Solar radiation prediction using RBF neural networks and cloudiness indices
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 radiati...
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| Other Authors: | , |
| Format: | conferenceObject |
| Language: | eng |
| Published: |
2013
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| Online Access: | http://hdl.handle.net/10400.1/2238 |
| Country: | Portugal |
| Oai: | oai:sapientia.ualg.pt:10400.1/2238 |
| Summary: | 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. |
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