205 shared publications
Centre for Water Systems, University of Exeter, North Park Road, Exeter EX4 4QF, UK
104 shared publications
Professor, Centre for Water Systems, College of Engineering, Mathematics, and Physical Sciences, Univ. of Exeter, North Park Rd., Harrison Bldg., Exeter EX4 4QF, UK
103 shared publications
IRPI, Consiglio Nazionale delle Ricerche, via Madonna Alta 126, 06128 Perugia, Italy
90 shared publications
Dipartimento di Ingegneria, Università degli Studi di Ferrara, Via Saragat 1E, 44122 Ferrara, Italy
85 shared publications
Dipartimento di Ingegneria, Università degli Studi di Ferrara, Ferrara 44121, Italy Via Giuseppe Saragat, 1
(2003 - 2017)
Short-term water demand forecasting is a useful tool for water distribution system management. In fact, an accurate prediction of water consumptions of a network or a part of it can support the scheduling of the main devices of the network, such as pumping stations or valves.
In this paper a model for short term water demand forecasting is proposed. The model is structured in order to provide at each hour the water demand forecast for the next 24 hour basing on coefficients estimated according to a short moving window of previously observed data.
More in details, the hourly forecast is performed in two steps: in the first step the average water demand for the next 24 hours (Q24) is forecasted multiplying the average water consumption observed in the last 24 hours by a previously estimated coefficient; in the second step, the water consumption of each of the next 24 hours is forecasted multiplying the forecasted Q24 by hourly coefficients. The coefficients’ values (both the one used to forecast the Q24 and those used to forecast the hourly values) are updated at each hour on the basis of the water demands observed in the last n (e.g. n=4) weeks.
The model is applied to a real case study; the analysis of the results, and their comparison with those provided by another short term water demand forecasting model already presented in the scientific literature, highlights that the proposed model provides an accurate and robust forecast, resulting in an efficient tool for real time management of water distribution networks requiring a very small effort for its parameterization.