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Predicting solar radiation with RBF neural networks
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Autores
Orientador(es)
Resumo(s)
In this paper radial basis function neural networks are applied to the prediction
of global solar radiation. The networks are employed as one-step-ahead predictors of the
solar radiation time series and iterated over time to obtain longer term predictions. Several models are compared varying the input dimension, the network size and the time series sampling rate. An empiric rule is proposed for network input selection. All networks are trained using one data set and evaluated for prediction performance on unseen data. Predictor performance is assessed taking root mean square measures of the error over the prediction horizon. The aim of this work is to select a model to be used in a climate simulator for an hydroponic greenhouse.
Descrição
Palavras-chave
Neural Networks Greenhouse Environmental Control Radial Basis Functions Solar Radiation Prediction Time Series
Contexto Educativo
Citação
Ferreira, P. M.; Ruano, A. E. Predicting solar radiation with RBF neural networks. Trabalho apresentado em 6th Portuguese Conference on Automatic Control (Controlo 2004), In 6th Portuguese Conference on Automatic Control (Controlo 2004), Faro, 2004.