Open AccessFrom probabilistic forecasts to statistical scenarios of short‐term wind power production
Author(s) -
Pinson Pierre,
Madsen Henrik,
Nielsen Henrik Aa.,
Papaefthymiou George,
Klöckl Bernd
Publication year - 2009
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.284
Subject(s) - probabilistic logic , wind power , term (time) , probabilistic forecasting , covariance matrix , covariance , computer science , econometrics , production (economics) , gaussian , time horizon , mathematical optimization , statistics , mathematics , engineering , algorithm , economics , artificial intelligence , physics , quantum mechanics , electrical engineering , macroeconomics
Short‐term (up to 2–3 days ahead) probabilistic forecasts of wind power provide forecast users with highly valuable information on the uncertainty of expected wind generation. Whatever the type of these probabilistic forecasts, they are produced on a per horizon basis, and hence do not inform on the development of the forecast uncertainty through forecast series. However, this additional information may be paramount for a large class of time‐dependent and multistage decision‐making problems, e.g. optimal operation of combined wind‐storage systems or multiple‐market trading with different gate closures. This issue is addressed here by describing a method that permits the generation of statistical scenarios of short‐term wind generation that accounts for both the interdependence structure of prediction errors and the predictive distributions of wind power production. The method is based on the conversion of series of prediction errors to a multivariate Gaussian random variable, the interdependence structure of which can then be summarized by a unique covariance matrix. Such matrix is recursively estimated in order to accommodate long‐term variations in the prediction error characteristics. The quality and interest of the methodology are demonstrated with an application to the test case of a multi‐MW wind farm over a period of more than 2 years. Copyright © 2008 John Wiley & Sons, Ltd.