Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Functional clustering for electricity demand data: A case study

시간단위 전력수요자료의 함수적 군집분석: 사례연구

  • Yoon, Sanghoo (WISE institute, Hankook University of Foreign Studies) ;
  • Choi, Youngjean (WISE institute, Hankook University of Foreign Studies)
  • 윤상후 (한국외국어대학교 차세대도시농림융합기상사업단) ;
  • 최영진 (한국외국어대학교 차세대도시농림융합기상사업단)
  • Received : 2015.05.06
  • Accepted : 2015.07.01
  • Published : 2015.07.31
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Abstract

It is necessary to forecast the electricity demand for reliable and effective operation of the power system. In this study, we try to categorize a functional data, the mean curve in accordance with the time of daily power demand pattern. The data were collected between January 1, 2009 and December 31, 2011. And it were converted to time series data consisting of seasonal components and error component through log transformation and removing trend. Functional clustering by Ma et al. (2006) are applied and parameters are estimated using EM algorithm and generalized cross validation. The number of clusters is determined by classifying holidays or weekdays. Monday, weekday (Tuesday to Friday), Saturday, Sunday or holiday and season are described the mean curve of daily power demand pattern.

전력시스템의 안정적이고 효과적인 운영을 위해선 전력수요예측이 필요하다. 본 연구에서는 일별전력수요패턴의 시간에 따른 커브를 군집분석 하려고 한다. 2009년 1월 1일부터 2011년 12월 31일까지의 일별 시간단위 전력수요 자료는 추세성분 제거와 로그변환을 통해 계절성분과 오차성분으로 구성된 시계열자료로 변환되었다. 변환된 자료는 Ma 등 (2006)이 제안한 함수적 군집모형을 사용하여 분석되었고, 모수는 EM알고리즘과 일반화교차검정을 통해 추정되었다. 군집의 수는 휴일과 평일을 잘 분류하는 10개로 결정하였다. 분석결과 월요일, 평일 (화요일~금요일), 토요일, 일요일 또는 공휴일과 계절요인으로 전력수요 평균곡선이 설명된다. 함수적 군집분석을 통한 전력수요패턴의 과학적인 분류는 향후 단기전력수요예측에 활용된다.

Keywords

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