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Neural network architectures for short-term load forecasting

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4 Author(s)
K. Y. Lee ; Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA ; Tae-Il Choi ; Chao-Chee Ku ; June Ho Park

Different neural network architectures are presented for short-term load forecasting. The fully connected recurrent neural network (FRNN), where all neurons are coupled to one another, is difficult to train and to converge in a short time. The diagonal recurrent neural network (DRNN) is a modified model of FRNN. It requires fewer weights than FRNN and rapid convergence has been demonstrated. A dynamic backpropagation algorithm coupled with adaptive learning rate guarantees even faster convergence. Many experiments are conducted to provide the one-day ahead load forecast, and the results are compared. The effect of temperatures and functional-link net mapping are also studied by including them as the network's inputs. The forecasting accuracy for weekend load can be improved by using a separate weekend load model

Published in:

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on  (Volume:7 )

Date of Conference:

27 Jun-2 Jul 1994