New Approaches to Modelling Occupant Comfort
A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Energy, Physics, Environment, and Systems".
Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 37910
Special Issue Editors
Interests: thermal comfort; indoor environmental quality; high performance buildings; adaptive behaviors; sensor technologies; thermal physiology; psychophysics; occupant satisfaction; personal comfort systems; machine learning
Interests: thermal comfort; thermal adaptation; occupant behavior; occupant satisfaction; healthy buildings; alliesthesia; psychophysical modelling
Special Issue Information
Dear Colleagues,
Energy spent on heating and cooling indoor environments is responsible for the largest share of electricity consumption in buildings. Reducing the carbon footprint of the built environment relies on a balance of energy efficiency measures that simultaneously ensure occupant thermal comfort. Overlaying this challenge is a shift towards building designs and layouts that promote well-being, health, and improve occupants’ experience of their space. As such, long-standing paradigms of thermal neutrality and steady-state conditions should be reconsidered. Successfully navigating this changing landscape requires novel approaches to understanding the psychophysical relationship between thermal environments and occupant perception to increase the resilience of humans and buildings in a rapidly changing world.
This Special Issue of Buildings will focus on innovative research efforts to model the thermal experience of building occupants. Of particular interest are works building upon comfort theories reflecting the dynamics involved, such as the adaptive thermal comfort theory or thermal alliesthesia, to better understand the relationship between climate, comfort and energy use in buildings. This includes diverse focus areas such as (i) modifications to existing or new comfort indices, (ii) personal comfort systems, (iii) post occupancy evaluations, (iv) application of building sensor networks, and (v) machine learning techniques. Works that employ laboratory studies, field studies, and numerical simulations are invited. Meta-analyses of existing databases such as the ASHRAE Global Thermal Comfort Database II are also encouraged.
Dr. Thomas Parkinson
Prof. Dr. Marcel Schweiker
Guest Editors
Manuscript Submission Information
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Keywords
- Adaptive
- Thermal comfort
- Comfort models
- HVAC
- Climate
- Occupant behavior
- Energy efficiency
- Buildings
- Statistical modeling
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