전기학회논문지 (The Transactions of The Korean Institute of Electrical Engineers)

  • 제62권2호
  • /
  • Pages.149-154
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  • 2013
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  • 1975-8359(pISSN)
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  • 2287-4364(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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평일 단기전력수요 예측을 위한 최적의 지수평활화 모델 계수 선정

Optimal Coefficient Selection of Exponential Smoothing Model in Short Term Load Forecasting on Weekdays

  • 송경빈 (숭실대학교 전기공학과) ;
  • 권오성 (숭실대학교 전기공학과) ;
  • 박정도 (위덕대학교 에너지전기공학부)
  • Song, Kyung-Bin (Department of Electrical Engineering, Soongsil University) ;
  • Kwon, Oh-Sung (Department of Electrical Engineering, Soongsil University) ;
  • Park, Jeong-Do (Division of Energy & Electrical Engineering, Uiduk University)
  • 투고 : 2012.07.03
  • 심사 : 2012.12.27
  • 발행 : 2013.02.01
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초록

Short term load forecasting for electric power demand is essential for stable power system operation and efficient power market operation. High accuracy of the short term load forecasting can keep the power system more stable and save the power market operation cost. We propose an optimal coefficient selection method for exponential smoothing model in short term load forecasting on weekdays. In order to find the optimal coefficient of exponential smoothing model, load forecasting errors are minimized for actual electric load demand data of last three years. The proposed method are verified by case studies for last three years from 2009 to 2011. The results of case studies show that the average percentage errors of the proposed load forecasting method are improved comparing with errors of the previous methods.

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참고문헌

  1. A.D. Papalexopoulos and T.C. Hesterberg, "A Regression-Based Approach to Short-Term System Load Forecasting," IEEE Trans. on Power Systems, vol.4, no.4, pp.1535-1547, Nov. 1990.
  2. Nima Amjady, "Short-term hourly load forecasting using time-series modeling with peak load estimation capability," IEEE Trans. on Power Systems, vol.16, no.4, pp.798-805, Nov. 2001. https://doi.org/10.1109/59.962429
  3. D.J. Trudnowski, et al., "Real-Time Very Short-Term Load Prediction for Power-System Automatic Generation Control," IEEE Trans. on Control Systems Technology, vol. 9, no.2, pp.254-260, Mar. 2001. https://doi.org/10.1109/87.911377
  4. T. Senjyu, H. Takara, and T. Funabashi, "One-Hour-Ahead Load Forecasting Using Neural Network," IEEE Trans. on Power Systems, vol.17, no.1, pp.113-118, Feb. 2002. https://doi.org/10.1109/59.982201
  5. Ying Chen, et al, "Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks," IEEE Trans. on Power Systems, vol.25, no.1, pp.322-330, Feb. 2010.
  6. Kyung-Bin Song, Seong-Kwan Ha, "An Algorithm of Short-Term Load Forecasting", Trans. KIEE, vol. 53A, no. 10, Oct. 2004.
  7. Korea Power Exchange, "Short-Term Load Forecaster(KSLF)", Version. 1.0.1.8.
  8. Korean Electric Power Research Institute, "Development of the Integrated System for Power System Operational Planning and Analysis", TR.94YJ15.J1998.89, pp. 20-21. December 1998.
  9. Korea Power Exchange, "Electricity Market Rules", pp. 207, Dec. 2011.
  10. Oh-Sung Kwon, Rae-Jun Park, Kyung-Bin Song, Sung-Kwan Joo, Jeong-Do Park, Burm-Sup Cho, Ki-Jun Shin, "Coefficient selection technique of exponential smoothing model for weekday load forecasting", KIEE Power Engineering Society, The proceeding of the Autumn Conference, pp. 295-296, Nov. 2010.

피인용 문헌

  1. Short-Term Electric Load Forecasting for the Consecutive Holidays Using the Power Demand Variation Rate vol.27, pp.6, 2013, https://doi.org/10.5207/JIEIE.2013.27.6.017
  2. Functional clustering for electricity demand data: A case study vol.26, pp.4, 2015, https://doi.org/10.7465/jkdi.2015.26.4.885
  3. Daily Maximum Electric Load Forecasting for the Next 4 Weeks for Power System Maintenance and Operation vol.63, pp.11, 2014, https://doi.org/10.5370/KIEE.2014.63.11.1497