Research on Maintenance and Fault Prediction Model of Intelligent Building Driven by Internet of Things

With the rapid development of Internet of Things (IoT) technology, its application in the field of intelligent buildings is increasingly extensive, which significantly improves the energy efficiency management and fault prediction capabilities of intelligent buildings. The purpose of this paper is to discuss the construction and optimization method of maintenance and fault prediction model of intelligent building driven by IoT. Through in-depth analysis of sensor data in intelligent buildings, statistical characteristics, time series characteristics, frequency characteristics and correlation characteristics are extracted, and the running state and potential faults of building systems are fully captured. On this basis, this paper proposes a fault prediction model based on ensemble learning, which combines random forest and AdaBoost algorithm to effectively improve the prediction accuracy and model stability. The experimental results show that the model performs well in fault prediction tasks in typical intelligent building scenes, with high accuracy, recall and F1 score, and its performance is stable under different data sets. In addition, by introducing L1 and L2 regularization, the model complexity is effectively controlled and the risk of over-fitting is reduced. The research in this paper not only provides a new solution for the maintenance and fault prediction of intelligent buildings, but also provides a useful reference for the future development of intelligent building management systems.