Electricity load classification using K-means clustering algorithm

K-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...

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Main Author: Somboon Nuchprayoon
Format: Conference Proceeding
Published: 2018
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84949986766&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/45401
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Institution: Chiang Mai University
id th-cmuir.6653943832-45401
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spelling th-cmuir.6653943832-454012018-01-24T06:09:53Z Electricity load classification using K-means clustering algorithm Somboon Nuchprayoon K-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. 2018-01-24T06:09:53Z 2018-01-24T06:09:53Z 2014-01-01 Conference Proceeding 2-s2.0-84949986766 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84949986766&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/45401
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
description K-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.
format Conference Proceeding
author Somboon Nuchprayoon
spellingShingle Somboon Nuchprayoon
Electricity load classification using K-means clustering algorithm
author_facet Somboon Nuchprayoon
author_sort Somboon Nuchprayoon
title Electricity load classification using K-means clustering algorithm
title_short Electricity load classification using K-means clustering algorithm
title_full Electricity load classification using K-means clustering algorithm
title_fullStr Electricity load classification using K-means clustering algorithm
title_full_unstemmed Electricity load classification using K-means clustering algorithm
title_sort electricity load classification using k-means clustering algorithm
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84949986766&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/45401
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