A Novel Time-Series Transformation and Machine-Learning-Based Method for NTL Fraud Detection in Utility Companies

Abstract

Several approaches have been proposed to detect any malicious manipulation caused by electricity fraudsters. Some of the significant approaches are Machine Learning algorithms and data-based methods that have shown advantages compared to the traditional methods, and they are becoming predominant in recent years. In this study, a novel method is introduced to detect the fraudulent NTL loss in the smart grids in a two-stage detection process. In the first stage, the time-series readings are enriched by adding a new set of extracted features from the detection of sudden Jump patterns in the electricity consumption and the Autoregressive Integrated moving average (ARIMA). In the second stage, the distributed random forest (DRF) generates the learned model. The proposed model is applied to the public SGCC dataset, and the approach results have reported 98% accuracy and F1-score. Such results outperform the other recently reported state-of-the-art methods for NTL detection that are applied to the same SGCC dataset.