IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i5p1197-d1350099.html
   My bibliography  Save this article

Data-Driven Management Systems for Wave-Powered Renewable Energy Communities

Author

Listed:
  • Saqib Iqbal

    (Department of Electronic Engineering, Queen Mary University of London, London E1 4NS, UK)

  • Kamyar Mehran

    (Department of Electronic Engineering, Queen Mary University of London, London E1 4NS, UK)

Abstract

This research focus on the essential task of precise prediction for power generation and energy consumption of wave energy converters (WECs) within the framework of contemporary wave-powered renewable energy sources (RESs). Utilizing real-time wave data, we introduce a deep learning methodology featuring a long short-term memory (LSTM) model. Additionally, we propose an online management system for RESs aimed at optimizing interactions among WECs, energy storage systems (ESSs), super capacitor (SC), and load. This approach leads to significant enhancements in mean square error (MSE) for critical variables such as wave height, time period, and direction, improving predictive accuracy by factors of 8.37, 9.30, and 16.14, respectively. Through diverse scenario-based experimental evaluations, our solution exhibits competitive performance when compared to benchmark strategies and ideal solutions. These findings underscore the potential of the LSTM-NN model to advance the efficiency and reliability of wave energy forecasting and management systems. As wave energy technology evolves, this study contributes to ongoing efforts to enhance practical applicability, especially in coastal regions with substantial wave energy potential.

Suggested Citation

  • Saqib Iqbal & Kamyar Mehran, 2024. "Data-Driven Management Systems for Wave-Powered Renewable Energy Communities," Energies, MDPI, vol. 17(5), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1197-:d:1350099
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/5/1197/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/5/1197/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fusco, Francesco & Nolan, Gary & Ringwood, John V., 2010. "Variability reduction through optimal combination of wind/wave resources – An Irish case study," Energy, Elsevier, vol. 35(1), pages 314-325.
    2. Sui, Quan & Zhang, Rui & Wu, Chuantao & Wei, Fanrong & Lin, Xiangning & Li, Zhengtian, 2020. "Stochastic scheduling of an electric vessel-based energy management system in pelagic clustering islands," Applied Energy, Elsevier, vol. 259(C).
    3. Ali, Mumtaz & Prasad, Ramendra, 2019. "Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 281-295.
    4. Nor Liza Tumeran & Siti Hajar Yusoff & Teddy Surya Gunawan & Mohd Shahrin Abu Hanifah & Suriza Ahmad Zabidi & Bernardi Pranggono & Muhammad Sharir Fathullah Mohd Yunus & Siti Nadiah Mohd Sapihie & Asm, 2023. "Model Predictive Control Based Energy Management System Literature Assessment for RES Integration," Energies, MDPI, vol. 16(8), pages 1-27, April.
    5. Trinadh Pamulapati & Muhammed Cavus & Ishioma Odigwe & Adib Allahham & Sara Walker & Damian Giaouris, 2022. "A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective," Energies, MDPI, vol. 16(1), pages 1-34, December.
    Full references (including those not matched with items on IDEAS)

