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

Mahsa Dehghan Manshadi, Majid Ghassemi, Seyed Milad Mousavi, Amir H. Mosavi and Levente Kovacs
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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

Energies, 2021, vol. 14, issue 16, 1-17

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.

Keywords: wind turbine; computational fluid dynamics; deep learning; long short-term memory; energy; artificial intelligence; renewable energy; machine learning; data science; energy conversion (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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