Conveners
INVITED SFdS
- Yannig Goude (EDF R&D)
Unbiased assessment of the predictivity of models learnt by supervised machine-learning methods requires knowledge of the learnt function over a reserved test set (not used by the learning algorithm). Indeed, some industrial context requires the model predictivity to be estimated on a test set strictly disjoint from the learning set, which excludes cross-validation techniques. The quality of...
Traditional mid-term electricity forecasting models rely on calendar and meteorological information such as temperature and wind speed to achieve high performance. However depending on such variables has drawbacks, as they may not be informative enough during extreme weather. While ubiquitous, textual sources of information are hardly included in prediction algorithms for time series, despite...
The development of electric vehicles is a major lever towards low-carbon transport. It comes with a growing number of charging infrastructures that can be used as flexible assets for the grid. To enable this smart-charging, an effective daily forecast of charging behaviours is necessary. The purpose of our work is to evaluate the performance of models for predicting load curves and charging...