基于长短期记忆网络的电费回收风险分析方法
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(国网甘肃省电力公司,兰州 730030)

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王林信(1967),男,甘肃泰安人,硕士,高级工程师,主要从事电力营销管理等工作;余向前(1973),男,北京人,硕士,高级工程师,主要从事工程技术等工作;欧阳燕(1975),女,甘肃庆阳人,学士,高级会计师,主要从事经济管理等工作;陈元楷(1982),男,甘肃平凉人,学士,高级会计师,主要从事经济管理等工作;张晓庆(1979),女,陕西渭南人,学士,高级经济师,主要从事电费财务管理等工作。

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国网甘肃省电力公司科技项目(LH18L498-S )


Risk analysis of electricity tariff recovery based on long-and-short term memory network
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(State Grid Gansu Electric Power Company, Lanzhou 730030, China)

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    摘要:

    新冠疫情在全世界的蔓延,对国内的经济发展造成了较大的影响,供电企业电费回收压力日益增大。针对新冠疫情形势下电费回收风险分析准确性差、催费针对性不强的问题,提出了一种基于长短期记忆网络的电费风险分析方法。首先,建立供电用户分类体系,通过AP聚类,实现对供电用户的分级分类;其次,通过供电用户征信、司法裁判等信息综合对供电用户的信用进行评估;再次,通过长短期记忆网络,结合用户的历史的缴费信息和用户信用进行电费回收分析,预测可能存在的欠费风险。最后,在某地区供电公司进行实例运行,其运行结果验证了所提方法的可行性和有效性。

    Abstract:

    With the spread of COVID-19 in the whole world,the domestic economic development has been greatly affected, and the pressure of power supply company’s electricity recovery has been increasing. In order to solve the problem of poor accuracy of electricity charge recovery and less pertinence in the situation of COVID- 19, a risk analysis method based on long term and short term memory network is proposed. Firstly, the classification system of power supply users is established, and the classification of power supply users is realized by AP clustering. Secondly, the credit of power supply users is evaluated through the information of power supply users’credit investigation and judicial judgment. Thirdly,through the long-term and short-term memory network, combined with the user’s historical payment information and the user’s credit, the electricity charge recovery analysis is carried out, and the possible arrears risk is predicted. Finally, an example is run in a regional power supply company, and the results verify the feasibility and effectiveness of the proposed method.

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王林信,余向前,欧阳燕,陈元楷,张晓庆.基于长短期记忆网络的电费回收风险分析方法[J].电力需求侧管理,2023,25(1):104-109

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  • 收稿日期:2022-09-02
  • 最后修改日期:2022-11-30
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  • 在线发布日期: 2023-02-07
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