Short-Term Predictability of Photovoltaic Production over Italy
This work addresses the need for accurate PV production forecasting to manage Italy's power grid, but it is incremental as it applies existing methods to a specific dataset.
The study tackled the problem of forecasting photovoltaic (PV) power production in Italy without on-site measurements, using weather forecasts and a support vector machine (SVM) model, achieving errors under 10% in summer but above 20% in winter for lead times of one to ten days.
Photovoltaic (PV) power production increased drastically in Europe throughout the last years. About the 6% of electricity in Italy comes from PV and for an efficient management of the power grid an accurate and reliable forecasting of production would be needed. Starting from a dataset of electricity production of 65 Italian solar plants for the years 2011-2012 we investigate the possibility to forecast daily production from one to ten days of lead time without using on site measurements. Our study is divided in two parts: an assessment of the predictability of meteorological variables using weather forecasts and an analysis on the application of data-driven modelling in predicting solar power production. We calibrate a SVM model using available observations and then we force the same model with the predicted variables from weather forecasts with a lead time from one to ten days. As expected, solar power production is strongly influenced by cloudiness and clear sky, in fact we observe that while during summer we obtain a general error under the 10% (slightly lower in south Italy), during winter the error is abundantly above the 20%.