Operation and performance of VRF systems: Mining a large-scale dataset

Qian, Mingyang; Yan, Da; Hong, Tianzhen; Liu, Hua

doi:10.1016/j.enbuild.2020.110519

Title: Operation and performance of VRF systems: Mining a large-scale dataset

Journal Article · Fri Jan 01 00:00:00 EST 2021 · Energy and Buildings

DOI:https://doi.org/10.1016/j.enbuild.2020.110519· OSTI ID:1783137

Qian, Mingyang ^[1]; Yan, Da ^[1]; Hong, Tianzhen ^[2]; Liu, Hua ^[3]

Tsinghua Univ., Beijing (China)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
State Key Laboratory of Air-conditioning Equipment and System Energy Conservation, Zhuhai (China)

The energy consumption of air-conditioning systems has gained increasing attention as it contributes significantly to the global building energy use. The variable refrigerant flow (VRF) system is a common air-conditioning system applied widely in residential and office buildings in China. Understanding the actual operation and performance of VRF systems is fundamental for the energy-efficient design and operation of VRF systems. Previous research on VRF system operation used either limited field data covering certain building types and climate zones or used a questionnaire to obtain a larger dataset. However, they did not capture the wide applications of VRF systems quantitatively across all building types, climate zones, and operating conditions. To fill this gap, statistical and clustering analysis was conducted on the newly proposed key performance indicators of approximately 287,000 VRF systems for residential and commercial buildings in all five climate zones in China. In this work, the main findings are: (1) VRF systems are mainly used for cooling in all climate zones in China; (2) among all building types, the duration of use is lowest in residential buildings and highest in hotels and medical buildings; (3) the distribution of the ideal VRF cooling coefficient of performance (COP) is similar across all climate zones and building types; whereas the COPs of ideal VRF heating in the Severe Cold region and Cold regions are lower than those in other climate zones; and (4) partial load operations for VRF systems are common in residential buildings and office buildings due to the part-time-part-space operation mode. These findings can inform the actual application of VRF systems in China, supporting the design, operation, industry standard development, and performance optimization of VRF systems.

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Research Organization:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: National Key Research and Development Program of China; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office; National Natural Science Foundation of China (NSFC)

Grant/Contract Number:: AC02-05CH11231; 2019YFE0100300; 51778321

OSTI ID:: 1783137

Journal Information:: Energy and Buildings, Vol. 230; ISSN 0378-7788

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (30)

