Sunspot number-based neural network model for global solar radiation estimation in Ghardaïa

Abstract

In this investigation, the estimation of global solar radiation was meticulously carried out within Ghardaïa city, a region situated in Southern Algeria, utilizing a sophisticated multilayer perceptron (MLP) neural network architecture. This research primarily concentrated on developing a predictive model based on a singular input parameter, specifically, the sunspot numbers, to forecast global solar radiation levels. The model's formulation was rooted in empirical data collected over an extensive period from 1984 to 2000, which was used for training the neural network. To assess the model's predictive accuracy and robustness, data from the years 2001 to 2004 were employed for validation purposes. The outcomes of this study were highly satisfactory, indicating that the MLP-based model possesses a significant predictive capability for Diffuse Global Solar Radiation (DGSR). This is substantiated by robust statistical metrics, including a normalized Root Mean Square Error (nRMSE) of 0.076, reflecting the model's accuracy in prediction, and a correlation coefficient (R) of 93.16%, denoting a strong correlation between the predicted and observed values. These results underscore the model's efficacy and potential application in accurately estimating global solar radiation in the specified region.