Classification Based on the Support Vector Machine for Determining Operational Targets for Controlling Electricity Usage With Conventional Meters: A Case Study of Industrial and Business Tariff Customers From PT PLN (Persero) Indonesia

Abstract

Electricity theft remains a significant challenge for PT PLN (Persero), Indonesia’s primary electricity provider, serving over 89 million customers as of 2023. The study focuses on industrial and business tariff customers, using a dataset from 2019 to 2023, which includes monthly consumption data from PLN’s postpaid customers across thirty operational units with the highest Electricity Use Control (P2TL) levels, covering customers with a maximum power of 6,600 VA. This approach differs from previous studies that rely on open or smart meter data, as this study uses conventional meters for data collection. In the dataset used for this research, losses from confirmed electricity theft amounted to approximately IDR 19 billion. This research aims to improve the detection of electricity theft through a machine learning-based model utilizing the Support Vector Machine (SVM) classification technique. The goal is to enhance the P2TL mechanism by accurately identifying potential targets for field verification. Various SVM kernels were tested, including Radial Basis Function (RBF), Linear, Polynomial (Poly), and Sigmoid, alongside classifiers such as SVM, Logistic Regression, Decision Tree, and Naïve Bayes. Results show that the SVM model, particularly with the RBF kernel, achieves optimal performance, with balanced precision and recall, especially with 30 months of historical data. This optimized model contributes to improving PLN’s operational efficiency, offering more accurate identification of electricity theft cases, leading to substantial financial savings by reducing losses from unpaid consumption. The findings offer practical benefits for reducing electricity theft and improving PLN’s monitoring system, especially in industrial and business sectors.