A machine learning approach to enhance indoor thermal comfort in a changing climate
(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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3416d156-c038-4295-affc-2410e5e6ba9a
- author
- Kramer, Tobias ; Garcia-Hansen, Veronica ; Omrani, Sara ; Nik, Vahid M. LU and Chen, Dong
- organization
- publishing date
- 2021-11-18
- 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 & 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}}, }