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A Machine Learning approach to enhance indoor thermal comfort in a changing climate

Kramer, Tobias ; Garcia-Hansen, Veronica ; Nik, Sara Omrani Vahid M. ; Chen, Dong

2021 International Hybrid Conference on Carbon Neutral Cities - Energy Efficiency and Renewables in the Digital Era, CISBAT 2021, Lausanne, Virtual, Switzerland, 2021, Vol.2042 (1), p.12070 [Peer Reviewed Journal]

IOP Publishing

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  • Title:
    A Machine Learning approach to enhance indoor thermal comfort in a changing climate
  • Author: Kramer, Tobias ; Garcia-Hansen, Veronica ; Nik, Sara Omrani Vahid M. ; Chen, Dong
  • Subjects: artificial intelligence ; Building Technologies ; Civil Engineering ; computational design ; Engineering and Technology ; Husbyggnad ; Samhällsbyggnadsteknik ; Teknik ; thermal comfort
  • Is Part Of: 2021 International Hybrid Conference on Carbon Neutral Cities - Energy Efficiency and Renewables in the Digital Era, CISBAT 2021, Lausanne, Virtual, Switzerland, 2021, Vol.2042 (1), p.12070
  • Description: 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.
  • Publisher: IOP Publishing
  • Language: English
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