王林信, 周盛成, 罗世刚, 江元, 王琼, 马莉. 基于改进词向量模型的电力缴费用户画像关键技术研究[J]. 电力信息与通信技术, 2022, 20(2): 42-48. DOI: 10.16543/j.2095-641x.electric.power.ict.2022.02.006
引用本文: 王林信, 周盛成, 罗世刚, 江元, 王琼, 马莉. 基于改进词向量模型的电力缴费用户画像关键技术研究[J]. 电力信息与通信技术, 2022, 20(2): 42-48. DOI: 10.16543/j.2095-641x.electric.power.ict.2022.02.006
WANG Linxin, ZHOU Shengcheng, LUO Shigang, JIANG Yuan, WANG Qiong, MA Li. Research on Key Technology of Power Payment User Portrait Based on Improved Word Vector Model[J]. Electric Power Information and Communication Technology, 2022, 20(2): 42-48. DOI: 10.16543/j.2095-641x.electric.power.ict.2022.02.006
Citation: WANG Linxin, ZHOU Shengcheng, LUO Shigang, JIANG Yuan, WANG Qiong, MA Li. Research on Key Technology of Power Payment User Portrait Based on Improved Word Vector Model[J]. Electric Power Information and Communication Technology, 2022, 20(2): 42-48. DOI: 10.16543/j.2095-641x.electric.power.ict.2022.02.006

基于改进词向量模型的电力缴费用户画像关键技术研究

Research on Key Technology of Power Payment User Portrait Based on Improved Word Vector Model

  • 摘要: 以准确识别电力缴费过程中的敏感用户为目标,文章基于改进词向量模型设计了一种新的电力缴费用户画像方法。首先通过改进K-均值算法对电力用户缴费行为大数据展开聚类处理,实现电网部门缴费用户的电量、电价、电费、停电类诉求数据聚类。然后基于改进词向量模型设计用户画像方法,并将聚类结果导入其中,通过构建用户画像识别电力缴费用户敏感类型。实验结果显示,该方法对4种敏感类型用户的分类结果较准确,分类结果差值仅有1个。且该方法对电网部门缴费用户的电量、电价、电费、停电类诉求数据聚类效果较好,查全率、准确率、F值均大于0.95,证明其聚类效果较好,可准确识别电力缴费过程中的敏感用户。

     

    Abstract: Aiming at accurate identification of sensitive users in the process of power payment, this paper designs a new power payment user portrait method based on improved word vector model. First of all, the improved K-means algorithm is used to cluster the big data of power users' payment behaviors, so as to realize the clustering of power supply, electricity price, electricity fee and power failure demand data of power payment users. Then the user portrait method is designed based on the improved word vector model, and the clustering results are imported into the method, and the sensitive types of power payment users are identified by constructing the user portrait. Experimental results show that this method is accurate for the classification of four sensitive types of users, and the difference between the classification results is only 1. Moreover, this method has a good clustering effect on the power, electricity price, electricity fee and power failure demand data of power payment users in the power grid department. The recall rate, accuracy rate and F value are all greater than 0.95, which proves that this method has a good clustering effect and can accurately identify sensitive users in the process of power payment.

     

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