Wind farm power uncertainty quantification using a mean-variance estimation method Khosravi, Abbas, Nahavandi, Saeid, Creighton, Douglas and Jaafar, Jafreezal 2012, Wind farm power uncertainty quantification using a mean-variance estimation method, in POWERCON 2012 : Proceedings of the 2012 IEEE International Conference on Power System Technology, IEEE, Piscataway, N.J., pp. 1-6, doi: 10.1109/PowerCon.2012.6401280. Attached Files Name Description MIMEType Size Downloads Title Wind farm power uncertainty quantification using a mean-variance estimation method Author(s) Khosravi, Abbas orcid.org/0000-0001-6927-0744 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Creighton, Douglas orcid.org/0000-0002-9217-1231 Jaafar, Jafreezal Conference name Power System Technology. Conference (2012 : Auckland, New Zealand) Conference location Auckland, New Zealand Conference dates 30 Oct.-2 Nov. 2012 Title of proceedings POWERCON 2012 : Proceedings of the 2012 IEEE International Conference on Power System Technology Editor(s) [Unknown] Publication date 2012 Conference series Power System Technology Conference Start page 1 End page 6 Total pages 6 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) uncertainty neural networks prediction intervals wind power Summary This paper proposes an innovative optimized parametric method for construction of prediction intervals (PIs) for uncertainty quantification. The mean-variance estimation (MVE) method employs two separate neural network (NN) models to estimate the mean and variance of targets. A new training method is developed in this study that adjusts parameters of NN models through minimization of a PI-based cost functions. A simulated annealing method is applied for minimization of the nonlinear non-differentiable cost function. The performance of the proposed method for PI construction is examined using monthly data sets taken from a wind farm in Australia. PIs for the wind farm power generation are constructed with five confidence levels between 50% and 90%. Demonstrated results indicate that valid PIs constructed using the optimized MVE method have a quality much better than the traditional MVE-based PIs. ISBN 9781467328685 Language eng DOI 10.1109/PowerCon.2012.6401280 Field of Research 080108 Neural, Evolutionary and Fuzzy Computation Socio Economic Objective 850509 Wind Energy HERDC Research category E1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30051759 Document type: Conference Paper Collections: Institute for Intelligent Systems Research and Innovation (IISRI) 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 Thu, 04 Apr 2013, 12:34:31 EST Thu, 04 Apr 2013, 12:40:06 EST Tue, 14 May 2013, 12:16:18 EST Wed, 15 May 2013, 08:24:09 EST Wed, 12 Jun 2013, 12:29:37 EST Thu, 13 Jun 2013, 08:47:32 EST Wed, 02 Jul 2014, 16:09:28 EST Fri, 17 Oct 2014, 12:02:02 EST Wed, 21 Oct 2015, 14:44:13 EST Wed, 21 Oct 2015, 18:06:42 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 7 times in Scopus Search Google Scholar Access Statistics: 718 Abstract Views, 7 File Downloads  -  Detailed Statistics Created: Thu, 04 Apr 2013, 12:34:31 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.