Machine Learning Nowcasting of PV Energy Using Satellite Data

  • Alejandro CatalinaEmail author
  • Alberto Torres-Barrán
  • Carlos M. Alaíz
  • José R. Dorronsoro


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.


Photovoltaic energy Nowcasting EUMETSAT Support vector regression Lasso Clear sky models 



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.


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Authors and Affiliations

  1. 1.Dpto. Ing. InformáticaUniversidad Autónoma de MadridMadridSpain
  2. 2.Inst. de Ciencias Matemáticas ICMATMadridSpain
  3. 3.Instituto de Ingeniería del ConocimientoMadridSpain

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