A Comparison of Short-Term Water Demand Forecasting Models

E. Pacchin, F. Gagliardi, S. Alvisi () and M. Franchini
Additional contact information
E. Pacchin: University of Ferrara
F. Gagliardi: University of Ferrara
S. Alvisi: University of Ferrara
M. Franchini: University of Ferrara

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2019, vol. 33, issue 4, No 16, 1497 pages

Abstract: Abstract 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 (αβ_WDF and Bakk_WDF), a probabilistic Markov chain-based model (HMC_WDF) and a naïve benchmark model. The comparison is made by applying the models to seven real-life cases, making reference to the water demands observed over 2 years 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.

Keywords: Water demand; Short-term forecasting; Moving window (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11269-019-02213-y

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