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Elena Pacchin   Ms.  Graduate Student or Post Graduate 
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Elena Pacchin published an article in February 2017.
Top co-authors
Marco Franchini

133 shared publications

University of Ferrara, Ferrara, Italy

S. Alvisi

57 shared publications

Department of Engineering, University of Ferrara, via Saragat, 1, 44122 Ferrara, Italy

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Publications
Article 3 Reads 6 Citations A Short-Term Water Demand Forecasting Model Using a Moving Window on Previously Observed Data Elena Pacchin, Stefano Alvisi, Marco Franchini Published: 28 February 2017
Water, doi: 10.3390/w9030172
DOI See at publisher website ABS Show/hide abstract
In this article, a model for forecasting water demands over a 24-h time window using solely a pair of coefficients whose value is updated at every forecasting step is presented. The first coefficient expresses the ratio between the average water demand over the 24 h that follow the time the forecast is made and the average water demand over the 24 h that precede it. The second coefficient expresses the relationship between the average water demand in a generic hour falling within the 24-h forecasting period and the average water demand over that period. These coefficients are estimated using the information available in the weeks prior to the time of forecasting and, therefore, the model does not require any actual calibration process. The length of the time series necessary to implement the model is thus just a few weeks (3–4 weeks) and the model can be parameterized and used without there being any need to collect hourly water demand data for long periods. The application of the model to a real-life case and a comparison with results provided by another model already proposed in the scientific literature show it to be effective, robust, and easy to use.
CONFERENCE-ARTICLE 7 Reads 0 Citations <strong>A short-term water demand forecasting model based on a short moving window of previously observed data</strong> Elena Pacchin, Stefano Alvisi, Marco Franchini Published: 16 November 2016
The 1st International Electronic Conference on Water Sciences, doi: 10.3390/ecws-1-d002
DOI See at publisher website ABS Show/hide abstract

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.

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