In this article a model for forecasting water demands over a 24-hour 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 hours that follow the time the forecast is made and the average water demand over the 24 hours that precede it. The second coefficient expresses the relationship between the average water demand in a generic hour falling within the 24-hour 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.

A Short-Term Water Demand Forecasting Model Using a Moving Window on Previously Observed Data

PACCHIN, Elena
Primo
;
ALVISI, Stefano
Secondo
;
FRANCHINI, Marco
Ultimo
2017

Abstract

In this article a model for forecasting water demands over a 24-hour 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 hours that follow the time the forecast is made and the average water demand over the 24 hours that precede it. The second coefficient expresses the relationship between the average water demand in a generic hour falling within the 24-hour 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.
2017
Pacchin, Elena; Alvisi, Stefano; Franchini, Marco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2368255
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