Intelligent Real-Time Air Quality Index Classification for Smart Home Digital Twins

This paper investigates the application of machine learning and deep learning models for intelligent real-time Air Quality Index (AQI) classification within a smart home digital twin context. Leveraging sensor data encompassing CO2 and TVOC levels, we perform a comparative analysis of eight models: Transformer Neural Network (TNN), Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), Recurrent Neural Networks (RNN), Support Vector Machines (SVM), Random Forest (RF), Gradient Boosting (GB), and K-Nearest Neighbors (KNN). These models aim to accurately classify air quality into six categories corresponding to AQI levels, ranging from Good to Hazardous, which are critical for assessing health risks. The performance of each model is rigorously evaluated using metrics including accuracy, precision, recall, F1-score, and ROC curves. Our findings demonstrate that the implemented models exhibit strong performance. This high-accuracy classification enables the smart home digital twin to move beyond passive monitoring, enabling proactive environmental control. For instance, the digital twin can use this real-time AQI classification to automatically adjust HVAC systems, trigger air purifiers when indoor air quality degrades, and potentially inform occupancy schedules. This integration allows for intelligent, adaptive management of the home's environment, ensuring optimal indoor air quality and occupant well-being. The paper also discusses the limitations of each model and suitable application scenarios for intelligent AQI management within the digital twin framework, offering valuable insights for the selection of appropriate air quality classification models in smart home environments.