Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912381634871296 |
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| author | Lin, Chensen Tie, Ruian Yi, Shihong Zhong, Xiaohui Li, Hao |
| author_facet | Lin, Chensen Tie, Ruian Yi, Shihong Zhong, Xiaohui Li, Hao |
| contents | High-resolution wind information is essential for wind energy planning and power forecasting, particularly in regions with complex terrain. However, most AI-based weather forecasting models operate at kilometer-scale resolution, constrained by the reanalysis datasets they are trained on. Here we introduce FuXi-CFD, an AI-based downscaling framework designed to generate detailed three-dimensional wind fields at 30-meter horizontal resolution, using only coarse-resolution atmospheric inputs. The model is trained on a large-scale dataset generated via computational fluid dynamics (CFD), encompassing a wide range of terrain types, surface roughness, and inflow conditions. Remarkably, FuXi-CFD predicts full 3D wind structures -- including vertical wind and turbulent kinetic energy -- based solely on horizontal wind input at 10 meters above ground, the typical output of AI-based forecast systems. It achieves CFD-comparable accuracy while reducing inference time from hours to seconds. By bridging the resolution gap between regional forecasts and site-specific wind dynamics, FuXi-CFD offers a scalable and operationally efficient solution to support the growing demands of renewable energy deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12732 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields Lin, Chensen Tie, Ruian Yi, Shihong Zhong, Xiaohui Li, Hao Atmospheric and Oceanic Physics High-resolution wind information is essential for wind energy planning and power forecasting, particularly in regions with complex terrain. However, most AI-based weather forecasting models operate at kilometer-scale resolution, constrained by the reanalysis datasets they are trained on. Here we introduce FuXi-CFD, an AI-based downscaling framework designed to generate detailed three-dimensional wind fields at 30-meter horizontal resolution, using only coarse-resolution atmospheric inputs. The model is trained on a large-scale dataset generated via computational fluid dynamics (CFD), encompassing a wide range of terrain types, surface roughness, and inflow conditions. Remarkably, FuXi-CFD predicts full 3D wind structures -- including vertical wind and turbulent kinetic energy -- based solely on horizontal wind input at 10 meters above ground, the typical output of AI-based forecast systems. It achieves CFD-comparable accuracy while reducing inference time from hours to seconds. By bridging the resolution gap between regional forecasts and site-specific wind dynamics, FuXi-CFD offers a scalable and operationally efficient solution to support the growing demands of renewable energy deployment. |
| title | Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2505.12732 |