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.
2024
Ma, Jun; Panic, Dimitrije; Yus, Roberto; Bouloukakis, Georgios
Co-zyBench: Using Co-Simulation and Digital Twins to Benchmark Thermal Comfort Provision in Smart Buildings Proceedings Article
In: The 22nd International Conference on Pervasive Computing and Communications (PerCom), 2024.
Abstract | Links | BibTeX | Tags: benchmarking, digital twin, energy efficiency, personalized thermal comfort
@inproceedings{ma2024co,
title = {Co-zyBench: Using Co-Simulation and Digital Twins to Benchmark Thermal Comfort Provision in Smart Buildings},
author = {Jun Ma and Dimitrije Panic and Roberto Yus and Georgios Bouloukakis},
url = {https://inria.hal.science/hal-04514006/, PDF},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {The 22nd International Conference on Pervasive Computing and Communications (PerCom)},
abstract = {Heating, Ventilation, and Air Conditioning (HVAC) systems account for 40% to 50% of energy usage in commercial buildings. Thus, innovative ways to control and manage HVAC systems while preserving occupants' comfort are required. State-of-the-art solutions employ pervasive systems with sensors or smart devices to gauge individual thermal sensations, yet assessing these methods is challenging. Real-world experiments are expensive, limited in access, and often overlook occupant and regional diversity. To address this, we introduce Co-zyBench, a benchmark tool using a Digital Twin (DT) approach for evaluating personalized thermal comfort systems. It employs a co-simulation middleware interfacing between a DT of the smart building and its HVAC system and another DT representing occupants' dynamic thermal preferences in various spaces. The DTs that support Co-zyBench are generated based on information, including data captured by sensors, of the space in which the thermal comfort system has to be evaluated. Co-zyBench incorporates metrics for energy consumption, thermal comfort, and occupant equality. It also features reference DTs based on standard buildings, HVAC systems, and occupants with diverse thermal preferences.
},
keywords = {benchmarking, digital twin, energy efficiency, personalized thermal comfort},
pubstate = {published},
tppubtype = {inproceedings}
}
Heating, Ventilation, and Air Conditioning (HVAC) systems account for 40% to 50% of energy usage in commercial buildings. Thus, innovative ways to control and manage HVAC systems while preserving occupants' comfort are required. State-of-the-art solutions employ pervasive systems with sensors or smart devices to gauge individual thermal sensations, yet assessing these methods is challenging. Real-world experiments are expensive, limited in access, and often overlook occupant and regional diversity. To address this, we introduce Co-zyBench, a benchmark tool using a Digital Twin (DT) approach for evaluating personalized thermal comfort systems. It employs a co-simulation middleware interfacing between a DT of the smart building and its HVAC system and another DT representing occupants' dynamic thermal preferences in various spaces. The DTs that support Co-zyBench are generated based on information, including data captured by sensors, of the space in which the thermal comfort system has to be evaluated. Co-zyBench incorporates metrics for energy consumption, thermal comfort, and occupant equality. It also features reference DTs based on standard buildings, HVAC systems, and occupants with diverse thermal preferences.
Ma, Jun; Panic, Dimitrije; Yus, Roberto; Bouloukakis, Georgios
Artifact: Co-zyBench: a thermal comfort provision benchmark for smart buildings Proceedings Article
In: 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2024.
Links | BibTeX | Tags: benchmarking, digital twin, energy efficiency, personalized thermal comfort
@inproceedings{ma2024artifact,
title = {Artifact: Co-zyBench: a thermal comfort provision benchmark for smart buildings},
author = {Jun Ma and Dimitrije Panic and Roberto Yus and Georgios Bouloukakis},
url = {https://inria.hal.science/hal-04514009/, PDF},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)},
keywords = {benchmarking, digital twin, energy efficiency, personalized thermal comfort},
pubstate = {published},
tppubtype = {inproceedings}
}
2023
Ma, Jun; Bouloukakis, Georgios; Kattepur, Ajay; Yus, Roberto; Conan., Denis
DEMSA: a DT-enabled Middleware for Self-adaptive Smart Spaces Proceedings Article
In: The 1st International Workshop on Middleware for Digital Twin (Midd4DT) – held in conjunction with ACM/IFIP/Usenix Middleware 2023, pp. 1-6, Association for Computing Machinery, 2023, ISBN: 979-8-4007-0461-1.
Abstract | Links | BibTeX | Tags: digital twin, middleware, self-adaptive system
@inproceedings{ma2023demsa,
title = {DEMSA: a DT-enabled Middleware for Self-adaptive Smart Spaces},
author = { Jun Ma and Georgios Bouloukakis and Ajay Kattepur and Roberto Yus and Denis Conan.},
url = {https://hal.science/hal-04514015, PDF},
doi = {10.1145/3631319.3632303},
isbn = {979-8-4007-0461-1},
year = {2023},
date = {2023-12-01},
urldate = {2023-12-01},
booktitle = {The 1st International Workshop on Middleware for Digital Twin (Midd4DT) – held in conjunction with ACM/IFIP/Usenix Middleware 2023},
pages = {1-6},
publisher = {Association for Computing Machinery},
abstract = {Heating, Ventilation, and Air Conditioning (HVAC) systems account for a significant portion of energy consumption within buildings. In order to balance the effect of thermal comfort vis-a-vis energy savings, HVAC control strategies have been proposed. However, the strategies are static and do not take into account dynamic changes of consumers, hence creating sub-optimal outcomes. This paper proposes DEMSA, a Digital Twin (DT)-enabled middleware for the self-adaptation of smart buildings. The DEMSA middleware interconnects and coordinates intelligent data exchange be- tween the building edge server, digital twin and Artificial Intelligence (AI) planning nodes in order to invoke appropriate strategies. Moreover, DEMSA is paired with a self-adaptive mechanism that can detect the anomaly of generated planning and adaptively modify it. This process ensures balancing building energy consumption and thermal comfort requirements, without human intervention. The DEMSA middleware is described over a real smart space scenario.
},
keywords = {digital twin, middleware, self-adaptive system},
pubstate = {published},
tppubtype = {inproceedings}
}
Heating, Ventilation, and Air Conditioning (HVAC) systems account for a significant portion of energy consumption within buildings. In order to balance the effect of thermal comfort vis-a-vis energy savings, HVAC control strategies have been proposed. However, the strategies are static and do not take into account dynamic changes of consumers, hence creating sub-optimal outcomes. This paper proposes DEMSA, a Digital Twin (DT)-enabled middleware for the self-adaptation of smart buildings. The DEMSA middleware interconnects and coordinates intelligent data exchange be- tween the building edge server, digital twin and Artificial Intelligence (AI) planning nodes in order to invoke appropriate strategies. Moreover, DEMSA is paired with a self-adaptive mechanism that can detect the anomaly of generated planning and adaptively modify it. This process ensures balancing building energy consumption and thermal comfort requirements, without human intervention. The DEMSA middleware is described over a real smart space scenario.