ARTIFICIAL NEURAL NETWORK AND WAVELET DECOMPOSITION IN THE FORECAST OF GLOBAL HORIZONTAL SOLAR RADIATION

• Autor(a) principal: Teixeira Júnior,Luiz Albino
• Data de Publicação: 2015
• Outros Autores: Souza,Rafael Morais de, Menezes,Moisés Lima de, Cassiano,Keila Mara, Pessanha,José Francisco Moreira, Souza,Reinaldo Castro
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Pesquisa operacional (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382015000100073
• topic: wavelet decomposition; artificial neural networks; forecasts

This paper proposes a method (denoted by WD-ANN) that combines the Artificial Neural Networks (ANN) and the Wavelet Decomposition (WD) to generate short-term global horizontal solar radiation forecasting, which is an essential information for evaluating the electrical power generated from the conversion of solar energy into electrical energy. The WD-ANN method consists of two basic steps: firstly, it is performed the decomposition of level p of the time series of interest, generating p + 1 wavelet orthonormal components; secondly, the p + 1 wavelet orthonormal components (generated in the step 1) are inserted simultaneously into an ANN in order to generate short-term forecasting. The results showed that the proposed method (WD-ANN) improved substantially the performance over the (traditional) ANN method.