📢 [PhD positions] The research group ERSEI from Mines Paris - Université PSL Centre PERSEE is offering several PhD positions in data science for #renewable energy #prediction. PhDs will be under the supervision of Prof Georges Kariniotakis and in collaboration with Simon Camal and Panagiotis Andrianesis (possible start date until April 2024 or from September). Do not hesitate to share this information with interested candidates. PhD-1: "Artificial intelligence based prescriptive analytics for energy systems" (https://shorturl.at/fmBCH) PhD-3: "Seamless forecasting of local energy production and demand using multiple heterogeneous data sources" (https://shorturl.at/fgDM4) PhD-4: "Flexibility-aware forecasting of local energy demand" (https://shorturl.at/qstxF) PhD-5: "Optimization of flexibility services under multiple local uncertainties in the context of smart grids" (https://shorturl.at/dgA89)
Smart4RES Project
Fabrication de semi-conducteurs pour énergies renouvelables
Sophia-Antipolis, Alpes-maritimes 917 abonnés
Next Generation Modelling and Forecasting of Variable Renewable Generation for Large-scale Integration in Energy Systems
À propos
The Smart4RES project aims to bring substantial performance improvements to the whole model and value chain in renewable energy (RES) forecasting, with particular emphasis placed on optimizing synergies with storage and to support power system operation and participation in electricity markets. For that, it concentrates on a number of disruptive proposals to support ambitious objectives for the future of renewable energy forecasting. This is thought of in a context with steady increase in the quantity of data being collected and computational capabilities. And, this comes in combination with recent advances in data science and approaches to meteorological forecasting. This project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement no 864337.
- Site web
-
http://www.smart4res.eu
Lien externe pour Smart4RES Project
- Secteur
- Fabrication de semi-conducteurs pour énergies renouvelables
- Taille de l’entreprise
- 11-50 employés
- Siège social
- Sophia-Antipolis, Alpes-maritimes
- Type
- Non lucratif
Lieux
-
Principal
Obtenir l’itinéraire
06560 Sophia-Antipolis, Alpes-maritimes, FR
Nouvelles
-
Smart4RES Project a republié ceci
Senior Quantitative Analyst @ Ørsted | PhD in Energy Meteorology | Combining Forecasting and Optimization to Accelerate the Energy Transition
We're excited to share that our latest #article, "CRPS-based online learning for nonlinear probabilistic forecast combination," is now available online in the International Journal of Forecasting. In the article, we introduce an #online version of the beta-transformed linear pool that can perform nonlinear #forecast combination. This approach allows us to avoid the issue of increasing forecast dispersion that can arise with linear #probabilistic forecast combination while our approach also adapts in #realtime to changing regimes, which is relevant in applications such as #windpower forecasting. Our method is demonstrated to be effective using both synthetic time series and real-world time series from a wind farm in France. The article is publicly available until March 10, so be sure to check it out here: https://lnkd.in/dm2QSpgP. Additionally, you can find the #R #code to run the model on the synthetic time series yourself on https://lnkd.in/dMV9GMG7 A special thanks goes out to my co-authors Pierre Pinson, Simon Camal and Georges Kariniotakis as well as the Smart4RES Project who made it possible.
Dennis VAN DER MEER / Online probabilistic forecast combination · GitLab
git.persee.mines-paristech.fr