Figure 3. Different parts of a Searaser; 1) Buoy 2) Chamber 3) Valves 4) Generator 5) Anchor system

Fatemehsadat Mirshafiee1, Emad Shahbazi 2, Mohadeseh Safi 3, Rituraj Rituraj 4,*
1Department of Electrical and Computer Engineering, K.N. Toosi University of Technology, Tehran 1999143344 , Iran
2Department of Mechatronic, Amirkabir University of Technology, Tehran 158754413, Iran
3Department of Mechatronic, Electrical and Computer Engineering, University of Tehran, Tehran 1416634793, Iran
4 Faculty of Informatics, Obuda University, 1023, Budapest, Hungary

  • Correspondence:


본 연구는 지속가능한 에너지 변환기의 전력 및 수소 발생 모델링을 위한 데이터 기반 방법론을 제안합니다. 파고와 풍속을 달리하여 파고와 수소생산을 예측합니다.

또한 이 연구는 파도에서 수소를 추출할 수 있는 가능성을 강조하고 장려합니다. FLOW-3D 소프트웨어 시뮬레이션에서 추출한 데이터와 해양 특수 테스트의 실험 데이터를 사용하여 두 가지 데이터 기반 학습 방법의 비교 분석을 수행합니다.

결과는 수소 생산의 양은 생성된 전력의 양에 비례한다는 것을 보여줍니다. 제안된 재생 에너지 변환기의 신뢰성은 지속 가능한 스마트 그리드 애플리케이션으로 추가로 논의됩니다.

This study proposes a data-driven methodology for modeling power and hydrogen generation of a sustainable energy converter. The wave and hydrogen production at different wave heights and wind speeds are predicted. Furthermore, this research emphasizes and encourages the possibility of extracting hydrogen from ocean waves. By using the extracted data from FLOW-3D software simulation and the experimental data from the special test in the ocean, the comparison analysis of two data-driven learning methods is conducted. The results show that the amount of hydrogen production is proportional to the amount of generated electrical power. The reliability of the proposed renewable energy converter is further discussed as a sustainable smart grid application.

Key words

Cavity, Combustion efficiency, hydrogen fuel, Computational Fluent and Gambit.

Figure 1. The process of power and hydrogen production with Searaser.
Figure 1. The process of power and hydrogen production with Searaser.
Figure 2. The cross-section A-A of the two essential parts of a Searaser
Figure 2. The cross-section A-A of the two essential parts of a Searaser
Figure 3. Different parts of a Searaser; 1) Buoy 2) Chamber 3) Valves 4) Generator 5) Anchor system
Figure 3. Different parts of a Searaser; 1) Buoy 2) Chamber 3) Valves 4) Generator 5) Anchor system
Figure 4. The boundary conditions of the control volume
Figure 4. The boundary conditions of the control volume
Figure 5. The wind velocity during the period of the experimental test
Figure 5. The wind velocity during the period of the experimental test


