PREDICTIVE ENERGY MANAGEMENT IN INDUSTRIAL MICROGRIDS USING AI AND MACHINE LEARNING APPROACHES

Abstract

Industrial microgrids with integrated renewable energy sources and distributed generation require efficient energy management to optimize operational costs, maintain reliability, and ensure stability. Traditional energy management systems often fail to handle dynamic load patterns and renewable generation variability effectively. This paper proposes a predictive energy management framework for industrial microgrids using AI and machine learning techniques. The framework forecasts energy demand and renewable generation, optimizes energy dispatch, and coordinates storage systems to minimize cost and energy losses. Reinforcement learning and predictive modeling are integrated for adaptive decisionmaking under uncertainty. Simulation results demonstrate significant improvement in operational efficiency, reduced peak load, and enhanced utilization of renewable energy compared to conventional rule-based management strategies