Cubist Regression, Random Forest and Support Vector Regression for Solar Power Prediction

Main Article Content

Souhaila Chahboun
Mohamed Maaroufi

Abstract

At a time when the energy transition is inescapable and artificial intelligence is rapidly advancing in all directions, solar renewable energy output forecasting is becoming a popular concept, especially with the availability of large data sets and the critical requirement to forecast these energies, known to have a random nature. Therefore, the main goal of this study is to investigate and exploit artificial intelligence's revolutionary potential for the prediction of the electricity generated by solar photovoltaic panels. The main algorithms that will be studied in this article are cubist regression, random forest and support vector regression. This forecast is beneficial to both providers and consumers, since it will enable for more efficient use of solar renewable energy supplies, which intermittency makes their integration into the existing electrical networks a challenging task.

Article Details

How to Cite
[1]
S. . Chahboun and M. . Maaroufi, “Cubist Regression, Random Forest and Support Vector Regression for Solar Power Prediction”, J. Ren. Energies, vol. 1, no. 1, pp. 65 -, Jun. 2022.
Section
special

References

S. K. H. Chow, E. W. M. Lee, and D. H. W. Li, “Short-term prediction of photovoltaic energy generation by intelligent approach,” Energy Build., vol. 55, pp. 660–667, 2012, doi: 10.1016/j.enbuild.2012.08.011.

B. Wang, J. Che, B. Wang, and S. Feng, “A Solar Power Prediction Using Support Vector Machines Based on Multi-source Data Fusion,” 2018 Int. Conf. Power Syst. Technol. POWERCON 2018 - Proc., no. 201805280000160, pp. 4573–4577, 2019, doi: 10.1109/POWERCON.2018.8601672.

S. Jogunuri and F. T. Josh, “Artificial intelligence methods for solar forecasting for optimum sizing of PV systems: A review,” Res. J. Chem. Environ., vol. 24, no. I, pp. 174–180, 2020.

K. A. Baharin, H. Abd Rahman, M. Y. Hassan, and C. K. Gan, “Hourly Photovoltaics Power Output Prediction for Malaysia Using Support Vector Regression,” Appl. Mech. Mater., vol. 785, pp. 591–595, 2015, doi: 10.4028/www.scientific.net/amm.785.591.

M. H. D. M. Ribeiro and L. dos Santos Coelho, “Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series,” Appl. Soft Comput. J., vol. 86, p. 105837, 2020, doi: 10.1016/j.asoc.2019.105837.

L. Visser, T. Alskaif, and W. Van Sark, “Benchmark analysis of day-ahead solar power forecasting techniques using weather predictions,” Conf. Rec. IEEE Photovolt. Spec. Conf., pp. 2111–2116, 2019, doi: 10.1109/PVSC40753.2019.8980899.

K. John, N. M. Kebonye, P. C. Agyeman, and S. K. Ahado, “Comparison of Cubist models for soil organic carbon prediction via portable XRF measured data,” Environ. Monit. Assess., vol. 193, no. 4, pp. 1–15, 2021, doi: 10.1007/s10661-021-08946-x.

J. Hernández-Orallo, “ROC curves for regression,” Pattern Recognit., vol. 46, no. 12, pp. 3395–3411, 2013, doi: 10.1016/j.patcog.2013.06.014.