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. Attached Files Name Description MIMEType Size Downloads Title Uncertainty quantification for wind farm power generation Author(s) Khosravi, Abbas orcid.org/0000-0001-6927-0744 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 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 Collections: Institute for Intelligent Systems Research and Innovation (IISRI) 2018 ERA Submission GTP Research Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 04 Sep 2012, 09:19:58 EST Wed, 31 Oct 2012, 11:27:55 EST Wed, 31 Oct 2012, 11:29:24 EST Thu, 08 Nov 2012, 08:11:39 EST Thu, 29 Nov 2012, 10:35:52 EST Thu, 29 Nov 2012, 11:13:43 EST Wed, 22 May 2013, 11:40:08 EST Wed, 22 May 2013, 12:27:02 EST Thu, 23 May 2013, 08:56:49 EST Wed, 05 Jun 2013, 15:05:59 EST Wed, 05 Jun 2013, 15:46:19 EST Wed, 05 Jun 2013, 15:47:52 EST Wed, 02 Jul 2014, 15:57:06 EST Fri, 17 Oct 2014, 11:46:47 EST Mon, 23 Mar 2015, 09:18:06 EST Wed, 14 Oct 2015, 13:20:51 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 696 Abstract Views, 10 File Downloads  -  Detailed Statistics Created: Tue, 04 Sep 2012, 09:19:58 EST

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