Wind Energy Science
Wind Energy Science
WES
Articles | Volume 9, issue 6
Articles | Volume 9, issue 6
https://doi.org/10.5194/wes-9-1431-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-1431-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 9, issue 6
Research article
 | 
27 Jun 2024
Research article |  | 27 Jun 2024

Machine-learning-based estimate of the wind speed over complex terrain using the long short-term memory (LSTM) recurrent neural network

Cássia Maria Leme Beu and Eduardo Landulfo

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Short summary
Extrapolating the wind profile for complex terrain through the long short-term memory model outperformed the traditional power law methodology, which due to its universal nature cannot capture local features as the machine-learning methodology does. Moreover, considering the importance of investigating the wind potential and the need for alternative energy sources, it is motivating to find that a short observational campaign can produce better results than the traditional techniques.
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