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

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Julio Cesar Royer
Volmir Eugênio Wilhelm
Luiz Albino Teixeira Junior
Edgar Manuel Carreño Franco

Abstract

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).

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Author Biographies

Julio Cesar Royer, Federal Institute of Parana (IFPR)

Julio Cesar Royer is graduated in Computer Sciences at Universidade Federal de Santa Catarina (UFSC), Brasil (1989), has master degree in Computer Sciences at UFSC (2008), and is currently a doctorade student in Numerical Methods in Engineering, at Universidade Federal do Paraná (UFPR). He is currently a professor at IFPR, Foz do Iguaçu, PR, Brazil. His main interests are Dam Safety and Time Series Forecasting.

Volmir Eugênio Wilhelm, Federal University of Paraná (UFPR)

Volmir Eugenio Wilhelm is graduated in Mathematics at Universidade Federal do Parana (UFPR), Brazil (1991), with master and doctorade degree in Production Engineering at Universidade Federal de Santa Catarina (UFSC), Brazil (1993 and 2000). He is an Associated Professor at UFPR. His main interests are Production Engineering, with emphasis on Stochastic Processes, games theory, Linear Programming, Fuzzy Sets, Queue Theory and Data Envelopment Analysis.

Luiz Albino Teixeira Junior, Latin American Integration Federal University (UNILA)

Luiz Albino Teixeira Junior has bachelor degree in Mathematics from Faculdades Integradas de Cataguases (2005), máster degree in Electric Engineering from Pontifícia Universidade Católica do Rio de Janeiro (2009) and doctorate degree at Electric Engineering from Pontifícia Universidade Católica do Rio de Janeiro (2013). Has experience in Production Engineering, focusing on Timetable Series, acting on the following subjects: Singular Spectral Analysis (SSA), Time Series Forecasting, Artificial Neural Networks, Wavelet Theory and Time Series.

Edgar Manuel Carreño Franco, Western Parana State University (Unioeste)

Edgar Manual Carreño Franco is graduated in Electrical Engineering at Universidad Nacional de Colombia (1999), has master degree in Electrical Engineering at Universidad Tecnologica de Prereira, Colombia (2003), and Ph.D. degree in Electrical Engineering at UNESP-FEIS, Brazil (2008). He is currently a Full professor at UNIOESTE, Foz do Iguaçu, PR, Brazil. His main interests are Power Systems Planning, Mathematical Optimizagion and load forecasting

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