Unsupervised BLSTM Based Electricity Theft Detection with Training Data Contaminated

Abstract

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 paper, 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 (GMM) 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 stage; 2) An attention-based bidirectional Long Short-Term Memory (BLSTM) encoder-decoder scheme is employed to improve the robustness against the non-malicious changes in usage patterns leveraging the process of encoding and decoding. Quantifying the similarity of consumption patterns and reconstruction errors, the anomaly score is defined to improve detection performance. Experiments on a real dataset show that the proposed method outperforms the state-of-the-art unsupervised detectors.