Verda Lal Cakir
AI in Energy Digital Twining: A Reinforcement Learning-Based Adaptive Digital Twin Model for Green Cities
Cakir, Verda Lal; Duran, Kubra; Thomson, Craig; Broadbent, Matthew; Canberk, Berk
Authors
Kubra Duran
Dr Craig Thomson C.Thomson3@napier.ac.uk
Lecturer
Matthew Broadbent
Prof Berk Canberk B.Canberk@napier.ac.uk
Professor
Abstract
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.
Citation
Cakir, V. L., Duran, K., Thomson, C., Broadbent, M., & Canberk, B. (2024, June). AI in Energy Digital Twining: A Reinforcement Learning-Based Adaptive Digital Twin Model for Green Cities. Presented at ICC 2024 - IEEE International Conference on Communications, Denver, CO, USA
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | ICC 2024 - IEEE International Conference on Communications |
Start Date | Jun 9, 2024 |
End Date | Jun 13, 2024 |
Acceptance Date | Apr 3, 2023 |
Online Publication Date | Aug 20, 2024 |
Publication Date | 2024 |
Deposit Date | Oct 10, 2024 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Pages | 4767-4772 |
Series ISSN | 1938-1883 |
Book Title | ICC 2024 - IEEE International Conference on Communications |
ISBN | 9781728190556 |
DOI | https://doi.org/10.1109/icc51166.2024.10622773 |
Keywords | Index Terms-smart cities; green cities; digital twin; DT mod- eling; adaptive twining |
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