Adversarial super-resolution of climatological wind and solar data
Abstract
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 a50×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.
- Authors:
-
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Laboratory Directed Research and Development (LDRD) Program
- OSTI Identifier:
- 1660090
- Report Number(s):
- NREL/JA-2C00-75625
Journal ID: ISSN 0027-8424; MainId:6859;UUID:c2361be1-9c1b-ea11-9c2a-ac162d87dfe5;MainAdminID:14074
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Proceedings of the National Academy of Sciences of the United States of America
- Additional Journal Information:
- Journal Volume: 117; Journal Issue: 29; Journal ID: ISSN 0027-8424
- Publisher:
- National Academy of Sciences
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 29 ENERGY PLANNING, POLICY, AND ECONOMY; 17 WIND ENERGY; 14 SOLAR ENERGY; adversarial training; climate downscaling; deep learning
Citation Formats
Stengel, Karen, Glaws, Andrew, Hettinger, Dylan, and King, Ryan N. Adversarial super-resolution of climatological wind and solar data. United States: N. p., 2020.
Web. doi:10.1073/pnas.1918964117.
Stengel, Karen, Glaws, Andrew, Hettinger, Dylan, & King, Ryan N. Adversarial super-resolution of climatological wind and solar data. United States. https://doi.org/10.1073/pnas.1918964117
Stengel, Karen, Glaws, Andrew, Hettinger, Dylan, and King, Ryan N. Mon .
"Adversarial super-resolution of climatological wind and solar data". United States. https://doi.org/10.1073/pnas.1918964117. https://www.osti.gov/servlets/purl/1660090.
@article{osti_1660090,
title = {Adversarial super-resolution of climatological wind and solar data},
author = {Stengel, Karen and Glaws, Andrew and Hettinger, Dylan and King, Ryan N.},
abstractNote = {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 a50×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.},
doi = {10.1073/pnas.1918964117},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 29,
volume = 117,
place = {United States},
year = {Mon Jul 06 00:00:00 EDT 2020},
month = {Mon Jul 06 00:00:00 EDT 2020}
}
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