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Comparison of performance prediction of solar water heaters between artificial neural networks and conventional correlations

Razavi, J ; Sharif University of Technology | 2009

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  1. Type of Document: Article
  2. DOI: 10.1504/IJGEI.2009.023889
  3. Publisher: 2009
  4. Abstract:
  5. 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 snowed that ANN approach can give better approximation than the traditional theoretical correlations which was obtained by linear regression analysis. Copyright © 2009 Inderscience Enterprises Ltd
  6. Keywords:
  7. Heat transfer coefficient ; Neural network ; Performance evaluation ; Solar water heaters ; Artificial neural networks ; Comparison of performance ; Conventional methods ; Copper tubes ; Experimental datum ; Feed forwards ; Linear regression analysis ; Multi-layer neural networks ; Outlet temperatures ; Performance evaluation ; Polypropylene tubes ; Predictive methods ; Backpropagation ; Heat exchangers ; Heat transfer coefficients ; Heating equipment ; Neural networks ; Regression analysis ; Solar heating ; Artificial neural network ; Comparative study ; Copper ; Experimental study ; Heat transfer ; Performance assessment ; Solar power
  8. Source: International Journal of Global Energy Issues ; Volume 31, Issue 2 , 2009 , Pages 122-131 ; 09547118 (ISSN)
  9. URL: http://www.inderscience.com/offer.php?id=23889