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

TL;DR

This paper reviews how AI-driven surrogate modeling, especially digital twins, can optimize hybrid and sustainable energy systems by reducing computation time and improving accuracy.

Contribution

It provides a comprehensive overview of AI-based surrogate modeling techniques, focusing on digital twins in energy systems, highlighting recent developments and applications.

Findings

Surrogate models significantly reduce computation time in energy system analysis.

Digital twins enable real-time monitoring and optimization of energy systems.

AI enhances the accuracy and applicability of surrogate models in sustainable energy.

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.