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.