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

Mumtaz Ali, Ramendra Prasad, Yong Xiang, Adarsh Sankaran, Ravinesh C. Deo, Fuyuan Xiao and Shuyu Zhu

Renewable Energy, 2021, vol. 177, issue C, 1031-1044

Abstract: The peak period of an energy-generating wave is one of the most important parameters that describe the spectral shape of the oceanic wave, as this indicates the duration for which the waves prevail with respect to their maximum extractable energy. In this paper, a half-hourly peak wave energy period (TP) forecast model is constructed using a suite of statistically significant lagged inputs based on the partial auto-correlation function with an extreme learning machine model developed and its predictive utility is benchmarked against deep learning models, i.e., convolutional neural network (CNN/CovNet) and recurrent neural network (RNN) models and other traditional M5tree, Conditional Maximization based Multiple Linear Regression (MLR-ECM) and MLR models. The objective model (ELM) vs. the comparison models (CNN, RNN, M5tree, MLR-ECM, and MLR) were trained and validated independently on the test dataset obtained from coastal zones of eastern Australia that have a high potential for implementation of wave energy generation systems. The outcomes ascertain that the ELM model can generate significantly accurate predictions of the half-hourly peak wave energy period, providing a good level of accuracy relative to deep learning models in selected coastal study zones. The study establishes the practical usefulness of the ELM model as being a noteworthy methodology for the applications in renewable and sustainable energy resource management systems.

Keywords: Deep learning; RNN; CNN; ELM; Peak wave energy period; Coastal waves (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:177:y:2021:i:c:p:1031-1044

DOI: 10.1016/j.renene.2021.06.052

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