Features Selection and Optimized Neural Network Architecture for Modelling Flows in Solar Collectors

Abstract

Selection of the most relevant features for training of an artificial neural network (ANN) is of significant importance affecting largely the complexity and accuracy of the trained network. Moreover, deciding the topology and architecture of an ANN including the training algorithm, number of hidden layers, activation functions and neurons in each layer is challenging for the effectiveness of an ANN to solve a particular problem. In this paper, we present a novel strategy to result into an optimized topological architecture for an ANN and selection method for the most important experimental features available for training. The proposed method is found to efficiently design and model flows in solar collectors and subsequently estimating pressure loss coefficients for flows in the tee junctions vastly used in solar collectors with high accuracy. Experimental results demonstrate that the number of features used in training of ANN can be reduced up to 85% by identifying the features upon which the pressure loss coefficients’ estimation shows strong dependency. Performance analysis of various training algorithms has been carried out with variation in hidden layers, activation functions and neurons that results into minimum mean square error leading to minimal percent relative error.