A Comparison of Short-Term Water Demand Forecasting Models

This paper presents a comparison of different short-term water demand forecasting models. The comparison regards six models that differ in terms of: forecasting technique, type of forecast (deterministic or probabilistic) and the amount of data necessary for calibration. Specifically, the following are compared: a neural-network based model (ANN_WDF), a pattern-based model (Patt_WDF), two pattern-based models relying on the moving-window technique (_WDF and Bakk_WDF), a probabilistic Markov chain-based model (HMC_WDF) and a naive benchmark model. The comparison is made by applying the models to seven real-life cases, making reference to the water demands observed over 2years in district-metered areas/water distribution networks of different sizes serving a different number and type of users. The models are applied in order to forecast the hourly water demands over a 24-h time horizon. The comparison shows that a) models based on different techniques provide comparable, medium-high forecasting accuracies, but also that b) short-term water demand forecasting models based on moving-window techniques are generally the most robust and easier to set up and parameterize.