Swarm intelligence-based multi-layer kernel meta extreme learning machine for tidal current to power prediction

Dokur, E, Erdogan, N ORCID: https://orcid.org/0000-0003-1621-2748 and Yuzgec, U, 2025. Swarm intelligence-based multi-layer kernel meta extreme learning machine for tidal current to power prediction. Renewable Energy: 122516. ISSN 0960-1481

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Abstract

Tidal energy, with its predictable and consistent nature, offers a scalable ocean renewable resource that can diversify the energy generation mix for countries with suitable coastal conditions. Accurate tidal current-to-power forecasting is essential to optimize power system management, improve grid stability, and inform the design of power processing and storage units. This study proposes a novel hybrid model integrating Swarm Decomposition with a Multi-Layer Kernel Meta Extreme Learning Machine to forecast non-stationary tidal currents. The Swarm Decomposition isolates key oscillatory components, reducing noise and improving feature extraction, while the kernel-based architecture enhances generalization and scalability by minimizing the need for extensive parameter tuning, resulting in higher forecasting accuracy and computational efficiency. The model is validated on two real-world tidal current datasets from distinct locations, incorporating seasonal variations, and compared against well-established extreme learning machines and deep learning models. A sensitivity analysis of signal decomposition parameters demonstrated their impact on decomposition quality and computational cost. The proposed model outperformed superior performance on both tidal datasets, achieving a 5-fold reduction in mean squared error and increased R2 from 0.9653 to 0.9933. These findings highlight the model’s robustness and adaptability to diverse tidal conditions, making it a reliable tool for tidal power forecasting.

Item Type:	Journal article
Publication Title:	Renewable Energy
Creators:	Dokur, E., Erdogan, N. and Yuzgec, U.
Publisher:	Elsevier BV
Date:	5 February 2025
ISSN:	0960-1481
Identifiers:	Number Type 10.1016/j.renene.2025.122516 DOI 2368984 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Borcherds
Date Added:	07 Feb 2025 16:21
Last Modified:	07 Feb 2025 16:22
URI:	https://irep.ntu.ac.uk/id/eprint/52993

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