mlsanityData to 14 Oct 2026

AI in Energy Digital Twining: A Reinforcement Learning-based Adaptive Digital Twin Model for Green Cities

arXiv 2401.16449 ↗First posted 28 Jan 2024cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 23 references that are in the map. First posted on arXiv 28 Jan 2024.

Builds on

2 of 23 references.

References · 2

  1. 2022Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Surveycited by 8
  2. 2023Digital Twin-Native AI-Driven Service Architecture for Industrial Networkscited by 1

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