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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Cited articles

Almeida, L. B.: Multilayer perceptrons, in: Handbook of Neural Computation, CRC Press, ISBN 9780429142772, 1997. a
Al-Shaikhi, A., Nuha, H., Mohandes, M., Rehman, S., and Adrian, M.: Vertical wind speed extrapolation model using long short-term memory and particle swarm optimization, Energ. Sci. Eng., 10, 4580–4594, https://doi.org/10.1002/ese3.1291, 2022. a, b
Bali, V., Kumar, A., and Gangwar, S.: Deep Learning based Wind Speed Forecasting-A Review, in: IEEE 2019 9th International Conference on Cloud Computing, Data Science & Engineering, 10–11 January 2019, Noida, India, https://doi.org/10.1109/confluence.2019.8776923, 2019. a, b, c
Baquero, L., Torio, H., and Leask, P.: Machine Learning Algorithms for Vertical Wind Speed Data Extrapolation: Comparison and Performance Using Mesoscale and Measured Site Data, Energies, 15, 5518, https://doi.org/10.3390/en15155518, 2022. a, b, c
Beu, C. M. L.: cassiabeu/doi.org-10.5194-wes-2023-104: v1.1, Zenodo [code], https://doi.org/10.5281/zenodo.12168778, 2024. a
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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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