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Solar radiation prediction using RBF neural networks and cloudiness indices
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Orientador(es)
Resumo(s)
In this paper, Artificial Neural Networks are
applied to multi-step long term solar radiation prediction.
The networks are trained as one-step-ahead predictors and iterated over time to obtain multi-step longer term predictions.
Auto-regressive and Auto-regressive with exogenous inputs solar radiation models are compared, considering cloudiness indices
as inputs in the latter case. These indices are obtained through pixel classification of ground-to-sky images. The input-output
structure of the neural network models is selected using evolutionary computation methods.
Descrição
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Contexto Educativo
Citação
Crispim, E. M.; Ferreira, P. M.; Ruano, A. E. Solar radiation prediction using RBF Neural Networks and cloudiness indices, Trabalho apresentado em The 2006 IEEE International Joint Conference on Neural Network Proceedings, In Proceedings of the 2006 IEEE International Joint Conference on Neural Network Proceedings, Vancouver, BC, Canada, 2006.