An Improved Wind Power Prediction via a Novel Wind Ramp Identification Algorithm

Accurate wind power prediction during ramp events remains challenging due to wind speed volatility. This study proposes a hybrid forecasting framework combining improved variational mode decomposition (VMD), a novel ramp factor (RF), and the Informer model. First, a dynamic adaptive VMD method is employed to filter noise and identify abrupt wind speed changes. Subsequently, a similar period matching algorithm, enhanced by the RF and wind speed similarity coefficients, captures historical convergence features. Finally, the Informer network fuses these features with NWP data. Experimental results demonstrate that the proposed method significantly outperforms existing models in accuracy during ramp events, enhancing grid stability.