Global model for short-term load forecasting using artificial neural networks

A global model is presented for short-term electric load forecasting using artificial neural networks. The model predicts the complete curve of the 24 hourly values for the next day. The development of this model consists of three phases: a prior one, in which, starting from historical data, each day is classified according to its load profile by means of self-organising feature maps; the second consists of building and training the neural networks for each class; and the third is an on-line operation phase, in which the prediction is carried out by previously trained recurrent neural networks. The historical data correspond to the central Spanish area from 1989 to 1999. Extensive testing shows that this method has better forecasting accuracy and robustness than statistical techniques, and a greater ability to adapt to different meteorological and social environments than other neural methods. The results obtained in testing are found to be very accurate