4 research outputs found
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 1: of Validity of the Stages of Change in Steps instrument (SoC-Step) for achieving the physical activity goal of 10,000 steps per day
The SoC-Step, a Stages of Change instrument relevant to the physical activity goal of 10,000 steps per day. (PDF 175 kb
Additional file 1: of The effectiveness of a web 2.0 physical activity intervention in older adults – a randomised controlled trial
CONSORT checklist. (DOC 218 kb
Additional file 3: of The effectiveness of a web 2.0 physical activity intervention in older adults – a randomised controlled trial
TIDieR checklist of the interventions. (PDF 129 kb