摘要
客户既是企业最大的财富,也是风险的最大来源,拖欠电费、违约用电等行为时有发生。在交易过程中对客户信誉信息收集调查和风险评估,制定针对性的防范和服务策略,对企业具有非常重要的作用。为了降低电费回收风险,提高企业经营效益和管理手段,需要对客户进行评价分级,电力企业可以利用现有资源和数据,建立客户电费信誉信息档案,为客户提供差异化的服务,并规避风险。以营销业务应用系统中客户的海量历史用电数据为依据,再借鉴客户经理、催费员等电力人员工作经验,梳理风险用户的业务规则,并构建多维度的电费风险分析指标体系。通过机器学习的大数据分析方式对用电行为与电费风险之前的潜在关系进行研究,实现对风险用户的精准定位。
Customers are not only the greatest wealth of enterprises,but also the biggest source of risk. Default of electricity charges,breach of contract and other behaviors occur from time to time. In the course of transaction,it is very important for enterprises to collect and investigate customer reputation information,to make risk assessment,and to formulate targeted prevention and service strategies. In order to reduce the risk of electricity tariff recovery and improve the business efficiency and management means of enterprises,customers need to be evaluated and graded. Electric power enterprises can use existing resources and data to establish customer credit information files,provide customers with differentiated services and avoid risks. Based on the huge amount of historical electricity data of customers in the marketing business application system,then draw lessons from the work experience of electric personnel such as customer managers and fee collectors,comb out the business rules of risk users,and build a multi-dimensional electricity risk analysis index system. The potential relationship between power consumption behavior and electricity risk was studied through the data analysis method of machine learning,and the precise location of risk users was realized.
作者
厉建宾
吴彬彬
朱雅魁
王悦
安亚刚
赵莎莎
LI Jianbin;WU Binbin;ZHU Yakui;Wang Yue;An Yagang;Zhao Shasha(State Grid Hebei Electric Power Research Institute,Shijiazhuang 050000 Hebei,China)
出处
《电力大数据》
2019年第2期1-6,共6页
Power Systems and Big Data
关键词
电费风险
预测
防控
electricity risk
prediction
prevention and control
