Hybrid Deep Learning Model for Wave Height Prediction in Australia's Wave Energy Region
Article
Article Title | Hybrid Deep Learning Model for Wave Height Prediction in Australia's Wave Energy Region |
---|---|
ERA Journal ID | 17759 |
Article Category | Article |
Authors | Ahmed, Abul Abrar Masrur, Jui, S Janifer Jabin, AL-Musaylh, Mohanad S., Raj, Nawin, Saha, Reepa, Deo, Ravinesh C and Saha, Sanjoy Kumar |
Journal Title | Applied Soft Computing |
Journal Citation | 150 |
Article Number | 111003 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 1568-4946 |
1872-9681 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.asoc.2023.111003 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1568494623010219 |
Abstract | Waves are emerging as a renewable energy resource, but the harnessing of such energy remains among the least developed in terms of renewable energy technologies on a regional or a global basis. To generate usable energy, wave heights must be predicted in near-real-time, which is the driving force for wave energy converters. This study develops a hybrid Convolutional Neural Network-Long Short-Term Memory-Bidirectional Gated Recurrent Unit forecast system (CLSTM-BiGRU) trained to accurately predict significant wave height (Hsig) at multiple forecasting horizons (30 minutes, 0.5H; 2 hours, 02H; 3 hours, 03H and 6 hours, 06H. In this model, convolutional neural networks (CNNs), long-short-term memories (LSTMs), and bidirectional gated recurrent units (BiGRUs) are employed to predict Hsig. To construct the proposed CLSTM-BiGRU model, historical wave properties, including maximum wave height, zero-up crossing wave period, peak energy wave period, sea surface temperature, and significant wave heights are analysed. Several wave energy generation sites in Queensland, Australia were tested using the hybrid deep learning CLSTM-BiGRU model. Based on statistical score metrics, scatterplots, and error evaluations, the hybrid CLSTM-BiGRU model generates more accurate forecasts than the benchmark models. This study established the practical utility of the hybrid CLSTM-BiGRU model for modelling Hsig and therefore shows the model could have significant implications for wave and ocean energy generation systems, tidal or wave height monitoring as well as sustainable wave energy resource evaluation where a prediction of wave heights is required. |
Keywords | deep learning mode; significant wave height; wave energy; renewable energy; sea level monitoring system |
ANZSRC Field of Research 2020 | 490199. Applied mathematics not elsewhere classified |
4101. Climate change impacts and adaptation | |
3708. Oceanography | |
Byline Affiliations | University of Melbourne |
Academic Registrar's Office | |
School of Mathematics, Physics and Computing | |
Southern Technical University, Iraq | |
University of Alabama, United States | |
LOS Cable Solutions, Norway |
https://research.usq.edu.au/item/z28v4/hybrid-deep-learning-model-for-wave-height-prediction-in-australia-s-wave-energy-region
Download files
151
total views45
total downloads1
views this month0
downloads this month