An Improved Regularization Stochastic Configuration Network for Robust Wind Speed Prediction

To address the limitations of Stochastic Configured Networks (SCNs) in wind speed prediction, specifically insufficient regularization capability and a high risk of overfitting, this paper proposes a novel Regularized Stochastic Configured Network (RSCN). By integrating L1 and L2 regularization techniques from Elastic Net, RSCNs achieve feature sparsity while preserving prediction accuracy. Furthermore, a dynamic loss coefficient and a penalty term based on historical training loss are introduced to adaptively modulate the regularization strength during model training. Experimental results demonstrate that RSCNs achieve superior prediction performance and enhanced stability across four benchmark regression datasets and two real-world wind speed datasets. Compared with conventional SCNs and the swarm intelligence optimization-based variant HPO-SCNs, RSCNs significantly reduce the performance gap between training and test sets while maintaining high predictive accuracy. On average, improvements in R2, MAE, and RMSE exceed 50% reduction in error discrepancies. The proposed method offers an effective solution for wind power forecasting by effectively balancing generalization ability and computational efficiency, thereby holding practical significance for real-world applications.