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신경회로망과 하절기 온도 민감도를 이용한 단기 전력 수요 예측Short-Term Load Forecasting Using Neural Networks and the Sensitivity of Temperatures in the Summer Season
- Other Titles
- Short-Term Load Forecasting Using Neural Networks and the Sensitivity of Temperatures in the Summer Season
- Issue Date
- Jun-2005
- Publisher
- 대한전기학회
- Keywords
- Load Forecasting; Neural Networks; General Exponential Smoothing; Temperature Sensitivity; Load Forecasting; Neural Networks; General Exponential Smoothing; Temperature Sensitivity
- Citation
- 전기학회논문지 A권, v.54, no.6(A), pp.259 - 266
- Journal Title
- 전기학회논문지 A권
- Volume
- 54
- Number
- 6(A)
- Start Page
- 259
- End Page
- 266
- ISSN
- 1229-2443
- 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.
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Related Researcher
- Song, Kyung Bin
- College of Engineering (School of Electrical Engineering)