The accuracy of wind power prediction is crucial for ensuring the sustainable and stable operation of power grids. To address the issue of inadequate prediction accuracy caused by the volatility and stochasticity of wind power data, this study proposes a decomposition-prediction model based on the Successive Variational Mode Decomposition (SVMD) algorithm for data decomposition, combined with a Bidirectional Temporal Convolutional Network (BiTCN) and Bidirectional Long Short-Term Memory Network (BiLSTM) for prediction. The Splendid Fairy-wren Optimization Algorithm enhanced with Newtonian method (SFOA-N) is employed to optimize SVMD′s penalty factor and the hyperparameters of the prediction model, thereby improving local search capability. To resolve the technical challenge that the exponentially growing dilation rate in BiTCN struggles to adapt to complex patterns across different time series, an innovative dynamic dilation rate prediction module is proposed. This module automatically adjusts dilation rates according to varying segments of input data, significantly enhancing prediction performance. Experimental results demonstrate that compared with standalone BiTCN models, the optimized SVMD-IBiTCN-BiLSTM model achieves a coefficient of determination of 0.998 2, with mean absolute percentage error, root mean square error, and mean absolute Error reduced by 3.57, 9.94, and 7.21 respectively, demonstrating superior forecasting accuracy.