PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Assessing the Feasibility of Wave Energy Harvesting through Coastal Wave Modeling and Forecasting with Long Short-Term Memory Optimization using Univariate Time Series Approach
Version 1
: Received: 12 April 2023 / Approved: 13 April 2023 / Online: 13 April 2023 (03:03:33 CEST)
Version 2
: Received: 17 May 2023 / Approved: 18 May 2023 / Online: 18 May 2023 (07:09:21 CEST)
How to cite:
Lawal, Z.K.; Yassin, H.; Lai, D.T.C.; Idris, A.C. Assessing the Feasibility of Wave Energy Harvesting through Coastal Wave Modeling and Forecasting with Long Short-Term Memory Optimization using Univariate Time Series Approach. Preprints2023, 2023040282. https://doi.org/10.20944/preprints202304.0282.v1
Lawal, Z.K.; Yassin, H.; Lai, D.T.C.; Idris, A.C. Assessing the Feasibility of Wave Energy Harvesting through Coastal Wave Modeling and Forecasting with Long Short-Term Memory Optimization using Univariate Time Series Approach. Preprints 2023, 2023040282. https://doi.org/10.20944/preprints202304.0282.v1
Lawal, Z.K.; Yassin, H.; Lai, D.T.C.; Idris, A.C. Assessing the Feasibility of Wave Energy Harvesting through Coastal Wave Modeling and Forecasting with Long Short-Term Memory Optimization using Univariate Time Series Approach. Preprints2023, 2023040282. https://doi.org/10.20944/preprints202304.0282.v1
APA Style
Lawal, Z.K., Yassin, H., Lai, D.T.C., & Idris, A.C. (2023). Assessing the Feasibility of Wave Energy Harvesting through Coastal Wave Modeling and Forecasting with Long Short-Term Memory Optimization using Univariate Time Series Approach. Preprints. https://doi.org/10.20944/preprints202304.0282.v1
Chicago/Turabian Style
Lawal, Z.K., Daphne Teck Ching Lai and Azam Che Idris. 2023 "Assessing the Feasibility of Wave Energy Harvesting through Coastal Wave Modeling and Forecasting with Long Short-Term Memory Optimization using Univariate Time Series Approach" Preprints. https://doi.org/10.20944/preprints202304.0282.v1
Abstract
. Accurate coastal wave direction and speed forecasts are crucial in coastal and marine engineering, marine energy, maritime transport, fisheries, naval navigation, environmental research, and risk management. Approximately 269 km of Brunei Darussalam's coastline can generate between 15 and 126 GW of wave energy. As part of the preliminary feasibility study of wave energy harvesting in Brunei Darussalam and net zero commitment, in this study, we used two numerical methods, namely, finite difference and spectral element methods, for modeling and simulation of wave speed and direction. The mean error between numerical and analytical solutions was calculated in each simulation. Explanatory data analysis was used to provide insight into the study data. We then proposed wave direction and speed forecasting models using Long Short-Term Memory (LSTM) stacking on the data computed from the Acoustic Doppler Current Profiler (ADCP) sensor data. A univariate time series forecasting approach was adopted for this research. KerasTuner hyperparameter tuning API was used for tuning and optimizing hyperparameters, leading us to build models with the least training and test errors. Seven separate prediction experiments were conducted for wave speed and direction in degree and radian units for the next 1, 3, 6, 8, 10, 12, and 24 hours, respectively. Mean squared error (MSE) was used as a metric for both training and testing. The experimental results show that wave speed forecast has the lowest MSEs compared to direction, regardless of the unit of measure, but has a longer runtime. Moreover, the forecast of direction in the degree unit has the least errors compared to the radian unit; the running time of the latter is higher than that of the former. In the future, we intend to use advanced multivariate time series techniques to forecast wave speed and direction.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
