Research on Operation Data Mining and Analysis of VRF Air-Conditioning Systems Based on ARM and MLR Methods to Enhance Building Sustainability

Jiayin Zhu, Xin Liu, Zihan Xu, Xingtao Zhang (), Congcong Du, Yabin Guo and Ruixin Li
Additional contact information
Jiayin Zhu: School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Xin Liu: School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Zihan Xu: School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Xingtao Zhang: School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Congcong Du: School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Yabin Guo: School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Ruixin Li: School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China

Sustainability, 2025, vol. 17, issue 20, 1-31

Abstract: With the increasing intelligence of modern air-conditioning systems, the difficulty of acquiring data from air-conditioning systems has been significantly reduced. However, analyzing the massive amounts of data collected and obtaining more valuable information still remains challenging, especially considering the internal relationships behind the data. The purpose of this study was to conduct operational experiments on VRF systems under different indoor set temperatures, indoor set air speeds, and terminal load rates. Then, the patterns of various operating parameters and energy consumption of VRF systems during winter operation were analyzed based on unsupervised methods. Three machine learning methods were primarily employed in this study, including correlation analysis, data regression analysis, and association rule analysis. Finally, a regression model was constructed for energy consumption based on eight typical characteristic parameters. The experimental results showed that the system was stable to a certain degree at different wind speeds. Among the characteristic parameters, fixed frequency 1 exhaust temperature, compressor frequency, and other parameters have a significant positive effect on energy consumption, while fixed frequency 1 shell top oil temperature, inlet and outlet pipe temperature difference, and other parameters have a negative effect. The research results provide a reference for air conditioning system data mining and building sustainability.

Keywords: VRF system; correlation analysis; multiple linear regression analysis; association rule analysis; data mining; building sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/20/8974/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/20/8974/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:20:p:8974-:d:1767984

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().