Open AccessFraud Detection in Customers’ Electricity Consumption in Nigeria Using Support Vector Machine and C4.5 Decision Tree Algorithms
Author(s) -
J.A. Sarumi
Publication year - 2021
Publication title -
advances in multidisciplinary and scientific research journal
Language(s) - English
Resource type - Journals
ISSN - 2488-8699
DOI - 10.22624/aims/cisdi/v12n1p6
Subject(s) - support vector machine , computer science , electricity , decision tree , anomaly detection , metric (unit) , analytics , data mining , machine learning , consumption (sociology) , algorithm , random forest , statistical classification , artificial intelligence , engineering , social science , sociology , electrical engineering , operations management
Over the years, Electricity theft has been estimated to cost billions of Naira per year in Nigeria. To reduce electricity theft, electric utilities are leveraging data collected by using data analytics to identify abnormal consumption trends and possible fraud. In this study, use of data analytics in detecting electricity theft, and a metric that leverages this threat model in order to evaluate and compare anomaly detectors. Data mining technology have helped several industries and sectors in improving their various forms of technology, this study therefore employ machine learning algorithms for the classification of fraud detection in the electricity consumption of costumers. In this project, Support Vector Machine (SVM) and C4.5 Decision tree classification algorithms were employed for fraud detection using customer electricity consumption data. SVM and C4.5 achieved 63.4% and 65.9% accuracy respectively. Thus, C4.5 outperformed SVM based on the dataset experimented in this project. Keywords: Active databases, distributed processing, intelligent processing, optimization, query processor