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Short-Term Predictability of Photovoltaic Production over Italy

Machine Learning 2014-09-30 v1 Applications

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%.

Keywords

Cite

@article{arxiv.1409.8202,
  title  = {Short-Term Predictability of Photovoltaic Production over Italy},
  author = {Matteo De Felice and Marcello Petitta and Paolo M. Ruti},
  journal= {arXiv preprint arXiv:1409.8202},
  year   = {2014}
}

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Submitted to Renewable Energy