IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i8p871-d536566.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
    School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK)

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.

Suggested Citation

  • Seyed Milad Mousavi & Majid Ghasemi & Mahsa Dehghan Manshadi & Amir Mosavi, 2021. "Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory," Mathematics, MDPI, vol. 9(8), pages 1-16, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:8:p:871-:d:536566
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/8/871/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/8/871/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Giorgi, Giuseppe & Gomes, Rui P.F. & Henriques, João C.C. & Gato, Luís M.C. & Bracco, Giovanni & Mattiazzo, Giuliana, 2020. "Detecting parametric resonance in a floating oscillating water column device for wave energy conversion: Numerical simulations and validation with physical model tests," Applied Energy, Elsevier, vol. 276(C).
    2. Tunde Aderinto & Hua Li, 2018. "Ocean Wave Energy Converters: Status and Challenges," Energies, MDPI, vol. 11(5), pages 1-26, May.
    3. Sinsel, Simon R. & Riemke, Rhea L. & Hoffmann, Volker H., 2020. "Challenges and solution technologies for the integration of variable renewable energy sources—a review," Renewable Energy, Elsevier, vol. 145(C), pages 2271-2285.
    4. Li, L. & Gao, Y. & Ning, D.Z. & Yuan, Z.M., 2021. "Development of a constraint non-causal wave energy control algorithm based on artificial intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    5. Reikard, Gordon & Robertson, Bryson & Bidlot, Jean-Raymond, 2015. "Combining wave energy with wind and solar: Short-term forecasting," Renewable Energy, Elsevier, vol. 81(C), pages 442-456.
    6. Antonio Manuel Gómez-Orellana & Juan Carlos Fernández & Manuel Dorado-Moreno & Pedro Antonio Gutiérrez & César Hervás-Martínez, 2021. "Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux," Energies, MDPI, vol. 14(2), pages 1-33, January.
    7. Gomes, R.P.F. & Henriques, J.C.C. & Gato, L.M.C. & Falcão, A.F.O., 2012. "Hydrodynamic optimization of an axisymmetric floating oscillating water column for wave energy conversion," Renewable Energy, Elsevier, vol. 44(C), pages 328-339.
    8. Chenhua Ni & Xiandong Ma, 2018. "Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs," Energies, MDPI, vol. 11(8), pages 1-18, August.
    9. Erick López & Carlos Valle & Héctor Allende & Esteban Gil & Henrik Madsen, 2018. "Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory," Energies, MDPI, vol. 11(3), pages 1-22, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Hai & Shi, Xiaodan & Kong, Weihua & Kong, Lingji & Hu, Yongli & Wu, Xiaoping & Pan, Hongye & Zhang, Zutao & Pan, Yajia & Yan, Jinyue, 2025. "Advanced wave energy conversion technologies for sustainable and smart sea: A comprehensive review," Renewable Energy, Elsevier, vol. 238(C).
    2. Fatemehsadat Mirshafiee & Emad Shahbazi & Mohadeseh Safi & Rituraj Rituraj, 2023. "Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven Methods: A Sustainable Smart Grid Case Study," Energies, MDPI, vol. 16(1), pages 1-20, January.
    3. Jialin Liu & Chen Gong & Suhua Chen & Nanrun Zhou, 2023. "Multi-Step-Ahead Wind Speed Forecast Method Based on Outlier Correction, Optimized Decomposition, and DLinear Model," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
    4. Chengcheng Gu & Hua Li, 2022. "Review on Deep Learning Research and Applications in Wind and Wave Energy," Energies, MDPI, vol. 15(4), pages 1-19, February.
    5. Panyu Tang & Mahdi Aghaabbasi & Mujahid Ali & Amin Jan & Abdeliazim Mustafa Mohamed & Abdullah Mohamed, 2022. "How Sustainable Is People’s Travel to Reach Public Transit Stations to Go to Work? A Machine Learning Approach to Reveal Complex Relationships," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    6. Stavropoulou, Charitini & Katsidoniotaki, Eirini & Faedo, Nicolás & Göteman, Malin, 2025. "Multi-fidelity surrogate modeling of nonlinear dynamic responses in wave energy farms," Applied Energy, Elsevier, vol. 380(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gomes, Rui P.F. & Gato, Luís M.C. & Henriques, João C.C. & Portillo, Juan C.C. & Howey, Ben D. & Collins, Keri M. & Hann, Martyn R. & Greaves, Deborah M., 2020. "Compact floating wave energy converters arrays: Mooring loads and survivability through scale physical modelling," Applied Energy, Elsevier, vol. 280(C).
