Time-adaptive quantile-copula for wind power probabilistic forecasting

• Tipo: Artigo em Revista Científica Internacional
• Data: 2012

Autores

Ricardo Bessa (Autor) FEUP; Vladimiro Miranda (Autor) FEUP; Audun Botterud (Autor) Outra; Jianhui Wang (Autor) Outra; Zhi Zhou (Autor) Outra

Revista

Título: Renewable Energy

Vol. 40 Nº 1

Páginas: 29-39

ISSN: 0960-1481

Editora: Elsevier

Indexação

ISI Web of Knowledge - 98 Citações

ISI Web of Science

Scopus - 120 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia electrónica

Outras Informações

ID Authenticus: P-002-BYE

DOI: 10.1016/j.renene.2011.08.015

Resumo (PT): This paper presents a novel time-adaptive quantile-copula estimator for kernel density forecast and a discussion of how to select the adequate kernels for modeling the different variables of the problem. Results are presented for different case-studies and compared with splines quantile regression (QR). The datasets used are from NREL's Eastern Wind Integration and Transmission Study, and from a real wind farm located in the Midwest region of the United States. . The new probabilistic forecasting model is elegant and simple and yet displays advantages over the traditional QR approach. Especially notable is the quality of the results achieved with the time-adaptive version, namely when evaluated in terms of forecast calibration, which is a characteristic that is advantageous for both system operators and wind power producers.

Abstract (EN): This paper presents a novel time-adaptive quantile-copula estimator for kernel density forecast and a discussion of how to select the adequate kernels for modeling the different variables of the problem. Results are presented for different case-studies and compared with splines quantile regression (QR). The datasets used are from NREL's Eastern Wind Integration and Transmission Study, and from a real wind farm located in the Midwest region of the United States. The new probabilistic prediction model is elegant and simple and yet displays advantages over the traditional QR approach. Especially notable is the quality of the results achieved with the time-adaptive version, namely when evaluated in terms of prediction calibration, which is a characteristic that is advantageous for both system operators and wind power producers.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11