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Maximization of energy absorption for a wave energy converter using the deep machine learning

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Cited by:

  1. Neshat, Mehdi & Nezhad, Meysam Majidi & Sergiienko, Nataliia Y. & Mirjalili, Seyedali & Piras, Giuseppe & Garcia, Davide Astiaso, 2022. "Wave power forecasting using an effective decomposition-based convolutional Bi-directional model with equilibrium Nelder-Mead optimiser," Energy, Elsevier, vol. 256(C).
  2. Kushal A. Prasad & Aneesh A. Chand & Nallapaneni Manoj Kumar & Sumesh Narayan & Kabir A. Mamun, 2022. "A Critical Review of Power Take-Off Wave Energy Technology Leading to the Conceptual Design of a Novel Wave-Plus-Photon Energy Harvester for Island/Coastal Communities’ Energy Needs," Sustainability, MDPI, vol. 14(4), pages 1-55, February.
  3. Samuel-Soma M. Ajibade & Festus Victor Bekun & Festus Fatai Adedoyin & Bright Akwasi Gyamfi & Anthonia Oluwatosin Adediran, 2023. "Machine Learning Applications in Renewable Energy (MLARE) Research: A Publication Trend and Bibliometric Analysis Study (2012–2021)," Clean Technol., MDPI, vol. 5(2), pages 1-21, April.
  4. Abbas, Ahmed K. & Bashikh, Ali A. & Abbas, Hayder & Mohammed, Haider Q., 2019. "Intelligent decisions to stop or mitigate lost circulation based on machine learning," Energy, Elsevier, vol. 183(C), pages 1104-1113.
  5. Qin, Hao & Su, Haowen & Wen, Zhixuan & Liang, Hongjian, 2025. "Latching control of a point absorber wave energy converter in irregular wave environments coupling computational fluid dynamics and deep reinforcement learning," Applied Energy, Elsevier, vol. 396(C).
  6. Li, Wei & Becker, Denis Mike, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Energy, Elsevier, vol. 237(C).
  7. Shadmani, Alireza & Nikoo, Mohammad Reza & Gandomi, Amir H. & Chen, Mingjie & Nazari, Rouzbeh, 2024. "Advancements in optimizing wave energy converter geometry utilizing metaheuristic algorithms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
  8. 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).
  9. Han, Zhi & Cao, Feifei & Tao, Ji & Shi, Hongda, 2023. "Study on the energy capture spectrum (ECS) of a multi-DoF buoy under random waves," Energy, Elsevier, vol. 279(C).
  10. Li, Yunzhu & Liu, Tianyuan & Wang, Yuqi & Xie, Yonghui, 2022. "Deep learning based real-time energy extraction system modeling for flapping foil," Energy, Elsevier, vol. 246(C).
  11. Gao, Yu & Ke, Li & Liu, Kun & Zhang, Ming & Gao, Zhenguo, 2025. "Latching control strategies for improving performance of coupled linear-bistable wave energy converters," Energy, Elsevier, vol. 324(C).
  12. 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.
  13. Liang, Hongjian & Qin, Hao & Su, Haowen & Wen, Zhixuan & Mu, Lin, 2024. "Environmental-Sensing and adaptive optimization of wave energy converter based on deep reinforcement learning and computational fluid dynamics," Energy, Elsevier, vol. 297(C).
  14. He, Guanghua & Luan, Zhengxiao & Zhang, Wei & He, Runhua & Liu, Chaogang & Yang, Kaibo & Yang, Changhao & Jing, Penglin & Zhang, Zhigang, 2023. "Review on research approaches for multi-point absorber wave energy converters," Renewable Energy, Elsevier, vol. 218(C).
  15. Aleix Maria-Arenas & Aitor J. Garrido & Eugen Rusu & Izaskun Garrido, 2019. "Control Strategies Applied to Wave Energy Converters: State of the Art," Energies, MDPI, vol. 12(16), pages 1-19, August.
  16. Zhao, Huai & Zhang, Haicheng & Bi, Rengui & Xi, Ru & Xu, Daolin & Shi, Qijia & Wu, Bo, 2020. "Enhancing efficiency of a point absorber bistable wave energy converter under low wave excitations," Energy, Elsevier, vol. 212(C).
  17. Ni, Wenchi & Tian, Gengqing & Xie, Guangci & Ma, Yong, 2024. "Power prediction of oscillating water column power generation device based on physical information embedded neural network," Energy, Elsevier, vol. 306(C).
  18. Lu, Kai-Hung & Hong, Chih-Ming & Xu, Qiangqiang, 2019. "Recurrent wavelet-based Elman neural network with modified gravitational search algorithm control for integrated offshore wind and wave power generation systems," Energy, Elsevier, vol. 170(C), pages 40-52.
  19. 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).
  20. Liu, Yue & Zhang, Xiantao & Dong, Qing & Chen, Gang & Li, Xin, 2024. "Phase-resolved wave prediction with linear wave theory and physics-informed neural networks," Applied Energy, Elsevier, vol. 355(C).
  21. Del Pozo Gonzalez, Hector & Bianchi, Fernando D. & Dominguez-Garcia, Jose Luis & Gomis-Bellmunt, Oriol, 2023. "Co-located wind-wave farms: Optimal control and grid integration," Energy, Elsevier, vol. 272(C).
  22. Natarajan, Senthil Kumar & Cho, Il Hyoung, 2026. "Techno-economic optimization of a wave energy converter array in front of a vertical seawall with artificial neural networks," Energy, Elsevier, vol. 342(C).
  23. Mahmoodi, Kumars & Nepomuceno, Erivelton & Razminia, Abolhassan, 2022. "Wave excitation force forecasting using neural networks," Energy, Elsevier, vol. 247(C).
  24. Neshat, Mehdi & Sergiienko, Nataliia Y. & Rafiee, Ashkan & Mirjalili, Seyedali & Gandomi, Amir H. & Boland, John, 2024. "MetaWave Learner: Predicting wave farms power output using effective meta-learner deep gradient boosting model: A case study from Australian coasts," Energy, Elsevier, vol. 304(C).
  25. Li, L. & Gao, Y. & Ning, D.Z. & Yuan, Z.M., 2021. "Development of a constraint non-causal wave energy control algorithm based on artificial intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
  26. Ji, Xingda & Li, Liang, 2026. "Latching control of a bi-oscillator wave energy converter considers both the signal transmission and actuator execution latency effects," Energy, Elsevier, vol. 342(C).
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