Solar energy production: Short-term forecasting and risk management
Author
Abstract
Suggested Citation
DOI: 10.1016/j.ifacol.2016.07.790
Note: View the original document on HAL open archive server: https://polytechnique.hal.science/hal-01272152v3
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Cited by:
- Voyant, Cyril & Motte, Fabrice & Fouilloy, Alexis & Notton, Gilles & Paoli, Christophe & Nivet, Marie-Laure, 2017. "Forecasting method for global radiation time series without training phase: Comparison with other well-known prediction methodologies," Energy, Elsevier, vol. 120(C), pages 199-208.
- Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
- Michel Fliess & Cédric Join & Cyril Voyant, 2018. "Prediction bands for solar energy: New short-term time series forecasting techniques," Post-Print hal-01736518, HAL.
- Voyant, Cyril & Notton, Gilles & Darras, Christophe & Fouilloy, Alexis & Motte, Fabrice, 2017. "Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case," Energy, Elsevier, vol. 125(C), pages 248-257.
More about this item
Keywords
confidence bands; time series; intelligent knowledge-based systems; forecasts; persistence; solar energy; volatility; risk; normality tests;All these keywords.
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ENE-2016-03-10 (Energy Economics)
- NEP-FOR-2016-03-10 (Forecasting)
- NEP-RMG-2016-03-10 (Risk Management)
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