  1. Kalbasi, R., Jahangiri, M., Dehshiri, S.J.H., Dehshiri, S.S.H., Ebrahimi, S., Etezadi, Z.A.S. and Karimipour, A., 2021. Finding the
    best station in Belgium to use residential-scale solar heating, one-year dynamic simulation with considering all system losses:
    economic analysis of using ETSW. Sustainable Energy Technologies and Assessments, 45, p.101097.
  2. Megura M, Gunderson R. Better poison is the cure? Critically examining fossil fuel companies, climate change framing, and
    corporate sustainability reports. Energy Research & Social Science. 2022 Mar 1;85:102388.
  3. Holechek JL, Geli HM, Sawalhah MN, Valdez R. A global assessment: can renewable energy replace fossil fuels by 2050?.
    Sustainability. 2022 Jan;14(8):4792.
  4. Ahmad M, Kumar A, Ranjan R. Recent Developments of Tidal Energy as Renewable Energy: An Overview. River and Coastal
    Engineering. 2022:329-43.
  5. Amini E, Mehdipour H, Faraggiana E, Golbaz D, Mozaffari S, Bracco G, Neshat M. Optimization of hydraulic power take-off
    system settings for point absorber wave energy converter. Renewable Energy. 2022 Jun 4.
  6. Claywell, R., Nadai, L., Felde, I., Ardabili, S. 2020. Adaptive neuro-fuzzy inference system and a multilayer perceptron model
    trained with grey wolf optimizer for predicting solar diffuse fraction. Entropy, 22(11), p.1192.
  7. McLeod I, Ringwood JV. Powering data buoys using wave energy: a review of possibilities. Journal of Ocean Engineering and
    Marine Energy. 2022 Jun 20:1-6.
  8. Olsson G. Water interactions: A systemic view: Why we need to comprehend the water-climate-energy-food-economics-lifestyle connections.
  9. Malkowska A, Malkowski A. Green Energy in the Political Debate. InGreen Energy 2023 (pp. 17-39). Springer, Cham.
  10. Mayon R, Ning D, Ding B, Sergiienko NY. Wave energy converter systems–status and perspectives. InModelling and Optimisation of Wave Energy Converters (pp. 3-58). CRC Press.
  11. Available online at: (9/27/2022)
  12. Mousavi SM, et al.,. Deep learning for wave energy converter modeling using long short-term memory. Mathematics. 2021 Apr
  13. Mega V. The Energy Race to Decarbonisation. InHuman Sustainable Cities 2022 (pp. 105-141). Springer, Cham.
  14. Li R, Tang BJ, Yu B, Liao H, Zhang C, Wei YM. Cost-optimal operation strategy for integrating large scale of renewable energy
    in China’s power system: From a multi-regional perspective. Applied Energy. 2022 Nov 1;325:119780.
  15. Ardabili S., Abdolalizadeh L., Mako C., Torok B., Systematic Review of Deep Learning and Machine Learning for Building
    Energy, Frontiers in Energy Research, 10, 2022.
  16. Penalba M, Aizpurua JI, Martinez-Perurena A, Iglesias G. A data-driven long-term metocean data forecasting approach for the
    design of marine renewable energy systems. Renewable and Sustainable Energy Reviews. 2022 Oct 1;167:112751.
  17. Torabi, M., Hashemi, S., Saybani, M.R., 2019. A Hybrid clustering and classification technique for forecasting short‐term energy
    consumption. Environmental progress & sustainable energy, 38(1), pp.66-76.
  18. Rivera FP, Zalamea J, Espinoza JL, Gonzalez LG. Sustainable use of spilled turbinable energy in Ecuador: Three different energy
    storage systems. Renewable and Sustainable Energy Reviews. 2022 Mar 1;156:112005.
  19. Raza SA, Jiang J. Mathematical foundations for balancing single-phase residential microgrids connected to a three-phase distribution system. IEEE Access. 2022 Jan 6;10:5292-303.
  20. Takach M, Sarajlić M, Peters D, Kroener M, Schuldt F, von Maydell K. Review of Hydrogen Production Techniques from Water
    Using Renewable Energy Sources and Its Storage in Salt Caverns. Energies. 2022 Feb 15;15(4):1415.
  21. Lv Z, Li W, Wei J, Ho F, Cao J, Chen X. Autonomous Chemistry Enabling Environment-Adaptive Electrochemical Energy
    Storage Devices. CCS Chemistry. 2022 Jul 7:1-9.
  22. Dehghan Manshadi, Mahsa, Milad Mousavi, M. Soltani, Amir Mosavi, and Levente Kovacs. 2022. “Deep Learning for Modeling
    an Offshore Hybrid Wind–Wave Energy System” Energies 15, no. 24: 9484.
  23. Ishaq H, Dincer I, Crawford C. A review on hydrogen production and utilization: Challenges and opportunities. International
    Journal of Hydrogen Energy. 2022 Jul 22;47(62):26238-64.
  24. Maguire JF, Woodcock LV. On the Thermodynamics of Aluminum Cladding Oxidation: Water as the Catalyst for Spontaneous
