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

Francesco Salamone,Italo Meroni,Federico Dell’Aquila,Matteo Ghellere,Alice Bellazzi,Valentino Megale,Walter Vitaletti,Gianfranco Damato,Ludovico Danza,Lorenzo Belussi

doi:10.1051/e3sconf/202019704001

Abstract

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) 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.

Highlights

Users spent much of their time indoors thereby the quality of the environments inside buildings and the occupants’ satisfaction and well-being is topical today [1]
The classical approach on Thermal Comfort (TC) is based on the studies of Fanger [4] that found that TC is influenced by six factors related to the environment: air temperature (AT), relative humidity (RH), air velocity (AV), and mean radiant temperature (RT) and to users’ conditions: metabolic rate (MET) and thermal insulation of clothing (Iclo)
Fanger’s model established two indexes to quantify the thermal comfort of occupants based on the heat balance approach: the Predicted Mean Vote (PMV) and the Predicted Percentage of Dissatisfied (PPD)

Summary

Introduction

Users spent much of their time indoors thereby the quality of the environments inside buildings and the occupants’ satisfaction and well-being is topical today [1]. The classical approach on TC is based on the studies of Fanger [4] that found that TC is influenced by six factors related to the environment: air temperature (AT), relative humidity (RH), air velocity (AV), and mean radiant temperature (RT) and to users’ conditions: metabolic rate (MET) and thermal insulation of clothing (Iclo). Fanger’s model established two indexes to quantify the thermal comfort of occupants based on the heat balance approach: the Predicted Mean Vote (PMV) and the Predicted Percentage of Dissatisfied (PPD). Starting from the experience of Fanger, the increase in use of new technological solutions have allowed improving the classical assessment to overcome the limits of the steady-state model and those inspired to this approach, paving the way for the development of adaptive models where people are actively involved in the control of the thermal environment [5,6]

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Conclusion