AI-Driven Optimization for Off-Grid Renewable Energy Systems: A Hybrid Solar-Wind-Battery Approach

Off-grid renewable energy systems are essential for providing sustainable electricity in remote and underserved areas. However, their reliability and efficiency are often hindered by the intermittent nature of renewable resources. This research presents an AI-driven optimization framework employing machine learning (ML) algorithms to enhance the performance of hybrid solar-wind-battery systems. By integrating historical meteorological data, load profiles, and component degradation patterns, a neural-network-based model was developed to forecast energy generation and consumption. A genetic algorithm was then applied to optimize energy dispatch and storage. The results demonstrate up to 15% improvement in energy utilization efficiency and a 20% reduction in battery cycling losses. The proposed system provides a scalable, intelligent control mechanism suitable for real-world deployment in rural electrification projects.