Abstract
Every building is equipped with HVAC (heating, ventilation, and air-conditioning) systems to ensure user comfort. These are known to be the primary contributors to the buildings' high energy consumption. Inefficient operation of these HVAC systems frequently results in a significant amount of energy loss. Therefore, it is essential that these always operate effectively. To address this problem, the paper focuses on a novel framework for detecting and identifying HVAC faults in buildings, so that energy consumption waste due to HVAC faults can be eliminated. Existing approaches suffer from issues such as the deployment of expensive and specialized hardware, manual inspection of HVAC systems, and the use of large amounts of training data for fault detection. However, the developed framework overcomes these issues by establishing functional relationship between factors that are affected by operation of HVACs, mainly temperature and power consumption. The functional relationship is derived for faulty and nonfaulty HVACs, leading to a model-based detection and identification of HVAC faults. The developed framework is applied and thoroughly tested on a set of 48 HVACs installed in a building (situated in Mumbai, India). Out of these HVACs, a total of 31 HVACs were found to be faulty. It was observed that the faulty behavior from these HVACs was leading to an excess energy consumption of about 49%.










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Agarwal, A. Minimizing Energy Wastage in Buildings by Identifying HVAC Faults Using Functional Relationship of Facets. SN COMPUT. SCI. 4, 640 (2023). https://doi.org/10.1007/s42979-023-02046-y
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DOI: https://doi.org/10.1007/s42979-023-02046-y