Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities
Abstract The increasing demand for electricity in daily life highlights the need for Smart Cities (SC) to use energy efficiently. Both technical and Non‐Technical Losses (NTL), particularly those resulting from electricity theft, present powerful obstacles; NTL alone can reach billions of dollars. A...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-02-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.13056 |
| _version_ | 1797333717832695808 |
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| author | Arshid Ali Laiq Khan Nadeem Javaid Muhammad Aslam Abdulaziz Aldegheishem Nabil Alrajeh |
| author_facet | Arshid Ali Laiq Khan Nadeem Javaid Muhammad Aslam Abdulaziz Aldegheishem Nabil Alrajeh |
| author_sort | Arshid Ali |
| collection | DOAJ |
| description | Abstract The increasing demand for electricity in daily life highlights the need for Smart Cities (SC) to use energy efficiently. Both technical and Non‐Technical Losses (NTL), particularly those resulting from electricity theft, present powerful obstacles; NTL alone can reach billions of dollars. Although Machine Learning (ML) based approaches for NTL detection have been embraced by numerous utilities, there is still a lack of thorough analysis of these methods. Limited research exists on NTL identification evaluation criteria and unbalanced data management in the context of SC. This research compares ML algorithms and data balancing methods to optimize electricity consumption detection. The given research applied the 15 ML techniques of Logistic regression, Bernoulli naive Bayes, Gaussian naive Bayes, K‐Nearest Neighbour, perceptron, passive‐aggressive classifier, quadratic discriminant analysis, SGD classifier, ridge classifier, linear discriminant analysis, decision tree, nearest centroid classifier, multi‐nomial naive Bayes, complement naive Bayes and dummy classifier. While SMOTE, AdaSyn, NRAS, and CCR are considered for data balancing. AUC, F1‐score, and seven relevant performance metrics were used for comparison. We have also implemented SHapely Additive exPlanations (SHAP) for feature importance and model interpretation. Results show varying classifier performance with different balancing methods, emphasizing data preprocessing's role in NTL detection for smart grid security. |
| first_indexed | 2024-03-08T08:09:02Z |
| format | Article |
| id | doaj.art-fce5c2043d584988aecbb178f1af0baf |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-03-08T08:09:02Z |
| publishDate | 2024-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-fce5c2043d584988aecbb178f1af0baf2024-02-02T10:00:26ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952024-02-0118341344510.1049/gtd2.13056Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart citiesArshid Ali0Laiq Khan1Nadeem Javaid2Muhammad Aslam3Abdulaziz Aldegheishem4Nabil Alrajeh5Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad PakistanDepartment of Electrical and Computer Engineering COMSATS University Islamabad Islamabad PakistanDepartment of Computer Science COMSATS University Islamabad Islamabad PakistanDepartment of Computer Science Aberystwyth University Aberystwyth UKDepartment of Urban Planning, College of Architecture and Planning King Saud University Riyadh Saudi ArabiaDepartment of Biomedical Technology, College of Applied Medical Sciences King Saud University Riyadh Saudi ArabiaAbstract The increasing demand for electricity in daily life highlights the need for Smart Cities (SC) to use energy efficiently. Both technical and Non‐Technical Losses (NTL), particularly those resulting from electricity theft, present powerful obstacles; NTL alone can reach billions of dollars. Although Machine Learning (ML) based approaches for NTL detection have been embraced by numerous utilities, there is still a lack of thorough analysis of these methods. Limited research exists on NTL identification evaluation criteria and unbalanced data management in the context of SC. This research compares ML algorithms and data balancing methods to optimize electricity consumption detection. The given research applied the 15 ML techniques of Logistic regression, Bernoulli naive Bayes, Gaussian naive Bayes, K‐Nearest Neighbour, perceptron, passive‐aggressive classifier, quadratic discriminant analysis, SGD classifier, ridge classifier, linear discriminant analysis, decision tree, nearest centroid classifier, multi‐nomial naive Bayes, complement naive Bayes and dummy classifier. While SMOTE, AdaSyn, NRAS, and CCR are considered for data balancing. AUC, F1‐score, and seven relevant performance metrics were used for comparison. We have also implemented SHapely Additive exPlanations (SHAP) for feature importance and model interpretation. Results show varying classifier performance with different balancing methods, emphasizing data preprocessing's role in NTL detection for smart grid security.https://doi.org/10.1049/gtd2.13056learning (artificial intelligence)lossessmart meterssmart power grids |
| spellingShingle | Arshid Ali Laiq Khan Nadeem Javaid Muhammad Aslam Abdulaziz Aldegheishem Nabil Alrajeh Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities IET Generation, Transmission & Distribution learning (artificial intelligence) losses smart meters smart power grids |
| title | Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities |
| title_full | Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities |
| title_fullStr | Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities |
| title_full_unstemmed | Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities |
| title_short | Exploiting machine learning to tackle peculiar consumption of electricity in power grids: A step towards building green smart cities |
| title_sort | exploiting machine learning to tackle peculiar consumption of electricity in power grids a step towards building green smart cities |
| topic | learning (artificial intelligence) losses smart meters smart power grids |
| url | https://doi.org/10.1049/gtd2.13056 |
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