Traditional and Intelligent Buildings – Perceptions of Thermal Comfort

Traditional and Intelligent Buildings – Perceptions of Thermal Comfort

KRAWCZYK Natalia

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Abstract. The article presents the perception of thermal comfort in two buildings, intelligent and traditional. 32 people aged 18 to 22 and one women aged 52 participated in the study. Two indicators were analyzed, PMV (Predicted Mean Vote) and PPD (Predicted Percentage of Dissatisfied). The analysis consisted in comparing the actual feelings of the respondents with the results based on Fanger’s model. The assessment of air humidity and thermal preferences are also shown.

Keywords
Thermal Comfort, Predicted Mean Vote, Thermal Sensation Vote, Microclimate

Published online 7/20/2022, 6 pages
Copyright © 2022 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: KRAWCZYK Natalia, Traditional and Intelligent Buildings – Perceptions of Thermal Comfort, Materials Research Proceedings, Vol. 24, pp 96-101, 2022

DOI: https://doi.org/10.21741/9781644902059-15

The article was published as article 15 of the book Terotechnology XII

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

References
[1] ISO International Organisation for Standardization, Ergonomics of the thermal environment – Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria, International Standard ISO 7730, 2005.
[2] PN-EN 16798-1:2019, Energy Performance of Buildings-Ventilation for Buildings-Part 1: Indoor Environmental Input Parameters for Design and Assessment of Energy Performance of Buildings Addressing Indoor Air Quality, Thermal Environment, Lighting and Acustics, 2019.
[3] N. Krawczyk, J. Krakowiak. The comparison of thermal comfort test results in selected traditional and modern buildings, E3S Web of Conferences 286 (2021) art. 02008. https://doi.org/10.1051/e3sconf/202128602008
[4] R.Z. Homoda, K.S.H. Saharia, H.A.F. Almurib, F.H. Nagi. RLF and TS fuzzy model identification of indoor thermal comfort based on PMV/PPD, Build. Environ. 49 (2012) 141-153. https://doi.org/10.1016/j.buildenv.2011.09.012
[5] A. Jindal. Thermal comfort study in naturally ventilated school classrooms in composite climate of India, Build. Environ. 142 (2018) 34-46. https://doi.org/10.1016/j.buildenv.2018.05.051
[6] N. Krawczyk, Comparison of thermal comfort in a traditional and intelligent building, E3S Web of Conferences 336 (2022) art. 00019. https://doi.org/10.1051/e3sconf/202233600019
[7] A. Białek, L. Dębska, N. Krawczyk. Assessment of light intensity and productivity in the intelligent building – case study, E3S Web of Conferences 336 (2022) art. 00011. https://doi.org/10.1051/e3sconf/202233600011
[8] G. Majewski, M. Telejko, Ł.J. Orman. Preliminary results of thermal comfort analysis in selected buildings, E3S Web of Conf. 17 (2017) art. 56. https://doi.org/10.1051/e3sconf/20171700056
[9] Ł.J. Orman, N. Radek, J. Pietraszek, M. Szczepaniak. Analysis of enhanced pool boiling heat transfer on laser – textured surfaces, Energies 13 (2020) art. 2700. https://doi.org/10.3390/en13112700
[10] N. Radek, J. Pietraszek, A. Gądek-Moszczak, Ł.J. Orman, A. Szczotok. The morphology and mechanical properties of ESD coatings before and after laser beam machining, Materials 13 (2020) art. 2331. https://doi.org/10.3390/ma13102331
[11] G. Majewski, Ł.J. Orman, M. Telejko, N. Radek, J. Pietraszek, A. Dudek. Assessment of thermal comfort in the intelligent buildings in view of providing high quality indoor environment, Energies 13 (2020) art. 1973. https://doi.org/10.3390/en13081973
[12] J. Pietraszek, E. Skrzypczak-Pietraszek. The uncertainty and robustness of the principal component analysis as a tool for the dimensionality reduction. Solid State Phenom. 235 (2015) 1-8. https://doi.org/10.4028/www.scientific.net/SSP.235.1
[13] J. Pietraszek, N. Radek, A.V. Goroshko. Challenges for the DOE methodology related to the introduction of Industry 4.0. Production Engineering Archives 26 (2020) 190-194. https://doi.org/10.30657/pea.2020.26.33
[14] J. Pietraszek, A. Gadek-Moszczak, N. Radek. The estimation of accuracy for the neural network approximation in the case of sintered metal properties. Studies in Computational Intelligence 513 (2014) 125-134. https://doi.org/10.1007/978-3-319-01787-7_12
[15] J. Pietraszek, R. Dwornicka, A. Szczotok. The bootstrap approach to the statistical significance of parameters in the fixed effects model. ECCOMAS Congress 2016 – Proceedings of the 7th European Congress on Computational Methods in Applied Sciences and Engineering 3, 6061-6068. https://doi.org/10.7712/100016.2240.9206