The ultra-short-term wind power point-interval forecasting model based on improved variational mode decomposition and bidirectional gated recurrent unit improved by improved sparrow search algorithm and attention mechanism

Ensuring the efficient scheduling of power systems and enhancing the grid's renewable energy integration efficiency heavily relies on the precision and dependability of wind power prediction. Based on this, an ultra-short-term wind power point-interval prediction framework is proposed. Firstly, the wind power data is preprocessed. Secondly, Random Forest(RF) is used to filter the factors affecting wind power data to eliminate redundant features. Third, using improved sparrow search algorithm(ISSA) to ascertain the optimal parameters in variational mode decomposition(VMD), the wind power sequence is decomposed to obtain a more regular sequence. Then, combining ISSA, bidirectional gated recurrent unit(BiGRU) and Attention, the ISSA-BiGRU-Attention model is constructed for the prediction of wind power subsequences. Finally, using the kernel density estimation(KDE) of Grid Search(GS)-Cross-Validation(CV), prediction intervals for wind power at varying confidence levels are calculated. The experimental results show that the RF-ISSA-VMD-ISSA-BiGRU-Attention prediction model has the great prediction accuracy compared with the comparison models. In dataset 1, the model possesses a good prediction result with an R2 of 0.997408 which is an improvement of 7.98 % compared with LSTM. In addition, the GS-CV-KDE interval prediction model enhances the practicability of prediction outcomes and offers more efficient prediction information for the assurance of power system safety and reliability.