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Early Warning Of The Risk Of High Voltage Electricity Customer's Electricity Fee Delays

Posted on:2020-11-20Degree:MasterType:Thesis
Country:ChinaCandidate:J ZengFull Text:PDF
GTID:2392330596993643Subject:Statistics
Abstract/Summary:PDF Full Text Request
Electric energy is a special commodity to maintain the normal operation and management of power companies,and it is also one of the important economic sources to promote the sustainable development of power enterprises.Power companies charge electricity fees by providing electricity and power services to customers.The amount of electricity charged reflects the efficiency and ability of the power company.Electric power companies provide high quality and safe electric power for customers by building advanced electric power service network.It is the responsibility of power companies to provide continuous and safe power services for users,and it is also the basic guarantee for power companies to have sources of funds.Therefore,only by ensuring that all the electricity charges of the power company are recovered on time and smoothly,can the healthy development of the power company be guaranteed.In the process of the continuous development of power service network,the phenomenon that the majority of electricity users are in arrears of electricity charges is increasing sharply.Electric power companies are facing serious risk of electricity tariff recovery.The increasing amount of electricity arrears will seriously restrict the production and development of power companies.As a result,power companies can not provide stable and sustainable power resources for society,affecting the pace of social production and people's quality of life.At present,the operation mode adopted by domestic power grid companies is to use electricity first and then pay for it.This will lead to the problem of electricity arrears and the increasing amount of electricity arrears.In the huge electricity consumption data of power companies,the power consumption of high-voltage customers accounts for an astonishing proportion,and their monthly electricity charges account for more than 70%of the total electricity charges collected by power companies,which has a very large potential risk of electricity tariff recovery.Once the phenomenon of default of payment,arrears and increasing amount of arrears occurs,it will bring irreparable losses to power companies and seriously restrict the production and development of power companies.Therefore,it is imminent to realize the early warning of the risk of high-voltage electricity customers' electricity bill arrears.In order to establish an early warning model for the risk of high-voltage electricity customers' electricity bill arrears,this paper attempts to start with the basic electricity data.Specific research work is as follows:Firstly,the background and significance of this topic are analyzed in detail,and the research status and main research results of electricity customer default risk early warning are explained.This paper analyses the existing problems and shortcomings of the research results,and expounds the necessity and practical significance of establishing the early warning system for the risk of high-voltage electricity customers' electricity arrears.Pearson correlation coefficient method was used to screen the seven main factors affecting electricity bill arrears,and the dimension effects among different variables were eliminated by data standardization.Then,according to these seven main factors,we use neural network to predict the risk score value of high-voltage customers,and then use the risk score value and seven main factors to establish logistic regression to predict the risk of high-voltage customers,change the risk management model from labor-intensive to technical,and timely warn high-risk electricity customers.Experiments show that the hybrid forecasting model of neural network and logistic regression established in this paper has high forecasting accuracy and good consistency with the actual situation.It has certain practical significance.It can accurately predict the high-voltage risky customers and avoid the irreparable losses caused by default of high-voltage users to power companies.
Keywords/Search Tags:risk prediction, neural network, logical regression, high voltage customers, power grid
PDF Full Text Request
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