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Short-term power load forecasting by neural network with stochastic back-propagation learning algorithm

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3 Author(s)
Rey-Chue Hwang ; Dept. of Electr. Eng., J-Shou Univ., Kaohsiung, Taiwan ; Huang-Chu Huang ; Jer-Guang Hsieh

In this paper, a short-term power load forecaster based on a neural network with stochastic back-propagation learning algorithm is developed. This modified learning rule can effectively help the load forecaster escape from a local minimum while it is trained. Consequently, the proposed load forecaster has more accurate prediction in forecasting operation. As a comparison, the same experiments are also performed by using a neural network with a traditional back-propagation learning rule which has constant learning rate and momentum

Published in:

Power Engineering Society Winter Meeting, 2000. IEEE  (Volume:3 )

Date of Conference:

23-27 Jan 2000