In this paper a comparison among six short-term water demand forecasting models is presented. The models differ in terms of forecasting technique, type of prevision (deterministic or probabilistic) and data requirement for calibration. In particular, the compared models are: an Artificial Neural Network based model, a model based on periodic patterns, both requiring a calibration over a year of historically observed water demands, two models that take into account the periodic behaviours using observed data only on a restricted time window preceding the time of forecast, a probabilistic model based on Markov chain and a Naïve model. All the models are evaluated applying them to seven real-life case studies, consisting in two-year time series of hourly water demands observed in districts/networks...