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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
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    References listed on IDEAS

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    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. 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.
    4. 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.
    5. 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.
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