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Artificial Neural Networks Modeling of a Shallow Solar Pond

  • Abdelkrim Terfai
  • Younes Chiba
  • Mohamed Najib Bouaziz
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 62)

Abstract

The aim of this work is to use multi-layered feed-forward back-propagation artificial neural networks and multiple linear regressions models to predict the efficiency of the shallow solar pond. For this purpose, the experimental data collection including wind speed, solar radiation, ambient air temperature, inlet temperature of fluid and mass flow rate of the heat transfer fluid was used in order to predict pond water temperature, outlet temperature of the fluid, rate of heat the heat transfer fluid and instantaneous collection efficiency of a shallow solar pond. In addition, the obtained results are presented and discussed.

Keywords

Renewable energy Solar energy Shallow solar pond Artificial neural networks Numerical simulation 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Abdelkrim Terfai
    • 1
  • Younes Chiba
    • 1
  • Mohamed Najib Bouaziz
    • 1
  1. 1.LBMPT, Mechanical Engineering Department, Faculty of TechnologyUniversity Yahia FaresMedeaAlgeria

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