Articles | Volume 5, issue 1
https://doi.org/10.5194/wes-5-199-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-5-199-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Feb 2020
Research article |
| 05 Feb 2020
| 05 Feb 2020
The Power Curve Working Group's assessment of wind turbine power performance prediction methods
Joseph C. Y. Lee
CORRESPONDING AUTHOR
National Wind Technology Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA
Peter Stuart
Renewable Energy Systems, Kings Langley, Hertfordshire, England, UK
Andrew Clifton
Stuttgart Wind Energy, Institute of Aircraft Design and Manufacture, University of Stuttgart, Stuttgart, Germany
M. Jason Fields
National Wind Technology Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA
Jordan Perr-Sauer
Computational Science Center, National Renewable Energy Laboratory,
Golden, Colorado 80401, USA
Lindy Williams
Computational Science Center, National Renewable Energy Laboratory,
Golden, Colorado 80401, USA
Lee Cameron
Renewable Energy Systems, Kings Langley, Hertfordshire, England, UK
Taylor Geer
DNV GL, Portland, Oregon 97204, USA
Paul Housley
SSE plc, Glasgow, Scotland, UK
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- A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network Z. Luo et al. https://doi.org/10.1109/ACCESS.2020.2994077
- Modeling the Wind Turbine Power Curve: A Comparison of Neural Networks and Classical Regression M. Asilsoy https://doi.org/10.47495/okufbed.1716847
- Characterizing atmospheric stability in complex terrain N. Agarwal & J. Lundquist https://doi.org/10.5194/wes-11-883-2026
- Research and Analysis of the Impact of Local Climatic Conditions on Wind Turbine Generation—Case Study J. Kusznier et al. https://doi.org/10.3390/en18246429
- Analysis of Barriers to South Africa’s Energy Transition: Perspectives from industry experts P. Molepo et al. https://doi.org/10.1016/j.esd.2025.101777
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- An explainable AI framework for robust and transparent data-driven wind turbine power curve models S. Letzgus & K. Müller https://doi.org/10.1016/j.egyai.2023.100328
- SCADA data analysis for long-term wind turbine performance assessment: A case study D. Astolfi et al. https://doi.org/10.1016/j.seta.2022.102357
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- Comparative Analysis of Estimated Small Wind Energy Using Different Probability Distributions in a Desert City in Northwestern México J. Burgos-Peñaloza et al. https://doi.org/10.3390/en17133323
- Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear D. Kim et al. https://doi.org/10.1016/j.energy.2020.119051
- Quantifying the impact of wind shear coefficient on annual energy production of coastal wind farm in balochistan, Pakistan J. Yasir et al. https://doi.org/10.1177/27533735251319764
- Building a Classification Map of Wind Turbine Characteristics Compatible with the Winds of Middle and Southern Regions in Iraq F. Hadi et al. https://doi.org/10.3390/wind6020015
- Impact of inflow conditions and turbine placement on the performance of offshore wind turbines exceeding 7 MW K. Vratsinis et al. https://doi.org/10.5194/wes-11-1803-2026
- Uncertainty-Aware Wind Turbine Power Curve Modelling with Rotor Turbulence Intensity Asymmetry Using Two-Stage Gaussian Process Regression S. Baisthakur et al. https://doi.org/10.1088/1742-6596/3224/2/022052
- Machine- and deep-learning models for wind turbine icing prediction across multiple horizons: the influence of ice sensors and weather forecasts A. Kallarappayi et al. https://doi.org/10.1016/j.coldregions.2026.104940
- A New Wind Turbine Power Performance Assessment Approach: SCADA to Power Model Based with Regression-Kriging P. Zhang et al. https://doi.org/10.3390/en15134820
- Power curve performance of coastal turbines subject to low turbulence intensity offshore winds Y. Sakagami et al. https://doi.org/10.1007/s40430-022-03942-9
- Research challenges and needs for the deployment of wind energy in hilly and mountainous regions A. Clifton et al. https://doi.org/10.5194/wes-7-2231-2022
27 citations as recorded by crossref.
