AI-Powered EB Smart Meter

This paper discusses a simulation-based smart electricity meter that uses AI and integrates Internet of Things (IoT), cloud computing, and machine learning to improve energy monitoring and management.The proposed system tackles key issues found in traditional electricity meters, including manual reading, no real-time visibility of usage, lack of predictive analytics, and the inability to identify unusual usage patterns.The system has a four-layer architecture.It includes a hardware sensing layer, a network communication layer, a cloud infrastructure layer, and a user interface layer.It processes electrical parameters like voltage, current, and power consumption with rule-based logic for real-time monitoring and safety.Long Short-Term Memory (LSTM) models help forecast energy consumption in the short and long term.Anomaly detection features identify unusual usage behavior that may signal faults or unauthorized consumption.The system is tested in a simulated environment that mimics real-world electricity usage, communication flow, and user interaction.The experimental results show that the proposed architecture can provide real-time monitoring, anomaly alerts, and accurate energy consumption predictions with low latency under simulated conditions.While the current version is limited to simulation, the results suggest it has strong potential for real-world use in smart grid and intelligent energy management applications.