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COMPARISON OF THERMAL COMFORT PARAMETERS USING DIFFERENT FEATURE SELECTION ALGORITHMS

Cilt: 33 Sayı: 3 19 Aralık 2025
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COMPARISON OF THERMAL COMFORT PARAMETERS USING DIFFERENT FEATURE SELECTION ALGORITHMS

Abstract

This study aims to examine the environmental parameters affecting user thermal comfort in an amphitheater belonging to an educational building and to compare the effects of these parameters using different feature selection algorithms. Thermal comfort indicators PPD and environmental parameters were calculated using measurements from the Testo 480 device. Subjective data were obtained through surveys measuring users' thermal acceptability and thermal comfort perception. The RandomForestRegressor, SHAP, and CorrelationAttributeEval algorithms were used to compare the order of influence of environmental parameters affecting students' thermal comfort. The results showed that surface temperature and indoor air temperature are the most influential parameters on user comfort. In the SHAP and CorrelationAttributeEval algorithms, surface temperature was identified as the most influential parameter, while in the RandomForestRegressor algorithm, indoor temperature was identified as the most significant parameter. Additionally, when compared with survey results, the environmental parameter with the least effect on user comfort was found to yield the same result as the CorrelationAttributeEval algorithm. These findings provide important insights into better interpreting the factors affecting user comfort and optimizing thermal comfort in similar spaces.

Keywords

Thermal comfort , AI algorithms , Educational building , Environmental Parameters , Feature selection algorithms

Kaynakça

  1. Alsahaf, A., Petkov, N., Shenoy, V., & Azzopardi, G. (2022). A framework for feature selection through boosting. Expert Systems with Applications, 187. https://doi.org/10.1016/j.eswa.2021.115895
  2. ANSI/ASHRAE Standard 55-2017. (2017). Thermal Environmental Conditions for Human Occupancy. Available at: www.ashrae.org/technology
  3. Aparicio-Ruiz, P., Barbadilla-Martín, E., Guadix, J., & Muñuzuri, J. (2021). A field study on adaptive thermal comfort in Spanish primary classrooms during summer season. Building and Environment, 203. https://doi.org/10.1016/j.buildenv.2021.108089
  4. Bai, Y., Dong, Z., & Liu, L. (2025). Hybrid feature selection-based machine learning methods for thermal preference prediction in diverse seasons
  5. and building environments. Building and Environment, 269. https://doi.org/10.1016/j.buildenv.2024.112450
  6. Caner, I., & Ilten, N. (2020). Evaluation of occupants’ thermal perception in a university hospital in Turkey. Proceedings of the Institution of Civil Engineers: Engineering Sustainability, 173(8), 414–428. https://doi.org/10.1680/jensu.19.00059
  7. CEN, E. 15251:2007. (2007). Indoor Environmental Input Parameters for Design and Assessment of Energy Performance of Buildings Addressing Indoor Air Quality, Thermal Environment, Lighting and Acoustics.
  8. Colgan, S., Quille, K., Mchugh, S., & Vasic, J. (2019). Predicting Student Success. Early for a VTOS Student. Paper presented at the International Conference on Engaging Pedagogy (ICEP), University of Limerick, Ireland.
  9. Cui, X., Lee, M., Koo, C., & Hong, T. (2024). Energy consumption prediction and household feature analysis for different residential building types using machine learning and SHAP: Toward energy-efficient buildings. Energy and Buildings, 309. https://doi.org/10.1016/j.enbuild.2024.113997
  10. De Giuli, V., Da Pos, O., & De Carli, M. (2012). Indoor environmental quality and pupil perception in Italian primary schools. Building and Environment, 56, 335–345. https://doi.org/10.1016/j.buildenv.2012.03.024

Kaynak Göster

APA
Özlük, R., & Göksal Özbalta, T. (2025). COMPARISON OF THERMAL COMFORT PARAMETERS USING DIFFERENT FEATURE SELECTION ALGORITHMS. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 33(3), 1964-1974. https://doi.org/10.31796/ogummf.1717391
AMA
1.Özlük R, Göksal Özbalta T. COMPARISON OF THERMAL COMFORT PARAMETERS USING DIFFERENT FEATURE SELECTION ALGORITHMS. ESOGÜ Müh Mim Fak Derg. 2025;33(3):1964-1974. doi:10.31796/ogummf.1717391
Chicago
Özlük, Resul, ve Türkan Göksal Özbalta. 2025. “COMPARISON OF THERMAL COMFORT PARAMETERS USING DIFFERENT FEATURE SELECTION ALGORITHMS”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 33 (3): 1964-74. https://doi.org/10.31796/ogummf.1717391.
EndNote
Özlük R, Göksal Özbalta T (01 Aralık 2025) COMPARISON OF THERMAL COMFORT PARAMETERS USING DIFFERENT FEATURE SELECTION ALGORITHMS. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 33 3 1964–1974.
IEEE
[1]R. Özlük ve T. Göksal Özbalta, “COMPARISON OF THERMAL COMFORT PARAMETERS USING DIFFERENT FEATURE SELECTION ALGORITHMS”, ESOGÜ Müh Mim Fak Derg, c. 33, sy 3, ss. 1964–1974, Ara. 2025, doi: 10.31796/ogummf.1717391.
ISNAD
Özlük, Resul - Göksal Özbalta, Türkan. “COMPARISON OF THERMAL COMFORT PARAMETERS USING DIFFERENT FEATURE SELECTION ALGORITHMS”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 33/3 (01 Aralık 2025): 1964-1974. https://doi.org/10.31796/ogummf.1717391.
JAMA
1.Özlük R, Göksal Özbalta T. COMPARISON OF THERMAL COMFORT PARAMETERS USING DIFFERENT FEATURE SELECTION ALGORITHMS. ESOGÜ Müh Mim Fak Derg. 2025;33:1964–1974.
MLA
Özlük, Resul, ve Türkan Göksal Özbalta. “COMPARISON OF THERMAL COMFORT PARAMETERS USING DIFFERENT FEATURE SELECTION ALGORITHMS”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, c. 33, sy 3, Aralık 2025, ss. 1964-7, doi:10.31796/ogummf.1717391.
Vancouver
1.Resul Özlük, Türkan Göksal Özbalta. COMPARISON OF THERMAL COMFORT PARAMETERS USING DIFFERENT FEATURE SELECTION ALGORITHMS. ESOGÜ Müh Mim Fak Derg. 01 Aralık 2025;33(3):1964-7. doi:10.31796/ogummf.1717391