LAPSE:2023.28254
Published Article
LAPSE:2023.28254
Operation Pattern Recognition of the Refrigeration, Heating and Hot Water Combined Air-Conditioning System in Building Based on Clustering Method
April 11, 2023
Air-conditioning system operation pattern recognition plays an important role in the fault diagnosis and energy saving of the building. Most machine learning methods need labeled data to train the model. However, the difficulty of obtaining labeled data is much greater than that of unlabeled data. Therefore, unsupervised clustering models are proposed to study the operation pattern recognition of the refrigeration, heating and hot water combined air-conditioning (RHHAC) system. Clustering methods selected in this study include K-means, Gaussian mixture model clustering (GMMC) and spectral clustering. Further, correlation analysis is used to eliminate the redundant characteristic variables of the clustering model. The operating data of the RHHAC system are used to evaluate the performance of proposed clustering models. The results show that clustering models, after removing redundant variables by correlation analysis, can also identify the defrosting operation mode. Moreover, for the GMMC model, the running time is reduced from 27.80 s to 10.04 s when the clustering number is 5. The clustering performance of the original feature set model is the best when the number of clusters of the spectral clustering model is two and three. The clustering hit rate is 98.99%, the clustering error rate is 0.58% and the accuracy is 99.42%.
Record ID
Keywords
air conditioning system, clustering, correlation analysis, defrosting operation mode, pattern recognition
Subject
Suggested Citation
Guo Y, Liu J, Liu C, Zhu J, Lu J, Li Y. Operation Pattern Recognition of the Refrigeration, Heating and Hot Water Combined Air-Conditioning System in Building Based on Clustering Method. (2023). LAPSE:2023.28254
Author Affiliations
Guo Y: School of Water Conservancy and Civil Engineering, Zhengzhou University, Zhengzhou 450001, China [ORCID]
Liu J: Key Laboratory of Low-Grade Energy Utilization Technologies and Systems, Chongqing University, Chongqing 400044, China
Liu C: School of Water Conservancy and Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Zhu J: School of Water Conservancy and Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Lu J: School of Water Conservancy and Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Li Y: School of Water Conservancy and Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Liu J: Key Laboratory of Low-Grade Energy Utilization Technologies and Systems, Chongqing University, Chongqing 400044, China
Liu C: School of Water Conservancy and Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Zhu J: School of Water Conservancy and Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Lu J: School of Water Conservancy and Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Li Y: School of Water Conservancy and Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Journal Name
Processes
Volume
11
Issue
3
First Page
812
Year
2023
Publication Date
2023-03-08
Published Version
ISSN
2227-9717
Version Comments
Original Submission
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PII: pr11030812, Publication Type: Journal Article
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LAPSE:2023.28254
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doi:10.3390/pr11030812
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[v1] (Original Submission)
Apr 11, 2023
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Apr 11, 2023
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