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

• Main Authors: Luiz Albino Teixeira Júnior, Rafael Morais de Souza, Moisés Lima de Menezes, Keila Mara Cassiano, José Francisco Moreira Pessanha, Reinaldo Castro Souza
• Format: Article
• Language: English
• Published: Sociedade Brasileira de Pesquisa Operacional 2015-04-01
• Series: Pesquisa Operacional
• Subjects: wavelet decomposition; artificial neural networks; forecasts
• Online Access: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382015000100073&lng=en&tlng=en

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.