ANALYSIS OF THE WATER USE IN DIFFERENT TYPES OF BUILDINGS

Year 2019, Volume: 3 Issue: 2, 53 - 62, 01.12.2019

Dimiter Alitchkov

Abstract

The analysis of the water use of the
different consumers is an important issue for the proper design, performance
and management not only for the water supply and sewerage systems in the
buildings, but also for the urban water infrastructure as a whole.  Water use changes with time due to many
climatic, socio-economic, cultural and technical factors and is tightly
connected with the development of the society and technologies. When the change
becomes substantial, there is a need of upgrading and verification of the
design parameters and methods, but also the construction practices and
maintenance requirements as well as the corresponding regulations, so that they
become adequate with current and future development. Analysis of the different
methods characterizing the water use in the buildings on quantative basis as
well as the determination of its seasonal, daily, hourly or shorter period of
time variation is made. The advantages and disadvantages of water demand
mathematical models are discussed and on that basis of that, a statistical
method for estimation of the parameters of hybrid stochastic-regression water
demand model is recommended to be used. The approach gives contemporary
theoretical basis of water demand on different spatial and temporal scales and
can be used for analysis of water consumption not only in the different types
of buildings but also in the settlements. 

Keywords

Statistical method, Stochastic-regression model, Water demand, Water use

References

• Adamowski J. F. (2008). "Peak daily water demand forecast modellinging using artificial neural networks." Journal of Water Resources Planning and Management, Vol. 134, No 2, pp.119-128.
• Alitchkov D. K. (1998). "Implementation of stochastic model for simulation of the flow rates in the water supply and drainage systems for buildings." Proc.,CIB W62 Symposium on Water Supply and Drainage for Buildings, Rotterdam, Netherlands.
• Alvisi S., Franchini M., Marinelli A. (2007). "A short-term, pattern-based model for water-demand forecasting." Journal of Hydroinformatics, Vol. 9, No 1.
• Brentan B. M., Luvizotto J., Herrera M., Izquierdo J., Perez-Garca R. (2017). "Hybrid regression model for near real-time urban water demand forecasting." Journal of Computantional and applied mathematics, Vol. 309.
• Buchberger S. (2018). "Estimating Peak Water Demands in Buildings with Efficient Fixtures." Proc., Progress and Prognosis, Emerging Water Technology Symposium, Cincinnati, USA .
• Dobromislov A., Verbitzkii A. S., Ljakmund A. L. (2007). " Handbook for estimation of the flow rates in water supply and drainage system of buildings and reagions(in Russian)." Santehniiproekt, Moscow.
• Gagliardi F., Alvisi S., Kaplan Z., Franchini M. (2017). "A probabilistic short-term water demand forecasting model based on the Markov Chain." Journal Water, Vol. 507, No 9.
• Gargano R. et al. (2017). "Probabilistic models for the peak residential water demand." Journal of Water, Vol. 417, No 9.
• Ghiassi M., Zimba D., Saidane H. (2008). "Urban water demand forecasting with dynamic artificial neural network model." Journal of Water Recources Planning and Management, Vol. 34, No 2, pp. 138-146.
• Herrera M., Torgo L., Izquiero J., Perz-Garcia R.(2010). "Predictive models for forecasting hourly urban water demand." Journal of Hydrology, Vol. 387.
• House-Peters L.A., Chang H. (2011). "Urban water deman modeling:Review of conceps, methods, and organizing principles. "Water Resources Research, Vol. 47, No5.
• Konen T. P., Goncalves O. M. (1993). "Summery of mathematical models for the design of water distribution systems within buildings." Proc., 20th CIBW062 International symposium of water supply and drainage systems in buildings, Porto, Portugal.
• Shrestha D., Solomatine D. (2007). "Predicting hydrological models uncertainty: use of machine leaning." Proc., 32-nd IAHR World Congress, Venice, Italy.
• Tiwari M. K., Adamowski J. (2013). Urban water demand forecasting and uncertanty assessment using ensemble wavelet-bootstrap-neural network models." Jurnal of Water resources research, Vol. 49, No10.
• Verbitsky A. S. (1993). "Mathematical models for calculation of water supply networks based on their stochastic characteristics." Integrated Computer Applications in Water Supply, Vol.1, Research Studies Press, Hertfordshire.
• Wong L. T., Mui Kwok-Wai (2018). "Review of demand models for water systems in buildings including a Bayesian approach." Journal of Water, Vol. 10, No.8.