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Global oceanic wave energy resource dataset—with the Maritime Silk Road as a case study

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  • Zheng, Chong-wei

Abstract

Construction of wave energy resource dataset (WERD) is the key base for achieving rational and efficient utilization of wave energy, which is beneficial for easing the energy crisis and protecting the fragile marine ecological environment. However, the research on the WERD construction is scarce. Even more, the existing WERD mainly contains the traditional focus of wave energy (climatic feature). This study proposed to create a global oceanic WERD, with the Maritime Silk Road as a case study, which comprehensively includes not only the traditional focus of wave energy, but also adds seven new modules: short-term forecast, climatic variation of wave energy, long-term projection of wave energy, wave energy classification, characteristics of swell energy, wave climate characteristics, as well as detailed study of wave energy of key nodes, in hope of providing scientific reference, data service and decision support for the site selection, daily operation and long term plan of wave energy development.

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  • 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.
    • Handle: RePEc:eee:renene:v:169:y:2021:i:c:p:843-854
    DOI: 10.1016/j.renene.2021.01.058
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    4. 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.
    5. Zheng, Zihao & Ali, Mumtaz & Jamei, Mehdi & Xiang, Yong & Abdulla, Shahab & Yaseen, Zaher Mundher & Farooque, Aitazaz A., 2023. "Multivariate data decomposition based deep learning approach to forecast one-day ahead significant wave height for ocean energy generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    6. Chongwei Zheng, 2023. "An Overview and Countermeasure of Global Wave Energy Classification," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    7. Kong, Weihua & He, Liujin & Hao, Daning & Wu, Xiaoping & Xiao, Luo & Zhang, Zutao & Xu, Yongsheng & Azam, Ali, 2023. "A wave energy harvester based on an ultra-low frequency synergistic PTO for intelligent fisheries," Renewable Energy, Elsevier, vol. 217(C).

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