Artificial neural network and wavelet neural network approaches for modelling of a solar air heater

• Published in: Expert systems with applications 2009-10, Vol.36 (8), p.11240-11248
• Main Authors: Esen, Hikmet, Ozgen, Filiz, Esen, Mehmet, Sengur, Abdulkadir
• Format: Article
• Language: English
• Subjects: Artificial neural network; Efficiency; Predict; Solar air heater; Temperature; Wavelet neural network
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2009.02.073

This paper reports on a modelling study of new solar air heater (SAH) system by using artificial neural network (ANN) and wavelet neural network (WNN) models. 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 ANN and WNN methods, it has a superior capability for generalization, and this capability is independent on the dimensionality of the input data’s. In this study, an ANN and WNN based methods were intended to adopt SAH system for efficient modelling. To evaluate prediction capabilities of different types of neural network models (ANN and WNN), their best architecture and effective training parameters should be found. The performance of the proposed methodology was evaluated by using several statistical validation parameters. Comparison between predicted and experimental results indicates that the proposed WNN model can be used for estimating the some parameters of SAHs with reasonable accuracy.