.m. [Information interfaces and presentation]: e.g., HCI
Technological advancements over the past few decades
have allowed us to live more comfortable lives at the cost
of consuming increased amounts of energy. Devices like
refrigerators, air conditioning units, or home entertainment
systems provide quality of life improvements at the cost of
placing strain on limited global resources. As recognition
and awareness of this trend have grown, there have been
increasing calls for citizens to use resources responsibly.
Investigations into how energy is consumed in homes provide
valuable suggestions for how this goal can be achieved and
indicate that temperature regulation systems (such as
furnaces and air conditioners) are a key target. They are
reported to be responsible for nearly 25% of the consumption
in an average American household [
Despite the high-energy consumption (and costs) incurred
by these systems, studies indicate that there are problems
with devices intended to increase their e ciency. For
instance, programmable thermostats are capable of decreasing
the energy use of a temperature regulation system by
relaxing the maintained temperatures during particular time
periods, such as when homes are empty during the
workday. However, 14% of users are reported to not own such a
system and, of those that do possess one, over 40% do not
use it [
These ndings also suggest that, generally, people have
poor awareness of how their consumption choices and
behavior a ects the environment. Sensing technologies capable
of capturing a user's activity can be combined with digital
displays of consumption (e.g., websites, ambient displays,
mobile devices) to address this issue and raise awareness of
the impact of particular choices. Such systems and devices,
which are intended to inform users about the current state
of their consumption and their impact on the environment,
are known as "eco-feedback technology" [
Research in psychology can inform the development of
systems that change a user's behavior. In a recent survey paper,
He et al. [
An earlier review of environmental psychology papers
performed by Abrahamse et al. [
We suggest that mobile devices are an ideal platform on which to o er motivation to users and convey bene ts incompatible with the functionality of stationary devices. For example, we can use sensors provided by mobile phones (e.g., location sensor, accelerometer, etc.) to infer the user's current activity and predict future ones. This functionality can be used to provide just-in-time feedback about the outcome of actions and provide live recommendations of alternatives.
Integrating all this literature, this paper introduces a mobile application designed to give users feedback about their past behavior, provide timely recommendations to promote behavior change related to temperature regulation power consumption, and to support users in the achievement of the long-term goal of consuming less power in temperature regulation while they are not at home. The application we developed allows users to remotely and conveniently control the thermostat in their apartment using their phone. We believe that the system can be deployed most e ectively in the motivations stages of Preparation, Action, and Maintenance identi ed in the Transtheoretical model described above. The remainder of the paper provides a description of the application and outlines the structure of a study that we intend to run as the next stage of our work.
We designed a system to help people reduce power
consumption resulting from domestic heating and cooling
devices. It informs the user about his behavior by giving an
overview over past consumption in the form of a graph.
Studies have shown [
Through a mobile phone application, the user is o ered remote control over the thermostat. This allows error recovery in case the user forgot to set the thermostat before leaving the house. In addition to supporting manual and programmatic control of the at-home temperature, we also o er automatic control over the thermostat, reducing energy consumption when the user is not at home, and returning the at-home temperature to a desired level when the system predicts the user is returning home. In doing so, the system aims to support the user in setting the right temperatures.
The advantage the system o ers a user is not only error recovery through remote control, but also feedback if a behavior was environmentally positive or negative. Additionally it provides positive reinforcement if the behavior the day before was positive or timely suggestions on how to improve the behavior in case it was not positive.
To support the automated control of the thermostat, we
gather continuous location information using a mobile
application. Using the current location and past movement
data we are able to determine the time a user is predicted
to return home. We leverage a robust location prediction
algorithm developed by our research group, which aims to
predict future destinations based on prior and current
movement data ([
In addition to location information we also collect inside, outside, and the thermostat's temperature setting for the user's apartment. This information is used to provide feedback on the user's behavior and also to give recommendations on how to improve an environmentally negative behavior. To understand a user's reasoning for changing the thermostat's set temperature, we also query the user immediately after a manual change to a higher temperature has occurred.
The system was based on a commercial home automation
system from Insteon [
Home automation - the use of small modules to extend appliances with remote control and automation features - is a commercially available technology. The Insteon system used in this work allows the remote regulation of home temperature and also provides support for the calculation of power consumption. Calculating the power consumption of heating or cooling devices (e.g., furnaces or air conditioner) is a complex problem, because it not only depends on the length of time a device is active, but also on a set of complex variables including: the e ciency of the heating/cooling device, the volume of the home space, the current inside and outside temperature, the thickness and material of the walls, the number of walls exposed to outside conditions, and the number and kind of windows in the walls, to name a few issues. Because measuring these data is challenging for individual users the system presented in this paper used a simpli ed set of calculations using an equation provided by the U.S. Department of Energy:
AHC(city) = Cadj ( hhddddcuitsy ) (1 + S (TwAvg
hddcity=us S
= = = = = =
Annual Heating Costs for city Adjusted consumption (gas furnace) in mBTU Average number of Heating Degree Days Savings per degree of setback temperature in percent Set temperature as weighted average Typical indoor temperature during heating season
This equation assumes standard values for some of these variables and uses di erences between inside and outside temperature and the number of hours the user was at home and away from home to calculate the power consumption. We believe the results of this calculation to be su ciently indicative of real consumption to serve as e ective feedback. 2.2
The mobile phone application was developed and deployed on T-Mobile G1 phones running Google Android. It consists of several components and is the core of the system described in this paper. Its key function is to collect and transmit its current location to a central server in real time and support the motivational techniques described in the beginning of this chapter. Using the transmitted location it enables us to estimate whether or not a user is at home, or whether they are likely to return home in the near future. Furthermore we are able to calculate the power consumption while the user is at home or out and about in the world. The application provides an interface composed of three main components: a temperature control screen which allows remote control over the home automation system, a graph overview showing past behavior, and a recommendation screen. Each of these components is introduced in the following subsections. 2.3
This interface component allows users to view and control the current temperature in their homes. This feature was intended to support error recovery (for instance, by allowing correction after forgetting to adjust the temperature settings prior to leaving home) but also to provide a sense of security and control - to reassure users that they remain in control of the system even though it incorporates signi cant automated elements. Such privacy and lack of control concerns are often raised with novel technology incorporating automation or context sensing control and can result in users developing negative opinions about the systems. By providing a manual override, the system presented in this paper hopes to avoid this problem. In addition to remote control we also ask the user why he increased the temperature in the apartment, because we also strive to understand a user's reasoning it.
