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

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.