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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | An active learning approach to home heating in the smart grid |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| ISBN | 978-1-57735-633-2 |
| Page Range | 2892 - 2899 |
| Event Title | International Joint Conference on Artificial Intelligence (IJCAI) |
| Event Type | conference |
| Event Location | Beijing, China |
| Event Start Date | August 3 - 2013 |
| Event End Date | August 9 - 2013 |
| Place of Publication | Palo Alto, USA |
| Publisher | IJCAI |
| Abstract Text | A key issue for the realization of the smart grid vision is the implementation of effective demand-side management. One possible approach involves exposing dynamic energy prices to end-users. In this paper, we consider a resulting problem on the user’s side: how to adaptively heat a home given dynamic prices. The user faces the challenge of having to react to dynamic prices in real time, trading off his comfort with the costs of heating his home to a certain temperature. We propose an active learning approach to adjust the home temperature in a semiautomatic way. Our algorithm learns the user’s preferences over time and automatically adjusts the temperature in real-time as prices change. In addition, the algorithm asks the user for feedback once a day. To find the best query time, the algorithm solves an optimal stopping problem. Via simulations, we show that our algorithm learns users’ preferences quickly, and that using the expected utility loss as the query criterion outperforms standard approaches from the active learning literature. |
| Official URL | http://dl.acm.org/citation.cfm?id=2540128.2540545 |
| Other Identification Number | merlin-id:9041 |
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