Forecasting the high-resolution variability of renewable production using advanced weather forecasting products

Authors :: Simon Camal
Georges Kariniotakis
Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques (PERSEE)
Mines Paris - PSL (École nationale supérieure des mines de Paris)
Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)
European Project: 864337,Smart4RES
Source :: HAL, 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022, 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022, Jun 2022, Manchester-Online, United Kingdom
Abstract: International audience

Subjects :: renewable energy forecasting
[STAT.AP]Statistics [stat]/Applications [stat.AP]
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
operational forecasting
[SPI.NRJ]Engineering Sciences [physics]/Electric power
data science
[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]
renewable energy sources
energy meteorology
smart grid
energy analytics
ComputingMilieux_MISCELLANEOUS

Database :: OpenAIRE
Journal :: HAL, 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022, 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022, Jun 2022, Manchester-Online, United Kingdom
Accession number :: edsair.dedup.wf.001..ff191b23e7035ae8cb02a14e11b34053

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