The quick shift from fossil fuels to renewable energy sources like solar, wind, and hydro has brought new challenges in balancing energy generation, demand, and grid stability. Renewable energy is uncertain because it relies on changing environmental conditions. This makes accurate forecasting essential for reliable and efficient energy management. Recent developments in Artificial Intelligence (AI), especially machine learning and deep learning, have shown great promise in tackling these challenges by offering strong and flexible forecasting models. This paper looks at AI-based forecasting methods that improve the accuracy of renewable energy predictions and assist with effective grid management. It examines different approaches, such as neural networks, hybrid models, and probabilistic forecasting frameworks, considering their methods, performance, and suitability for various renewable energy sources. The paper also illustrates how AI-based forecasting helps with cost reduction, sustainability, and the integration of smart grid systems. It discusses limitations like data quality, computational needs, and model clarity, while proposing directions for future research. By bringing together existing advancements and pointing out key gaps, this study highlights how AI can change renewable energy management systems and support global sustainability goals.
Ingole et al. (2025) studied this question.