Wind Energy Science
Wind Energy Science
WES
Articles | Volume 5, issue 3
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.
Special issue:
https://doi.org/10.5194/wes-5-959-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 5, issue 3
Research article
 | 
26 Jul 2020
Research article |  | 26 Jul 2020

Decreasing wind speed extrapolation error via domain-specific feature extraction and selection

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

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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.
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