Optimization of thermal energy storage systems for domestic solar thermal applications

Abstract

The current study presents a methodical framework to fully explore the possibility and potential of Artificial neural network (ANN) modeling to predict the performance of an indirect hybrid solar thermal storage system involving phase change materials (PCM) for domestic water heating application. A fully validated and tested numerical model simulating the performance of an indirect hybrid solar thermal storage system developed by others in previous work is adapted in the current study to produce the training, validation, and testing data necessary to adequately train and test the ANN model. To fully test the predictive and generalization ability of the ANN model; the studied system parameters (i.e., collector area, tank volume, demand temperature, PCM melting temperature, and PCM volume fraction) were varied over an extended range of values. To effectively train the ANN model; the training set must be optimized (i.e., Sampling method, and number of training samples). Three sampling techniques were investigated as follows: