Prediction of the solar radiation using RBF neural networks and ground-to-sky images

Crispim, E. M.; Ferreira, P. M.; Ruano, Antonio

http://hdl.handle.net/10400.1/2359

Use this identifier to reference this record.

Name:	Description:	Size:	Format:
paper19.pdf		31.14 KB	Adobe PDF	Download

Send Feedback

Authors

Crispim, E. M.

Ferreira, P. M.

Ruano, Antonio

Abstract(s)

In this study, Artificial Neural Networks are applied to multistep 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 radiationmodels 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.

URI

http://hdl.handle.net/10400.1/2359

Citation

Crispim, E. M.; Ferreira, P.M.; Ruano, A. E. Prediction of the solar radiation using RBF neural networks and ground-to-sky images, Trabalho apresentado em Global Education Techology Symposium (GETS 2006), In Proceedings of the Global Education Techology Symposium (GETS 2006), Faro, 2006.