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

• Type: Article in International Scientific Journal
• Date: 2011

Authors

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

Journal

Title: Wind Energy

Vol. 14 No. 5

Pages: 625-636

ISSN: 1095-4244

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 32 Citations

ISI Web of Science

Scopus - 36 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12