Prognóstico de Radiação Solar Através Modelos que Combinam as Técnicas Wavelet e Redes Neurais (Forecast of Solar Radiation Through Models that Combine the Wavelet and Neural Networks Techniques)

O prognostico de variaveis meteorologicas, como radiacao solar, sempre foi de grande importância para a tomada de decisao em ocasiao de ocorrencias de eventos incomuns. Nesse contexto e justificavel a busca por modelos matematicos e estatisticos que produzam melhores prognosticos para tais variaveis. Desta forma investiga-se a estrategia de conjuncao que se compoe de duas tecnicas muito utilizadas no tratamento de serie temporal; a transformada Wavelet que mostra analiticamente o sinal no dominio do tempo e da frequencia; e as RNA’s a quais sao modelos de inteligencia artificial. A combinacao dessas duas tecnicas, o que se denomina modelo hibrido, tem se mostrado eficaz no prognostico de variaveis meteorologicas. Os dados diarios de radiacao solar sao do Instituto Agronomico do Parana/PR coletados no periodo de 1990 ate 1995. Neste trabalho sao estudadas conjuncoes de modelos hibridos com Redes Neurais e tecnicas Wavelets, apresentando o resumo de alguns dos modelos sintetizados na literatura, para o prognostico de radiacao solar. Tais modelos hibridos estudados se mostraram satisfatorios no prognostico dessa variavel, pois apresentaram um melhor desempenho em relacao aos modelos que nao sao hibridos, sendo o modelo que se mostrou mais eficiente no prognostico foi o que utiliza as sub-series da decomposicao como entrada da Rede Neural, pois apresenta regressao com valores significativos (R proximo a 1). A B S T R A C T The prognosis of meteorological variables such as solar radiation has always been of great importance for decision making in time of occurrence of unusual events. In this context it is justifiable to search for mathematical and statistical models that produce better prognosis for these variables. Thus investigates the combination strategy which uses two techniques widely used in the treatment time series; Wavelet transform analytically shows that the signal in the time domain and frequency; and the RNA's which models of artificial intelligence. The combination of these two techniques, which is called the hybrid models, has proven effective in predicting meteorological variables. Daily data of solar radiation are the Agronomic Institute of Parana / PR collected from 1990 to 1995. In this work conjunctions of hybrid models with neural networks and wavelets techniques are studied, presenting a summary of some of the synthesized models in the literature for the prediction of solar radiation. Such hybrid models studied were satisfactory prognosis with this variable because it showed better performance compared to models that are not hybrids in the model that is more efficient prognosis was that uses the sub-series decomposition as input the Network neural, it presents significant regression values (R close to 1). Keywords: Wavelet, Neural Network, Forecast.