Contexte et atouts du poste
Context
This Starting Research Position (SRP) will be co-supervised by Claire Monteleoni at INRIA Paris, and Boutheina Oueslati, Emmanuel Neau, and Yannig Goude, at EDF Lab, Saclay. The position will by financed by an INRIA-EDF Défi and a Choose France Chair in AI.
Mission confiée
Research project
Overview
This INRIA Starting Research Position will focus on improving projections and reducing uncertainty on future wind trends and anticipating periods of low wind, using innovative approaches based on new machine/deep learning techniques.
Transitioning our energy preduction to renewable sources is a clear path towards reducing CO2 emissions, which in turn is key to mitigating the most severe risks of of climate change. Most of these sources (e.g., solar, wind, and hydro) are variable in the short term, depending on local weather conditions, but are also evolving in a changing climate. The latest IPCC report suggests a decrease in average wind in Europe of between 8 and 10% for a warming scenario of 1.5°C and an increase in strong winds. Significant uncertainties are, however, still present on the processes that drive this future decrease in wind, in particular on the respective contributions of climate change and internal variability (Carvalho et al., 2021, Wohland et al., 2021). Therefore, current wind turbines may not be well-adapted to future wind conditions, either in their location or in their operating characteristics. Understanding and anticipating the future evolution of wind and its impact on wind power production is an important issue for the current and future electricity system in order to ensure its proper functioning in terms of flexibility but also means of adaptation.
Approach
Building on past work by members of the hosting teams on statistical and machine learning-based methods for downscaling spatiotemporal data, this project will explore existing methods and develop new methods to improve wind power forecasting at the necessary spatial and temporal scales. These AI-driven approaches will be used to analyse the evolution of the historical wind in Europe, the associated atmospheric dynamics and the link with the evolution of roughness based on observations, in order to reduce uncertainty on future predictions.
Specific tasks include:
Principales activités
Compétences
Technical skills and level required :
Languages :
Avantages
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