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

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 corre...

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Bibliographic Details
Published in:Energies (Basel) 2020-11, Vol.13 (21), p.5758
Main Authors: Feng, Xiaofeng, Hui, Hengyu, Liang, Ziyang, Guo, Wenchong, Que, Huakun, Feng, Haoyang, Yao, Yu, Ye, Chengjin, Ding, Yi
Format: Article
Language:English
Subjects:
Accuracy
Data structures
data-driven approaches
Datasets
Electric power
Electricity
Electricity consumption
Electricity distribution
electricity theft detection
Methods
Neural networks
smart meters
Support vector machines
text convolutional neural networks (TextCNN)
Theft
time-series classification
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13215758