P. Kossieris, and C. Makropoulos, Exploring the statistical and distributional properties of residential water demand at fine time scales, Water, 10 (10), 1481, doi:10.3390/w10101481, 2018.
Residential water demand consists one of the most uncertain factors posing extra difficulties in the efficient planning and management of urban water systems. Currently, high resolution data from smart meters provide the means for a better understanding and modelling of this variable at a household level and fine temporal scales. Having this in mind, this paper examines the statistical and distributional properties of residential water demand at a 15-minute and hourly scale, which are the temporal scales of interest for the majority of urban water modeling applications. Towards this, we investigate large residential water demand records of different characteristics. The analysis indicates that the studied characteristics of the marginal distribution of water demand vary among households as well as on the basis of different time intervals. Both month-to-month and hour-to-hour analysis reveal that the mean value and the probability of no demand exhibit high variability while the changes in the shape characteristics of the marginal distributions of the nonzero values are significantly less. The investigation of performance of 10 probabilistic models reveals that Gamma and Weibull distributions can be used to adequately describe the nonzero water demand records of different characteristics at both time scales.
Full text (23829 KB)
Our works referenced by this work:
|1.||S.M. Papalexiou, D. Koutsoyiannis, and A. Montanari, Can a simple stochastic model generate rich patterns of rainfall events?, Journal of Hydrology, 411 (3-4), 279–289, 2011.|
|2.||S.M. Papalexiou, D. Koutsoyiannis, and C. Makropoulos, How extreme is extreme? An assessment of daily rainfall distribution tails, Hydrology and Earth System Sciences, 17, 851–862, doi:10.5194/hess-17-851-2013, 2013.|
|3.||P. Kossieris, Panayiotakis, K. Tzouka, E. Rozos, and C. Makropoulos, An e-Learning approach for improving household water efficiency, Procedia Engineering, WDSA 2014, Bari, Italy, Water Distribution Systems Analysis, 2014.|
|4.||P. Kossieris, S. Kozanis, A. Hashmi, E. Katsiri, L. Vamvakeridou-Lyroudia, R. Farmani, C. Makropoulos, and D. Savic, A web-based platform for water efficient households, Procedia Engineering, 89, 1128–1135, 2014.|
|5.||S.M. Papalexiou, and D. Koutsoyiannis, A global survey on the seasonal variation of the marginal distribution of daily precipitation, Advances in Water Resources, 94, 131–145, doi:10.1016/j.advwatres.2016.05.005, 2016.|
|6.||E. Creaco, P. Kossieris, L. Vamvakeridou-Lyroudia, C. Makropoulos, Z. Kapelan, and D. Savic, Parameterizing residential water demand pulse models through smart meter readings, Environmental Modelling and Software, 80, 33–40, 2016.|
|7.||P. Kossieris, C. Makropoulos, E. Creaco, L. Vamvakeridou-Lyroudia, and D. Savic, Assessing the applicability of the Bartlett-Lewis model in simulating residential water demands, Procedia Engineering, 154, 123–131, 2016.|
|8.||P. Kossieris, C. Makropoulos, C. Onof, and D. Koutsoyiannis, A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett-Lewis based model with adjusting procedures, Journal of Hydrology, 556, 980–992, doi:10.1016/j.jhydrol.2016.07.015, 2018.|
|9.||I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Stochastic periodic autoregressive to anything (SPARTA): Modelling and simulation of cyclostationary processes with arbitrary marginal distributions, Water Resources Research, 54 (1), 161–185, WRCR23047, doi:10.1002/2017WR021394, 2018.|
|10.||I. Tsoukalas, S.M. Papalexiou, A. Efstratiadis, and C. Makropoulos, A cautionary note on the reproduction of dependencies through linear stochastic models with non-Gaussian white noise, Water, 10 (6), 771, doi:10.3390/w10060771, 2018.|
Our works that reference this work:
|1.||P. Kossieris, I. Tsoukalas, C. Makropoulos, and D. Savic, Simulating marginal and dependence behaviour of water demand processes at any fine time scale, Water, 11 (5), 885, doi:10.3390/w11050885, 2019.|