A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection
Adaptive boosting; Crime; Decision trees; Deep neural networks; Neural networks; Supervised learning; Comparative studies; Comprehensive analysis; Electricity theft detection; Future research directions; Learning classifiers; Pre-processing method; Predictive accuracy; Supervised learning methods; L...
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2023
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my.uniten.dspace-265072023-05-29T17:11:18Z A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection Bohani F.A. Suliman A. Saripuddin M. Sameon S.S. Md Salleh N.S. Nazeri S. 56288042200 25825739000 57220806580 36683226000 54946009300 55372569700 Adaptive boosting; Crime; Decision trees; Deep neural networks; Neural networks; Supervised learning; Comparative studies; Comprehensive analysis; Electricity theft detection; Future research directions; Learning classifiers; Pre-processing method; Predictive accuracy; Supervised learning methods; Learning systems 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. � 2021 Farah Aqilah Bohani et al. Final 2023-05-29T09:11:18Z 2023-05-29T09:11:18Z 2021 Article 10.1155/2021/9136206 2-s2.0-85112621433 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112621433&doi=10.1155%2f2021%2f9136206&partnerID=40&md5=24e1581e15b7ef26b6c4a844177bf8b6 https://irepository.uniten.edu.my/handle/123456789/26507 2021 9136206 All Open Access, Gold Hindawi Limited Scopus |
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Adaptive boosting; Crime; Decision trees; Deep neural networks; Neural networks; Supervised learning; Comparative studies; Comprehensive analysis; Electricity theft detection; Future research directions; Learning classifiers; Pre-processing method; Predictive accuracy; Supervised learning methods; Learning systems |
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56288042200 |
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56288042200 Bohani F.A. Suliman A. Saripuddin M. Sameon S.S. Md Salleh N.S. Nazeri S. |
| format |
Article |
| author |
Bohani F.A. Suliman A. Saripuddin M. Sameon S.S. Md Salleh N.S. Nazeri S. |
| spellingShingle |
Bohani F.A. Suliman A. Saripuddin M. Sameon S.S. Md Salleh N.S. Nazeri S. A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection |
| author_sort |
Bohani F.A. |
| title |
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_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_sort |
comprehensive analysis of supervised learning techniques for electricity theft detection |
| publisher |
Hindawi Limited |
| publishDate |
2023 |
| _version_ |
1806427299112812544 |
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13.214268 |