A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection
There are many methods or algorithms applicable for detecting electricity theft. However, comparative studies on supervised learning methods for electricity theft detection are still insufficient. In this paper, comparisons based on predictive accuracy, recall, precision, AUC, and F1-score of severa...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/9136206 |
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| author | Farah Aqilah Bohani Azizah Suliman Mulyana Saripuddin Sera Syarmila Sameon Nur Shakirah Md Salleh Surizal Nazeri |
| author_facet | Farah Aqilah Bohani Azizah Suliman Mulyana Saripuddin Sera Syarmila Sameon Nur Shakirah Md Salleh Surizal Nazeri |
| author_sort | Farah Aqilah Bohani |
| collection | DOAJ |
| description | There are many methods or algorithms applicable for detecting electricity theft. However, comparative studies on supervised learning methods for electricity theft detection are still insufficient. In this paper, comparisons based on predictive accuracy, recall, precision, AUC, and F1-score of several supervised learning methods such as decision tree (DT), artificial neural network (ANN), deep artificial neural network (DANN), and AdaBoost are presented and their performances are analyzed. A public dataset from the State Grid Corporation of China (SGCC) was used for this study. The dataset consisted of power consumption in kWh unit. Based on the analysis results, the DANN outperforms compared to other supervised learning classifiers such as ANN, AdaBoost, and DT in recall, F1-Score, and AUC. A future research direction is the experiments can be performed on other supervised learning algorithms with different types of datasets and suitable preprocessing methods can be applied to produce better performance. |
| format | Article |
| id | doaj-art-9e2b06c5dbf244e3b27ddf9e0c7a7125 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-9e2b06c5dbf244e3b27ddf9e0c7a71252025-02-03T01:27:21ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552021-01-01202110.1155/2021/91362069136206A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft DetectionFarah Aqilah Bohani0Azizah Suliman1Mulyana Saripuddin2Sera Syarmila Sameon3Nur Shakirah Md Salleh4Surizal Nazeri5College of Computing & Informatics, Universiti Tenaga Nasional, Putrajaya Campus, Jalan Ikram-Uniten, 43000 Kajang, Selangor, MalaysiaCollege of Computing & Informatics, Universiti Tenaga Nasional, Putrajaya Campus, Jalan Ikram-Uniten, 43000 Kajang, Selangor, MalaysiaCollege of Computing & Informatics, Universiti Tenaga Nasional, Putrajaya Campus, Jalan Ikram-Uniten, 43000 Kajang, Selangor, MalaysiaCollege of Computing & Informatics, Universiti Tenaga Nasional, Putrajaya Campus, Jalan Ikram-Uniten, 43000 Kajang, Selangor, MalaysiaCollege of Computing & Informatics, Universiti Tenaga Nasional, Putrajaya Campus, Jalan Ikram-Uniten, 43000 Kajang, Selangor, MalaysiaCollege of Computing & Informatics, Universiti Tenaga Nasional, Putrajaya Campus, Jalan Ikram-Uniten, 43000 Kajang, Selangor, MalaysiaThere are many methods or algorithms applicable for detecting electricity theft. However, comparative studies on supervised learning methods for electricity theft detection are still insufficient. In this paper, comparisons based on predictive accuracy, recall, precision, AUC, and F1-score of several supervised learning methods such as decision tree (DT), artificial neural network (ANN), deep artificial neural network (DANN), and AdaBoost are presented and their performances are analyzed. A public dataset from the State Grid Corporation of China (SGCC) was used for this study. The dataset consisted of power consumption in kWh unit. Based on the analysis results, the DANN outperforms compared to other supervised learning classifiers such as ANN, AdaBoost, and DT in recall, F1-Score, and AUC. A future research direction is the experiments can be performed on other supervised learning algorithms with different types of datasets and suitable preprocessing methods can be applied to produce better performance.http://dx.doi.org/10.1155/2021/9136206 |
| spellingShingle | Farah Aqilah Bohani Azizah Suliman Mulyana Saripuddin Sera Syarmila Sameon Nur Shakirah Md Salleh Surizal Nazeri A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection Journal of Electrical and Computer Engineering |
| title | A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection |
| title_full | A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection |
| title_fullStr | A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection |
| title_full_unstemmed | A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection |
| title_short | A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection |
| title_sort | comprehensive analysis of supervised learning techniques for electricity theft detection |
| url | http://dx.doi.org/10.1155/2021/9136206 |
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