GMM clustering for heating load patterns in-depth identification and prediction model accuracy improvement of district heating system Lu, Yakai; Tian, Zhe; Peng, Peng Energy and Buildings, Vol. 190 https://doi.org/10.1016/j.enbuild.2019.02.014	journal	May 2019
IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings Yan, Da; Hong, Tianzhen; Dong, Bing Energy and Buildings, Vol. 156 https://doi.org/10.1016/j.enbuild.2017.09.084	journal	December 2017
Single and multi-family residential central all-air HVAC system operational characteristics in cooling-dominated climate Cetin, Kristen Sara; Novoselac, Atila Energy and Buildings, Vol. 96 https://doi.org/10.1016/j.enbuild.2015.03.039	journal	June 2015
Big-data for building energy performance: Lessons from assembling a very large national database of building energy use Mathew, Paul A.; Dunn, Laurel N.; Sohn, Michael D. Applied Energy, Vol. 140 https://doi.org/10.1016/j.apenergy.2014.11.042	journal	February 2015
Extending the virtual refrigerant charge sensor (VRC) for variable refrigerant flow (VRF) air conditioning system using data-based analysis methods Li, Guannan; Hu, Yunpeng; Chen, Huanxin Applied Thermal Engineering, Vol. 93 https://doi.org/10.1016/j.applthermaleng.2015.10.050	journal	January 2016
Achieving better energy-efficient air conditioning – A review of technologies and strategies Chua, K. J.; Chou, S. K.; Yang, W. M. Applied Energy, Vol. 104 https://doi.org/10.1016/j.apenergy.2012.10.037	journal	April 2013
Clustering and statistical analyses of air-conditioning intensity and use patterns in residential buildings An, Jingjing; Yan, Da; Hong, Tianzhen Energy and Buildings, Vol. 174 https://doi.org/10.1016/j.enbuild.2018.06.035	journal	September 2018
Development and application of the load responsive control of the evaporating temperature in a VRF system for cooling energy savings Yun, Geun Young; Lee, Je Hyeon; Kim, Han Jun Energy and Buildings, Vol. 116 https://doi.org/10.1016/j.enbuild.2016.02.012	journal	March 2016
A survey on energy consumption and energy usage behavior of households and residential building in urban China Hu, Shan; Yan, Da; Guo, Siyue Energy and Buildings, Vol. 148 https://doi.org/10.1016/j.enbuild.2017.03.064	journal	August 2017
Power consumption and energy efficiency of VRF system based on large scale monitoring virtual sensors Qian, Mingyang; Yan, Da; Liu, Hua Building Simulation, Vol. 13, Issue 5 https://doi.org/10.1007/s12273-020-0670-x	journal	July 2020
Divide and Conquer? ${k}$-Means Clustering of Demand Data Allows Rapid and Accurate Simulations of the British Electricity System Green, Richard; Staffell, Iain; Vasilakos, Nicholas IEEE Transactions on Engineering Management, Vol. 61, Issue 2 https://doi.org/10.1109/TEM.2013.2284386	journal	May 2014
Comparative study of the cooling energy performance of variable refrigerant flow systems and variable air volume systems in office buildings Yu, Xinqiao; Yan, Da; Sun, Kaiyu Applied Energy, Vol. 183 https://doi.org/10.1016/j.apenergy.2016.09.033	journal	December 2016
Evaluation of the energy performance of variable refrigerant flow systems using dynamic energy benchmarks based on data mining techniques Liu, Jiangyan; Wang, Jiangyu; Li, Guannan Applied Energy, Vol. 208 https://doi.org/10.1016/j.apenergy.2017.09.116	journal	December 2017
Clustering time-series energy data from smart meters Lavin, Alexander; Klabjan, Diego Energy Efficiency, Vol. 8, Issue 4 https://doi.org/10.1007/s12053-014-9316-0	journal	November 2014
Abnormal energy identification of variable refrigerant flow air-conditioning systems based on data mining techniques Liu, Jiahui; Liu, Jiangyan; Chen, Huanxin Applied Thermal Engineering, Vol. 150 https://doi.org/10.1016/j.applthermaleng.2018.12.133	journal	March 2019
Development of a virtual variable-speed compressor power sensor for variable refrigerant flow air conditioning system Guo, Yabin; Li, Guannan; Chen, Huanxin International Journal of Refrigeration, Vol. 74 https://doi.org/10.1016/j.ijrefrig.2016.09.025	journal	February 2017
Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions Li, Guannan; Hu, Yunpeng; Chen, Huanxin Applied Energy, Vol. 185 https://doi.org/10.1016/j.apenergy.2016.10.091	journal	January 2017
Occupancy schedules learning process through a data mining framework D’Oca, Simona; Hong, Tianzhen Energy and Buildings, Vol. 88 https://doi.org/10.1016/j.enbuild.2014.11.065	journal	February 2015
Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches Fan, Cheng; Yan, Da; Xiao, Fu Building Simulation, Vol. 14, Issue 1 https://doi.org/10.1007/s12273-020-0723-1	journal	October 2020
A Cluster Separation Measure Davies, David L.; Bouldin, Donald W. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-1, Issue 2 https://doi.org/10.1109/TPAMI.1979.4766909	journal	April 1979
Reduction of HVAC system runtime due to occupancy-controlled smart thermostats in contemporary multi-unit residential building suites Stopps, H.; Touchie, M. F. IOP Conference Series: Materials Science and Engineering, Vol. 609 https://doi.org/10.1088/1757-899X/609/6/062013	journal	September 2019
A novel efficient SVM-based fault diagnosis method for multi-split air conditioning system’s refrigerant charge fault amount Sun, Kaizheng; Li, Guannan; Chen, Huanxin Applied Thermal Engineering, Vol. 108 https://doi.org/10.1016/j.applthermaleng.2016.07.109	journal	September 2016
A review of recent advancements of variable refrigerant flow air-conditioning systems Wan, Hanlong; Cao, Tao; Hwang, Yunho Applied Thermal Engineering, Vol. 169 https://doi.org/10.1016/j.applthermaleng.2019.114893	journal	March 2020
A data-driven predictive model of city-scale energy use in buildings Kontokosta, Constantine E.; Tull, Christopher Applied Energy, Vol. 197 https://doi.org/10.1016/j.apenergy.2017.04.005	journal	July 2017
New method for measuring field performance of variable refrigerant flow systems based on compressor set energy conservation Zhang, Guohui; Liu, Wanlong; Xiao, Hansong Applied Thermal Engineering, Vol. 154 https://doi.org/10.1016/j.applthermaleng.2019.03.122	journal	May 2019
Modeling heating and cooling loads by artificial intelligence for energy-efficient building design Chou, Jui-Sheng; Bui, Dac-Khuong Energy and Buildings, Vol. 82 https://doi.org/10.1016/j.enbuild.2014.07.036	journal	October 2014
Development of a control algorithm aiming at cost-effective operation of a VRF heating system Moon, Jin Woo; Yang, Young Kwon; Choi, Eun Ji Applied Thermal Engineering, Vol. 149 https://doi.org/10.1016/j.applthermaleng.2018.12.044	journal	February 2019
Evaluation of thermal imbalance of ground source heat pump systems in residential buildings in China Qian, Mingyang; Yan, Da; An, Jingjing Building Simulation, Vol. 13, Issue 3 https://doi.org/10.1007/s12273-020-0606-5	journal	January 2020
A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings Miller, Clayton; Nagy, Zoltán; Schlueter, Arno Renewable and Sustainable Energy Reviews, Vol. 81 https://doi.org/10.1016/j.rser.2017.05.124	journal	January 2018
Influence of occupant behavior on the energy performance of variable refrigerant flow systems for office buildings: A case study Zhang, Guohui; Li, Xianting; Shi, Wenxing Journal of Building Engineering, Vol. 22 https://doi.org/10.1016/j.jobe.2018.12.020	journal	March 2019

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