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

Hikmet Esen, Mustafa Inalli, Abdulkadir Sengur and Mehmet Esen

Renewable Energy, 2008, vol. 33, issue 8, 1814-1823

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.

Keywords: Ground coupled heat pump performance; Support vector machine; Forecast; Artificial neural network; Adaptive neuro-fuzzy inference system (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (25)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:33:y:2008:i:8:p:1814-1823

DOI: 10.1016/j.renene.2007.09.025

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