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

Khosravi, Abbas and Nahavandi, Saeid 2014, An optimized mean variance estimation method for uncertainty quantification of wind power forecasts, International journal of electrical power and energy systems, vol. 61, pp. 446-454, doi: 10.1016/j.ijepes.2014.03.060.

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Title An optimized mean variance estimation method for uncertainty quantification of wind power forecasts
Author(s) Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name International journal of electrical power and energy systems
Volume number 61
Start page 446
End page 454
Total pages 9
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2014-10
ISSN 0142-0615
Keyword(s) Mean-variance
Prediction interval
Uncertainty
Wind farm
Science & Technology
Technology
Engineering, Electrical & Electronic
Engineering
PREDICTION INTERVALS
MODELS
SPEED
CONSTRUCTION
NETWORKS
Summary 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.
Language eng
DOI 10.1016/j.ijepes.2014.03.060
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 850509 Wind Energy
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Persistent URL http://hdl.handle.net/10536/DRO/DU:30069967

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