AI in Energy Digital Twining: A Reinforcement Learning-based Adaptive Digital Twin Model for Green Cities
This work addresses energy efficiency and resource optimization for green cities, but it appears incremental as it builds on existing digital twin and reinforcement learning methods.
The paper tackled the problem of dynamic smart city environments by proposing a reinforcement learning-based adaptive digital twin model, achieving 55% higher querying performance, 20% lower overhead, and 25% lower energy consumption.
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.