An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis
Nowadays, electricity theft has been a major problem worldwide. Although many single-classification algorithms or an ensemble of single learners (i.e., homogeneous ensemble learning) have proven able to automatically identify suspicious customers in recent years, after the accuracy of these methods...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/19/7423 |
| _version_ | 1797479377364058112 |
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| author | Rui Xia Yunpeng Gao Yanqing Zhu Dexi Gu Jiangzhao Wang |
| author_facet | Rui Xia Yunpeng Gao Yanqing Zhu Dexi Gu Jiangzhao Wang |
| author_sort | Rui Xia |
| collection | DOAJ |
| description | Nowadays, electricity theft has been a major problem worldwide. Although many single-classification algorithms or an ensemble of single learners (i.e., homogeneous ensemble learning) have proven able to automatically identify suspicious customers in recent years, after the accuracy of these methods reaches a certain level, it still cannot be improved even if it continues to be optimized. To break through this bottleneck, a heterogeneous ensemble learning method with stacking integrated structure of different strong individual learners for detection of electricity theft is presented in this paper. Firstly, we use the grey relation analysis (GRA) method to select the heterogeneous strong classifier combination of LG + LSTM + KNN as the base model layer of stacking structure based on the principle of the highest comprehensive evaluation index value. Secondly, the support vector machine (SVM) model with relatively good results of the stacking overall structure experiment is selected as the model of the meta-model layer. In this way, a heterogeneous integrated learning model for electricity theft detection of the stacking structure is constructed. Finally, the experiments of this model are conducted on electricity consumption data from State Grid Corporation of China, and the results show that the detection performance of the proposed method is better than that of the existing state-of-the-art detection method (where the area under receiver operating characteristic curve (AUC) value is 0.98675). |
| first_indexed | 2024-03-09T21:45:00Z |
| format | Article |
| id | doaj.art-66300ab266bd4fbda6c941f6410df5ac |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T21:45:00Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-66300ab266bd4fbda6c941f6410df5ac2023-11-23T20:18:51ZengMDPI AGEnergies1996-10732022-10-011519742310.3390/en15197423An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation AnalysisRui Xia0Yunpeng Gao1Yanqing Zhu2Dexi Gu3Jiangzhao Wang4College of Electrical and Information Engineering, Hunan University, Changsha 410082, ChinaCollege of Electrical and Information Engineering, Hunan University, Changsha 410082, ChinaCollege of Electrical and Information Engineering, Hunan University, Changsha 410082, ChinaCollege of Electrical and Information Engineering, Hunan University, Changsha 410082, ChinaCollege of Electrical and Information Engineering, Hunan University, Changsha 410082, ChinaNowadays, electricity theft has been a major problem worldwide. Although many single-classification algorithms or an ensemble of single learners (i.e., homogeneous ensemble learning) have proven able to automatically identify suspicious customers in recent years, after the accuracy of these methods reaches a certain level, it still cannot be improved even if it continues to be optimized. To break through this bottleneck, a heterogeneous ensemble learning method with stacking integrated structure of different strong individual learners for detection of electricity theft is presented in this paper. Firstly, we use the grey relation analysis (GRA) method to select the heterogeneous strong classifier combination of LG + LSTM + KNN as the base model layer of stacking structure based on the principle of the highest comprehensive evaluation index value. Secondly, the support vector machine (SVM) model with relatively good results of the stacking overall structure experiment is selected as the model of the meta-model layer. In this way, a heterogeneous integrated learning model for electricity theft detection of the stacking structure is constructed. Finally, the experiments of this model are conducted on electricity consumption data from State Grid Corporation of China, and the results show that the detection performance of the proposed method is better than that of the existing state-of-the-art detection method (where the area under receiver operating characteristic curve (AUC) value is 0.98675).https://www.mdpi.com/1996-1073/15/19/7423electricity theftstacking structureanalytic hierarchy processentropy weight methodgrey relation analysis |
| spellingShingle | Rui Xia Yunpeng Gao Yanqing Zhu Dexi Gu Jiangzhao Wang An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis Energies electricity theft stacking structure analytic hierarchy process entropy weight method grey relation analysis |
| title | An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis |
| title_full | An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis |
| title_fullStr | An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis |
| title_full_unstemmed | An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis |
| title_short | An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis |
| title_sort | efficient method combined data driven for detecting electricity theft with stacking structure based on grey relation analysis |
| topic | electricity theft stacking structure analytic hierarchy process entropy weight method grey relation analysis |
| url | https://www.mdpi.com/1996-1073/15/19/7423 |
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