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Wind farm power uncertainty quantification using a mean-variance estimation method

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4 Author(s)
Khosravi, A. ; Centre for Intell. Syst. Res. (CISR), Deakin Univ., Geelong, VIC, Australia ; Nahavandi, S. ; Creighton, D. ; Jaafar, J.

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.

Published in:

Power System Technology (POWERCON), 2012 IEEE International Conference on

Date of Conference:

Oct. 30 2012-Nov. 2 2012