Bayesian Hybrid Collaborative Filtering-Based Residential Electricity Plan Recommender System

The deregulation of the electricity market enables residential customers to select suitable electricity retailing plans. This paper proposes a Bayesian hybrid collaborative filtering-based electricity plan recommender system (BHCF-EPRS), which is constructed in a two-stage model integrated with model-based and memory-based collaborative filteringmethods. Bayesian inference is developed for missing feature estimation and user classification. Free from the requirements on total electricity use data and historical plan transaction data, the BHCF-EPRS can recommend suitable retailers and plans based on some easily obtainable features quantifying home appliance usage patterns. The BHCF-EPRS is verified to be a reliable recommender system with low error in full-ranking recommendation and high precision in top-N recommendation, which can improve the competitive operation of the electricity market.