LAPSE:2023.20152
Published Article
LAPSE:2023.20152
Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory
March 10, 2023
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.
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Keywords
Artificial Intelligence, Computational Fluid Dynamics, data science, deep learning, Energy, Energy Conversion, long short-term memory, Machine Learning, Renewable and Sustainable Energy, wind turbine
Subject
Suggested Citation
Dehghan Manshadi M, Ghassemi M, Mousavi SM, Mosavi AH, Kovacs L. Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory. (2023). LAPSE:2023.20152
Author Affiliations
Dehghan Manshadi M: Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1999143344, Iran
Ghassemi M: Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1999143344, Iran
Mousavi SM: Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1999143344, Iran [ORCID]
Mosavi AH: Institute of Software Design and Development, Obuda University, 1034 Budapest, Hungary [ORCID]
Kovacs L: 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 Un [ORCID]
Ghassemi M: Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1999143344, Iran
Mousavi SM: Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1999143344, Iran [ORCID]
Mosavi AH: Institute of Software Design and Development, Obuda University, 1034 Budapest, Hungary [ORCID]
Kovacs L: 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 Un [ORCID]
Journal Name
Energies
Volume
14
Issue
16
First Page
4867
Year
2021
Publication Date
2021-08-09
Published Version
ISSN
1996-1073
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Original Submission
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PII: en14164867, Publication Type: Journal Article
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LAPSE:2023.20152
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doi:10.3390/en14164867
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[v1] (Original Submission)
Mar 10, 2023
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