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

Author

Listed:
  • 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
    Faculty of Informatics, Selye University, 94501 Komarom, Slovakia)

  • M. Soltani

    (Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, Iran
    Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
    Waterloo Institute for Sustainable Energy (WISE), University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Amir Mosavi

    (German Research Center for Artificial Intelligence, 26129 Oldenburg, Germany
    Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, 81243 Bratislava, Slovakia)

  • Levente Kovacs

    (Biomatics and Applied Artificial Intelligence Institution, John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
    Physiological Controls Research Center, Obuda University, 1034 Budapest, Hungary)

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.

Suggested Citation

  • Mahsa Dehghan Manshadi & Milad Mousavi & M. Soltani & Amir Mosavi & Levente Kovacs, 2022. "Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System," Energies, MDPI, vol. 15(24), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9484-:d:1003237
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    References listed on IDEAS

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    1. Ashkan Safari & Hamed Kheirandish Gharehbagh & Morteza Nazari Heris, 2023. "DeepVELOX: INVELOX Wind Turbine Intelligent Power Forecasting Using Hybrid GWO–GBR Algorithm," Energies, MDPI, vol. 16(19), pages 1-22, September.

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