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Short-Term Load Forecasting Using Neural Networks and the Sensitivity of Temperatures in the Summer Season  

Ha Seong-Kwan (중부발전(주))
Kim Hongrae (순천향대 공대 정보기술공학부)
Song Kyung-Bin (숭실대 공대 전기제어시스템공학부)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers A / v.54, no.6, 2005 , pp. 259-266 More about this Journal
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
Load Forecasting; Neural Networks; General Exponential Smoothing; Temperature Sensitivity;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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