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An optimized mean variance estimation method for uncertainty quantification of wind power forecasts

journal contribution
posted on 2014-10-01, 00:00 authored by Abbas KhosraviAbbas Khosravi, Saeid Nahavandi
A statistical optimized technique for rapid development of reliable prediction intervals (PIs) is presented in this study. The mean-variance estimation (MVE) technique is employed here for quantification of uncertainties related with wind power predictions. In this method, two separate neural network models are used for estimation of wind power generation and its variance. A novel PI-based training algorithm is also presented to enhance the performance of the MVE method and improve the quality of PIs. For an in-depth analysis, comprehensive experiments are conducted with seasonal datasets taken from three geographically dispersed wind farms in Australia. Five confidence levels of PIs are between 50% and 90%. Obtained results show while both traditional and optimized PIs are hypothetically valid, the optimized PIs are much more informative than the traditional MVE PIs. The informativeness of these PIs paves the way for their application in trouble-free operation and smooth integration of wind farms into energy systems. © 2014 Elsevier Ltd. All rights reserved.

History

Journal

International journal of electrical power and energy systems

Volume

61

Pagination

446 - 454

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

0142-0615

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal