Analysis of ARMA solar forecasting models using ground measurements and satellite images
Franco Marchesoni1,3, Philippe Lauret2, Alvaro Gomez3, Rodrigo Alonso-Suárez1
1Laboratorio de Energía Solar, Universidad de la República, Montevideo, Uruguay
/2Université de la Réunion, Saint-Denis, France
/3Instituto de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de la República, Montevideo, Uruguay

As the solar PV share in the electricity grids is growing year by year, solar irradiance forecasting is becoming increasingly important. In this work we study the performance of a recursive formulation of ARMA models suitable for operational context using the Pampa Humeda region as a case study. Results are promising, as this simple adaptive algorithm does not require historical data and outperform persistence at all lead times. We also evaluate the improvement produced by adding satellite data and short-term local variability as exogenous inputs. It is found that the spatially averaged satellite albedo is a useful input variable, improving the forecast performance, while the variability produce negligible performance changes under this kind of models.