Abstract
Satellite-measured radiances are obviously of great interest for photovoltaic (PV) energy prediction. In this work we will use them together with clear sky irradiance estimates for the nowcasting of PV energy productions over peninsular Spain. We will feed them directly into two linear Machine Learning models, Lasso and linear Support Vector Regression (SVR), and two highly non-linear ones, Deep Neural Networks (in particular, Multilayer Perceptrons, MLPs) and Gaussian SVRs. We shall also use a simple clear sky-based persistence model for benchmarking purposes. We consider prediction horizons of up to 6 h, with Gaussian SVR being statistically better than the other models at each horizon, since its errors increase slowly with time (with an average of 1.92% for the first three horizons and of 2.89% for the last three). MLPs performance is close to that of the Gaussian SVR for the longer horizons (with an average of 3.1%) but less so at the initial ones (average of 2.26%), being nevertheless significantly better than the linear models. As it could be expected, linear models give weaker results (in the initial horizons, Lasso and linear SVR have already an error of 3.21% and 3.46%, respectively), but we will take advantage of the spatial sparsity provided by Lasso to try to identify the concrete areas with a larger influence on PV energy nowcasts.





Similar content being viewed by others
References
Alaíz CM, Dorronsoro JR (2015) The generalized group lasso. In: Proceedings of the 2015 international joint conference on neural networks, pp 1–8
Antonanzas J, Osorio N, Escobar R, Urraca R, Martinez de Pison FJ, Antonanzas-Torres F (2016) Review of photovoltaic power forecasting. Solar Energy 136:78–111
Antonanzas J, Pozo-Vázquez D, Fernandez-Jimenez LA, Martinez de Pison FJ (2017) The value of day-ahead forecasting for photovoltaics in the spanish electricity market. Solar Energy 158:140–146
Bishop CM (2006) Pattern recognition and machine learning. Springer, Berlin
Catalina A, Torres-Barràn A, Dorronsoro JR (2017) Satellite based nowcasting of PV energy over peninsular Spain. In: Proceedings of IWANN 2017, international work conference in neural networks, lecture notes in computer science 10305. Springer, pp 685-697
Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27:1–27:27. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed 24 Dec 2018
Chollet F (2015) Keras: deep learning library for theano and tensorflow
EUMETSAT. European European Organisation for the Exploitation of Meteorological Satellites. http://www.eumetsat.int/. Accessed 24 Dec 2018
EUMETSAT (2004) Msg-1/seviri solar channels calibration. Commissioning Activity Report, pp 1–39
Fan R-E, Chang K-W, Hsieh C-J, Wang X-R, Lin C-J (2008) Liblinear: a library for large linear classification. J Mach Learn Res 9:1871–1874
Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: JMLR W&CP: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010), vol 9, pp 249–256
Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: JMLR W&CP: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2011)
Hammer A, Heinemann D, Hoyer C, Kuhlemann R, Lorenz E, Müller R, Beyer HG (2003) Solar energy assessment using remote sensing technologies. Remote Sens Environ 86(3):423–432
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer, Berlin
Holmgren WF, Hansen CW, Mikofski MA (2018) pvlib python: a python package for modeling solar energy systems. J Open Source Softw 3:884
Ineichen P, Perez R (2002) A new airmass independent formulation for the linke turbidity coefficient. Solar Energy 73(3):151–157
Inman RH, Pedro H, Coimbra C (2013) Solar forecasting methods for renewable energy integration. Prog Energy Combust Sci 39(6):533–576
Kühnert J, Lorenz E, Heinemann D (2013) Satellite-based irradiance and power forecasting for the German energy market. In: Kleissl J (ed) Solar energy forecasting and resource assessment. Academic Press, Cambridge, pp 267–297
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. CoRR. arxiv:1412.6980
Marquez R, Coimbra CFM (2013) Intra-hour DNI forecasting based on cloud tracking image analysis. Solar Energy 91:327–336
Mohammed AA, Yaqub W, Aung Z (2015) Probabilistic forecasting of solar power: an ensemble learning approach. In: Neves-Silva R, Jain CL, Howlett JR (eds) Intelligent Decision Technologies: Proceedings of the 7th KES International Conference on Intelligent Decision Technologies (KES-IDT 2015). Springer, pp 449–458
Myers DR (2005) Solar radiation modeling and measurements for renewable energy applications: data and model quality. Energy 30(9):1517–1531
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830
Schölkopf B, Smola AJ (2001) Learning with Kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge
Torres-Barrán A, Alonso Á, Dorronsoro JR (2017) Regression tree ensembles for wind energy and solar radiation prediction. Neurocomputing. https://doi.org/10.1016/j.neucom.2017.05.104
Wan C, Zhao J, Song Y, Zhao X, Lin J, Zechun H (2015) Photovoltaic and solar power forecasting for smart grid energy management. J Power Energy Syst 1:38–46
Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83
Wolff B, Kühnert J, Lorenz E, Kramer O, Heinemann D (2016) Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data. Solar Energy 135:197–208
Acknowledgements
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 was 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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Catalina, A., Torres-Barrán, A., Alaíz, C.M. et al. Machine Learning Nowcasting of PV Energy Using Satellite Data. Neural Process Lett 52, 97–115 (2020). https://doi.org/10.1007/s11063-018-09969-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-018-09969-1