In this work, identification of 24-hours-ahead water demand prediction model based on historical water demand data is considered. As part of the identification procedure, the input variable selection algorithm based on partial mutual information is implemented. It is shown that meteorological data on a daily basis are not relevant for the water demand prediction in the sense of partial mutual information for the analysed water distribution systems of the cities of Tavira, Algarve, Portugal and Evanton East, Scotland, UK. Water demand prediction system is modelled using artificial neural networks, which offer a great potential for the identification of complex dynamic systems. The adaptive tuning procedure of model parameters is also developed in order to enable the model to adapt to changes in the system. A significant improvement of the prediction ability of such a model in relation to the model with fixed parameters is shown when a certain trend is present in the water demand profile.
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Research Article|
April 22 2015
Adaptable urban water demand prediction system Available to Purchase
G. Banjac;
1Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia
E-mail: [email protected]
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M. Vašak;
M. Vašak
1Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia
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M. Baotić
M. Baotić
1Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia
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Water Supply (2015) 15 (5): 958–964.
Article history
Received:
January 19 2015
Accepted:
April 01 2015
Citation
G. Banjac, M. Vašak, M. Baotić; Adaptable urban water demand prediction system. Water Supply 1 October 2015; 15 (5): 958–964. doi: https://doi.org/10.2166/ws.2015.048
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