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Chapitre D'ouvrage Année : 2018

Day-ahead probabilistic forecast of solar irradiance: a Stochastic Differential Equation approach

Résumé

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 proba-bilistic forecast: the input parameters of the SDE model are the Arome deterministic forecast 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.
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Dates et versions

hal-01625651 , version 1 (28-10-2017)

Identifiants

Citer

Jordi Badosa, Emmanuel Gobet, Maxime Grangereau, Daeyoung Kim. Day-ahead probabilistic forecast of solar irradiance: a Stochastic Differential Equation approach. Renewable Energy: Forecasting and Risk Management, 254, Springer International Publishing, pp.73-93, 2018, Springer Proceedings in Mathematics & Statistics, ⟨10.1007/978-3-319-99052-1_4⟩. ⟨hal-01625651⟩
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