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Title: OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information

Abstract

Thermal comfort is a decisive factor for the well-being, productivity, and overall satisfaction of commercial building occupants. Many commercial building automation systems either use a fixed zone-wide temperature set-point for all occupants or they rely on extensive sensor deployments with frequent online interaction with occupants. This results in inadequate comfort levels or significant training effort from users, respectively. However, the increasing ubiquity of cheap, depth-based occupancy tracking systems has enabled an improvement in inferential capabilities. We propose the novel system OccuTherm to model thermal comfort of occupants. We conducted a laboratory study with 77 participants to collect data for the implementation of a thermal comfort model that derives thermal comfort using the human body shape. Based on the comparison with model baselines and ablations, we show that our approach infers thermal comfort of individuals with 60 % accuracy when body shape information is taken into account; 6 % more than state-of-the-art approaches. We make our code, mobile app, datasets, and models freely available.

Authors:
;  [1];  [2];  [3]; ORCiD logo
  1. National University of Singapore
  2. Technical University of Munich
  3. Bosch Research & Technology Center
Publication Date:
Research Org.:
Carnegie Mellon Univ., Pittsburgh, PA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1576189
Report Number(s):
DOE-CMU-07682
DOE Contract Number:  
EE0007682
Resource Type:
Conference
Resource Relation:
Conference: 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. BuildSys ’19. New York, NY, USA, 2019; Related Information: http://doi.acm.org/10.1145/3359427.3361915
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Thermal Comfort, Human Studies, Machine Learning

Citation Formats

Francis, Jonathan, Quintana, Matias, von Frankenberg, Nadine, Munir, Sirajum, and Berges, Mario. OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information. United States: N. p., 2019. Web. doi:10.1145/3360322.3360858.
Francis, Jonathan, Quintana, Matias, von Frankenberg, Nadine, Munir, Sirajum, & Berges, Mario. OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information. United States. doi:10.1145/3360322.3360858.
Francis, Jonathan, Quintana, Matias, von Frankenberg, Nadine, Munir, Sirajum, and Berges, Mario. Thu . "OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information". United States. doi:10.1145/3360322.3360858.
@article{osti_1576189,
title = {OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information},
author = {Francis, Jonathan and Quintana, Matias and von Frankenberg, Nadine and Munir, Sirajum and Berges, Mario},
abstractNote = {Thermal comfort is a decisive factor for the well-being, productivity, and overall satisfaction of commercial building occupants. Many commercial building automation systems either use a fixed zone-wide temperature set-point for all occupants or they rely on extensive sensor deployments with frequent online interaction with occupants. This results in inadequate comfort levels or significant training effort from users, respectively. However, the increasing ubiquity of cheap, depth-based occupancy tracking systems has enabled an improvement in inferential capabilities. We propose the novel system OccuTherm to model thermal comfort of occupants. We conducted a laboratory study with 77 participants to collect data for the implementation of a thermal comfort model that derives thermal comfort using the human body shape. Based on the comparison with model baselines and ablations, we show that our approach infers thermal comfort of individuals with 60 % accuracy when body shape information is taken into account; 6 % more than state-of-the-art approaches. We make our code, mobile app, datasets, and models freely available.},
doi = {10.1145/3360322.3360858},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {11}
}

Conference:
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