Bayesian Learning of Consumer Preferences for Residential Demand Response
This work addresses energy saving automation for residential consumers facing dynamic pricing, though it appears incremental as it builds on existing Bayesian methods for a specific application.
The paper tackles the problem of learning residential consumer preferences for automated demand response under real-time electricity tariffs by proposing a Bayesian learning algorithm to estimate comfort level functions from appliance use history. In numeric experiments using simulated data, the algorithm outperforms popular regression analysis tools.
In coming years residential consumers will face real-time electricity tariffs with energy prices varying day to day, and effective energy saving will require automation - a recommender system, which learns consumer's preferences from her actions. A consumer chooses a scenario of home appliance use to balance her comfort level and the energy bill. We propose a Bayesian learning algorithm to estimate the comfort level function from the history of appliance use. In numeric experiments with datasets generated from a simulation model of a consumer interacting with small home appliances the algorithm outperforms popular regression analysis tools. Our approach can be extended to control an air heating and conditioning system, which is responsible for up to half of a household's energy bill.