Advanced IoT-Enabled Indoor Thermal Comfort Prediction Using SVM and Random Forest Models

Abstract

Predicting thermal comfort within indoor environments is essential for enhancing human health, productivity, and well-being. This study uses interdisciplinary approaches, integrating insights from engineering, psychology, and data science to develop sophisticated machine learning models that predict thermal comfort. Traditional methods often depend on subjective human input and can be inefficient. In contrast, this research applies Support Vector Machines (SVM) and Random Forest algorithms, celebrated for their precision and speed in handling complex datasets. The advent of the Internet of Things (IoT) further revolutionizes building management systems by introducing adaptive control algorithms and enabling smarter, IoT-driven architectures. We focus on the comparative analysis of SVM and Random Forest in predicting indoor thermal comfort, discussing their respective advantages and limitations under various environmental conditions and building designs. The dataset we used included comprehensive thermal comfort data, which underwent rigorous preprocessing to enhance model training and testing—80% of the data was used for training and the remaining 20% for testing. The models were evaluated based on their ability to accurately mirror complex interactions between environmental factors and occupant comfort levels. The results indicated that while both models performed robustly, Random Forest demonstrated greater stability and slightly higher accuracy in most scenarios. The paper proposes potential strategies for incorporating additional predictive features to further refine the accuracy of these models, emphasizing the promise of machine learning in advancing indoor comfort optimization.

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