Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System

Mahsa Dehghan Manshadi, Milad Mousavi, M. Soltani (), Amir Mosavi () and Levente Kovacs
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Mahsa Dehghan Manshadi: Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, Iran
Milad Mousavi: Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, Iran
M. Soltani: Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, Iran
Amir Mosavi: German Research Center for Artificial Intelligence, 26129 Oldenburg, Germany
Levente Kovacs: Biomatics and Applied Artificial Intelligence Institution, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary

Energies, 2022, vol. 15, issue 24, 1-16

Abstract: The combination of an offshore wind turbine and a wave energy converter on an integrated platform is an economical solution for the electrical power demand in coastal countries. Due to the expensive installation cost, a prediction should be used to investigate whether the location is suitable for these sites. For this purpose, this research presents the feasibility of installing a combined hybrid site in the desired coastal location by predicting the net produced power due to the environmental parameters. For combining these two systems, an optimized array includes ten turbines and ten wave energy converters. The mathematical equations of the net force on the two introduced systems and the produced power of the wind turbines are proposed. The turbines’ maximum forces are 4 kN, and for the wave energy converters are 6 kN, respectively. Furthermore, the comparison is conducted in order to find the optimum system. The comparison shows that the most effective system of desired environmental condition is introduced. A number of machine learning and deep learning methods are used to predict key parameters after collecting the dataset. Moreover, a comparative analysis is conducted to find a suitable model. The models’ performance has been well studied through generating the confusion matrix and the receiver operating characteristic (ROC) curve of the hybrid site. The deep learning model outperformed other models, with an approximate accuracy of 0.96.

Keywords: renewable energy; artificial intelligence; machine learning; comparative analysis; wind turbine; energy; deep learning; big data; wave energy; wave power; offshore (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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