Electric demand is significantly influenced by weather conditions which regulate the request of energy for conditioning and lighting. Within this context the first objective of the presentation is to give an overview, based on data for the 1999-2008 period, of the response of Italian electric demand to temperature and cloud cover variability. Then a daily electric demand forecast model will be introduced: it is based on the electric demand of the past and on temperature and cloud cover data. Up to now the model has been tested considering observed meteorological data, rather than forecasted ones: they are based on a network of 28 Air-Force synoptic stations which covers the whole Italian territory. Finally the results of the application of the model to the 1999-2008 period will be discussed. They show that the introduction of the meteorological variables leads to a sensible enhancement of the predictive accuracy with respect to the case in which meteorological information has not been considered. In fact, considering temperature and cloud cover, a significant reduction of the global Mean Absolute Percentage Error was observed.