Comparison Between Neural Network and Finite Element Models for the Prediction of Groundwater Temperatures in Heat Pump (GWHP) Systems

Abstract

A fundamental aspect in groundwater heat pump (GWHP) plant design is the correct evaluation of the Thermally Affected Zone (TAZ) that develops around the injection well. This is particularly important to avoid interference with previously existing groundwater uses (wells) and underground structures. Temperature anomalies are detected through numerical methods. Computational fluid dynamic (CFD) models are widely used in this field because they offer the opportunity to calculate the time evolution of the thermal plume produced by a heat pump. The drawback of these models is the computational time. This paper aims to propose the use of neural networks to determine the time evolution of the groundwater temperature downstream of an installation as a function of the possible utilization profiles of the heat pump. The main advantage of neural network modeling is the possibility of evaluating a large number of scenarios in a very short time, which is very useful for the preliminary analysis of future multiple installations and optimal planning of urban energy systems. The neural network is trained using the results from a CFD model (FEFLOW) under several operating conditions. The final results appeared to be reliable and the temperature anomalies around the injection well appeared to be predicted well.