A Machine Learning Approach for Predicting Thermal Comfort in a Bangkok Academic Library: Integrating Logit and Random Forest Models
Article Sidebar
Main Article Content
Abstract
A study of thermal comfort was conducted in a 4-story academic library located in Bangkok, Thailand. Since limited thermal comfort studies have been conducted for academic libraries located in a tropical climate. The field measurements by a set of sensors and questionnaire surveys were conducted among 234 library occupants. The measurements led to the average air temperature, relative humidity, and air velocity of about 24.39°C, 55.39%, and 0.073 m/s, respectively. Hence, the thermal comfort was evaluated using the Predicted Mean Vote (PMV) and the Predicted Percentage of Dissatisfied (PPD) based on the ASHRAE 55 standard, and the Actual Mean Vote (AMV) from the occupants’ perception. PMV and AMV were found to be -0.74 and -0.12, implying that the library environment was slightly cooled. Although the conventional standard PMV was widely used, it might not accurately represent real-time thermal preferences and cannot be practically integrated into the Heating, Ventilation, and Air-Conditioning (HVAC) control. In addition to the conventional method, two Machine Learning (ML) models, Random Forest (RF) and Support Vector Machine (SVM), were investigated for their predictive performance. For the ML model development, five environmental variables (Ta, Tmrt, Va, RH, and CLO) were used. It was found that the RF model performed well with an R-squared of about 0.9876, while SVM achieved only 0.8067. Therefore, the key findings support the application of the ML approach, especially RF, to enhance HVAC control for energy management strategies while maintaining the thermal comfort of library occupants in academic buildings located in tropical climates.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Article Accepting Policy
The editorial board of Thai-Nichi Institute of Technology is pleased to receive articles from lecturers and experts in the fields of engineering and technology written in Thai or English. The academic work submitted for publication must not be published in any other publication before and must not be under consideration of other journal submissions. Therefore, those interested in participating in the dissemination of work and knowledge can submit their article to the editorial board for further submission to the screening committee to consider publishing in the journal. The articles that can be published include solely research articles. Interested persons can prepare their articles by reviewing recommendations for article authors.
Copyright infringement is solely the responsibility of the author(s) of the article. Articles that have been published must be screened and reviewed for quality from qualified experts approved by the editorial board.
The text that appears within each article published in this research journal is a personal opinion of each author, nothing related to Thai-Nichi Institute of Technology, and other faculty members in the institution in any way. Responsibilities and accuracy for the content of each article are owned by each author. If there is any mistake, each author will be responsible for his/her own article(s).
The editorial board reserves the right not to bring any content, views or comments of articles in the Journal of Thai-Nichi Institute of Technology to publish before receiving permission from the authorized author(s) in writing. The published work is the copyright of the Journal of Thai-Nichi Institute of Technology.
References
Thai Meteorological Department. “Thailand Weather.” TMD.go.th. Accessed: Feb 24, 2026. [Online]. Available: www.tmd.go.th
IPCC, “Climate change 2023: Synthesis report,” Intergovernmental Panel on Climate Change, Geneva, Switzerland, Mar. 19, 2023. Accessed: Feb. 1, 2026. [Online]. Available: https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_FullVolume.pdf
Thermal Environmental Conditions for Human Occupancy, ANSI/ASHRAE Standard 55-2023, 2023.
P. O. Fanger, Thermal Comfort: Analysis and Applications in Environmental Engineering. Copenhagen, Denmark: Danish Technical Press, 1970.
N. Assymkhan and A. Kartbayev, “Advanced IoT-enabled indoor thermal comfort prediction using SVM and random forest models,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 8, pp. 1040–1050, 2024.
Y. Boutahri and A. Tilioua, “Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings,” Results Eng., vol. 22, Jun. 2024, Art. no. 102148.
P. Pourabrishami, “Evaluation of thermal comfort in library buildings in the tropical climate of Ghana (Case Study: Balma Library, Accra, Ghana),” M.S. thesis, Dept. Architecture, Univ. Liverpool, Liverpool, U.K., 2024. [Online]. Available: https://livrepository.liverpool.ac.uk/3185189/1/201669519_Sep2024.pdf
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, ISO 7730:2005, 2005.
R. J. de Dear and G. S. Brager, “Developing an adaptive model of thermal comfort and preference,” presented at the ASHRAE Winter Meeting and AHR Expo, San Francisco, CA, USA, Jan. 1998.
Z. Q. Fard, Z. S. Zomorodian, and S. S. Korsarvi, “Application of machine learning in thermal comfort studies: A review of methods, performance and challenges,” Energy Build., vol. 256, Feb. 2022, Art. no. 111771.
P. Triracheewin, W. Wongsuwan, and P. Chaiwiwatworakul, “Evaluation of thermal comfort: case study of the Faculty of Law, Chulalongkorn University,” in Proc. 5th Thai-Nichi Inst. Technol. Academic Conf. (TNIAC), Bangkok, Thailand, May 2019, pp. 59–64.
L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, Oct. 2001.
C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., vol. 20, pp. 273–297, Sep. 1995.
J. S. Cramer, Logit Models from Economics and Other Fields. Cambridge, U.K.: Cambridge Univ. Press, 2010.
M. A. Humphreys and J. F. Nicol, “The validity of ISO-PMV for predicting comfort votes in every-day thermal environments,” Energy Build., vol. 34, no. 6, pp. 667–684, Jul. 2002.
EIT Air Conditioning and Ventilation Standard, EIT Standard 3003-50, 2007.
A. Aryal, P. Chaiwiwatworakul, and S. Chirarattananon, “An experimental study of thermal performance of the radiant ceiling cooling in office building in Thailand,” Energy Build., vol. 283, Mar. 2023, Art. no. 112849.
H. Huang and B. R. Hughes, “Review of HVAC forecasting and control strategies for improved building performance,” Build. Environ., vol. 287, Jan. 2026, Art. no. 113797.
