A Framework for Environment Thermal Comfort Prediction Model

Abstract

In recent years, the integration of Internet of Things (IoT) technologies across diverse domains has accelerated efforts toward real-time environmental monitoring. Ensuring a responsive and adaptive ecosystem is essential for maintaining optimal living and working conditions. IoT-enabled sensors autonomously gather and transmit environmental data, facilitating the classification and analysis of ambient conditions. The pervasive role of IoT lies in its ability to seamlessly interconnect devices and enable dynamic data exchange across systems. This study investigates the evaluation of thermal comfort levels through advanced classification techniques. A machine learning framework is employed to train and validate predictive models using a comprehensive benchmark dataset comprising 100,000 samples, each reflecting key environmental attributes. The proposed approach enhances both the reliability and precision of predictive algorithms. Experimental findings demonstrate that the thermal comfort prediction system offers robust support for intelligent automation in smart learning environments.