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Unsupervised Blstm-Based Electricity Theft Detection With Training Data Contaminated, Qiushi Liang, Shengjie Zhao, Jiangfan Zhang, Hao Deng

Electrical and Computer Engineering Faculty Research & Creative Works

Electricity theft can cause economic damage and even increase the risk of outage. Recently, many methods have implemented electricity theft detection on smart meter data. However, how to conduct detection on the dataset without any label still remains challenging. In this article, we propose a novel unsupervised two-stage approach under the assumption that the training set is contaminated by attacks. Specifically, the method consists of two stages: (1) a Gaussian mixture model is employed to cluster consumption patterns with respect to different habits of electricity usage, and with the goal of improving the accuracy of the model in the posterior …