Machine learning methods for solar radiation forecasting: A review

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• 作者：Cyril Voyant^a ; ^b ; ^{voyant@univ-corse.fr} ; Gilles Notton^a ; Soteris Kalogirou^c ; Marie-Laure Nivet^a ; Christophe Paoli^a ; ^d ; Fabrice Motte^a ; Alexis Fouilloy^a
• 关键词：Solar radiation forecasting ; Machine learning ; Artificial neural networks ; Support vector machines ; Regression
• 刊名：Renewable Energy
• 出版年：2017
• 出版时间：May 2017
• 年：2017
• 卷：105
• 期：Complete
• 页码：569-582
• 全文大小：1306 K
• 卷排序：105

文摘

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.