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Modeling a ground-coupled heat pump system by a support vector machine

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  • Esen, Hikmet
  • Inalli, Mustafa
  • Sengur, Abdulkadir
  • Esen, Mehmet

Abstract

This paper reports on a modeling study of ground coupled heat pump (GCHP) system performance (COP) by using a support vector machine (SVM) method. A GCHP system is a multi-variable system that is hard to model by conventional methods. As regards the SVM, it has a superior capability for generalization, and this capability is independent of the dimensionality of the input data. In this study, a SVM based method was intended to adopt GCHP system for efficient modeling. The Lin-kernel SVM method was quite efficient in modeling purposes and did not require a pre-knowledge about the system. The performance of the proposed methodology was evaluated by using several statistical validation parameters. It is found that the root-mean squared (RMS) value is 0.002722, the coefficient of multiple determinations (R2) value is 0.999999, coefficient of variation (cov) value is 0.077295, and mean error function (MEF) value is 0.507437 for the proposed Lin-kernel SVM method. The optimum parameters of the SVM method were determined by using a greedy search algorithm. This search algorithm was effective for obtaining the optimum parameters.

Suggested Citation

  • Esen, Hikmet & Inalli, Mustafa & Sengur, Abdulkadir & Esen, Mehmet, 2008. "Modeling a ground-coupled heat pump system by a support vector machine," Renewable Energy, Elsevier, vol. 33(8), pages 1814-1823.
    • Handle: RePEc:eee:renene:v:33:y:2008:i:8:p:1814-1823
    DOI: 10.1016/j.renene.2007.09.025
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    4. Hou, Gaoyang & Taherian, Hessam & Song, Ying & Jiang, Wei & Chen, Diyi, 2022. "A systematic review on optimal analysis of horizontal heat exchangers in ground source heat pump systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    5. Lee, C.K., 2011. "Effects of multiple ground layers on thermal response test analysis and ground-source heat pump simulation," Applied Energy, Elsevier, vol. 88(12), pages 4405-4410.
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    7. Sivasakthivel, T. & Murugesan, K. & Thomas, H.R., 2014. "Optimization of operating parameters of ground source heat pump system for space heating and cooling by Taguchi method and utility concept," Applied Energy, Elsevier, vol. 116(C), pages 76-85.
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    9. Soni, Suresh Kumar & Pandey, Mukesh & Bartaria, Vishvendra Nath, 2016. "Hybrid ground coupled heat exchanger systems for space heating/cooling applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 724-738.
    10. Dong, Shengming & Zhang, Yufeng & He, Zhonglu & Deng, Na & Yu, Xiaohui & Yao, Sheng, 2018. "Investigation of Support Vector Machine and Back Propagation Artificial Neural Network for performance prediction of the organic Rankine cycle system," Energy, Elsevier, vol. 144(C), pages 851-864.
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    12. Aikins, Kojo Atta & Choi, Jong Min, 2012. "Current status of the performance of GSHP (ground source heat pump) units in the Republic of Korea," Energy, Elsevier, vol. 47(1), pages 77-82.
    13. Taghavifar, Hamid & Mardani, Aref, 2014. "A comparative trend in forecasting ability of artificial neural networks and regressive support vector machine methodologies for energy dissipation modeling of off-road vehicles," Energy, Elsevier, vol. 66(C), pages 569-576.
    14. Mladenović, Igor & Sokolov-Mladenović, Svetlana & Milovančević, Milos & Marković, Dušan & Simeunović, Nenad, 2016. "Management and estimation of thermal comfort, carbon dioxide emission and economic growth by support vector machine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 466-476.
    15. Razavi, M. & Dehghani-sanij, A.R. & Khani, M.R. & Dehghani, M.R., 2015. "Comparing meshless local Petrov–Galerkin and artificial neural networks methods for modeling heat transfer in cisterns," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 521-529.
    16. Mojumder, Juwel Chandra & Ong, Hwai Chyuan & Chong, Wen Tong & Izadyar, Nima & Shamshirband, Shahaboddin, 2017. "The intelligent forecasting of the performances in PV/T collectors based on soft computing method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1366-1378.
    17. Gang, Wenjie & Wang, Jinbo, 2013. "Predictive ANN models of ground heat exchanger for the control of hybrid ground source heat pump systems," Applied Energy, Elsevier, vol. 112(C), pages 1146-1153.
    18. Zanchini, Enzo & Lazzari, Stefano & Priarone, Antonella, 2012. "Long-term performance of large borehole heat exchanger fields with unbalanced seasonal loads and groundwater flow," Energy, Elsevier, vol. 38(1), pages 66-77.
    19. Aira, Roberto & Fernández-Seara, José & Diz, Rubén & Pardiñas, Ángel Á., 2017. "Experimental analysis of a ground source heat pump in a residential installation after two years in operation," Renewable Energy, Elsevier, vol. 114(PB), pages 1214-1223.
    20. Sivasakthivel, T. & Murugesan, K. & Sahoo, P.K., 2014. "Optimization of ground heat exchanger parameters of ground source heat pump system for space heating applications," Energy, Elsevier, vol. 78(C), pages 573-586.
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    22. Sebarchievici, Calin & Sarbu, Ioan, 2015. "Performance of an experimental ground-coupled heat pump system for heating, cooling and domestic hot-water operation," Renewable Energy, Elsevier, vol. 76(C), pages 148-159.
    23. Dong, Rentao & Xu, Jiuping & Lin, Bo, 2017. "ROI-based study on impact factors of distributed PV projects by LSSVM-PSO," Energy, Elsevier, vol. 124(C), pages 336-349.

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