Performance Prediction of Solar Collectors Using Artificial Neural Networks

A new approach based on artificial neural network (ANN) was developed in this study to determine the performance of solar collectors. The experiments were performed under the meteorological conditions of Beijing. Performance parameters obtained from the experimentation were used as training data. The backpropagation learning algorithm and logistic sigmoid transfer function were used in the ANN. Ambient temperature of collector, solar identity, declination angle, azimuth angle and tilt angle are used in the input layer and the efficiency and heating capacity are outputs. The results showed that the ANN with 10 neurons in the hidden layer is the most suitable algorithm with maximum correlation coefficient (R2), minimum root mean square error (RMSE) and low coefficient of variance (COV). Simulation results conformed that the use of ANN for performance prediction of solar collectors is acceptable.