@inproceedings{7563f1f38e6f4996aec4ec891dce9103,
title = "Day-Ahead Probabilistic Forecast of Solar Irradiance: A Stochastic Differential Equation Approach",
abstract = "In this work, we derive a probabilistic forecast of the solar irradiance during a day at a given location, using a stochastic differential equation (SDE for short) model. We propose a procedure that transforms a deterministic forecast into a probabilistic forecast: the input parameters of the SDE model are the AROME numerical weather predictions computed at day \$\$D-1\$\$ for the day D. The model also accounts for the maximal irradiance from the clear sky model. The SDE model is mean-reverting towards the deterministic forecast and the instantaneous amplitude of the noise depends on the clear sky index, so that the fluctuations vanish as the index is close to 0 (cloudy) or 1 (sunny), as observed in practice. Our tests show a good adequacy of the confidence intervals of the model with the measurement.",
keywords = "Probabilistic forecast, Solar power, Stochastic differential equation",
author = "Jordi Badosa and Emmanuel Gobet and Maxime Grangereau and Daeyoung Kim",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; Workshop on Forecasting and Risk Management for Renewable Energy, 2017 ; Conference date: 07-06-2017 Through 09-06-2017",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-99052-1\_4",
language = "English",
isbn = "9783319990514",
series = "Springer Proceedings in Mathematics and Statistics",
publisher = "Springer New York LLC",
pages = "73--93",
editor = "Mathilde Mougeot and Dominique Picard and Peter Tankov and Riwal Plougonven and Philippe Drobinski",
booktitle = "Renewable Energy",
}