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Forecasting Conditional Extreme Quantiles for Wind Energy

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Probabilistic forecasting of distribution tails (i.e., quantiles below .05 and above .95) is challenging for nonparametric approaches since data for extreme events are scarce. A poor forecast of extreme quantiles can have a high impact in various power system decision-aid problems. An alternative approach more robust to data sparsity is extreme value theory (EVT), which uses parametric functions for modelling distribution’s tails. In this work, we apply conditional EVT estimators to historical data by directly combining gradient boosting trees with a truncated generalized Pareto distribution. The parametric function parameters are conditioned by covariates such as wind speed or direction from a numerical weather predictions grid. The results for a wind power plant located in Galicia, Spain, show that the proposed method outperforms state-of-the-art methods in terms of quantile score.

Author(s):

Carla Gonçalves    
INESC TEC and FCUP
Portugal

Laura Cavalcante    
INESC TEC
Portugal

Margarida Brito    
FCUP
Portugal

Ricardo J. Bessa    
INESC TEC
Portugal

João Gama    
INESC TEC and FEP
Portugal

 

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