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Clustering and classification to characterize daily electricity demand
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시간단위 전력사용량 시계열 패턴의 군집 및 분류분석

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Type
Academic journal
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The Korean Data and Information Science Society Journal of the Korean Data And Information Science Society Vol.28 No.2 KCI Excellent Accredited Journal
Published
2017.3
Pages
395 - 406 (12page)

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Clustering and classification to characterize daily electricity demand
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The purpose of this study is to identify the pattern of daily electricity demand through clustering and classification. The hourly data was collected by KPS (Korea Power Exchange) between 2008 and 2012. The time trend was eliminated for conducting the pattern of daily electricity demand because electricity demand data is times series data. We have considered k-means clustering, Gaussian mixture model clustering, and functional clustering in order to find the optimal clustering method. The classification analysis was conducted to understand the relationship between external factors, day of the week, holiday, and weather. Data was divided into training data and test data. Training data consisted of external factors and clustered number between 2008 and 2011. Test data was daily data of external factors in 2012. Decision tree, random forest, Support vector machine, and Naive Bayes were used. As a result, Gaussian model based clustering and random forest showed the best prediction performance when the number of cluster was 8.
#군집분석 #기계학습 #Cluster analysis #Electricity demand #분류분석 #전력수요량 #Classification analysis #Machine learn-ing

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[학술지(정기간행물)] - Breiman, L. / 2001 / Random forests / Machine learning 45 : 5 ~ 32 google schola [학술지(정기간행물)] - Cho, H. / 2013 / Modeling and forecasting daily electricity load curves : A hybrid approach / Journal of the American Statistical Association 108 : 7 ~ 21 google schola [인터넷자원] - Dimitriadou, E. / / Misc Functions of the Department of Statistics (e1071), TU Wien. R package version 1.5-7 google schola [인터넷자원] - Fraley, C. / / mclust: Normal mixture modelling for model-based clustering, classification, and density estimation google schola [학술지(정기간행물)] - Hwang, H. M. / 2015 / Load forecasting using hierarchical clustering method for building / Journal of the Korean Institute of Illuminating and Electrical Installation Engineers : 59 ~ 65 google schola

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UCI(KEPA) : I410-ECN-0101-2018-041-001369262