Application of ANN model to predict the performance of solar air heater using relevant input parameters

Abstract

In this study, the thermal performance of solar air heater (SAH) has been predicted using artificial neural network (ANN) with relevant input parameters. To complete this aim, two different types of SAHs were developed using roughened (arc shaped wire rib) and smooth duct. Many researchers have been used system parameters, operating parameters and meteorological parameters to predict the performance of SAH by analytical or conventional approach and ANN technique, but performance prediction by using relevant input parameters has not been done so far. Therefore, neural model that has been developed with relevant input parameters is considered in this study. Total ten parameters are used to find out the relevant parameters for prediction. Seven different neural models have been constructed using these parameters. In each, 10 to 20 neurons have been selected to find out optimal model. It has been found that ANN-II with 8-14-1 is the optimal model as compared to other models. The values of SSE, MRE and R2 were found to be 0.02138, 1.82% and 0.99387 respectively, for ANN-II. The effectiveness of neural model has been examined by comparing with Group method of data handling (GMDH) approach and found that the ANN performed better than GMDH model. In addition to this, sensitivity analysis has been performed to find out the most sensitive input parameter and observed that the mass flow rate of air is the most effective input parameter.