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Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets
This dataset contains turbine- and plant-level power outputs for 252,500 cases of diverse wind plant layouts operating under a wide range of yawing and atmospheric conditions. The power outputs were computed using the Gaussian wake model in NREL's FLOw Redirection and Induction in Steady State (FLORIS) model, version 2.3.0. The 252,500 cases include 500 unique wind plants generated randomly by a specialized Plant Layout Generator (PLayGen) that samples randomized realizations of wind plant layouts from one of four canonical configurations: (i) cluster, (ii) single string, (iii) multiple string, (iv) parallel string. Other wind plant layout parameters were also randomly sampled, including the number of turbines (25-200) and the mean turbine spacing (3D-10D, where D denotes the turbine rotor diameter). For each layout, 500 different sets of atmospheric conditions were randomly sampled. These include wind speed in 0-25 m/s, wind direction in 0 deg.-360 deg., and turbulence intensity chosen from low (6%), medium (8%), and high (10%). For each atmospheric inflow scenario, the individual turbine yaw angles were randomly sampled from a one-sided truncated Gaussian on the interval 0 deg.-30 deg. oriented relative to wind inflow direction.
This random data is supplemented with a collection of yaw-optimized samples where FLORIS was used to determine turbine yaw angles that maximize power production for the entire plant. To generate this data, a subset of cases were selected (50 atmospheric conditions from 50 layouts each for a total of additional 2,500 cases) for which FLORIS was re-run with wake steering control optimization. The IEA onshore reference turbine, which has a 130 m rotor diameter, a 110 m hub height, and a rated power capacity of 3.4 MW was used as the turbine for all simulations.
The simulations were performed using NREL's Eagle high performance computing system in February 2021 as part of the Spatial Analysis for Wind Technology Development project funded by the U.S. Department of Energy Wind Energy Technologies Office. The data was collected, reformatted, and preprocessed for this OEDI submission in May 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided.
The .h5 data file structure can be found in the GitHub repository under explore_wind_plant_data_h5.ipynb.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"019:20"
]
|
| contactPoint |
{
"fn": "Ryan King",
"@type": "vcard:Contact",
"hasEmail": "mailto:ryan.king@nrel.gov"
}
|
| dataQuality |
true
|
| description | This dataset contains turbine- and plant-level power outputs for 252,500 cases of diverse wind plant layouts operating under a wide range of yawing and atmospheric conditions. The power outputs were computed using the Gaussian wake model in NREL's FLOw Redirection and Induction in Steady State (FLORIS) model, version 2.3.0. The 252,500 cases include 500 unique wind plants generated randomly by a specialized Plant Layout Generator (PLayGen) that samples randomized realizations of wind plant layouts from one of four canonical configurations: (i) cluster, (ii) single string, (iii) multiple string, (iv) parallel string. Other wind plant layout parameters were also randomly sampled, including the number of turbines (25-200) and the mean turbine spacing (3D-10D, where D denotes the turbine rotor diameter). For each layout, 500 different sets of atmospheric conditions were randomly sampled. These include wind speed in 0-25 m/s, wind direction in 0 deg.-360 deg., and turbulence intensity chosen from low (6%), medium (8%), and high (10%). For each atmospheric inflow scenario, the individual turbine yaw angles were randomly sampled from a one-sided truncated Gaussian on the interval 0 deg.-30 deg. oriented relative to wind inflow direction. This random data is supplemented with a collection of yaw-optimized samples where FLORIS was used to determine turbine yaw angles that maximize power production for the entire plant. To generate this data, a subset of cases were selected (50 atmospheric conditions from 50 layouts each for a total of additional 2,500 cases) for which FLORIS was re-run with wake steering control optimization. The IEA onshore reference turbine, which has a 130 m rotor diameter, a 110 m hub height, and a rated power capacity of 3.4 MW was used as the turbine for all simulations. The simulations were performed using NREL's Eagle high performance computing system in February 2021 as part of the Spatial Analysis for Wind Technology Development project funded by the U.S. Department of Energy Wind Energy Technologies Office. The data was collected, reformatted, and preprocessed for this OEDI submission in May 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided. The .h5 data file structure can be found in the GitHub repository under explore_wind_plant_data_h5.ipynb. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "FLORIS Data on AWS",
"format": "HTML",
"accessURL": "https://data.openei.org/s3_viewer?bucket=nrel-pds-windai&prefix=wind_plant_power%2Ffloris%2F",
"mediaType": "text/html",
"description": "FLORIS data for wind plant power production for various layouts/wind flow scenarios on AWS. Data is from a simulation in February 2021, run as part of the Spatial Analysis for Wind Technology Development project."
},
{
"@type": "dcat:Distribution",
"title": "GitHub Repository",
"format": "HTML",
"accessURL": "https://github.com/NREL/windAI_bench/tree/main/FLORIS_PLayGen",
"mediaType": "text/html",
"description": "Repository that contains demo notebooks to explore the data as well as baseline ML models."
},
{
"@type": "dcat:Distribution",
"title": "Wind AI Bench Data Registry on AWS",
"format": "HTML",
"accessURL": "https://registry.opendata.aws/nrel-pds-windai/",
"mediaType": "text/html",
"description": "AWS public dataset program registry page for data released under the Wind AI Bench. The registry page contains information about dataset documentation, access, and contact, for each of the Wind AI Data Lake datasets."
},
{
"@type": "dcat:Distribution",
"title": "FLORIS Informational Link",
"format": "html",
"accessURL": "https://www.nrel.gov/wind/floris.html",
"mediaType": "text/html",
"description": "FLORIS webpage at NREL"
},
{
"@type": "dcat:Distribution",
"title": "Optimal Yaw List.csv",
"format": "csv",
"mediaType": "text/csv",
"description": "A csv file containing a list of the layouts and scenarios for which yaw optimized simulations were performed.",
"downloadURL": "https://data.openei.org/files/5884/opt_yaw_list.csv"
}
]
|
| DOI | 10.25984/2222588 |
| identifier | https://data.openei.org/submissions/5884 |
| issued | 2021-02-12T07:00:00Z |
| keyword |
[
"AI",
"FLORIS",
"Flow Redirection and Induction in Steady State",
"Gaussian wake model",
"ML",
"artificial intelligence",
"benchmark",
"code",
"data",
"energy",
"machine learning",
"model",
"optimization",
"plant-level",
"power",
"power production",
"processed data",
"python",
"simulation",
"turbine-level",
"wake steering",
"wakes",
"wind",
"wind energy",
"wind plant",
"wind plant layout",
"wind power",
"wind turbine",
"yaw angle"
]
|
| landingPage | https://data.openei.org/submissions/5884 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2024-01-02T18:23:00Z |
| programCode |
[
"019:010",
"019:023"
]
|
| projectNumber | FY23 AOP 1.3.0.403 |
| projectTitle | Foundational AI for Wind Energy |
| publisher |
{
"name": "National Renewable Energy Laboratory",
"@type": "org:Organization"
}
|
| spatial |
"{"type":"Polygon","coordinates":[[[-180,-83],[180,-83],[180,83],[-180,83],[-180,-83]]]}"
|
| title | Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets |
