A Survey of Computational Intelligence Techniques for Wind Power Uncertainty Quantification in Smart Grids Quan, H, Khosravi, Abbas, Yang, D and Srinivasan, D 2020, A Survey of Computational Intelligence Techniques for Wind Power Uncertainty Quantification in Smart Grids, IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 11, pp. 4582-4599, doi: 10.1109/TNNLS.2019.2956195. Attached Files Name Description MIMEType Size Downloads Title A Survey of Computational Intelligence Techniques for Wind Power Uncertainty Quantification in Smart Grids Author(s) Quan, H Khosravi, Abbas orcid.org/0000-0001-6927-0744 Yang, D Srinivasan, D Journal name IEEE Transactions on Neural Networks and Learning Systems Volume number 31 Issue number 11 Start page 4582 End page 4599 Total pages 18 Publisher IEEE Place of publication Piscataway, N.J. Publication date 2020 ISSN 2162-237X 2162-2388 Keyword(s) Science & Technology Technology Computer Science, Artificial Intelligence Computer Science, Hardware & Architecture Computer Science, Theory & Methods Engineering, Electrical & Electronic Computer Science Engineering Uncertainty Wind power generation Wind speed Wind forecasting Forecasting Stochastic processes Computational intelligence decision-making neural network (NN) prediction interval (PI) uncertainty quantification wind power FUZZY-LOGIC SYSTEMS OPTIMAL PREDICTION INTERVALS CONSTRAINED UNIT COMMITMENT NEURAL-NETWORK ROBUST OPTIMIZATION SPINNING RESERVE LOAD ENERGY FLOW GENERATION Language eng DOI 10.1109/TNNLS.2019.2956195 Indigenous content off HERDC Research category C1 Refereed article in a scholarly journal Persistent URL http://hdl.handle.net/10536/DRO/DU:30145297 Document type: Journal Article Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description 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 Fri, 13 Nov 2020, 07:04:45 EST Sat, 14 Nov 2020, 04:00:41 EST Mon, 16 Nov 2020, 06:54:47 EST Sat, 09 Jan 2021, 01:39:37 EST Tue, 19 Jan 2021, 05:06:28 EST Wed, 24 Feb 2021, 13:44:55 EST Wed, 24 Feb 2021, 13:46:39 EST Wed, 24 Feb 2021, 15:00:35 EST Filtered Full Citation counts:   Cited 4 times in TR Web of Science Cited 7 times in Scopus Search Google Scholar Access Statistics: 12 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Fri, 13 Nov 2020, 07:04:45 EST

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