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The solar collector is the heart of any solar energy collection system designed for operation in the low to medium temperature ranges. So, an efficient design of solar collector system, giving optimum performance is required. Though system performance is optimized by many different techniques, however, intelligent system design is an useful technique to optimize the efficiency of such systems. One of the intelligence techniques is Artificial Neural Network (ANN), and it is used in modeling, simulation and control of the system. ANN tool is faster and more accurate to solve complex and nonlinear problems as compared to other conventional techniques. ANN technique is applied in the field of Science, Engineering, Medicine, Defense, Business and Manufacturing etc. The main task of ANN tool is training of structure, which is done by collected experimental data of solar energy systems and in this method separate programming is not required as in other conventional methods. The aim of this study is to review the applications of ANN to predict the performance of solar energy collector and to identify the research gap for future work. Published research works presented in this paper, show that the ANN technique is very appropriate tool to predict the performance of solar collector systems [ABSTRACT FROM AUTHOR]

The exergetic performance of a roughened solar air heater has been predicted using an artificial neural network (ANN) model. The exergetic values have been derived from the data collected by conducting experiments on solar air heater using transverse wire ribs as a roughness element on the absorber plate with relative roughness heights 0.009571, 0.01400, 0.017142 and constant relative roughness pitch 10, at local weather condition of Jamshedpur (22.77° N and 86.14° E), India. The ANN model was structured with seven input parameters such as experimental time, solar radiation intensity, roughness size, atmospheric temperature, mean air temperature, absorber plate temperature and mass flow rate of air in input layer and five parameters such as exergy inlet, exergy outlet, exergy efficiency, exergy destruction and improvement potential in output layer. Levenberg-Marquardt (LM), scaled conjugate gradient (SCG) and Polak-Ribiére conjugate gradient (CGP) learning algorithms have been used for training the proposed model. The LM learning algorithms with 7 neurons in the hidden layer are found to be optimal on the basis of statistical error analysis. The value of R2 for the predicted model of exergy outlet, exergy inlet, exergetic efficiency, exergy destruction and improvement potential were 0.99584, 0.99997, 0.99517, 0.99997 and 0.99983 respectively, which yields the satisfactory performance of the ANN model. The values of mean square error (MSE), the coefficient of variance (COV), the and mean relative error (MRE) are found to be very low for predicted values of exergetic performance parameters that are desirable. The statistical results exhibit that the proposed multi-layered perceptron (MLP) ANN model successfully predicts the exergetic performance of a roughened solar air heater. [ABSTRACT FROM AUTHOR]