Design and Implementation of Electric Charge Arrears Prediction System

Abstract

Electric charge is the primary income for the power company. However, collecting electric charge is much difficult due to the existence of the risky consumer which makes the huge impact on the normal operation and development of the company. So the arrear problem of the risky customers has become one of the focus problems. Based on the gettable electric data from some areas, this paper proposed an integral system which can predict risky customers according to the various scenarios. In the system, the Random Forest (RF) model and Extreme Learning Machine (ELM) model are integrated that can effectively analyze the obvious features of the risky customers and predict the potential risky customers. In the experiment part, it has shown that our system applied to arrear risky customers' prediction has higher performance.