Short-term wave power forecasting with hybrid multivariate variational mode decomposition model integrated with cascaded feedforward neural networks
Article
Article Title | Short-term wave power forecasting with hybrid multivariate variational mode decomposition model integrated with cascaded feedforward neural networks |
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ERA Journal ID | 4067 |
Article Category | Article |
Authors | Ali, Mumtaz, Prasad, Ramendra, Jamei, Mehdi, Malik, Anurag, Xiang, Yong, Abdulla, Shahab, Deo, Ravinesh C., Farooque, Aitazaz A. and Labban, Abdulhaleem H. |
Journal Title | Renewable Energy |
Journal Citation | 221 |
Article Number | 119773 |
Number of Pages | 14 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0960-1481 |
1879-0682 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.renene.2023.119773 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0960148123016889?dgcid=coauthor |
Abstract | Wave power is an emerging renewable energy technology that has not reached its full potential. For wave power plants, a reliable forecast system is crucial to managing intermittency. We propose a novel robust short-term wave power (Pw) forecasting method, MVMD-CFNN, based on a multivariate variational mode decomposition hybridized with cascaded feedforward neural networks. By using cross-correlation, we were able to determine the significant input predictor lags. To overcome the non-linearity and non-stationarity issues, the proposed MVMD method is then used to demarcate the significant lags into intrinsic mode functions (IMFs). To forecast the short-term PW, the MVMD-CFNN model incorporated the IMFs into cascaded feedforward neural networks. Validation and benchmarking of the MVMD-CFNN model at two stations in Queensland, Australia has been conducted against standalone cascaded feedforward neural networks, boosted regression trees, extreme learning machines, and hybrid models, MVMD-BRT and MVMD-ELM. According to the results, the MVMD-CFNN predicts PW accurately against the benchmark models. The outcomes of this research can contribute to the application and implementation of clean energy worldwide for sustainable energy generation. |
Keywords | Wave power prediction ; Renewable energy resources ; Sustainable energy management ; Artificial intelligence methods for renewable ; energy |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 461199. Machine learning not elsewhere classified |
461103. Deep learning | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | UniSQ College |
University of Prince Edward Island, Canada | |
Al-Ayen University, Iraq | |
University of the South Pacific, Fiji | |
Shahid Chamran University of Ahvaz, Iran | |
Punjab Agricultural University, India | |
Deakin University | |
School of Mathematics, Physics and Computing | |
King Abdulaziz University, Saudi Arabia |
https://research.usq.edu.au/item/z38q7/short-term-wave-power-forecasting-with-hybrid-multivariate-variational-mode-decomposition-model-integrated-with-cascaded-feedforward-neural-networks
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