Probabilistic Forecasting of Regional Wind Power Generation for the EEM20 Competition: a Physics-oriented Machine Learning Approach
| Autor: | Simon Camal, Georges Kariniotakis, Valentin Mahler, Kevin Bellinguer |
|---|---|
| Přispěvatelé: | Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques (PERSEE), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), KTH, IEEE, European Project: 864337,Smart4RES, 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) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Energy meteorology
020209 energy Wind power forecasting 02 engineering and technology Smart grid 7. Clean energy Competition (economics) Electric power system Variable renewable energy [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] 0202 electrical engineering electronic engineering information engineering Econometrics Energy market Wind energy [STAT.AP]Statistics [stat]/Applications [stat.AP] Wind power Competition business.industry [SPI.NRJ]Engineering Sciences [physics]/Electric power 020207 software engineering [MATH.MATH-PR]Mathematics [math]/Probability [math.PR] Probabilistic forecasting [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] business Analogs Forecasting |
| Zdroj: | 17th European Energy Market Conference, EEM 2020 17th European Energy Market Conference, EEM 2020, KTH, IEEE, Sep 2020, Stockholm (by visio), Sweden HAL 2020 17th International Conference on the European Energy Market (EEM) |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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