Articles | Volume 5, issue 3
https://doi.org/10.5194/wes-5-959-2020
© 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-5-959-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Jul 2020
Research article |
| 26 Jul 2020
| 26 Jul 2020
Decreasing wind speed extrapolation error via domain-specific feature extraction and selection
Daniel Vassallo
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, Indiana, USA
Raghavendra Krishnamurthy
Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, Indiana, USA
Pacific Northwest National Laboratory, Washington, USA
Harindra J. S. Fernando
Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, Indiana, USA
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- Rejoinder to the discussion on A high‐resolution bilevel skew‐t stochastic generator for assessing Saudi Arabia's wind energy resources F. Tagle et al. 10.1002/env.2659
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- Enhanced habitat loss of the Himalayan endemic flora driven by warming-forced upslope tree expansion X. Wang et al. 10.1038/s41559-022-01774-3
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- Validation of Reanalysis-Based Offshore Wind Resource Characterization Using Lidar Buoy Observations L. Sheridan et al. 10.4031/MTSJ.54.6.13
- Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan C. Ho et al. 10.3390/en16031374
- New methods to improve the vertical extrapolation of near-surface offshore wind speeds M. Optis et al. 10.5194/wes-6-935-2021
- 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
- Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks Applied to Lidar Measurements A. García-Gutiérrez et al. 10.3390/s21113659
- Assessing the reliability of wind power operations under a changing climate with a non-Gaussian bias correction J. Zhang et al. 10.1214/21-AOAS1460
- Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques D. Hatfield et al. 10.5194/wes-8-621-2023
- Machine learning for predicting offshore vertical wind profiles F. Rouholahnejad et al. 10.1088/1742-6596/2626/1/012023
- 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
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- Enabling Virtual Met Masts for wind energy applications through machine learning-methods S. Schwegmann et al. 10.1016/j.egyai.2022.100209
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21 citations as recorded by crossref.
- 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
- 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
- Rejoinder to the discussion on A high‐resolution bilevel skew‐t stochastic generator for assessing Saudi Arabia's wind energy resources F. Tagle et al. 10.1002/env.2659
- Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data C. Hallgren et al. 10.5194/wes-9-821-2024
- Enhanced habitat loss of the Himalayan endemic flora driven by warming-forced upslope tree expansion X. Wang et al. 10.1038/s41559-022-01774-3
- Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error D. Vassallo et al. 10.5194/wes-6-295-2021
- New ridge regression, artificial neural networks and support vector machine for wind speed prediction Y. Zheng et al. 10.1016/j.advengsoft.2023.103426
- Validation of Reanalysis-Based Offshore Wind Resource Characterization Using Lidar Buoy Observations L. Sheridan et al. 10.4031/MTSJ.54.6.13
- Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan C. Ho et al. 10.3390/en16031374
- New methods to improve the vertical extrapolation of near-surface offshore wind speeds M. Optis et al. 10.5194/wes-6-935-2021
- 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
- Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks Applied to Lidar Measurements A. García-Gutiérrez et al. 10.3390/s21113659
- Assessing the reliability of wind power operations under a changing climate with a non-Gaussian bias correction J. Zhang et al. 10.1214/21-AOAS1460
- Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques D. Hatfield et al. 10.5194/wes-8-621-2023
- Machine learning for predicting offshore vertical wind profiles F. Rouholahnejad et al. 10.1088/1742-6596/2626/1/012023
- 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
- Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains T. Su & Y. Zhang 10.5194/gmd-17-6319-2024
- Deep-learning-derived planetary boundary layer height from conventional meteorological measurements T. Su & Y. Zhang 10.5194/acp-24-6477-2024
- Machine Learning Algorithms for Vertical Wind Speed Data Extrapolation: Comparison and Performance Using Mesoscale and Measured Site Data L. Baquero et al. 10.3390/en15155518
- On the estimation of boundary layer heights: a machine learning approach R. Krishnamurthy et al. 10.5194/amt-14-4403-2021
- Enabling Virtual Met Masts for wind energy applications through machine learning-methods S. Schwegmann et al. 10.1016/j.egyai.2022.100209
Latest update: 20 Nov 2024
Short summary
Model error and uncertainty is a challenge in the wind energy industry, potentially leading to mischaracterization of millions of dollars' worth of wind resource. This paper combines meteorological knowledge with machine learning techniques, specifically artificial neural networks (ANNs), to better extrapolate wind speeds. It is found that ANNs can reduce power-law extrapolation error by up to 52 % while simultaneously reducing uncertainty. A test case is shown to help decipher the ANN results.
Model error and uncertainty is a challenge in the wind energy industry, potentially leading to...
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