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All Journal JURNAL NASIONAL TEKNIK ELEKTRO
Monister Yaw Kwarteng
Kwame Nkrumah University of Science and Technology
Electrical & Electronics Engineering

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Journal : JURNAL NASIONAL TEKNIK ELEKTRO

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ANN-Based Electricity Theft Classification Technique for Limited Data Distribution Systems Monister Yaw Kwarteng; Francis Boafo Effah; Daniel Kwegyir; Emmanuel Asuming Frimpong
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 1: March 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n1.1072.2023

Abstract

Electricity theft has been a challenge for distribution systems over the years. Theft presents a massive cost to the system operators and other issues such as transformer overloading, line loading, etc. It has become crucial for measures to be implemented to combat illegal electricity consumption. This work sought to develop an artificial neural network-based electricity theft classifier for distribution systems with limited data, i.e., systems that can only provide consumption data alone and no auxiliary data. First, a novel data pre-processing method was proposed for the systems with consumption data only. Again, synthetic minority oversampling is employed to deal with the unbalance problem in the theft detection dataset. Afterwards, an artificial neural network (ANN)-based classifier was proposed to classify customers as normal or fraudulent. The proposed method was tested on actual electricity theft data from the Electricity Company of Ghana (ECG) and its performance compared to random forest (RF) and logistic regression (LR) classifiers. The proposed ANN-based classifier performed exceptionally by producing the best results over RF and LR regarding precision, recall, F1-score, and accuracy of 99.49%, 100%, 99.75%, and 99.74%, respectively.
Co-Authors Daniel Kwegyir Emmanuel Asuming Frimpong Francis Boafo Effah