Dataset for Evaluation of Extreme Weather Impacts on Utility-Scale Photovoltaic Plant Performance in the United States
This dataset is a fusion of three data types (operations and maintenance tickets, weather data, and production data) that was used to support machine learning analysis and evaluation of drivers for low performance at photovoltaic (PV) sites during compound, extreme weather events. After being processed with machine learning, the data was used in the "Evaluation of Extreme Weather Impacts on Utility-scale Photovoltaic Plant Performance in the United States" manuscript. Additional details are captured in the associated manuscript.
Resources
2 resources available
-
Evaluation of Extreme Weather Impacts on Utility-Scale Photovoltaic Plant Performance in the United States
117508 -
Data for PV Performance Machine Learning Analysis.xlsx
XLSX
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"019:20"
]
|
| contactPoint |
{
"fn": "Thushara Gunda",
"@type": "vcard:Contact",
"hasEmail": "mailto:tgunda@sandia.gov"
}
|
| dataQuality |
true
|
| description | This dataset is a fusion of three data types (operations and maintenance tickets, weather data, and production data) that was used to support machine learning analysis and evaluation of drivers for low performance at photovoltaic (PV) sites during compound, extreme weather events. After being processed with machine learning, the data was used in the "Evaluation of Extreme Weather Impacts on Utility-scale Photovoltaic Plant Performance in the United States" manuscript. Additional details are captured in the associated manuscript. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "Evaluation of Extreme Weather Impacts on Utility-Scale Photovoltaic Plant Performance in the United States",
"format": "117508",
"accessURL": "https://doi.org/10.1016/j.apenergy.2021.117508",
"mediaType": "application/octet-stream",
"description": "This paper was written using the data provide in this submission. The paper investigates the effects of compound weather events on photovoltaic plant performance using machine learning."
},
{
"@type": "dcat:Distribution",
"title": "Data for PV Performance Machine Learning Analysis.xlsx",
"format": "xlsx",
"mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
"description": "This dataset was used to support machine learning analysis and evaluation of drivers for low performance at PV sites during compound, extreme weather events. The dataset includes information regarding location, climate, storms, storm factors (such as rainfall and windspeed), and other factors for low production PV. ",
"downloadURL": "https://data.openei.org/files/4055/oedi_data.xlsx"
}
]
|
| DOI | 10.25984/1812011 |
| identifier | https://data.openei.org/submissions/4055 |
| issued | 2021-04-01T06:00:00Z |
| keyword |
[
"AI",
"analysis",
"artificial intelligence",
"climate region",
"data",
"data fusion",
"energy",
"extreme weather",
"flood",
"flooding",
"humidity zone",
"hurricanes",
"lightning",
"machine learning",
"maintenance",
"photovoltaic",
"power",
"production",
"pv",
"rain",
"snow",
"solar",
"storm",
"temperature zone",
"weather",
"wind",
"wind speed"
]
|
| landingPage | https://data.openei.org/submissions/4055 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2024-06-14T15:17:14Z |
| programCode |
[
"019:000",
"019:008",
"019:023"
]
|
| projectNumber |
"34172"
|
| projectTitle | Operation and Maintenance of PV Systems: Data Science, Analysis, and Standards |
| publisher |
{
"name": "Sandia National Laboratories",
"@type": "org:Organization"
}
|
| spatial |
"{"type":"Polygon","coordinates":[[[-125.1877,30.202354530848773],[-67.89410000000002,30.202354530848773],[-67.89410000000002,48.80270683429179],[-125.1877,48.80270683429179],[-125.1877,30.202354530848773]]]}"
|
| title | Dataset for Evaluation of Extreme Weather Impacts on Utility-Scale Photovoltaic Plant Performance in the United States |
