Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/10039
Title: Machine learning methods for solar radiation forecasting: A review
Authors: Voyant, Cyril 
Notton, Gilles 
Kalogirou, Soteris A. 
Nivet, Marie Laure 
Paoli, Christophe 
Motte, Fabrice 
Fouilloy, Alexis 
Keywords: Artificial neural networks;Support vector machines;Machine learning;Regression;Solar radiation forecasting
Category: Mechanical Engineering
Field: Engineering and Technology
Issue Date: 1-May-2017
Publisher: Elsevier Ltd
Source: Renewable Energy, 2017, Volume 105, Pages 569-582
metadata.dc.doi: http://dx.doi.org/10.1016/j.renene.2016.12.095
Abstract: 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.
URI: http://ktisis.cut.ac.cy/handle/10488/10039
ISSN: 09601481
Rights: © 2017 Elsevier Ltd
Type: Article
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