Vertical Wind Profile Estimation Using Hybrid Convolutional Neural Networks and Bidirectional Long Short-Term Memory-Reference-Cited by-同舟云学术

Vertical Wind Profile Estimation Using Hybrid Convolutional Neural Networks and Bidirectional Long Short-Term Memory

Published:2023-02-21 Issue:5 Volume:48 Page:6915-6924
ISSN:2193-567X
Container-title:Arabian Journal for Science and Engineering
language:en
Short-container-title:Arab J Sci Eng

Author:

Al-Shaikhi Ali,Nuha Hilal H.,Lawal Abdulmajid,Rehman Shafiqur,Mohandes Mohamed^ORCID

Funder

KFUPM

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

https://link.springer.com/content/pdf/10.1007/s13369-023-07665-4.pdf

Reference23 articles.

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3. Newman, J.F.; Klein, P.M.: The impacts of atmospheric stability on the accuracy of wind speed extrapolation methods. Resources 3, 81–105 (2014)

4. Banuelos-Ruedas, F.; Angeles-Camacho, C.; Sebastin, R.M.: Methodologies used in the extrapolation of wind speed data at different heights and its impact in the wind energy resource assessment in a region. Wind Farm – Tech. Regul., Potential Estim Siting Assess 97, 114 (2011)

5. Gupta, D.; Kumar, V.; Ayus, I.; Vasudevan, M.; Natarajan, N.: Short-term prediction of wind power density using convolutional LSTM network. FME Trans. 49(3), 653–663 (2021)

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