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Accurately predicting heat transfer performance of ground heat exchanger for ground-coupled heat pump systems using data mining methods

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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.

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References

  1. Soni SK, Pandey M, Bartaria VN (2015) Ground coupled heat exchangers: a review and applications. Renew Sustain Energy Rev 47:83–92

    Article  Google Scholar 

  2. Fujimitsu Y, Fukuoka K, Ehara S, Takeshita H, Abe F (2010) Evaluation of subsurface thermal environmental change caused by a ground-coupled heat pump system. Curr Appl Phys 10(2):S113–S116

    Article  Google Scholar 

  3. Esen H, Inalli M, Esen M (2007) A techno-economic comparison of ground-coupled and air-coupled heat pump system for space cooling. Build Environ 42(5):1955–1965

    Article  Google Scholar 

  4. Hwang Y, Lee JK, Jeong YM, Koo KM, Lee DH, Kim IK, Jin SW, Kim SH (2009) Cooling performance of a vertical ground-coupled heat pump system installed in a school building. Renew Energy 34(3):578–582

    Article  Google Scholar 

  5. Galgaro A, Emmi G, Zarrella A, Carli MD (2014) Possible applications of ground coupled heat pumps in high geothermal gradient zones. Energy Build 79:12–22

    Article  Google Scholar 

  6. Robert F, Gosselin L (2014) New methodology to design ground coupled heat pump systems based on total cost minimization. Appl Therm Eng 62(2):481–491

    Article  Google Scholar 

  7. Esen H, Inalli M, Esen M, Pihtili K (2007) Energy and exergy analysis of a ground-coupled heat pump system with two horizontal ground heat exchangers. Build Environ 42(10):3606–3615

    Article  Google Scholar 

  8. Go GH, Lee SR, Nikhil NV, Yoon S (2015) A new performance evaluation algorithm for horizontal GCHPs (ground coupled heat pump systems) that considers rainfall infiltration. Energy 83:766–777

    Article  Google Scholar 

  9. Yang H, Cui P, Fang Z (2010) Vertical-borehole ground-coupled heat pumps: a review of models and systems. Appl Energy 87(1):16–27

    Article  Google Scholar 

  10. Guo Y, Zhang G, Zhou J, Wu J, Shen W (2012) A techno-economic comparison of a direct expansion ground-source and a secondary loop ground-coupled heat pump system for cooling in a residential building. Appl Therm Eng 35:29–39

    Article  Google Scholar 

  11. Chen J, Xia L, Li B, Mmereki D (2015) Simulation and experimental analysis of optimal buried depth of the vertical U-tube ground heat exchanger for a ground-coupled heat pump system. Renew Energy 73:46–54

    Article  Google Scholar 

  12. Fidorów N, Szulgowska-Zgrzywa M (2015) The influence of the ground coupled heat pump’s labor on the ground temperature in the boreholes—study based on experimental data. Appl Therm Eng 82:237–245

    Article  Google Scholar 

  13. Esen H, Inalli M, Esen Y (2009) Temperature distributions in boreholes of a vertical ground-coupled heat pump system. Renew Energy 34(12):2672–2679

    Article  Google Scholar 

  14. Yang W, Zhang S, Chen Y (2014) A dynamic simulation method of ground coupled heat pump system based on borehole heat exchange effectiveness. Energy Build 77:17–27

    Article  Google Scholar 

  15. Michopoulos A, Papakostas KT, Kyriakis N (2011) Potential of autonomous ground-coupled heat pump system installations in Greece. Appl Energy 88(6):2122–2129

    Article  Google Scholar 

  16. Man Y, Yang H, Wang J, Fang Z (2012) In situ operation performance test of ground coupled heat pump system for cooling and heating provision in temperate zone. Appl Energy 97:913–920

    Article  Google Scholar 

  17. Alalaimi M, Lorente S, Anderson R, Bejan A (2013) Effect of size on ground-coupled heat pump performance. Int J Heat Mass Transf 64:115–121

    Article  Google Scholar 

  18. Zhang C, Guo Z, Liu Y, Cong X, Peng D (2014) A review on thermal response test of ground-coupled heat pump systems. Renew Sustain Energy Rev 40:851–867

