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Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven Methods: A Sustainable Smart Grid Case Study

Author

Listed:
  • Fatemehsadat Mirshafiee

    (Department of Electrical and Computer Engineering, K.N. Toosi University of Technology, Tehran 1999143344, Iran)

  • Emad Shahbazi

    (Department of Mechatronic, Amirkabir University of Technology, Tehran 158754413, Iran)

  • Mohadeseh Safi

    (Department of Mechatronic, Electrical and Computer Engineering, University of Tehran, Tehran 1416634793, Iran)

  • Rituraj Rituraj

    (Doctoral School of Applied Informatics and Applied Mathematics, Faculty of Informatics, Obuda University, 1023 Budapest, Hungary)

Abstract

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 the 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.

Suggested Citation

  • Fatemehsadat Mirshafiee & Emad Shahbazi & Mohadeseh Safi & Rituraj Rituraj, 2023. "Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven Methods: A Sustainable Smart Grid Case Study," Energies, MDPI, vol. 16(1), pages 1-20, January.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:1:p:502-:d:1022849
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    References listed on IDEAS

    as
    1. Safarian, Sahar & Ebrahimi Saryazdi, Seyed Mohammad & Unnthorsson, Runar & Richter, Christiaan, 2020. "Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant," Energy, Elsevier, vol. 213(C).
    2. Raju Ahamed & Kristoffer McKee & Ian Howard, 2022. "A Review of the Linear Generator Type of Wave Energy Converters’ Power Take-Off Systems," Sustainability, MDPI, vol. 14(16), pages 1-42, August.
    3. Seyed Milad Mousavi & Majid Ghasemi & Mahsa Dehghan Manshadi & Amir Mosavi, 2021. "Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory," Mathematics, MDPI, vol. 9(8), pages 1-16, April.
    4. Zulfiqar Ahmad & Hua Zhong & Amir Mosavi & Mehreen Sadiq & Hira Saleem & Azeem Khalid & Shahid Mahmood & Narjes Nabipour, 2020. "Machine Learning Modeling of Aerobic Biodegradation for Azo Dyes and Hexavalent Chromium," Mathematics, MDPI, vol. 8(6), pages 1-17, June.
    5. Li, Ru & Tang, Bao-Jun & Yu, Biying & Liao, Hua & Zhang, Chen & Wei, Yi-Ming, 2022. "Cost-optimal operation strategy for integrating large scale of renewable energy in China’s power system: From a multi-regional perspective," Applied Energy, Elsevier, vol. 325(C).
    6. Mahmoodi, Kumars & Nepomuceno, Erivelton & Razminia, Abolhassan, 2022. "Wave excitation force forecasting using neural networks," Energy, Elsevier, vol. 247(C).
    7. Mahdi Takach & Mirza Sarajlić & Dorothee Peters & Michael Kroener & Frank Schuldt & Karsten von Maydell, 2022. "Review of Hydrogen Production Techniques from Water Using Renewable Energy Sources and Its Storage in Salt Caverns," Energies, MDPI, vol. 15(4), pages 1-17, February.
    8. Jerry L. Holechek & Hatim M. E. Geli & Mohammed N. Sawalhah & Raul Valdez, 2022. "A Global Assessment: Can Renewable Energy Replace Fossil Fuels by 2050?," Sustainability, MDPI, vol. 14(8), pages 1-22, April.
    9. 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.
    10. Amir Mosavi & Pedram Ghamisi & Yaser Faghan & Puhong Duan, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Papers 2004.01509, arXiv.org.
    11. Jafar Tavoosi & Amir Abolfazl Suratgar & Mohammad Bagher Menhaj & Amir Mosavi & Ardashir Mohammadzadeh & Ehsan Ranjbar, 2021. "Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction," Sustainability, MDPI, vol. 13(6), pages 1-21, March.
    12. Reza Khakian & Mehrdad Karimimoshaver & Farshid Aram & Soghra Zoroufchi Benis & Amir Mosavi & Annamaria R. Varkonyi-Koczy, 2020. "Modeling Nearly Zero Energy Buildings for Sustainable Development in Rural Areas," Energies, MDPI, vol. 13(10), pages 1-19, May.
