Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC)

Description

The Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) data is a collection of 4km hourly wind, solar, temperature, humidity, and pressure fields for the contiguous United States under various climate change scenarios. Sup3rCC is downscaled Global Climate Model (GCM) data. The downscaling process was performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data (linked below as "Sup3r GitHub Repo"). The data includes both historical and future weather years, although the historical years represent the historical climate, not the actual historical weather that we experienced. You cannot use Sup3rCC data to study historical weather events, although other sup3r datasets may be intended for this. The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar capacity. Please note that all climate change data is only a representation of the possible future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty.

Resources

Name Format Description Link
21 Sup3rCC data in OEDI S3 Viewer https://data.openei.org/s3_viewer?bucket=nrel-pds-sup3rcc
21 Journal publication detailing the Sup3rCC project. https://www.nature.com/articles/s41560-024-01507-9
21 Documentation page for NREL's renewable energy resource datasets including Sup3rCC https://nrel.github.io/rex/misc/examples.nrel_data.html
21 The Super-Resolution for Renewable Resource Data (sup3r) software uses generative adversarial networks to create synthetic high-resolution wind and solar spatiotemporal data from coarse low-resolution inputs. https://github.com/NREL/sup3r/tree/main/examples/sup3rcc
21 The Super-Resolution for Renewable Resource Data (sup3r) software uses generative adversarial networks to create synthetic high-resolution wind and solar spatiotemporal data from coarse low-resolution inputs. https://github.com/NREL/sup3r
28 OEDI documentation markdown for the Sup3rCC dataset detailing version log, directory structure, data format, and code examples. https://github.com/openEDI/documentation/blob/main/Sup3rCC.md
21 Released to the public as part of the Department of Energy's Open Energy Data Initiative, these data represent a serially complete collection of hourly 4km wind, solar, temperature, humidity, and pressure fields for the Continental United States under climate change scenarios. Sup3rCC is downscaled Global Climate Model (GCM) data. For example, the initial file set tagged "sup3rcc_conus_mriesm20_ssp585_r1i1p1f1" is downscaled from MRI ESM 2.0 for climate change scenario SSP5 8.5 and variant label r1i1p1f1. The downscaling process is performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data. The data includes both historical and future weather years, although the historical years represent the historical average climate, not true historical weather. The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar power generation. Please note that all climate change data is only a representation of the *possible* future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty. https://registry.opendata.aws/nrel-pds-sup3rcc/

Tags

  • power-systems
  • generative-adversarial-learning
  • high-resolution
  • generative-machine-learning
  • weather
  • energy-systems
  • solar
  • generative-adversarial-network
  • irradiance
  • energy-planning
  • dni
  • gan
  • renewable-energy
  • wind
  • climate
  • contiguous-united-states
  • power
  • ghi
  • climate-change
  • temperature
  • resource-data
  • sup3rcc
  • windspeed
  • machine-learning
  • energy

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