基于并行分类算法的电力客户欠费预警

针对供电企业"先消费后付款"的经营模式可能造成用电客户因失信引发的欠费风险,需要在用电客户欠费行为发生之前实时快速地分析海量的用电用户的数据,给出潜在的欠费客户名单的问题,提出一种基于并行分类算法的电力客户欠费预警方法。首先,该方法使用基于Spark的随机森林(RF)分类算法对欠费用户进行建模;其次,根据用户以往历史用电行为和缴费记录使用时间序列进行预测得到其未来用电和缴费行为特征;最后,使用之前得到的模型对用户进行分类得到未来潜在高危险欠费用户。将该方法与并行化后的支持向量机(SVM)算法和在线序列极限学习机(OSELM)算法进行对比分析,实验结果表明,所提方法相对于对比算法在准确率上有较大提高,便于电费回收管理人员进行提前催缴,确保电费回收的及时性,有利于电力企业进行客户欠费风险管理。

Electricity customers arrears alert based on parallel classification algorithm

The "consumption first and replenishment afterward" operation model of the power supply companies may cause the risk of arrears due to poor credit of some power consumers. Therefore, it is necessary to analyze of the tremendous user data in real-time and quickly before the arrears' happening and provide a list of the potential customers in arrear. In order to solve the problem, a method for arrears alert of power consumers based on the parallel classification algorithm was proposed. Firstly, the arrear behaviors were modeled by the parallel Random Forest (RF) classification algorithm based on the Spark framework. Secondly, based on previous consumption behaviors and payment records, the future characteristics of consumption and payment behavior were predicted by time series. Finally, the list of the potential hig-risk customers in arrear was obtained by using the obtained model for classifying users. The proposed algorithm was compared with the parallel Support Vector Machine (SVM) algorithm and Online Sequential Extreme Learning Machine (OSELM) algorithm. The experimental results demonstrate that, the prediction accuracy of the proposed algorithm performs better than the other algorithms in comparison. Therefore, the proposed method is a convenient way for electricity recycling management to remind the customers of paying the electricity bills ahead of time, which can ensure timeliness electricity recovery. Moreover, the proposed method is also beneficial for consumer arrear risk management of the power supply companies.