Modelling of a new solar air heater through least-squares support vector machines - Esen, Hikmet; Ozgen, Filiz; Esen, Mehmet; Sengur, Abdulkadir (Pergamon-Elsevier Science Ltd, 2009)
This paper reports on a modelling study of new solar air heater (SAH) system efficiency by using leastsquares support vector machine (LS-SVM) method. In this study, a device for inserting an absorbing plate made of aluminium cans into the double-pass channel in a flat-plate SAH. A SAH system is a multi-variable system that is hard to model by conventional methods. As regards the LS-SVM, it has a superior capability for generalization, and this capability is independent on the dimensionality of the input data. In this study, a LS-SVM based method was intended to adopt SAH system for efficient modelling. For modelling, different mass flow rates in flow duct and collector types are used and then for obtaining the optimum LS-SVM parameters, such as regularization parameter, and optimum kernel function and parameters, several tests have been carried out. The performance of the proposed methodology was evaluated by using several statistical validation parameters. It is found that root mean squared error (RMSE) value is 0.0024, the coefficient of multiple determinations (R(2)) value is 0.9997 and coefficient of variation (cov) value is 2.1194 for the proposed radial basis function (RBF)-kernel LS-SVM method at 0.03 kg/s air mass flow rate. It is found that RMSE value is 0.0135, R(2) value is 0.9991 and cov value is 2.9868 for the proposed RBF-kernel LS-SVM method at 0.05 kg/s air mass flow rate. Comparison between predicted and experimental results indicates that the proposed LS-SVM model can be used for estimating the efficiency of SAHs with reasonable accuracy. (C) 2009 Elsevier Ltd. All rights reserved.
Keywords: Solar air heater; Least-squares support vector machine; Predict; Efficiency; Kernel