Generation of statistical scenarios of short-term wind power production

Pierre Pinson; George Papaefthymiou; Bernd Klockl; Henrik Aalborg Nielsen

doi:10.1109/PCT.2007.4538366

Generation of statistical scenarios of short-term wind power production

Pierre Pinson, George Papaefthymiou, Bernd Klockl, Henrik Aalborg Nielsen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

395 Downloads (Pure)

Abstract

Short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a paramount 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. This issue is addressed here by describing a method that permits to generate statistical scenarios of wind generation that accounts for the interdependence structure of prediction errors, in plus of respecting predictive distributions of wind generation. The approach is evaluated on the test case of a multi-MW wind farm over a period of more than two years. Its interest for a large range of applications is discussed.

Original language	English
Title of host publication	IEEE PowerTech Conference 2007, Lausanne, Switzerland
Publisher	IEEE
Publication date	2007
ISBN (Print)	978-1-4244-2189-3
DOIs	https://doi.org/10.1109/PCT.2007.4538366
Publication status	Published - 2007
Event	IEEE PowerTech Conference 2007, Lausanne, Switzerland - Duration: 1 Jan 2007 → …

Conference

Conference	IEEE PowerTech Conference 2007, Lausanne, Switzerland
Period	01/01/2007 → …

Bibliographical note

Copyright: 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

Access to Document

10.1109/PCT.2007.4538366

PinsonFinal published version, 2.21 MB

Fingerprint Dive into the research topics of 'Generation of statistical scenarios of short-term wind power production'. Together they form a unique fingerprint.

Cite this

@inproceedings{287302af80dc4c9ab9ee232ecd29577e,

title = "Generation of statistical scenarios of short-term wind power production",

abstract = "Short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a paramount 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. This issue is addressed here by describing a method that permits to generate statistical scenarios of wind generation that accounts for the interdependence structure of prediction errors, in plus of respecting predictive distributions of wind generation. The approach is evaluated on the test case of a multi-MW wind farm over a period of more than two years. Its interest for a large range of applications is discussed.",

author = "Pierre Pinson and George Papaefthymiou and Bernd Klockl and Nielsen, {Henrik Aalborg}",

note = "Copyright: 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE; IEEE PowerTech Conference 2007, Lausanne, Switzerland ; Conference date: 01-01-2007",

year = "2007",

doi = "10.1109/PCT.2007.4538366",

language = "English",

isbn = "978-1-4244-2189-3",

booktitle = "IEEE PowerTech Conference 2007, Lausanne, Switzerland",

publisher = "IEEE",

address = "United States",

}

TY - GEN

T1 - Generation of statistical scenarios of short-term wind power production

AU - Pinson, Pierre

AU - Papaefthymiou, George

AU - Klockl, Bernd

AU - Nielsen, Henrik Aalborg

N1 - Copyright: 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

PY - 2007

Y1 - 2007

N2 - Short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a paramount 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. This issue is addressed here by describing a method that permits to generate statistical scenarios of wind generation that accounts for the interdependence structure of prediction errors, in plus of respecting predictive distributions of wind generation. The approach is evaluated on the test case of a multi-MW wind farm over a period of more than two years. Its interest for a large range of applications is discussed.

AB - Short-term (up to 2-3 days ahead) probabilistic forecasts of wind power provide forecast users with a paramount 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. This issue is addressed here by describing a method that permits to generate statistical scenarios of wind generation that accounts for the interdependence structure of prediction errors, in plus of respecting predictive distributions of wind generation. The approach is evaluated on the test case of a multi-MW wind farm over a period of more than two years. Its interest for a large range of applications is discussed.

U2 - 10.1109/PCT.2007.4538366

DO - 10.1109/PCT.2007.4538366

M3 - Article in proceedings

SN - 978-1-4244-2189-3

BT - IEEE PowerTech Conference 2007, Lausanne, Switzerland

PB - IEEE

T2 - IEEE PowerTech Conference 2007, Lausanne, Switzerland

Y2 - 1 January 2007

ER -