Super-Resolution for Renewable Energy Resource Data with Wind from Reanalysis (Sup3rWind)

                                            [
  "019:20"
]
                                        

                                            {
  "fn": "Brandon Benton",
  "@type": "vcard:Contact",
  "hasEmail": "mailto:brandon.benton@nrel.gov"
}
                                        

                                            true
                                        

                                            [
  {
    "@type": "dcat:Distribution",
    "title": "Sup3rWind Data and Models in S3",
    "format": "HTML",
    "accessURL": "https://data.openei.org/s3_viewer?bucket=nrel-pds-wtk&prefix=sup3rwind%2F",
    "mediaType": "text/html",
    "description": "The Super-Resolution for Renewable Energy Resource Data with Wind from Reanalysis (Sup3rWind) data is a collection of high-resolution historical wind, temperature, humidity, and pressure fields. Sup3rWind data is produced by downscaling ECMWF Reanalysis Version 5 data (ERA5) to 2-km spatial and 5-minute temporal resolution (hourly for temperature, humidity, and pressure). 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"). It improves the representation of terrain driven wind flows, extreme wind events, and preserves important spatiotemporal patterns for use in energy system planning and operations."
  },
  {
    "@type": "dcat:Distribution",
    "title": "Sup3rWind Ukraine Data API",
    "format": "HTML",
    "accessURL": "https://developer.nrel.gov/docs/wind/wind-toolkit/sup3rwind-ukraine-download/",
    "mediaType": "text/html",
    "description": "Gives instructions for downloading and using the Sup3rWind Ukraine Data API. This API allows users to create large downloadable data archives via a data request. The data available covers Ukraine, Moldova, and part of Romania for the years 2000 to 2023."
  },
  {
    "@type": "dcat:Distribution",
    "title": "Sup3r GitHub Repo",
    "format": "HTML",
    "accessURL": "https://github.com/NREL/sup3r",
    "mediaType": "text/html",
    "description": "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. "
  },
  {
    "@type": "dcat:Distribution",
    "title": "Sup3rWind Data and Model Usage Examples",
    "format": "HTML",
    "accessURL": "https://github.com/NREL/sup3r/tree/main/examples/sup3rwind",
    "mediaType": "text/html",
    "description": "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. "
  },
  {
    "@type": "dcat:Distribution",
    "title": "Wind Integration National Dataset Registry on AWS",
    "format": "HTML",
    "accessURL": "https://registry.opendata.aws/nrel-pds-wtk/",
    "mediaType": "text/html",
    "description": "A registry of the NREL Wind Integration National Dataset. References the "WIND AWS S3 Bucket" data link. Released to the public as part of the Department of Energy's Open Energy Data Initiative,  the Wind Integration National Dataset (WIND) is an update and expansion of the Eastern Wind Integration Data Set and Western Wind Integration Data Set. It supports the next generation of wind integration studies. "
  },
  {
    "@type": "dcat:Distribution",
    "title": "Sup3rWind Ukraine Publication",
    "format": "HTML",
    "accessURL": "https://www.mdpi.com/1996-1073/18/14/3769",
    "mediaType": "text/html",
    "description": "Journal publication detailing the Sup3rWind Ukraine project."
  }
]
                                        

                                            [
  "ERA5",
  "GAN",
  "ML",
  "South America",
  "Sup3rWind",
  "Ukraine",
  "data",
  "data assimilation",
  "downscaling",
  "energy",
  "energy planning",
  "energy systems",
  "generative",
  "generative adversarial learning",
  "high-resolution",
  "machine learning",
  "model",
  "power",
  "power systems",
  "processed data",
  "renewable energy",
  "resource data",
  "sup3r",
  "temperature",
  "weather",
  "wind",
  "windspeed"
]
                                        

                                            [
  "019:010"
]
                                        

                                            {
  "name": "National Renewable Energy Lab (NREL)",
  "@type": "org:Organization"
}
                                        

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