Blending-Based Ensemble Learning Low-Voltage Station Area Theft Detection
In order to improve the efficiency of electricity theft detection, the power theft detection area and users should be better integrated, we proposed a Blending ensemble learning electricity theft detection model based on the Base Learner Selection Strategy (BLSS). Firstly, the adaptive synthetic (AD...
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/1/31 |
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| author | Dunchu Chen Wenwu Li Jie Fang |
| author_facet | Dunchu Chen Wenwu Li Jie Fang |
| author_sort | Dunchu Chen |
| collection | DOAJ |
| description | In order to improve the efficiency of electricity theft detection, the power theft detection area and users should be better integrated, we proposed a Blending ensemble learning electricity theft detection model based on the Base Learner Selection Strategy (BLSS). Firstly, the adaptive synthetic (ADASYN) sampling method is used to process the unbalanced power consumption data, and the sample distribution of training data is balanced. Secondly, the BLSS selection method is used to screen the optimal base learner combination and construct the Blending ensemble learning model. Then, based on the historical data, the model makes a short-term prediction of the power consumption of the station area the next day, and focuses on the verification of the suspected energy-stealing station area where the Root Mean Square Percentage Error (RSPE) exceeds the threshold, so as to lock in the potential energy stealing users. Finally, through the comparison and verification of real examples, the search scope for electricity theft inspections was reduced by 79.17%, greatly improving the detection efficiency of the power supply company. At the same time, the model’s electricity theft detection and recognition accuracy rate can be as high as 97.50%. The Blending ensemble learning electricity stealing detection model based on the BLSS base learner selection method has strong electricity stealing detection and recognition ability. |
| format | Article |
| id | doaj-art-1c60cbd6b9734c6c9d85af42ac3e162b |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-1c60cbd6b9734c6c9d85af42ac3e162b2025-01-10T13:16:53ZengMDPI AGEnergies1996-10732024-12-011813110.3390/en18010031Blending-Based Ensemble Learning Low-Voltage Station Area Theft DetectionDunchu Chen0Wenwu Li1Jie Fang2College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, ChinaCollege of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, ChinaCollege of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, ChinaIn order to improve the efficiency of electricity theft detection, the power theft detection area and users should be better integrated, we proposed a Blending ensemble learning electricity theft detection model based on the Base Learner Selection Strategy (BLSS). Firstly, the adaptive synthetic (ADASYN) sampling method is used to process the unbalanced power consumption data, and the sample distribution of training data is balanced. Secondly, the BLSS selection method is used to screen the optimal base learner combination and construct the Blending ensemble learning model. Then, based on the historical data, the model makes a short-term prediction of the power consumption of the station area the next day, and focuses on the verification of the suspected energy-stealing station area where the Root Mean Square Percentage Error (RSPE) exceeds the threshold, so as to lock in the potential energy stealing users. Finally, through the comparison and verification of real examples, the search scope for electricity theft inspections was reduced by 79.17%, greatly improving the detection efficiency of the power supply company. At the same time, the model’s electricity theft detection and recognition accuracy rate can be as high as 97.50%. The Blending ensemble learning electricity stealing detection model based on the BLSS base learner selection method has strong electricity stealing detection and recognition ability.https://www.mdpi.com/1996-1073/18/1/31blending combination strategyensemble learningelectricity stealing detectionunbalanced data |
| spellingShingle | Dunchu Chen Wenwu Li Jie Fang Blending-Based Ensemble Learning Low-Voltage Station Area Theft Detection Energies blending combination strategy ensemble learning electricity stealing detection unbalanced data |
| title | Blending-Based Ensemble Learning Low-Voltage Station Area Theft Detection |
| title_full | Blending-Based Ensemble Learning Low-Voltage Station Area Theft Detection |
| title_fullStr | Blending-Based Ensemble Learning Low-Voltage Station Area Theft Detection |
| title_full_unstemmed | Blending-Based Ensemble Learning Low-Voltage Station Area Theft Detection |
| title_short | Blending-Based Ensemble Learning Low-Voltage Station Area Theft Detection |
| title_sort | blending based ensemble learning low voltage station area theft detection |
| topic | blending combination strategy ensemble learning electricity stealing detection unbalanced data |
| url | https://www.mdpi.com/1996-1073/18/1/31 |
| work_keys_str_mv | AT dunchuchen blendingbasedensemblelearninglowvoltagestationareatheftdetection AT wenwuli blendingbasedensemblelearninglowvoltagestationareatheftdetection AT jiefang blendingbasedensemblelearninglowvoltagestationareatheftdetection |