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IoT intelligent agent based cloud management system by integrating machine learning algorithm for HVAC systems
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DOI：10.1016/j.ijrefrig.2022.10.022.png)
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Abstract
En 中文
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.
Keywords:
Machine learning
IoT
HVAC system
Fault detection and diagnosis
Cloud smart management
Apprentissage automatique
Internet des objets
Syste`me CVC
De?tection et diagnostic des d
faillances
Gestion intelligente du nuage
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