Publication:
A Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detection

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dc.citedby7
dc.contributor.authorBohani F.A.en_US
dc.contributor.authorSuliman A.en_US
dc.contributor.authorSaripuddin M.en_US
dc.contributor.authorSameon S.S.en_US
dc.contributor.authorMd Salleh N.S.en_US
dc.contributor.authorNazeri S.en_US
dc.contributor.authorid56288042200en_US
dc.contributor.authorid25825739000en_US
dc.contributor.authorid57220806580en_US
dc.contributor.authorid36683226000en_US
dc.contributor.authorid54946009300en_US
dc.contributor.authorid55372569700en_US
dc.date.accessioned2023-05-29T09:11:18Z
dc.date.available2023-05-29T09:11:18Z
dc.date.issued2021
dc.descriptionAdaptive 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 systemsen_US
dc.description.abstractThere 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9136206
dc.identifier.doi10.1155/2021/9136206
dc.identifier.scopus2-s2.0-85112621433
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85112621433&doi=10.1155%2f2021%2f9136206&partnerID=40&md5=24e1581e15b7ef26b6c4a844177bf8b6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26507
dc.identifier.volume2021
dc.publisherHindawi Limiteden_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleJournal of Electrical and Computer Engineering
dc.titleA Comprehensive Analysis of Supervised Learning Techniques for Electricity Theft Detectionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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