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    史玉良, 荣以平, 朱伟义. 基于用电特征分析的窃电行为识别方法[J]. 计算机研究与发展, 2018, 55(8): 1599-1608. DOI: 10.7544/issn1000-1239.2018.20180216
    引用本文: 史玉良, 荣以平, 朱伟义. 基于用电特征分析的窃电行为识别方法[J]. 计算机研究与发展, 2018, 55(8): 1599-1608. DOI: 10.7544/issn1000-1239.2018.20180216
    Shi Yuliang, Rong Yiping, Zhu Weiyi. Stealing Behavior Recognition Method Based on Electricity Characteristics Analysis[J]. Journal of Computer Research and Development, 2018, 55(8): 1599-1608. DOI: 10.7544/issn1000-1239.2018.20180216
    Citation: Shi Yuliang, Rong Yiping, Zhu Weiyi. Stealing Behavior Recognition Method Based on Electricity Characteristics Analysis[J]. Journal of Computer Research and Development, 2018, 55(8): 1599-1608. DOI: 10.7544/issn1000-1239.2018.20180216

    基于用电特征分析的窃电行为识别方法

    Stealing Behavior Recognition Method Based on Electricity Characteristics Analysis

    • 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.

       

      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.

       

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