Electricity Theft Detection in Power Grids with Deep Learning and Random Forests
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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
|
| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/4136874 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832560818106925056 |
|---|---|
| author | Shuan Li Yinghua Han Xu Yao Song Yingchen Jinkuan Wang Qiang Zhao |
| author_facet | Shuan Li Yinghua Han Xu Yao Song Yingchen Jinkuan Wang Qiang Zhao |
| author_sort | Shuan Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-910fd867e1174a8793cc914676907ba4 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-910fd867e1174a8793cc914676907ba42025-02-03T01:26:36ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552019-01-01201910.1155/2019/41368744136874Electricity Theft Detection in Power Grids with Deep Learning and Random ForestsShuan Li0Yinghua Han1Xu Yao2Song Yingchen3Jinkuan Wang4Qiang Zhao5School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, ChinaSchool of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, ChinaSchool of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, ChinaSchool of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang 110819, ChinaSchool of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, ChinaAs 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.http://dx.doi.org/10.1155/2019/4136874 |
| spellingShingle | Shuan Li Yinghua Han Xu Yao Song Yingchen Jinkuan Wang Qiang Zhao Electricity Theft Detection in Power Grids with Deep Learning and Random Forests Journal of Electrical and Computer Engineering |
| title | Electricity Theft Detection in Power Grids with Deep Learning and Random Forests |
| title_full | Electricity Theft Detection in Power Grids with Deep Learning and Random Forests |
| title_fullStr | Electricity Theft Detection in Power Grids with Deep Learning and Random Forests |
| title_full_unstemmed | Electricity Theft Detection in Power Grids with Deep Learning and Random Forests |
| title_short | Electricity Theft Detection in Power Grids with Deep Learning and Random Forests |
| title_sort | electricity theft detection in power grids with deep learning and random forests |
| url | http://dx.doi.org/10.1155/2019/4136874 |
| work_keys_str_mv | AT shuanli electricitytheftdetectioninpowergridswithdeeplearningandrandomforests AT yinghuahan electricitytheftdetectioninpowergridswithdeeplearningandrandomforests AT xuyao electricitytheftdetectioninpowergridswithdeeplearningandrandomforests AT songyingchen electricitytheftdetectioninpowergridswithdeeplearningandrandomforests AT jinkuanwang electricitytheftdetectioninpowergridswithdeeplearningandrandomforests AT qiangzhao electricitytheftdetectioninpowergridswithdeeplearningandrandomforests |