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신경회로망과 하절기 온도 민감도를 이용한 단기 전력 수요 예측
Short-Term Load Forecasting Using Neural Networks and the Sensitivity of Temperatures in the Summer Season

하성관    (중부발전(주)   ); 김홍래    (순천향대 공대 정보기술공학부   ); 송경빈    (숭실대 공대 전기제어시스템공학부  );
  • 초록

    Short-term load forecasting algorithm using neural networks and the sensitivity of temperatures in the summer season is proposed. In recent 10 years, many researchers have focused on artificial neural network approach for the load forecasting. In order to improve the accuracy of the load forecasting, input parameters of neural networks are investigated for three training cases of previous 7-days, 14-days, and 30-days. As the result of the investigation, the training case of previous 7-days is selected in the proposed algorithm. Test results show that the proposed algorithm improves the accuracy of the load forecasting.


  • 주제어

    Load Forecasting .   Neural Networks .   General Exponential Smoothing .   Temperature Sensitivity.  

  • 참고문헌 (28)

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  • 이 논문을 인용한 문헌 (4)

    1. Jo, Nam-Hoon 2006. "SVM Load Forecasting using Cross-Validation" 전기학회논문지. The transactions of the Korean Institute of Electrical Engineers. A / A, 전력기술부문, 55(11): 485~491     
    2. Jo, Nam-Hoon ; Song, Kyung-Bin ; Roh, Young-Su ; Kang, Dae-Seung 2006. "A Study on the Short-term Load Forecasting using Support Vector Machine" 전기학회논문지. The transactions of the Korean Institute of Electrical Engineers. A / A, 전력기술부문, 55(7): 306~312     
    3. Han, Jung-Hee ; Baek, Jong-Kwan 2010. "The Load Forecasting in Summer Considering Day Factor" 한국산학기술학회논문지 = Journal of the Korea Academia-Industrial cooperation Society, 11(8): 2793~2800     
    4. Kim, Meekyeong ; Hong, Chuleui 2016. "The Artificial Neural Network based Electric Power Demand Forecast using a Season and Weather Informations" Journal of the Institute of Electronics and Information Engineers = 전자공학회논문지, 53(1): 71~78     

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