TY  - CONF
KW  - Australia; Confidence levels; Data sets; Estimation methods; Mean variance; Neural network model; Non-differentiable; Parametric method; Prediction interval; Simulated annealing method; Training methods; Uncertainty; Uncertainty quantifications; Wind farm
KW  -  Cost functions; Electric utilities; Estimation; Neural networks; Simulated annealing
KW  -  Wind power
TI  - Wind farm power uncertainty quantification using a mean-variance estimation method
ID  - scholars2614
N2  - 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.
N1  - cited By 15; Conference of 2012 IEEE International Conference on Power System Technology, POWERCON 2012 ; Conference Date: 30 October 2012 Through 2 November 2012; Conference Code:95313
AV  - none
CY  - Auckland
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84873320883&doi=10.1109%2fPowerCon.2012.6401280&partnerID=40&md5=73abe2d6f61ce94267e154cac13f7d78
A1  - Khosravi, A.
A1  - Nahavandi, S.
A1  - Creighton, D.
A1  - Jaafar, J.
SN  - 9781467328685
Y1  - 2012///
ER  -