Performance evaluation of ground source heat pump using linear and nonlinear regressions and artificial neural networks

Ground source heat pump (GSHP) systems have been widely used in both Northern Europe and China and have received a large amount of research attention due to their role in heat exchange of heating and cooling indoor temperature. Previous investigations have demonstrated that numerous design parameters have remarkable influences on the heat transfer performance of GSHP. Moreover, it is found that the design-ordinated provisions are not available for the direct prediction of the heat transfer performance of GSHP. To this end, this paper presents three numerical approaches (i.e., linear regression, nonlinear regression and artificial neural networks) to evaluate the heat transfer rate of GSHP regarding the fixed variables. An experimental database of GSHP applied in China is first collected containing 79 test results measured by authors and 33 experimental measurements from available literatures. Aiming to obtain the prediction models with high accuracy, the extensive and important variables (i.e., soil thermal conductivity, vertical well depth, well diameter, U-tube thickness, water flow rate and water temperature difference) reported in all test programs of the compiled experimental database are then set as the input parameters in the numerical approaches. The results show that the developed artificial neural networks (ANN) can provide more accurate predictions on the tested heat transfer rate of GSHP compared to the linear and nonlinear regressions. Finally, the trained ANN model is employed to conduct the parameter study to predict the influence of the input variables on the heat transfer rate and heat transfer rate variation of GSHP. The evaluation results demonstrate that increasing the well diameter and the U-tube thickness can lead to decrease the heat transfer rate of GSHP, whilst other variables (i.e., soil thermal conductivity, vertical well depth, water flow rate and water temperature difference) have an improvement in the heat transfer rate of GSHP.