Power Customer Arrears Risk Assessment And Prediction Model Based On HIFAHP Method

The tariff recovery management has been the focus of the power supply enterprises.For a long time,the power supply enterprises have always adopted the market rule that customers can use electricity before buy it.While the recovery tariff cycle is always long and the measures to urge the fee are backward,then the tariff recovery has been a significant problem in the power supply enterprises.In addition,there is no effective risk analysis method for arrears,and the power supply enterprises haven’t establish differentiation risk control strategy.At present,only manual or systematic analysis tools can be used to identify the risk of the tariff recovery.On one hand,we can’t carry on the comprehensive analysis without considering the basic information,industry characteristics and behavior characteristics of customers.On the other hand,because of the backward of the practice point of risk prevention,we can’t form early standardized warning mechanism which will make it difficult to formulate differentiated control measures in time and increases the operational risk of the enterprises.In view of this,we should build a set of management method(Hesitant Intuitionistic Fuzzy Analytic Hierarchy Process)to estimate and predict arrears by using big data analysis technology based on the massive historical data information of customers.Then the power supply enterprises will improve the risk prevention and control ability,reduce the risk of enterprise operation and predict the risk of arrears efficiently and accurately.1)The electric power customers will be divided into two categories based on the situation of the tariff recovery management in the power supply and the industry risk assessment theory to construct the risk evaluation index system of power customers’ arrears respectively.2)For the problem of arrears in power system combined with the hesitate intuitionistic fuzzy sets and analytic hierarchy process,HIFAHP was proposed.We construct the hesitate intuitionistic judgment matrix;The second we propose the consistency test method of the intuition judgment matrix and the correction method of the nonconformance judgment matrix;The last,using the normal distribution weighting method and the weights of positions to determine attribute weights,then we will get the score functions of hesitant intuitionistic fuzzy matrix to evaluate the risk of electricity customer arrears.3)On the basis of the risk assessment of power customers’ arrears and the software resources of China’s big data platform,considering the characteristics of two types of power customers,the Logistic regression algorithm and the decision Tree Algorithm are used to predict the default risk of the first type of power customers and the second type of power customers.This paper takes four electric customers as the practical application background,on the basis of the risk assessment index system for power customers we established a multi-attribute evaluation matrix and get the score functions of hesitant intuitionistic fuzzy matrix thus we will identify high-risk power customers.Using the Logistic regression algorithm and decision Tree Algorithm forecasted the arrears risk of the first type of power customers and the second type of power customers,according to the predicted results,take appropriate measures,the efficiency of electric charge recovery is improved,therefore,the index system and prediction models have good feasibility and effectiveness.