User needs for location-aware mobile services
Personal and Ubiquitous Computing
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
A bright green perspective on sustainable choices
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
It's not all about "Green": energy use in low-income communities
Proceedings of the 11th international conference on Ubiquitous computing
Planning-based prediction for pedestrians
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
One size does not fit all: applying the transtheoretical model to energy feedback technology design
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The design of eco-feedback technology
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The smart thermostat: using occupancy sensors to save energy in homes
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
PreHeat: controlling home heating using occupancy prediction
Proceedings of the 13th international conference on Ubiquitous computing
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Reducing the large energy consumption of temperature regulation systems is a challenge for researchers and practitioners alike. In this paper, we explore and compare two common types of solutions: A manual systems that encourages reduced energy use, and an intelligent automatic control system. We deployed an eco-feedback system with the ability to remotely control one's thermostat to ten participants for three months. Participants appreciated the ability to remotely control the thermostat, and controlled their heating system with 78.8% accuracy, a 6.3% improvement over not having this system. However, despite having feedback and remote control, they still wasted a lot of energy heating when away from home for the day. Using data from our deployment, we developed TherML, an occupancy prediction algorithm that uses GPS data from a user's smartphone to automatically control the indoor temperature of a home with 92.1% accuracy. We compare TherML to other state-of-the-art techniques, and show that the higher accuracy of our approach optimizes both energy usage and user comfort. We end with recommendations for a mixed initiative system that leverages aspects of both the manual and automated approaches that can better match heating control to users' routines and preferences.