AI-driven forecasting for efficient integration of renewable energy systems

The global transition to renewable energy is critical for climate change mitigation, energy security, and the achievement of sustainable development goals. Nevertheless, the intermittent and unpredictable characteristics of renewable energy sources, particularly solar and wind, pose substantial challenges for reliable power grid operation and energy management. Artificial Intelligence (AI) has emerged as a transformative technology that enhances forecasting accuracy and enables intelligent decision-making for renewable energy integration. This paper reviews recent advances in AI-driven forecasting techniques that facilitate the efficient integration of renewable energy systems into power grids. Relevant studies were identified through a systematic search of peer-reviewed literature published in major scientific databases over the past decade, with an emphasis on works that applied AI methods to renewable energy forecasting and energy management. Studies were selected and synthesized based on criteria including methodological rigor, relevance to grid integration, and demonstrated impact on operational performance. The review examines the application of machine learning, deep learning, reinforcement learning, and hybrid AI models in forecasting renewable energy generation, electricity demand, energy storage management, and grid optimization. The review also discusses data acquisition and preprocessing methods, forecasting architectures, performance evaluation metrics, and real-world case studies that demonstrate the effectiveness of AI-enabled energy management systems. The findings indicate that AI-based forecasting substantially improves prediction accuracy, enhances grid stability, optimizes energy dispatch, reduces operational costs, and supports greater penetration of renewable energy resources. Despite these advancements, challenges related to data quality, computational complexity, cybersecurity, model interpretability, and regulatory frameworks continue to impede large-scale deployment. The paper concludes by identifying emerging research directions, such as explainable AI, federated learning, digital twins, edge intelligence, and physics-informed machine learning, as promising strategies for developing resilient, intelligent, and sustainable future power grids.