Comparative evaluation of recurrent and attention-based deep learning models for wind speed forecasting

Wind speed forecasting is critical for enhancing wind energy output and improving efficiency. Although traditional statistical and physical models are commonly used but fail to capture the complex, non-linear, and time-varying nature of meteorological data. Therefore, this study explores four deep l...

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Bibliografiska uppgifter

Huvudupphov:	Al Qahtani Nouf J., Abolouz Mohamed S., Mohandes Mohamed A., Rehman Shafiqur
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	University of Belgrade - Faculty of Mechanical Engineering, Belgrade 2025-01-01
Serie:	FME Transactions
Ämnen:	deep learning architectures wind speed short-term prediction recurrent neural network (rnn) gated recurrent unit (gru) long short-term memory (lstm) transformer
Länkar:	https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2025/1451-20922504693A.pdf
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