Publications:
2025
Ma, Jun; Panic, Dimitrije; Yus, Roberto; Bouloukakis, Georgios
A customizable benchmarking tool for evaluating personalized thermal comfort provisioning in smart spaces using Digital Twins Journal Article
In: Pervasive and Mobile Computing, vol. 112, pp. 102076, 2025, ISSN: 1574-1192.
Abstract | Links | BibTeX | Tags: benchmarking, digital twin, Energy consumption, Individual thermal comfort
@article{MA2025102076,
title = {A customizable benchmarking tool for evaluating personalized thermal comfort provisioning in smart spaces using Digital Twins},
author = {Jun Ma and Dimitrije Panic and Roberto Yus and Georgios Bouloukakis},
url = {https://www.sciencedirect.com/science/article/pii/S1574119225000653},
doi = {https://doi.org/10.1016/j.pmcj.2025.102076},
issn = {1574-1192},
year = {2025},
date = {2025-01-01},
journal = {Pervasive and Mobile Computing},
volume = {112},
pages = {102076},
abstract = {Providing proper thermal comfort to individual occupants is crucial to improve well-being and work efficiency. However, Heating, Ventilation, and Air Conditioning (HVAC) systems are responsible for a large portion of energy consumption and CO2 emissions in buildings. To combat the current energy crisis and climate change, innovative ways have been proposed to leverage pervasive and mobile computing systems equipped with sensors and smart devices for occupant thermal comfort satisfaction and efficient HVAC management. However, evaluating these thermal comfort provision solutions presents considerable difficulties. Conducting experiments in the real world poses challenges such as privacy concerns and the high costs of installing and maintaining sensor infrastructure. On the other hand, experiments with simulations need to accurately model real-world conditions and ensure the reliability of the simulated data. To address these challenges, we present Co-zyBench, an innovative benchmarking tool that leverages Digital Twin (DT) technology to assess personalized thermal comfort provision systems. Our benchmark employs a simulation-based DT for the building and its HVAC system, another DT for simulating the dynamic behavior of its occupants, and a co-simulation middleware to achieve a seamless connection of the DTs. Our benchmark includes mechanisms to generate DTs based on data such as architectural models of buildings, sensor readings, and occupant thermal sensation data. It also includes reference DTs based on standard buildings, HVAC configurations, and various occupant thermal profiles. As a result of the evaluation, the benchmark generates a report based on expected energy consumption, carbon emission, thermal comfort, and occupant equity metrics. We present the evaluation results of state-of-the-art thermal comfort provisioning systems within a DT based on a real building and several reference DTs.},
keywords = {benchmarking, digital twin, Energy consumption, Individual thermal comfort},
pubstate = {published},
tppubtype = {article}
}
Providing proper thermal comfort to individual occupants is crucial to improve well-being and work efficiency. However, Heating, Ventilation, and Air Conditioning (HVAC) systems are responsible for a large portion of energy consumption and CO2 emissions in buildings. To combat the current energy crisis and climate change, innovative ways have been proposed to leverage pervasive and mobile computing systems equipped with sensors and smart devices for occupant thermal comfort satisfaction and efficient HVAC management. However, evaluating these thermal comfort provision solutions presents considerable difficulties. Conducting experiments in the real world poses challenges such as privacy concerns and the high costs of installing and maintaining sensor infrastructure. On the other hand, experiments with simulations need to accurately model real-world conditions and ensure the reliability of the simulated data. To address these challenges, we present Co-zyBench, an innovative benchmarking tool that leverages Digital Twin (DT) technology to assess personalized thermal comfort provision systems. Our benchmark employs a simulation-based DT for the building and its HVAC system, another DT for simulating the dynamic behavior of its occupants, and a co-simulation middleware to achieve a seamless connection of the DTs. Our benchmark includes mechanisms to generate DTs based on data such as architectural models of buildings, sensor readings, and occupant thermal sensation data. It also includes reference DTs based on standard buildings, HVAC configurations, and various occupant thermal profiles. As a result of the evaluation, the benchmark generates a report based on expected energy consumption, carbon emission, thermal comfort, and occupant equity metrics. We present the evaluation results of state-of-the-art thermal comfort provisioning systems within a DT based on a real building and several reference DTs.