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Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory

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Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, Iran
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Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
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John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
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School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Alessandro Niccolai
Mathematics 2021, 9(8), 871; https://doi.org/10.3390/math9080871
Received: 28 March 2021 / Revised: 10 April 2021 / Accepted: 12 April 2021 / Published: 15 April 2021
Accurate forecasts of ocean waves energy can not only reduce costs for investment, but it is also essential for the management and operation of electrical power. This paper presents an innovative approach based on long short-term memory (LSTM) to predict the power generation of an economical wave energy converter named “Searaser”. The data for analysis is provided by collecting the experimental data from another study and the exerted data from a numerical simulation of Searaser. The simulation is performed with Flow-3D software, which has high capability in analyzing fluid–solid interactions. The lack of relation between wind speed and output power in previous studies needs to be investigated in this field. Therefore, in this study, wind speed and output power are related with an LSTM method. Moreover, it can be inferred that the LSTM network is able to predict power in terms of height more accurately and faster than the numerical solution in a field of predicting. The network output figures show a great agreement, and the root mean square is 0.49 in the mean value related to the accuracy of the LSTM method. Furthermore, the mathematical relation between the generated power and wave height was introduced by curve fitting of the power function to the result of the LSTM method. View Full-Text
Keywords: Searaser; renewable energy; machine learning; long short term memory; deep neural network; deep learning; recurrent neural network; data science; big data; internet of things (IoT) Searaser; renewable energy; machine learning; long short term memory; deep neural network; deep learning; recurrent neural network; data science; big data; internet of things (IoT)
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MDPI and ACS Style

Mousavi, S.M.; Ghasemi, M.; Dehghan Manshadi, M.; Mosavi, A. Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory. Mathematics 2021, 9, 871. https://doi.org/10.3390/math9080871

AMA Style

Mousavi SM, Ghasemi M, Dehghan Manshadi M, Mosavi A. Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory. Mathematics. 2021; 9(8):871. https://doi.org/10.3390/math9080871

Chicago/Turabian Style

Mousavi, Seyed M., Majid Ghasemi, Mahsa Dehghan Manshadi, and Amir Mosavi. 2021. "Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory" Mathematics 9, no. 8: 871. https://doi.org/10.3390/math9080871

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