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Uncertainty quantification for wind farm power generation

Khosravi, Abbas, Nahavandi, Saeid, Creighton, Doug and Naghavizadeh, Reihaneh 2012, Uncertainty quantification for wind farm power generation, in IJCNN/WCCI 2012 : Proceedings of the 2012 International Joint Conference on Neural Networks, IEEE, [Piscataway, N. J.], pp. 309-314.

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Title Uncertainty quantification for wind farm power generation
Author(s) Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Creighton, DougORCID iD for Creighton, Doug orcid.org/0000-0002-9217-1231
Naghavizadeh, Reihaneh
Conference name International Joint Conference on Neural Networks (2012 : Brisbane, Qld.)
Conference location Brisbane, Qld.
Conference dates 10-15 Jun. 2012
Title of proceedings IJCNN/WCCI 2012 : Proceedings of the 2012 International Joint Conference on Neural Networks
Editor(s) [unknown]
Publication date 2012
Conference series International Joint Conference on Neural Networks
Start page 309
End page 314
Total pages 6
Publisher IEEE
Place of publication [Piscataway, N. J.]
Keyword(s) neural networks
prediction intervals
uncertainty
wind energy
Summary Accurate forecasting of wind farm power generation is essential for successful operation and management of wind farms and to minimize risks associated with their integration into energy systems. However, due to the inherent wind intermittency, wind power forecasts are highly prone to error and often far from being perfect. The purpose of this paper is to develop statistical methods for quantifying uncertainties associated with wind power generation forecasts. Prediction intervals (PIs) with a prescribed confidence level are constructed using the delta and bootstrap methods for neural network forecasts. The moving block bootstrap method is applied to preserve the correlation structure in wind power observations. The effectiveness and efficiency of these two methods for uncertainty quantification is examined using two month datasets taken from a wind farm in Australia. It is demonstrated that while all constructed PIs are theoretically valid, bootstrap PIs are more informative than delta PIs, and are therefore more useful for decision-making.
ISBN 9781467314893
Language eng
Field of Research 080110 Simulation and Modelling
Socio Economic Objective 850509 Wind Energy
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2012, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048266

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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