标题:Accurately predicting heat transfer performance of ground heat exchanger for ground-coupled heat pump systems using data mining methods
作者:Zhuang, Zhaoyi; Ben, Xianye; Yan, Rui; Pang, Jianhua; Li, Yongbin
作者机构:[Zhuang, Zhaoyi; Li, Yongbin] Shandong Jianzhu Univ, Coll Thermal Energy Engn, Jinan 250101, Shandong, Peoples R China.; [Ben, Xianye; Pang, Jianhua 更多
通讯作者:Ben, Xianye
通讯作者地址:[Ben, XY]Shandong Univ, Sch Informat Sci & Engn, 27 Shanda South Rd, Jinan 250100, Shandong, Peoples R China.
来源:NEURAL COMPUTING & APPLICATIONS
出版年:2017
卷:28
期:12
页码:3993-4010
DOI:10.1007/s00521-016-2307-7
关键词:Ground-coupled heat pump (GCHP); Ground heat exchanger (GHE); Partial; least squares regression (PLSR); Support vector regression (SVR); M5; Model Tree
摘要:Nowadays, the ground-coupled heat pump (GCHP) systems have been recognized as one of the most energy-efficient systems for heating, cooling and hot water supply in both residential and commercial buildings. However, the heat transfer of ground heat exchanger (GHE) involves in large spatial scales, long time span and complex influential factors. We develop a data mining framework constructed by using 1998 experimental data to study the effects of 12 input variables composed of seven borehole parameters, two U-tube parameters, two ground parameters and one circulating liquid parameter to accurately predict the heat transfer performance of GHE for GCHP systems in 10 years. Hence, selecting a suitable input configuration to improve the energy efficiency has important sustainability benefits. The role of each of independent variable explaining the output variables is analyzed by partial least squares regression. Furthermore, support vector regression and M5 Model Tree are, respectively, used to predict the heat transfer performance. Extensive simulations show that we can predict the average quantity of heat exchanger, temperature of ground around GHE, inlet temperature of heat pump unit with very low level of error.
收录类别:EI;SCOPUS;SCIE
WOS核心被引频次:1
Scopus被引频次:1
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962883656&doi=10.1007%2fs00521-016-2307-7&partnerID=40&md5=32a72df446c0f49386fca4fe7ec096e4
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