Social Information Filtering Based Electricity Retail Plan Recommender System for Smart Grid End Users

Abstract

Rapid growth of data in smart grids provides great potentials for the utility to discover knowledge of demand side and design proper Demand Side Management (DSM) schemes to optimize the grid operation. The overloaded data also impose challenges on the data analytics and decision making. This paper introduces the service computing technique into the smart grid, and propose a personalized electricity retail plan recommender system for residential users. The proposed personalized recommender sys-tem (PRS) is based on the collaborative filtering (CF) technique. The energy consumption data of users are firstly collected from the smart meter, and then key energy consumption features of the users are extracted and stored into a user knowledge database (UKD), together with the information of their chosen electricity retail plans. For a target user, the recommender system analyzes his/her energy consumption pattern, find users having similar energy consumption patterns with him/her from the UKD, and then recommend most suitable pricing plan to the target user. Experiments are conducted based on actual smart meter data and retail plan data to verify the effectiveness of the proposed PRS.