Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/12363
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dc.contributor.authorZobaa, AF-
dc.contributor.authorVaccaro, A-
dc.contributor.authorLai, LL-
dc.date.accessioned2016-03-16T16:33:30Z-
dc.date.available2016-03-03-
dc.date.available2016-03-16T16:33:30Z-
dc.date.issued2016-
dc.identifier.citationIEEE Transactions on Industrial Informatics, 12(2): pp. 820 - 823, (2016)en_US
dc.identifier.issn1941-0050-
dc.identifier.urihttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7425220-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/12363-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.subjectData mining-
dc.subjectFeature extraction-
dc.subjectHeuristic algorithms-
dc.subjectPower system dynamics-
dc.titleEnabling technologies and methodologies for knowledge discovery and data mining in smart gridsen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1109/TII.2016.2524562-
dc.relation.isPartOfIEEE Transactions on Industrial Informatics-
pubs.publication-statusPublished online-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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