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 and Mustafa, Mohd. Wazir and Al-Shqeerat, Khalil and Al-rimy, Bander 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 & Distribution, 16 (6). pp. 1257-1275. ISSN 1350-2360

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Abstract

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.

Item Type: Article
Identification Number: https://doi.org/10.1049/gtd2.12386
Dates:
DateEvent
29 December 2021Accepted
11 February 2022Published Online
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Faisal Saeed
Date Deposited: 07 Mar 2022 14:14
Last Modified: 07 Mar 2022 14:14
URI: http://www.open-access.bcu.ac.uk/id/eprint/12928

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