A hybrid model for smart grid theft detection based on deep learning
A hybrid deep learning model was proposed to effectively detect electricity theft in smart grids.The hybrid model employed a deep learning convolutional neural network (AlexNet) to tackle the curse of dimensionality, significantly enhancing data processing accuracy and efficiency.It further improved...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2024-02-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024027/ |
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| _version_ | 1841533880043044864 |
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| author | Yinling LIAO Jincan LI Bing WANG Jun ZHANG Yaoyuan LIANG |
| author_facet | Yinling LIAO Jincan LI Bing WANG Jun ZHANG Yaoyuan LIANG |
| author_sort | Yinling LIAO |
| collection | DOAJ |
| description | A hybrid deep learning model was proposed to effectively detect electricity theft in smart grids.The hybrid model employed a deep learning convolutional neural network (AlexNet) to tackle the curse of dimensionality, significantly enhancing data processing accuracy and efficiency.It further improved classification accuracy by differentiating between normal and abnormal electricity usage using adaptive boosting (AdaBoost).To resolve the issue of class imbalance, undersampling techniques were utilized, ensuring balanced performance across various data classes.Additionally, the artificial bee colony algorithm was used to optimize hyperparameters for both AdaBoost and AlexNet, effectively boosting overall model performance.The effectiveness of this hybrid model was evaluated using real smart meter datasets from an electricity company.Compared to similar models, this hybrid model achieves accuracy, precision, recall, F1-score, Matthews correlation coefficient (MCC), and area under the curve-receiver operating characteristic curve (AUC-ROC) scores of 88%, 86%, 84%, 85%, 78%, and 91%, respectively.The proposed model not only increases the accuracy of electricity usage monitoring, but also offers a new perspective for intelligent analysis in power systems. |
| format | Article |
| id | doaj-art-2ecc87bc4bdd4a3aa40af43c363b4cd2 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2024-02-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-2ecc87bc4bdd4a3aa40af43c363b4cd22025-01-15T02:48:33ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012024-02-0140728259555900A hybrid model for smart grid theft detection based on deep learningYinling LIAOJincan LIBing WANGJun ZHANGYaoyuan LIANGA hybrid deep learning model was proposed to effectively detect electricity theft in smart grids.The hybrid model employed a deep learning convolutional neural network (AlexNet) to tackle the curse of dimensionality, significantly enhancing data processing accuracy and efficiency.It further improved classification accuracy by differentiating between normal and abnormal electricity usage using adaptive boosting (AdaBoost).To resolve the issue of class imbalance, undersampling techniques were utilized, ensuring balanced performance across various data classes.Additionally, the artificial bee colony algorithm was used to optimize hyperparameters for both AdaBoost and AlexNet, effectively boosting overall model performance.The effectiveness of this hybrid model was evaluated using real smart meter datasets from an electricity company.Compared to similar models, this hybrid model achieves accuracy, precision, recall, F1-score, Matthews correlation coefficient (MCC), and area under the curve-receiver operating characteristic curve (AUC-ROC) scores of 88%, 86%, 84%, 85%, 78%, and 91%, respectively.The proposed model not only increases the accuracy of electricity usage monitoring, but also offers a new perspective for intelligent analysis in power systems.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024027/AlexNetadaptive boostingdeep driven modeltheft detectionfeature extraction |
| spellingShingle | Yinling LIAO Jincan LI Bing WANG Jun ZHANG Yaoyuan LIANG A hybrid model for smart grid theft detection based on deep learning Dianxin kexue AlexNet adaptive boosting deep driven model theft detection feature extraction |
| title | A hybrid model for smart grid theft detection based on deep learning |
| title_full | A hybrid model for smart grid theft detection based on deep learning |
| title_fullStr | A hybrid model for smart grid theft detection based on deep learning |
| title_full_unstemmed | A hybrid model for smart grid theft detection based on deep learning |
| title_short | A hybrid model for smart grid theft detection based on deep learning |
| title_sort | hybrid model for smart grid theft detection based on deep learning |
| topic | AlexNet adaptive boosting deep driven model theft detection feature extraction |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024027/ |
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