scholarly journals Analysis of MNF and FAVAD Models for Leakage Characterization by Exploiting Smart-Metered Data: The Case of the Gorino Ferrarese (FE-Italy) District

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 643
Author(s):  
Irene Marzola ◽  
Stefano Alvisi ◽  
Marco Franchini
Keyword(s):  
Time Series ◽  
Water Balance ◽  
Water Consumption ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Utilities ◽  
Time Step ◽  
Minimum Night Flow ◽  
The Relationship ◽  
District Metered Area

Leakages in water distribution systems have great economic and environmental impacts and are a major issue for water utilities. In this work, the water balance and the Minimum Night Flow (MNF) method for evaluating the amount of water loss, as well as the power and Fixed and Variable Area Discharge (FAVAD) equations for analyzing the relationship between leakage and pressure, were applied to a fully monitored District Metered Area (DMA) located in Gorino Ferrarese (FE, Italy). Time series of (a) the water consumption of each user, (b) the DMA inflow, and (c) the pressure at the DMA inlet point were monitored with a 5 min time step. The results of an analysis carried out by exploiting the collected time series highlighted that: (a) The application of the MNF method based on literature values can lead to significant inaccuracies in the presence of users with irregular consumption, and (b) the estimation of the parameters of the power and FAVAD equations is highly affected by the amounts and types of observed data used.

scholarly journals Minimum Night Flow Analysis and Application of the Fixed and Variable Area Discharges Model for Characterizing Leakage in the Gorino Ferrarese (FE-Italy) District

2020 ◽  
Vol 2 (1) ◽  
pp. 8
Author(s):  
Irene Marzola ◽  
Stefano Alvisi ◽  
Marco Franchini
Keyword(s):  
Time Series ◽  
Distribution Systems ◽  
Water Distribution ◽  
Flow Analysis ◽  
Water Utilities ◽  
Smart Meters ◽  
Minimum Night Flow ◽  
Major Interest ◽  
The Relationship ◽  
District Metered Area

Leakage in water distribution systems is an important issue and of major interest for water utilities. In this study, the Minimum Night Flow (MNF) method to quantify the amount of water lost and the equations representing the relationship between pressure and leakage in power and FAVAD (Fixed and Variable Area Discharge) forms were applied to a District Metered Area (DMA) located in Gorino Ferrarese (FE, Italy) equipped with smart meters. The analysis carried out by exploiting the collected time series of user water consumption, DMA inflow, and pressure highlighted that: (a) the MNF method can lead to significant inaccuracy in leakage estimation in the presence of users with irregular consumptions, when based on literature values, and (b) the estimation of the parameters of the power and FAVAD equation is highly affected by the number and types of observed data used.

scholarly journals Optimal Time Step Length to Minimize Uncertainty of Zonal Water Balance Calculation in Drinking Water Distribution Systems

10.29007/5r9x ◽  
2018 ◽  
Author(s):  
Attila Bibok ◽  
Roland Fülöp
Keyword(s):  
Drinking Water ◽  
Water Balance ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Training Dataset ◽  
Optimal Time ◽  
Time Step ◽  
Balance Calculation ◽  
Drinking Water Distribution

Water balance calculation is a well-known and adapted method to analyze district-metered areas (DMAs) of drinking water distribution system. Other application of water balance results can be training dataset for consumption forecast, monitoring and managing water loss. The vast amount of application makes it a very powerful tool, which is in contrary sensitive to the accuracy of the calculation. The uncertainty of flow metering decreases as the length of the time step decreases, however, the uncertainty of stored volume measurement in tanks increases. Investigation of the limitations of water balance calculation in regard to uncertainty is necessary to develop an analytical solution for optimal calculation time step.

scholarly journals Modeling and clustering water demand patterns from real-world smart meter data

2017 ◽  
Vol 10 (2) ◽  
pp. 75-82 ◽  
Author(s):  
Nicolas Cheifetz ◽  
Zineb Noumir ◽  
Allou Samé ◽  
Anne-Claire Sandraz ◽  
Cédric Féliers ◽  
...  
Keyword(s):  
Time Series ◽  
Water Consumption ◽  
Water Demand ◽  
Smart Grids ◽  
Water Distribution ◽  
Smart Cities ◽  
Distribution Network ◽  
Seasonal Patterns ◽  
Smart Meters ◽  
Time Step

