Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia

Ali, Mumtaz, Prasad, R, Xiang, Yong, Sankaran, A, Deo, RC, Xiao, F and Zhu, S 2021, Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia, Renewable Energy, vol. 177, pp. 1031-1044, doi: 10.1016/j.renene.2021.06.052.

Attached Files
Name Description MIMEType Size Downloads

Title Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia
Author(s) Ali, MumtazORCID iD for Ali, Mumtaz orcid.org/0000-0002-6975-5159
Prasad, R
Xiang, YongORCID iD for Xiang, Yong orcid.org/0000-0003-3545-7863
Sankaran, A
Deo, RC
Xiao, F
Zhu, S
Journal name Renewable Energy
Volume number 177
Start page 1031
End page 1044
Total pages 14
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2021
ISSN 0960-1481
Keyword(s) deep learning
RNN
CNN
ELM
Peak wave energy period
Coastal Waves
Language eng
DOI 10.1016/j.renene.2021.06.052
Indigenous content off
Field of Research 0906 Electrical and Electronic Engineering
0913 Mechanical Engineering
0915 Interdisciplinary Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30152397

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 34 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Sun, 13 Jun 2021, 19:33:51 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.