Please use this identifier to cite or link to this item: http://nopr.niscpr.res.in/handle/123456789/10106
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dc.contributor.authorAzadeh, Ali-
dc.contributor.authorSaberi, Morteza-
dc.contributor.authorAnvari, Mona-
dc.contributor.authorMoghaddam, M-
dc.date.accessioned2010-08-21T08:28:59Z-
dc.date.available2010-08-21T08:28:59Z-
dc.date.issued2010-09-
dc.identifier.issn0975-1084 (Online); 0022-4456 (Print)-
dc.identifier.urihttp://hdl.handle.net/123456789/10106-
dc.description672-679en_US
dc.description.abstractThis study proposes a non-parametric efficiency frontier analysis method based on artificial neural network (ANN) andK-Means algorithm for measuring efficiency of electricity distribution units (EDUs). Effect of return to scale of EDU on itsefficiency is included and EDU used for correction is selected based on its scale. K-Means algorithm is used to cluster EDUs toincrease their homogeneousness by handling outlines and noise. Proposed approach was applied to 31 EDUs in Iran . This is firststudy using integrated ANN-K-Means algorithm for improved performance assessment of EDUs. ANN-K-Means algorithm iscompared with earlier models to show its advantages and superiorities in prediction and forecasting.en_US
dc.language.isoen_USen_US
dc.publisherCSIRen_US
dc.sourceJSIR Vol.69(09) [September 2010]en_US
dc.subjectArtificial neural networken_US
dc.subjectElectricity distribution unitsen_US
dc.subjectImproved performance assessmenten_US
dc.subjectK-Means algorithmen_US
dc.titleAn integrated ANN-K-Means algorithm for improved performanceassessment of electricity distribution unitsen_US
dc.typeArticleen_US
Appears in Collections:JSIR Vol.69(09) [September 2010]

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