Thermal performance estimation of a v-corrugated solar air heater using ann techniques

Abstract

A solar air heater (SAH) is a unique form of solar thermal collector that utilizes solar energy emitted from the sun to produce heated air. Various experimental and theoretical investigations have been undertaken to improve the poor thermal performance of SAHs. The difficulties related to these studies drew attention toward a reliable soft computing technique exemplified by the Artificial Neural Network (ANN) technique. The current work applied actual meteorological data from Miskolc City, Hungary, to an ANN model with the structure of a Multi-layer Perceptron (MLP) to forecast the energy performance of a V-corrugated solar-powered air heater. Seven input parameters and one output parameter make up the ANN structure, with a single hidden layer. For the purpose of selecting the most effective network for predicting output parameters, ten neurons have been assessed. The suggested ANN model was trained with 336 data sets using the Levenberg-Marquardt (LM) learning technique. The comparison of anticipated and real thermal performance values shows a very good agreement. The statistical error analysis showed that the optimal ANN model structure of 7-8-1 can reliably and accurately predict SAH’s thermal performance and thus it can save both time and cost.