1. UN General Assembly, "Transforming our world: The 2030 Agenda for Sustainable Development,", Oct. 21, 2015, A/RES/70/1, available at: https://www.refworld.org/docid/57b6e3e44.html [accessed Jun. 7, 2022].
2. GSE, "ARG/elt 4/10 --- Procedure to improve the predictability of the electricity input from implants using renewable energy sources that are not programmable about not relevant production units,", Jan. 2010.
3. Nespoli, A., et al., "Day-ahead photovoltaic forecasting: A comparison of the most effective techniques," Energies, Vol. 12, No. 9, 1621, 2019.
4. Massa, A., G. Oliveri, M. Salucci, N. Anselmi, and P. Rocca, "Learning-by-examples techniques as applied to electromagnetics," Journal of Electromagnetic Waves and Applications, Vol. 32, No. 4, 516-541, 2018.
5. Salucci, M., M. Arrebola, T. Shan, and M. Li, "Artificial intelligence: New frontiers in real-time inverse scattering and electromagnetic imaging," IEEE Trans. Antennas Propag., DOI: 10.1109/TAP.2022.3177556.
6. Massa, A., D. Marcantonio, X. Chen, M. Li, and M. Salucci, "DNNs as applied to electromagnetics, antennas, and propagation --- A review," IEEE Antennas Wireless Propag. Lett., Vol. 18, No. 11, 2225-2229, Nov. 2019.
7. Elshennawy, W., "Large intelligent surface-assisted wireless communication and path loss prediction model based on electromagnetics and machine learning algorithms," Progress In Electromagnetics Research C, Vol. 119, 65-79, 2022.
8. Salucci, M., N. Anselmi, G. Oliveri, P. Calmon, R. Miorelli, C. Reboud, and A. Massa, "Real-time NDT-NDE through an innovative adaptive partial least squares SVR inversion approach," IEEE Trans. Geosci. Remote Sens., Vol. 54, No. 11, 6818-6832, Nov. 2016.
9. Liu, F., Y. Wu, H. Duan, and R. Du, "SVR-CMT Algorithm for null broadening and sidelobe control," Progress In Electromagnetics Research, Vol. 163, 39-50, 2018.
10. Salucci, M., L. Tenuti, G. Oliveri, and A. Massa, "Efficient prediction of the EM response of reflectarray antenna elements by an advanced statistical learning method," IEEE Trans. Antennas Propag., Vol. 66, No. 8, 3995-4007, Aug. 2018.
11. Salucci, M., J. Vrba, I. Merunka, and A. Massa, "Real-time brain stroke detection through a learning-by-examples technique --- An experimental assessment," Microw. Opt. Technol. Lett., Vol. 59, No. 11, 2796-2799, Aug. 2017.
12. Hosseinzadeh, S. and M. Shaghaghi, "GPR data regression and clustering by the fuzzy support vector machine and regression," Progress In Electromagnetics Research M, Vol. 93, 175-184, 2020.
13. Salucci, M., G. Oliveri, and A. Massa, "Real-time electrical impedance tomography of the human chest by means of a learning-by-examples method," IEEE J. Electromagn., RF, Microw. Med. Biol., Vol. 3, No. 2, 88-96, Jun. 2019.
14. Li, H., B. Zhu, and J. Chen, "Optimal design of photonic band-gap structure based on Kriging surrogate model," Progress In Electromagnetics Research M, Vol. 52, 1-8, 2016.
15. Salucci, M., N. Anselmi, G. Oliveri, P. Rocca, S. Ahmed, P. Calmon, R. Miorelli, C. Reboud, and A. Massa, "A nonlinear kernel-based adaptive learning-by-examples method for robust NDE-NDT of conductive tubes," Journal of Electromagnetic Waves and Applications, Vol. 33, No. 6, 669-696, Feb. 2019.
16. Rayala, R. and S. Raghavan, "Hexagon shape SIW bandpass filter with CSRRS using artificial neural networks optimization," Progress In Electromagnetics Research Letters, Vol. 104, 47-55, 2022.
17. Oliveri, G., M. Salucci, and A. Massa, "Towards efficient reflectarray digital twins --- An EM-driven machine learning perspective," IEEE Trans. Antennas Propag., DOI: 10.1109/TAP.2022.3155204.
18. Tanaka, K., K. Uchida, K. Ogimi, T. Goya, A. Yona, and T. Senjyu, "Optimal operation by controllable loads based on smart grid topology considering insolation forecasted error," IEEE Trans. Smart Grid, Vol. 2, No. 3, 438-444, Sep. 2011.
