HYBRID ATTENTION-BASED DEEP LEARNING MODEL FOR WIND SPEED FORECASTING IN RENEWABLE ENERGY APPLICATIONS

Accurate wind speed forecasting (WSF) is critical for the efficient integration of wind energy into power systems. In this study, a novel hybrid model combining Ensemble Empirical Mode Decomposition (EEMD), Convolutional Neural Network (CNN), and Attention-based Long Short-Term Memory (ALSTM)—referred to as EEMD-CNN-ALSTM—is proposed for short-term wind speed prediction. The model leverages EEMD to decompose complex and non-stationary wind speed signals into more manageable components, CNN to extract localized spatial features, and ALSTM to selectively capture temporal dependencies using attention mechanisms. The proposed model is evaluated using 1-hour interval wind speed data collected from two distinct wind farms: Garden City and Idalia. Performance is assessed using standard metrics including RMSE, MAE, MSE, and R². Results demonstrate that the EEMD-CNN-ALSTM model consistently outperforms benchmark models and other hybrid architectures. Across both wind farms, the proposed model achieves the lowest error rates and highest predictive accuracy, confirming its robustness and effectiveness in handling the complexities of WSF.