Investigation of thermal performance of unidirectional flow porous bed solar air heater using MLP, GRNN, and RBF models of ANN technique

In the present work, Multi-layer perceptron (MLP), Generalized regression neural network (GRNN), Radial basis function (RBF) and Multiple linear regression (MLR) models has been used to predict the thermal performance of unidirectional flow porous bed solar air heater. These four models have been constructed on the basis of actual experimental data and calculated values. Total 96 experimental data sets have been used in the present work. In GRNN, RBF and MLP models, six input parameters such as mass flow rate, wind speed, atmospheric temperature, inlet fluid temperature, fluid mean temperature and solar intensity were used in input layer, and one variable, the thermal efficiency was used in output layer. Same parameters were used in MLR model. It is observed that GRNN model is the best model due to lowest error and highest value of R2 as compared to MLP, RBF and MLR model performances. It is found that the value of MAE, RMSE and R2 for GRNN model are 1.1128E−03, 5.9284E−06 and 0.99758 respectively, and the model efficiency is 0.99760, which is the highest value as compared to other model. These results confirmed that the GRNN model is appropriate model to predict the thermal performance of solar air heater.