Feature Ranking for Predicting Occupant Thermal Comfort Using Artificial Neural Networks (ANNs)

Mohammad Nyme Uddin¹, Minhyun Lee¹, Xuange Zhang¹, Xue Cui¹, Soleman Rakib², Md. Iktekar Alam Imran², Tanvin Hasan², Anisuzzaman Khan²

1. The Hong Kong Polytechnic University
2. International University of Business Agriculture and Technology

Published in Proceedings of the International Conference on Smart and Sustainable Built Environment (SASBE 2024), edited by Ali GhaffarianHoseini, Amirhosein GhaffarianHoseini, Farzad Rahimian and Mahesh Babu Purushothaman. Springer Nature, Lecture Notes in Civil Engineering, volume 591, 2025, pages 874 to 883. DOI 10.1007/978-981-96-4051-5_84.

Abstract

Indoor comfort refers to the overall satisfaction and well-being of occupants in terms of thermal and visual conditions within the building. This study utilizes Artificial Neural Networks (ANNs) to predict occupant thermal comfort in a naturally ventilated educational building situated in Dhaka, Bangladesh. The primary aim is to identify the most significant features or feature rankings that have a substantial impact on occupant thermal comfort. Four feature selection methods, namely Principal components analysis (PCA), Tree-based (Random Forest), Recursive Feature Elimination (RFE), and Lasso regularization, were employed to assess feature importance and rankings. The results of the feature ranking analysis consistently highlight certain features as influential across the different approaches. Notably, as Floor Area, No of Windows, Lighting Level, Study Level, and CO2 emerged as significant factors in predicting occupant thermal comfort. Additionally, features such as “Temperature”, “Humidity”, “Room Orientation”, “No of Fans”, and “No of Lights”, demonstrated varying degrees of significance. These findings provide valuable insights into the factors that contribute to occupant thermal comfort in the context of a naturally ventilated educational building. By understanding the optimal features or feature rankings, stakeholders can make informed decisions and implement strategies to enhance indoor comfort conditions.

Keywords

Session

Presented in Recorded Presentations, Session IV, Saturday 9 November 2024, 16:15 to 18:15, room WG 201, Auckland University of Technology. Session chair Dr Dat Doan.

How to Cite

Uddin, M. N., Lee, M., Zhang, X., Cui, X., Rakib, S., Imran, M. I. A., Hasan, T., & Khan, A. (2025). Feature Ranking for Predicting Occupant Thermal Comfort Using Artificial Neural Networks (ANNs). In A. GhaffarianHoseini, A. GhaffarianHoseini, F. Rahimian, & M. B. Purushothaman (Eds.), Proceedings of the International Conference on Smart and Sustainable Built Environment (SASBE 2024) (Lecture Notes in Civil Engineering, Vol. 591, pp. 874–883). Springer Nature Singapore. https://doi.org/10.1007/978-981-96-4051-5_84

About the Conference

Presented at SASBE 2024, the International Conference on Smart and Sustainable Built Environment, held in Auckland from 7 to 9 November 2024 and chaired by Professors Ali and Amirhosein GhaffarianHoseini, founders of GDI Academy. The version of record is published by Springer Nature; this page is the conference archive record kept by GDI Academy.