A novel hybrid deep learning model for ultra-short-term prediction of wind speed

Accurate ultra-short-term wind speed prediction is critical for various engineering applications. Although decomposition-integration methods are widely used for this purpose, several challenges remain. This study addresses these challenges by integrating two-stage feature extraction, a combination weight prediction model, and intelligent integration to improve prediction accuracy. In the decomposition phase, two advanced methods are employed to reduce data complexity and extract comprehensive features. During reconstruction, multiscale sample entropy is applied to balance computational efficiency with model complexity. To overcome the limitations of individual forecasting models, a combined prediction model incorporating two deep learning approaches is developed, with weights adaptively optimized using the Sparrow Search Algorithm. Additionally, to address the variability in subsequence contributions, intelligent weight integration based on deep learning models is implemented, ensuring robust and accurate final predictions. Validation using wind speed data from three Hong Kong Observatory weather stations demonstrates that the proposed method outperforms benchmark models, achieving more-satisfactory accuracy, stability, and robustness.