Open AccessA Machine Learning approach to enhance indoor thermal comfort in a changing climate
Author(s) -
Tobias Kramer,
Veronica Garcia Hansen,
Sara Omrani,
Vahid M. Nik,
Dong Chen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2042/1/012070
Subject(s) - ashrae 90.1 , thermal comfort , leverage (statistics) , exploit , computer science , workflow , architectural engineering , simulation , machine learning , engineering , meteorology , database , physics , computer security
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.