Performance prediction of ground-water heat pump system using artificial neural networks

Abstract

This paper describes an application of artificial neural networks (ANNs) to predict the performance of a ground-water heat pump system (GWHP). In order to gather data for training and testing the proposed ANN model, an experimental GWHP system was operated at steady state conditions. Utilizing some experimental data for training, an ANN model based on a multi-layered perception/back propagation was developed. The performances of the ANN predictions were tested using experimental data not employed in the training process. The predictions usually agreed well with the experimental values with the coefficients of multiple determinations in the range of 0.947- 0.9999, and mean relative errors in the range of 1.3%-3.47%. The ANN approach shows high accuracy and reliability for predicting the performance of GWHP systems.