ABSTRACT Guangxi, located along China's southern coast, is prone to typhoons and features complex terrain, making wind speed forecasting challenging. Accurate prediction of near‐surface maximum wind speed is crucial for improving wind energy utilization and supporting carbon neutrality goals. This study proposes a novel prediction model using the eXtreme Gradient Boosting (XGBoost) algorithm integrated with a Bayesian Optimization Algorithm (BOA) and based on the k‐nearest neighbor mutual information feature selection algorithm (KNN‐MIFSA). Data from 93 meteorological stations in Guangxi (2016–2021) with a 3‐h temporal resolution were used. The model incorporates dynamic and thermal factors, including high‐altitude and surface variables, to predict maximum wind speed. Two key improvements were made in the prediction modeling: (1) KNN‐MIFSA was employed to select highly correlated features and eliminate redundant variables, and (2) BOA was used to optimize XGBoost parameters, enhancing model generalizability. The improved model was tested for 6 prediction lead times (12–72 h) from 2020 to 2021. Results show that, after adjusting parameters and processing factors, the new model reduced the mean absolute error (MAE) by 18.9%–30.06% and the root mean square error (RMSE) by 40.18%–65.83% compared to the original XGBoost model. For maximum wind speeds above level 6, MAE and RMSE of the new model were reduced by up to 40.41% and 30.92%, respectively, across lead times (12–72 h). The model demonstrates consistent performance and significantly improved accuracy, offering a promising approach for wind speed prediction in regions with complex terrain.
Huang et al. (Fri,) studied this question.