Abstract
In this work we will study the use of satellite-measured irradiances as well as clear sky radiance estimates as features for the nowcasting of photovoltaic energy productions over Peninsular Spain. We will work with three Machine Learning models (Lasso and linear and Gaussian Support Vector Regression-SVR) plus a simple persistence model. We consider prediction horizons of up to three hours, for which Gaussian SVR is the clear winner, with a quite good performance and whose errors increase slowly with time. Possible ways to further improve these results are also proposed.
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Acknowledgments
With partial support from Spain’s grants TIN2013-42351-P, TIN2016-76406-P, TIN2015-70308-REDT and S2013/ICE-2845 CASI-CAM-CM. Work supported also by project FACIL–Ayudas Fundación BBVA a Equipos de Investigación Científica 2016, and the UAM–ADIC Chair for Data Science and Machine Learning. The second author is also supported by the FPU–MEC grant AP-2012-5163. We thank Red Eléctrica de España for useful discussions and making available PV energy data and gratefully acknowledge the use of the facilities of Centro de Computación Científica (CCC) at UAM.
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Catalina, A., Torres-Barrán, A., Dorronsoro, J.R. (2017). Satellite Based Nowcasting of PV Energy over Peninsular Spain. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_59
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DOI: https://doi.org/10.1007/978-3-319-59153-7_59
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