Performance Analysis of a Hybrid Photovoltaic Thermal Single Pass Air Collector Using ANN

  • Deepali Kamthania
  • Sujata Nayak
  • G. N. Tiwari
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)


This paper presents the performance analysis of semi transparent hybrid photovoltaic single pass air collector considering four weather conditions (a, b, c and d type) of New Delhi weather station of India using ANN technique. The MATLAB 7.1 neural networks toolbox has been used for defining and training of ANN for calculations of thermal, electrical, overall thermal energy and overall exergy. The ANN models use ambient temperature, number of clear days, global and diffuse radiation as input parameters. The transfer function, neural network configuration and learning parameters have been selected based on highest convergence during training and testing of networks. About 3000 sets of data from four weather stations (Bangalore, Mumbai, Srinagar, and Jodhpur) have been given as input for training and data of the fifth weather station (New Delhi) has been used for testing purpose. ANN model has been tested with Levenberg-Marquardt training algorithm to select the best training algorithm. The feedforward back-propagation algorithm with logsig transfer function has been used in this analysis. The results of ANN model have been compared with analytical values on the basis of root mean square error.


Hybrid photovoltaic thermal (HPVT) Single pass air collector (SPAC) Log Sigmoid (logsig) Root Mean Square Error (RMSE) Lavenberg - Marguardt (LM) Artificial Neural Network (ANN) 


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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Deepali Kamthania
    • 1
  • Sujata Nayak
    • 1
  • G. N. Tiwari
    • 1
  1. 1.Center for Energy StudiesIndian Institute of Technology DelhiNew DelhiIndia

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