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

Seyed Milad Mousavi, Majid Ghasemi, Mahsa Dehghan Manshadi and Amir Mosavi
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Seyed Milad Mousavi: Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, Iran
Majid Ghasemi: Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, Iran
Mahsa Dehghan Manshadi: Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, Iran
Amir Mosavi: Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany

Mathematics, 2021, vol. 9, issue 8, 1-16

Abstract: 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.

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) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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