Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique

Abstract

In the present study three different types of neural models: multi-layer perceptron (MLP), generalized regression neural network (GRNN) and radial basis function (RBF) has been used to predict the exergetic efficiency of roughened solar air heater. The experiments were conducted at NIT Jamshedpur, India, using two different types of absorber plate: arc shape wire rib roughened with relative roughness height 0.0395, relative roughness pitch 10 and angle of attack 60°, and smooth absorber plates for 7 days. Total 210 data sets were collected from the experiments. Mass flow rate, relative humidity, wind speed, ambient air temperature, inlet air temperature, mean air temperature, average plate temperature and solar intensity were selected as input parameters in input layer to estimate the exergetic efficiency. In the first part of study, MLP model has been used. In this model 10–20 neurons with LM learning algorithm were used in hidden layer for optimal model selection. It has been found that LM-18 is an optimal model. In second part, GRNN model was used. The GRNN model was simulated experimentally at different spread constants and found that keeping spread constant as 1.5, optimal results have been obtained. In the third part, RBF model was used. For optimal model, 1–5 spread constant at interval of 0.5 have been used. It has been found that by taking spread constant 3.5, best results are obtained. In the last part of the study, all neural models are compared on the basis of statistical error analysis. It has been found that RBF model is better than GRNN and MLP models due to lowest value of RMSE and MAE and highest value of R2 and ME. After RBF model, GRNN model performs better results as compared to MLP model. It has been found that the values of RMSE, MAE and R2 were 0.001652, 2.86E-04 and 0.99999 respectively for RBF model.