Stochastic simulation model of water demand in residential buildings

Abstract

Over the years, researchers have been conducting studies to investigate the water consumption profile in buildings; these studies have contributed to the accumulation of knowledge regarding the correct sizing of hydraulic systems in buildings. In the context of the methods for the characterization of system demand or loading values, the procedures commonly employed to obtain the project flow rate were primarily proposed in the mid-20th century. These models require revision and adaptation to the current water consumption values. In recent years, certain researchers have proposed simulation models with an application focus on water distribution systems owing to the random and temporal behavior of water demand in this system type. In this study, a water-demand stochastic simulation model in residential buildings is proposed, which encompasses the behavioral modelling of users and their interaction with the system to improve the design process of water distribution systems. Therefore, geographical and population factors (quantity, distribution, and organization) were considered for the behavioral modelling of users; regarding the system modelling, aspects related to the hydraulic system were considered, such as the relation between system components, the type of sanitary appliance, and the number of available devices. Different simulations—with several different types of showers—were conducted using the proposed model. Comparing the flows obtained from the simulation and from the Brazilian standard, for all system components, the decrease in the project flow rate varied from 4% to 61%. In terms of material consumption regarding the pipe (PVC), the decrease varied from 25% to 63%. Practical application: When assessing potential designs for components in water distribution systems in buildings robust information is required for water demand across different time scales. The use of simulation models represents an important advance for the dimensioning process of these components, since it is possible to know a wider range of information about the system demand possibilities. The use of this type of model, as discussed in this article, will equip the designer with an enhanced decision making capacity.

Comparing Actual and Designed Water Demand in Australian Multilevel Residential Buildings

“…Wong and Mui (2018) used Bayes' theorem to predict peak demand in residential buildings. Ferreira and Goncalves (2020) developed a stochastic water demand model to analyze how building plumbing systems behave over an extended period.…”

“…Gad and Abd-Elaal (2016) state that current methods used to size the water supply network in residential buildings do not consider reliability of a system to overcome unexpected demand or system failures. Additionally, Ferreira and Goncalves (2020) highlight that traditional pipe sizing methods ignore how a plumbing system operates outside the peak period. Ferreira and Goncalves (2020) suggest there may be opportunities for further reduction to pipe sizing and economics savings if designers considered whole-system behavior.…”

“…Additionally, Ferreira and Goncalves (2020) highlight that traditional pipe sizing methods ignore how a plumbing system operates outside the peak period. Ferreira and Goncalves (2020) suggest there may be opportunities for further reduction to pipe sizing and economics savings if designers considered whole-system behavior. In addition, existing research has a strong focus toward residential buildings.…”

User-based fuzzy end-use modeling of indoor urban residential water demand

“…13 Accordingly, many researchers came up with different methods of estimating the water demands in residential buildings and for pipe sizing in BWSS. Amongst several approaches for residential water demand modeling proposed by researchers, some important ones such as simplified mathematical models, 14 Poisson's Rectangular Pulse model, 15 Neyman-Scott Rectangular Pulse model, 16 End-use models, [17][18][19][20][21] and models using softcomputing techniques 2,19,22 can be cited.…”