Prediction of the outlet flow temperature in a flat plate solar collector using artificial neural network

In the current research, the efficiency of a solar flat plate collector (SFPC) was examined experimentally, while the system was modeled with an artificial neural network (ANN) under semi-arid weather conditions of Rafsanjan, Iran. Based on the backpropagation algorithm, a feedforward neural network was established to estimate and forecast the outlet flow temperature of SFPC. To identify the most appropriate model, the ANN topology hidden layer, the number of hidden neurons, iteration, and statistical indicators were analyzed. In the first ANN modeling (CASE I), five parameters, including solar radiation, inlet flow temperature, flow rate, ambient temperature, and wind speed, were applied in the input layer of the network, while the output flow temperature (subsequently efficiency) was in the output layer. In the second artificial neural network modeling (CASE II), the wind speed was omitted from the input of the ANN model. Results showed that the ANN with four inputs yields more accurate results for both estimation and prediction of outlet flow temperature.