Resumen
In spite of the significant developments in machine learning methods employed for short-term electrical load forecasting on a Country level, the complexity and diversity of the problem points to the need for investing more research effort in the selection of representative input datasets for the training. This is demonstrated in the example of the Greek electricity system, where careful selection and quality assurance of input data resulted in quite acceptable levels of prediction accuracy, even when training standard, robust feed-forward artificial neural networks.