IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i3p538-d311840.html
   My bibliography  Save this article

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

Author

Listed:
  • Katarzyna Gładyszewska-Fiedoruk

    (Department of HVAC Engineering, Faculty of Civil and Environmental Engineering, Bialystok University of Technology, 15-351 Bialystok, Poland)

  • Maria Jolanta Sulewska

    (Department of Geotechnics and Structural Mechanics, Faculty of Civil and Environmental Engineering, Bialystok University of Technology, 15-351 Bialystok, Poland)

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.

Suggested Citation

  • Katarzyna Gładyszewska-Fiedoruk & Maria Jolanta Sulewska, 2020. "Thermal Comfort Evaluation Using Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANNs)," Energies, MDPI, vol. 13(3), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:538-:d:311840
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/3/538/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/3/538/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Miguel Ángel Campano & Samuel Domínguez-Amarillo & Jesica Fernández-Agüera & Juan José Sendra, 2019. "Thermal Perception in Mild Climate: Adaptive Thermal Models for Schools," Sustainability, MDPI, vol. 11(14), pages 1-23, July.
    2. Zhang, Zijun & Kusiak, Andrew & Zeng, Yaohui & Wei, Xiupeng, 2016. "Modeling and optimization of a wastewater pumping system with data-mining methods," Applied Energy, Elsevier, vol. 164(C), pages 303-311.
    3. Boris Kingma & Wouter van Marken Lichtenbelt, 2015. "Energy consumption in buildings and female thermal demand," Nature Climate Change, Nature, vol. 5(12), pages 1054-1056, December.
    4. Vicente López-Chao & Antonio Amado Lorenzo & Jorge Martin-Gutiérrez, 2019. "Architectural Indoor Analysis: A Holistic Approach to Understand the Relation of Higher Education Classrooms and Academic Performance," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Łukasz Mamica & Jakub Głowacki & Kamil Makieła, 2021. "Determinants of the Energy Poverty of Polish Students during the COVID-19 Pandemic," Energies, MDPI, vol. 14(11), pages 1-15, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuehong Qiu & Zeming Deng & Chujuan Jiang & Kaigong Wei & Lijun Zhu & Jieting Zhang & Can Jiao, 2022. "The Associations of Meteorological and Environmental Factors with Memory Function of the Older Age in Urban Areas," IJERPH, MDPI, vol. 19(9), pages 1-14, April.
    2. Alicia Alonso & Jesús Llanos & Rocío Escandón & Juan J. Sendra, 2021. "Effects of the COVID-19 Pandemic on Indoor Air Quality and Thermal Comfort of Primary Schools in Winter in a Mediterranean Climate," Sustainability, MDPI, vol. 13(5), pages 1-17, March.
    3. Carla Balocco & Lorenzo Leoncini, 2020. "Energy Cost for Effective Ventilation and Air Quality for Healthy Buildings: Plant Proposals for a Historic Building School Reopening in the Covid-19 Era," Sustainability, MDPI, vol. 12(20), pages 1-16, October.
    4. Filipe, Jorge & Bessa, Ricardo J. & Reis, Marisa & Alves, Rita & Póvoa, Pedro, 2019. "Data-driven predictive energy optimization in a wastewater pumping station," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    5. Panagiotis Karadimos & Leonidas Anthopoulos, 2023. "Machine Learning-Based Energy Consumption Estimation of Wastewater Treatment Plants in Greece," Energies, MDPI, vol. 16(21), pages 1-20, November.
    6. Zong Woo Geem & Jin-Hong Kim, 2016. "Sustainable Optimization for Wastewater Treatment System Using PSF-HS," Sustainability, MDPI, vol. 8(4), pages 1-13, March.
    7. Wang, Zhe & Hong, Tianzhen, 2020. "Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    8. Xiaoli Feng & Baoyun Qiu & Yongxing Wang, 2020. "Optimizing Parallel Pumping Station Operations in an Open-Channel Water Transfer System Using an Efficient Hybrid Algorithm," Energies, MDPI, vol. 13(18), pages 1-19, September.
    9. Tom Y Chang & Agne Kajackaite, 2019. "Battle for the thermostat: Gender and the effect of temperature on cognitive performance," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-10, May.
    10. Itziar Sobrino-García, 2021. "Artificial Intelligence Risks and Challenges in the Spanish Public Administration: An Exploratory Analysis through Expert Judgements," Administrative Sciences, MDPI, vol. 11(3), pages 1-22, September.
    11. Vo, Nguyen Dat & Oh, Dong Hoon & Kang, Jun-Ho & Oh, Min & Lee, Chang-Ha, 2020. "Dynamic-model-based artificial neural network for H2 recovery and CO2 capture from hydrogen tail gas," Applied Energy, Elsevier, vol. 273(C).
    12. Vicente López-Chao & Antonio Amado Lorenzo & Jose Luis Saorín & Jorge De La Torre-Cantero & Dámari Melián-Díaz, 2020. "Classroom Indoor Environment Assessment through Architectural Analysis for the Design of Efficient Schools," Sustainability, MDPI, vol. 12(5), pages 1-12, March.
    13. Brewer, Dylan, 2022. "Equilibrium sorting and moral hazard in residential energy contracts," Journal of Urban Economics, Elsevier, vol. 129(C).
    14. Somasundaram, Jeeva & Koch, Ingrid & Lim, Noah, 2023. "Raising the AC temperature in the tropics, one degree at a time," Energy Economics, Elsevier, vol. 128(C).
    15. Sana Ehsan & Farhat Abbas & Muhammad Ibrahim & Bashir Ahmad & Aitazaz A. Farooque, 2021. "Thermal Discomfort Levels, Building Design Concepts, and Some Heat Mitigation Strategies in Low-Income Communities of a South Asian City," IJERPH, MDPI, vol. 18(5), pages 1-18, March.
    16. Marie Davidová & Shanu Sharma & Dermott McMeel & Fernando Loizides, 2022. "Co-De|GT: The Gamification and Tokenisation of More-Than-Human Qualities and Values," Sustainability, MDPI, vol. 14(7), pages 1-20, March.
    17. Akbaş, Halil & Bilgen, Bilge, 2017. "An integrated fuzzy QFD and TOPSIS methodology for choosing the ideal gas fuel at WWTPs," Energy, Elsevier, vol. 125(C), pages 484-497.
    18. Guo, Hongshan & Luo, Yongqiang & Meggers, Forrest & Simonetti, Marco, 2019. "Human body exergy consumption models’ evaluation and their sensitivities towards different environmental conditions," Energy, Elsevier, vol. 183(C), pages 1075-1088.
    19. Arturo Realyvásquez-Vargas & Aidé Aracely Maldonado-Macías & Karina Cecilia Arredondo-Soto & Yolanda Baez-Lopez & Teresa Carrillo-Gutiérrez & Guadalupe Hernández-Escobedo, 2020. "The Impact of Environmental Factors on Academic Performance of University Students Taking Online Classes during the COVID-19 Pandemic in Mexico," Sustainability, MDPI, vol. 12(21), pages 1-22, November.
    20. Zhang, Chaobo & Xue, Xue & Zhao, Yang & Zhang, Xuejun & Li, Tingting, 2019. "An improved association rule mining-based method for revealing operational problems of building heating, ventilation and air conditioning (HVAC) systems," Applied Energy, Elsevier, vol. 253(C), pages 1-1.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:538-:d:311840. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.