    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. Ali, Mumtaz & Prasad, Ramendra & Xiang, Yong & Deo, Ravinesh C., 2020. "Near real-time significant wave height forecasting with hybridized multiple linear regression algorithms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    2. Thomas Kelly & Thomas Dooley & John Campbell & John V. Ringwood, 2013. "Comparison of the Experimental and Numerical Results of Modelling a 32-Oscillating Water Column (OWC), V-Shaped Floating Wave Energy Converter," Energies, MDPI, vol. 6(8), pages 1-33, August.
    3. Elisabetta Tedeschi & Jonas Sjolte & Marta Molinas & Maider Santos, 2013. "Stochastic Rating of Storage Systems in Isolated Networks with Increasing Wave Energy Penetration," Energies, MDPI, vol. 6(5), pages 1-20, May.
    4. Gao, Qiang & Khan, Salman Saeed & Sergiienko, Nataliia & Ertugrul, Nesimi & Hemer, Mark & Negnevitsky, Michael & Ding, Boyin, 2022. "Assessment of wind and wave power characteristic and potential for hybrid exploration in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    5. Pasquale Contestabile & Enrico Di Lauro & Paolo Galli & Cesare Corselli & Diego Vicinanza, 2017. "Offshore Wind and Wave Energy Assessment around Malè and Magoodhoo Island (Maldives)," Sustainability, MDPI, vol. 9(4), pages 1-24, April.
    6. Wu, Chuantao & Zhou, Dezhi & Lin, Xiangning & Sui, Quan & Wei, Fanrong & Li, Zhengtian, 2022. "A novel energy cooperation framework for multi-island microgrids based on marine mobile energy storage systems," Energy, Elsevier, vol. 252(C).
    7. Seongwoo Lee & Joonho Seon & Byungsun Hwang & Soohyun Kim & Youngghyu Sun & Jinyoung Kim, 2024. "Recent Trends and Issues of Energy Management Systems Using Machine Learning," Energies, MDPI, vol. 17(3), pages 1-24, January.
    8. Gao, Ruobin & Li, Ruilin & Hu, Minghui & Suganthan, Ponnuthurai Nagaratnam & Yuen, Kum Fai, 2023. "Dynamic ensemble deep echo state network for significant wave height forecasting," Applied Energy, Elsevier, vol. 329(C).
    9. Ali, Mumtaz & Prasad, Ramendra & Xiang, Yong & Jamei, Mehdi & Yaseen, Zaher Mundher, 2023. "Ensemble robust local mean decomposition integrated with random forest for short-term significant wave height forecasting," Renewable Energy, Elsevier, vol. 205(C), pages 731-746.
    10. Gallagher, Sarah & Tiron, Roxana & Whelan, Eoin & Gleeson, Emily & Dias, Frédéric & McGrath, Ray, 2016. "The nearshore wind and wave energy potential of Ireland: A high resolution assessment of availability and accessibility," Renewable Energy, Elsevier, vol. 88(C), pages 494-516.
    11. Wang, Huaizhi & Xue, Wenli & Liu, Yitao & Peng, Jianchun & Jiang, Hui, 2020. "Probabilistic wind power forecasting based on spiking neural network," Energy, Elsevier, vol. 196(C).
    12. Zhou, Dezhi & Wu, Chuantao & Sui, Quan & Lin, Xiangning & Li, Zhengtian, 2022. "A novel all-electric-ship-integrated energy cooperation coalition for multi-island microgrids," Applied Energy, Elsevier, vol. 320(C).
    13. Zhao, Ning & You, Fengqi, 2020. "Can renewable generation, energy storage and energy efficient technologies enable carbon neutral energy transition?," Applied Energy, Elsevier, vol. 279(C).
    14. Foley, A.M. & Leahy, P.G. & Li, K. & McKeogh, E.J. & Morrison, A.P., 2015. "A long-term analysis of pumped hydro storage to firm wind power," Applied Energy, Elsevier, vol. 137(C), pages 638-648.
    15. Yuchen Zhang & Zhenquan Zhang & Jun Wang & Jian Qin & Shuting Huang & Gang Xue & Yanjun Liu, 2024. "Research on Excitation Estimation for Ocean Wave Energy Generators Based on Extended Kalman Filtering," Energies, MDPI, vol. 17(3), pages 1-17, February.
    16. Elham M. Al-Ali & Yassine Hajji & Yahia Said & Manel Hleili & Amal M. Alanzi & Ali H. Laatar & Mohamed Atri, 2023. "Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model," Mathematics, MDPI, vol. 11(3), pages 1-19, January.
    17. Clark, Caitlyn E. & Miller, Annalise & DuPont, Bryony, 2019. "An analytical cost model for co-located floating wind-wave energy arrays," Renewable Energy, Elsevier, vol. 132(C), pages 885-897.
    18. Iglesias, G. & Carballo, R., 2011. "Wave resource in El Hierro—an island towards energy self-sufficiency," Renewable Energy, Elsevier, vol. 36(2), pages 689-698.
    19. Mohamed, M.H. & Janiga, G. & Pap, E. & Thévenin, D., 2011. "Multi-objective optimization of the airfoil shape of Wells turbine used for wave energy conversion," Energy, Elsevier, vol. 36(1), pages 438-446.
    20. Pennock, Shona & Coles, Daniel & Angeloudis, Athanasios & Bhattacharya, Saptarshi & Jeffrey, Henry, 2022. "Temporal complementarity of marine renewables with wind and solar generation: Implications for GB system benefits," Applied Energy, Elsevier, vol. 319(C).

    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:jeners:v:17:y:2024:i:5:p:1197-:d:1350099. 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.