Electricity Theft Detection in Power Grids with Deep Learning and Random Forests
Joint Authors
Wang, Jinkuan
Zhao, Qiang
Li, Shuan
Yao, Xu
Yingchen, Song
Han, Yinghua
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-10-03
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
As one of the major factors of the nontechnical losses (NTLs) in distribution networks, the electricity theft causes significant harm to power grids, which influences power supply quality and reduces operating profits.
In order to help utility companies solve the problems of inefficient electricity inspection and irregular power consumption, a novel hybrid convolutional neural network-random forest (CNN-RF) model for automatic electricity theft detection is presented in this paper.
In this model, a convolutional neural network (CNN) firstly is designed to learn the features between different hours of the day and different days from massive and varying smart meter data by the operations of convolution and downsampling.
In addition, a dropout layer is added to retard the risk of overfitting, and the backpropagation algorithm is applied to update network parameters in the training phase.
And then, the random forest (RF) is trained based on the obtained features to detect whether the consumer steals electricity.
To build the RF in the hybrid model, the grid search algorithm is adopted to determine optimal parameters.
Finally, experiments are conducted based on real energy consumption data, and the results show that the proposed detection model outperforms other methods in terms of accuracy and efficiency.
American Psychological Association (APA)
Li, Shuan& Han, Yinghua& Yao, Xu& Yingchen, Song& Wang, Jinkuan& Zhao, Qiang. 2019. Electricity Theft Detection in Power Grids with Deep Learning and Random Forests. Journal of Electrical and Computer Engineering،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1173723
Modern Language Association (MLA)
Li, Shuan…[et al.]. Electricity Theft Detection in Power Grids with Deep Learning and Random Forests. Journal of Electrical and Computer Engineering No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1173723
American Medical Association (AMA)
Li, Shuan& Han, Yinghua& Yao, Xu& Yingchen, Song& Wang, Jinkuan& Zhao, Qiang. Electricity Theft Detection in Power Grids with Deep Learning and Random Forests. Journal of Electrical and Computer Engineering. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1173723
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1173723