Construction of neural network-based prediction intervals for short-term electrical load forecasting Quan, Hao, Srinivasan, Dipti, Khosravi, Abbas, Nahavandi, Saeid and Creighton, Doug 2013, Construction of neural network-based prediction intervals for short-term electrical load forecasting, in CIASG 2013 : Proceedings of the 2013 IEEE Computational Intelligence Applications in Smart Grid, IEEE, Piscataway, N. J., pp. 66-72. Attached Files Name Description MIMEType Size Downloads Title Construction of neural network-based prediction intervals for short-term electrical load forecasting Author(s) Quan, Hao Srinivasan, Dipti Khosravi, Abbas orcid.org/0000-0001-6927-0744 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Creighton, Doug orcid.org/0000-0002-9217-1231 Conference name IEEE Computational Intelligence Applications in Smart Grid (2013 : Singapore) Conference location Singapore Conference dates 16-19 Apr. 2013 Title of proceedings CIASG 2013 : Proceedings of the 2013 IEEE Computational Intelligence Applications in Smart Grid Editor(s) [Unknown] Publication date 2013 Conference series IEEE Symposium on Computational Intelligence series Start page 66 End page 72 Total pages 7 Publisher IEEE Place of publication Piscataway, N. J. Keyword(s) electrical power system neural network prediction interval short term load forecasting short-term electrical loads wind and solar power Summary Short-term load forecasting (STLF) is of great importance for control and scheduling of electrical power systems. The uncertainty of power systems increases due to the random nature of climate and the penetration of the renewable energies such as wind and solar power. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in datasets. To quantify these potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for construction of prediction intervals (PIs). A newly proposed method, called lower upper bound estimation (LUBE), is applied to develop PIs using NN models. The primary multi-objective problem is firstly transformed into a constrained single-objective problem. This new problem formulation is closer to the original problem and has fewer parameters than the cost function. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Two case studies from Singapore and New South Wales (Australia) historical load datasets are used to validate the PSO-based LUBE method. Demonstrated results show that the proposed method can construct high quality PIs for load forecasting applications. ISBN 1467360023 9781467360029 Language eng Field of Research 080108 Neural, Evolutionary and Fuzzy Computation Socio Economic Objective 850699 Energy Storage, Distribution and Supply not elsewhere classified HERDC Research category E1 Full written paper - refereed Copyright notice ©2013, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30062968 Document type: Conference Paper Collections: Centre for Intelligent Systems Research GTP Research   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 07 May 2014, 10:18:20 EST Wed, 07 May 2014, 10:25:17 EST Thu, 08 May 2014, 09:29:55 EST Thu, 08 May 2014, 15:39:56 EST Thu, 08 May 2014, 15:43:53 EST Thu, 08 May 2014, 15:46:32 EST Fri, 09 May 2014, 08:44:15 EST Mon, 02 Jun 2014, 12:36:13 EST Wed, 02 Jul 2014, 16:55:31 EST Tue, 15 Jul 2014, 15:02:46 EST Fri, 17 Oct 2014, 12:45:09 EST Wed, 21 Oct 2015, 15:05:05 EST Sat, 15 Apr 2017, 02:33:34 EST Filtered Full Citation counts:   Cited 7 times in TR Web of Science Cited 9 times in Scopus Search Google Scholar Access Statistics: 400 Abstract Views, 7 File Downloads  -  Detailed Statistics Created: Wed, 07 May 2014, 10:18:19 EST by Barb Robertson

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