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

AUTOR(ES)

Teixeira Júnior, Luiz Albino, Souza, Rafael Morais de, Menezes, Moisés Lima de, Cassiano, Keila Mara, Pessanha, José Francisco Moreira, Souza, Reinaldo Castro

FONTE

Pesqui. Oper.

DATA DE PUBLICAÇÃO

2015-04

RESUMO

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.

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