A personalized electricity tariff recommender system based on advanced metering infrastructure and collaborative filtering

Abstract Deregulation of electricity retail markets and the advancement of energy informatics have been supporting the transition of electricity retail business into an electronic business, where different electricity retailers can provide different electricity tariff plans to end users through digital media. In this context, end users are facing an information filtering challenge of choosing the most suitable tariff plans from a set of candidate tariff plans. This paper proposes a new personalized recommendation system that makes intelligent electricity tariff recommendations to end users. The proposed approach starts by collecting a group of end users’ electricity consumption profiles through the advanced metering infrastructure and, based on this information, it infers the preference of individual users on each tariff plan. Based on the inferred preference degree, a new matrix factorization is established based on a collaborative filtering algorithm that is capable of recommending most suitable tariff plans to an arbitrary target user. The proposed recommendation system is validated against a number of scenarios that are generated based on simulated tariff plan sets and on a modified Australian “Smart Grid, Smart City” dataset.