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dc.contributor.authorJohannesen, Nils Jakob
dc.contributor.authorKolhe, Mohan Lal
dc.contributor.authorGoodwin, Morten
dc.date.accessioned2023-03-30T12:34:03Z
dc.date.available2023-03-30T12:34:03Z
dc.date.created2022-08-25T11:32:54Z
dc.date.issued2022
dc.identifier.citationJohannesen, N. J., Kolhe, M. L. & Goodwin, M. (2022). Evaluating Anomaly Detection Algorithms through different Grid scenarios using k-Nearest Neighbor, iforest and Local Outlier Factor. In 7th International Conference on Smart and Sustainable Technologies, (pp. 1-6). IEEE.en_US
dc.identifier.isbn978-1-6654-8828-0
dc.identifier.urihttps://hdl.handle.net/11250/3061191
dc.descriptionAuthor's accepted manuscripten_US
dc.description© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractDetection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage. The available advanced information and communicating platform and computational capability renders smart grid prone to attacks with extreme social, financial and physical effects. The smart network enables energy management of smart appliances contributing support for ancillary services. Cyber threats could affect operation of smart appliances and hence the ancillary services, which might lead to stability and security issues. In this work, an overview is presented of different methods used in anomaly detection, performance evaluation of 3 models, the k-Nearest Neighbor, local outlier factor and isolated forest on recorded smart meter data from urban area and rural regionen_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.titleEvaluating Anomaly Detection Algorithms through different Grid scenarios using k-Nearest Neighbor, iforest and Local Outlier Factoren_US
dc.typeChapteren_US
dc.typePeer revieweden_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© 2022 IEEEen_US
dc.subject.nsiVDP::Teknologi: 500en_US
dc.source.pagenumber1-6en_US
dc.source.journal7th International Conference on Smart and Sustainable Technologiesen_US
dc.identifier.doihttps://doi.org/10.23919/SpliTech55088.2022.9854355
dc.identifier.cristin2045953
cristin.qualitycode1


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