Abstract Electricity theft is the main reason for non-technical losses (NTL) in distribution networks, which can lead to great economic losses in power supply enterprises. Efficient and accurate detection of abnormal power consumption patterns is a key part of demand side management. With the popular use of smart meters, it is more efficient and reliable to collect customers’ power consumption data, which make on-line monitoring of power consumption possible. In this paper, a detection algorithm based on local matrix reconstruction (LMR) is proposed and utilized to detect abnormal power consumption patterns in power systems. In this algorithm, five daily load characteristics are used to replace high-dimensional daily load curves to characterize power consumption patterns. Then, principal component analysis (PCA) is applied to calculate weighted reconstruction errors in a local scope. The reconstruction error of each sample is compared with its adjacent samples in order to calculate local outlier scores, which represent the abnormal degree of each load sample. Using two open source datasets, the detection performance of the proposed method is verified to be effective and efficient.

中文翻译：

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基于局部矩阵重构的异常用电模式检测数据挖掘

摘要 窃电是配电网非技术性损失（NTL）的主要原因，会给供电企业带来巨大的经济损失。高效准确地检测异常用电模式是需求侧管理的关键部分。随着智能电表的普及，采集客户用电数据更加高效可靠，使在线监测用电成为可能。在本文中，提出了一种基于局部矩阵重构（LMR）的检测算法，并将其用于检测电力系统中的异常功耗模式。在该算法中，五个日负荷特性被用来代替高维日负荷曲线来表征电力消耗模式。然后，应用主成分分析 (PCA) 来计算局部范围内的加权重建误差。将每个样本的重构误差与其相邻样本进行比较，以计算局部异常值分数，该分数代表每个负载样本的异常程度。使用两个开源数据集，验证了所提出方法的检测性能是有效和高效的。