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

Abid Hossain Khan, Salauddin Omar, Nadia Mushtary, Richa Verma, Dinesh Kumar and Syed Alam ()
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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

A chapter in Handbook of Smart Energy Systems, 2023, pp 2837-2859 from Springer

Abstract: 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.

Keywords: Artificial intelligence; Hybrid energy system; Digital twin; Machine learning; Surrogate modeling (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-030-97940-9_147

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