A Machine Learning approach for personal thermal comfort perception evaluation: experimental campaign under real and virtual scenarios

Salamone Francesco, Bellazzi Alice, Belussi Lorenzo, Damato Gianfranco, Danza Ludovico, Dell'Aquila Federico, Ghellere Matteo, Megale Valentino, Meroni Italo, Vitaletti Walter
2020 E3S Web of Conferences  
Personal Thermal Comfort models differ from the steady-state methods because they consider personal user feedback as target value. Today, the availability of integrated "smart" devices following the concept of the Internet of Things and Machine Learning (ML) techniques allows developing frameworks reaching optimized indoor thermal comfort conditions. The article investigates the potential of such approach through an experimental campaign in a test cell, involving 25 participants in a Real (R)
more » ... nts in a Real (R) and Virtual (VR) scenario, aiming at evaluating the effect of external stimuli on personal thermal perception, such as the variation of colours and images of the environment. A dataset with environmental parameters, biometric data and the perceived comfort feedbacks of the participants is defined and managed with ML algorithms in order to identify the most suitable one and the most influential variables that can be used to predict the Personal Thermal Comfort Perception (PTCP). The results identify the Extra Trees classifier as the best algorithm. In both R and VR scenario a different group of variables allows predicting PTCP with high accuracy.
doi:10.1051/e3sconf/202019704001 doaj:e3825935ca2749bbaf47352abe7a2ba3 fatcat:na2fekgnjzdmvgmnmkpft5ewfi