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

2020 ◽  
Vol 197 ◽  
pp. 04001
Author(s):  
Francesco Salamone ◽  
Alice Bellazzi ◽  
Lorenzo Belussi ◽  
Gianfranco Damato ◽  
Ludovico Danza ◽  
...  
Keyword(s):  
Machine Learning ◽  
Thermal Comfort ◽  
Environmental Parameters ◽  
Smart Devices ◽  
Thermal Perception ◽  
Biometric Data ◽  
Experimental Campaign ◽  
Machine Learning Approach ◽  
Comfort Perception ◽  
External Stimuli

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.

scholarly journals Evaluation of the Visual Stimuli on Personal Thermal Comfort Perception in Real and Virtual Environments Using Machine Learning Approaches

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1627 ◽  
Author(s):  
Francesco Salamone ◽  
Alice Bellazzi ◽  
Lorenzo Belussi ◽  
Gianfranco Damato ◽  
Ludovico Danza ◽  
...  
Keyword(s):  
Machine Learning ◽  
Thermal Comfort ◽  
Visual Stimuli ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
Learning Approaches ◽  
Biometric Data ◽  
Target Value ◽  
Learning Techniques ◽  
Comfort Perception

Personal Thermal Comfort models consider personal user feedback as a target value. The growing development of integrated “smart” devices following the concept of the Internet of Things and data-processing algorithms based on Machine Learning techniques allows developing promising frameworks to reach the best level of indoor thermal comfort closest to the real needs of users. The article investigates the potential of a new approach aiming at evaluating the effect of visual stimuli on personal thermal comfort perception through a comparison of 25 participants’ feedback exposed to a real scenario in a test cell and the same environment reproduced in Virtual Reality. The users’ biometric data and feedback about their thermal perception along with environmental parameters are collected in a dataset and managed with different Machine Learning techniques. The most suitable algorithm, among those selected, and the influential variables to predict the Personal Thermal Comfort Perception are identified. The Extra Trees classifier emerged as the most useful algorithm in this specific case. In real and virtual scenarios, the most important variables that allow predicting the target value are identified with an average accuracy higher than 0.99.

Developing smart devices with automated Machine learning Approach: A review

2021 ◽  
Author(s):  
Sunil A. Patel ◽  
Sanjay P. Patel ◽  
Yagna Bhupendra Kumar Adhyaru ◽  
Santosh Maheshwari ◽  
Pankaj Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smart Devices ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Automated Machine Learning

scholarly journals Field study on thermal comfort in naturally ventilated and air-conditioned university classrooms

2019 ◽  
Vol 29 (6) ◽  
pp. 851-859 ◽  
Author(s):  
Michael Fabozzi ◽  
Alessandro Dama
Keyword(s):  
Thermal Comfort ◽  
Field Study ◽  
Thermal Environment ◽  
Thermal Sensation ◽  
Thermal Perception ◽  
Adaptive Models ◽  
Adaptive Comfort ◽  
Comfort Perception ◽  
Comfort Parameters ◽  
University Classrooms

Maintaining a satisfactory thermal environment is of primary importance, especially when the goal is to maximize learning such as in schools or universities. This paper presents a field study conducted in Milan during summer 2017 in 16 classrooms of Politecnico di Milano, including both naturally ventilated (NV) and air-conditioned (AC) environments. This study asked 985 students to report their thermal perception and their responses were evaluated according to the measured thermal comfort parameters to assess the prediction as given by Fanger and adaptive models, according to ANSI/ASHRAE 55-2017 and EN 15251:2007 standards. Furthermore, an analysis regarding potential effects of gender in comfort perception was performed. The results confirmed the fitness of Fanger’s model for the prediction of occupants’ thermal sensations in AC classrooms with a reasonable accuracy. In NV classrooms, the Adaptive model was proven to be suitable for predicting students’ comfort zone according to ASHRAE 55 Standard, while the adaptive comfort temperatures recommended by EN 15251 were not acceptable for a large number of students. No significant differences in thermal comfort perception between genders have been observed, except for two NV classrooms in which females’ thermal sensation votes had resulted closer to neutrality in comparison to males, who expressed a warmer thermal sensation.

scholarly journals Modeling Autonomic Pupillary Responses from External Stimuli using Machine Learning

2019 ◽  
Author(s):  
David Lary
Keyword(s):  
Machine Learning ◽  
Input Data ◽  
Environmental Parameters ◽  
Pupil Dilation ◽  
Autonomic Response ◽  
Data Sets ◽  
Biometric Data ◽  
Validation Data ◽  
Pupillary Responses ◽  
Non Linear

The human body exhibits a variety of autonomic responses. For example, changing light intensity provokes a change in the pupil dilation. In the past, formulae for pupil size based on luminance have been derived using traditional empirical approaches. In this paper, we present a different approach to a similar task by using machine learning to ex- amine the multivariate non-linear autonomic response of pupil dilation as a function of a comprehensive suite of more than four hundred environmental parameters leading to the provision of quantitative empirical models. The objectively optimized empirical machine learning models use a multivariate non-linear non-parametric supervised regression algorithm employing an ensemble of regression trees which receive input data from both spectral and biometric data. The models for predicting the participant’s pupil diameters from the input data had a fidelity of at least 96.9% for both the training and independent validation data sets. The most important inputs were the light levels (irradiance) of the wavelengths near 562 nm. This coincides with the peak sensitivity of the long-wave photosensitive cones in the retina, which exhibit a maximum absorbance around λmax = 562.8 ± 4.7 nm.

scholarly journals Application of IoT and Machine Learning techniques for the assessment of thermal comfort perception.

2018 ◽  
Vol 148 ◽  
pp. 798-805 ◽  
Author(s):  
Francesco Salamone ◽  
Lorenzo Belussi ◽  
Cristian Currò ◽  
Ludovico Danza ◽  
Matteo Ghellere ◽  
...  
Keyword(s):  
Machine Learning ◽  
Thermal Comfort ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Comfort Perception
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