The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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A study on electricity demand forecasting based on time series clustering in smart grid

스마트 그리드에서의 시계열 군집분석을 통한 전력수요 예측 연구

  • Sohn, Hueng-Goo (Department of Applied Statistics, Chung-Ang University) ;
  • Jung, Sang-Wook (Department of Applied Statistics, Chung-Ang University) ;
  • Kim, Sahm (Department of Applied Statistics, Chung-Ang University)
  • 손흥구 (응용통계학과, 중앙대학교) ;
  • 정상욱 (응용통계학과, 중앙대학교) ;
  • 김삼용 (응용통계학과, 중앙대학교)
  • Received : 2015.12.17
  • Accepted : 2015.12.30
  • Published : 2016.02.29
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Abstract

This paper forecasts electricity demand as a critical element of a demand management system in Smart Grid environment. We present a prediction method of using a combination of predictive values by time series clustering. Periodogram-based normalized clustering, predictive analysis clustering and dynamic time warping (DTW) clustering are proposed for time series clustering methods. Double Seasonal Holt-Winters (DSHW), Trigonometric, Box-Cox transform, ARMA errors, Trend and Seasonal components (TBATS), Fractional ARIMA (FARIMA) are used for demand forecasting based on clustering. Results show that the time series clustering method provides a better performances than the method using total amount of electricity demand in terms of the Mean Absolute Percentage Error (MAPE).

본 논문은 ICT기반 시장에서의 수요관리시스템에서의 핵심 요소인 전력 수요 예측을 위하여, 전체 사용량을 기반으로 예측 하는 방식이 아닌, 시계열 기반 군집분석을 통한 군집별 예측량의 결합을 실시하였다. 시계열 군집 분석 방법으로서 Periodogram 기반의 정규화 군집분석, 예측 기반의 군집분석, DTW(Dynamic Time Warping)를 이용하여 군집화를 시도하였으며, 군집 별 수요예측 모형으로서 DSHW(Double Seasonal Holt-Winters) 모형, TBATS(Trigonometric, Box-Cox transform, ARMA errors, Trend and Seasonal components) 모형, FARIMA(Fractional ARIMA) 모형을 사용하여 예측을 실시하였다. 전체 사용량을 기반으로 예측 하는 방식이 아닌, 군집분석을 통한 군집별 예측량의 결합이 더 낮은 MAPE로 나타남에 따라 우수한 예측 방법으로 판단되었다.

Keywords

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