Publications:
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}
}