ABSTRACT
In order to better understand the opportunities and challenges of an intelligent system in the home, we studied the lived experience of a thermostat, the Nest. The Nest utilizes machine learning, sensing, and networking technology, as well as eco-feedback features. To date, we have conducted six interviews and one diary study. Our findings show that improved interfaces through web and mobile applications changed the interactions between users and their home system. Intelligibility and accuracy of the machine learning and sensing technology influenced the way participants perceive and adapt to the system. The convenient control over the system combined with limitations of the technology may have prevented the desired energy savings. These findings assert that thoughtful, continuous involvement from users is critical to the desired system performance and the success of interventions to promote sustainable choices. We suggest that an intelligent system in the home requires improved intelligibility and a better way in which users can provide deliberate input to the system.
- Catch.com: https://catch.com/.Google Scholar
- Energy Savers: Space Heating and Cooling: http://www.energysavers.gov/your_home/space_heating_cooling/index.cfm/mytopic=12300.Google Scholar
- Fogg, B. J. 2009. A behavior model for persuasive design. Proceedings of the 4th International Conference on Persuasive Technology. ACM, New York, NY, 40._ Google ScholarDigital Library
- Gupta, M., Intille, S. and Larson, K. 2009. Adding GPS-control to traditional thermostats: An exploration of potential energy savings and design challenges. Pervasive Computing. Springer-Verlag Berlin, Heidelberg, 95--114. Google ScholarDigital Library
- Lim, B. Y. and Dey, A. K. 2011. Investigating intelligibility for uncertain context-aware applications. Proceedings of the 13th international conference on Ubiquitous computing. ACM, New York, NY, 415--424. Google ScholarDigital Library
- Lui, T. J., Stirling, W. and Marcy, H. O. 2010. Get Smart. Power and Energy Magazine, IEEE. 8, 3 (2010), 66--78.Google Scholar
- Nest | The Learning Thermostat | Home: http://www.nest.com/.Google Scholar
- Peffer, T., Pritoni, M., Meier, A., Aragon, C., and Perry, D. How people use thermostats in homes: A review. Building and Environment, 46(12), 2529--2541.Google Scholar
- Pierce, J., Schiano, D. J. and Paulos, E. 2010. Home, habits, and energy: examining domestic interactions and energy consumption. Proceedings of the 28th international conference on Human factors in computing systems. ACM, New York, NY, 1985--1994. Google ScholarDigital Library
- Scott, J., Bernheim Brush, A., Krumm, J., Meyers, B., Hazas, M., Hodges, S. and Villar, N. 2011. PreHeat: controlling home heating using occupancy prediction. Proceedings of the 13th international conference on Ubiquitous computing. ACM, New York, NY, 281--290. Google ScholarDigital Library
- Strengers, Y. A. A. 2011. Designing eco-feedback systems for everyday life. Proceedings of the 2011 annual conference on Human factors in computing systems. ACM, New York, NY, 2135--2144. Google ScholarDigital Library
- Whitworth, B. 2005. Polite computing. Behaviour & Information Technology. 24, 5 (2005), 353--363.Google ScholarCross Ref
Index Terms
- Living with an intelligent thermostat: advanced control for heating and cooling systems
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