19. Capizzi, G., C. Napoli, and F. Bonanno, "Innovative second-generation wavelets construction with recurrent neural networks for solar radiation forecasting," IEEE Trans. Neural Netw. Learn. Syst., Vol. 23, No. 11, 1805-1815, Nov. 2012.
20. Lorenz, E., T. Scheidsteger, J. Hurka, D. Heinemann, and C. Kurz, "Regional PV power prediction for improved grid integration," Progress in Photovoltaic: Research and Applications, Vol. 19, 757-771, 2011.
21. Knight, K. M., S. A. Klein, and J. A. Duffie, "A methodology for the synthesis of hourly weather data," Solar Energy, Vol. 46, No. 2, 109-120, 1991.
22. Liu, J., W. Fang, X. Zhang, and C. Yang, "An improved photovoltaic power forecasting model with the assistance of aerosol index data," IEEE Trans. Sustain. Energy, Vol. 6, No. 2, 434-442, Apr. 2015.
23. Shi, J., W. J. Lee, Y. Liu, Y. Yang, and P. Wang, "Forecasting power output of photovoltaic systems based on weather classification and support vector machines," IEEE Trans. Ind. Appl., Vol. 48, No. 3, 1064-1069, May 2012.
24. Yona, A., T. Senijyu, A. Y. Seber, T. Funabashi, H. Sekine, and C. H. Kim, "Application of neural network to 24-hour-ahead generating power forecasting for PV system," Proceedings of Power and Energy Society General Meeting --- Conversion and Delivery of Electrical Energy in 21st Century, 1-6, 2008.
25. Wang, F., Z. Mi, S. Su, M. Chen, and C. Zhang, "practical model for single-step power prediction of grid-connected PV plant using artificial neural network," IEEE Innovative Smart Grid Technologies Asia (ISGT), 1-4, 2011.
26. Fonseca, J. G., T. OOzeki, T. Takashima, G. Koshimizu, Y. Uchida, and K. Ogimoto, "Photovoltaic power production forecasts with support vector regression: A study on the forecast horizon," 37th IEEE Photovoltaic Specialists Conference (PVSC) 2011, 1-6, Seattle, WA, USA, Jun. 2011.
27. Mellit, A. and A. M. Pavan, "A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy," Solar Energy, Vol. 84, No. 5, 807-821, 2010.
28. Shi, J., W.-J. Lee, Y. Liu, Y. Yang, and P. Wang, "Forecasting power output of photovoltaic system based on weather classification and support vector machine," Industry Applications Society Annual Meeting (IAS), 1-6, Orlando, FL, USA, Oct. 2011.
29. Shi, J., W.-J. Lee, Y. Liu, Y. Yang, and P. Wang, "Forecasting power output of photovoltaic systems based on weather classification and support vector machine," IEEE Trans. on Industry App., Vol. 46, No. 3, 1064-1069, Jun. 2012.
30. Luque, A. and S. Hegedus, Handbook of Photovoltaic Science and Engineering, 2nd Ed., Wiley, New York, 2011.
31. Colli, A. and W. J. Zaaiman, "Maximum-power-based PV performance validation method: Application to single-axis tracking and fixed-tilt c-Si systems in the Italian Alpine region," IEEE J. Photovolt., Vol. 2, No. 4, 555-563, Oct. 2012.
32. Cristianini, N. and J. S. Taylor, An Introduction to Support Vector Machine, Cambridge University Press, Cambridge, U.K., 2000.
33. Ito, K. and R. Nakano, "Optimizing support vector regression hyperparameters based on cross-validation," Proceedings of the International Joint Conference on Neural Networks, Vol. 3, 2077-2082, Jul. 2003.
34. Smola, A. J. and B. Scholkopf, "From regularization operators to support vector kernels," Neural Information Processing Systems, MIT Press, Cambridge, MA, 1997.
35. Mellit, A., A. Massi Pavan, and V. Lughi, "Short-term forecasting of power production in a large-scale photovoltaic plant," Sol. Energy J., Vol. 105, 401-413, Jul. 2014.
36. Yang, C., A. A. Thatte, and L. Xie, "Multitime-scale data-driven spatio-temporal forecast of photovoltaic generation," IEEE Trans. Sustain. Energy, Vol. 6, 104-112, Jan. 2015.
37. Yang, H. T., C. Huang, Y. C. Huang, and Y. Pai, "A weather-based hybrid method for one-day ahead hourly forecasting of PV power output," IEEE Trans. Sustain. Energy, Vol. 5, 917-926, Jul. 2014.
38. Chen, C., S. Duan, T. Cai, and B. Liu, "Online 24-h solar power forecasting based on weather type classification using artificial neural network," Sol. Energy J., Vol. 85, 2856-2870, Nov. 2011.