@ARTICLE{Ghritlahre_Harish_Kumar_Solar_2020,
 author={Ghritlahre, Harish Kumar and Chandrakar, Purvi and Ahmad, Ashfaque},
 volume={vol. 41},
 number={No 3},
 journal={Archives of Thermodynamics},
 pages={255-282},
 howpublished={online},
 year={2020},
 publisher={The Committee of Thermodynamics and Combustion of the Polish Academy of Sciences and The Institute of Fluid-Flow Machinery Polish Academy of Sciences},
 abstract={Solar air heater (SAH) is an important device for solar energy utilization which is used for space heating, crop drying, timber seasoning etc. Its performance mainly depends on system parameters, operating parameters and meteorological parameters. Many researchers have been used these parameters to predict the performance of SAH by analytical or conventional approach and artificial neural network (ANN) technique, but performance prediction of SAH by using relevant input parameters has not been done so far. Therefore, relevant input parameters have been considered in this study. Total ten parameters were used such as mass flow rate, ambient temperature, wind speed, relative humidity, fluid inlet temperature, fluid mean temperature, plate temperature, wind direction, solar elevation and solar intensity to find out the relevant parameters for ANN prediction. Seven different neural models have been constructed using these parameters. In each model 10 to 20 neurons have been selected to find out the optimal model. The optimal neural models for ANN-I, ANN-II, ANN-III, ANN-IV, ANN-V, ANN-VI and ANN-VII were obtained as 10-17-1, 8-14-1, 6-16-1, 5- 14-1, 4-17-1, 3-16-1 and 2-14-1, respectively. It has been found that ANN-II model with 8-14-1 is the optimal model as compared to other neural models. Values of the sum of squared errors, mean relative error, and coefficient of determination were found to be 0.02138, 1.82% and 0.99387, respectively, which shows that the ANN-II developed with mass flow rate, ambient temperature, inlet and mean temperature of air, plate temperature, wind speed and direction, relative humidity, and relevant input parameters performed better. The above results show that these eight parameters are relevant for prediction.},
 type={Article},
 title={Solar air heater performance prediction using artificial neural network technique with relevant input variables},
 URL={http://ochroma.man.poznan.pl/Content/117618/PDF/10_paper.pdf},
 doi={10.24425/ather.2020.134579},
 keywords={Artificial Neural Network, Solar air heater, Thermal performance, Multi-layer perceptron},
}