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Optimizing Indoor Thermal Comfort Prediction Using Machine Learning Models

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Comparative analysis highlights random forest's superior accuracy over support vector machines for predicting thermal comfort.

Random forest model outperforms support vector machines in predicting indoor thermal comfort.
Analysis reveals that random forest achieves higher stability and predictive accuracy than support vector machines.
Machine learning approaches utilize extensive datasets, improving predictions of thermal comfort beyond traditional methods.
Integration of the Internet of Things enhances model performance, enabling smart control in building systems.

Assymkhan et al. (2025) studied this question.

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View Full Paper

Ask AI

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Authors

Comparative analysis highlights random forest's superior accuracy over support vector machines for predicting thermal comfort.

Random forest model outperforms support vector machines in predicting indoor thermal comfort.
Analysis reveals that random forest achieves higher stability and predictive accuracy than support vector machines.
Machine learning approaches utilize extensive datasets, improving predictions of thermal comfort beyond traditional methods.
Integration of the Internet of Things enhances model performance, enabling smart control in building systems.

Assymkhan et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark