Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel-Tree boosting classifier-A novel sequentially executed supervised machine learning approach
This paper presents a novel, sequentially executed supervised machine learning-based electric theft detection framework using a Jaya-optimized combined Kernel and Tree Boosting (KTBoost) classifier. It utilizes the intelligence of the XGBoost algorithm to estimate the missing values in the acquired...
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| Format: | Article |
| Language: | English |
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John Wiley and Sons Inc
2022
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| Online Access: | http://eprints.utm.my/104448/1/MohdWazirMustafa2022_ElectricTheftDetectionInAdvancedMetering.pdf |
| _version_ | 1796867659441111040 |
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| author | Hussain, Saddam Mustafa, Mohd. Wazir Al-Shqeerat, Khalil Hamdi Ateyeh Al-rimy, Bander Ali Saleh Saeed, Faisal |
| author_facet | Hussain, Saddam Mustafa, Mohd. Wazir Al-Shqeerat, Khalil Hamdi Ateyeh Al-rimy, Bander Ali Saleh Saeed, Faisal |
| author_sort | Hussain, Saddam |
| collection | ePrints |
| description | This paper presents a novel, sequentially executed supervised machine learning-based electric theft detection framework using a Jaya-optimized combined Kernel and Tree Boosting (KTBoost) classifier. It utilizes the intelligence of the XGBoost algorithm to estimate the missing values in the acquired dataset during the data pre-processing phase. An oversampling algorithm based on the Robust-SMOTE technique is utilized to avoid the unbalanced data class distribution issue. Afterward, with the aid of few very significant statistical, temporal, and spectral features extracted from the acquired kWh dataset, the complex underlying data patterns are comprehended to enhance the accuracy and detection rate of the classifier. For effectively classifying the consumers into “Honest” and “Fraudster,” the ensemble machine learning-based classifier KTBoost, with Jaya algorithm optimized hyperparameters, is utilized. Finally, the developed model is re-trained using a reduced set of highly important features to minimize the computational resources without compromising the performance of the developed model. The outcome of this study reveals that the proposed theft detection method achieves the highest accuracy (93.38%), precision (95%), and recall (93.18%) among all the studied methods, thus signifying its importance in the studied area of research. |
| first_indexed | 2024-03-05T21:30:36Z |
| format | Article |
| id | utm.eprints-104448 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:30:36Z |
| publishDate | 2022 |
| publisher | John Wiley and Sons Inc |
| record_format | dspace |
| spelling | utm.eprints-1044482024-02-08T07:58:35Z http://eprints.utm.my/104448/ Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel-Tree boosting classifier-A novel sequentially executed supervised machine learning approach Hussain, Saddam Mustafa, Mohd. Wazir Al-Shqeerat, Khalil Hamdi Ateyeh Al-rimy, Bander Ali Saleh Saeed, Faisal TK Electrical engineering. Electronics Nuclear engineering This paper presents a novel, sequentially executed supervised machine learning-based electric theft detection framework using a Jaya-optimized combined Kernel and Tree Boosting (KTBoost) classifier. It utilizes the intelligence of the XGBoost algorithm to estimate the missing values in the acquired dataset during the data pre-processing phase. An oversampling algorithm based on the Robust-SMOTE technique is utilized to avoid the unbalanced data class distribution issue. Afterward, with the aid of few very significant statistical, temporal, and spectral features extracted from the acquired kWh dataset, the complex underlying data patterns are comprehended to enhance the accuracy and detection rate of the classifier. For effectively classifying the consumers into “Honest” and “Fraudster,” the ensemble machine learning-based classifier KTBoost, with Jaya algorithm optimized hyperparameters, is utilized. Finally, the developed model is re-trained using a reduced set of highly important features to minimize the computational resources without compromising the performance of the developed model. The outcome of this study reveals that the proposed theft detection method achieves the highest accuracy (93.38%), precision (95%), and recall (93.18%) among all the studied methods, thus signifying its importance in the studied area of research. John Wiley and Sons Inc 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/104448/1/MohdWazirMustafa2022_ElectricTheftDetectionInAdvancedMetering.pdf Hussain, Saddam and Mustafa, Mohd. Wazir and Al-Shqeerat, Khalil Hamdi Ateyeh and Al-rimy, Bander Ali Saleh and Saeed, Faisal (2022) Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel-Tree boosting classifier-A novel sequentially executed supervised machine learning approach. IET Generation, Transmission and Distribution, 16 (6). pp. 1257-1275. ISSN 1751-8687 http://dx.doi.org/10.1049/gtd2.12386 DOI : 10.1049/gtd2.12386 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Hussain, Saddam Mustafa, Mohd. Wazir Al-Shqeerat, Khalil Hamdi Ateyeh Al-rimy, Bander Ali Saleh Saeed, Faisal Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel-Tree boosting classifier-A novel sequentially executed supervised machine learning approach |
| title | Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel-Tree boosting classifier-A novel sequentially executed supervised machine learning approach |
| title_full | Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel-Tree boosting classifier-A novel sequentially executed supervised machine learning approach |
| title_fullStr | Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel-Tree boosting classifier-A novel sequentially executed supervised machine learning approach |
| title_full_unstemmed | Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel-Tree boosting classifier-A novel sequentially executed supervised machine learning approach |
| title_short | Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel-Tree boosting classifier-A novel sequentially executed supervised machine learning approach |
| title_sort | electric theft detection in advanced metering infrastructure using jaya optimized combined kernel tree boosting classifier a novel sequentially executed supervised machine learning approach |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/104448/1/MohdWazirMustafa2022_ElectricTheftDetectionInAdvancedMetering.pdf |
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