A Bayesian Model for Optimizing Thermal Comfort and Indoor Air Quality

A Bayesian Model for Optimizing Thermal Comfort and Indoor Air Quality

Akdoğan, Merve; Alaçam, Sema; Töreyin, Behçet Uğur

Full Article:

This study focuses on the usage of a probabilistic approach on determining the best course of action in a specific environment within the domain of architecture. More specifically, Bayesian decision theory is applied on a simplified problem of maintaining thermal comfort and air quality. An already existing comprehensive dataset is used and narrowed down for the purpose of the study. Environment measurements (indoor and outdoor temperature, indoor CO2 level and air humidity) are taken as input variables and user preferences (open or closed window) are taken as outputs in order to address the problem as a binary classification problem. The paper can be regarded as a preliminary study on the usage of probabilistic approaches in the discipline of architecture.

This study focuses on the usage of a probabilistic approach on determining the best course of action in a specific environment within the domain of architecture. More specifically, Bayesian decision theory is applied on a simplified problem of maintaining thermal comfort and air quality. An already existing comprehensive dataset is used and narrowed down for the purpose of the study. Environment measurements (indoor and outdoor temperature, indoor CO2 level and air humidity) are taken as input variables and user preferences (open or closed window) are taken as outputs in order to address the problem as a binary classification problem. The paper can be regarded as a preliminary study on the usage of probabilistic approaches in the discipline of architecture.

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DOI: 10.5151/sigradi2023-396

Referências bibliográficas

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Como citar:

Akdoğan, Merve; Alaçam, Sema; Töreyin, Behçet Uğur; "A Bayesian Model for Optimizing Thermal Comfort and Indoor Air Quality", p-350-361. In: . São Paulo: Blucher, 2024.
ISSN 23186968, DOI 10.5151/sigradi2023-396