An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence

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Dokur, E, Erdogan, N ORCID: https://orcid.org/0000-0003-1621-2748, Salari, ME, Yuzgec, U and Murphy, J, 2024. An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence. IET Renewable Power Generation. ISSN 1752-1424

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Official URL: https://doi.org/10.1049/rpg2.12961

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.

Item Type:	Journal article
Publication Title:	IET Renewable Power Generation
Creators:	Dokur, E., Erdogan, N., Salari, M.E., Yuzgec, U. and Murphy, J.
Publisher:	Institution of Engineering and Technology (IET)
Date:	7 February 2024
ISSN:	1752-1424
Identifiers:	Number Type 10.1049/rpg2.12961 DOI 1865264 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Jeremy Silvester
Date Added:	22 Feb 2024 09:30
Last Modified:	22 Feb 2024 09:30
URI:	https://irep.ntu.ac.uk/id/eprint/50913