Articles | Volume 9, issue 6
https://doi.org/10.5194/wes-9-1431-2024
https://doi.org/10.5194/wes-9-1431-2024
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

Model code and software

cassiabeu/doi.org-10.5194-wes-2023-104: v1.1 C. M. L. Beu https://doi.org/10.5281/zenodo.12168778

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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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