IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-97940-9_147.html
   My bibliography  Save this book chapter

Digital Twin and Artificial Intelligence Incorporated with Surrogate Modeling for Hybrid and Sustainable Energy Systems

In: Handbook of Smart Energy Systems

Author

Listed:
  • Abid Hossain Khan

    (Bangladesh University of Engineering and Technology)

  • Salauddin Omar

    (Ahsanullah University of Science and Technology)

  • Nadia Mushtary

    (Bangladesh University of Engineering and Technology)

  • Richa Verma

    (Indian Institute of Technology Delhi)

  • Dinesh Kumar

    (University of Bristol)

  • Syed Alam

    (Missouri University of Science and Technology)

Abstract

Surrogate modeling has brought about a revolution in computation in the branches of science and engineering. Backed by Artificial Intelligence, a surrogate model can present highly accurate results with a significant reduction in computation time than computer simulation of actual models. Surrogate modeling techniques have found their use in numerous branches of science and engineering, energy system modeling being one of them. Since the idea of hybrid and sustainable energy systems is spreading rapidly in the modern world for the paradigm of the smart energy shift, researchers are exploring the future application of artificial intelligence-based surrogate modeling in analyzing and optimizing hybrid energy systems. One of the promising technologies for assessing applicability for the energy system is the digital twin, which can leverage surrogate modeling. This work presents a comprehensive framework/review on Artificial Intelligence-driven surrogate modeling and its applications with a focus on the digital twin framework and energy systems. The role of machine learning and artificial intelligence in constructing an effective surrogate model is explained. After that, different surrogate models developed for different sustainable energy sources are presented. Finally, digital twin surrogate models and associated uncertainties are described.

Suggested Citation

  • Abid Hossain Khan & Salauddin Omar & Nadia Mushtary & Richa Verma & Dinesh Kumar & Syed Alam, 2023. "Digital Twin and Artificial Intelligence Incorporated with Surrogate Modeling for Hybrid and Sustainable Energy Systems," Springer Books, in: Michel Fathi & Enrico Zio & Panos M. Pardalos (ed.), Handbook of Smart Energy Systems, pages 2837-2859, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-97940-9_147
    DOI: 10.1007/978-3-030-97940-9_147
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. Ghafariasl, Parviz & Mahmoudan, Alireza & Mohammadi, Mahmoud & Nazarparvar, Aria & Hoseinzadeh, Siamak & Fathali, Mani & Chang, Shing & Zeinalnezhad, Masoomeh & Garcia, Davide Astiaso, 2024. "Neural network-based surrogate modeling and optimization of a multigeneration system," Applied Energy, Elsevier, vol. 364(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:spr:sprchp:978-3-030-97940-9_147. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.