    Combustion. Journal of Failure Analysis and Prevention. 2022 Sep 10:1-5.
  25. Mohammadi, M. R., Hadavimoghaddam, F., Pourmahdi, M., Atashrouz, S., Munir, M. T., Hemmati-Sarapardeh, A., … & Mohaddespour, A. (2021). Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state.
    Scientific reports, 11(1).
  26. Ma S, Qin J, Xiu X, Wang S. Design and performance evaluation of an underwater hybrid system of fuel cell and battery. Energy
    Conversion and Management. 2022 Jun 15;262:115672.
  27. Ahamed R, McKee K, Howard I. A Review of the Linear Generator Type of Wave Energy Converters’ Power Take-Off Systems.
    Sustainability. 2022 Jan;14(16):9936.
  28. Nejad, H.D., Nazari, M., Nazari, M., Mardan, M.M.S., 2022. Fuzzy State-Dependent Riccati Equation (FSDRE) Control of the
    Reverse Osmosis Desalination System With Photovoltaic Power Supply. IEEE Access, 10, pp.95585-95603.
  29. Zou S, Zhou X, Khan I, Weaver WW, Rahman S. Optimization of the electricity generation of a wave energy converter using
    deep reinforcement learning. Ocean Engineering. 2022 Jan 15;244:110363.
  30. Wu J, Qin L, Chen N, Qian C, Zheng S. Investigation on a spring-integrated mechanical power take-off system for wave energy
    conversion purpose. Energy. 2022 Apr 15;245:123318.
  31. Papini G, Dores Piuma FJ, Faedo N, Ringwood JV, Mattiazzo G. Nonlinear Model Reduction by Moment-Matching for a Point
    Absorber Wave Energy Conversion System. Journal of Marine Science and Engineering. 2022 May;10(5):656.
  32. Forbush DD, Bacelli G, Spencer SJ, Coe RG, Bosma B, Lomonaco P. Design and testing of a free floating dual flap wave energy
    converter. Energy. 2022 Feb 1;240:122485.
  33. Rezaei, M.A., 2022. A New Hybrid Cascaded Switched-Capacitor Reduced Switch Multilevel Inverter for Renewable Sources
    and Domestic Loads. IEEE Access, 10, pp.14157-14183.
  34. Lin Z, Cheng L, Huang G. Electricity consumption prediction based on LSTM with attention mechanism. IEEJ Transactions on
    Electrical and Electronic Engineering. 2020;15(4):556-562.
  35. Tavoosi, J., Mohammadzadeh, A., Pahlevanzadeh, B., Kasmani, M.B., 2022. A machine learning approach for active/reactive
    power control of grid-connected doubly-fed induction generators. Ain Shams Engineering Journal, 13(2), p.101564.
  36. Ghalandari, M., 2019. Flutter speed estimation using presented differential quadrature method formulation. Engineering Applications of Computational Fluid Mechanics, 13(1), pp.804-810.
  37. Li Z, Bouscasse B, Ducrozet G, Gentaz L, Le Touzé D, Ferrant P. Spectral wave explicit navier-stokes equations for wavestructure interactions using two-phase computational fluid dynamics solvers. Ocean Engineering. 2021 Feb 1;221:108513.
  38. Zhou Y. Ocean energy applications for coastal communities with artificial intelligencea state-of-the-art review. Energy and AI.
    2022 Jul 29:100189.
  39. Miskati S, Farin FM. Performance evaluation of wave-carpet in wave energy extraction at different coastal regions: an analytical
    approach (Doctoral dissertation, Department of Mechanical and Production Engineering).
  40. Gu C, Li H. Review on Deep Learning Research and Applications in Wind and Wave Energy. Energies. 2022 Feb 17;15(4):1510.
  41. Aazami, R., 2022. Optimal Control of an Energy-Storage System in a Microgrid for Reducing Wind-Power Fluctuations. Sustainability, 14(10), p.6183.
  42. Kabir M, Chowdhury MS, Sultana N, Jamal MS, Techato K. Ocean renewable energy and its prospect for developing economies.
    InRenewable Energy and Sustainability 2022 Jan 1 (pp. 263-298). Elsevier.
  43. Babajani A, Jafari M, Hafezisefat P, Mirhosseini M, Rezania A, Rosendahl L. Parametric study of a wave energy converter
    (Searaser) for Caspian Sea. Energy Procedia. 2018 Aug 1;147:334-42.
  44. He J. Coherence and cross-spectral density matrix analysis of random wind and wave in deep water. Ocean Engineering.
  45. Ijadi Maghsoodi, A., 2018. Renewable energy technology selection problem using integrated h-swara-multimoora approach.
    Sustainability, 10(12), p.4481.
  46. Band, S.S., Ardabili, S., Sookhak, M., Theodore, A., Elnaffar, S., Moslehpour, M., Csaba, M., Torok, B., Pai, H.T., 2022. When
    Smart Cities Get Smarter via Machine Learning: An In-depth Literature Review. IEEE Access.
