An electric power large customer electricity charge recovery risk early warning method based on deep learning

Abstract

The invention discloses an electric power large customer electric charge recovery risk early warning method based on deep learning, which comprises the following steps of: establishing an electric charge recovery risk index system of an electric power large customer based on electric quantity and electric charge data inside the electric power, and combining business, tax administration and court information related to an enterprise; Filtering weak influence indexes based on a risk index weight coefficient obtained by an entropy method, and rejecting overlapping action indexes by adopting correlation analysis to obtain a client electricity charge recovery risk early warning index; And performing historical data training to obtain an electricity charge recovery risk early warning deep learning model, and performing client electricity charge recovery risk early warning. The risk early warning model provided by the invention is accurate and effective, risk customers can be accurately positioned, and the electricity charge recovery efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of intelligent application of electric power marketing, and in particular relates to a method for early warning of electricity bill recovery risk for major electric power customers based on deep learning. Background technique [0002] For a long time, the electricity charge recovery rate has been an important assessment index for power companies, and its quality is directly related to the operating efficiency and level of power companies. Large power customers have large contract capacity and large electricity consumption. Whether they pay their bills in a timely manner will largely affect the recovery rate of electricity charges. Therefore, in recent years, electric power companies have always regarded the risk of electricity charge recovery as one of the core indicators of their operational risk, and have also carried out a lot of research work on this topic, and achieved certain results. [0003] At pres...

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Problems solved by technology

[0003] At present, work such as business expansion, installation, and electricity inspections are mainly concerned with the electricity consumption information required to complete professional work, but there is insufficient active understanding of important economic information such as the customer's industry classification, operating conditions, development trends, and legal background. Insufficient attention has been paid to the customer's operating status and possible arrears risks, and the role of pre-prevention and in-process control has not been fully utilized, and the risk of electricity charge recovery cannot be effectively reduced

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