Abnormal Electricity Consumption Behaviors Detection Based on Improved Deep Auto-Encoder

doi:10.11930/j.issn.1004-9649.201910005

Abstract

Abstract: In order to accurately detect the abnormal electricity consumption behaviors for reducing the operating costs of power companies, a detection method of abnormal electricity consumption behaviors is proposed based on the improved deep auto-encoder (DAE). Firstly, the data of normal electricity users are employed as training samples, and the effective features of the data are automatically extracted by AE; and then the data is reconstructed to calculate the detection threshold. Because the effective data characteristics are destroyed by the abnormal behaviors, the abnormal behaviors can be detected through comparing the difference between the reconstruction error and the detection threshold. To improve the feature extraction ability and the robustness of AE network, the sparse restrictions and the noise coding are introduced into the auto-encoder, and the hyper-parameters of AE network are optimized through the particle swarm optimization algorithm to improve the learning efficiency and generalization ability. The proposed model is validated by the electricity consumption dataset of domestic and business users of a region in Fujian province, and the abnormal detection accuracy is higher than 92%, which indicates that the proposed method has a powerful ability in feature extraction and abnormal behavior detection.

Key words: abnormal electricity consumption behavior, auto-encoder, sparse restriction, noise, feature extraction, data reconstruction

LIN Nvgui, HONG Lanxiu, HUANG Daoshan, YI Yang, LIU Zhixuan, XU Qifeng. Abnormal Electricity Consumption Behaviors Detection Based on Improved Deep Auto-Encoder[J]. Electric Power, 2020, 53(6): 18-26.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201910005

https://www.electricpower.com.cn/EN/Y2020/V53/I6/18

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