[1] Sharadga, H., Hajimirza, S., Balog, R.S.: Time series forecasting of solar power generation for large-scale photovoltaic plants. Renew. Energy 150, 797–807 (2020)

[2] Puah, B.K., et al.: A regression unsupervised incremental learning algorithm for solar irradiance prediction. Renew. Energy 164, 908–925 (2021)

[3] Huang, L., Kang, J., Wan, M., Fang, L., Zhang, C., Zeng, Z.: Solar radiation prediction using different machine learning algorithms and implications for extreme climate events. Front. Earth Sci. 9, 596860 (2021)

[4] Qiu, J., An, X.J., Wu, Z.G., Li, F.F.: Forecasting solar irradiation based on influencing factors determined by linear correlation and stepwise regression. Theor. Appl. Climatol. 140(1), 253–269 (2020)

[5] Narvaez, G., Giraldo, L.F., Bressan, M., Pantoja, A.: Machine learning for site-adaptation and solar radiation forecasting. Renew. Energy 167, 333–342 (2021)

[6] Haldun, A.: Feature selection for support vector machines using generalized benders decomposition. Eur. J. Oper. Res. 244(1), 210–218 (2015)

[7] Haykin, S.: Neural Networks, a Comprehensive Foundation. Macmillan College Publishing Company, N.Y. (1994)

[8] Rahul, K., Awad, M.: efficient learning machines: theories, concepts, and applications for engineers and system designers, Apress Open (2015). https://doi.org/10.1007/978-1-4302-5990-9

[9] Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting, Springer (1997)

[10] Alhoniemi, E., Simula, O., Vesanto, J.: Analysis of complex systems using the self organising map, Laboratory of Computer and Information Science, Helsinki University of Technology, Finland (1997)

[11] Bezdek, J.C.: Pattern Recognition with fuzzy Objective Function Algorithms. Plenum, New York (1981)

[12] Jared, D.S.G., et al.: Annual daily irradiance analysis of clusters in mexico by machine learning algorithms. Remote Sens. 16(4), 709 (2024)