Articles | Volume 6, issue 1
https://doi.org/10.5194/wes-6-295-2021
© Author(s) 2021. 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-6-295-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Mar 2021
Research article |
| 01 Mar 2021
| 01 Mar 2021
Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error
Daniel Vassallo
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Indiana, USA
Raghavendra Krishnamurthy
Pacific Northwest National Laboratory, Washington, USA
Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Indiana, USA
Harindra J. S. Fernando
Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Indiana, USA
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Total article views: 2,341 (including HTML, PDF, and XML)
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Total article views: 932 (including HTML, PDF, and XML)
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Cited
16 citations as recorded by crossref.
- Estimation of Planetary Boundary Layer Height From Lidar by Combining Gradient Method and Machine Learning Algorithms H. Li et al. 10.1109/TGRS.2023.3329122
- Exogenous atmospheric variables as wind speed predictors in machine learning A. Dalton & B. Bekker 10.1016/j.apenergy.2022.119257
- Explainable Machine Learning (XML) to predict external wind pressure of a low-rise building in urban-like settings D. Meddage et al. 10.1016/j.jweia.2022.105027
- Enabling Virtual Met Masts for wind energy applications through machine learning-methods S. Schwegmann et al. 10.1016/j.egyai.2022.100209
- Towards Sustainable Urban Mobility: Leveraging Machine Learning Methods for QA of Meteorological Measurements in the Urban Area D. Sládek et al. 10.3390/su16135713
- A review of physics-based machine learning in civil engineering S. Vadyala et al. 10.1016/j.rineng.2021.100316
- A multi-dimensional interpretable wind speed forecasting model with two-stage feature exploring B. Wu et al. 10.1016/j.renene.2025.124028
- Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil E. Gomaa et al. 10.1016/j.heliyon.2023.e18819
- Research on adaptive combined wind speed prediction for each season based on improved gray relational analysis Y. Zhu 10.1007/s11356-022-22957-2
- Estimating the urban atmospheric boundary layer height from remote sensing applying machine learning techniques G. de Arruda Moreira et al. 10.1016/j.atmosres.2021.105962
- Fusing physics-inferred information from stochastic model with machine learning approaches for degradation prediction Z. Li et al. 10.1016/j.ress.2022.109078
- On the estimation of boundary layer heights: a machine learning approach R. Krishnamurthy et al. 10.5194/amt-14-4403-2021
- Improving Surface Wind Speed Forecasts Using an Offline Surface Multilayer Model With Optimal Ground Forcing J. Feng et al. 10.1029/2022MS003072
- A Comprehensive Degradation Modeling Comparison From Statistical to Artificial Intelligence Models for Curing Oven Chains H. Misaii et al. 10.1002/asmb.2930
- Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting D. Vassallo et al. 10.3390/en13205488
- Decreasing wind speed extrapolation error via domain-specific feature extraction and selection D. Vassallo et al. 10.5194/wes-5-959-2020
14 citations as recorded by crossref.
- Estimation of Planetary Boundary Layer Height From Lidar by Combining Gradient Method and Machine Learning Algorithms H. Li et al. 10.1109/TGRS.2023.3329122
- Exogenous atmospheric variables as wind speed predictors in machine learning A. Dalton & B. Bekker 10.1016/j.apenergy.2022.119257
- Explainable Machine Learning (XML) to predict external wind pressure of a low-rise building in urban-like settings D. Meddage et al. 10.1016/j.jweia.2022.105027
- Enabling Virtual Met Masts for wind energy applications through machine learning-methods S. Schwegmann et al. 10.1016/j.egyai.2022.100209
- Towards Sustainable Urban Mobility: Leveraging Machine Learning Methods for QA of Meteorological Measurements in the Urban Area D. Sládek et al. 10.3390/su16135713
- A review of physics-based machine learning in civil engineering S. Vadyala et al. 10.1016/j.rineng.2021.100316
- A multi-dimensional interpretable wind speed forecasting model with two-stage feature exploring B. Wu et al. 10.1016/j.renene.2025.124028
- Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil E. Gomaa et al. 10.1016/j.heliyon.2023.e18819
- Research on adaptive combined wind speed prediction for each season based on improved gray relational analysis Y. Zhu 10.1007/s11356-022-22957-2
- Estimating the urban atmospheric boundary layer height from remote sensing applying machine learning techniques G. de Arruda Moreira et al. 10.1016/j.atmosres.2021.105962
- Fusing physics-inferred information from stochastic model with machine learning approaches for degradation prediction Z. Li et al. 10.1016/j.ress.2022.109078
- On the estimation of boundary layer heights: a machine learning approach R. Krishnamurthy et al. 10.5194/amt-14-4403-2021
- Improving Surface Wind Speed Forecasts Using an Offline Surface Multilayer Model With Optimal Ground Forcing J. Feng et al. 10.1029/2022MS003072
- A Comprehensive Degradation Modeling Comparison From Statistical to Artificial Intelligence Models for Curing Oven Chains H. Misaii et al. 10.1002/asmb.2930
Latest update: 06 Sep 2025
Short summary
Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting and is especially useful when combined with other forecasting techniques. This study utilizes a popular machine learning algorithm, random forest, in an attempt to predict the forecasting error of a statistical forecasting model. Various atmospheric characteristics are used as random forest inputs in an effort to discern the most useful atmospheric information for this purpose.
Machine learning is quickly becoming a commonly used technique for wind speed and power...
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