Adelaide Research & Scholarship
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https://hdl.handle.net/2440/139925
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| Type: | Journal article |
| Title: | Wind turbine power output prediction using a new hybrid neuro-evolutionary method |
| Author: | Neshat, M. Nezhad, M.M. Abbasnejad, E. Mirjalili, S. Groppi, D. Heydari, A. Tjernberg, L.B. Astiaso Garcia, D. Alexander, B. Shi, Q. Wagner, M. |
| Citation: | Energy, 2021; 229:120617-1-120617-24 |
| Publisher: | Elsevier |
| Issue Date: | 2021 |
| ISSN: | 0360-5442 1873-6785 |
| Statement of Responsibility: | Mehdi Neshat, Meysam Majidi Nezhad, Ehsan Abbasnejad, Seyedali Mirjalili, Daniele Groppi, Azim Heydari, Lina Bertling Tjernberg, Davide Astiaso Garcia, Bradley Alexander, Qinfeng Shi, Markus Wagner |
| Abstract: | Abstract not available |
| Keywords: | Neuro-evolutionary algorithms; Alternating optimisation algorithm; Recurrent deep learning; Long short-term memory neural network; Adaptive variational mode decomposition; Power prediction model; Wind turbin; Power curve |
| Description: | Available online 18 April 2021 |
| Rights: | © 2021 Elsevier Ltd. All rights reserved. |
| DOI: | 10.1016/j.energy.2021.120617 |
| Published version: | http://dx.doi.org/10.1016/j.energy.2021.120617 |
| Appears in Collections: | Australian Institute for Machine Learning publications Computer Science publications |
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