Forecasting conditional extreme quantiles for wind energy

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Gonçalves, C (Autor) Outra; Cavalcante, L (Autor) Outra; Margarida Brito (Autor) FCUP; Bessa, RJ (Autor) Outra; João Gama (Autor) FEP

Revista

Título: Electric Power Systems Research

Vol. 190

ISSN: 0378-7796

Editora: Elsevier

Indexação

ISI Web of Knowledge - 16 Citações

Scopus - 20 Citações

Outras Informações

ID Authenticus: P-00S-HZ4

DOI: 10.1016/j.epsr.2020.106636

Abstract (EN): Probabilistic forecasting of distribution tails (i.e., quantiles below 0.05 and above 0.95) is challenging for non-parametric 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. © 2020 Elsevier B.V.

Idioma: Inglês

Tipo (Avaliação Docente): Científica