Thermal Comfort Evaluation Using Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANNs)

Abstract

The thermal sensations of people differ from each other, even if they are in the same thermal conditions. The research was carried out in a didactic teaching room located in the building of the Faculty of Civil and Environmental Engineering in Poland. Tests on the temperature were carried out simultaneously with questionnaire surveys. The purpose of the survey was to define sensations regarding the thermal comfort of people in the same room, in different conditions of internal and external temperatures. In total 333 questionnaires were analyzed. After the discriminant and neural analyses it was found that it is not possible to forecast the thermal comfort assessment in the room based on the analyzed variables: gender, indoor air temperature, external wall radiant temperature, and outdoor air temperature. The thermal comfort assessments of men and women were similar and overlapped. The results of this study confirm that under the same thermal conditions about 85% of respondents assess thermal comfort as good, and about 15% of respondents assess thermal comfort as bad. The test results presented in this article are similar to the results of tests carried out by other authors in other climatic conditions.

Determinants of the Energy Poverty of Polish Students during the COVID-19 Pandemic

“…The problem of experiencing discomfort when someone is visiting, indicated by the Krakow students afflicted by energy poverty (by nearly 20% of them) may lead to weakening social relations and, in consequence, even to severe social withdrawal that is possible in extreme cases of this type [52]. However, we must bear in mind that the feeling of discomfort is based on the individual preferences regarding temperature which differ for every person [53]. Students who experience inappropriate temperature (heating) due to excessive costs more often than others feel ill or become sick (group average at 47.5% and 30%, respectively vs. 26.9% and 18.8% for others).…”

Thermal Comfort in Buildings: Scientometric Analysis and Systematic Review

“…(Fanger, 1970;Haghighat et al, 2000;ISO, 2005;La Gennusa et al, 2007;Hoof, 2008;ASHRAE, 2010;De Dear, 2011;Orosa and Oliveira, 2011;Chen and Chang, 2012;Halawa and Van Hoof, 2012;Li, Yu and Li, 2012;Langevin, Wen and Gurian, 2013;Maiti, 2014;Wang et al, 2014;Martínez et al, 2015;Gangisetti et al, 2016;Moon and Jung, 2016;Martinez-Molina et al, 2017b;Alzahrani et al, 2018;B. Yang et al, 2018;Deng and Chen, 2018;Elizabeth Amudhini Stephen, 2018;Hang and Kim, 2018;Hong et al, 2018b;Jiang et al, 2018a;Zhang et al, 2018Zhang et al, , 2020Escandón, Ascione, et al, 2019b;Haddad, Osmond and King, 2019;Hellwig et al, 2019;Jindal, 2019;Kwak and Huh, 2019;Ma, Liu and Shang, 2019;Piasecki et al, 2019;Tewari et al, 2019;Xu, Li and Zhang, 2019;Ali et al, 2020;Gładyszewska-Fiedoruk and Sulewska, 2020;…”

“…Several studies have incorporated industry 4.0 technologies into the thermal comfort models to cope with the various factors influencing both models of thermal comfort. Artificial Intelligence, for examplethe use of Artificial Neural Network (ANN) methods, has been incorporated into several studies (Li, Yu and Li, 2012;Moon and Jung, 2016;Alzahrani et al, 2018;Deng and Chen, 2018;Escandón, Ascione, et al, 2019b;Ma, Liu and Shang, 2019;Gładyszewska-Fiedoruk and Sulewska, 2020;Palladino, Nardi and Buratti, 2020;. ANN can provide the personalisation of thermal comfort settings (Karyono et al, 2020).…”

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

“…2019 ), etc. Moving on, LDA has been employed for thermal comfort evaluation (Gładyszewska-Fiedoruk and Sulewska 2020 ), sensor-based occupancy detection (Fayed et al. 2019 ).…”