Collaborative Filtering-Based Electricity Plan Recommender System

Owning to electricity market deregulation, residential customers now enjoy the freedom to choose their preferred electricity retailers. This paper investigates the application of recommender system, a fast-developing technique in machine learning, into the task of recommending electricity plans for the individual residential customer. Based on a collaborative filtering strategy, an electricity plan recommender system (EPRS) is developed. By providing easily obtainable data of some household appliances, residential customers of the EPRS are recommended with predicted ratings of different plans, which can provide effective guidance to customers in the selection of suitable plans and proper tariffs. Different numerical tests are carried out to evaluate the performance of the EPRS. The EPRS outperforms other strategies in the accuracy of recommendation result and is verified to be a promising solution to electricity plan recommendation task.