Abstract Abnormal electricity consumption (AEC) caused huge economic losses to power supply enterprises in the past years, and also posed severe threats to the safety of peoples’ daily live. An accurate AEC detection is crucial to reducing the non-technical losses (NTLs) suffered by power supply enterprises and the State Grid. Comparing with the huge amount of electricity data flow, AEC data are relative few, that makes the AEC detection a typical imbalanced learning problem. To address this issue, two effective AEC detection algorithms from the perspective of data balancing and data weighting, respectively, are studied in this paper: (i) the K-means clustering and synthetic minority oversampling (K-means SMOTE) technique combining with the artificial neural network (ANN) trained by kernel extreme learning machine (KELM), and (ii) the deep weighted ELM (DWELM), that builds on an improved multiclass AdaBoost imbalanced learning algorithm (AdaBoost-ID) and an enhanced deep representation network based ELM (EH-DrELM). Experiments on the electricity consumption data of State Grid Zhejiang Electric Power Corporation are presented to show the effectiveness of the proposed algorithms. Comparisons to many state-of-the-art methods are provided for the superiority demonstration.

中文翻译：

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基于不平衡学习算法的智能异常用电检测

摘要 近年来，异常用电(AEC)给供电企业造成了巨大的经济损失，也对人们的日常生活安全构成了严重威胁。准确的AEC检测对于减少供电企业和国家电网遭受的非技术损失（NTL）至关重要。与海量的电力数据流相比，AEC数据相对较少，这使得AEC检测成为典型的不平衡学习问题。为了解决这个问题，本文分别从数据平衡和数据加权的角度研究了两种有效的 AEC 检测算法：(i) K-means 聚类和合成少数过采样 (K-means SMOTE) 技术结合由内核极限学习机（KELM）训练的人工神经网络（ANN），(ii) 深度加权 ELM (DWELM)，它建立在改进的多类 AdaBoost 不平衡学习算法 (AdaBoost-ID) 和基于增强型深度表示网络的 ELM (EH-DrELM) 之上。以国网浙江省电力公司的用电数据为实验对象，验证了所提算法的有效性。提供了与许多最先进方法的比较以进行优越性演示。