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dc.contributor.authorSomboon Nuchprayoonen_US
dc.date.accessioned2018-09-04T09:50:48Z-
dc.date.available2018-09-04T09:50:48Z-
dc.date.issued2014-01-01en_US
dc.identifier.other2-s2.0-84949986766en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84949986766&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/53522-
dc.description.abstractK-means clustering method is applied to classify electricity load data into five groups. The load groups are super-peak, peak, cycling, intermediate, and base. On the other hand, when only three groups are needed, the peak load is combined with the cycling load and the intermediate load is combined with the base load. The classification is performed both on annual basis and seasonal basis and shown by using load duration curves. The attributes of load group are load level and duration. The proposed method has been implemented by using statistical analysis software SPSS and tested with the hourly generation data of Thailand during 2009-2011.en_US
dc.subjectEngineeringen_US
dc.titleElectricity load classification using K-means clustering algorithmen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleIET Conference Publicationsen_US
article.volume2014en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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