Recent Advances in Artificial Intelligence for Renewable Energy System Optimization
DOI:
https://doi.org/10.54518/jaei.2.2.2024.1295Keywords:
Artificial Intelligence, Deep Learning, Energy Optimization, Machine Learning, Renewable Energy SystemsAbstract
The rapid development of renewable energy systems has increased the need for optimization methods capable of improving operational efficiency, reliability, and flexibility. The intermittent nature of renewable energy sources creates significant challenges in system management, making Artificial Intelligence (AI) a promising solution for intelligent optimization. This study aims to analyze the development of AI algorithms for renewable energy system optimization using a Systematic Literature Review (SLR) approach. The review examines scientific articles published between 2019 and 2023 that discuss the implementation of AI techniques, including Machine Learning, Deep Learning, Artificial Neural Networks, and Reinforcement Learning in renewable energy applications. The findings indicate that AI significantly improves renewable energy forecasting accuracy, optimizes system configuration, enhances energy storage management, and increases the operational efficiency and reliability of smart grids and microgrids. Furthermore, integrating AI with optimization algorithms contributes to reducing operational costs while supporting sustainable energy utilization. However, several challenges remain, including computational complexity, model generalization, and real-time implementation. Overall, this study provides a comprehensive overview of recent advances in AI-based renewable energy optimization and offers valuable insights for developing more adaptive, efficient, and sustainable energy management models.
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