'Good' or 'bad' wind power forecasts: a relative concept

• Tipo: Artigo em Revista Científica Internacional
• Data: 2011

Autores

Ricardo Jorge Bessa (Autor) FEUP; Vladimiro Miranda (Autor) FEUP; Audun Botterud (Autor) FEUP; Jianhui Wang (Autor) FEUP

Revista

Título: Wind Energy

Vol. 14 Nº 5

Páginas: 625-636

ISSN: 1095-4244

Editora: Wiley-Blackwell

Indexação

ISI Web of Knowledge - 32 Citações

ISI Web of Science

Scopus - 36 Citações

Classificação Científica

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

Outras Informações

ID Authenticus: P-002-PXG

DOI: 10.1002/we.444

Abstract (EN): This paper reports a study on the importance of the training criteria for wind power forecasting and calls into question the generally assumed neutrality of the 'goodness' of particular forecasts. The study, focused on the Spanish Electricity Market as a representative example, combines different training criteria and different users of the forecasts to compare them in terms of the benefits obtained. In addition to more classical criteria, an information theoretic learning training criterion, called parametric correntropy, is introduced as a means to correct problems detected in other criteria and achieve more satisfactory compromises among conflicting criteria, namely forecasting value and quality. We show that the interests of wind farm owners may lead to a preference for biased forecasts, which may be in conflict with the larger needs of secure operating policies. The ideas and conclusions are supported by results from three real wind farms. Copyright (c) 2010 John Wiley & Sons, Ltd.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12