基于模糊聚类的异常用电行为识别研究

窃电等异常用电行为的识别是用电检查的重点和难点。由于数据采集问题，以往研究大多专注于大客户窃电行为方面，对居民窃电行为的研究相对较为薄弱。针对小用户级别的窃电等窃电监测问题，提出一种基于聚类的异常用电行为识别方法。该方案首先从智能电表收集的数据中提取用电特征，然后使用模糊聚类分析数据结构，提取出正常用户的行为特征。在真实数据集上的实验结果表明了该方法的有效性。

Fuzzy clustering based abnormal power consumption behavior detection on resident level

Detection of abnormal power consumption behavior including energy theft is an important and difficult part of electricity inspection. Due to data aggregation problems, previous studies have mostly focused on electricity stealing behaviors of large customers, and research on residents'' electricity stealing behaviors is relatively weak. This article extracts electricity characteristics from data collected by smart meters, and then uses fuzzy clustering to analyze the data structure to extract behavior characteristics of normal users. Experimental results on real-world dataset show the applicability of the proposed method.