Artificial Intelligence-Driven Optimization of Renewable Energy Systems: Enhancing Efficiency, Reliability, and Integration

Abstract

Abstract

 The global transition to renewable energy sources (RES) such as solar, wind, and hydropower has introduced new challenges in system operation, grid integration, and energy management due to intermittency, variability, and distributed generation. Artificial Intelligence (AI) provides advanced solutions to address these challenges, enabling predictive modeling, optimization, fault detection, and intelligent control. This review paper presents a comprehensive analysis of AI-driven approaches for renewable energy systems (RES), covering data acquisition, preprocessing, model development, evaluation metrics, testing, validation, and deployment. Techniques such as machine learning (ML), deep learning (DL), reinforcement learning (RL), and hybrid models are discussed, emphasizing their role in energy forecasting, predictive maintenance, system optimization, and demand-side management. Representative datasets and applications are summarized in tabular form, with limited mathematical formulations to illustrate predictive modeling and optimization. Results demonstrate that AI enhances energy yield, reliability, and operational efficiency while reducing costs. Real-time deployment, edge computing, and integration with smart grids are analyzed. The novelty of AI-enhanced renewable energy systems lies in adaptive control, predictive analytics, and integration of distributed energy resources. Future research directions focus on explainable AI, multi-source data fusion, real-time energy management, and resilient smart grids.

Keywords: Renewable Energy, Artificial Intelligence, Machine Learning, Predictive Maintenance, Smart Grid