Modelling of a vertical ground coupled heat pump system by using artificial neural networks

Abstract

This paper describes the applicability of artificial neural networks (ANNs) to estimate of performance of a vertical ground coupled heat pump (VGCHP) system used for cooling and heating purposes experimentally. The system involved three heat exchangers in the different depths at 30 (VB1), 60 (VB2) and 90 (VB3) m. The experimental results were obtained in cooling and heating seasons of 2006–2007. ANNs have been used in varied applications and they have been shown to be particularly useful in system modeling and system identification. In this study, the back-propagation learning algorithm with three different variants, namely Levenberg–Marguardt (LM), Pola–Ribiere conjugate gradient (CGP), and scaled conjugate gradient (SCG), and tangent sigmoid transfer function were used in the network so that the best approach could be found. The most suitable algorithm and neuron number in the hidden layer were found as LM with 8 neurons for both cooling and heating modes.