Title: Bayesian Learning of Consumer Preferences for Residential Demand Response,
Abstract: 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.