A comparison between pattern-based and neural network short-term water demand forecasting models

Abstract

In this paper, two models are set up in order to forecast hourly water demands up to 24 h ahead and are contrasted with each other. The first model (hereinafter referred to as the Patt model) is based on the representation of the periodic patterns that typically characterize water demands, such as seasonal and weekly patterns of daily water demands and daily patterns of hourly water demands. The second model is based on artificial neural networks (hereinafter referred to as ANN models). Both the models have been applied to three case studies, representing water distribution systems managed by HERA S.p.A., characterized by very different numbers of users served, and consequently very different average water demands, ranging from 900 L/s for the first case study (CS1) to about 8 L/s and 1.5 L/s for the second (CS2) and third (CS3) case studies, respectively. The results show that in general, both the models, Patt and ANN, provide good accuracy for the CS1. The performances of both the models tend to decrease for CS2 and, particularly, for CS3. In particular, in the validation phase, the Patt model is more accurate than the ANN model for the CS1; for the CS2, the accuracy of the two models are very similar, and for the CS3 the accuracy of the ANN model is slightly higher than that of the Patt model.