INTELLIGENT PREDICTIVE CONTROL OF MICROGRID BY AI LOAD FORECASTING

This paper presents an intelligent energy management system for microgrids that integrates AI-based load forecasting with predictive and adaptive control strategies. The proposed framework combines demand estimation, proactive scheduling, and real-time dispatch optimization within a unified hierarchical architecture. Evaluated over a 6- hour control horizon, the system achieves a 93% reduction in operating cost compared to conventional rule-based methods and 75% reduction compared to scheduling-only approaches, while attaining a renewable self-sufficiency rate of 78.8%.