Abstract. Nowadays, drinking water utilities need an acute comprehension of the water demand on their distribution network, in order to efficiently operate the optimization of resources, manage billing and propose new customer services. With the emergence of smart grids, based on automated meter reading (AMR), a better understanding of the consumption modes is now accessible for smart cities with more granularities. In this context, this paper evaluates a novel methodology for identifying relevant usage profiles from the water consumption data produced by smart meters. The methodology is fully data-driven using the consumption time series which are seen as functions or curves observed with an hourly time step. First, a Fourier-based additive time series decomposition model is introduced to extract seasonal patterns from time series. These patterns are intended to represent the customer habits in terms of water consumption. Two functional clustering approaches are then used to classify the extracted seasonal patterns: the functional version of K-means, and the Fourier REgression Mixture (FReMix) model. The K-means approach produces a hard segmentation and K representative prototypes. On the other hand, the FReMix is a generative model and also produces K profiles as well as a soft segmentation based on the posterior probabilities. The proposed approach is applied to a smart grid deployed on the largest water distribution network (WDN) in France. The two clustering strategies are evaluated and compared. Finally, a realistic interpretation of the consumption habits is given for each cluster. The extensive experiments and the qualitative interpretation of the resulting clusters allow one to highlight the effectiveness of the proposed methodology.

scholarly journals Models for forecasting water demand using time series analysis: a case study in Southern Brazil

2021 ◽  
Author(s):  
Danielle C. M. Ristow ◽  
Elisa Henning ◽  
Andreza Kalbusch ◽  
Cesar E. Petersen
Keyword(s):  
Time Series ◽  
Water Consumption ◽  
Water Demand ◽  
Web Application ◽  
Water Resource Management ◽  
Distribution Systems ◽  
Water Distribution ◽  
Demand Forecasting ◽  
Model Quality

Abstract Technology has been increasingly applied in search for excellence in water resource management. Tools such as demand-forecasting models provide information for utility companies to make operational, tactical and strategic decisions. Also, the performance of water distribution systems can be improved by anticipating consumption values. This work aimed to develop models to conduct monthly urban water demand forecasts by analyzing time series, and adjusting and testing forecast models by consumption category, which can be applied to any location. Open language R was used, with automatic procedures for selection, adjustment, model quality assessment and forecasts. The case study was conducted in the city of Joinville, with water consumption forecasts for the first semester of 2018. The results showed that the seasonal ARIMA method proved to be more adequate to predict water consumption in four out of five categories, with mean absolute percentage errors varying from 1.19 to 15.74%. In addition, a web application to conduct water consumption forecasts was developed.

scholarly journals Modeling and Clustering Water Demand Patterns from Real-World Smart Meter Data

2017 ◽  
Author(s):  
Nicolas Cheifetz ◽  
Zineb Noumir ◽  
Allou Samé ◽  
Anne-Claire Sandraz ◽  
Cédric Féliers ◽  
...  
Keyword(s):  
Time Series ◽  
Water Consumption ◽  
Water Demand ◽  
Smart Grids ◽  
Water Distribution ◽  
Smart Cities ◽  
Distribution Network ◽  
Seasonal Patterns ◽  
Smart Meters ◽  
Time Step

<p><strong>Abstract.</strong> Nowadays, drinking water utilities need an acute comprehension of the water demand on their distribution network, in order to efficiently operate the optimization of resources, the management of billing and to propose new customer services. With the emergence of smart grids, based on Automated Meter Reading (AMR), a better understanding of the consumption modes is now accessible for smart cities with more granularities. In this context, this paper evaluates a novel methodology for identifying relevant usage profiles from the water consumption data produced by smart meters. The methodology is fully data-driven using the consumption time series which are seen as functions or curves observed with an hourly time step. First, a Fourier-based additive time series decomposition model is introduced to extract seasonal patterns from time series. These patterns are intended to represent the customer habits in terms of water consumption. Two functional clustering approaches are then used to classify the extracted seasonal patterns: the functional version of K-means, and the Fourier REgression Mixture (FReMix) model. The K-means approach produces a hard segmentation and K representative prototypes. On the other hand, the FReMix is a generative model and produces also K profiles as well as a soft segmentation based on the posterior probabilities. The proposed approach is applied to a smart grid deployed on the largest Water Distribution Network (WDN) in France. The two clustering strategies are evaluated and compared. Finally, a realistic interpretation of the consumption habits is given for each cluster. The extensive experiments and the qualitative interpretation of the resulting clusters allow to highlight the effectiveness of the proposed methodology.</p>

scholarly journals WDSchain: A Toolbox for Enhancing the Security Using Blockchain Technology in Water Distribution System

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1944
Author(s):  
Haitham H. Mahmoud ◽  
Wenyan Wu ◽  
Yonghao Wang
Keyword(s):  
Time Series ◽  
Distribution Systems ◽  
Water Distribution ◽  
Time Series Data ◽  
Series Data ◽  
Process Time ◽  
Time Interval ◽  
Security Level ◽  
Data Validation ◽  
Simulated Time

This work develops a toolbox called WDSchain on MATLAB that can simulate blockchain on water distribution systems (WDS). WDSchain can import data from Excel and EPANET water modelling software. It extends the EPANET to enable simulation blockchain of the hydraulic data at any intended nodes. Using WDSchain will strengthen network automation and the security in WDS. WDSchain can process time-series data with two simulation modes: (1) static blockchain, which takes a snapshot of one-time interval data of all nodes in WDS as input and output into chained blocks at a time, and (2) dynamic blockchain, which takes all simulated time-series data of all the nodes as input and establishes chained blocks at the simulated time. Five consensus mechanisms are developed in WDSchain to provide data at different security levels using PoW, PoT, PoV, PoA, and PoAuth. Five different sizes of WDS are simulated in WDSchain for performance evaluation. The results show that a trade-off is needed between the system complexity and security level for data validation. The WDSchain provides a methodology to further explore the data validation using Blockchain to WDS. The limitations of WDSchain do not consider selection of blockchain nodes and broadcasting delay compared to commercial blockchain platforms.

scholarly journals Burst Detection in Water Distribution Systems: The Issue of Dataset Collection

2020 ◽  
Vol 10 (22) ◽  
pp. 8219
Author(s):  
Andrea Menapace ◽  
Ariele Zanfei ◽  
Manuel Felicetti ◽  
Diego Avesani ◽  
Maurizio Righetti ◽  
...  
Keyword(s):  
Water Supply ◽  
Water Consumption ◽  
Distribution System ◽  
Smart Grids ◽  
Distribution Systems ◽  
Water Distribution ◽  
Stochastic Modelling ◽  
Water Supply Systems ◽  
Consumption Data ◽  
Supply Systems

Developing data-driven models for bursts detection is currently a demanding challenge for efficient and sustainable management of water supply systems. The main limit in the progress of these models lies in the large amount of accurate data required. The aim is to present a methodology for the generation of reliable data, which are fundamental to train anomaly detection models and set alarms. Thus, the results of the proposed methodology is to provide suitable water consumption data. The presented procedure consists of stochastic modelling of water request and hydraulic pipes bursts simulation to yield suitable synthetic time series of flow rates, for instance, inlet flows of district metered areas and small water supply systems. The water request is obtained through the superimposition of different components, such as the daily, the weekly, and the yearly trends jointly with a random normal distributed component based on the consumption mean and variance, and the number of users aggregation. The resulting request is implemented into the hydraulic model of the distribution system, also embedding background leaks and bursts using a pressure-driven approach with both concentrated and distributed demand schemes. This work seeks to close the gap in the field of synthetic generation of drinking water consumption data, by establishing a proper dedicated methodology that aims to support future water smart grids.

Kinetic aspects of biofilm formation on surfaces exposed to drinking water

1995 ◽  
Vol 32 (8) ◽  
pp. 61-65 ◽  
Author(s):  
D. van der Kooij ◽  
H. S. Vrouwenvelder ◽  
H. R. Veenendaal
Keyword(s):  
Drinking Water ◽  
Biofilm Formation ◽  
Distribution Systems ◽  
Water Distribution ◽  
Formation Rate ◽  
Water Distribution Systems ◽  
Acetate Concentration ◽  
Drinking Water Distribution Systems ◽  
Drinking Water Distribution ◽  
The Relationship

Biofilm formation in drinking water distribution systems should be limited to prevent the multiplication of undesirable bacteria and other organisms. Certain types of drinking water with an AOC concentration below 10 μg of acetate-C eq/l can support the growth of Aeromonas. Therefore, the effect of acetate at a concentration of 10 μg of C/l on the biofilm formation rate (BFR) of drinking water with a low AOC concentration (3.2 μg C/l) was determined. Drinking water without acetate had a BFR of 3.9 pg ATP/cm2.day, whereas a BFR value of 362 pg ATP/cm2.day was found with acetate added. These data indicate that a low acetate concentration strongly affects biofilm formation, and that only a small fraction of AOC is available for biofilm formation. Aeromonads did not multiply in the biofilm despite their ability to grow at a concentration of 10 μg of acetate-C/l. Further investigations are needed to elucidate the relationship between substrate concentration and biofilm formation in drinking water distribution systems and the growth of undesirable bacteria in these biofilms.

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