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 Estimating High Resolution Temporal Scale of Water Demand Time Series – Disaggregation Approach (Case Study)

10.29007/4vfl ◽  
2018 ◽  
Author(s):  
Peyman Yousefi ◽  
Gholamreza Naser ◽  
Hadi Mohammadi
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Water Demand ◽  
Distribution System ◽  
Urban Areas ◽  
Water Distribution ◽  
Demand Forecasting ◽  
Temporal Scale ◽  
A New Technique ◽  
Cascade Method

A comprehensive understanding of water demand and its availability is essential for decision-makers to manage their resources and understand related risks effectively. Historical data play a crucial role in developing an integrated plan for management of water distribution system. The key is to provide high-resolution temporal-scale of demand data in urban areas. In the literature, many studies on water demand forecasting are available; most of them were focused on monthly-scales. Since monitoring of time series is a prolonged and costly procedure, the popularity of disaggregation methods is a most recent desirable trend. The objective of this research is to transfer low-resolution into high-resolution temporal scale using random cascade disaggregation and non-linear deterministic methods. This study defines a new technique to apply previously proposed random cascade method to disaggregate continuous data of the city of Peachland. The accuracy of the results is more than 90%. It represents a satisfactory application of the models. The proposed approach helps operators to have access to daily demand without acquiring high-resolution temporal scale values. Although the disaggregated values may not be precisely equal with observed values, it offers a practical solution for the low equipped WDS and leads to lesser number of drinking water-related problems.

Generation of synthetic cross-correlated water demand time series

2013 ◽  
Vol 13 (4) ◽  
pp. 977-986 ◽  
Author(s):  
N. Ansaloni ◽  
S. Alvisi ◽  
M. Franchini
Keyword(s):  
Time Series ◽  
Water Demand ◽  
Distribution System ◽  
Water Distribution ◽  
Cross Correlation ◽  
Time Lags ◽  
Cross Correlations ◽  
The Cross ◽  
Water Districts

This paper presents a procedure for generating synthetic district-level series of hourly water demand coefficients cross-correlated in space (between districts) and time. The procedure consists of two steps: (1) generation of hourly water demand coefficients which respect, for each hour of the day, pre-assigned means and variances; and (2) introduction of the cross-correlation at different time lags through the application of a method which implies the reordering of the data generated at step 1. The procedure was applied to a case study of the Ferrara water distribution system with the aim of generating cross-correlated synthetic series of hourly water demand coefficients for the 19 water districts making it up. It was observed that the application of the method for introducing the cross-correlation (step 2) causes numerical problems when a large number of water districts are involved and the cross-correlations are considered at many time lags; this problem is solved by carrying out an appropriate regularization of the observed cross-correlation matrix. The results obtained show that overall the proposed procedure constitutes a valid tool for generating synthetic water demand time series with pre-assigned characteristics in terms of means, variances and cross-correlation at different time lags.

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 Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
B. M. Brentan ◽  
G. Meirelles ◽  
M. Herrera ◽  
E. Luvizotto ◽  
J. Izquierdo
Keyword(s):  
Water Demand ◽  
Distribution Systems ◽  
Water Distribution ◽  
Demand Forecasting ◽  
Principal Component ◽  
Correlation Study ◽  
Self Organizing Maps ◽  
Water Demand Forecasting ◽  
Predictive Variables ◽  
Short Term Forecasting

Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems (WDSs) management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there is growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve this purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and social aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies three algorithms, namely, classical Principal Component Analysis (PCA) and machine learning powerful algorithms such as Self-Organizing Maps (SOMs) and Random Forest (RF). We show that these last algorithms help corroborate the results found by PCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a correlation study of three district metered areas (DMAs) from Franca, a Brazilian city, exploring weather and social variables to improve the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day appear to be the most important predictive variables to build an accurate regression model.

Commercial greenhouse water demand sensitivity analysis: single crop case study

2016 ◽  
Vol 16 (5) ◽  
pp. 1185-1197 ◽  
Author(s):  
Dean C. J. Rice ◽  
Rupp Carriveau ◽  
David S.-K. Ting
Keyword(s):  
Sensitivity Analysis ◽  
Water Demand ◽  
Water Distribution ◽  
Demand Forecasting ◽  
Time Of Day ◽  
Bell Peppers ◽  
Demand Forecasting Model ◽  
Large Water ◽  
Demand Sensitivity

Today water distribution utilities are trying to improve operational efficiency through increased demand intelligence from their largest customers. Moving to prognostic operations allows utilities to optimally schedule and scale resources to meet demand more reliably and economically. Commercial greenhouses are large water consumers. In order to produce effective forecasting models for greenhouse water demand, the factors that drive demand must be enumerated and prioritized. In this study greenhouse water demand was modeled using artificial neural networks trained with a dataset containing eight input factors for a commercial greenhouse growing bell peppers. The dataset contained water usage, climatic and temporal data for the years 2012–2014. This model was then evaluated using the Extended Fourier Amplitude Sensitivity Test, a global sensitivity analysis, in order to determine the importance, or sensitivity, of each input factor. It was found that time of day, solar radiation, and outdoor temperature (°C) had the largest effects on the model output. These outputs could be used to contribute to the generation of a simplified demand-forecasting model.

