Forecasting conditional extreme quantiles for wind energy

• Type: Article in International Scientific Journal
• Date: 2021

Authors

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

Journal

Title: Electric Power Systems Research

Vol. 190

ISSN: 0378-7796

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 16 Citations

Scopus - 20 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific