Hourly solar radiation forecasting on SAURAN network datasets using deep learning method: La Reunion and Durban cases study - ENERGY-LAB
Communication Dans Un Congrès Année : 2018

Hourly solar radiation forecasting on SAURAN network datasets using deep learning method: La Reunion and Durban cases study

Mathieu Delsaut
Patrick Jeanty
Miloud Bessafi
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Jean-Pierre Chabriat

Résumé

The purpose of this article is to describe the data pretreatment (smoothing, normalizing) and present hourly forecasting method using XGBoost deep learning tool on the global horizontal irradiance (GHI). This method will be applied on two sites with different typical meteorological profiles. An estimation of prediction skills will be given and discussed against classical persistence model.
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Dates et versions

hal-04521454 , version 1 (26-03-2024)

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  • HAL Id : hal-04521454 , version 1

Citer

Mathieu Delsaut, Claire Quatrehomme, Patrick Jeanty, Miloud Bessafi, Jean-Pierre Chabriat. Hourly solar radiation forecasting on SAURAN network datasets using deep learning method: La Reunion and Durban cases study. 5th Southern African Solar Energy Conference (SASEC 2018), Jun 2018, Durban South Africa, South Africa. ⟨hal-04521454⟩
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