Scaling Water Consumption Statistics

Vertommen, Ina; Magini, Roberto; Cunha, Maria da Conceição

doi:10.1061/(asce)wr.1943-5452.0000467

Cited by 21 publications

(27 citation statements)

References 20 publications

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“…The temporal resolution is a fundamental choice when modeling water demand (e.g., [15][16][17][18][19]), leading to significant reductions of the peak demand when the time step increases [20,21]. Therefore, time scaling effects were also investigated and further equations that take into account such phenomenon were presented.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Models for the Peak Residential Water Demand

Gargano

Tricarico

Granata

et al. 2017

Water

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Peak water demand is one of the most stringent operative conditions for a Water Distribution System (WDS), not only for the intensity of the event itself, but also for its recurring nature. The estimation of the maximum water demand is a crucial aspect in both the design and management processes. Studies in the past have tackled this issue with deterministic approaches, even if peak phenomena are distinctly random. In this work, probabilistic models have been developed to study and forecast the daily maximum residential water demand. Some probability distributions have been tested by means of statistical inferences on different data samples related to three monitored WDS. The parameter estimations of the proposed equations have been related to the number of supplied users. Furthermore, this work investigates time scaling effects on the effectiveness of the proposed distributions and relations. Corrective factors that take into account the effect of time averaging step on the above-mentioned parameters have been proposed.

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Section: Introductionmentioning

confidence: 99%

Probabilistic Models for the Peak Residential Water Demand

Gargano

Tricarico

Granata

et al. 2017

Water

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“…The scaling laws are used to estimate the values of demands statistics for any number of users, on the base of the demand signal of a single user. For the sake of brevity they will not be reported here, an extensive explanation can be found in [24]. Then, a Monte Carlo simulation is used to determine 1500 water demand scenarios.…”

Section: Data Analysis: Demands Scenarios and Corresponding Water Prementioning

confidence: 99%

Artificial Neural Networks and Entropy-based Methods to Determine Pressure Distribution in Water Distribution Systems

Ridolfi

Servili

Magini

et al. 2014

Procedia Engineering

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Pressure determination in water distribution systems (WDS) is important because it generally drives the operational actions for leakage and failure management, backwater intrusion and demand control. This determination would ideally be done through pressure monitoring at every junction in the distribution system. However, due to limited resources, it is only possible to monitor at a limited number of nodes. To this end, this work explores the use of an Artificial Neural Network (ANN) to estimate pressure distributions in a WDS using the available data at the monitoring nodes as inputs. The optimal subset of monitoring nodes are chosen through an entropy-based method. Finally, pressure values are compared to synthetic pressure measures estimated through a hydraulic model.

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“…Other studies have built on the initial research by Buchberger et al (2003) but have primarily looked at the aggregation of many homes for the purpose of distribution system modeling. However, one subsequent study (Vertommen et al ) presented data collected for indoor water use for 82 single‐family residences in the town of Latina, Italy. Each home was monitored for four total days, consisting of four consecutive Mondays, with a temporal resolution of 1 s. The purpose of the study was to compare measured data with theoretical scaling laws.…”

Section: Previous High‐frequency Evaluations For Individual Homesmentioning

confidence: 99%

Use of High‐Frequency Data to Detect Household Leaks as Event Outliers

McCary

Heaney

2018

Journal AWWA

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This article presents the results of a prototype highfrequency water use evaluation using 1 min data collected for three single-family homes in Hillsborough County, Fla., over a period of one year. Automatic meter reading meter registers were used with shortrange wireless communication that allowed for ease of data collection by driving by and downloading the data from the meter registers. This analysis quantifies potential leaks by looking at outlier events in terms of intensity, duration, frequency, and volume. The homes have separate indoor and irrigation meters, which enables the analysis to compare outliers across indoor water use and irrigation separately. The term "aggregate event" is used because specific end uses (for example, showers) are not quantified. The aggregates of end-use events are summarized over continuous durations to analyze the most significant leak events. The results are promising, and the techniques used in the study are currently being applied to a larger study area.

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Scaling Water Consumption Statistics

Cited by 21 publications

References 20 publications

Probabilistic Models for the Peak Residential Water Demand

Probabilistic Models for the Peak Residential Water Demand

Artificial Neural Networks and Entropy-based Methods to Determine Pressure Distribution in Water Distribution Systems

Use of High‐Frequency Data to Detect Household Leaks as Event Outliers

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