Hybrid Predictive Maintenance for Building Systems: Integrating Rule-Based and Machine Learning Models for Fault Detection Using a High-Resolution Danish Dataset

This study evaluates the effectiveness of six machine learning models, Artificial Neural Networks (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression (LR), for predictive maintenance in building systems. Utilizing a high-resolution dataset collected every five minutes from six office rooms at Aalborg University in Denmark over a ten-month period (27 February 2023 to 31 December 2023), we defined rule-based conditions to label historical faults in HVAC, lighting, and occupancy systems, resulting in over 100,000 fault instances. XGBoost outperformed other models, achieving an accuracy of 95%, precision of 93%, recall of 94%, and an F1-score of 0.93, with a computation time of 60 s. The model effectively predicted critical faults such as “Light_On_No_Occupancy” (1149 occurrences) and “Damper_Open_No_Occupancy” (8818 occurrences), demonstrating its potential for real-time fault detection and energy optimization in building management systems. Our findings suggest that implementing XGBoost in predictive maintenance frameworks can significantly enhance fault detection accuracy, reduce energy waste, and improve operational efficiency.

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.