基于长短期记忆网络算法的电费回收风险预警

Application of Long Short-term Memory Network Algorithm in Tariff Recovery Risk Early Warning for Large Power Customers

电力大客户电费回收风险一直都是电力公司关注的热点,但由于缺乏外部数据支撑和高效预警模型,使电费回收风险预警成为了一个难题。文中首先综合电力客户的电量、电费数据,以及电力客户在工商、税务、法院等部门的风险信息,建立电力大客户的电费回收风险指标体系。其次,基于熵值法得到的风险指标权重系数,过滤弱影响指标,采用相关性分析剔除重叠作用指标,得到客户电费回收风险预警指标。最后,基于深度学习中的长短期记忆(long short-term memory,LSTM)网络算法进行了客户电费回收风险预警。算例结果表明,提出的风险预警模型精确有效,且LSTM在准确率、查准率和查全率3个指标上较Logistic回归更加精准,能够精准定位风险客户,提高电费回收效率。

For a long time,tariff recovery risk of large power customers is a hot spot for electric power company. But because of the lack of external data support and efficient early warning model,tariff recovery risk early warning has become a difficult problem. Based on electricity consumption and electricity charge data,combining with the relevant business,tax and court information of the enterprise,the paper established a serial of tariff recovery risk indexes for large power customers. Secondly,the entropy method（ EM） is adopted to evaluate tariff recovery risk assessment of customers＇ electricity bills,and the customer risk level is divided according to the distribution of risk score. Weak influence indexes were filtered by weight coefficients and overlapping indexes were dropped by correlation analysis. Tariff recovery risk early warning model was carried out by Long Short-Term Memory（ LSTM） network. Numerical example results show that the proposed risk early warning model was accurate and effective,and the result gained by LSTM is better than Logistic regression in accuracy,precision and recall. The tariff recovery risk early warning results can accurately locate high risk customers and improve tariff recovery efficiency.