ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (8): 1599-1608.doi: 10.7544/issn1000-1239.2018.20180216

所属专题: 2018数据挖掘前沿进展专题

• 人工智能 • 上一篇    下一篇

基于用电特征分析的窃电行为识别方法

史玉良1,2,荣以平3,朱伟义3   

  1. 1(山东大学软件学院 济南 250100);2(山大地纬软件股份有限公司 济南 250100);3(国网山东省电力公司 济南 250001) (shiyuliang@sdu.edu.cn)
  • 出版日期: 2018-08-01
  • 基金资助: 
    山东省泰山产业领军人才工程专项经费(tscy20150305);山东省重点研发计划(2016GGX101008,2016ZDJS01A09);山东省自然科学基金重大基础研究项目(ZR2017ZB0419) This work was supported by the TaiShan Industrial Experts Programme of Shandong Province (tscy20150305), the Primary Research and Development Plan of Shandong Province (2016GGX101008, 2016ZDJS01A09), and the Major Basic Research Project of Natural Science Foundation of Shandong Province (ZR2017ZB0419).

Stealing Behavior Recognition Method Based on Electricity Characteristics Analysis

Shi Yuliang1,2, Rong Yiping3,Zhu Weiyi3   

  1. 1(School of Software, Shandong University, Jinan 250100);2(Dareway Software Co., Ltd., Jinan 250100);3(State Grid Shandong Electric Power Company, Jinan 250001)
  • Online: 2018-08-01

摘要: 反窃电工作是实现电力企业用电管理不可或缺的环节.针对山东省用电用户数量多、分布面积广、窃电现象逐年上升、检测人员不足等特点,对获取的用户窃电行为数据进行合理的分析、处理,提出一种基于用电特征分析的窃电行为识别方法,实现对窃电嫌疑用户的筛查.该方法首先基于采集样本,以过滤式算法和规则阈值设定的方式,实现采集样本数据的特征提取,从而提高采集数据的有效性;随后以逻辑回归算法构建用户窃电行为诊断模型,实现对窃电嫌疑用户的判定;此外,采用推送、排查、处理和反馈的闭环工作机制不断优化模型,并以国网山东省电力公司用电信息采集系统、营销业务应用系统提供数据进行算例分析,验证了所述方法的可行性与适用性.

关键词: 过滤式算法, 规则阈值设定, 特征提取, 用户窃电行为诊断模型, 闭环反馈优化

Abstract: Anti-stealing electricity is an indispensable component of electricity enterprise management. In view of the current problems such as the large number of users, the wide distribution area, the increasing year-on-year power stealing, and the lack of supervision personnel, this paper analyzes and handles the data of power stealing behavior, and proposes a stealing behavior recognition method which can identify the stealing users. First, based on the collected samples, this method adopts a filtering algorithm and a regular threshold to implement feature extraction, so as to improve the effectiveness of the collected data. Then, the user’s stealing behavior diagnosis model is constructed based on the logistic regression algorithm to realize the determination of suspected users. In addition, this paper uses the closed-loop working mechanism which continues to update data by the way of pushing, shooting, processing and feedback to continuously optimize the model. According to the collected data provided by the power consumption information collection system and marketing business application system of State Grid Shandong Electric Power Company, the experimental results prove the feasibility and applicability of the method.

Key words: filter algorithm, rule threshold setting, feature extraction, stealing behavior diagnosis model, closed-loop feedback optimization

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