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. © 2012 IEEE.