Paper Info
Title
Accurately predicting heat transfer performance of ground heat exchanger for ground-coupled heat pump systems using data mining methods.
Abstract
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.
Year
DOI
Venue
2017
10.1007/s00521-016-2307-7
Neural Computing and Applications
Keywords
Field
DocType
Ground-coupled heat pump (GCHP), Ground heat exchanger (GHE), Partial least squares regression (PLSR), Support vector regression (SVR), M5 Model Tree
Data mining,Efficient energy use,Heat pump,Support vector machine,Partial least squares regression,Borehole,Heat transfer,Heat exchanger,Variables,Mathematics
Journal
Volume
Issue
ISSN
28
12
1433-3058
Citations 
PageRank 
References 
0
0.34
4
Authors
5
Name
Order
Citations
PageRank
Zhaoyi Zhuang100.68
Xianye Ben213110.56
Rui Yan3885.22
Jianhua Pang400.34
Yongbin Li537.49