Construction of optimal prediction intervals for load forecasting problems Khosravi, Abbas, Nahavandi, Saeid and Creighton, Doug 2010, Construction of optimal prediction intervals for load forecasting problems, IEEE transactions on power systems, vol. 25, no. 3, pp. 1496-1503, doi: 10.1109/TPWRS.2010.2042309. Attached Files Name Description MIMEType Size Downloads nahavandi-constructionof-2010.pdf Published version application/pdf 622.52KB 357 Title Construction of optimal prediction intervals for load forecasting problems Author(s) Khosravi, Abbas orcid.org/0000-0001-6927-0744 Nahavandi, Saeid Creighton, Doug orcid.org/0000-0002-9217-1231 Journal name IEEE transactions on power systems Volume number 25 Issue number 3 Start page 1496 End page 1503 Total pages 8 Publisher IEEE Place of publication Piscataway, N.J. Publication date 2010-03-11 ISSN 0885-8950 1558-0679 Keyword(s) load forecasting neural network prediction interval Summary Short-term load forecasting is fundamental for the reliable and efficient operation of power systems. Despite its importance, accurate prediction of loads is problematic and far remote. Often uncertainties significantly degrade performance of load forecasting models. Besides, there is no index available indicating reliability of predicted values. The objective of this study is to construct prediction intervals for future loads instead of forecasting their exact values. The delta technique is applied for constructing prediction intervals for outcomes of neural network models. Some statistical measures are developed for quantitative and comprehensive evaluation of prediction intervals. According to these measures, a new cost function is designed for shortening length of prediction intervals without compromising their coverage probability. Simulated annealing is used for minimization of this cost function and adjustment of neural network parameters. Demonstrated results clearly show that the proposed methods for constructing prediction interval outperforms the traditional delta technique. Besides, it yields prediction intervals that are practically more reliable and useful than exact point predictions. Notes Date of Publication: 11 March 2010. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. Language eng DOI 10.1109/TPWRS.2010.2042309 Field of Research 080108 Neural, Evolutionary and Fuzzy Computation Socio Economic Objective 970109 Expanding Knowledge in Engineering HERDC Research category C1 Refereed article in a scholarly journal HERDC collection year 2010 Copyright notice ©2010, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30030994 Document type: Journal Article Collections: Centre for Intelligent Systems Research Open Access Collection Related Links Link Description Connect to published version (restricted access) 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. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Mon, 01 Nov 2010, 16:27:53 EST Mon, 01 Nov 2010, 16:29:06 EST Mon, 08 Nov 2010, 14:41:52 EST Tue, 09 Nov 2010, 09:43:39 EST Tue, 09 Nov 2010, 09:44:14 EST Tue, 09 Nov 2010, 10:35:22 EST Tue, 09 Nov 2010, 10:35:55 EST Tue, 09 Nov 2010, 10:37:34 EST Tue, 09 Nov 2010, 10:38:09 EST Tue, 09 Nov 2010, 10:41:24 EST Tue, 09 Nov 2010, 10:43:46 EST Thu, 19 May 2011, 13:37:49 EST Wed, 02 Jul 2014, 14:50:44 EST Fri, 17 Oct 2014, 10:30:18 EST Tue, 06 Sep 2016, 15:59:23 EST Filtered Full Citation counts:   Cited 62 times in TR Web of Science Cited 76 times in Scopus Search Google Scholar Access Statistics: 598 Abstract Views, 359 File Downloads  -  Detailed Statistics Created: Mon, 01 Nov 2010, 16:27:47 EST by Sandra Dunoon

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.