Determination of efficiency of flat-plate solar collectors using neural network approach

In this study, a new formula based on artificial neural network (ANN) technique was developed to determine the efficiency of flat-plate solar collectors. In practice, the ANN model can be used for modeling the efficiency of solar collectors with complex structures when other models may have difficulties. Logistic sigmoid transfer function was used in the network. Meteorological data of summer session (from July to September) for Ankara were used as training data in order to train the neural network. Surface temperature in collector, date, time, solar radiation, declination angle, azimuth angle and tilt angle are used in the input layer of the network. The efficiency of flat-plate solar collector is in the output layer. The results show that the maximum and minimum deviations were found 2.558484 and 0.001969, respectively. The advantages of ANN model compared to the conventional testing methods are speed, simplicity, and the capacity of the ANN to learn from examples.