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Using Quantile Regression to Extend an Existing Wind Power Forecasting System with Probabilistic Forecasts

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Authors:
  • Nielsen, Henrik Aalborg ;
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    Department of Informatics and Mathematical Modeling, Technical University of Denmark
  • Madsen, Henrik ;
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    Orcid logo0000-0003-0690-3713
    Department of Informatics and Mathematical Modeling, Technical University of Denmark
  • Nielsen, Torben Skov
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    Department of Informatics and Mathematical Modeling, Technical University of Denmark
DOI:
10.1002/we.180
Abstract:
For operational planning it is important to provide information about the situation-dependent uncertainty of a wind power forecast. Factors which influence the uncertainty of a wind power forecast include the predictability of the actual meteorological situation, the level of the predicted wind speed (due to the non-linearity of the power curve) and the forecast horizon. With respect to the predictability of the actual meteorological situation a number of explanatory variables are considered, some inspired by the literature. The article contains an overview of related work within the field. An existing wind power forecasting system (Zephyr/WPPT) is considered and it is shown how analysis of the forecast error can be used to build a model of the quantiles of the forecast error. Only explanatory variables or indices which are predictable are considered, whereby the model obtained can be used for providing situation-dependent information regarding the uncertainty. Finally, the article contains directions enabling the reader to replicate the methods and thereby extend other forecast systems with situation-dependent information on uncertainty. Copyright © 2005 John Wiley & Sons, Ltd.
Type:
Conference paper
Language:
English
Published in:
Wind Energy, 2006, Vol 9, Issue 1-2, p. 95-108
Keywords:
Wind power forecasting; Uncertainty; Quantile regression; Additive model
Main Research Area:
Science/technology
Publication Status:
Published
Review type:
Peer Review
Conference:
European Wind Energy Conference & Exhibition 2004
Submission year:
2006
Scientific Level:
Scientific
ID:
8758750

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