Machine learning methods for solar radiation forecasting: A review

• Published in: Renewable energy 2017-05, Vol.105, p.569-582
• Main Authors: Voyant, Cyril, Notton, Gilles, Kalogirou, Soteris, Nivet, Marie-Laure, Paoli, Christophe, Motte, Fabrice, Fouilloy, Alexis
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Computer Science; Engineering Sciences; Information Retrieval; Machine learning; Regression; Solar radiation forecasting; Support vector machines
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2016.12.095

Forecasting the output power of solar systems is required for the good operation of the power grid or for the optimal management of the energy fluxes occurring into the solar system. Before forecasting the solar systems output, it is essential to focus the prediction on the solar irradiance. The global solar radiation forecasting can be performed by several methods; the two big categories are the cloud imagery combined with physical models, and the machine learning models. In this context, the objective of this paper is to give an overview of forecasting methods of solar irradiation using machine learning approaches. Although, a lot of papers describes methodologies like neural networks or support vector regression, it will be shown that other methods (regression tree, random forest, gradient boosting and many others) begin to be used in this context of prediction. The performance ranking of such methods is complicated due to the diversity of the data set, time step, forecasting horizon, set up and performance indicators. Overall, the error of prediction is quite equivalent. To improve the prediction performance some authors proposed the use of hybrid models or to use an ensemble forecast approach. •Overview of forecasting methods of solar irradiation using machine learning approaches.•Performance ranking of such methods is complicated.•ANN and ARIMA methods are equivalent in term of quality of prediction.•Predictor ensemble methodology is always better than simple predictors.•SVM, regression trees and random forests, as the results given are very promising.