A machine learning approach to enhance indoor thermal comfort in a changing climate
Paper i proceeding, 2021

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.

thermal comfort

artificial intelligence

computational design


Tobias Kramer

Queensland University of Technology (QUT)

Veronica Garcia-Hansen

Queensland University of Technology (QUT)

Sara Omrani

Queensland University of Technology (QUT)

Vahid Nik

Lunds universitet

Chalmers, Arkitektur och samhällsbyggnadsteknik, Byggnadsteknologi

Dong Chen

Commonwealth Scientific and Industrial Research Organisation (CSIRO)

Journal of Physics: Conference Series

17426588 (ISSN) 17426596 (eISSN)

Vol. 2042 1 012070

2021 International Hybrid Conference on Carbon Neutral Cities - Energy Efficiency and Renewables in the Digital Era, CISBAT 2021
Lausanne, Virtual, Switzerland,


Annan samhällsbyggnadsteknik




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