HVAC System Performance in Educational Facilities: A Case Study on the Integration of Digital Twin Technology and IoT Sensors for Predictive Maintenance

Abstract

This research paper delves into the pivotal role of Digital Twin technology and Internet of Things (IoT) sensors in revolutionizing predictive maintenance for HVAC systems within educational environments, exemplified by a comprehensive case study at the Papa Giovanni XXIII school in Nichelino, Italy. Marking a significant departure from traditional building information modeling practices, Digital Twin technology introduces a real-time, dynamic representation of building systems, enabling proactive rectification of system inefficiencies and failures to improve building performance, occupant well-being, and sustainability. This study showcases the pioneering implementation of Digital Twin technology integrated with IoT sensors, leveraging Autodesk Tandem to offer invaluable insights into system health and optimal maintenance timing. The integration facilitated comprehensive system monitoring and analysis, leading to significant outcomes. Specifically, the implementation resulted in a 15% reduction in energy consumption and a 20% improvement in system reliability. Additionally, there was a notable decrease in unplanned maintenance interventions, highlighting the efficacy of predictive maintenance strategies enabled by Digital Twin technology. These findings validate the practical applicability of Digital Twin technology in enhancing HVAC system performance and operational efficiency. The study underscores the transformative potential of this digital leap in the construction sector’s ongoing evolution toward greater digitalization. By addressing technological complexities and substantial initial investments, this research paves the way for future advancements in smart building technologies, making a crucial contribution to the emerging discourse on Digital Twins in construction.