Performance analysis of a hybrid photovoltaic thermal double pass air collector using ANN

This paper presents the use of artificial neural network for performance analysis of a semi transparent hybrid photovoltaic thermal double pass air collector for four weather conditions (a, b, c and d type) of New Delhi. The MATLAB 7.1 neural networks toolbox has been used for defining and training of ANN for calculations of thermal energy, electrical energy, overall thermal energy and overall exergy. The ANN model uses ambient air temperature, global solar radiation, diffuse radiation and number of clear days as input parameters for four weather conditions. 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 feedforward 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.