摘要
随着电力系统规模的不断扩大、电网业务逐步呈现多样化的形态,电力企业对于把控金融风险的难度也逐步增加。如何对电力企业用户存在的逾期缴费等金融风险问题进行精准评估与分析预测,是目前电力金融领域面临的一个重要问题。在此背景下,文章首先通过改进的随机森林算法对电力企业客户的特征要素进行重要性评估,给出该客户发生逾期缴费等金融风险事件影响的大小;然后,利用随机森林方法中的分类预测机制对其是否会有逾期缴费风险进行预测,并将该算法与逻辑回归算法进行对比;最后,通过算例仿真,结果表明所述方法能在一定程度上对电力客户缴费的金融风险行为进行评估预测。
With the continuous expansion of the scale of the power system and the gradual diversification of the power grid business,the difficulty for power enterprises to control financial risks has gradually increased.How to accurately evaluate,analyze and predict the financial risk problems such as overdue payment of power enterprise users is an important issue in the field of power finance.In this context,this paper first evaluates the importance of the characteristic elements of the power enterprise customer with the improved random forest algorithm,and gives the size of the impact of financial risk events such as overdue payment of the customer.Then,the classification prediction mechanism of random forest method is used to predict whether it will have the risk of overdue payment,and the algorithm is compared with the logistic regression algorithm.Finally,through the simulation of an example,the results show that the method can evaluate and predict the financial risk behavior of power customers'payment to a certain extent.
作者
余锦河
田举
丁颖
YU Jinhe;TIAN Ju;DING Ying(Customer Service Center,State Grid Corporation of China,Dongli District,Tianjin 300309,China)
出处
《电力信息与通信技术》
2023年第11期63-69,共7页
Electric Power Information and Communication Technology
基金
国网客服中心-电力营销业务-2020年网上国网服务后台-设计开发实施项目(71993118000D)。
关键词
金融数据
企业风险
数据挖掘
随机森林算法
分析预测
financial data
enterprise risk
data mining
random forest algorithm
analyze and predict
