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

2024-01-28 · 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.

📄 PDF Abstract BibTeX arXiv:2401.16449

Code (0)

등록된 구현이 없습니다.

Adaptive Federated Learning and Digital Twin for Industrial Internet of Things

2020-10-25 · Wen Sun, Shiyu Lei, Lu Wang, Zhiqiang Liu 외

Industrial Internet of Things (IoT) enables distributed intelligent services varying with the dynamic and realtime industrial devices to achieve Industry 4.0 benefits. In this paper, we consider a new architecture of dig…

Clustering Deep Reinforcement Learning Federated Learning Reinforcement Learning (RL)

Adaptive Sensor Steering Strategy Using Deep Reinforcement Learning for Dynamic Data Acquisition in Digital Twins

2025-04-14 · Collins O. Ogbodo, Timothy J. Rogers, Mattia Dal Borgo, David J. Wagg

This paper introduces a sensor steering methodology based on deep reinforcement learning to enhance the predictive accuracy and decision support capabilities of digital twins by optimising the data acquisition process. T…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning

A Resolution-Adaptive 8 mm$^\text{2}$ 9.98 Gb/s 39.7 pJ/b 32-Antenna All-Digital Spatial Equalizer for mmWave Massive MU-MIMO in 65nm CMOS

2021-07-23 · Oscar Castañeda, Zachariah Boynton, Seyed Hadi Mirfarshbafan, Shimin Huang 외

All-digital millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO) receivers enable extreme data rates but require high power consumption. In order to reduce power consumption, this paper pr…

All

Digital Twin-Assisted Adaptive Multi-Agent DRL for Intelligent Spectrum and Resource Management in Open-RAN UAV-Enabled 6G Networks

2026-05-31 · Marwan Dhuheir, Thang X. Vu, Symeon Chatzinotas arxiv

The evolution toward 6G wireless networks envisions a seamlessly intelligent, Open-RAN-enabled architecture where unmanned aerial vehicles (UAVs) play a pivotal role in extending coverage, enhancing resilience, and ensur…

Reinforcement Learning

ANSR-DT: An Adaptive Neuro-Symbolic Learning and Reasoning Framework for Digital Twins

2025-01-15 · Safayat Bin Hakim, Muhammad Adil, Alvaro Velasquez, Houbing Herbert Song

In this paper, we propose an Adaptive Neuro-Symbolic Learning and Reasoning Framework for digital twin technology called ``ANSR-DT." Digital twins in industrial environments often struggle with interpretability, real-tim…

Decision Making Event Detection reinforcement-learning Reinforcement Learning