An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence
Abstract While wave energy is regarded as one of the prominent renewable energy resources to diversify global low‐carbon generation capacity, operational reliability is the main impediment to the wide deployment of the related technology. Current experience in wave energy systems demonstrates that o...
Published in: | IET Renewable Power Generation |
---|---|
Main Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Wiley
2024
|
Subjects: | |
Online Access: | https://doi.org/10.1049/rpg2.12961 https://doaj.org/article/582d0e13e7c74f06ad628e33c14ba5d5 |
id |
ftdoajarticles:oai:doaj.org/article:582d0e13e7c74f06ad628e33c14ba5d5 |
---|---|
record_format |
openpolar |
spelling |
ftdoajarticles:oai:doaj.org/article:582d0e13e7c74f06ad628e33c14ba5d5 2024-09-15T18:23:42+00:00 An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence Emrah Dokur Nuh Erdogan Mahdi Ebrahimi Salari Ugur Yuzgec Jimmy Murphy 2024-02-01T00:00:00Z https://doi.org/10.1049/rpg2.12961 https://doaj.org/article/582d0e13e7c74f06ad628e33c14ba5d5 EN eng Wiley https://doi.org/10.1049/rpg2.12961 https://doaj.org/toc/1752-1416 https://doaj.org/toc/1752-1424 1752-1424 1752-1416 doi:10.1049/rpg2.12961 https://doaj.org/article/582d0e13e7c74f06ad628e33c14ba5d5 IET Renewable Power Generation, Vol 18, Iss 3, Pp 348-360 (2024) artificial intelligence forecasting theory multilayer perceptrons neural nets optimisation particle swarm optimisation Renewable energy sources TJ807-830 article 2024 ftdoajarticles https://doi.org/10.1049/rpg2.12961 2024-08-05T17:49:57Z Abstract While wave energy is regarded as one of the prominent renewable energy resources to diversify global low‐carbon generation capacity, operational reliability is the main impediment to the wide deployment of the related technology. Current experience in wave energy systems demonstrates that operation and maintenance costs are dominant in their cost structure due to unplanned maintenance resulting in energy production loss. Accurate and high performance simulation forecasting tools are required to improve the efficiency and safety of wave converters. This paper proposes a new methodology for significant wave height forecasting. It is based on incorporating swarm decomposition (SWD) and multi‐strategy random weighted grey wolf optimizer (MsRwGWO) into a multi‐layer perceptron (MLP) forecasting model. This approach takes advantage of the SWD approach to generate more stable, stationary, and regular patterns of the original signal, while the MsRwGWO optimizes the MLP model parameters efficiently. As such, forecasting accuracy has improved. Real wave datasets from three buoys in the North Atlantic Sea are used to test and validate the forecasting performance of the proposed model. Furthermore, the performance is evaluated through a comparison analysis against deep‐learning based state‐of‐the‐art forecasting models. The results show that the proposed approach significantly enhances the model's accuracy. Article in Journal/Newspaper North Atlantic Directory of Open Access Journals: DOAJ Articles IET Renewable Power Generation 18 3 348 360 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
artificial intelligence forecasting theory multilayer perceptrons neural nets optimisation particle swarm optimisation Renewable energy sources TJ807-830 |
spellingShingle |
artificial intelligence forecasting theory multilayer perceptrons neural nets optimisation particle swarm optimisation Renewable energy sources TJ807-830 Emrah Dokur Nuh Erdogan Mahdi Ebrahimi Salari Ugur Yuzgec Jimmy Murphy An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence |
topic_facet |
artificial intelligence forecasting theory multilayer perceptrons neural nets optimisation particle swarm optimisation Renewable energy sources TJ807-830 |
description |
Abstract While wave energy is regarded as one of the prominent renewable energy resources to diversify global low‐carbon generation capacity, operational reliability is the main impediment to the wide deployment of the related technology. Current experience in wave energy systems demonstrates that operation and maintenance costs are dominant in their cost structure due to unplanned maintenance resulting in energy production loss. Accurate and high performance simulation forecasting tools are required to improve the efficiency and safety of wave converters. This paper proposes a new methodology for significant wave height forecasting. It is based on incorporating swarm decomposition (SWD) and multi‐strategy random weighted grey wolf optimizer (MsRwGWO) into a multi‐layer perceptron (MLP) forecasting model. This approach takes advantage of the SWD approach to generate more stable, stationary, and regular patterns of the original signal, while the MsRwGWO optimizes the MLP model parameters efficiently. As such, forecasting accuracy has improved. Real wave datasets from three buoys in the North Atlantic Sea are used to test and validate the forecasting performance of the proposed model. Furthermore, the performance is evaluated through a comparison analysis against deep‐learning based state‐of‐the‐art forecasting models. The results show that the proposed approach significantly enhances the model's accuracy. |
format |
Article in Journal/Newspaper |
author |
Emrah Dokur Nuh Erdogan Mahdi Ebrahimi Salari Ugur Yuzgec Jimmy Murphy |
author_facet |
Emrah Dokur Nuh Erdogan Mahdi Ebrahimi Salari Ugur Yuzgec Jimmy Murphy |
author_sort |
Emrah Dokur |
title |
An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence |
title_short |
An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence |
title_full |
An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence |
title_fullStr |
An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence |
title_full_unstemmed |
An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence |
title_sort |
integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence |
publisher |
Wiley |
publishDate |
2024 |
url |
https://doi.org/10.1049/rpg2.12961 https://doaj.org/article/582d0e13e7c74f06ad628e33c14ba5d5 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
IET Renewable Power Generation, Vol 18, Iss 3, Pp 348-360 (2024) |
op_relation |
https://doi.org/10.1049/rpg2.12961 https://doaj.org/toc/1752-1416 https://doaj.org/toc/1752-1424 1752-1424 1752-1416 doi:10.1049/rpg2.12961 https://doaj.org/article/582d0e13e7c74f06ad628e33c14ba5d5 |
op_doi |
https://doi.org/10.1049/rpg2.12961 |
container_title |
IET Renewable Power Generation |
container_volume |
18 |
container_issue |
3 |
container_start_page |
348 |
op_container_end_page |
360 |
_version_ |
1810463961916637184 |