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 (R 2 ) 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.
Highlights
Renewable energy technologies are the clean sources of energy that have a much lower environmental impact than conventional energy technologies
The results showed solar chimney power plants at high latitudes produce as much as 85% of the same plants in the southern locations
2000 pattern numbers, which have been gained from experimental device, are used for both training the polynomial neural network according to R2 and root-mean square error (RMSE) and prediction output data
Summary
Renewable energy technologies are the clean sources of energy that have a much lower environmental impact than conventional energy technologies. Xu et al [18] carried out numerical simulations on air flow, heat transfer and power output characteristics of the solar chimney power plant This model included energy storage layer and turbine which was similar to the Manzanares prototype. They proposed mathematical model of flow and heat transfer for the solar chimney power plant system. An exhaustive theoretical model for the performance evaluation of a solar chimney power plant was proposed by Li et al [25] and the results were verified by the experimental data of the Manzanares pilot They evaluated the effects of chimney height and collector radius on the power output of a solar chimney. 2000 pattern numbers, which have been gained from experimental device, are used for both training the polynomial neural network according to R2 and RMSE and prediction output data
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