Forecasting method for global radiation time series without training phase: comparison with other well-known prediction methodologies

Cyril Voyant 1 Fabrice Motte 1 Fouilloy Alexis Gilles Notton 1 Christophe Paoli 1 Marie Laure Nivet 1
1 SPE - Sciences pour l'environnement
Abstract : Integration of unpredictable renewable energy sources into electrical networks intensifies the complexity of the grid management due to their intermittent and unforeseeable nature. Because of the strong increase of solar power generation the prediction of solar yields becomes more and more important. Electrical operators need an estimation of the future production. For nowcasting and short term forecasting, the usual technics based on machine learning need large historical data sets of good quality during the training phase of predictors. However data are not always available and induce an advanced maintenance of meteorological stations, making the method inapplicable for poor instrumented or isolated sites. In this work, we propose intuitive methodologies based on the Kalman filter use (also known as linear quadratic estimation), able to predict a global radiation time series without the need of historical data. The accuracy of these methods is compared to other classical data driven methods, for different horizons of prediction and time steps. The proposed approach shows interesting capabilities allowing to improve quasi-systematically the prediction. For one to ten hour horizons Kalman model performances are competitive in comparison to more sophisticated models such as ANN which require both consistent historical data sets and computational resources.
Keywords : model free forecasting Time Series forecasting ARMA Kalman filter Artificial Neural Networks
Document type :
Journal articles
Complete list of metadatas
https://hal.archives-ouvertes.fr/hal-01423959
Contributor : Cyril Voyant <>
Submitted on : Sunday, January 1, 2017 - 2:35:07 PM
Last modification on : Friday, August 23, 2019 - 9:06:15 AM

Links full text

openaccess

Identifiers

Collections

UNIV-CORSE | UNIV-CORSE-SPE | SPE

Citation

Cyril Voyant, Fabrice Motte, Fouilloy Alexis, Gilles Notton, Christophe Paoli, et al.. Forecasting method for global radiation time series without training phase: comparison with other well-known prediction methodologies. Energy, Elsevier, 2016, 120, pp.196-208. ⟨10.1016/j.energy.2016.12.118⟩. ⟨hal-01423959⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

181

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD