Supporting renewable energy planning and operation with data-driven high-resolution ensemble weather forecast
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arXiv
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866912455542702080 |
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| author | Wang, Jingnan Chao, Jie Yang, Shangshang Ren, Kaijun Deng, Kefeng Chen, Xi Liu, Yaxin Wen, Hanqiuzi Xiao, Ziniu Zhang, Lifeng Wang, Xiaodong Guan, Jiping Pan, Baoxiang |
| author_facet | Wang, Jingnan Chao, Jie Yang, Shangshang Ren, Kaijun Deng, Kefeng Chen, Xi Liu, Yaxin Wen, Hanqiuzi Xiao, Ziniu Zhang, Lifeng Wang, Xiaodong Guan, Jiping Pan, Baoxiang |
| contents | The planning and operation of renewable energy, especially wind power, depend crucially on accurate, timely, and high-resolution weather information. Coarse-grid global numerical weather forecasts are typically downscaled to meet these requirements, introducing challenges of scale inconsistency, process representation error, computation cost, and entanglement of distinct uncertainty sources from chaoticity, model bias, and large-scale forcing. We address these challenges by learning the climatological distribution of a target wind farm using its high-resolution numerical weather simulations. An optimal combination of this learned high-resolution climatological prior with coarse-grid large scale forecasts yields highly accurate, fine-grained, full-variable, large ensemble of weather pattern forecasts. Using observed meteorological records and wind turbine power outputs as references, the proposed methodology verifies advantageously compared to existing numerical/statistical forecasting-downscaling pipelines, regarding either deterministic/probabilistic skills or economic gains. Moreover, a 100-member, 10-day forecast with spatial resolution of 1 km and output frequency of 15 min takes < 1 hour on a moderate-end GPU, as contrast to $\mathcal{O}(10^3)$ CPU hours for conventional numerical simulation. By drastically reducing computational costs while maintaining accuracy, our method paves the way for more efficient and reliable renewable energy planning and operation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04396 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Supporting renewable energy planning and operation with data-driven high-resolution ensemble weather forecast Wang, Jingnan Chao, Jie Yang, Shangshang Ren, Kaijun Deng, Kefeng Chen, Xi Liu, Yaxin Wen, Hanqiuzi Xiao, Ziniu Zhang, Lifeng Wang, Xiaodong Guan, Jiping Pan, Baoxiang Machine Learning Atmospheric and Oceanic Physics The planning and operation of renewable energy, especially wind power, depend crucially on accurate, timely, and high-resolution weather information. Coarse-grid global numerical weather forecasts are typically downscaled to meet these requirements, introducing challenges of scale inconsistency, process representation error, computation cost, and entanglement of distinct uncertainty sources from chaoticity, model bias, and large-scale forcing. We address these challenges by learning the climatological distribution of a target wind farm using its high-resolution numerical weather simulations. An optimal combination of this learned high-resolution climatological prior with coarse-grid large scale forecasts yields highly accurate, fine-grained, full-variable, large ensemble of weather pattern forecasts. Using observed meteorological records and wind turbine power outputs as references, the proposed methodology verifies advantageously compared to existing numerical/statistical forecasting-downscaling pipelines, regarding either deterministic/probabilistic skills or economic gains. Moreover, a 100-member, 10-day forecast with spatial resolution of 1 km and output frequency of 15 min takes < 1 hour on a moderate-end GPU, as contrast to $\mathcal{O}(10^3)$ CPU hours for conventional numerical simulation. By drastically reducing computational costs while maintaining accuracy, our method paves the way for more efficient and reliable renewable energy planning and operation. |
| title | Supporting renewable energy planning and operation with data-driven high-resolution ensemble weather forecast |
| topic | Machine Learning Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2505.04396 |