Dynamic neural networks to analyze the behavior of phase change materials embedded in building envelopes
Abstract Heat transfer during melting and solidifying processes in phase change materials, is a highly non-linear phenomenon. Artificial Neural Networks seem a suitable approach to deal with highly non-linear processes. The present work uses dynamic neural networks to simulate the behavior of a phase change material layer of a building envelope, which typically are under strongly varying conditions, with temperatures and heat fluxes changing at different rates inside a wide range of values. In the present work, the experimental procedure to obtain the training dataset is explained; then, an ANN is trained using experimental data, together with additional data related to the thermal behavior of PCMs; finally, generalization of the ANN is analyzed, and the results are evaluated. Initial training attempts, show a perfect match between experimental data and simulation results of the neural networks, with absolute mean errors below the accuracy of the fluximeters. However, when analyzing the simulations carried out with data different to those used to train the networks, the results differ strongly to those expected. This means that neural networks seem to have a bad generalization when dealing with phase change materials under strongly varying conditions, probably due to the high variability of the input data, and the strongly non-linear behavior of phase change processes.