IoT intelligent agent based cloud management system by integrating machine learning algorithm for HVAC systems

Collection of extensive sensor data from HVAC chillers has facilitated the development of smart cloud man-agement systems to increase the energy efficiency of buildings. However, limited by computing resource and algorithm performance, smart energy management technique and its fault diagnosis method in the literature suffer from low response speed and generalization capacity. To this end, a novel IoT intelligent agent based cloud management system for HVAC systems to improve operational efficiency and safety. The smart cloud manage-ment system integrates fundamental framework with machine learning algorithm for fault detection and diag-nosis. After preprocessing the data collected by IoT agents, an algorithm is constructed to predict virtual sensor values based on fault-free conditions. The calculated residuals of the actual values and virtual values on both normal and faulty conditions are used as inputs to an extreme gradient boosting algorithm that predicts the fault level. The diagnosis results are compared with other methods such as support vector machine, multi-layer perceptron and random forest. The k-fold cross validation indicated that the proposed methodology can ach-ieve superior overall generalization performance with 67.8%, 70.5% and 71.6% while that of the conventional method were 59.4%, 63.9% and 68.3%. This study will contribute to the practical applications of smart cloud management system in building energy systems.

Automated summary

The collection of sensor data from HVAC chillers has enabled the development of smart cloud management systems to improve energy efficiency in buildings. However, current smart energy management techniques and fault diagnosis methods have limitations in terms of response speed and generalization capacity. This study proposes a novel IoT intelligent agent-based cloud management system for HVAC systems, integrating a fundamental framework with a machine learning algorithm for fault detection and diagnosis. By preprocessing the data and using an extreme gradient boosting algorithm, the proposed methodology achieves superior overall generalization performance compared to conventional methods.