ABSTRACT Accurate long‐term wind speed forecasting is pivotal for the strategic planning of renewable energy infrastructure, particularly for assessing the techno‐economic feasibility of wind‐powered green hydrogen facilities. However, capturing the complex spatiotemporal dependencies in climate data remains a significant challenge. This study proposes a hybrid deep learning framework designed to enhance 1‐ to 10‐year wind speed forecasts. The proposed architecture integrates graph neural networks (GNN) to extract inter‐variable correlations and feature‐space dynamics among meteorological parameters, coupled with advanced sequence modeling layers to capture temporal patterns. We rigorously evaluated the framework using multi‐variable climate data from NASA's Power Data Access Viewer, comparing a GNN‐Transformer model against a GNN‐GRU variant, as well as standard baselines (LSTM, CNN) and state‐of‐the‐art hybrids (e.g., MST‐GNN). The results demonstrate that the proposed hybrid framework significantly outperforms standalone models. Specifically, the GNN‐Transformer achieved a Mean Absolute Error (MAE) of 0.53 m/s for 10‐year forecasts, representing a 30.27% improvement over a standard Transformer. Furthermore, our comparative analysis reveals that the GNN‐GRU variant achieved superior practical performance with an MAE of 0.44 m/s. These findings provide two key contributions: (1) establishing a robust GNN‐based framework that advances long‐term forecasting accuracy for green hydrogen site planning, and (2) offering empirical evidence that while Transformers offer theoretical complexity, simpler recurrent architectures like GRU may yield better stability in specific long‐term climatological tasks.
Baghaei et al. (Thu,) studied this question.