An active learning approach to home heating in the smart grid
作者： Sven Seuken , Mike Shann
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摘要: A key issue for the realization of smart grid vision is implementation effective demand-side management. One possible approach involves exposing dynamic energy prices to end-users. In this paper, we consider a resulting problem on user's side: how adaptively heat home given prices. The user faces challenge having react in real time, trading off his comfort with costs heating certain temperature. We propose an active learning adjust temperature semi-automatic way. Our algorithm learns preferences over time and automatically adjusts real-time as change. addition, asks feedback once day. To find best query solves optimal stopping problem. Via simulations, show that our users' quickly, using expected utility loss criterion outperforms standard approaches from literature.
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ijcai.org参考文章(24)
Burr Settles, Active Learning Literature Survey University of Wisconsin-Madison Department of Computer Sciences. ,(2009)
Leen Peeters, Richard de Dear, Jan Hensen, William D’haeseleer, Thermal comfort in residential buildings: Comfort values and scales for building energy simulation Applied Energy. ,vol. 86, pp. 772- 780 ,(2009) , 10.1016/J.APENERGY.2008.07.011
Zhe Yu, L. McLaughlin, Liyan Jia, M. C. Murphy-Hoye, A. Pratt, Lang Tong, Modeling and stochastic control for Home Energy Management power and energy society general meeting. pp. 1- 9 ,(2012) , 10.1109/PESGM.2012.6345471
Thomas M. Cover, Joy A. Thomas, Elements of information theory ,(1991)
Nicolo Cesa-Bianchi, Claudio Gentile, Luca Zaniboni, Manfred Warmuth, Worst-Case Analysis of Selective Sampling for Linear Classification Journal of Machine Learning Research. ,vol. 7, pp. 1205- 1230 ,(2006)
Andreas Krause, Cheng S. Ong, Contextual Gaussian Process Bandit Optimization neural information processing systems. ,vol. 24, pp. 2447- 2455 ,(2011)
David J. C. MacKay, Information-based objective functions for active data selection Neural Computation. ,vol. 4, pp. 590- 604 ,(1992) , 10.1162/NECO.1992.4.4.590
Daphne Koller, Ronald Parr, Urszula Chajewska, Making Rational Decisions Using Adaptive Utility Elicitation national conference on artificial intelligence. pp. 363- 369 ,(2000)
Liyan Jia, Lang Tong, Optimal pricing for residential demand response: A stochastic optimization approach allerton conference on communication, control, and computing. pp. 1879- 1884 ,(2012) , 10.1109/ALLERTON.2012.6483451
N. Cesa-Bianchi, G. Lugosi, G. Stoltz, Minimizing regret with label efficient prediction IEEE Transactions on Information Theory. ,vol. 51, pp. 2152- 2162 ,(2005) , 10.1109/TIT.2005.847729