Comparison of performance prediction of solar water heaters between Artificial Neural Networks and conventional correlations. (19th March 2009)

• Record Type: Journal Article
• Authors: Razavi, J.; Riazi, M.R.; Raoufi, F.; Sadeghi, A.
• Is Part Of: International journal of global energy issues. Volume 31:Number 2(2009)
• Page Start: 122
• Page End: 131
• Publication Date: 2009-03-19
• Subjects: heat transfer coefficient -- artificial neural networks -- ANNs -- performance evaluation -- solar water heaters -- outlet temperature -- polypropylene tubes -- copper tubes -- linear regression analysis; Power resources -- Periodicals; 333.79
• Languages: English
• ISSNs: 0954-7118

Abstract

The aim of this study was to develop a predictive method for heat transfer coefficients in solar water heaters and their performance evaluation of such heaters with different materials used as heat collectors. Two approaches have been used: conventional method and an Artificial Neural Network (ANN) to predict the performance of solar water heaters and to compare these two approaches. This performance is measured in terms of outlet temperature by using a set of conventional feed forward multi-layer neural networks. The actual experimental data which were used as our network's input gathered from published literature (for polypropylene tubes) and from the experiments carried out recently using copper tubes. The results of this study showed that ANN approach can give better approximation than the traditional theoretical correlations which was obtained by linear regression analysis.