基于贝叶斯网络的电力客户停电敏感度预测

精准预测停电敏感的电力客户群体,能够有效感知客户用电需求,提升客户用电满意度,助力提高电力服务水平。文中提出基于贝叶斯网络构建电力客户停电敏感度预测模型,从95598客服平台、营销业务系统、用电信息采集系统获取分析数据,结合客户基本信息、用电信息、智能电能表计量信息以及用户用电交互行为,定义客户停电敏感度数据标签,对用户的停电投诉进行分析与预测。采用K折交叉验证法对停电敏感度预测模型进行实验验证。实验表明,基于贝叶斯网络构建的电力客户停电敏感度预测模型,在停电投诉分析应用中具备较高的精准度,验证了模型的有效性。

Prediction of power customer outage sensitivity based on Bayesian network

Accurate prediction of sensitive power customer groups can perceive customer demand and improve customer satisfaction with electricity consumption and the level of power service effectively. A power customer outage sensitivity prediction model based on Bayesian network is proposed to predict the power customer outage complaints, which defines customer power outage sensitivity data labels by customer basic information, power consumption information, smart meter energy measurement information, and user power interaction behavior, coming from 95598 customer service platform, marketing system and power information collection system.It experimentally verifies the power outage sensitivity analysis model using K-folding cross validation method, shows that the power outage sensitivity prediction model based on bayesian network has high precision in the application of power outage complaint analysis, and the experimental results demonstrate the effectiveness of the prediction model.