Short-Term Predictability of Photovoltaic Production over Italy

TL;DR

This study evaluates the short-term predictability of photovoltaic power production in Italy using weather forecasts and data-driven models, highlighting seasonal differences in forecast accuracy.

Contribution

It introduces a method combining weather forecast data with SVM models to predict solar power output without on-site measurements, assessing predictability over multiple lead times.

Findings

Summer forecast errors are under 10%.

Winter forecast errors exceed 20%.

Predictability varies seasonally and geographically.

Abstract

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%.