    Article  Google Scholar 

  19. Zhai XQ, Yu X, Yang Y, Wang RZ (2013) Experimental investigation and performance analysis of a ground-coupled heat pump system. Geothermics 48:112–120

    Article  Google Scholar 

  20. Edwards KC, Finn DP (2015) Generalised water flow rate control strategy for optimal part load operation of ground source heat pump systems. Appl Energy 150:50–60

    Article  Google Scholar 

  21. Fujii H, Inatomi T, Itoi R, Uchida Y (2007) Development of suitability maps for ground-coupled heat pump systems using groundwater and heat transport models. Geothermics 36(5):459–472

    Article  Google Scholar 

  22. Shrestha G, Uchida Y, Yoshioka M, Fujii H, Ioka S (2015) Assessment of development potential of ground-coupled heat pump system in Tsugaru Plain, Japan. Renew Energy 76:249–257

    Article  Google Scholar 

  23. Safa AA, Fung AS, Kumar R (2015) Comparative thermal performances of a ground source heat pump and a variable capacity air source heat pump systems for sustainable houses. Appl Therm Eng 81:279–287

    Article  Google Scholar 

  24. Esen H, Inalli M, Sengur A, Esen M (2008) Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system. Energy Build 40(6):1074–1083

    Article  Google Scholar 

  25. Esen H, Inalli M, Sengur A, Esen M (2008) Modeling a ground-coupled heat pump system by a support vector machine. Renew Energy 33(8):1814–1823

    Article  Google Scholar 

  26. Sun W, Hu P, Lei F, Zhu N, Jiang Z (2015) Case study of performance evaluation of ground source heat pump system based on ANN and ANFIS models. Appl Therm Eng 87:586–594

    Article  Google Scholar 

  27. Tsanas A, Xifara A (2012) Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy Build 49:560–567

    Article  Google Scholar 

  28. Tian W, Song J, Li Z, de Wilde P (2014) Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis. Appl Energy 135:320–328

    Article  Google Scholar 

  29. Catalina T, Iordache V, Caracaleanu B (2013) Multiple regression model for fast prediction of the heating energy demand. Energy Build 57:302–312

    Article  Google Scholar 

  30. Cheng MY, Cao MT (2014) Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines. Appl Soft Comput 22:178–188

    Article  Google Scholar 

  31. Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometr Intell Lab Syst 58(2):109–130

    Article  Google Scholar 

  32. Ben Xianye, Meng Weixiao, Yan Rui, Wang Kejun (2013) Kernel coupled distance metric learning for gait recognition and face recognition. Neurocomputing 120:577–589

    Article  Google Scholar 

  33. Xiong W, Du B, Zhang L et al (2015) R2FP: rich and robust feature pooling for mining visual data. In: Proceedings of IEEE international conference on data mining (ICDM)

  34. Du B, Wang Z, Zhang L et al (2015) Exploring representativeness and informativeness for active learning. IEEE Trans Cybern. doi:10.1109/TCYB.2015.2496974

    Google Scholar 

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Acknowledgments

In this paper, the research was sponsored by the Natural Science Foundation of China (Grant Nos. 61571275, 61302157), Shandong University of Science and Technology Plan Projects (Grant No. J15LG03) and Shandong Co-Innovation Center of Green Team Construction Funds (Grant No. LSXT201519).

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Authors and Affiliations

  1. College of Thermal Energy Engineering, Shandong Jianzhu University, Jinan, 250101, People’s Republic of China

    Zhaoyi Zhuang & Yongbin Li

  2. School of Information Science and Engineering, Shandong University, No. 27, Shanda South Road, Jinan, 250100, People’s Republic of China

    Xianye Ben & Jianhua Pang

  3. Computer Science Department, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

    Rui Yan

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  1. Zhaoyi Zhuang
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  2. Xianye Ben
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  4. Jianhua Pang
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  5. Yongbin Li
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Correspondence to Xianye Ben.

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Zhuang, Z., Ben, X., Yan, R. et al. Accurately predicting heat transfer performance of ground heat exchanger for ground-coupled heat pump systems using data mining methods. Neural Comput & Applic 28, 3993–4010 (2017). https://doi.org/10.1007/s00521-016-2307-7

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  • DOI: https://doi.org/10.1007/s00521-016-2307-7

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