Smart Load Balancing and Real-Time Threat Prediction for National Energy Infrastructure

Abstract: The increasing complexity and demand on national energy infrastructures necessitate advanced solutions for efficient load management and threat mitigation. This paper explores the integration of Artificial Intelligence (AI) in smart load balancing and real-time threat prediction within national energy grids. By leveraging AI algorithms, energy systems can dynamically forecast demand, optimize energy distribution, and preemptively identify potential threats, ensuring stability and resilience. The study delves into current methodologies, challenges, and future prospects of AI-driven energy management systems.​ Keywords: Smart Load Balancing, Real-Time Threat Prediction, National Energy Infrastructure, Artificial Intelligence (AI), Machine Learning, Cybersecurity, Smart Grid, Supervisory Control and Data Acquisition (SCADA) Systems, Internet of Things (IoT), Anomaly Detection, Predictive Analytics, Demand Response, Energy Management Systems (EMS), Distributed Energy Resources (DER), Grid Resilience, Advanced Metering Infrastructure (AMI), Dynamic Line Rating (DLR), Cyber-Physical Systems, Intrusion Detection Systems (IDS), Energy Forecasting.