Machine-learning-based estimate of the wind speed over complex terrain using the long short-term memory (LSTM) recurrent neural network
Abstract R 2) was greater than 90 % up to 100 m for input variables up to a 40 m height only. However, the performance of the model improved when the 60 m wind speed was added to the input dataset. Furthermore, we found that the LSTM model trained on one site with 40 and 60 m observational data and when applied to other sites also outperformed the power law. Our results show that the machine-learning techniques, particularly LSTM, are a promising tool for accurately estimating the wind speed profiles over complex terrain, even for short observational campaigns.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Machine-learning-based estimate of the wind speed over complex terrain using the long short-term memory (LSTM) recurrent neural network ; volume:9 ; number:6 ; year:2024 ; pages:1431-1450 ; extent:20
Wind energy science ; 9, Heft 6 (2024), 1431-1450 (gesamt 20)
- Classification
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Natürliche Ressourcen, Energie und Umwelt
- Creator
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Leme Beu, Cássia Maria
Landulfo, Eduardo
- DOI
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10.5194/wes-9-1431-2024
- URN
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urn:nbn:de:101:1-2408061107104.140580368017
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:57 AM CEST
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Associated
- Leme Beu, Cássia Maria
- Landulfo, Eduardo
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