Simulating Residential Water Demand with a Stochastic End-Use Model

Blokker, Mirjam; Vreeburg, J.H.G.; Dijk, J. C. van

doi:10.1061/(asce)wr.1943-5452.0000002

Cited by 295 publications

(313 citation statements)

References 10 publications

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“…Therefore, the single-use events obtained from the automatic disaggregation algorithms developed in this study are significantly more reliable, in terms of duration, average flow rate and shape than those resulting from a manual processing and cropping the water consumption flow traces. These results have direct implications in the probability functions used to characterize water consumption events frequency, duration and intensity [30][31][32][33][34]. Obviously, more accurate classification techniques can be developed as processing experience is gained and larger and more reliable data sets are available for training the algorithms.…”

Section: R1mentioning

confidence: 99%

Water End Use Disaggregation Based on Soft Computing Techniques

Pastor-Jabaloyes

Arregui

Cobacho

2018

Water

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Disaggregating residential water end use events through the available commercial tools needs a great investment in time to manually process smart metering data. Therefore, it is extremely difficult to achieve a homogenous and sufficiently large corpus of classified single-use events capable of accurately describe residential water consumption. The main goal of the present paper is to develop an automatic tool that facilitates the disaggregation of the individual water consumptions events from the raw flow trace. The proposed disaggregation methodology is conducted through two actions that are iteratively performed: first, the use of an advanced two-step filter, whose calibration is automatically conducted by the Elitist Non-Dominated Sorting Genetic Algorithm NSGA-II; and second, a cropping algorithm based on the filtered water consumption flow traces. As a secondary goal, yet complementary to the main one, a semiautomatic massive classification process has been developed, so that the resulting single-use events can be easily categorized in the different water end uses in a household. This methodology was tested using water consumption data from two different case studies. The characteristics of the households taken as reference and their occupants were unequivocally dissimilar from each other. In addition, the monitoring equipment used to obtain the consumption flow traces had completely different technical specifications. The results obtained from the processing of the two studies show that the automatic disaggregation is both robust and accurate, and produces significant time saving compared to the standard manual analysis.

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Section: R1mentioning

confidence: 99%

Water End Use Disaggregation Based on Soft Computing Techniques

Pastor-Jabaloyes

Arregui

Cobacho

2018

Water

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“…While the scope and output scales of these models are different, the majority of these models predict water end-uses at small temporal and spatial scales including daily or sub-daily scale and at per person or household scale [2,19,[21][22][23]. Blokker et al [21] showed the importance of end-use water demand predictions at small time scales (per second or per minute at household scale) for modelling of water quality of drinking water distribution systems. Rathnayaka et al [24] showed the importance of end-use water demand predictions at small time and spatial scales for planning water supply sources employing the fit-for-purpose water use concept.…”

Section: Introductionmentioning

confidence: 99%

“…Available water end-use data were used to build a number of end-use models across the world [2,9,[19][20][21][22]. While the scope and output scales of these models are different, the majority of these models predict water end-uses at small temporal and spatial scales including daily or sub-daily scale and at per person or household scale [2,19,[21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Seasonal Demand Dynamics of Residential Water End-Uses

Rathnayaka

Malano

Maheepala

et al. 2015

Water

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Water demand prediction by end-use at an appropriate spatial and temporal resolution is essential for planning water supply systems that will supply water from a diversified set of sources on a fit-for-purpose basis. Understanding seasonal, daily and sub-daily water demand including peak demand by end-uses is an essential planning requirement to implement a fit-for-purpose water supply strategy. Studies in the literature assume that all indoor water uses except evaporative cooler water use are weather independent and do not exhibit seasonal variability. This paper presents an analysis undertaken to examine seasonal variability of residential water end-uses. The analysis was repeated using two sets of data to ensure the validity of findings. The study shows that shower water use is significantly different between winter and summer, in addition to irrigation, evaporative cooler and pool water end-uses, while other water end-uses are not. Weather is shown to be a significant determinant of shower water use; in particular it affects shower duration which increases with lower temperature. Further analysis on OPEN ACCESSWater 2015, 7 203shower water use suggests that it is driven by behavioural factors in addition to weather, thus providing useful insights to improve detailed end-use water demand predictions.

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“…The importance of temporal and spatial aspects of water demand is motivated, as are the difficulties of modeling these factors. Blokker et al [2010] implemented a stochastic simulation for predicting demand based on end use probabilities for pulse duration, intensity, and time of day of water usage. Aksela and Aksela [2011] presented a probabilistic prediction model based on mixtures of Gaussians.…”

Section: Predicting or Reducing Demandmentioning

confidence: 99%

Water use signature patterns for analyzing household consumption using medium resolution meter data

Cardell-Oliver

2013

Water Resour. Res.

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[1] Providers of potable water to households and businesses are charged with conserving water. Addressing this challenge requires accurate information about how water is actually being used. So smart meters are being deployed on a large scale by water providers to collect medium resolution water use data. This paper presents water use signature patterns, the first technique designed for medium resolution meters for discovering patterns that explain how households use water. Signature patterns are clusters (subsets) of water meter readings specified by patterns on volumes and calendar dates. Four types of signature pattern are introduced in this paper: continuous flow days; exceptional peak use days; programmed patterns with recurrent hours; and normal use partitioned by season and period of the day. Signature patterns for each household are calculated using efficient selection rules that scale for city populations and years of data collection. Data from a real-world, large-scale, smart metering trial are analyzed using water use signature patterns. The results demonstrate that water use behaviors are distinctive, for both individuals and populations. Signatures can identify behaviors that are promising targets for water conservation. Pattern discovery can be automated with an efficient and scalable computer program. By identifying relevant consumption patterns in medium resolution meter data, water use signature patterns can help to achieve the water conservation potential of large-scale smart metering.Citation: Cardell-Oliver, R. (2013), Water use signature patterns for analyzing household consumption using medium resolution meter data, Water Resour. Res., 49,[8589][8590][8591][8592][8593][8594][8595][8596][8597][8598][8599]

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Simulating Residential Water Demand with a Stochastic End-Use Model

Cited by 295 publications

References 10 publications

Water End Use Disaggregation Based on Soft Computing Techniques

Water End Use Disaggregation Based on Soft Computing Techniques

Seasonal Demand Dynamics of Residential Water End-Uses

Water use signature patterns for analyzing household consumption using medium resolution meter data

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