Imbalanced learning algorithm based intelligent abnormal electricity consumption detection

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.