Modeling and estimation of solar radiation of Karachi through artificial neural network (ANN) using temperature and dew-point

  • Atif Idrees Department of Basic Sciences, DHA Suffa University, Karachi, Pakistan | Department of Mathematics, University of Karachi, Karachi, Pakistan
  • Naeem sadiq Institute of Space Science and Technology, University of Karachi, Karachi Pakistan
  • Mahwish Mobeen Khan Department of Applied Chemistry and Chemical Technology, University of Karachi, Karachi, Pakistan
  • Syed Ahmed Hassan Department of Mathematics, University of Karachi, Karachi, Pakistan
  • Zaheer Uddin Khan Department of Physics, University of Karachi, Karachi, Pakistan

Abstract

The most influential source of energy in our lives is solar energy. Solar energy reaches the earth in three different forms, i.e., Global, diffused, and Direct Solar Radiation. The Solar flux at the earth's surface depends on the intensity of these radiations and is a function of the values of latitude and longitude. The earth's temperature and hence dewpoint are greatly affected by solar flux. This idea is used for predicting solar radiation with input parameters, temperature, and dewpoint along with day number and month. The method of prediction of solar radiation used in the study is Artificial Neural Network (ANN). ANN has four variables in the input, ten neurons in the hidden layer, and three output parameters GSR. DSR and BSR. Six different types of errors, namely, Root Mean Square error (RMSE), Mean Absolute Error (MABE), Mean percent error (MAPE), Chi-square, Coefficient of Determination, Kolmogorov Smirnov, have been calculated for training, testing, and validation mode to check the accuracy of estimation. The values of all the errors are low, which indicates the prediction of solar radiation is reliable.

Published
Jul 21, 2023
How to Cite
IDREES, Atif et al. Modeling and estimation of solar radiation of Karachi through artificial neural network (ANN) using temperature and dew-point. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 42, n. 3, p. 119-128, july 2023. ISSN 2413-7219. Available at: <https://publications.muet.edu.pk/index.php/muetrj/article/view/2777>. Date accessed: 24 may 2024. doi: http://dx.doi.org/10.22581/muet1982.2303.13.
Section
Articles
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License