Neural network modelling of flat-plate solar collectors

In this study, different approaches to the modelling of flat-plate solar collectors are introduced and analysed. Among the physically based models, the heat network model and Hottel–Vhillier (H–V) models are discussed. The parameters of the latter model are identified for three different types of these solar collectors. The identification exhibited good agreement with the measured values. Finally, modelling simulations with an artificial neural network (ANN) technique were carried out. A sensitivity study was performed on the parameters of the neural network. The possible ANN structures, the size of training data set, the number of hidden neurons, and the type of training algorithm were analysed in order to identify the most appropriate model. The same ANN structures were trained and validated for the three solar collectors, using data generated from the H–V model and long-term (17 days) measurements.