You are not logged in.

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, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, Saeid
Creighton, Douglas
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
Collection: Centre for Intelligent Systems Research
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 3 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 282 Abstract Views, 6 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.