  47. Shamshirband, S., Rabczuk, T., Nabipour, N. and Chau, K.W., 2020. Prediction of significant wave height; comparison between
    nested grid numerical model, and machine learning models of artificial neural networks, extreme learning and support vector
    machines. Engineering Applications of Computational Fluid Mechanics, 14(1), pp.805-817.
  48. Liu, Z., Mohammadzadeh, A., Turabieh, H., Mafarja, M., 2021. A new online learned interval type-3 fuzzy control system for
    solar energy management systems. IEEE Access, 9, pp.10498-10508.
  49. Bavili, R.E., Mohammadzadeh, A., Tavoosi, J., Mobayen, S., Assawinchaichote, W., Asad, J.H. 2021. A New Active Fault Tolerant Control System: Predictive Online Fault Estimation. IEEE Access, 9, pp.118461-118471.
  50. Akbari, E., Teimouri, A.R., Saki, M., Rezaei, M.A., Hu, J., Band, S.S., Pai, H.T., 2022. A Fault-Tolerant Cascaded SwitchedCapacitor Multilevel Inverter for Domestic Applications in Smart Grids. IEEE Access.
  51. Band, S.S., Ardabili, S., 2022. Feasibility of soft computing techniques for estimating the long-term mean monthly wind speed.
    Energy Reports, 8, pp.638-648.
  52. Tavoosi, J., Mohammadzadeh, A., Pahlevanzadeh, B., Kasmani, M.B., 2022. A machine learning approach for active/reactive
    power control of grid-connected doubly-fed induction generators. Ain Shams Engineering Journal, 13(2), p.101564.
  53. Ponnusamy, V. K., Kasinathan, P., Madurai Elavarasan, R., Ramanathan, V., Anandan, R. K., Subramaniam, U., … & Hossain,
    E. A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid. Sustainability, 2021; 13(23),
  54. Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. Artificial intelligence in sustainable energy industry:
    Status Quo, challenges and opportunities. Journal of Cleaner Production, 2021; 289, 125834.
  55. Wang, G., Chao, Y., Cao, Y., Jiang, T., Han, W., & Chen, Z. A comprehensive review of research works based on evolutionary
    game theory for sustainable energy development. Energy Reports, 2022; 8, 114-136.
  56. Iranmehr H., Modeling the Price of Emergency Power Transmission Lines in the Reserve Market Due to the Influence of Renewable Energies, Frontiers in Energy Research, 9, 2022
  57. Farmanbar, M., Parham, K., Arild, Ø., & Rong, C. A widespread review of smart grids towards smart cities. Energies, 2019;
    12(23), 4484.
  58. Quartier, N., Crespo, A. J., Domínguez, J. M., Stratigaki, V., & Troch, P. Efficient response of an onshore Oscillating Water
    Column Wave Energy Converter using a one-phase SPH model coupled with a multiphysics library. Applied Ocean Research,
    2021; 115, 102856.
  59. Mahmoodi, K., Nepomuceno, E., & Razminia, A. Wave excitation force forecasting using neural networks. Energy, 2022; 247,
  60. Wang, H., Alattas, K.A., 2022. Comprehensive review of load forecasting with emphasis on intelligent computing approaches.
    Energy Reports, 8, pp.13189-13198.
  61. Clemente, D., Rosa-Santos, P., & Taveira-Pinto, F. On the potential synergies and applications of wave energy converters: A
    review. Renewable and Sustainable Energy Reviews, 2021; 135, 110162.
  62. Felix, A., V. Hernández-Fontes, J., Lithgow, D., Mendoza, E., Posada, G., Ring, M., & Silva, R. Wave energy in tropical regions:
    deployment challenges, environmental and social perspectives. Journal of Marine Science and Engineering, 2019; 7(7), 219.
  63. Farrok, O., Ahmed, K., Tahlil, A. D., Farah, M. M., Kiran, M. R., & Islam, M. R. Electrical power generation from the oceanic
    wave for sustainable advancement in renewable energy technologies. Sustainability, 2020; 12(6), 2178.
  64. Guo, B., & Ringwood, J. V. A review of wave energy technology from a research and commercial perspective. IET Renewable
    Power Generation, 2021; 15(14), 3065-3090.
  65. López-Ruiz, A., Bergillos, R. J., Lira-Loarca, A., & Ortega-Sánchez, M. A methodology for the long-term simulation and uncertainty analysis of the operational lifetime performance of wave energy converter arrays. Energy, 2018; 153, 126-135.
  66. Safarian, S., Saryazdi, S. M. E., Unnthorsson, R., & Richter, C. Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant. Energy, 2020; 213, 118800.
  67. Kushwah, S. An oscillating water column (OWC): the wave energy converter. Journal of The Institution of Engineers (India):
    Series C, 2021; 102(5), 1311-1317.
