Personalized Thermal Comfort Model for a Multiple Occupancy Office Building
Publication: Computing in Civil Engineering 2023
ABSTRACT
Building occupants have different thermal comforts at the static indoor set-point temperatures due to inter- and intra-individual differences in preferences, needs, and activities. Despite advances in intelligent building management/control systems, occupants’ dissatisfaction with the indoor thermal environment is still widespread. This study developed a personalized thermal comfort model to predict individual thermal preferences in multiple occupancy. The proposed model integrates both physiological signals (e.g., heart rate, skin temperature, and skin conductance) and environmental parameters (e.g., air temperature, relative humidity, and CO2) in the process of predicting individual thermal preferences. The collected data was trained through the proposed model using various machine learning-driven classification algorithms and sought the most reliable personalized predictive model. To evaluate the performance of the proposed model, a case study is conducted in multiple occupancies in an office building. The results demonstrate that each person has a different powerful classification model to accurately predict their thermal preferences.
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Published In
Computing in Civil Engineering 2023
Pages: 824 - 830
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Architectural engineering
- Building management
- Building systems
- Buildings
- Case studies
- Commercial buildings
- Engineering fundamentals
- Engineering mechanics
- Facilities (by type)
- Measurement (by type)
- Methodology (by type)
- Research methods (by type)
- Smart buildings
- Structural engineering
- Structures (by type)
- Systems engineering
- Systems management
- Temperature effects
- Temperature measurement
- Thermal analysis
- Thermodynamics
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