Bayesian Learning of Consumer Preferences for Residential Demand Response

IFAC-PapersOnLine. 2016. Vol. 49. No. 32. P. 24–29.

Губко М. В., Kuznetsov S., Neznanov A., Ignatov D. I.

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.

Keywords: машинное обучение, machine learning, моделирование поведения, Bayesian learning, smart home, умный дом, Байесовское обучение, smart power applications, electrical appliances, rational behaviour simulation, electricity saving, real-time electricity price schedule, экономия электричества, электрические устройства, умное потребление энергии, онлайновые тарифы на электроэнергию, моделирование рационального поведения