Abstract
In this paper we tackle a problem of solar radiation prediction with Soft-Computing Techniques. We introduce new atmospheric input variables in the problem, which help to obtain an accurate prediction of solar radiation. We test the performance of two state-of-the art algorithms: Extreme Learning Machines and Support Vector regression algorithms, in a real problem of solar radiation prediction in Murcia, Spain, where we have obtained excellent results with the proposed techniques.
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Salcedo-Sanz, S., Casanova-Mateo, C., Pastor-Sánchez, A., Gallo-Marazuela, D., Labajo-Salazar, A., Portilla-Figueras, A. (2013). Direct Solar Radiation Prediction Based on Soft-Computing Algorithms Including Novel Predictive Atmospheric Variables. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_39
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DOI: https://doi.org/10.1007/978-3-642-41278-3_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41277-6
Online ISBN: 978-3-642-41278-3
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