    2. Erfan Amini & Rojin Asadi & Danial Golbaz & Mahdieh Nasiri & Seyed Taghi Omid Naeeni & Meysam Majidi Nezhad & Giuseppe Piras & Mehdi Neshat, 2021. "Comparative Study of Oscillating Surge Wave Energy Converter Performance: A Case Study for Southern Coasts of the Caspian Sea," Sustainability, MDPI, vol. 13(19), pages 1-21, October.
    3. Hall, Carrie & Sheng, Wanan & Wu, Yueqi & Aggidis, George, 2024. "The impact of model predictive control structures and constraints on a wave energy converter with hydraulic power take off system," Renewable Energy, Elsevier, vol. 224(C).
    4. Gradowski, M. & Gomes, R.P.F. & Alves, M., 2020. "Hydrodynamic optimisation of an axisymmetric floating Oscillating Water Column type wave energy converter with an enlarged inner tube," Renewable Energy, Elsevier, vol. 162(C), pages 1519-1532.
    5. Oikonomou, Charikleia L.G. & Gomes, Rui P.F. & Gato, Luís M.C., 2021. "Unveiling the potential of using a spar-buoy oscillating-water-column wave energy converter for low-power stand-alone applications," Applied Energy, Elsevier, vol. 292(C).
    6. Zhang, Yongxing & Zhao, Yongjie & Sun, Wei & Li, Jiaxuan, 2021. "Ocean wave energy converters: Technical principle, device realization, and performance evaluation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    7. Mehdi Neshat & Nataliia Y. Sergiienko & Erfan Amini & Meysam Majidi Nezhad & Davide Astiaso Garcia & Bradley Alexander & Markus Wagner, 2020. "A New Bi-Level Optimisation Framework for Optimising a Multi-Mode Wave Energy Converter Design: A Case Study for the Marettimo Island, Mediterranean Sea," Energies, MDPI, vol. 13(20), pages 1-23, October.
    8. Tunde Aderinto & Hua Li, 2020. "Effect of Spatial and Temporal Resolution Data on Design and Power Capture of a Heaving Point Absorber," Sustainability, MDPI, vol. 12(22), pages 1-17, November.
    9. Bruno Cárdenas & Lawrie Swinfen-Styles & James Rouse & Seamus D. Garvey, 2021. "Short-, Medium-, and Long-Duration Energy Storage in a 100% Renewable Electricity Grid: A UK Case Study," Energies, MDPI, vol. 14(24), pages 1-28, December.
    10. Licheri, Fabio & Ghisu, Tiziano & Cambuli, Francesco & Puddu, Pierpaolo, 2022. "Detailed investigation of the local flow-field in a Wells turbine coupled to an OWC simulator," Renewable Energy, Elsevier, vol. 197(C), pages 583-593.
    11. Ivan S. Maksymov, 2023. "Analogue and Physical Reservoir Computing Using Water Waves: Applications in Power Engineering and Beyond," Energies, MDPI, vol. 16(14), pages 1-26, July.
    12. Abadie, Luis Mª & Chamorro, José M., 2023. "Investment in wind-based hydrogen production under economic and physical uncertainties," Applied Energy, Elsevier, vol. 337(C).
    13. Reguieg, Zakaria & Bouyakoub, Ismail & Mehedi, Fayçal, 2025. "Integrated optimization of power quality and energy management in a photovoltaic-battery microgrid," Renewable Energy, Elsevier, vol. 241(C).
    14. Pasta, Edoardo & Faedo, Nicolás & Mattiazzo, Giuliana & Ringwood, John V., 2023. "Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    15. José Carlos Ugaz Peña & Christian Luis Medina Rodríguez & Gustavo O. Guarniz Avalos, 2023. "Study of a New Wave Energy Converter with Perturb and Observe Maximum Power Point Tracking Method," Sustainability, MDPI, vol. 15(13), pages 1-18, July.
    16. Hamilton, James & Negnevitsky, Michael & Wang, Xiaolin, 2022. "The role of modified diesel generation within isolated power systems," Energy, Elsevier, vol. 240(C).
    17. Costa, Marcelo Azevedo & Ruiz-Cárdenas, Ramiro & Mineti, Leandro Brioschi & Prates, Marcos Oliveira, 2021. "Dynamic time scan forecasting for multi-step wind speed prediction," Renewable Energy, Elsevier, vol. 177(C), pages 584-595.
    18. Adriano Silva Bastos & Tâmara Rita Costa de Souza & Dieimys Santos Ribeiro & Mirian de Lourdes Noronha Motta Melo & Carlos Barreira Martinez, 2023. "Wave Energy Generation in Brazil: A Georeferenced Oscillating Water Column Inventory," Energies, MDPI, vol. 16(8), pages 1-24, April.
    19. Andersen, Allan Dahl & Markard, Jochen, 2020. "Multi-technology interaction in socio-technical transitions: How recent dynamics in HVDC technology can inform transition theories," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    20. Jianzhong Zhou & Han Liu & Yanhe Xu & Wei Jiang, 2018. "A Hybrid Framework for Short Term Multi-Step Wind Speed Forecasting Based on Variational Model Decomposition and Convolutional Neural Network," Energies, MDPI, vol. 11(9), pages 1-18, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:8:p:871-:d:536566. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.