This study presents an artificial intelligence (AI)-driven framework for high-accuracy wind speed prediction and its application in improving wind energy forecasting across multiple regions in China, with a focus on Shandong and Hunan provinces. By integrating advanced machine learning techniques—such as Long Short-Term Memory (LSTM) networks and gradient boosting models—the proposed approach fuses multi-source meteorological data, high-resolution topographic features, and historical wind power generation records to effectively capture the complex spatiotemporal dynamics of wind behavior. The first key result demonstrates the superior accuracy of the wind speed predictions, where both mean absolute error (MAE) and root mean square error (RMSE) are significantly lower than those of traditional physical and statistical models. In Shandong’s coastal zones and Hunan’s mountainous terrain, the model achieves over 95% prediction accuracy for short-term forecasts and maintains strong performance in day-ahead scenarios, offering a reliable basis for grid operations and energy market trading. Second, this research highlights the pivotal role of accurate wind speed forecasting in enhancing the predictability and grid integration of wind energy—a critical component of China’s renewable energy strategy. By reducing uncertainty in power output estimation, the AI system enables more efficient wind farm scheduling, reduces curtailment, and supports stable electricity market participation. Enhanced forecasting precision leads to higher utilization rates of wind resources, thereby promoting grid stability and advancing decarbonization objectives. Furthermore, the framework demonstrates strong scalability and adaptability, making it suitable for deployment in other regions with high wind energy penetration. These findings underscore the synergistic potential of AI innovation and the sustainable transformation of energy systems.

Wind Speed Prediction, Wind Energy Forecasting, Artificial Intelligence, Machine Learning