An integrated AI-based software architecture for load forecasting, controlled anomaly detection, and predictive energy management
The results demonstrate that the joint integration of forecasting, anomaly detection, and predictive control provides a computationally coherent framework for supporting energy-management efficiency, operational reliability, and data-driven decision-making in intelligent energy systems.