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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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    1. Wang, Junlei & Geng, Linfeng & Ding, Lin & Zhu, Hongjun & Yurchenko, Daniil, 2020. "The state-of-the-art review on energy harvesting from flow-induced vibrations," Applied Energy, Elsevier, vol. 267(C).
    2. Meka, Rajitha & Alaeddini, Adel & Bhaganagar, Kiran, 2021. "A robust deep learning framework for short-term wind power forecast of a full-scale wind farm using atmospheric variables," Energy, Elsevier, vol. 221(C).
    3. Kouadri, Abdelmalek & Hajji, Mansour & Harkat, Mohamed-Faouzi & Abodayeh, Kamaleldin & Mansouri, Majdi & Nounou, Hazem & Nounou, Mohamed, 2020. "Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems," Renewable Energy, Elsevier, vol. 150(C), pages 598-606.
    4. Guangyu Qin & Qingyou Yan & Jingyao Zhu & Chuanbo Xu & Daniel M. Kammen, 2021. "Day-Ahead Wind Power Forecasting Based on Wind Load Data Using Hybrid Optimization Algorithm," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
    5. Yang, Mao & Shi, Chaoyu & Liu, Huiyu, 2021. "Day-ahead wind power forecasting based on the clustering of equivalent power curves," Energy, Elsevier, vol. 218(C).
    6. Peng Qian & Xiange Tian & Jamil Kanfoud & Joash Lap Yan Lee & Tat-Hean Gan, 2019. "A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network," Energies, MDPI, vol. 12(18), pages 1-15, September.
    7. Martin, Sean & Jung, Sungmoon & Vanli, Arda, 2020. "Impact of near-future turbine technology on the wind power potential of low wind regions," Applied Energy, Elsevier, vol. 272(C).
    8. Shahram Hanifi & Xiaolei Liu & Zi Lin & Saeid Lotfian, 2020. "A Critical Review of Wind Power Forecasting Methods—Past, Present and Future," Energies, MDPI, vol. 13(15), pages 1-24, July.
    9. Chang, Yuanhong & Chen, Jinglong & Qu, Cheng & Pan, Tongyang, 2020. "Intelligent fault diagnosis of Wind Turbines via a Deep Learning Network Using Parallel Convolution Layers with Multi-Scale Kernels," Renewable Energy, Elsevier, vol. 153(C), pages 205-213.
    10. Hossein Moayedi & Amir Mosavi, 2021. "An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework," Energies, MDPI, vol. 14(4), pages 1-18, February.
    11. Ali Mostafaeipour & Mostafa Rezaei & Mehdi Jahangiri & Mojtaba Qolipour, 2020. "Feasibility analysis of a new tree-shaped wind turbine for urban application: A case study," Energy & Environment, , vol. 31(7), pages 1230-1256, November.
    12. Chen, Wanqiu & Qiu, Yingning & Feng, Yanhui & Li, Ye & Kusiak, Andrew, 2021. "Diagnosis of wind turbine faults with transfer learning algorithms," Renewable Energy, Elsevier, vol. 163(C), pages 2053-2067.
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    Cited by:

    1. Bin Yang & Xin Zhu & Boan Wei & Minzhang Liu & Yifan Li & Zhihan Lv & Faming Wang, 2023. "Computer Vision and Machine Learning Methods for Heat Transfer and Fluid Flow in Complex Structural Microchannels: A Review," Energies, MDPI, vol. 16(3), pages 1-24, February.
    2. Ali Akbar Firoozi & Farzad Hejazi & Ali Asghar Firoozi, 2024. "Advancing Wind Energy Efficiency: A Systematic Review of Aerodynamic Optimization in Wind Turbine Blade Design," Energies, MDPI, vol. 17(12), pages 1-30, June.
    3. 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.
    4. 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.
    5. 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.
    6. 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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