The graph overview is one of the core feedback
mechanisms in the application. As illustrated in Figure 1, it
o ers users a consumption overview split into daily, weekly,
and monthly activity. Each sub-graph includes a goal line
highlighting the di erence between intended and achieved
levels of consumption. This is highlighted using a simple
red, yellow, and green color scheme. This visualization is
designed so that users can see at a glance their performance
over days, weeks and months. By including these longer
periods of time, the system aims to convey a sense of mounting
achievement and provide motivation to continue with and/or
improve sustainable behaviors. In order to further support
this and provide positive reinforcement, a small smiley face
is shown if users achieve green behavior for more than half
of the days in a particular week or month. As described in
[
The recommendations screen aims to provide recommendations to users on how to achieve consumption goals through behaving sustainably. Once again, to present a clear visual representation we adopted a simple red, yellow and green color scheme for this UI. The user is shown a green screen when he behaves sustainably the previous day, by turning down the temperature while away from home to at least 60°F and turning down the temperature to 65°F while at home. If the user sets a higher temperature while at home, he is shown a yellow screen. If the temperature is consistently high throughout the day, the system will show him a red screen. For the yellow and red screen we give the user a recommendation on how to achieve sustainable behavior, which is in essence an explanation about why his behavior was not sustainable. We also suggest wearing additional clothing to compensate for cool temperatures at home.
In addition to recommendations and behavior indications we also inform users about the environmental impact of the wasteful behavior of leaving temperature regulation systems active in empty homes. This is done simply by calculating and presenting the number of 60W light bulbs that could have be powered by the wasted energy.
The recommendations screen is the rst screen shown to
the user upon application startup. Every morning the phone
noti es the user about the previous days consumption data
by ashing the onboard LED and vibrating. The goal of this
feedback is to provide positive reinforcement for sustainable
behavior and to inform users about the impact of their
behavior on the environment. In this way, we hope to
provide an incentive for users to alter unsustainable behaviors.
Our application informs users about problematic behaviors,
highlights the impact of these behaviors, and gives speci c
recommendations on how to enact change. By showing this
information in the mornings, prior to regular departures to
work, we hope to be able in uence the user's behavior in
a timely and appropriate fashion. Studies have shown [
In order to test the system and evaluate whether or not it can in uence a user's behavior and awareness of environmental issues, a eld study of the system is planned. This will take place in winter 2010/2011 and will use a group of 20 recruited participants residing in a city which experiences sub-zero temperatures and signi cant snowfall for several months. The group will be split into 2 sub-groups, with each group initially being in a manual condition where the user has to change the temperature on his own volition or an automatic condition where the system automatically sets the away-from-home temperature The study will be separated into two phases and each phase will go for 4 weeks, with a study length of 8 weeks in total. After 4 weeks the two sub-groups will switch conditions.
Prior to the study, GPS location data will be captured from each user and used to train the location prediction model and to calculate baseline consumption data. In order to measure environmental attitudes and temperature comfort levels both before and after the study participants will be required to ll out a survey every week. The survey is split into two categories, a temperature survey that will be distributed weekly and a survey to measure environmental attitude, locus-of-control, and self-e cacy that will be conducted at the beginning, middle, and end of the study. The temperature comfort survey will give us data on how the subjective comfort of the user changes through use of our system. We believe that a successful application that aims at changing a users environmental behavior also has to consider a user's comfort level. A system that disregards an individual's subjective comfort level could be unsuccessful because the user rejects it. One goal of our system is to change a user's environmental behavior, thus we need a way to evaluate the impact our two versions of the system have on the environmental awareness of the users. Because we also give the user information about his environmental behavior and recommend actions to change environmentally bad behavior, we measure the locus-of-control and self-e cacy of a user at the beginning, middle, and end of the study. This information will tell us if there was a change, for example, from external locus-of-control, the state where outside sources in uence events happening in our life's, to internal locus-of-control, the state where the user himself a ects events. Measuring self-e cacy will tell us if our system had a self-empowering e ect on users, where they increasingly got con dent to in uence their environmental behavior.
In this paper we rst gave an introduction into the problem space of home power consumption and explained some of the problems with the current use of thermostats. Additionally we highlight how research results from psychology can help to in uence a user's behavior and reduce power consumption resulting from heating/cooling devices.
We then described a working prototype of a mobile application designed to in uence a user's sustainable practices. Our prototype allows remote control and automated control over the thermostat, gives the user a goal to achieve, provides daily feedback on how well this goal was met, recommendations to achieve the goal (or positive reinforcement in case the goal met), and an overview over past consumption behavior. Our immediate next work is to conduct a study of this prototype with 20 people in the fall.