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Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory

Author

Listed:
  • Mahsa Dehghan Manshadi

    (Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1999143344, Iran)

  • Majid Ghassemi

    (Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1999143344, Iran)

  • Seyed Milad Mousavi

    (Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1999143344, Iran)

  • Amir H. Mosavi

    (Institute of Software Design and Development, Obuda University, 1034 Budapest, Hungary)

  • Levente Kovacs

    (Biomatics Institute, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
    ELKH SZTAKI Institute, P.O. Box 63, 1518 Budapest, Hungary
    Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary)

Abstract

From conventional turbines to cutting-edge bladeless turbines, energy harvesting from wind has been well explored by researchers for more than a century. The vortex bladeless wind turbine (VBT) is considered an advanced design that alternatively harvests energy from oscillation. This research investigates enhancing the output electrical power of VBT through simulation of the fluid–solid interactions (FSI), leading to a comprehensive dataset for predicting procedure and optimal design. Hence, the long short-term memory (LSTM) method, due to its time-series prediction accuracy, is proposed to model the power of VBT from the collected data. To find the relationship between the parameters and the variables used in this research, a correlation matrix is further presented. According to the value of 0.3 for the root mean square error (RMSE), a comparative analysis between the simulation results and their predictions indicates that the LSTM method is suitable for modeling. Furthermore, the LSTM method has significantly reduced the computation time so that the prediction time of desired values has been reduced from an average of two and a half hours to two minutes. In addition, one of the most important achievements of this study is to suggest a mathematical relation of output power, which helps to extend it in different sizes of VBT with a high range of parameter variations.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4867-:d:611283
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    References listed on IDEAS

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    Cited by:

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    2. Mahsa Dehghan Manshadi & Milad Mousavi & M. Soltani & Amir Mosavi & Levente Kovacs, 2022. "Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System," Energies, MDPI, vol. 15(24), pages 1-16, December.
    3. Enas Taha Sayed & Abdul Ghani Olabi & Abdul Hai Alami & Ali Radwan & Ayman Mdallal & Ahmed Rezk & Mohammad Ali Abdelkareem, 2023. "Renewable Energy and Energy Storage Systems," Energies, MDPI, vol. 16(3), pages 1-26, February.
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    5. Ali Javaid & Umer Javaid & Muhammad Sajid & Muhammad Rashid & Emad Uddin & Yasar Ayaz & Adeel Waqas, 2022. "Forecasting Hydrogen Production from Wind Energy in a Suburban Environment Using Machine Learning," Energies, MDPI, vol. 15(23), pages 1-13, November.

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