An Efficient Method for Detecting Abnormal Electricity Behavior

Chao Tang, Yunchuan Qin, Yumeng Liu, Huilong Pi and Zhuo Tang ()
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Chao Tang: College of Computer Science and Technology, Hunan University, Changsha 410012, China
Yunchuan Qin: College of Computer Science and Technology, Hunan University, Changsha 410012, China
Yumeng Liu: Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
Huilong Pi: College of Computer Science and Technology, Hunan University, Changsha 410012, China
Zhuo Tang: College of Computer Science and Technology, Hunan University, Changsha 410012, China

Energies, 2024, vol. 17, issue 11, 1-16

Abstract: The non-technical losses caused by abnormal power consumption behavior of power users seriously affect the revenue of power companies and the quality of power supply. To assist electric power companies in improving the efficiency of power consumption audit and regulating the power consumption behavior of users, this paper proposes a power consumption anomaly detection method named High-LowDAAE (Autoencoder model for dual adversarial training of high low-level temporal features). High-LowDAAE adds an extra “discriminator” named AE3 to USAD (UnSupervised Anomaly Detection on Multivariate Time Series), which performs the same function as AE2 in USAD. AE3 performs the same function as AE2 in USAD, i.e., it is trained against AE1 to enhance its ability to reconstruct average data. However, AE3 differs from AE2 because the two “discriminators” correspond to the high-level and low-level time series features output from the shared encoder network. By utilizing different levels of temporal features to reconstruct the data and conducting adversarial training, AE1 can reconstruct the time-series data more efficiently, thus improving the accuracy of detecting abnormal electricity usage. In addition, to enhance the model’s feature extraction ability for time-series data, the self-encoder is constructed with a long short-term memory (LSTM) network, and the fully connected layer in the USAD model is no longer used. This modification improves the extraction of temporal features and provides richer hidden features for the adversarial training of the dual “discriminators”. Finally, the ablation and comparison experiments are conducted using accurate electricity consumption data from users, and the results show that the proposed method has higher accuracy.

Keywords: non-technical losses; unsupervised anomaly detection; dual-confrontation training (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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