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Determination of Efficiency of Hybrid Photovoltaic Thermal Air Collectors Using Artificial Neural Network Approach for Different PV Technology

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Author(s): Deepali Kamthania | G. N. Tiwari

Journal: BVICAM's International Journal of Information Technology
ISSN 0973-5658

Volume: 4;
Issue: 1;
Date: 2012;
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Keywords: Artificial neural network (ANN) | Efficiency | Photovoltaic thermal (PVT) | Levenberg–Marquardt (LM) | Multi-layer perceptron (MLP) | Mean Bias Error (MBE) | Single pass (SP) | Double pass (DP). 2

ABSTRACT
In this paper an attempt has been made to determine efficiency of semi transparent hybrid photovoltaic thermal double pass air collector for different PV technology and compare it with single pass air collector using artificial neural network (ANN) technique for New Delhi weather station of India. The MATLAB 7.1 neural networks toolbox has been used for defining and training of ANN for determination of thermal, electrical, overall thermal and overall exergy efficiency of the system. The ANN model uses ambient air temperature, number of sunshine hours, number of clear days, temperature coefficient, cell efficiency, 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 network. About 2000 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. It has been observed that the best transfer function for a given configuration is logsig. The feed forward back-propagation algorithm has been used in this analysis. Further the results of ANN model have been compared with analytical values on the basis of root mean square error.
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