Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms

The increasing cost of energy and the prevalence of electricity theft pose significant financial and operational challenges for energy providers. Traditional fraud detection methods often fail to identify sophisticated unauthorized consumption, particularly in non-smart-grid environments. This study proposes an advanced machine learning-based methodology for detecting energy fraud, leveraging real-world data from energy distribution networks. This approach integrates multiple machine learning models—k-nearest neighbors (kNN), decision trees, random forest, and artificial neural networks (ANNs)—to improve detection accuracy and efficiency. Experimental results demonstrate an 89.5% fraud detection accuracy, significantly outperforming conventional methods. Furthermore, the implementation of this model led to an estimated financial loss reduction of EUR 45,200. By analyzing historical consumption patterns, anomaly detection techniques, and geospatial data, the proposed system enhances fraud detection capabilities across both smart and non-smart grids. Future research will focus on real-time detection, scalability, and the integration of external data sources to further refine predictive accuracy.