Articles | Volume 5, issue 2
https://doi.org/10.5194/wes-5-489-2020
© 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-5-489-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Apr 2020
Research article |
| 17 Apr 2020
| 17 Apr 2020
The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds
National Renewable Energy Laboratory, Golden, Colorado, USA
Mike Optis
National Renewable Energy Laboratory, Golden, Colorado, USA
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Total article views: 2,986 (including HTML, PDF, and XML)
Thereof 2,580 with geography defined
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Total article views: 736 (including HTML, PDF, and XML)
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Cited
21 citations as recorded by crossref.
- High-resolution satellite observations to account for coastal gradient in wind resource assessment: application to French coastal areas M. Cathelain et al. 10.1088/1742-6596/2505/1/012027
- Characterization of wind speed and directional shear at the AWAKEN field campaign site M. Debnath et al. 10.1063/5.0139737
- Machine-learning-based estimate of the wind speed over complex terrain using the long short-term memory (LSTM) recurrent neural network C. Leme Beu & E. Landulfo 10.5194/wes-9-1431-2024
- How generalizable is a machine-learning approach for modeling hub-height turbulence intensity? N. Bodini et al. 10.1088/1742-6596/2265/2/022028
- Enabling Virtual Met Masts for wind energy applications through machine learning-methods S. Schwegmann et al. 10.1016/j.egyai.2022.100209
- New methods to improve the vertical extrapolation of near-surface offshore wind speeds M. Optis et al. 10.5194/wes-6-935-2021
- A temporal model for vertical extrapolation of wind speed and wind energy assessment P. Crippa et al. 10.1016/j.apenergy.2021.117378
- Characterization of local wind profiles: a random forest approach for enhanced wind profile extrapolation F. Rouholahnejad & J. Gottschall 10.5194/wes-10-143-2025
- Validation of Reanalysis-Based Offshore Wind Resource Characterization Using Lidar Buoy Observations L. Sheridan et al. 10.4031/MTSJ.54.6.13
- On the estimation of boundary layer heights: a machine learning approach R. Krishnamurthy et al. 10.5194/amt-14-4403-2021
- Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble N. Bodini et al. 10.5194/wes-6-1363-2021
- Long-term uncertainty quantification in WRF-modeled offshore wind resource off the US Atlantic coast N. Bodini et al. 10.5194/wes-8-607-2023
- Wind plants can impact long-term local atmospheric conditions N. Bodini et al. 10.1038/s41598-021-02089-2
- Machine learning for predicting offshore vertical wind profiles F. Rouholahnejad et al. 10.1088/1742-6596/2626/1/012023
- High-resolution wind speed forecast system coupling numerical weather prediction and machine learning for agricultural studies — a case study from South Korea J. Shin et al. 10.1007/s00484-022-02287-1
- The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques L. Canché-Cab et al. 10.1007/s10462-024-10962-5
- Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks Applied to Lidar Measurements A. García-Gutiérrez et al. 10.3390/s21113659
- High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 synthetic aperture radar (SAR) data and machine learning L. de Montera et al. 10.5194/wes-7-1441-2022
- Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques D. Hatfield et al. 10.5194/wes-8-621-2023
- Wind speed vertical extrapolation model validation under uncertainty J. Quick et al. 10.1016/j.renene.2024.122028
- Can reanalysis products outperform mesoscale numerical weather prediction models in modeling the wind resource in simple terrain? V. Pronk et al. 10.5194/wes-7-487-2022
21 citations as recorded by crossref.
- High-resolution satellite observations to account for coastal gradient in wind resource assessment: application to French coastal areas M. Cathelain et al. 10.1088/1742-6596/2505/1/012027
- Characterization of wind speed and directional shear at the AWAKEN field campaign site M. Debnath et al. 10.1063/5.0139737
- Machine-learning-based estimate of the wind speed over complex terrain using the long short-term memory (LSTM) recurrent neural network C. Leme Beu & E. Landulfo 10.5194/wes-9-1431-2024
- How generalizable is a machine-learning approach for modeling hub-height turbulence intensity? N. Bodini et al. 10.1088/1742-6596/2265/2/022028
- Enabling Virtual Met Masts for wind energy applications through machine learning-methods S. Schwegmann et al. 10.1016/j.egyai.2022.100209
- New methods to improve the vertical extrapolation of near-surface offshore wind speeds M. Optis et al. 10.5194/wes-6-935-2021
- A temporal model for vertical extrapolation of wind speed and wind energy assessment P. Crippa et al. 10.1016/j.apenergy.2021.117378
- Characterization of local wind profiles: a random forest approach for enhanced wind profile extrapolation F. Rouholahnejad & J. Gottschall 10.5194/wes-10-143-2025
- Validation of Reanalysis-Based Offshore Wind Resource Characterization Using Lidar Buoy Observations L. Sheridan et al. 10.4031/MTSJ.54.6.13
- On the estimation of boundary layer heights: a machine learning approach R. Krishnamurthy et al. 10.5194/amt-14-4403-2021
- Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble N. Bodini et al. 10.5194/wes-6-1363-2021
- Long-term uncertainty quantification in WRF-modeled offshore wind resource off the US Atlantic coast N. Bodini et al. 10.5194/wes-8-607-2023
- Wind plants can impact long-term local atmospheric conditions N. Bodini et al. 10.1038/s41598-021-02089-2
- Machine learning for predicting offshore vertical wind profiles F. Rouholahnejad et al. 10.1088/1742-6596/2626/1/012023
- High-resolution wind speed forecast system coupling numerical weather prediction and machine learning for agricultural studies — a case study from South Korea J. Shin et al. 10.1007/s00484-022-02287-1
- The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques L. Canché-Cab et al. 10.1007/s10462-024-10962-5
- Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks Applied to Lidar Measurements A. García-Gutiérrez et al. 10.3390/s21113659
- High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 synthetic aperture radar (SAR) data and machine learning L. de Montera et al. 10.5194/wes-7-1441-2022
- Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques D. Hatfield et al. 10.5194/wes-8-621-2023
- Wind speed vertical extrapolation model validation under uncertainty J. Quick et al. 10.1016/j.renene.2024.122028
- Can reanalysis products outperform mesoscale numerical weather prediction models in modeling the wind resource in simple terrain? V. Pronk et al. 10.5194/wes-7-487-2022
Latest update: 02 Apr 2025
Short summary
An accurate assessment of the wind resource at hub height is necessary for an efficient and bankable wind farm project. Conventional techniques for wind speed vertical extrapolation include a power law and a logarithmic law. Here, we propose a round-robin validation to assess the benefits that a machine-learning-based approach can provide in vertically extrapolating wind speed at a location different from the training site – the most practically useful application for the wind energy industry.
An accurate assessment of the wind resource at hub height is necessary for an efficient and...
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