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. Attached Files Name Description MIMEType Size Downloads Title An optimized mean variance estimation method for uncertainty quantification of wind power forecasts Author(s) Khosravi, Abbas orcid.org/0000-0001-6927-0744 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 Document type: Journal Article Collections: Institute for Intelligent Systems Research and Innovation (IISRI) 2018 ERA Submission GTP Research Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges   Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 24 Feb 2015, 09:32:28 EST Tue, 24 Feb 2015, 09:33:09 EST Tue, 24 Feb 2015, 09:59:33 EST Tue, 24 Feb 2015, 10:29:37 EST Tue, 03 Mar 2015, 05:05:39 EST Thu, 05 Mar 2015, 09:43:25 EST Thu, 05 Mar 2015, 09:45:44 EST Tue, 17 Mar 2015, 14:34:29 EST Filtered Full Citation counts:   Cited 27 times in TR Web of Science Cited 35 times in Scopus Search Google Scholar Access Statistics: 746 Abstract Views, 7 File Downloads  -  Detailed Statistics Created: Tue, 24 Feb 2015, 09:33:09 EST