Time Adaptive Conditional Kernel Density Estimation for Wind Power 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; Emil Constantinescu (Author) Other

Journal

Title: IEEE Transactions on Sustainable Energy

Vol. 3 No. 4

Pages: 660-669

ISSN: 1949-3029

Publisher: IEEE

Indexing

ISI Web of Knowledge - 74 Citations

Scopus - 100 Citations

Scientific classification

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

CORDIS: Technological sciences > Engineering > Electrical engineering

Other information

Authenticus ID: P-005-A4J

DOI: 10.1109/tste.2012.2200302

Abstract (EN): This paper reports the application of a new kernel density estimation model based on the Nadaraya-Watson estimator, for the problem of wind power uncertainty forecasting. The new model is described, including the use of kernels specific to the wind power problem. A novel time-adaptive approach is presented. The quality of the new model is benchmarked against a splines quantile regression model currently in use in the industry. The case studies refer to two distinct wind farms in the United States and show that the new model produces better results, evaluated with suitable quality metrics such as calibration, sharpness, and skill score.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10