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

• Autor(a) principal: Royer, Julio Cesar
• Data de Publicação: 2016
• Outros Autores: Wilhelm, Volmir Eugênio, Teixeira Junior, Luiz Albino, Franco, Edgar Manuel Carreño
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Independent Journal of Management & Production
• Texto Completo: http://www.ijmp.jor.br/index.php/ijmp/article/view/393
• topic: Solar Radiation Time Series; Wavelet Decomposition; Artificial Neural Networks; Forecasts

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).