DOI: 10.1073/pnas.1918964117
论文题名: Adversarial super-resolution of climatological wind and solar data
作者: Stengel K. ; Glaws A. ; Hettinger D. ; King R.N.
刊名: Proceedings of the National Academy of Sciences of the United States of America
ISSN: 0027-8424
出版年: 2020
卷: 117, 期: 29 起始页码: 16805
结束页码: 16815
语种: 英语
英文关键词: Adversarial training
; Climate downscaling
; Deep learning
Scopus关键词: article
; climate change
; deep learning
; noise
; perception
; renewable energy
; turbulent flow
; validation study
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 50× resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation on arbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change's Fifth Assessment Report. © 2020 National Academy of Sciences. All rights reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/164126
Appears in Collections: 气候变化与战略
There are no files associated with this item.
作者单位: Stengel, K., Computational Science Center, National Renewable Energy Laboratory, Golden, CO 80401, United States; Glaws, A., Computational Science Center, National Renewable Energy Laboratory, Golden, CO 80401, United States; Hettinger, D., Strategic Energy Analysis Center, National Renewable Energy Laboratory, Golden, CO 80401, United States; King, R.N., Computational Science Center, National Renewable Energy Laboratory, Golden, CO 80401, United States
Recommended Citation:
Stengel K.,Glaws A.,Hettinger D.,et al. Adversarial super-resolution of climatological wind and solar data[J]. Proceedings of the National Academy of Sciences of the United States of America,2020-01-01,117(29)