APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO PREDICT HEAT TRANSFER FROM BURIED PIPE FOR GROUND SOURCE HEAT PUMP APPLICATIONS

Abstract

The earth is an energy resource which has more suitable and stable temperatures than air. Ground Source Heat Pumps (GSHPs) were developed to use ground energy for residential heating. The most important part of a GSHP is the Ground Heat Exchanger (GHE) that consists of pipes buried in the soil and is used for transferring heat between the soil and the heat exchanger of the GSHP. Soil composition, density, moisture and burial depth of pipes affect the size of a GHE. Design of GSHP systems in different regions of US and Europe is performed using data from an experimental model. However, there are many more techniques including some complex calculations for sizing GHEs. An experimental study was carried out to investigate heat transfer in soil. A three-layer network is used for predicting heat transfer from a buried pipe. Measured fluid inlet temperatures were used in the artificial neural network model and the fluid outlet temperatures were obtained. The number of the neurons in the hidden layer was determined by a trial and error process together with cross-validation of the experimental data taken from literature evaluating the performance of the network and standard sensitivity analysis. Also, the results of the trained network were compared with the numerical study.