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.
Citation Formats
TY - DATA
AB - 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.
AU - Ramos, Dakota
A2 - Glaws, Andrew
A3 - King, Ryan
A4 - Harrison-Atlas, Dylan
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Renewable Energy Laboratory
DO - 10.25984/2222588
KW - energy
KW - power
KW - benchmark
KW - machine learning
KW - artificial intelligence
KW - wind energy
KW - wind turbine
KW - wind plant
KW - wakes
KW - wind plant layout
KW - FLORIS
KW - ML
KW - AI
KW - wind
KW - wake steering
KW - wind power
KW - code
KW - python
KW - yaw angle
KW - simulation
KW - turbine-level
KW - plant-level
KW - Gaussian wake model
KW - model
KW - power production
KW - Flow Redirection and Induction in Steady State
KW - optimization
KW - data
KW - processed data
LA - English
DA - 2021/02/12
PY - 2021
PB - National Renewable Energy Laboratory
T1 - Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets
UR - https://doi.org/10.25984/2222588
ER -
Ramos, Dakota, et al. Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets. National Renewable Energy Laboratory, 12 February, 2021, Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2222588.
Ramos, D., Glaws, A., King, R., & Harrison-Atlas, D. (2021). Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets. [Data set]. Open Energy Data Initiative (OEDI). National Renewable Energy Laboratory. https://doi.org/10.25984/2222588
Ramos, Dakota, Andrew Glaws, Ryan King, and Dylan Harrison-Atlas. Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets. National Renewable Energy Laboratory, February, 12, 2021. Distributed by Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2222588
@misc{OEDI_Dataset_5884,
title = {Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets},
author = {Ramos, Dakota and Glaws, Andrew and King, Ryan and Harrison-Atlas, Dylan},
abstractNote = {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.},
url = {https://data.openei.org/submissions/5884},
year = {2021},
howpublished = {Open Energy Data Initiative (OEDI), National Renewable Energy Laboratory, https://doi.org/10.25984/2222588},
note = {Accessed: 2025-04-23},
doi = {10.25984/2222588}
}
https://dx.doi.org/10.25984/2222588
Details
Data from Feb 12, 2021
Last updated Jan 2, 2024
Submitted Oct 13, 2023
Organization
National Renewable Energy Laboratory
Contact
Ryan King
Authors
Research Areas
Keywords
energy, power, benchmark, machine learning, artificial intelligence, wind energy, wind turbine, wind plant, wakes, wind plant layout, FLORIS, ML, AI, wind, wake steering, wind power, code, python, yaw angle, simulation, turbine-level, plant-level, Gaussian wake model, model, power production, Flow Redirection and Induction in Steady State, optimization, data, processed dataDOE Project Details
Project Name Foundational AI for Wind Energy
Project Number FY23 AOP 1.3.0.403