An empty-nest power user identification method based on weighted random forest algorithm - Zimeng LU; Jiayi CHEN; Jing LI; Yue XIE; Xinli JIANG; Lei HAN; Qian GUO (Dianxin kexue)
In view of the lack of effective technical means for the identification of empty-nesters by the government and the society,an empty-nesters prow user identification method based on weighted random forest algorithm was proposed.Firstly,some accurate labels of empty-nest users were obtained through questionnaires,and electricity characteristic library was drawn from three aspects:electricity consumption level,electricity consumption fluctuation and electricity consumption trend.Due to the data imbalance between empty-nest and non-empty-nest users,the weighted random forest algorithm was used to improve the data sensitivity phenomenon of machine learning.Finally,the algorithm model was put online in the power company’s acquisition system.The 2 000 unknown users of various types were identified,among which the identification accuracy of empty-nest users was 74.2%.The results show that the identification of empty-nesters from the perspective of electricity consumption can help power grid companies to understand the personalized and differentiated needs of empty-nesters,so as to provide users with more sophisticated services,and also assist the government and society to carry out assistance work.