A recent review in Engineering Proceedings examines how AI and machine learning address the variability of wind and solar power. By using deep learning and reinforcement learning, grid operators can better predict energy output, optimize battery storage, and reduce curtailment. While AI can lower energy costs and emissions, the transition faces hurdles. The study highlights the need for better cybersecurity, data quality, and explainable AI models to ensure grid stability and operator trust as decarbonization accelerates.
