An active learning approach to home heating in the smart grid

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

Additional file 2: of Compliance with the Australian 24-hour movement guidelines for the early years: associations with weight status

Venn diagram showing the proportion (%) of toddlers meeting no guidelines, physical activity, sedentary behavior, sleep guidelines and the combinations of these guidelines for girls (n = 104) and boys (n = 98). (TIFF 1521 kb

Additional file 1: of Compliance with the Australian 24-hour movement guidelines for the early years: associations with weight status

Mean BMI Z-scores according to level of compliance with the 24 h Movement Guidelines. (DOCX 17 kb

Additional file 2: of Acute effects of reducing sitting time in adolescents: a randomized cross-over study

Condition B: A ‘reduced sitting’ school day schedule. Description: A table demonstrating the protocol used to guide participants through the first condition: a ‘reduced sitting’ school day. (DOCX 12 kb