AI in Energy Digital Twining: A Reinforcement Learning-based Adaptive Digital Twin Model for Green Cities. (arXiv:2401.16449v1 [cs.LG])

Digital Twins (DT) have become crucial to achieve sustainable and effective
smart urban solutions. However, current DT modelling techniques cannot support
the dynamicity of these smart city environments. This is caused by the lack of
right-time data capturing in traditional approaches, resulting in inaccurate
modelling and high resource and energy consumption challenges. To fill this
gap, we explore spatiotemporal graphs and propose the Reinforcement
Learning-based Adaptive Twining (RL-AT) mechanism with Deep Q Networks (DQN).
By doing so, our study contributes …

arxiv become cities city cs.lg current data digital digital twin digital twins energy environments green modelling reinforcement reinforcement learning smart smart city solutions support sustainable time data twins urban