Abstract: It is normal for the modern population to spend 12 hours or more daily indoors where the level of comfort can be moderated. Yet, indoor occupants are similarly exposed to various air pollutants just as outdoors. Indoor air pollution could be detrimental toward the occupant's health noted by the United Nation Environment Programme (UNEP) in the Pollution Action Note, published on 7th of September 2021. According to the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standards, occupancy patterns could influence indoor air quality. Hence, this paper investigates the utilisation of machine learning algorithms in predicting occupancy patterns against indoor air quality (IAQ) variables such as humidity, temperature, light, and carbon dioxide (CO2). This study compares the performance of selected machine learning approaches, namely deep learning (LSTM, CNN), regression (ANN) and (SVR) models. In addition, it explores the diverse range of evaluation metrics utilized to evaluate the performance of machine learning in the specific context of Mean Squared Error (MSE) and Mean Absolute Error (MAE). In the training phase, the SVR model achieved the lowest MAE of 0.0826 and MSE of 0.0280 as compared to the other algorithms. The ANN model demonstrated slightly better generalization capabilities in the testing phase, while the LSTM model demonstrated robust performance in the test phase. Overall, the results highlighted the significant impact of occupancy behaviour on Indoor Air Quality (IAQ) variables and underscored the importance of advanced modelling techniques in IAQ monitoring and management, emphasizing the need for tailored approaches to address the complex relationship between occupancy patterns and IAQ variables.

Amir Hamzah Mohd Shaberi, Sumayyah Dzulkifly, Wang Shir Li and Yona Falinie A. Gaus, “Machine Learning Approaches for Predicting Occupancy Patterns and its Influence on Indoor Air Quality in Office Environments” International Journal of Advanced Computer Science and Applications(IJACSA), 15(9), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150987

@article{Shaberi2024,
title = {Machine Learning Approaches for Predicting Occupancy Patterns and its Influence on Indoor Air Quality in Office Environments},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150987},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150987},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {9},
author = {Amir Hamzah Mohd Shaberi and Sumayyah Dzulkifly and Wang Shir Li and Yona Falinie A. Gaus}
}