2 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 Retaining the general practitioner workforce in England: what matters to GPs? A cross-sectional study
The following supplementary tables are available: Table S1. Participant demographics and practice characteristics. Table S2. Logistic regression analysis (a) intention to leave practice within 5 years (b) and intention to take a career break within 5 years. Table S3. Factors influencing GPs’ decision to retire/leave practice: (a) work related (b) non-work related. Table S4. Factors that might retain GPs in practice: (a) work-related and (b) non-work related. In addition, a statement summarising adherence to the COREQ guidelines is also provided. (DOCX 31 kb