Abstract
This work was carried out based on a constructed solar chimney with 2 m height and 3 m diameter. The temperature distributions were assessed based on the practical climatic conditions. In this work, the experimental data of temperature were investigated by a group method of data handling (GMDH). This method was applied as an artificial intelligence approach to predict the temperature changes, and also to find out the quality of the experimental data and temperature. In this case, a data set of 2000 condition-parameters for 30 days operation of solar chimney was applied. In order to obtain the network input and output variables, eight and four temperature sensors were set, respectively. In this study, according to the value correlation coefficient (R2) and the root-mean square error (RMSE), the results of the trained networks have been reported. In the modeling and calculations, the ambient temperatures have been considered. Also temperature prediction was carried out with high accuracy. Finally, the results showed that the solar chimney’s experimental data were qualified with no noise and some formulas were obtained for each output based on the temperature input variables.
Abstract by the authors, reproduced under the CC BY 4.0 licence. Original publication via the link below.