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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., de Pison, F.M., Antonanzas-Torres, F.: Review of photovoltaic power forecasting. Sol. Energy 136, 78–111 (2016)
Fernández-Pascual, Á., Gala, Y., Dorronsoro, J.R.: Machine learning prediction of large area photovoltaic energy production. In: ECML PKDD Workshop on Data Analytics for Renewable Energy Integration DARE 2014, pp. 38–53 (2014)
Fonseca, J.G.S., Oozeki, T., Takashima, T., Koshimizu, G., Uchida, Y., Ogimoto, K.: Photovoltaic power production forecasts with support vector regression: a study on the forecast horizon. In: 2011 37th IEEE on Photovoltaic Specialists Conference (PVSC), pp. 002579–002583 (2011)
Hammer, A., Heinemann, D., Hoyer, C., Kuhlemann, R., Lorenz, E., Müller, R., Beyer, H.G.: Solar energy assessment using remote sensing technologies. Remote Sens. Environ. 86(3), 423–432 (2003)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg (2009)
Inman, R., Pedro, H., Coimbra, C.: Solar forecasting methods for renewable energy integration. Prog. Energy Combust. Sci. 39(6), 533–576 (2013)
Kühnert, J., Lorenz, E., Heinemann, D.: Satellite-based irradiance and power forecasting for the German energy market. In: Kleissl, J. (ed.) Solar Energy Forecasting and Resource Assessment, pp. 267–297. Academic Press (2013)
Kleissl, J.: Solar Energy Forecasting and Resource Assessment. Academic Press, Cambridge (2013)
Lorenz, E., Kühnert, J., Wolff, B., Hammer, A., Kramer, O., Heinemann, D.: PV power predictions on different spatial and temporal scales integrating PV measurements, satellite data and numerical weather predictions. In: Proceedings of the 29-th European Photovoltaic Solar Energy Conference and Exhibition (EU PVSEC), pp. 22–26 (2014)
Marquez, R., Coimbra, C.F.: Intra-hour DNI forecasting based on cloud tracking image analysis. Sol. Energy 91, 327–336 (2013)
Mohammed, A.A., Yaqub, W., Aung, Z.: Probabilistic forecasting of solar power: an ensemble learning approach. In: Neves-Silva, R., Jain, L.C., Howlett, R.J. (eds.) Intelligent Decision Technologies. SIST, vol. 39, pp. 449–458. Springer, Heidelberg (2015). doi:10.1007/978-3-319-19857-6_38
Pedro, H.T., Coimbra, C.F.: Assessment of forecasting techniques for solar power production with no exogenous inputs. Sol. Energy 86(7), 2017–2028 (2012)
Rana, M., Koprinska, I., Agelidis, V.G.: 2D-interval forecasts for solar power production. Sol. Energy 122, 191–203 (2015)
Schölkopf, B., Smola, A.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
Wolff, B., Kühnert, J., Lorenz, E., Kramer, O., Heinemann, D.: Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data. Sol. Energy 135, 197–208 (2016)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-50947-1_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50946-4
Online ISBN: 978-3-319-50947-1
eBook Packages: Computer ScienceComputer Science (R0)