1. Guermoui, M., Abdelaziz, R., Gairaa, K., Djemoui, L., & Benkaciali, S., 2020, New temperature-based predicting model for global solar radiation using support vector regression, International Journal of Ambient Energy. https://doi.org/10.1080/01430750.2019.1708792
2. Tuononen, M., O’Connor, E. J., & Sinclair, V. A., 2019, Evaluating solar radiation forecast uncertainty, Atmospheric Chemistry and Physics, 19(3): 1985–2000. https://doi.org/10.5194/acp-19-1985-2019
3. Basaran, K., Özçift, A., & Kılınç, D., 2019, A New Approach for Prediction of Solar Radiation with Using Ensemble Learning Algorithm, Arabian Journal for Science and Engineering, 44(8): 7159–7171. https://doi.org/10.1007/s13369-019-03841-7
4. Ghimire, S., Deo, R. C., Raj, N., & Mi, J., 2019, Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms, Applied Energy, 253: 113541. https://doi.org/10.1016/j.apenergy.2019.113541
5. Beyaztas, U., Salih, S. Q., Chau, K.-W., Al-Ansari, N., & Yaseen, Z. M., 2019, Construction of functional data analysis modeling strategy for global solar radiation prediction: application of cross-station paradigm, Engineering Applications of Computational Fluid Mechanics, 13(1): 1165–1181. https://doi.org/10.1080/19942060.2019.1676314
6. Ghimire, S., Deo, R. C., Downs, N. J., & Raj, N., 2019, Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia, Journal of Cleaner Production, 216: 288–310. https://doi.org/10.1016/j.jclepro.2019.01.158
7. Khatib, T., 2015, A Novel Approach for Solar Radiation Prediction Using Artificial Neural Networks, Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 37(22): 2429–2436. https://doi.org/10.1080/15567036.2012.713080
8. Kuhe, A., Achirgbenda, V. T., & Agada, M., 2019, Global solar radiation prediction for Makurdi, Nigeria, using neural networks ensemble, Energy Sources, Part A: Recovery, Utilization and Environmental Effects. https://doi.org/10.1080/15567036.2019.1637481
9. Wang, L., Kisi, O., Zounemat-Kermani, M., Salazar, G. A., Zhu, Z., & Gong, W., 2016, Solar radiation prediction using different techniques: Model evaluation and comparison, Renewable and Sustainable Energy Reviews, 61: 384-397. https://doi.org/10.1016/j.rser.2016.04.024
10. Yadav, A. K., & Chandel, S. S., 2014, May 1, Solar radiation prediction using Artificial Neural Network techniques: A review, Renewable and Sustainable Energy Reviews, 33: 772-781. https://doi.org/10.1016/j.rser.2013.08.055
11. Premalatha, N., & Valan Arasu, A., 2016, Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms, Journal of Applied Research and Technology, 14(3): 206–214. https://doi.org/10.1016/j.jart.2016.05.001
12. Iqdour, R., & Zeroual, A., 2007, Prediction of daily global solar radiation using fuzzy systems, International Journal of Sustainable Energy, 26(1): 19–29. https://doi.org/10.1080/14786450701265371
13. Reza Parsaei, M., Mollashahi, H., Darvishan, A., Mir, M., & Simoes, R., 2020, A new prediction model of solar radiation based on the neuro-fuzzy model, International Journal of Ambient Energy, 41(2): 189–197. https://doi.org/10.1080/01430750.2018.1456964
14. Voyant, C., Notton, G., Kalogirou, S., Nivet, M. L., Paoli, C., Motte, F., & Fouilloy, A., 2017, May 1, Machine learning methods for solar radiation forecasting: A review, Renewable Energy, 105: 569-582. https://doi.org/10.1016/j.renene.2016.12.095
15. Rabehi, A., Guermoui, M., & Lalmi, D., 2020, Hybrid models for global solar radiation prediction: a case study, International Journal of Ambient Energy, 41(1): 31–40. https://doi.org/10.1080/01430750.2018.1443498
16. Reikard, G., 2009, Predicting solar radiation at high resolutions: A comparison of time series forecasts, Solar Energy, 83(3): 342–349. https://doi.org/10.1016/j.solener.2008.08.007
17. Samadianfard, S., Majnooni-Heris, A., Qasem, S. N., Kisi, O., Shamshirband, S., & Chau, K., 2019, Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate, Engineering Applications of Computational Fluid Mechanics, 13(1): 142–157. https://doi.org/10.1080/19942060.2018.1560364
18. Suthar, M., Singh, G. K., & Saini, R. P., 2017, Effects of air pollution for estimating global solar radiation in India, International Journal of Sustainable Energy, 36(1): 20–27. https://doi.org/10.1080/14786451.2014.979348
19. Foued Chabane, Guellai, F., Michraoui, M. Y., Bensahal, D., Bima, A., & Moummi, N., 2019, Prediction of the Global Solar Radiation on Inclined Area, Applied Solar Energy (English translation of Geliotekhnika), 55(1): 41–47. https://doi.org/10.3103/S0003701X19010055
20. Chabane, F., Khadraoui, Z., & Bensahal, D., 2019, Prediction of Global Solar Radiation on the Horizontal Area with the Effect of Ambient Temperature Part: II, TECNICA ITALIANA-Italian Journal of Engineering Science, 63(1): 73–77. https://doi.org/10.18280/ti-ijes.630110
21. Chabane, F., Laznek, I., & Bensahal, D., 2018, Prediction of global solar radiation on the horizontal area with the effect of relative humidity part: I, TECNICA ITALIANA-Italian Journal of Engineering Science, 61+1(2): 115–118. https://doi.org/10.18280/ti-ijes.620209
22. Chabane, F., Ghedhifi, M. A., Bensahal, D., Bima, A., & Moummi, N., 2018, Forecast of global solar irradiation with a perfect model according to incline angle, Journal of Power of Technologies, 98(3): 245–254.
23. Chabane, F., Moummi, N., & Brima, A., 2016, Predictions of solar radiation distribution: Global, direct and diffuse light on horizontal surface, European Physical Journal Plus, 131(4): 1–8. https://doi.org/10.1140/epjp/i2016-16106-7