Performance prediction of ground-coupled heat pump system using NNCA-RBF neural networks

This paper describes an application of artificial neural networks (ANNs) based on improved Radial Basis Function (NNCA-RBF) to predict performance of a horizontal ground-coupled heat pump (GCHP) system. Performance forecasting is the precondition for the optimal control and energy saving operation of heat pump systems. 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 NNCA-RBFNN predictions usually agree well with the experimental values with correlation coefficients in the range of 0.9967-0.9998, mean relative errors in the range of 1.02-4.83% and root mean square errors in the range of 0.0147-0.058. The NNCA-RBFNN approach shows high accuracy and reliability for predicting the performance of GCHP systems.