Proceedings of the National Academy of Sciences of the United States of America ( IF 11.1 ) Pub Date : 2020-07-21 , DOI: 10.1073/pnas.1918964117 Karen Stengel 1 , Andrew Glaws 1 , Dylan Hettinger 2 , Ryan N King 3
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
中文翻译:
气候风和太阳数据的对抗性超分辨率。
决策者在决定未来能源,电力基础设施,交通网络,农业以及许多其他社会重要系统的发展时,对于决策者而言至关重要的是,反映不同气候情景的准确和高分辨率数据至关重要。但是,最新的长期全球气候模拟无法解决资源评估或运营规划所需的时空特征。我们引入对抗性深度学习方法,以超级解析全球气候模型中的风速和太阳辐照度输出,以达到足以评估可再生能源资源的规模。通过对抗性训练来改善我们网络的物理和感知性能,我们展示了