Intelligent Energy Ecosystems: A Hybrid Framework for Real-Time Prediction and Distributed Resource Allocation

Abstract

The increasing global demand for sustainable energy solutions has accelerated the integration of renewable energy systems, such as wind, solar, and hydroelectric power, with emerging technologies. This review explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in optimizing the efficiency, reliability, and scalability of these renewable energy systems. It highlights AI driven algorithms for predictive maintenance, energy forecasting, and dynamic grid management, addressing key challenges like energy storage, load balancing, and resource intermittency. Furthermore, the paper examines advanced ML techniques for improving grid resilience, optimizing energy storage solutions, and enhancing smart grid operations. By analyzing recent developments, this review provides insights into the potential of AI and ML in driving the next generation of intelligent renewable energy systems, with a particular focus on operational efficiency and sustainability.