Reliable forecasting of renewable power generation is critical for optimizing power system operation, enhancing resource allocation, and supporting sustainable renewable energy planning. This paper proposes DeepCognition Forecasting (DCF), a Cognitive AI‐inspired hybrid deep learning framework that integrates bidirectional long short‐term memory (Bi‐LSTM) networks for short‐term temporal perception and memory with Transformer encoders for long‐range contextual reasoning. The framework further incorporates a meta‐adaptive dual‐optimization mechanism that dynamically blends first‐order (Adam) and second‐order (Shampoo) optimizers during training. DCF is evaluated using multiyear hourly wind speed data obtained from the Open‐Meteo historical API. Experimental results show that the meta‐adaptive optimization strategy achieves faster convergence and lower final training loss than fixed‐optimizer baselines. Specifically, the adaptive approach reduces the final loss to 0.00689, outperforming Adam‐only (0.00711) and Shampoo‐only (0.02261) optimization. The learned optimizer mixing coefficient stabilizes around α ≈ 0.51, indicating balanced and stable optimizer contributions. Structural ablation experiments further highlight the importance of cognitive modularization, with removal of the Bi‐LSTM component increasing the loss to 0.00906. Sensitivity analyses demonstrate robustness across training conditions, with losses ranging from 0.00827 to 0.00958 for different sequence lengths and increasing gradually from 0.00847 to 0.01077 under noise perturbations. Comparative experiments against representative baselines, including the state‐of‐the‐art temporal fusion transformer (TFT), show substantially lower training loss for DCF (TFT: 0.04234). These results position DCF as a compact and empirically grounded framework for wind speed forecasting and renewable energy decision support.
El-saieed et al. (Thu,) studied this question.