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A machine learning approach to enhance indoor thermal comfort in a changing climate

Kramer, Tobias ; Garcia-Hansen, Veronica ; Omrani, Sara ; Nik, Vahid M. LU orcid and Chen, Dong (2021) 2021 International Hybrid Conference on Carbon Neutral Cities - Energy Efficiency and Renewables in the Digital Era, CISBAT 2021 In Journal of Physics: Conference Series 2042.
Abstract

This paper presents an alternative workflow for thermal comfort prediction. By using the leverage of Data Science & AI in combination with the power of computational design, the proposed methodology exploits the extensive comfort data provided by the ASHRAE Global Thermal Comfort Database II to generate more customised comfort prediction models. These models consider additional, often significant input parameters like location and specific building characteristics. Results from an early case study indicate that such an approach has the potential for more accurate comfort predictions that eventually lead to more efficient and comfortable buildings.

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author
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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Journal of Physics: Conference Series : CISBAT 2021 Carbon-neutral cities - energy efficiency and renewables in the digital era - CISBAT 2021 Carbon-neutral cities - energy efficiency and renewables in the digital era
series title
Journal of Physics: Conference Series
volume
2042
article number
012070
edition
1
conference name
2021 International Hybrid Conference on Carbon Neutral Cities - Energy Efficiency and Renewables in the Digital Era, CISBAT 2021
conference location
Lausanne, Virtual, Switzerland
conference dates
2021-09-08 - 2021-09-10
external identifiers
  • scopus:85120904267
ISSN
1742-6588
DOI
10.1088/1742-6596/2042/1/012070
language
English
LU publication?
yes
additional info
Publisher Copyright: © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.
id
3416d156-c038-4295-affc-2410e5e6ba9a
date added to LUP
2022-01-31 12:48:08
date last changed
2022-04-27 07:28:25
@inproceedings{3416d156-c038-4295-affc-2410e5e6ba9a,
  abstract     = {{<p>This paper presents an alternative workflow for thermal comfort prediction. By using the leverage of Data Science &amp; AI in combination with the power of computational design, the proposed methodology exploits the extensive comfort data provided by the ASHRAE Global Thermal Comfort Database II to generate more customised comfort prediction models. These models consider additional, often significant input parameters like location and specific building characteristics. Results from an early case study indicate that such an approach has the potential for more accurate comfort predictions that eventually lead to more efficient and comfortable buildings.</p>}},
  author       = {{Kramer, Tobias and Garcia-Hansen, Veronica and Omrani, Sara and Nik, Vahid M. and Chen, Dong}},
  booktitle    = {{Journal of Physics: Conference Series : CISBAT 2021 Carbon-neutral cities - energy efficiency and renewables in the digital era}},
  issn         = {{1742-6588}},
  language     = {{eng}},
  month        = {{11}},
  series       = {{Journal of Physics: Conference Series}},
  title        = {{A machine learning approach to enhance indoor thermal comfort in a changing climate}},
  url          = {{http://dx.doi.org/10.1088/1742-6596/2042/1/012070}},
  doi          = {{10.1088/1742-6596/2042/1/012070}},
  volume       = {{2042}},
  year         = {{2021}},
}