Hybrid Random Forest–Marine Predator Algorithm for High-Accuracy Energy Theft Detection

Abstract: Electrical energy theft is an issue that persists in the energy sector and has led to financial losses and inefficiencies. The detection of electrical energy theft is often difficult with traditional approaches, which are not effective in dealing with varying consumption patterns and imbalanced datasets. The aim of this paper is to present an effective and efficient hybrid framework that uses the classification power of the Random Forest algorithm and the optimization power of the Marine Predator Algorithm. The proposed hybrid framework is effective in detecting electrical energy theft with better accuracy compared to other models. The proposed framework is significant in the sense that it offers an effective and efficient solution to the detection of electrical energy theft in the energy sector. The proposed hybrid framework is significant in the sense that it offers an effective and efficient solution to the detection of electrical energy theft in the energy sector and is an additional contribution to the field of smart grid analytics.

Keywords: Random Forest (RF), Marine Predator Algorithm (MPA), Hybrid Machine Learning, Energy Theft Detection, Smart Grid Analytics.