IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i9p2013-d1131256.html
   My bibliography  Save this article

ConvGRU-RMWP: A Regional Multi-Step Model for Wave Height Prediction

Author

Listed:
  • Youjun Sun

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Huajun Zhang

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Shulin Hu

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Jun Shi

    (CSSC Marine Technology Co., Ltd., Shanghai 200136, China)

  • Jianning Geng

    (CSSC Marine Technology Co., Ltd., Shanghai 200136, China)

  • Yixin Su

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

Abstract

Accurate large-scale regional wave height prediction is important for the safety of ocean sailing. A regional multi-step wave height prediction model (ConvGRU-RMWP) based on ConvGRU is designed for the two problems of difficult spatial feature resolution and low accuracy of multi-step prediction in ocean navigation wave height prediction. For multi-step prediction, a multi-input multi-output prediction strategy is used, and wave direction and wave period are used as exogenous variables, which are combined with historical wave height data to expand the sample space. For spatial features, a convolutional gated recurrent neural network with an Encoder-Forecaster structure is used to extract and resolve multi-level spatial information. In contrast to time series forecasting methods that consider only backward and forward dependencies in the time dimension and a single assessment of the properties of the predictor variables themselves, this paper additionally considers spatial correlations and implied correlations among the meteorological variables. This model uses the wave height information of the past 24 h to predict the wave height information for the next 12 h. The prediction results in both space and time show that the model can effectively extract spatial and temporal correlations and obtain good multi-step wave height prediction results. The proposed method has a lower prediction error than the other five prediction methods and verifies the applicability of this model for three selected sea areas along the global crude oil transportation route, all of which have a lower prediction error.

Suggested Citation

  • Youjun Sun & Huajun Zhang & Shulin Hu & Jun Shi & Jianning Geng & Yixin Su, 2023. "ConvGRU-RMWP: A Regional Multi-Step Model for Wave Height Prediction," Mathematics, MDPI, vol. 11(9), pages 1-21, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2013-:d:1131256
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/9/2013/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/9/2013/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    2. Chengcheng Gu & Hua Li, 2022. "Review on Deep Learning Research and Applications in Wind and Wave Energy," Energies, MDPI, vol. 15(4), pages 1-19, February.
    3. Bingölbali, Bilal & Jafali, Halid & Akpınar, Adem & Bekiroğlu, Serkan, 2020. "Wave energy potential and variability for the south west coasts of the Black Sea: The WEB-based wave energy atlas," Renewable Energy, Elsevier, vol. 154(C), pages 136-150.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
    2. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
    3. Kamranzad, Bahareh & Takara, Kaoru, 2020. "A climate-dependent sustainability index for wave energy resources in Northeast Asia," Energy, Elsevier, vol. 209(C).
    4. Pasta, Edoardo & Faedo, Nicolás & Mattiazzo, Giuliana & Ringwood, John V., 2023. "Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    5. Zhu, Xiaoxun & Liu, Ruizhang & Chen, Yao & Gao, Xiaoxia & Wang, Yu & Xu, Zixu, 2021. "Wind speed behaviors feather analysis and its utilization on wind speed prediction using 3D-CNN," Energy, Elsevier, vol. 236(C).
    6. Lee, Yoonjae & Ha, Byeongmin & Hwangbo, Soonho, 2022. "Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea's energy transition policy," Renewable Energy, Elsevier, vol. 200(C), pages 69-87.
    7. Yanan Xue & Jinliang Yin & Xinhao Hou, 2024. "Short-Term Wind Power Prediction Based on Multi-Feature Domain Learning," Energies, MDPI, vol. 17(13), pages 1-25, July.
    8. Liu, Shuwei & Tian, Jianyan & Ji, Zhengxiong & Dai, Yuanyuan & Guo, Hengkuan & Yang, Shengqiang, 2024. "Research on multi-digital twin and its application in wind power forecasting," Energy, Elsevier, vol. 292(C).
    9. Vaia I. Kontopoulou & Athanasios D. Panagopoulos & Ioannis Kakkos & George K. Matsopoulos, 2023. "A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks," Future Internet, MDPI, vol. 15(8), pages 1-31, July.
    10. Yakai Qiao & Hui Chen & Bo Fu, 2024. "Multi-Wind Turbine Wind Speed Prediction Based on Weighted Diffusion Graph Convolution and Gated Attention Network," Energies, MDPI, vol. 17(7), pages 1-15, March.
    11. Wang, Pengfei & Zhang, Jiaxuan & Wan, Jiashuang & Wu, Shifa, 2022. "A fault diagnosis method for small pressurized water reactors based on long short-term memory networks," Energy, Elsevier, vol. 239(PC).
    12. Sareen, Karan & Panigrahi, Bijaya Ketan & Shikhola, Tushar & Sharma, Rajneesh, 2023. "An imputation and decomposition algorithms based integrated approach with bidirectional LSTM neural network for wind speed prediction," Energy, Elsevier, vol. 278(C).
    13. Lv, Yunlong & Hu, Qin & Xu, Hang & Lin, Huiyao & Wu, Yufan, 2024. "An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model," Energy, Elsevier, vol. 293(C).
    14. Zhang, Yagang & Zhang, Jinghui & Yu, Leyi & Pan, Zhiya & Feng, Changyou & Sun, Yiqian & Wang, Fei, 2022. "A short-term wind energy hybrid optimal prediction system with denoising and novel error correction technique," Energy, Elsevier, vol. 254(PC).
    15. Kamranzad, Bahareh & Lin, Pengzhi, 2020. "Sustainability of wave energy resources in the South China Sea based on five decades of changing climate," Energy, Elsevier, vol. 210(C).
    16. Zhang, Haipeng & Wang, Jianzhou & Qian, Yuansheng & Li, Qiwei, 2024. "Point and interval wind speed forecasting of multivariate time series based on dual-layer LSTM," Energy, Elsevier, vol. 294(C).
    17. Bashir, Hassan & Sibtain, Muhammad & Hanay, Özge & Azam, Muhammad Imran & Qurat-ul-Ain, & Saleem, Snoober, 2023. "Decomposition and Harris hawks optimized multivariate wind speed forecasting utilizing sequence2sequence-based spatiotemporal attention," Energy, Elsevier, vol. 278(PB).
    18. Dokur, Emrah & Erdogan, Nuh & Salari, Mahdi Ebrahimi & Karakuzu, Cihan & Murphy, Jimmy, 2022. "Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine," Energy, Elsevier, vol. 248(C).
    19. Cardo-Miota, Javier & Trivedi, Rohit & Patra, Sandipan & Khadem, Shafi & Bahloul, Mohamed, 2024. "Data-driven approach for day-ahead System Non-Synchronous Penetration forecasting: A comprehensive framework, model development and analysis," Applied Energy, Elsevier, vol. 362(C).
    20. Farah, Shahid & David A, Wood & Humaira, Nisar & Aneela, Zameer & Steffen, Eger, 2022. "Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2013-:d:1131256. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.