Artificial Intelligence (AI) in renewable energy forecasting and optimization

Smart, Ezekiel Ezekiel and Olanrewaju, Lois Oyindamola and Usman, Joseph and Otaru, Kabiru and Muhammad, Dauda Umar and Amalu, Prince Nnamdi and Popoola, Emmanuel Toba (2025) Artificial Intelligence (AI) in renewable energy forecasting and optimization. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 1100-1112. ISSN 2582-8266

The integration of Artificial Intelligence (AI) in renewable energy forecasting and optimization has significantly enhanced the efficiency and reliability of energy systems. The use of AI methods like reinforcement learning, deep learning, and machine learning, to increase the precision of forecasting energy from solar, wind, hydropower, and biomass is examined in this research. AI-driven optimization techniques have proven essential for grid integration, load balancing, energy storage management, and hybrid energy systems. Compared to conventional forecasting methods, AI models demonstrate superior accuracy by effectively processing large-scale, heterogeneous data. Additionally, AI facilitates real-time energy management and predictive maintenance, thereby increasing the sustainability of renewable energy infrastructures. Despite its advantages, challenges such as data quality, computational complexity, cybersecurity risks, and the need for explainable AI remain critical barriers to large-scale adoption. The paper further discusses emerging trends, including the potential of quantum computing and blockchain integration, in advancing AI-driven renewable energy solutions. In order to secure the ethical deployment of AI, future research should concentrate on creating more interpretable AI models, improving energy efficiency, and putting strong regulatory frameworks in place. The insights from this study provide valuable guidance for researchers, policymakers, and industry stakeholders in optimizing renewable energy systems.