AI-enabled smart metering framework for energy theft and anomaly detection in modern smart grids - Osifeko, Martins; Munda, Josiah (Edelweiss Applied Science and Technology, 2025)
The rapid expansion of smart grids and distributed renewable energy systems has increased the need for intelligent, real-time energy monitoring and management solutions. Traditional energy meters lack the computational intelligence to analyze usage patterns or detect anomalies, limiting their effectiveness in modern energy systems. This paper presents an AI-integrated smart energy metering framework that combines Internet of Things (IoT) technologies with machine learning (ML) algorithms for real-time energy monitoring, anomaly detection, and load forecasting. The proposed system employs low-cost IoT hardware for energy data acquisition, cloud-based storage for scalable data handling, and embedded AI models for predictive analytics and anomaly detection. A supervised learning model, trained using historical consumption data, predicts short-term demand, while an unsupervised learning algorithm detects abnormal consumption patterns indicative of energy theft, equipment malfunction, or system inefficiencies. Experimental results from prototype implementation demonstrate high prediction accuracy (R² = 0.94) and efficient anomaly identification with minimal false positives. The integration of AI with IoT can help utilities identify energy theft, thereby preventing lost revenue.