School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China

In face of the problem with mass electricity data processing, a strategy of electricity consumption behavior feature selection is put forward to reduce computing complexity and improve validity. Firstly, the validity and the relevance of electricity consumption feature are analyzed by its information entropy and related coefficient. On this basis, an evaluation criterion for electricity consumption feature is designed. Then a feature selection strategy is built to reduce the high dimensions of the feature vectors by reducing the redundancy of feature space. And the condensed feature set is devised using this strategy to select features. According to the feature selection strategy, a method of electricity consumption behavior analysis is put forward to make electricity consumption behavior analysis optimized. Finally, the simulation results of this method achieved by electricity consumption data show that it can effectively improve the accuracy of clustering and reduce computation time.