Smart Building Management System based on Digital Twin: A Case Study on Real-Time Environmental Monitoring and Thermal Comfort Prediction

As urban landscapes increasingly transform into smart cities, there is an increasing emphasis on smart buildings for sustainable and efficient management of resources. This evolution necessitates innovative approaches to efficiently manage energy resources, enhance occupant comfort, and ensure environmental sustainability. In response to these challenges, this study introduces a practical and novel smart building management system, which integrates Digital Twin (DT) technology with machine learning, primarily aimed at enhancing thermal comfort in buildings. The system, underpinned by a sensor network-based DT and real-time data visualization using the Unreal Engine, employs a Deep Neural Network (DNN) to predict thermal comfort. The empirical validation, conducted in a controlled laboratory setting, involves a comparative analysis of the DNN's performance against traditional models and user experience evaluation through the USE Questionnaire. Results demonstrate the DNN's superior predictive accuracy and high user satisfaction levels in usability and effectiveness. This research highlights the significant role of DT and machine learning in revolutionizing smart building operations, setting a foundation for future advancements in creating sustainable, efficient, and occupant-friendly smart cities.