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