Abstract
Satellite–measured irradiances can be an interesting source of information for the nowcasting of solar energy productions. Here we will consider the Machine Learning based prediction at hour H of the aggregated photovoltaic (PV) energy of Peninsular Spain using the irradiances measured by Meteosat’s visible and infrared channels at hours \(H, H-1, H-2\) and \(H-3\). We will work with Lasso and Support Vector Regression models and show that both give best results when using \(H-1\) irradiances to predict H PV energy, with SVR being slightly ahead. We will also suggest possible ways to improve our current results.
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Acknowledgments
With partial support from Spain’s grants TIN2013-42351-P (MINECO), the UAM-ADIC Chair for Data Science and Machine Learning and S2013/ICE-2845 CASI-CAM-CM (Comunidad de Madrid). The first author is kindly supported by the UAM-ADIC Chair for Data Science and Machine Learning and the second author by the FPU-MEC grant AP-2012-5163. We gratefully acknowledge the use of the facilities of Centro de Computación Científica (CCC) at UAM and thank Red Eléctrica de España for kindly supplying PV energy data.
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Catalina, A., Torres-Barrán, A., Dorronsoro, J.R. (2017). Machine Learning Prediction of Photovoltaic Energy from Satellite Sources. In: Woon, W., Aung, Z., Kramer, O., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2016. Lecture Notes in Computer Science(), vol 10097. Springer, Cham. https://doi.org/10.1007/978-3-319-50947-1_4
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DOI: https://doi.org/10.1007/978-3-319-50947-1_4
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