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Short-term wave power forecasting with hybrid multivariate variational mode decomposition model integrated with cascaded feedforward neural networks

Author

Listed:
  • Ali, Mumtaz
  • Prasad, Ramendra
  • Jamei, Mehdi
  • Malik, Anurag
  • Xiang, Yong
  • Abdulla, Shahab
  • Deo, Ravinesh C.
  • Farooque, Aitazaz A.
  • Labban, Abdulhaleem H.

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.

Suggested Citation

  • Ali, Mumtaz & Prasad, Ramendra & Jamei, Mehdi & Malik, Anurag & Xiang, Yong & Abdulla, Shahab & Deo, Ravinesh C. & Farooque, Aitazaz A. & Labban, Abdulhaleem H., 2024. "Short-term wave power forecasting with hybrid multivariate variational mode decomposition model integrated with cascaded feedforward neural networks," Renewable Energy, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:renene:v:221:y:2024:i:c:s0960148123016889
    DOI: 10.1016/j.renene.2023.119773
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