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Recognition Model of User Electricity Stealing Behavior Based on Joint Neural Network
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Xianyi LIU1, Xinghao SHI1, Yikang JIANG1, Xiumin PAN1, Le QU2, Feng HUANG2
Electric Drive | 2024, 54(3) : 61 - 67
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Electric Drive | 2024, 54(3): 61-67
Recognition Model of User Electricity Stealing Behavior Based on Joint Neural Network
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Xianyi LIU1, Xinghao SHI1, Yikang JIANG1, Xiumin PAN1, Le QU2, Feng HUANG2
Affiliations
  • 1 State Grid Shandong Electric Power Company Liaocheng Power Supply Company，Liaocheng 252000， Shandong，China
  • 2 State Grid Shandong Electric Power Company Gaotang County Power Supply Company，Liaocheng 252800，Shandong，China
Published: 2024-03-20 doi: 10.19457/j.1001-2095.dqcd24467
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Aiming at the problem of low recognition accuracy of electricity stealing behavior， an electricity stealing behavior recognition model based on joint neural network was proposed. Firstly， the acquired user electricity consumption data was processed， and the user electricity consumption data was two-dimensionally processed by using the Gramian angular field method. Then， for the electricity consumption data of different dimensions， a user electricity stealing behavior recognition model based on the joint neural network was proposed， and the features of the one-dimensional electricity consumption data and the two-dimensional electricity consumption data were extracted by using the convolutional neural network（CNN） and the bidirectional long short-term memory（BiLSTM） neural network. The case analysis shows that the proposed joint neural network model has an accuracy rate of more than 90% for the recognition of electricity stealing behavior， which proves that the established evaluation model provides a practical solution to the electricity stealing problem.

electricity stealing behavior  /  joint neural network  /  data mining
Xianyi LIU, Xinghao SHI, Yikang JIANG, Xiumin PAN, Le QU, Feng HUANG. Recognition Model of User Electricity Stealing Behavior Based on Joint Neural Network[J]. Electric Drive, 2024 , 54 (3) : 61 -67 . DOI: 10.19457/j.1001-2095.dqcd24467
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Year 2024 volume 54 Issue 3
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Article Info
doi: 10.19457/j.1001-2095.dqcd24467
  • Receive Date：2022-07-07
  • Online Date：2025-12-12
  • Published：2024-03-20
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History
  • Received：2022-07-07
  • Revised：2022-08-04
Affiliations
    1 State Grid Shandong Electric Power Company Liaocheng Power Supply Company，Liaocheng 252000， Shandong，China
    2 State Grid Shandong Electric Power Company Gaotang County Power Supply Company，Liaocheng 252800，Shandong，China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
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Genus		 种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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