Adaptive Intelligence in Microgrid Systems

Abstract

Microgrid systems, with diverse energy sources and decentralized control, are revolutionizing energy management. However, integrating renewable energy sources and consumer demands poses challenges to grid stability. Machine learning (ML) is a key tool for optimizing energy management and adaptive control in microgrids, enabling accurate load forecasting, renewable energy output prediction, and efficient resource utilization. This abstract discusses the potential of ML in revolutionizing microgrid systems by enabling adaptive control mechanisms, predictive maintenance, early fault detection, and proactive scheduling. ML also addresses cybersecurity concerns, providing sophisticated solutions for intrusion detection and secure data management. This approach optimizes operations, drives innovation, and ensures resilient, cost-effective, and sustainable energy infrastructures.