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Wind farm power uncertainty quantification using a mean-variance estimation method
conference contribution
posted on 2012-01-01, 00:00 authored by Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, Douglas CreightonDouglas Creighton, J JaafarThis 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.
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Event
Power System Technology. Conference (2012 : Auckland, New Zealand)Pagination
1 - 6Publisher
IEEELocation
Auckland, New ZealandPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2012-10-30End date
2012-11-02ISBN-13
9781467328685Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
POWERCON 2012 : Proceedings of the 2012 IEEE International Conference on Power System TechnologyUsage metrics
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