DocumentCode :
2772504
Title :
Solar radiation prediction using RBF Neural Networks and cloudiness indices
Author :
Crispim, Eduardo M. ; Ferreira, Pedro M. ; Ruano, António E.
Author_Institution :
Centre for Intelligent Systems, Faculty of Sciences and Technology, University of Algarve, Campus de Gambelas, 8005-139 Faro, Portugal. Email: ecrispim@ualg.pt
fYear :
2006
fDate :
16-21 July 2006
Firstpage :
2611
Lastpage :
2618
Abstract :
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.
Keywords :
Artificial neural networks; Crops; Energy consumption; Greenhouses; Neural networks; Predictive models; Production; Solar radiation; Strontium; Temperature dependence;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Print_ISBN :
0-7803-9490-9
Type :
conf
DOI :
10.1109/IJCNN.2006.247139
Filename :
1716449
Link To Document :
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