Ultra-Short-Term Wind Power Forecasting Based on Adaptive Selective Denoising and TimeMixer-Mamba

To improve the accuracy of ultra-short-term wind power forecasting, this paper proposes a forecasting method based on adaptive selective denoising and TimeMixer-Mamba. First, variational mode decomposition (VMD) decomposes the original wind power sequence into multiple intrinsic mode functions (IMFs). A comprehensive noise score combining the correlation coefficient and sample entropy is then constructed to adaptively identify high-noise IMFs, which are selectively denoised using wavelet packet decomposition (WPD) and an adaptive soft threshold (AST). Subsequently, TimeMixer-Mamba is developed to capture multiscale temporal patterns, long-term dependencies, and abrupt changes in wind power. The improved whale optimization algorithm (IWOA) is further employed to optimize the key model hyperparameters. The proposed method is evaluated on a real-world wind farm dataset with a 15-min forecasting horizon and a chronological 6:2:2 training/validation/test split. The proposed method achieves an RMSE of 1.89, an MAE of 0.97, and an R2 of 0.9855. The inclusion of WPD-AST provides a modest additional improvement, reducing RMSE from 1.93 to 1.89 and MAE from 1.01 to 0.97. These results support the effectiveness of the proposed method for ultra-short-term wind power forecasting within the evaluated setting.