Machine Learning Nowcasting of PV Energy Using Satellite Data

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.