AI in Energy Digital Twining: A Reinforcement Learning-based Adaptive
  Digital Twin Model for Green Cities
Lal Verda Cakir Kubra Duran Craig Thomson Matthew Broadbent Berk Canberk
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 to advancing Green Cities and showcases
tangible benefits in accuracy, synchronisation, resource optimization, and
energy efficiency. As a result, we note the spatiotemporal graphs are able to
offer a consistent accuracy and 55% higher querying performance when
implemented using graph databases. In addition, our model demonstrates
right-time data capturing with 20% lower overhead and 25% lower energy
consumption.