Time-adaptive quantile-copula for wind power probabilistic forecasting

• Type: Article in International Scientific Journal
• Date: 2012

Authors

Ricardo Bessa (Author) FEUP; Vladimiro Miranda (Author) FEUP; Audun Botterud (Author) Other; Jianhui Wang (Author) Other; Zhi Zhou (Author) Other

Journal

Title: Renewable Energy

Vol. 40 No. 1

Pages: 29-39

ISSN: 0960-1481

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 98 Citations

ISI Web of Science

Scopus - 120 Citations

Scientific classification

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

CORDIS: Technological sciences > Engineering > Electronic engineering

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11