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Short-term solar radiation forecasting by using an iterative combination of wavelet artificial neural networks

Independent Journal of Management & Production

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Title Short-term solar radiation forecasting by using an iterative combination of wavelet artificial neural networks
 
Creator Royer, Julio Cesar
Wilhelm, Volmir Eugênio
Teixeira Junior, Luiz Albino
Franco, Edgar Manuel Carreño
 
Subject Production Engineering; Numeric Methods; Statistics
Solar Radiation Time Series; Wavelet Decomposition; Artificial Neural Networks; Forecasts
 
Description The information provided by accurate forecasts of solar energy time series are considered essential for performing an appropriate prediction of the electrical power that will be available in an electric system, as pointed out in Zhou et al. (2011). However, since the underlying data are highly non-stationary, it follows that to produce their accurate predictions is a very difficult assignment. In order to accomplish it, this paper proposes an iterative Combination of Wavelet Artificial Neural Networks (CWANN) which is aimed to produce short-term solar radiation time series forecasting. Basically, the CWANN method can be split into three stages: at first one, a decomposition of level p, defined in terms of a wavelet basis, of a given solar radiation time series is performed, generating r+1 Wavelet Components (WC); at second one, these r+1 WCs are individually modeled by the k different ANNs, where k>5, and the 5 best forecasts of each WC are combined by means of another ANN, producing the combined forecasts of WC; and, at third one, the combined forecasts WC are simply added, generating the forecasts of the underlying solar radiation data. An iterative algorithm is proposed for iteratively searching for the optimal values for the CWANN parameters, as we will see. In order to evaluate it, ten real solar radiation time series of Brazilian system were modeled here. In all statistical results, the CWANN method has achieved remarkable greater forecasting performances when compared with a traditional ANN (described in Section 2.1).
 
Publisher Instituto Federal de Educação, Ciência e Tecnologia de São Paulo
 
Contributor Fundação Araucária
 
Date 2016-03-01
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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Format application/pdf
text/html
 
Identifier http://www.ijmp.jor.br/index.php/ijmp/article/view/393
10.14807/ijmp.v7i1.393
 
Source Independent Journal of Management & Production; Vol 7, No 1 (2016): Independent Journal of Management & Production; 271-288
2236-269X
 
Language eng
 
Relation http://www.ijmp.jor.br/index.php/ijmp/article/view/393/289
http://www.ijmp.jor.br/index.php/ijmp/article/view/393/506
http://www.ijmp.jor.br/index.php/ijmp/article/downloadSuppFile/393/160
http://www.ijmp.jor.br/index.php/ijmp/article/downloadSuppFile/393/161
 
Rights Copyright (c) 2016 Julio Cesar Royer, Volmir Eugênio Wilhelm, Luiz Albino Teixeira Junior, Edgar Manuel Carreño Franco
http://creativecommons.org/licenses/by/4.0