Probabilistic Forecasting of Regional Wind Power Generation for the EEM20 Competition: a Physics-oriented Machine Learning Approach - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

Probabilistic Forecasting of Regional Wind Power Generation for the EEM20 Competition: a Physics-oriented Machine Learning Approach

Résumé

Variable renewable energy has a growing impact on electricity markets and power systems in many regions of the world. In this context, the 17th International Conference on the European Energy Market EEM20 set up a competition to develop probabilistic forecasting tools of wind production at a regional level. This paper proposes an adaptive approach for regional wind power forecasting. A physics-oriented pre-processing of the data delivers analog weather patterns and wind-power-related variables, then a k-means clustering of wind farms further reduces the dimension of the problem. The generated representative features feed a Quantile Regression Forests model that produces sharp and reliable predictions. As a result, our model won the competition with a relative improvement of the average pinball loss of 6.7% and 14.7%, compared to the teams ranked second and third respectively.
Fichier principal
Vignette du fichier
EEM20_BellinguerMahlerCamalKariniotakis_postprint.pdf (1.19 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02952589 , version 1 (29-09-2020)

Identifiants

  • HAL Id : hal-02952589 , version 1

Citer

Kevin Bellinguer, Valentin Mahler, Simon Camal, Georges Kariniotakis. Probabilistic Forecasting of Regional Wind Power Generation for the EEM20 Competition: a Physics-oriented Machine Learning Approach. 17th European Energy Market Conference, EEM 2020, KTH, IEEE, Sep 2020, Stockholm (by visio), Sweden. ⟨hal-02952589⟩
322 Consultations
320 Téléchargements

Partager

Gmail Facebook X LinkedIn More