    13. Mina Farmanbar & Kiyan Parham & Øystein Arild & Chunming Rong, 2019. "A Widespread Review of Smart Grids Towards Smart Cities," Energies, MDPI, vol. 12(23), pages 1-18, November.
    14. Vinoth Kumar Ponnusamy & Padmanathan Kasinathan & Rajvikram Madurai Elavarasan & Vinoth Ramanathan & Ranjith Kumar Anandan & Umashankar Subramaniam & Aritra Ghosh & Eklas Hossain, 2021. "A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid," Sustainability, MDPI, vol. 13(23), pages 1-35, December.
    15. Rahmat Aazami & Omid Heydari & Jafar Tavoosi & Mohammadamin Shirkhani & Ardashir Mohammadzadeh & Amir Mosavi, 2022. "Optimal Control of an Energy-Storage System in a Microgrid for Reducing Wind-Power Fluctuations," Sustainability, MDPI, vol. 14(10), pages 1-14, May.
    16. Abteen Ijadi Maghsoodi & Arta Ijadi Maghsoodi & Amir Mosavi & Timon Rabczuk & Edmundas Kazimieras Zavadskas, 2018. "Renewable Energy Technology Selection Problem Using Integrated H-SWARA-MULTIMOORA Approach," Sustainability, MDPI, vol. 10(12), pages 1-18, November.
    17. Khalid Almutairi & Salem Algarni & Talal Alqahtani & Hossein Moayedi & Amir Mosavi, 2022. "A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    18. Mahsa Dehghan Manshadi & Majid Ghassemi & Seyed Milad Mousavi & Amir H. Mosavi & Levente Kovacs, 2021. "Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory," Energies, MDPI, vol. 14(16), pages 1-17, August.
    19. Omar Farrok & Koushik Ahmed & Abdirazak Dahir Tahlil & Mohamud Mohamed Farah & Mahbubur Rahman Kiran & Md. Rabiul Islam, 2020. "Electrical Power Generation from the Oceanic Wave for Sustainable Advancement in Renewable Energy Technologies," Sustainability, MDPI, vol. 12(6), pages 1-23, March.
    20. Amini, Erfan & Mehdipour, Hossein & Faraggiana, Emilio & Golbaz, Danial & Mozaffari, Sevda & Bracco, Giovanni & Neshat, Mehdi, 2022. "Optimization of hydraulic power take-off system settings for point absorber wave energy converter," Renewable Energy, Elsevier, vol. 194(C), pages 938-954.
    21. López-Ruiz, Alejandro & Bergillos, Rafael J. & Lira-Loarca, Andrea & Ortega-Sánchez, Miguel, 2018. "A methodology for the long-term simulation and uncertainty analysis of the operational lifetime performance of wave energy converter arrays," Energy, Elsevier, vol. 153(C), pages 126-135.
    22. Wu, Jinming & Qin, Liuzhen & Chen, Ni & Qian, Chen & Zheng, Siming, 2022. "Investigation on a spring-integrated mechanical power take-off system for wave energy conversion purpose," Energy, Elsevier, vol. 245(C).
    23. Hossein Moayedi & Amir Mosavi, 2021. "Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings," Energies, MDPI, vol. 14(5), pages 1-25, March.
    24. Mosavi, Amir & Faghan, Yaser & Ghamisi, Pedram & Duan, Puhong & Ardabili, Sina Faizollahzadeh & Hassan, Salwana & Band, Shahab S., 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," OSF Preprints jrc58, Center for Open Science.
    25. Amirhosein Mosavi & Yaser Faghan & Pedram Ghamisi & Puhong Duan & Sina Faizollahzadeh Ardabili & Ely Salwana & Shahab S. Band, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Mathematics, MDPI, vol. 8(10), pages 1-42, September.
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    1. Jimmy Gallegos & Paul Arévalo & Christian Montaleza & Francisco Jurado, 2024. "Sustainable Electrification—Advances and Challenges in Electrical-Distribution Networks: A Review," Sustainability, MDPI, vol. 16(2), pages 1-33, January.
    2. Hafize Nurgul Durmus Senyapar & Ramazan Bayindir, 2023. "The Research Agenda on Smart Grids: Foresights for Social Acceptance," Energies, MDPI, vol. 16(18), pages 1-31, September.

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