基于多尺度特征提取的电力客户欠费风险预测

针对用户拖欠电费频繁发生的现象,如何利用科学方法和技术手段来预测电力客户的欠费风险,降低自身的经营风险,是供电企业急需解决的问题。文中以某地高压用户为例,分析了导致用户欠费的影响因素,从欠费频度、违约时长等多个尺度提取用户欠费风险的特征,并基于逻辑回归算法建立模型用于预测用户欠费风险。模型的评价结果表明,在所获取的用户信息不够全面的情况下,模型的预测准确率、精确率和召回率等评价指标仍较为精准,特别是模型对风险用户的识别较为灵敏。该风险模型可用于指导供电企业制定欠费风险管理对策,提高管理水平。

Arrears risk prediction of large power customers based on multi-scale feature extraction

In view of the frequent occurrence of electricity arrears,how to use scientific methods and technical means to predict the arrears risk of power customers and reduce business risk is an urgent problem for the Power Grid Corp.Taking high-voltage customers from a certain areaas an example,this paper analyzes the factors affecting the recovery of electricity tariff,and extracts therisk features from multiple scales such as arrears frequency and duration,and then establishes a model based on logistic regression algorithm to predict the risk of user arrears.The model′s evaluation results show that the indicators such as the prediction accuracy,precision and recall rate are still relatively accurate when user information is not comprehensive enough.The model issensitive to the identification of risk users,and can be used to guide the power grid corp to formulate arrears risk management policiesand improving their management capabilities.