Open AccessProbabilistic wind power forecasts using local quantile regression
Author(s) -
Bremnes John Bjørnar
Publication year - 2004
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.107
Subject(s) - quantile regression , probabilistic logic , quantile , probabilistic forecasting , econometrics , wind power forecasting , wind power , consensus forecast , predictive power , computer science , power (physics) , engineering , electric power system , economics , artificial intelligence , philosophy , physics , epistemology , quantum mechanics , electrical engineering
Wind power forecasts are in various ways valuable for users in decision‐making processes. However, most forecasts are deterministic, and hence possibly important information about uncertainty is not available. Complete information about future production can be obtained by using probabilistic forecasts, and this article demonstrates how such forecasts can be created by means of local quantile regression. The approach has several advantages, such as no distributional assumptions and flexible inclusion of predictive information. In addition, it can be shown that, for some purposes, forecasts in terms of quantiles provide the type of information required to make optimal economic decisions. The methodology is applied to data from a wind farm in Norway. Copyright © 2004 John Wiley & Sons, Ltd.