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Advancements on Optimization Algorithms Applied to Wave Energy Assessment: An Overview on Wave Climate and Energy Resource

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  • Daniel Clemente

    (CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208 Matosinhos, Portugal
    Department of Civil Engineering, FEUP—Faculty of Engineering, University of Porto, rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal)

  • Felipe Teixeira-Duarte

    (CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208 Matosinhos, Portugal
    Department of Civil Engineering, FEUP—Faculty of Engineering, University of Porto, rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal)

  • Paulo Rosa-Santos

    (CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208 Matosinhos, Portugal
    Department of Civil Engineering, FEUP—Faculty of Engineering, University of Porto, rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal)

  • Francisco Taveira-Pinto

    (CIIMAR—Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208 Matosinhos, Portugal
    Department of Civil Engineering, FEUP—Faculty of Engineering, University of Porto, rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal)

Abstract

The wave energy sector has not reached a sufficient level of maturity for commercial competitiveness, thus requiring further efforts towards optimizing existing technologies and making wave energy a viable alternative to bolster energy mixes. Usually, these efforts are supported by physical and numerical modelling of complex physical phenomena, which require extensive resources and time to obtain reliable, yet limited results. To complement these approaches, artificial-intelligence-based techniques (AI) are gaining increasing interest, given their computational speed and capability of searching large solution spaces and/or identifying key study patterns. Under this scope, this paper presents a comprehensive review on the use of computational systems and AI-based techniques to wave climate and energy resource studies. The paper reviews different optimization methods, analyses their application to extreme events and examines their use in wave propagation and forecasting, which are pivotal towards ensuring survivability and assessing the local wave operational conditions, respectively. The use of AI has shown promising results in improving the efficiency, accuracy and reliability of wave predictions and can enable a more thorough and automated sweep of alternative design solutions, within a more reasonable timeframe and at a lower computational cost. However, the particularities of each case study still limit generalizations, although some application patterns have been identified—such as the frequent use of neural networks.

Suggested Citation

  • Daniel Clemente & Felipe Teixeira-Duarte & Paulo Rosa-Santos & Francisco Taveira-Pinto, 2023. "Advancements on Optimization Algorithms Applied to Wave Energy Assessment: An Overview on Wave Climate and Energy Resource," Energies, MDPI, vol. 16(12), pages 1-28, June.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4660-:d:1169172
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    References listed on IDEAS

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    1. Ali, Mumtaz & Prasad, Ramendra & Xiang, Yong & Deo, Ravinesh C., 2020. "Near real-time significant wave height forecasting with hybridized multiple linear regression algorithms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    2. Cornejo-Bueno, L. & Nieto-Borge, J.C. & García-Díaz, P. & Rodríguez, G. & Salcedo-Sanz, S., 2016. "Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm – Extreme Learning Machine approach," Renewable Energy, Elsevier, vol. 97(C), pages 380-389.
    3. 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.
    4. Teixeira-Duarte, Felipe & Clemente, Daniel & Giannini, Gianmaria & Rosa-Santos, Paulo & Taveira-Pinto, Francisco, 2022. "Review on layout optimization strategies of offshore parks for wave energy converters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    5. 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.
    6. Michele Acciaro & Thierry Vanelslander & Christa Sys & Claudio Ferrari & Athena Roumboutsos & Genevieve Giuliano & Jasmine Siu Lee Lam & Seraphim Kapros, 2014. "Environmental sustainability in seaports: a framework for successful innovation," Maritime Policy & Management, Taylor & Francis Journals, vol. 41(5), pages 480-500, September.
    7. Mahmoodi, Kumars & Nepomuceno, Erivelton & Razminia, Abolhassan, 2022. "Wave excitation force forecasting using neural networks," Energy, Elsevier, vol. 247(C).
    8. Tiron, Roxana & Mallon, Fionn & Dias, Frédéric & Reynaud, Emmanuel G., 2015. "The challenging life of wave energy devices at sea: A few points to consider," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 1263-1272.
    9. Ma, Yu & Sclavounos, Paul D. & Cross-Whiter, John & Arora, Dhiraj, 2018. "Wave forecast and its application to the optimal control of offshore floating wind turbine for load mitigation," Renewable Energy, Elsevier, vol. 128(PA), pages 163-176.
    10. Garcia-Teruel, A. & Forehand, D.I.M., 2021. "A review of geometry optimisation of wave energy converters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    11. Ali, Mumtaz & Prasad, Ramendra & Xiang, Yong & Sankaran, Adarsh & Deo, Ravinesh C. & Xiao, Fuyuan & Zhu, Shuyu, 2021. "Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia," Renewable Energy, Elsevier, vol. 177(C), pages 1031-1044.
    12. Clemente, D. & Rosa-Santos, P. & Taveira-Pinto, F., 2021. "On the potential synergies and applications of wave energy converters: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    13. Garcia-Teruel, Anna & DuPont, Bryony & Forehand, David I.M., 2020. "Hull geometry optimisation of wave energy converters: On the choice of the optimisation algorithm and the geometry definition," Applied Energy, Elsevier, vol. 280(C).
    14. Gómez-Orellana, A.M. & Guijo-Rubio, D. & Gutiérrez, P.A. & Hervás-Martínez, C., 2022. "Simultaneous short-term significant wave height and energy flux prediction using zonal multi-task evolutionary artificial neural networks," Renewable Energy, Elsevier, vol. 184(C), pages 975-989.
    15. Burgaç, Alper & Yavuz, Hakan, 2019. "Fuzzy Logic based hybrid type control implementation of a heaving wave energy converter," Energy, Elsevier, vol. 170(C), pages 1202-1214.
    16. Chih-Chiang Wei, 2017. "Nearshore Wave Predictions Using Data Mining Techniques during Typhoons: A Case Study near Taiwan’s Northeastern Coast," Energies, MDPI, vol. 11(1), pages 1-23, December.
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