Viability Prediction of Smart Meter Installation to Prevent Non-Technical Losses Using Naïve Bayes Classifier

Background: Energy regulators across the world resolved to curtail the liability of Non-Technical Losses (NTLs) in power by implementing the use of Smart Meters to measure consumed power. However, power regulators in developing countries are confronted with a huge metering gap in an era of unprecedented energy theft. This has resulted in deficits in revenue, an increase in debts and subsequently power cuts. Objective: The objective of this research is to predict whether the unmetered customers are eligible to be metered by identifying worthy and unworthy customers for metering given their bill payment history. Methods: The approach analyses the performance accuracy of some machine learning algorithms on small datasets by exploring the classification abilities of Deep learning, Naïve Bayes, Support Vector Machine and Extreme Learning Machine using data obtained from an electricity distribution company in Nigeria. Results: The performance analysis shows that Naïve Bayes classifier outperformed the Deep Learn-ing, Support Vector Machine and Extreme Learning Machine algorithms. Experiments in deep learning have shown that the alteration of batch sizes has a significant effect on the outputs. Conclusion: This paper presents a data-driven methodology for the prediction of consumers’ eligi-bility to be metered. The research has analysed the performance of deep learning, Naive Bayes, SVM and ELM on a small dataset. It is anticipated that the research will help utility companies in developing countries with large populations and huge metering gaps to prioritise the installation of smart meters based on consumer’s payment history. © 2021 Bentham Science Publishers.