Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1073/pnas.1918964117 |
Adversarial super-resolution of climatological wind and solar data | |
Karen Stengel; Andrew Glaws; Dylan Hettinger; Ryan N. King | |
2020-07-06 | |
发表期刊 | Proceedings of the National Academy of Science
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出版年 | 2020 |
英文摘要 | Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a |
领域 | 地球科学 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/282729 |
专题 | 地球科学 |
推荐引用方式 GB/T 7714 | Karen Stengel,Andrew Glaws,Dylan Hettinger,et al. Adversarial super-resolution of climatological wind and solar data[J]. Proceedings of the National Academy of Science,2020. |
APA | Karen Stengel,Andrew Glaws,Dylan Hettinger,&Ryan N. King.(2020).Adversarial super-resolution of climatological wind and solar data.Proceedings of the National Academy of Science. |
MLA | Karen Stengel,et al."Adversarial super-resolution of climatological wind and solar data".Proceedings of the National Academy of Science (2020). |
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