Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
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Regional Long-term/Mid-term Load Forecasting using SARIMA in South Korea

계절 ARIMA 모형을 이용한 국내 지역별 전력사용량 중장기수요예측

  • Ahn, Byung-Hoon (Department of Industrial and Management Engineering, Korea University) ;
  • Choi, Hoe-Ryeon (Department of Industrial and Management Engineering, Korea University) ;
  • Lee, Hong-Chul (Department of Industrial and Management Engineering, Korea University)
  • 안병훈 (고려대학교 산업경영공학과) ;
  • 최회련 (고려대학교 산업경영공학과) ;
  • 이홍철 (고려대학교 산업경영공학과)
  • Received : 2015.10.08
  • Accepted : 2015.12.04
  • Published : 2015.12.31
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Abstract

Load forecasting is needed to make supply and demand plan for a stable supply of electricity. It is also necessary for optimal operational plan of the power system planning. In particular, in order to ensure stable power supply, long-term load forecasting is important. And regional load forecasting is important for tightening supply stability. Regional load forecasting is known to be an essential process for the optimal state composition and maintenance of the electric power system network including transmission lines and substations to meet the load required for the area. Therefore, in this paper we propose a forecasting method using SARIMA during the 12 months (long-term/mid-term) load forecasting by 16 regions of the South Korea.

전력수요의 예측은 안정적인 전력공급을 위한 수급계획수립을 위해서 그리고 전력계통의 최적운영계획수립을 위해서도 필요하다. 특히 안정적인 전력수급확보를 위해서는 중장기 전력수요예측이 중요하고 공급안정성 강화를 위해서는 지역별 전력수요예측이 중요하다. 지역별 전력수요예측은 지역에 소요되는 부하를 충족시킬 수 있도록 송전선로 및 변전소 등의 계통망의 최적상태 구성 및 유지를 위한 필수적인 과정으로 알려져 있다. 따라서 본 논문은 12개월(중장기)동안 대한민국 시도별 16개 지역의 전력사용량을 SARIMA 모형을 이용하여 예측하는 방법을 제안한다.

Keywords

References

  1. SungHo Ryu, "2014 Load Forecasting accuracy Improving Plan in the summer", Korea Electric Association, Vol 7, pp. 66-72, 2014.
  2. HongTae Kim, "Monthly Industrial Trends", KB Financial Group Research Institute, Vol 12, pp. 8-22, 2013.
  3. CheChiang Hsu, ChiaYon Chen, "Regional load forecasting in Taiwan--applications of artificial neural networks", Energy conversion and Management 44, pp. 1941-1949, 2003. DOI: http://dx.doi.org/10.1016/S0196-8904(02)00225-X
  4. ZhanJun Qiao, FuLing Li, Yong Li, "Mid-long-Term Regional Load Forecasting based on Census X12-SARIMA Model", Przeglad Elektrotechniczny, pp. 224-227, 2013.
  5. G. Juberias, R. Yunta, J. G. Moreno, C. Mendivil, "A new ARIMA model for hourly load forecasting", Transmission and Distribution Conference, IEEE. pp. 314-319, 1999. DOI: http://dx.doi.org/10.1109/tdc.1999.755371
  6. D. C. Park, M. A. El-Sharkawi, R. J. Marks II, L. E. Atlas, M. J. Damborg, "Electric load forecasting using an artificial neural network", IEEE Transactions on PowerSystems, pp. 442-449, 1991. DOI: http://dx.doi.org/10.1109/59.76685
  7. Mohamed Mohandes, "Support vector machines for short-term electrical load forecasting", International Journal of Energy Research, pp. 335-345, 2002. DOI: http://dx.doi.org/10.1002/er.787
  8. KyungBin Song, YoungSik Baek, DugHun Hong, Gilsoo Jang, "Short-term load forecasting for the holidays using fuzzy linear regression method", IEEE Transactions on Power Systems, pp. 96-101, 2005.
  9. Nur Adilah Abd Jalil, Maizah Hura Ahmad, Norizan Mohamed, "Electricity load demand forecasting using exponential smoothing methods", World Applied Sciences Journal 22(11) pp. 1540-1543, 2013.
  10. Arash ASRARI, Dawood Seyed JAVAN, Mohammad Hossein JAVIDI, Mohammad MONFARED, "Application of Gray-fuzzy- Markov chain method for day-ahead electric load forecasting", Przeglad Elektrotechniczny-Electri cal Review, pp. 228-237, 2012.
  11. HyunWoo Jung, KyungBin Song, "Daily Maximum Electric Load Forecasting for the Next 4 Weeks for Power System Maintenance and Operation", The Transactions of The Korean Institute of Electrical Engineers pp. 1497-1502, 2014.
  12. SiYeon Kim, HyunWoo Jung, JeongDo Park, SeungMook Baek, WooSeon Kim, KyungHee Chon, KyungBin Song, "Weekly Maximum Electric Load Forecasting for 104 Weeks by Seasonal ARIMA Model", Journal of the Korean Institute of IIIuminating and Electrical Installation Engineer, pp. 50-56, 2014. DOI: http://dx.doi.org/10.5207/JIEIE.2014.28.1.050
  13. YongJin. Park, Bohyeun Wang, "Neuro-Fuzzy Model based Electrical Load Forecasting System: Hourly, Daily, and Weekly Forecasting", Journal of Korean Institute of Intelligent Systems, pp. 533-538, 2004. DOI: http://dx.doi.org/10.5391/JKIIS.2004.14.5.533
  14. BongWoo Nam, KyungBin Song, KyuHo Kim, JunMin Cha, "The Spatial Electric Load Forecasting Algorithm using the Multiple Regression Analysis Method", Journal of the Korean Institute of Illuminating and Electrical Installation Engineers, pp. 63-70, 2008. DOI: http://dx.doi.org/10.5207/JIEIE.2008.22.2.063
  15. HyoungRo Lee, Hyunjung Shin, "Electricity Demand Forecasting based on Support Vector Regression", IE interfaces, pp. 351-361, 2011.
  16. G. E. P. Box, G. M. Jenkins, "Time Series Analysis Forecasting", 1st Holden Day Inc. Sanfransisco, 1976.
  17. YoungJoo, Kim, "Study on Low Cost Carrier Demand Forecasting Using Seasonal ARIMA Model", The Hanyang Tourism Research institute, pp. 3-25, 2014.