Citation

Tan, Hui Hui and Tan, Yi Fei and Tan, Wooi Haw and Ooi, Chee Pun (2024) Enhanced Thermal Comfort Prediction Model by Addressing Outliers and Data Imbalance. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 118 (1). pp. 52-64. ISSN 2289-7879

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Abstract

The relationship between individuals and their thermal environment is pivotal not only for comfort but also for health and productivity. Thermal comfort, as defined by ASHRAE, reflects an individual's satisfaction with their ambient thermal conditions and can be gauged using the ASHRAE scale. In the past, traditional thermal comfort prediction models such as the Predicted Mean Vote (PMV) were used to evaluate thermal comfort.Nevertheless, the emergence of machine learning provides a more dynamic approach to predict thermal comfort of occupants. However, the subjective nature of thermal comfort introduces data ambiguities challenge which lead to the existence of outliers. Moreover, data imbalances within the dataset can cause the machine learning models to not learn the minority class effectively, resulting in the deterioration of the model. This research has developed an enhanced thermal comfort prediction model to predict the occupant’s thermal comfort by leveraging the outlier detection technique and synthetic data generator, particularly the Isolation Forest and SMOTE. The experiment showed that the proposed model is able to achieve an accuracy of 74.94%. This exhibited a slight improvement comparedto the findings in prior research of using Random Forest prediction model

Item Type:	Article
Uncontrolled Keywords:	Machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Jul 2024 03:27
Last Modified:	03 Jul 2024 03:27
URII:	http://shdl.mmu.edu.my/id/eprint/12601

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