A Novel Electricity Theft Detection Scheme Based on Text Convolutional Neural Networks

Xiaofeng Feng, Hengyu Hui, Ziyang Liang, Wenchong Guo, Huakun Que, Haoyang Feng, Yu Yao, Chengjin Ye and Yi Ding
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Xiaofeng Feng: Metrology Center of Guangdong Power Grid Corporation, Guangzhou 510080, China
Hengyu Hui: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Ziyang Liang: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Wenchong Guo: Metrology Center of Guangdong Power Grid Corporation, Guangzhou 510080, China
Huakun Que: Metrology Center of Guangdong Power Grid Corporation, Guangzhou 510080, China
Haoyang Feng: Metrology Center of Guangdong Power Grid Corporation, Guangzhou 510080, China
Yu Yao: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Chengjin Ye: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Yi Ding: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Energies, 2020, vol. 13, issue 21, 1-17

Abstract: Electricity theft decreases electricity revenues and brings risks to power usage’s safety, which has been increasingly challenging nowadays. As the mainstream in the relevant studies, the state-of-the-art data-driven approaches mainly detect electricity theft events from the perspective of the correlations between different daily or weekly loads, which is relatively inadequate to extract features from hours or more of fine-grained temporal data. In view of the above deficiencies, we propose a novel electricity theft detection scheme based on text convolutional neural networks (TextCNN). Specifically, we convert electricity consumption measurements over a horizon of interest into a two-dimensional time-series containing the intraday electricity features. Based on the data structure, the proposed method can accurately capture various periodical features of electricity consumption. Moreover, a data augmentation method is proposed to cope with the imbalance of electricity theft data. Extensive experimental results based on realistic Chinese and Irish datasets indicate that the proposed model achieves a better performance compared with other existing methods.

Keywords: data-driven approaches; electricity theft detection; smart meters; text convolutional neural networks (TextCNN); time-series classification (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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