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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.16449 |
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| _version_ | 1866914928165650432 |
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| author | Cakir, Lal Verda Duran, Kubra Thomson, Craig Broadbent, Matthew Canberk, Berk |
| author_facet | Cakir, Lal Verda Duran, Kubra Thomson, Craig Broadbent, Matthew Canberk, Berk |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16449 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI in Energy Digital Twining: A Reinforcement Learning-based Adaptive Digital Twin Model for Green Cities Cakir, Lal Verda Duran, Kubra Thomson, Craig Broadbent, Matthew Canberk, Berk Machine Learning 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. |
| title | AI in Energy Digital Twining: A Reinforcement Learning-based Adaptive Digital Twin Model for Green Cities |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.16449 |