A novel TimeDistill-Ensemble model for short-term wind power forecasting considering meteorological coupling effect

Accurate short-term wind power forecasting is essential for power system stability, peak-shaving, and renewable energy accommodation. However, achieving high-precision forecasting remains a significant challenge due to the inherent strong variability of wind energy and the complex coupling relationships among meteorological factors. To address this, this paper proposes a short-term wind power forecasting method that accounts for the coupling effects of meteorological factors. First, the mutual information method is employed to quantify the nonlinear dependencies between power output and meteorological variables, revealing latent coupling effects that traditional linear correlation analysis fails to capture, thereby facilitating the selection of key features. Second, an ensemble model incorporating TimeDistill is proposed for prediction. This model establishes a dual-teacher-student distillation architecture, utilizing BiLSTM and BiGRU as teacher networks to extract long-term dependencies and short-term dynamic features, respectively, guiding a Conv-MLP student network in learning complex temporal representations. Finally, the superiority of the proposed method is validated using measured data from a wind farm in Inner Mongolia. Experimental results demonstrate that, compared with LSTM and Transformer benchmark models, the proposed method reduces the Root Mean Square Error (RMSE) by 9.4% and 4.0%, respectively, providing an effective solution for real-time power dispatching.