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Multi-step-ahead significant wave height prediction using a hybrid model based on an innovative two-layer decomposition framework and LSTM

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  • Fu, Yang
  • Ying, Feixiang
  • Huang, Lingling
  • Liu, Yang

Abstract

As waves are being developed as a renewable energy source, the development of new predictive algorithms to forecast wave height has garnered considerable interest. This study proposes an innovative hybrid model to predict the wave height, including a two-layer decomposition framework and long short-term memory (LSTM). First, the original wave height series is effectively and quickly decomposed into multiple sub-sequences using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The refined composite multiscale entropy (RCMSE) is then applied to reconstruct these sub-sequences into high-frequency, medium-frequency, low-frequency, and trend components. The respective LSTM is adopted to predict the medium-frequency, low-frequency, and trend components and obtain the sub-results. Subsequently, a series of modes is obtained by the second decomposition of the high-frequency component with variational mode decomposition (VMD), and the sub-result is obtained by forecasting the modes with ensemble LSTM. Finally, we employ the ensemble LSTM to predict all sub-results and obtain the final wave height prediction result, rather than simply adding up all the sub-results linearly. The proposed hybrid model is tested geographically at two buoy stations in eastern New York and Gulf of Mexico. The results show that the proposed hybrid model is more accurate than other benchmark models.

Suggested Citation

  • Fu, Yang & Ying, Feixiang & Huang, Lingling & Liu, Yang, 2023. "Multi-step-ahead significant wave height prediction using a hybrid model based on an innovative two-layer decomposition framework and LSTM," Renewable Energy, Elsevier, vol. 203(C), pages 455-472.
  • Handle: RePEc:eee:renene:v:203:y:2023:i:c:p:455-472
    DOI: 10.1016/j.renene.2022.12.079
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    1. Reikard, Gordon & Robertson, Bryson & Bidlot, Jean-Raymond, 2015. "Combining wave energy with wind and solar: Short-term forecasting," Renewable Energy, Elsevier, vol. 81(C), pages 442-456.
    2. Zheng, Chong-wei & Li, Xue-hong & Azorin-Molina, Cesar & Li, Chong-yin & Wang, Qing & Xiao, Zi-niu & Yang, Shao-bo & Chen, Xuan & Zhan, Chao, 2022. "Global trends in oceanic wind speed, wind-sea, swell, and mixed wave heights," Applied Energy, Elsevier, vol. 321(C).
    3. Zheng, Chong-wei, 2021. "Global oceanic wave energy resource dataset—with the Maritime Silk Road as a case study," Renewable Energy, Elsevier, vol. 169(C), pages 843-854.
    4. Uihlein, Andreas & Magagna, Davide, 2016. "Wave and tidal current energy – A review of the current state of research beyond technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1070-1081.
    5. Huang, Weinan & Dong, Sheng, 2021. "Improved short-term prediction of significant wave height by decomposing deterministic and stochastic components," Renewable Energy, Elsevier, vol. 177(C), pages 743-758.
    6. Wu, Yueqi & Ma, Xiandong, 2022. "A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines," Renewable Energy, Elsevier, vol. 181(C), pages 554-566.
    7. Noor Ullah Khan & Munam Ali Shah & Carsten Maple & Ejaz Ahmed & Nabeel Asghar, 2022. "Traffic Flow Prediction: An Intelligent Scheme for Forecasting Traffic Flow Using Air Pollution Data in Smart Cities with Bagging Ensemble," Sustainability, MDPI, vol. 14(7), pages 1-23, March.
    8. Ali, Mumtaz & Prasad, Ramendra, 2019. "Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 281-295.
    9. Lavidas, George & Venugopal, Vengatesan, 2018. "Application of numerical wave models at European coastlines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 92(C), pages 489-500.
    10. Yang, Shaobo & Deng, Zegui & Li, Xingfei & Zheng, Chongwei & Xi, Lintong & Zhuang, Jucheng & Zhang, Zhenquan & Zhang, Zhiyou, 2021. "A novel hybrid model based on STL decomposition and one-dimensional convolutional neural networks with positional encoding for significant wave height forecast," Renewable Energy, Elsevier, vol. 173(C), pages 531-543.
    11. Zheng, Chong-wei & Wu, Di & Wu, Hai-lang & Guo, Jing & Shen, Chong & Tian, Chuan & Tian, Xin-long & Xiao, Zi-niu & Zhou, Wen & Li, Chong-yin, 2022. "Propagation and attenuation of swell energy in the Pacific Ocean," Renewable Energy, Elsevier, vol. 188(C), pages 750-764.
    12. Omer, Abdeen Mustafa, 2008. "Energy, environment and sustainable development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(9), pages 2265-2300, December.
    13. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
    14. Qu, Zongxi & Mao, Wenqian & Zhang, Kequan & Zhang, Wenyu & Li, Zhipeng, 2019. "Multi-step wind speed forecasting based on a hybrid decomposition technique and an improved back-propagation neural network," Renewable Energy, Elsevier, vol. 133(C), pages 919-929.
    15. Zhang, Tingting & Tang, Zhenpeng & Wu, Junchuan & Du, Xiaoxu & Chen, Kaijie, 2021. "Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm," Energy, Elsevier, vol. 229(C).
    16. Yin, Hao & Ou, Zuhong & Huang, Shengquan & Meng, Anbo, 2019. "A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition," Energy, Elsevier, vol. 189(C).
    Full references (including those not matched with items on IDEAS)

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