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

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지
DOI QR코드

DOI QR Code

The 24 Hourly Load Forecasting of the Election Day Using the Load Variation Rate

부하변동율을 이용한 선거일의 24시간 수요예측

  • 송경빈 (숭실대학교 전기공학부)
  • Received : 2010.04.05
  • Accepted : 2010.05.11
  • Published : 2010.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

Short-term electric load forecasting of power systems is essential for the power system stability and the efficient power system operation. An accurate load forecasting scheme improves the power system security and saves some economic losses in power system operations. Due to scarcity of the historical same type of holiday load data, most big electric load forecasting errors occur on load forecasting for the holidays. The fuzzy linear regression model has showed good accuracy for the load forecasting of the holidays. However, it is not good enough to forecast the load of the election day. The concept of the load variation rate for the load forecasting of the election day is introduced. The proposed algorithm shows its good accuracy in that the average percentage error for the short-term 24 hourly loads forecasting of the election days is 2.27%. The accuracy of the proposed 24 hourly loads forecasting of the election days is compared with the fuzzy linear regression method. The proposed method gives much better forecasting accuracy with overall average error of 2.27%, which improved about average error of 2% as compared to the fuzzy linear regression method.

Keywords

References

  1. "'10년 1월 전력계통 운영실적", 한국전력거래소 계통운영처, 2010년 2월.
  2. 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, 1990.
  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, 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, 2002. https://doi.org/10.1109/59.982201
  5. 위영민, 송경빈, 주성관, "특수일 최대 전력 수요 예측을 위한 결정계수를 사용한 데이터 마이닝", 전기학회 논문지, 제58권, 제1호, pp.18-22, 2009년 1월
  6. K.-B. Song, S.-K. Ha, J.-W. Park, D.-J. Kweon, and K.-H. Kim, "Hybrid Load forecasting Method With Analysis of Temperature Sensitivities", IEEE Transactions on Power Systems, Vol. 21. No. 2, pp. 869-876, May 2006. https://doi.org/10.1109/TPWRS.2006.873099
  7. K.-B. Song, Y.-S. Baek, D. H. Hong, and G. Jang, "Short-Term Load Forecasting for the Holidays Using Fuzzy Linear Regression Method", IEEE Transactions on Power Systems, Vol. 20, No. 1, pp.96-101, February 2005. https://doi.org/10.1109/TPWRS.2004.835632
  8. 남봉우, 송경빈, 김규호, 차준민, "다중회귀분석법을 이용한 지역 전력 수요예측 알고리즘", 조명 전기설비학회논문지, 제22권, 제2호, pp. 63-70, 2008년 2월
  9. 박정도, 송경빈, "태풍 발생 인접 주말의 수요예측 오차감소 방안", 전기학회논문지, 제58권, 제9호, pp.1700-1705, 2009년 9월
  10. 구본석, 백영식, 송경빈, "추석과 설날 연휴에 대한 전력수요예측 알고리즘 개선", 전기학회논문지, 제51D권, 제10호, pp.453-459, 2002년 10월
  11. "전력수급계획 및 운용해석 종합시스템 개발에 관한 연구(최종보고서)" 한국전력공사 전력연구원, 1998년 12월