Quantile-copula density forecast for wind power uncertainty modeling

• Type: Article in International Conference Proceedings Book
• Date: 2011

Authors

Bessa, RJ (Author) Other; Mendes, J (Author) Other; Vladimiro Miranda (Author) FEUP; Botterud, A (Author) Other; Wang, J (Author) Other; Zhou, Z (Author) Other

Conference proceedings International

Title: 2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011

2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011

Trondheim, 19 June 2011 through 23 June 2011

Indexing

Scopus - 18 Citations

Other information

Authenticus ID: P-008-0GT

DOI: 10.1109/ptc.2011.6019180

Abstract (EN): A probabilistic forecast, in contrast to a point forecast, provides to the end-user more and valuable information for decision-making problems such as wind power bidding into the electricity market or setting adequate operating reserve levels in the power system. One important requirement is to have flexible representations of wind power forecast (WPF) uncertainty, in order to facilitate their inclusion in several problems. This paper reports results of using the quantile-copula conditional Kernel density estimator in the WPF problem, and how to select the adequate kernels for modeling the different variables of the problem. The method was compared with splines quantile regression for a real wind farm located in the U.S. Midwest. © 2011 IEEE.

Language: English

Type (Professor's evaluation): Scientific