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

Author(s) : DU Z., CHEN S., ANDUV B., ZHU X., JIN X.

Type of article: IJR article

Summary

Collection of extensive sensor data from HVAC chillers has facilitated the development of smart cloud management 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 management system integrates fundamental framework with machine learning algorithm for fault detection and diagnosis. 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 achieve 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.

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Pages: 158-173

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Details

  • Original title: IoT intelligent agent based cloud management system by integrating machine learning algorithm for HVAC systems.
  • Record ID : 30030900
  • Languages: English
  • Subject: Technology
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 146
  • Publication date: 2023/02
  • DOI: http://dx.doi.org/10.1016/j.ijrefrig.2022.10.022

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