Bayesian Learning of Consumer Preferences for Residential Demand Response

TL;DR

This paper introduces a Bayesian learning algorithm to estimate consumer comfort preferences for residential demand response, enabling automated energy management in response to real-time electricity prices.

Contribution

The paper presents a novel Bayesian approach for learning consumer preferences from appliance usage data, outperforming traditional regression methods in simulation experiments.

Findings

Bayesian algorithm accurately estimates comfort levels from usage history.

Outperforms popular regression analysis tools in simulations.

Potential extension to HVAC control for energy savings.

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.