Heating and cooling potential of an earth-to-air heat exchanger using artificial neural network

In this article, we use the concept of artificial neural network and goal oriented design to propose a computer design tool that can help the designer to evaluate any aspect of earth-to-air heat exchanger and behavior of the final configuration. The present study focuses mostly on those aspects related to the passive heating or cooling performance of the building. Two models have been developed for this purpose, namely deterministic and intelligent. The deterministic model is developed by analyzing simultaneously coupled heat and mass transfer in ground whereas the intelligent model is a development of data driven artificial neural network model. Six variables influencing the thermal performance of the earth-to-air heat exchangers which were taken into account are length, humidity, ambient air temperature, ground surface temperature, ground temperature at burial depth and air mass flow rate. Furthermore, a sensitivity analysis was carried out in order to evaluate the impact of various factors involved in the energy balance equation at the burial depth. The model was validated against experimental data sets. Moreover, the developed algorithm is suitable for the calculation of the outlet air temperature and therefore of the heating and cooling potential of the earth-to-air heat exchanger system. The Intelligent model predicts earth-to-air heat exchanger outlet air temperature with an accuracy of ±2.6%, whereas, the deterministic model shows an accuracy of ±5.3%.