A Model for Hourly Solar Radiation Data Generation from Daily Solar Radiation Data Using a Generalized Regression Artificial Neural Network

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Khatib, Tamer ; Elmenreich, Wilfried (2015)
  • Publisher: Hindawi Publishing Corporation
  • Journal: International Journal of Photoenergy (issn: 1110-662X, eissn: 1687-529X)
  • Related identifiers: doi: 10.1155/2015/968024
  • Subject: TJ807-830 | Renewable energy sources | Article Subject

This paper presents a model for predicting hourly solar radiation data using daily solar radiation averages. The proposed model is a generalized regression artificial neural network. This model has three inputs, namely, mean daily solar radiation, hour angle, and sunset hour angle. The output layer has one node which is mean hourly solar radiation. The training and development of the proposed model are done using MATLAB and 43800 records of hourly global solar radiation. The results show that the proposed model has better prediction accuracy compared to some empirical and statistical models. Two error statistics are used in this research to evaluate the proposed model, namely, mean absolute percentage error and root mean square error. These values for the proposed model are 11.8% and −3.1%, respectively. Finally, the proposed model shows better ability in overcoming the sophistic nature of the solar radiation data.
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