Short-term load forecasting using an artificial neural network

This paper discusses an artificial neural network (ANN) model for short-term load forecasting. A two-step training method to cope with a shortage of training data and overfitting problems is proposed. A limit is conducted to the range where the ANN's weights are allowed to change in order to preserve the general relation between the inputs and the output of the ANN. The ANN trained with this two-step training method demonstrates improved accuracy over conventional methods, including ANNs which employ ordinary training algorithms.<
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