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Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions

Author

Listed:
  • Latifa A. Yousef

    (Renewable and Sustainable Energy Research Center, Technology Innovation Institute, Masdar City, Abu Dhabi P.O. Box 9639, United Arab Emirates)

  • Hibba Yousef

    (Biotechnology Research Center, Technology Innovation Institute, Masdar City, Abu Dhabi P.O. Box 9639, United Arab Emirates)

  • Lisandra Rocha-Meneses

    (Renewable and Sustainable Energy Research Center, Technology Innovation Institute, Masdar City, Abu Dhabi P.O. Box 9639, United Arab Emirates)

Abstract

This review paper provides a summary of methods in which artificial intelligence (AI) techniques have been applied in the management of variable renewable energy (VRE) systems, and an outlook to future directions of research in the field. The VRE types included are namely solar, wind and marine varieties. AI techniques, and particularly machine learning (ML), have gained traction as a result of data explosion, and offer a method for integration of multimodal data for more accurate forecasting in energy applications. The VRE management aspects in which AI techniques have been applied include optimized power generation forecasting and integration of VRE into power grids, including the aspects of demand forecasting, energy storage, system optimization, performance monitoring, and cost management. Future directions of research in the applications of AI for VRE management are proposed and discussed, including the issue of data availability, types and quality, in addition to explainable artificial intelligence (XAI), quantum artificial intelligence (QAI), coupling AI with the emerging digital twins technology, and natural language processing.

Suggested Citation

  • Latifa A. Yousef & Hibba Yousef & Lisandra Rocha-Meneses, 2023. "Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions," Energies, MDPI, vol. 16(24), pages 1-27, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:8057-:d:1299919
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    References listed on IDEAS

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    Cited by:

    1. Kingsley Ukoba & Kehinde O. Olatunji & Eyitayo Adeoye & Tien-Chien Jen & Daniel M. Madyira, 2024. "Optimizing renewable energy systems through artificial intelligence: Review and future prospects," Energy & Environment, , vol. 35(7), pages 3833-3879, November.

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