Bayesian Learning of Consumer Preferences for Residential Demand Response. Issue 32 (2016)

• Record Type: Journal Article
• Authors: Goubko, Mikhail V.; Kuznetsov, Sergey O.; Neznanov, Alexey A.; Ignatov, Dmitry I.
• Is Part Of: IFAC-PapersOnLine. Volume 49:Issue 32(2016)
• Page Start: 24
• Page End: 29
• Publication Date: 2016
• Subjects: smart power applications -- electrical appliances -- rational behaviour simulation -- electricity saving -- real-time electricity price schedule -- Bayesian learning; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2016.12.184
• Languages: English
• ISSNs: 2405-8963

Abstract

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.