eprintid: 17669
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/76/69
datestamp: 2023-12-19 03:24:00
lastmod: 2023-12-19 03:24:00
status_changed: 2023-12-19 03:08:28
type: article
metadata_visibility: show
creators_name: Hashmani, M.A.
creators_name: Umair, M.
creators_name: Keiichi, H.
title: Wave Parameters Prediction for Wave Energy Converter Site using Long Short-Term Memory
ispublished: pub
keywords: Brain; Forecasting; Mean square error; Water waves; Wave energy conversion, Batch sizes; Lstm; Maritime operation; Memory modeling; Parameter prediction; Peak wave period; Significant wave height; Wave energy converters; Wave period; Waves parameters, Long short-term memory
note: cited By 1
abstract: Forecasting the behaviour of various wave parameters is crucial for the safety of maritime operations as well as for optimal operations of wave energy converter (WEC) sites. For coastal WEC sites, the wave parameters of interest are significant wave height (Hs) and peak wave period (Tp). Numerical and statistical modeling, along with machine and deep learning models, have been applied to predict these parameters for the short and long-term future. For near-future prediction of Hs and Tp, this study investigates the possibility of optimally training a Long Short-Term Memory (LSTM) model on historical values of Hs and Tp only. Additionally, the study investigates the minimum amount of training data required to predict these parameters with acceptable accuracy. The Root Mean Square Error (RMSE) measure is used to evaluate the prediction ability of the model. As a result, it is identified that LSTM can effectively predict Hs and Tp given their historical values only. For Hs, it is identified that a 4-year dataset, 20 historical inputs, and a batch size of 256 produce the best results for three, six, twelve, and twenty-four-hour prediction windows at half-hourly step. It is also established that the future values of Tp can be optimally predicted using a 2-year dataset, 10 historical inputs, and a 128-batch size. However, due to the much dynamic nature of the peak wave period, it is discovered that the LSTM model yielded relatively low prediction accuracy as compared to Hs © 2022. International Journal of Advanced Computer Science and Applications.All Rights Reserved.
date: 2022
publisher: Science and Information Organization
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129862763&doi=10.14569%2fIJACSA.2022.0130358&partnerID=40&md5=f7fadfdc01ead32e39746c144fc1725c
id_number: 10.14569/IJACSA.2022.0130358
full_text_status: none
publication: International Journal of Advanced Computer Science and Applications
volume: 13
number: 3
pagerange: 481-487
refereed: TRUE
issn: 2158107X
citation:   Hashmani, M.A. and Umair, M. and Keiichi, H.  (2022) Wave Parameters Prediction for Wave Energy Converter Site using Long Short-Term Memory.  International Journal of Advanced Computer Science and Applications, 13 (3).  pp. 481-487.  ISSN 2158107X