The Transactions of the Korean Institute of Electrical Engineers A (대한전기학회논문지:전력기술부문A)

The Korean Institute of Electrical Engineers (대한전기학회)
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Short-Term Load Forecasting Using Neural Networks and the Sensitivity of Temperatures in the Summer Season

신경회로망과 하절기 온도 민감도를 이용한 단기 전력 수요 예측

  • 하성관 (중부발전(주)) ;
  • 김홍래 (순천향대 공대 정보기술공학부) ;
  • 송경빈 (숭실대 공대 전기제어시스템공학부)
  • Published : 2005.06.01
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Abstract

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.

Keywords

References

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