Abstract：With increasing demands for timeliness and update frequency in wind power prediction, the limitations of methods based on Numerical Weather Prediction (NWP) have become increasingly evident. The development of artificial intelligence meteorological models has provided an efficient source of meteorological forecasts for wind power prediction. Selecting an offshore wind farm in Yangjiang, Guangdong as the service object, the Pangu AI meteorological model and the numerical weather prediction model CMA-GD were applied to conduct simulation calculations for a total of 17 months in 2023 and 2024. Four sets of power prediction experiments were designed, which used different meteorological data forecast sources, different power prediction methods, and a wind speed correction method, to compare and analyze the improvement of wind power prediction. The experimental results show that the Pangu model achieves accuracy comparable to or even surpassing that of numerical weather prediction models. When using the generated site power curve for power prediction, the short-term power prediction accuracy based on the Pangu model meteorological forecast exceeds that based on CMA-GD, and even in some months of 2024, the prediction performance is comparable to that of using machine learning algorithms for power prediction. According to the November 2023 experimental results, the power prediction model based on corrected Pangu model forecasts and the ResMLP algorithm achieved the best power prediction performance, with a 24-hour prediction accuracy of 0.714 and a 96-hour prediction accuracy of 0.654. Applying this model to the period from January to June 2024 yielded stable results that met the grid assessment standards. This study provides a referable solution for applying the Pangu or other artificial intelligence meteorological models in wind power prediction operations.