Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Energies

DOI: 10.3390/en19215213

Authors: Xiaohui Xie, Zhong Wen, Qiujie Wang, Hechong Chen, Kezheng Xu, Wentao Zhang

Institutions: China Three Gorges University, State Grid Hubei Electric Power Co., Ltd. (China), State Grid Hubei Electric Power Company Electric Power Research Institute

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $3,137