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.

Stochastic simulation model of water demand in residential buildings

2019 ◽  
Vol 41 (5) ◽  
pp. 544-560
Author(s):  
Tiago de VG Ferreira ◽  
Orestes M Goncalves
Keyword(s):  
Flow Rate ◽  
Water Consumption ◽  
Water Demand ◽  
Distribution Systems ◽  
Water Distribution ◽  
Residential Buildings ◽  
Water Distribution Systems ◽  
Simulation Models ◽  
System Components ◽  
Stochastic Simulation Model

Over the years, researchers have been conducting studies to investigate the water consumption profile in buildings; these studies have contributed to the accumulation of knowledge regarding the correct sizing of hydraulic systems in buildings. In the context of the methods for the characterization of system demand or loading values, the procedures commonly employed to obtain the project flow rate were primarily proposed in the mid-20th century. These models require revision and adaptation to the current water consumption values. In recent years, certain researchers have proposed simulation models with an application focus on water distribution systems owing to the random and temporal behavior of water demand in this system type. In this study, a water-demand stochastic simulation model in residential buildings is proposed, which encompasses the behavioral modelling of users and their interaction with the system to improve the design process of water distribution systems. Therefore, geographical and population factors (quantity, distribution, and organization) were considered for the behavioral modelling of users; regarding the system modelling, aspects related to the hydraulic system were considered, such as the relation between system components, the type of sanitary appliance, and the number of available devices. Different simulations—with several different types of showers—were conducted using the proposed model. Comparing the flows obtained from the simulation and from the Brazilian standard, for all system components, the decrease in the project flow rate varied from 4% to 61%. In terms of material consumption regarding the pipe (PVC), the decrease varied from 25% to 63%. Practical application: When assessing potential designs for components in water distribution systems in buildings robust information is required for water demand across different time scales. The use of simulation models represents an important advance for the dimensioning process of these components, since it is possible to know a wider range of information about the system demand possibilities. The use of this type of model, as discussed in this article, will equip the designer with an enhanced decision making capacity.

scholarly journals Hybrid Model for Short-Term Water Demand Forecasting Based on Error Correction Using Chaotic Time Series

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1683
Author(s):  
Shan Wu ◽  
Hongquan Han ◽  
Benwei Hou ◽  
Kegong Diao
Keyword(s):  
Time Series ◽  
Error Correction ◽  
Hybrid Model ◽  
Water Demand ◽  
Distribution Systems ◽  
Demand Forecasting ◽  
Chaotic Time Series ◽  
Forecasting Model ◽  
Short Term ◽  
Water Demand Forecasting

Short-term water demand forecasting plays an important role in smart management and real-time simulation of water distribution systems (WDSs). This paper proposes a hybrid model for the short-term forecasting in the horizon of one day with 15 min time steps, which improves the forecasting accuracy by adding an error correction module to the initial forecasting model. The initial forecasting model is firstly established based on the least square support vector machine (LSSVM), the errors time series obtained by comparing the observed values and the initial forecasted values is next transformed into chaotic time series, and then the error correction model is established by the LSSVM method to forecast errors at the next time step. The hybrid model is tested on three real-world district metering areas (DMAs) in Beijing, China, with different demand patterns. The results show that, with the help of the error correction module, the hybrid model reduced the mean absolute percentage error (MAPE) of forecasted demand from (5.64%, 4.06%, 5.84%) to (4.84%, 3.15%, 3.47%) for the three DMAs, compared with using LSSVM without error correction. Therefore, the proposed hybrid model provides a better solution for short-term water demand forecasting on the tested cases.

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 Technological aspects of GEOSECMA for ArcGIS and EPANET integration - case study

2018 ◽  
Vol 45 ◽  
pp. 00029
Author(s):  
Maciej Kalinowski ◽  
Daniel Grzelak
Keyword(s):  
Water Demand ◽  
Data Exchange ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Network ◽  
Hydraulic Model ◽  
Point Of View ◽  
Operation Management ◽  
Water Companies

In general, GEOSECMA for GIS (GfA) and the EPANET hydraulic model are complementary tools from a technological, financial and operational point of view. It is in this way, that water companies can reduce substantial time and money, and provide extensive capability to support decisions in the planning, operation, management and the expansion of water distribution systems. This paper is an attempt to formulate technological assumptions and methodology for GfA-EPANET integration using the example of the water distribution network serving Ząbki, a medium-sized city in the Warsaw metropolitan area. This integration is understood as the process of the spatial physical and water demand data exchange between GfA and the water distribution hydraulic model with a structure tailored to the needs of water meter reading systems.

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