응용통계연구 (The Korean Journal of Applied Statistics)

  • 제34권4호
  • /
  • Pages.623-632
  • /
  • 2021
  • /
  • 1225-066X(pISSN)
  • /
  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지
DOI QR코드

DOI QR Code

최대 전력수요 예측을 위한 시계열모형 비교

Comparison of time series predictions for maximum electric power demand

  • 권숙희 (충북대학교 정보통계학과) ;
  • 김재훈 (충북대학교 정보통계학과) ;
  • 손석만 (충북대학교 정보통계학과) ;
  • 이성덕 (충북대학교 정보통계학과)
  • Kwon, Sukhui (Department of Information & Statistics, Chungbuk National University) ;
  • Kim, Jaehoon (Department of Information & Statistics, Chungbuk National University) ;
  • Sohn, SeokMan (Department of Information & Statistics, Chungbuk National University) ;
  • Lee, SungDuck (Department of Information & Statistics, Chungbuk National University)
  • 투고 : 2021.06.15
  • 심사 : 2021.07.07
  • 발행 : 2021.08.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구에서는 여러가지 시계열 모형 중 평활법(가법계절지수, 승법계절지수), 계절 ARIMA 모형, ARARCH 그리고 AR-GARCH 회귀모형을 이용하여 최대 전력수요를 예측하는 방법을 연구하였다. 이 때 가중 평균모형으로 추세를 갖는 시계열 모형과 온도에 대한 회귀 모형을 적절한 가중치로 예측 정확도를 높이는 방법도 연구하였다. 결과적으로 AR-GARCH 회귀모형으로 예측하는 것이 가중 우수함을 보였다.

Through this study, we studied how to consider environment variables (such as temperatures, weekend, holiday) closely related to electricity demand, and how to consider the characteristics of Korea electricity demand. In order to conduct this study, Smoothing method, Seasonal ARIMA model and regression model with AR-GARCH errors are compared with mean absolute error criteria. The performance comparison results of the model showed that the predictive method using AR-GARCH error regression model with environment variables had the best predictive power.

키워드

과제정보

분석을 도와준 숙명여자대학교 정선아 대학원생에게 진심으로 감사드립니다.

참고문헌

  1. Amjady N (2001). Short-term hourly load forecasting using time-series modeling with peak load estimation capability, IEEE Transactions on Power Systems, 16, 498-505. https://doi.org/10.1109/59.932287
  2. Box GEP, Jenkins GM, and Reinsel GC (1994). Time Series Analysis: Forecasting and Control, Princeton-Hall International.
  3. Engle RF (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica: Journal of the Econometric Society, 50, 987-1007. https://doi.org/10.2307/1912773
  4. Jung S and Kim S (2014). Electricity demand forecasting for daily peak load with seasonality and temperature effects, Journal of the Korean Data And Information Science Society, 27, 843-853.
  5. Kalimoldayev M, Drozdenko A, Koplyk I, Marinich T, Abdildayeva A, and Zhukabayeva T (2020). Analysis of modern approaches for the prediction of electric energy consumption, Open Engineering, 10, 350-361. https://doi.org/10.1515/eng-2020-0028
  6. Lee JH, Oh SJ, Yoon Y, Ahn YH, Kim JS, Cho WS, and Lee SD (2019). Analysis of time series to support decision making on V2G using energy consumption data, Journal of the Korean Data And Information Science Society, 30, 401-414. https://doi.org/10.7465/jkdi.2019.30.2.401
  7. Park J, Kim YB, and Jung CW (2013). Short-term forecasting of city gas daily demand, Journal of Korean Institute of Industrial Engineers, 39, 247-252. https://doi.org/10.7232/JKIIE.2013.39.4.247
  8. Taylor JW and Buizza R (2003). Using weather ensemble predictions in electricity demand forecasting, International Journal of Forecasting, 19, 57-70. https://doi.org/10.1016/S0169-2070(01)00123-6
  9. Winters PR (1960). Forecasting sales by exponentially weighted moving averages, Management Science, 6, 324-342. https://doi.org/10.1287/mnsc.6.3.324