Accurate ultra-short-term wind power forecasting is critical for maintaining the stability and efficiency of modern power systems,yet it remains challenging due to the high volatility and nonlinear dynamics of wind en...Accurate ultra-short-term wind power forecasting is critical for maintaining the stability and efficiency of modern power systems,yet it remains challenging due to the high volatility and nonlinear dynamics of wind energy.This study proposes a hybrid forecasting framework that integrates deep learning-based temporal modeling,fluctuation-aware feature engineering,and expert-driven hyperparameter optimization.A convolu-tional attention network is designed to capture both local temporal patterns and long-term dependencies in wind power time series,while financial technical indicators are incorporated to enhance the representation of shortterm fluctuation characteristics.To further improve forecasting accuracy and robustness,a mixture of experts strategy is employed to jointly optimize model hyperparameters and indicator construction.The final power forecasting is performed using a gradient boosting decision tree model with strong generalization capability.Experimental evaluations conducted on multiple real-world wind farm datasets demonstrate that the proposed framework consistently outperforms state of the art machine learning and deep learning approaches in terms of forecasting accuracy and efficiency.For example,on Dataset 1,the proposed method achieves a 5.03%reduction in mean square error compared with the strongest deep learning baseline.The results indicate that the proposed approach effectively captures both rapid fluctuations and underlying temporal trends,providing a reliable and practical solution for ultra-short-term wind power forecasting in complex operational environments.展开更多
基金National Natural Science Foundation of China(Project No.62366039)Inner Mongolia Autonomous Region Science and Technology Project(Project No.2023YFSH0066)Sci-entific Research Project of Inner Mongolia Higher Education Institutions(Project No.JMZD202301).
文摘Accurate ultra-short-term wind power forecasting is critical for maintaining the stability and efficiency of modern power systems,yet it remains challenging due to the high volatility and nonlinear dynamics of wind energy.This study proposes a hybrid forecasting framework that integrates deep learning-based temporal modeling,fluctuation-aware feature engineering,and expert-driven hyperparameter optimization.A convolu-tional attention network is designed to capture both local temporal patterns and long-term dependencies in wind power time series,while financial technical indicators are incorporated to enhance the representation of shortterm fluctuation characteristics.To further improve forecasting accuracy and robustness,a mixture of experts strategy is employed to jointly optimize model hyperparameters and indicator construction.The final power forecasting is performed using a gradient boosting decision tree model with strong generalization capability.Experimental evaluations conducted on multiple real-world wind farm datasets demonstrate that the proposed framework consistently outperforms state of the art machine learning and deep learning approaches in terms of forecasting accuracy and efficiency.For example,on Dataset 1,the proposed method achieves a 5.03%reduction in mean square error compared with the strongest deep learning baseline.The results indicate that the proposed approach effectively captures both rapid fluctuations and underlying temporal trends,providing a reliable and practical solution for ultra-short-term wind power forecasting in complex operational environments.
