Communications for Statistical Applications and Methods

  • 제19권6호
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  • Pages.869-876
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  • 2012
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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The Performance of Time Series Models to Forecast Short-Term Electricity Demand

  • Park, W.G. (Digital Inclusion Policy Division, NIA) ;
  • Kim, S. (Department of Applied Statistics, Chung-Ang University)
  • 투고 : 2012.09.25
  • 심사 : 2012.11.06
  • 발행 : 2012.11.30
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초록

In this paper, we applied seasonal time series models such as ARIMA, FARIMA, AR-GARCH and Holt-Winters in consideration of seasonality to forecast short-term electricity demand data. The results for performance evaluation on the time series models show that seasonal FARIMA and seasonal Holt-Winters models perform adequately under the criterion of Mean Absolute Percentage Error(MAPE).

키워드

참고문헌

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피인용 문헌

  1. A Study on the Suitability of Load Demand Forecasting Models for Island Area Using Weather Variables vol.13, pp.2, 2017, https://doi.org/10.7849/ksnre.2017.6.13.2.084
  2. A study on electricity demand forecasting based on time series clustering in smart grid vol.29, pp.1, 2016, https://doi.org/10.5351/KJAS.2016.29.1.193