Wind Energy Science
Wind Energy Science
WES
Articles | Volume 6, issue 1
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.
https://doi.org/10.5194/wes-6-295-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 6, issue 1
Research article
 | 
01 Mar 2021
Research article |  | 01 Mar 2021

Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error

Daniel Vassallo, Raghavendra Krishnamurthy, and Harindra J. S. Fernando

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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.
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