volume | PIER Journals

Abstract

In this paper, an innovative machine learning (ML) approach for the prediction of the output power generated by photovoltaic (PV) plants is presented. Toward this end, a two-step learning-by-examples (LBE) strategy based on support vector regression (SVR) is proposed to learn the complex relation among the heterogeneous parameters affecting the energy production of the power plant. More specifically, the first step is aimed at down-scaling the weather forecasts from the standard air temperature and the solar irradiance to the local module temperature and the plane-of-array (POA) irradiance. Then, the second step predicts the output power profile given the down-scaled forecasts estimated at the previous step. The advantages and the limitations of the proposed two-step approach have been experimentally analyzed exploiting a set of measurements acquired in a real PV plant. The obtained results are presented and discussed to point out the capabilities of the proposed LBE method to provide robust and reliable power predictions starting from simple weather forecasts.

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].
doi:10.3390/en12091621 Google Scholar

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.
doi:10.1080/09205071.2017.1402713 Google Scholar

3. Nespoli, A., et al. "Day-ahead photovoltaic forecasting: A comparison of the most effective techniques," Energies, Vol. 12, No. 9, 1621, 2019. Google Scholar

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.
doi:10.1109/LAWP.2019.2916369 Google Scholar

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.
doi:10.2528/PIERC22013002 Google Scholar

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.
doi:10.1109/TGRS.2016.2591439 Google Scholar

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. Google Scholar

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.
doi:10.1109/TAP.2018.2835566 Google Scholar

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.
doi:10.1002/mop.30821 Google Scholar

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.
doi:10.2528/PIERM20050805 Google Scholar

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.
doi:10.1109/JERM.2019.2893217 Google Scholar

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.
doi:10.2528/PIERM16091803 Google Scholar

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.
doi:10.1080/09205071.2019.1572546 Google Scholar

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.
doi:10.2528/PIERL22031901 Google Scholar

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. Google Scholar

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.
doi:10.1109/TSG.2011.2158563 Google Scholar

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.
doi:10.1109/TNNLS.2012.2216546 Google Scholar

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.
doi:10.1002/pip.1033 Google Scholar

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.
doi:10.1016/0038-092X(91)90023-P Google Scholar

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.
doi:10.1109/TSTE.2014.2381224 Google Scholar

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.
doi:10.1109/TIA.2012.2190816 Google Scholar

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. Google Scholar

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. Google Scholar

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. Google Scholar

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.
doi:10.1016/j.solener.2010.02.006 Google Scholar

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. Google Scholar

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.
doi:10.1109/TIA.2012.2190816 Google Scholar

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. Google Scholar

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.
doi:10.1109/JPHOTOV.2012.2203794 Google Scholar

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. Google Scholar

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. Google Scholar

34. Smola, A. J. and B. Scholkopf, "From regularization operators to support vector kernels," Neural Information Processing Systems, MIT Press, Cambridge, MA, 1997.
doi:10.1109/TSTE.2014.2359974 Google Scholar

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. Google Scholar

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. Google Scholar

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. Google Scholar

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. Google Scholar

Dr. Alessandro Polo