Spatio-Temporal Probabilistic Forecasting of Wind Speed Using Transformer-Based Diffusion Models

Spatio-temporal wind speed forecasting plays a crucial role in enhancing energy conversion efficiency and optimizing resource allocation, forming a cornerstone of sustainable development. However, existing methods for spatio-temporal wind speed forecasting face challenges in capturing intricate spatio-temporal dependencies and adapting to dynamic variations in wind speed data. To address these limitations, we propose the Probabilistic Spatio-Temporal Diffusion Transformer (PSTDT), a novel model that combines the generative power of denoising diffusion probabilistic models with the robust spatio-temporal modeling capabilities of Transformers. This approach introduces a dual-spatial attention module to model static positional relationships and dynamic dependencies from historical data, thereby capturing evolving spatial correlations. Additionally, PSTDT integrates a dual-phase temporal module for modeling crossperiod temporal dependencies alongside auto-regressive temporal features, enhancing its capacity to address the complexities of time-series forecasting. Furthermore, a temporal-adaptive layer normalization is incorporated to dynamically adjust normalization parameters, improving the model's forecast accuracy and stability. Extensive experiments demonstrate that PSTDT outperforms state-of-the-art methods across multiple datasets, reducing continuous ranked probability score by 8%-20% and mean absolute error by 7%-19%.