Interval type-2 fuzzy logic systems for load forecasting : a comparative study Khosravi, Abbas, Nahavandi, Saeid, Creighton, Doug and Srinivasan, Dipti 2012, Interval type-2 fuzzy logic systems for load forecasting : a comparative study, IEEE transactions on power systems, vol. 27, no. 3, pp. 1274-1282. Attached Files Name Description MIMEType Size Downloads Title Interval type-2 fuzzy logic systems for load forecasting : a comparative study Author(s) Khosravi, Abbas orcid.org/0000-0001-6927-0744 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Creighton, Doug orcid.org/0000-0002-9217-1231 Srinivasan, Dipti Journal name IEEE transactions on power systems Volume number 27 Issue number 3 Start page 1274 End page 1282 Total pages 9 Publisher IEEE Place of publication Piscataway, N. J Publication date 2012 ISSN 0885-8950 1558-0679 Keyword(s) load forecasting prediction interval type 2 fuzzy logic system Summary Accurate short term load forecasting (STLF) is essential for a variety of decision-making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with additional degrees of freedom, are an excellent tool for handling uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models precisely approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks and traditional type-1 Takagi-Sugeno-Kang (TSK) FLSs. Language eng Field of Research 080110 Simulation and Modelling Socio Economic Objective 850699 Energy Storage, Distribution and Supply not elsewhere classified HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2012, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30046865 Document type: Journal Article Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research 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. Versions Version Filter Type Mon, 13 Aug 2012, 12:35:36 EST Tue, 14 Aug 2012, 12:16:49 EST Tue, 14 Aug 2012, 12:18:32 EST Fri, 07 Jun 2013, 14:17:52 EST Fri, 07 Jun 2013, 14:59:49 EST Fri, 07 Jun 2013, 15:00:41 EST Wed, 02 Jul 2014, 15:50:17 EST Fri, 17 Oct 2014, 11:37:13 EST Filtered Full Citation counts:   Cited 108 times in TR Web of Science Cited 131 times in Scopus Search Google Scholar Access Statistics: 633 Abstract Views, 5 File Downloads  -  Detailed Statistics Created: Mon, 13 Aug 2012, 12:35:36 EST

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