Ultra-Short-Term Wind Power Forecasting Based on DT-DSCTransformer Model

作     者：Gao, Yanlong Xing, Feng Kang, Lipeng Zhang, Mingming Qin, Caiyan 

作者机构：Liaoning Univ Technol Sch Elect Engn Jinzhou 121001 Peoples R China Harbin Inst Technol Sch Mech Engn & Automat Shenzhen 518055 Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2025年第13卷

页      面：22919-22930页

核心收录：

基　　金：National Key Research and Development Program of China [2024YFE0212200] Fundamental Research Project of the Educational Department of Liaoning Province [LJ212410154011] 

主　　题：Predictive models Data models Wind power generation Accuracy Forecasting Transformers Correlation Prediction algorithms Computational modeling Wind speed Transformer wind power prediction distribution shift DT DSCAttention 

摘      要：When using the Transformer model for wind power prediction, the accuracy of the model predictions tends to be reduced due to the shift in the wind power data distribution, channel mixing, and the inability of the model to establish strong correlations. To address these challenges, this paper proposes an ultra-short-term wind power prediction model based on the DT-DSCTransformer. First, the model applies DT s self-learning standardization and de-standardization parameters to standardize the input and de-standardize the output, mitigating the impact forecasting of data distribution shifts on prediction accuracy. Second, the proposed De-Stationary Channel Attention (DSCAttention) mechanism is introduced. By incorporating De-Stationary Attention (DSAttention) into the channel attention mechanism while maintaining channel independence, the model establishes stronger inter-channel correlations, addressing the performance degradation caused by channel mixing and weak correlations. Finally, experimental analysis demonstrates that the proposed model achieves the highest prediction accuracy compared to commonly used time series forecasting models.