- A GIS-portal platform from the data perspective to energy hub digitalization solutions- A review and a case study M. Majidi Nezhad et al. https://doi.org/10.1016/j.rser.2025.116019
- Wind Resource Evaluation With Atmospheric Stability Across Different Surface Types Z. Hua et al. https://doi.org/10.1002/ese3.70459
- Aerodynamic effects of leading-edge erosion in wind farm flow modeling J. Visbech et al. https://doi.org/10.5194/wes-9-1811-2024
- Validating the next generation of turbine interaction models T. Levick et al. https://doi.org/10.1088/1742-6596/2257/1/012010
- Impact of atmospheric turbulence on performance and loads of wind turbines: knowledge gaps and research challenges B. Kosović et al. https://doi.org/10.5194/wes-11-509-2026
- Data-Driven wind turbine performance assessment and quantification using SCADA data and field measurements Y. Ding et al. https://doi.org/10.3389/fenrg.2022.1050342
- Offshore wind power multi-step forecasting based on multi-scale attention mechanism fusion network Y. Xiong & W. Wang https://doi.org/10.1016/j.engappai.2025.112026
- Wind turbine energy forecasting using real wind farm’s measurement data and performance of gene expression programming analytical model in comparison to traditional algorithms Z. Mehmood & Z. Wang https://doi.org/10.1080/15435075.2024.2415540
- A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network Z. Luo et al. https://doi.org/10.1109/ACCESS.2020.2994077
- Modeling the Wind Turbine Power Curve: A Comparison of Neural Networks and Classical Regression M. Asilsoy https://doi.org/10.47495/okufbed.1716847
- Characterizing atmospheric stability in complex terrain N. Agarwal & J. Lundquist https://doi.org/10.5194/wes-11-883-2026
- Research and Analysis of the Impact of Local Climatic Conditions on Wind Turbine Generation—Case Study J. Kusznier et al. https://doi.org/10.3390/en18246429
- Analysis of Barriers to South Africa’s Energy Transition: Perspectives from industry experts P. Molepo et al. https://doi.org/10.1016/j.esd.2025.101777
- Grand challenges in the digitalisation of wind energy A. Clifton et al. https://doi.org/10.5194/wes-8-947-2023
- An explainable AI framework for robust and transparent data-driven wind turbine power curve models S. Letzgus & K. Müller https://doi.org/10.1016/j.egyai.2023.100328
- SCADA data analysis for long-term wind turbine performance assessment: A case study D. Astolfi et al. https://doi.org/10.1016/j.seta.2022.102357
- A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset S. Kartal et al. https://doi.org/10.5194/wes-8-1533-2023
- Comparative Analysis of Estimated Small Wind Energy Using Different Probability Distributions in a Desert City in Northwestern México J. Burgos-Peñaloza et al. https://doi.org/10.3390/en17133323
- Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear D. Kim et al. https://doi.org/10.1016/j.energy.2020.119051
- Quantifying the impact of wind shear coefficient on annual energy production of coastal wind farm in balochistan, Pakistan J. Yasir et al. https://doi.org/10.1177/27533735251319764
- Building a Classification Map of Wind Turbine Characteristics Compatible with the Winds of Middle and Southern Regions in Iraq F. Hadi et al. https://doi.org/10.3390/wind6020015
- Impact of inflow conditions and turbine placement on the performance of offshore wind turbines exceeding 7 MW K. Vratsinis et al. https://doi.org/10.5194/wes-11-1803-2026
- Uncertainty-Aware Wind Turbine Power Curve Modelling with Rotor Turbulence Intensity Asymmetry Using Two-Stage Gaussian Process Regression S. Baisthakur et al. https://doi.org/10.1088/1742-6596/3224/2/022052
- Machine- and deep-learning models for wind turbine icing prediction across multiple horizons: the influence of ice sensors and weather forecasts A. Kallarappayi et al. https://doi.org/10.1016/j.coldregions.2026.104940
- A New Wind Turbine Power Performance Assessment Approach: SCADA to Power Model Based with Regression-Kriging P. Zhang et al. https://doi.org/10.3390/en15134820
- Power curve performance of coastal turbines subject to low turbulence intensity offshore winds Y. Sakagami et al. https://doi.org/10.1007/s40430-022-03942-9
- Research challenges and needs for the deployment of wind energy in hilly and mountainous regions A. Clifton et al. https://doi.org/10.5194/wes-7-2231-2022
Saved (final revised paper)
Latest update: 07 Jun 2026
Short summary
This work summarizes the results of the intelligence-sharing initiative of the Power Curve Working Group. Participants in this share exercise applied a handful of selected power curve modeling correction methods on their power performance test data, and they submitted the results for the coauthors to analyze. In this paper, we describe the share exercise, explain the analysis methodologies, and perform statistical tests to evaluate the correction methods in various inflow conditions.
This work summarizes the results of the intelligence-sharing initiative of the Power Curve...
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