Integrating Machine Learning and Digital Twins for Predictive Maintenance in Smart Buildings

This paper presents an AI-driven predictive maintenance framework integrated with a digital twin for smart buildings, aiming to optimize building management by predicting system failures and improving operational efficiency. The proposed methodology leverages Internet of Things (IoT) sensors and machine learning models to monitor critical systems such as HVAC (Heating, Ventilation, and Air Conditioning), electrical infrastructure, and structural elements. By analyzing real-time data streams and historical records, predictive algorithms identify anomalies and forecast equipment failures, enabling proactive maintenance. The integration of machine learning models—ranging from random forests to autoencoders—within the digital twin enhances real-time simulation of building systems, providing actionable insights and enabling what-if analyses. Testing results demonstrate that the framework improves the accuracy of failure predictions, with HVAC systems achieving a prediction accuracy of 92% and structural anomalies detected with an accuracy of 88%. The system also led to a 15% reduction in energy consumption and a 25% decrease in maintenance costs. While promising, the approach faces challenges in data quality, computational complexity, and scalability. Nonetheless, this research demonstrates the potential of AI-enhanced digital twins to extend the lifespan of building systems, reduce downtime, and contribute to sustainable building lifecycle management. Future work should focus on improving data integration, reducing costs through edge computing, and scaling the system for larger smart city applications.

Integrating Machine Learning and Digital Twins for Predictive Maintenance in Smart Buildings Keywords:

Predictive Maintenance , Digital Twin , Smart Buildings , Machine Learning , IoT , HVAC Systems