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Dynamic ensemble deep echo state network for significant wave height forecasting

Author

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  • Gao, Ruobin
  • Li, Ruilin
  • Hu, Minghui
  • Suganthan, Ponnuthurai Nagaratnam
  • Yuen, Kum Fai

Abstract

Forecasts of the wave heights can assist in the data-driven control of wave energy systems. However, the dynamic properties and extreme fluctuations of the historical observations pose challenges to the construction of forecasting models. This paper proposes a novel dynamic ensemble deep Echo state networks (ESN) to learn the dynamic characteristics of the significant wave height. The dynamic ensemble ESN creates a profound representation of the input and trains an independent readout module for each reservoir. To begin, numerous reservoir layers are built in a hierarchical order, adopting a reservoir pruning approach to filter out the poorer representations. Finally, a dynamic ensemble block is used to integrate the forecasts of all readout layers. The suggested model has been tested on twelve available datasets and statistically outperforms state-of-the-art approaches.

Suggested Citation

  • Gao, Ruobin & Li, Ruilin & Hu, Minghui & Suganthan, Ponnuthurai Nagaratnam & Yuen, Kum Fai, 2023. "Dynamic ensemble deep echo state network for significant wave height forecasting," Applied Energy, Elsevier, vol. 329(C).
    • Handle: RePEc:eee:appene:v:329:y:2023:i:c:s0306261922015185
    DOI: 10.1016/j.apenergy.2022.120261
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    References listed on IDEAS

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    Cited by:

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    4. Mahdavi-Meymand, Amin & Sulisz, Wojciech, 2023. "Application of nested artificial neural network for the prediction of significant wave height," Renewable Energy, Elsevier, vol. 209(C), pages 157-168.
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    6. Chen, Xinqiang & Lv, Siying & Shang, Wen-long & Wu, Huafeng & Xian, Jiangfeng & Song, Chengcheng, 2024. "Ship energy consumption analysis and carbon emission exploitation via spatial-temporal maritime data," Applied Energy, Elsevier, vol. 360(C).
    7. Wu, Han & Gao, Xiao-Zhi & Heng, Jia-Ni, 2024. "Bio-multisensory-inspired gate-attention coordination model for forecasting short-term significant wave height," Energy, Elsevier, vol. 294(C).
    8. Zhao, Lingxiao & Li, Zhiyang & Pei, Yuguo & Qu, Leilei, 2024. "Disentangled Seasonal-Trend representation of improved CEEMD-GRU joint model with entropy-driven reconstruction to forecast significant wave height," Renewable Energy, Elsevier, vol. 226(C).
    9. Liu, Yaru & Wang, Lei & Ng, Bing Feng, 2024. "A hybrid model-data-driven framework for inverse load identification of interval structures based on physics-informed neural network and improved Kalman filter algorithm," Applied Energy, Elsevier, vol. 359(C).

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