Disentangled Seasonal-Trend representation of improved CEEMD-GRU joint model with entropy-driven reconstruction to forecast significant wave height

Lingxiao Zhao, Zhiyang Li, Yuguo Pei and Leilei Qu

Renewable Energy, 2024, vol. 226, issue C

Abstract: In recent years, wave energy has gained popularity among marine researchers for its sustainability, cleanliness, high energy density and wide distribution. As one of the most important parameters of wave energy, Significant Wave Height (SWH) is difficult to predict accurately due to complex sea conditions and the ubiquitous chaotic phenomena in nature. The efficient use of wave energy cannot be achieved without accurate analysis and prediction of SWH. In this study, a novel DST-CEEMD-SampEn-GRU joint model is proposed, which combines Disentangled Seasonal-Trend (DST) to reduce the computational time of the Complementary Ensemble Empirical Mode Decomposition (CEEMD). This is combined with SampEn-based reconstruction to avoid the tremendous computational effort associated with IMF components of CEEMD. The final prediction is made using the Gate Recurrent Unit (GRU). In this paper, we demonstrate the effectiveness of CEEMD, SampEn and DST in improving accuracy through forward ablation experiments respectively. Finally, the proposed model is validated with multi-single machine learning models for muti-hour ahead SWH forecasting. The experimental results demonstrate that DST-CEEMD-SampEn-GRU is statistically superior to all the other methods.

Keywords: Significant wave height; Disentangled seasonal-trend (DST); SampEn; CEEMD; GRU (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148124004105
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:226:y:2024:i:c:s0960148124004105

DOI: 10.1016/j.renene.2024.120345

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().