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Guijo-Rubio, D., Durán-Rosal, A., Gutiérrez, P., Gómez-Orellana, A., Casanova-Mateo, C., Sanz-Justo, J., Salcedo-Sanz, S. and Hervás-Martínez, C. (2020) Evolutionary Artificial Neural Networks for Accurate Solar Radiation Prediction. Energy, 210, Article ID: 118374.
https://doi.org/10.1016/j.energy.2020.118374

has been cited by the following article:

• TITLE: Empirical Models for the Evaluation of Global Solar Radiation for the Site of Abeche in the Province of Ouaddaï, in Chad

  AUTHORS: Marcel Hamda Soulouknga, Abraham Dandoussou, Noel Djongyang

  KEYWORDS: Empirical Models, Statistical Indicators, Solar Radiation, Abeche

  JOURNAL NAME: Smart Grid and Renewable Energy, Vol.13 No.10, October 28, 2022

  ABSTRACT: The global sustainability plan for future development relies on solar radiation which is the main source of renewable energy. Thus, this work studies the performance of six models to estimate global solar radiation on a horizontal surface for the Abeche site in Chad. The data used in this work were collected at the General Directorate of National Meteorology of Chad. The reliability and accuracy of different models for estimating global solar radiation were validated by statistical indicators to identify the most accurate model. The results show that among all the models, the Sabbagh model has the best performance in estimating the global solar radiation. The average is 6.354 kWh/m2 with an average of -3.704%. This model is validated against NASA data which is widely used.