Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 7 March 2021 / Approved: 11 March 2021 / Online: 11 March 2021 (09:29:20 CET)
Version 2 : Received: 11 April 2021 / Approved: 13 April 2021 / Online: 13 April 2021 (09:51:25 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Mathematics 2021, 9, 871
DOI: 10.3390/math9080871

Abstract

Accurate forecasts of ocean waves energy can not only reduce costs for investment but it is also essential for management and operation of electrical power. This paper presents an innovative approach based on the Long Short Term Memory (LSTM) to predict the power generation of an economical wave energy converter named “Searaser”. The data for analyzing is provided by collecting the experimental data from another study and the exerted data from numerical simulation of searaser. The simulation is done with Flow-3D software which has high capability in analyzing the 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 the wind speed and output power are related with a 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 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 LSTM method.

Keywords

Searaser; Flow-3D; Prediction; Long short term memory; deep neural network; Root mean error.

Subject

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

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