An Experimental Water Consumption Regression Model for Typical Administrative Buildings in the Czech Republic

Pressure management is the basic step of reducing water losses from water supply systems (WSSs). The reduction of direct water losses is reliably achieved by reducing pressure in the WSSs. There is also a slight decrease in water consumption in connected properties. Nevertheless, consumption is also...

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Bibliographic Details
Published in:Water (Basel) Vol. 10; no. 4; p. 424
Main Authors: Rucka, Jan, Holesovsky, Jan, Suchacek, Tomas, Tuhovcak, Ladislav
Format: Journal Article
Language:English
Published: Basel MDPI AG 04.04.2018
Subjects:
buildings
Czech Republic
decision making
humidity
Maximum likelihood method
prediction
regression analysis
statistical inference
temperature
Water
water supply
Czech Republic
ISSN:2073-4441, 2073-4441
Online Access:Get full text
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Summary:Pressure management is the basic step of reducing water losses from water supply systems (WSSs). The reduction of direct water losses is reliably achieved by reducing pressure in the WSSs. There is also a slight decrease in water consumption in connected properties. Nevertheless, consumption is also affected by other factors, the quantification of which is not trivial. However, there is still a lack of much relevant information to enter into this analysis and subsequent decision making. This article focuses on water consumption and its prediction, using regression models designed for an experiment regarding an administrative building in the Czech Republic (CZ). The variables considered are pressure and climatological factors (temperature and humidity). The effects of these variables on the consumption are separately evaluated, subsequently multidimensional models are discussed with the common inclusion of selected combinations of predictors. Separate evaluation results in a value of the N3 coefficient, according to the FAVAD concept used for prediction of changes in water consumption related to pressure. The statistical inference is based on the maximum likelihood method. The proposed regression models are tested to evaluate their suitability, particularly, the models are compared using a cross-validation procedure. The significance tests for parameters and model reduction are based on asymptotic properties of the likelihood ratio statistics. Pressure is confirmed in each regression model as a significant variable.
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ISSN:2073-4441
2073-4441
DOI:10.3390/w10040424