Performance prediction between horizontal and vertical source heat pump systems for greenhouse heating with the use of artificial neural networks

Abstract

This paper presents the suitability of artificial neural networks (ANNs) to predict the performance and comparison between a horizontal and a vertical ground source heat pump system. Performance forecasting is the precondition for the optimal control and energy saving operation of heat pump systems. In this study, performance parameters such as air temperature entering condenser fan-coil unit, air temperature leaving condenser fan-coil unit, and ground temperatures (2 and 60 m) obtained experimental studies are input data; coefficient of performance of system (COPsys) is in output layer. The back propagation learning algorithm with three different variants such as Levenberg–Marguardt, Pola–Ribiere conjugate gradient, and scaled conjugate gradient, and also tangent sigmoid transfer function were used in the network so that the best approach can be found. The results showed that LM with three neurons in the hidden layer is the most suitable algorithm with maximum correlation coefficients R2 of 0.999, minimum root mean square RMS value and low coefficient variance COV. The reported results confirmed that the use of ANN for performance prediction of COPsys,H–V is acceptable in these studies.