  68. Pap, J., Mako, C., Illessy, M., Kis, N., 2022. Modeling Organizational Performance with Machine Learning. Journal of Open
    Innovation: Technology, Market, and Complexity, 8(4), p.177.
  69. Pap, J., Mako, C., Illessy, M., Dedaj, Z., Ardabili, S., Torok, B., 2022. Correlation Analysis of Factors Affecting Firm Performance
    and Employees Wellbeing: Application of Advanced Machine Learning Analysis. Algorithms, 15(9), p.300.
  70. Alanazi, A., 2022. Determining Optimal Power Flow Solutions Using New Adaptive Gaussian TLBO Method. Applied Sciences, 12(16), p.7959.
  71. Shakibjoo, A.D., Moradzadeh, M., Din, S.U., 2021. Optimized Type-2 Fuzzy Frequency Control for Multi-Area Power Systems.
    IEEE access, 10, pp.6989-7002.
  72. Zhang, G., 2021. Solar radiation estimation in different climates with meteorological variables using Bayesian model averaging
    and new soft computing models. Energy Reports, 7, pp.8973-8996.
  73. Cao, Y., Raise, A., Mohammadzadeh, A., Rathinasamy, S., 2021. Deep learned recurrent type-3 fuzzy system: Application for
    renewable energy modeling/prediction. Energy Reports, 7, pp.8115-8127.
  74. Tavoosi, J., Suratgar, A.A., Menhaj, M.B., 2021. Modeling renewable energy systems by a self-evolving nonlinear consequent
    part recurrent type-2 fuzzy system for power prediction. Sustainability, 13(6), p.3301.
  75. Bourouis, S., Band, S.S., 2022. Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound
    Images. Frontiers in Oncology, 12, p.834028.
  76. Mosavi, A.H., Mohammadzadeh, A., Rathinasamy, S., Zhang, C., Reuter, U., Levente, K. and Adeli, H., 2022. Deep learning
    fuzzy immersion and invariance control for type-I diabetes. Computers in Biology and Medicine, 149, p.105975.
  77. Almutairi, K., Algarni, S., Alqahtani, T., Moayedi, H., 2022. A TLBO-Tuned Neural Processor for Predicting Heating Load in
    Residential Buildings. Sustainability, 14(10), p.5924.
  78. Ahmad, Z., Zhong, H., 2020. Machine learning modeling of aerobic biodegradation for azo dyes and hexavalent chromium.
    Mathematics, 8(6), p.913.
  79. Mosavi, A., Shokri, M., Mansor, Z., Qasem, S.N., Band, S.S. and Mohammadzadeh, A., 2020. Machine learning for modeling
    the singular multi-pantograph equations. Entropy, 22(9), p.1041.
  80. Ardabili, S., 2019, September. Deep learning and machine learning in hydrological processes climate change and earth systems
    a systematic review. In International conference on global research and education (pp. 52-62). Springer, Cham.
  81. Moayedi, H., (2021). Suggesting a stochastic fractal search paradigm in combination with artificial neural network for early
    prediction of cooling load in residential buildings. Energies, 14(6), 1649.
  82. Rezakazemi, M., et al., 2019. ANFIS pattern for molecular membranes separation optimization. Journal of Molecular Liquids,
    274, pp.470-476.
  83. Mosavi, A., Faghan, Y., Ghamisi, P., Duan, P., Ardabili, S.F., Salwana, E. and Band, S.S., 2020. Comprehensive review of deep
    reinforcement learning methods and applications in economics. Mathematics, 8(10), p.1640.
  84. Samadianfard, S., Jarhan, S., Salwana, E., 2019. Support vector regression integrated with fruit fly optimization algorithm for
    river flow forecasting in Lake Urmia Basin. Water, 11(9), p.1934.
  85. Moayedi, H., (2021). Double-target based neural networks in predicting energy consumption in residential buildings. Energies,
    14(5), 1331.
  86. Choubin, B., 2019. Earth fissure hazard prediction using machine learning models. Environmental research, 179, p.108770.
  87. Mohammadzadeh S, D., Kazemi, S.F., 2019. Prediction of compression index of fine-grained soils using a gene expression programming model. Infrastructures, 4(2), p.26.
  88. Karballaeezadeh, N., Mohammadzadeh S, D., Shamshirband, S., Hajikhodaverdikhan, P., 2019. Prediction of remaining service
    life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road). Engineering Applications
    of Computational Fluid Mechanics, 13(1), pp.188-198.
  89. Rezaei, M. Et al., (2022). Adaptation of A Real-Time Deep Learning Approach with An Analog Fault Detection Technique for
    Reliability Forecasting of Capacitor Banks Used in Mobile Vehicles. IEEE Access v. 21 pp. 89-99.
  90. Khakian, R., et al., (2020). Modeling nearly zero energy buildings for sustainable development in rural areas. Energies, 13(10),