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

Katarzyna Gładyszewska-Fiedoruk , Maria Jolanta Sulewska

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.
Author Katarzyna Gładyszewska-Fiedoruk (FCEE / HVAC)
Katarzyna Gładyszewska-Fiedoruk,,
- Heating, Ventilation, Air Conditioning Department (HVAC Department)
, Maria Jolanta Sulewska (FCEE / DGSM)
Maria Jolanta Sulewska,,
- Department of Geotechnics and Structural Mechanics
Journal seriesEnergies, [ENERGIES], ISSN 1996-1073, (N/A 140 pkt)
Issue year2020
Vol13
No3
Pages1-14
Publication size in sheets0.65
Article number538
Keywords in Englishthermal comfort; survey research; statistical analysis; indoor air temperature; sensation of temperature by both men and women; analysis using Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANNs) methods
ASJC Classification1700 General Computer Science
DOIDOI:10.3390/en13030538
URL https://www.mdpi.com/1996-1073/13/3/538
Internal identifierROC 19-20
Languageen angielski
LicenseJournal (articles only); published final; Uznanie Autorstwa (CC-BY); with publication
Score (nominal)140
Score sourcejournalList
ScoreMinisterial score = 140.0, 12-02-2020, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2018 = 1.156; WoS Impact Factor: 2018 = 2.707 (2) - 2018=2.99 (5)
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