  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.
