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Short term load forecasting using a multilayer neural network with an adaptive learning algorithm

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3 Author(s)
Ku-Long Ho ; Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan ; Hsu, Yuan-Yih ; Chien-Chuen Yang

A multilayer feedforward neural network is proposed for short-term load forecasting. To speed up the training process, a learning algorithm for the adaptive training of neural networks is presented. The effectiveness of the neural network with the proposed adaptive learning algorithm is demonstrated by short-term load forecasting of the Taiwan power system. It is found that, once trained by the proposed learning algorithm, the neural network can yield the desired hourly load forecast efficiently and accurately. The proposed adaptive learning algorithm converges much faster than the conventional backpropagation-momentum learning method

Published in:

Power Systems, IEEE Transactions on  (Volume:7 ,  Issue: 1 )

Date of Publication:

Feb 1992

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