Timing shift-based bi-residual network model for the detection of electricity stealing
Abstract With the increasing number of electricity stealing users, the interests of countries are jeopardized and it brings economic burden to the government. However, due to the small-scale stealing and its random time coherence, it is difficult to find electricity stealing users. To solve this iss...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-04-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-022-00865-4 |
| _version_ | 1811290441639788544 |
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| author | Jie Lu Jingfu Li Wenjiang Feng Yongqi Zou Juntao Zhang Yuan Li |
| author_facet | Jie Lu Jingfu Li Wenjiang Feng Yongqi Zou Juntao Zhang Yuan Li |
| author_sort | Jie Lu |
| collection | DOAJ |
| description | Abstract With the increasing number of electricity stealing users, the interests of countries are jeopardized and it brings economic burden to the government. However, due to the small-scale stealing and its random time coherence, it is difficult to find electricity stealing users. To solve this issue, we first generate the hybrid dataset composed of real electricity data and specific electricity stealing data. Then, we put forward the timing shift-based bi-residual network (TS-BiResNet) model. It learns the features of electricity consumption data on two aspects, i.e., shallow features and deep features, and meanwhile takes time factor into consideration. The simulation results show that TS-BiResNet model can detect electricity stealing behaviors that are small scaled and randomly coherent with time. Besides, its detection accuracy is superior to the benchmark schemes, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), combined convolutional neural network and LSTM (CNN-LSTM) and Bi-ResNet. |
| first_indexed | 2024-04-13T04:13:00Z |
| format | Article |
| id | doaj.art-4b991e955c61408194c1c43af5d7b1f6 |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-04-13T04:13:00Z |
| publishDate | 2022-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-4b991e955c61408194c1c43af5d7b1f62022-12-22T03:03:02ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802022-04-012022111410.1186/s13634-022-00865-4Timing shift-based bi-residual network model for the detection of electricity stealingJie Lu0Jingfu Li1Wenjiang Feng2Yongqi Zou3Juntao Zhang4Yuan Li5School of Microelectronics and Communication Engineering, Chongqing UniversitySchool of Microelectronics and Communication Engineering, Chongqing UniversitySchool of Microelectronics and Communication Engineering, Chongqing UniversitySchool of Microelectronics and Communication Engineering, Chongqing UniversitySchool of Microelectronics and Communication Engineering, Chongqing UniversitySchool of Microelectronics and Communication Engineering, Chongqing UniversityAbstract With the increasing number of electricity stealing users, the interests of countries are jeopardized and it brings economic burden to the government. However, due to the small-scale stealing and its random time coherence, it is difficult to find electricity stealing users. To solve this issue, we first generate the hybrid dataset composed of real electricity data and specific electricity stealing data. Then, we put forward the timing shift-based bi-residual network (TS-BiResNet) model. It learns the features of electricity consumption data on two aspects, i.e., shallow features and deep features, and meanwhile takes time factor into consideration. The simulation results show that TS-BiResNet model can detect electricity stealing behaviors that are small scaled and randomly coherent with time. Besides, its detection accuracy is superior to the benchmark schemes, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), combined convolutional neural network and LSTM (CNN-LSTM) and Bi-ResNet.https://doi.org/10.1186/s13634-022-00865-4Electricity stealingDetection modelBi-ResNetTiming shift |
| spellingShingle | Jie Lu Jingfu Li Wenjiang Feng Yongqi Zou Juntao Zhang Yuan Li Timing shift-based bi-residual network model for the detection of electricity stealing EURASIP Journal on Advances in Signal Processing Electricity stealing Detection model Bi-ResNet Timing shift |
| title | Timing shift-based bi-residual network model for the detection of electricity stealing |
| title_full | Timing shift-based bi-residual network model for the detection of electricity stealing |
| title_fullStr | Timing shift-based bi-residual network model for the detection of electricity stealing |
| title_full_unstemmed | Timing shift-based bi-residual network model for the detection of electricity stealing |
| title_short | Timing shift-based bi-residual network model for the detection of electricity stealing |
| title_sort | timing shift based bi residual network model for the detection of electricity stealing |
| topic | Electricity stealing Detection model Bi-ResNet Timing shift |
| url | https://doi.org/10.1186/s13634-022-00865-4 |
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