scholarly journals Comparison of Bottom-Up and Top-Down Procedures for Water Demand Reconstruction

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 922 ◽  
Author(s):  
Diana Fiorillo ◽  
Enrico Creaco ◽  
Francesco De Paola ◽  
Maurizio Giugni
Keyword(s):  
Time Series ◽  
Water Demand ◽  
Second Phase ◽  
Top Down ◽  
Shift Parameter ◽  
Time Step ◽  
Bottom Up ◽  
Smart Water ◽  
Cross Correlations ◽  
Two Phases

This paper presents a comparison between two procedures for the generation of water demand time series at both single user and nodal scales, a top-down and a bottom-up procedure respectively. Both procedures are made up of two phases. The top-down procedure adopted includes a non-parametric disaggregation based on the K-nearest neighbours approach. Therefore, once the temporal aggregated water demand patterns have been defined (first phase), the disaggregation is used to generate water demand time series at lower levels of spatial aggregation (second phase). In the bottom-up procedure adopted, demand time series for each user and for each time step are generated applying a beta probability distribution with tunable bounds or a gamma distribution with shift parameter (first phase). Then, a Copula based re-sort is applied to the demand time series generated to impose existing rank cross-correlations between users and at all temporal lags (second phase). For the sake of comparison, two case studies were considered, both of which are related to a smart water network in Naples (Italy). The results obtained show that the bottom-up procedure performs significantly better than the top-down procedure in terms of rank-cross correlations at fine scale. However, the top-down procedure showed a better performance in terms of skewness and rank cross-correlation when the aggregated demands were considered. Finally, the level of aggregation in nodes was found to affect the performance of both the procedures considered.

scholarly journals Implementation of Simple Strategies to Improve Wellfield Management in Arid Regions: The Case Study of Wadi Al Arab Wellfield, Jordan

2019 ◽  
Vol 11 (21) ◽  
pp. 5903
Author(s):  
Mohammad Alqadi ◽  
Armin Margane ◽  
Marwan Al Raggad ◽  
HE Ali Subah ◽  
Markus Disse ◽  
...  
Keyword(s):  
Drinking Water ◽  
Monitoring System ◽  
Water Demand ◽  
Groundwater Resources ◽  
Decision Support Tool ◽  
Data Availability ◽  
Monitoring Strategy ◽  
Top Down ◽  
Bottom Up ◽  
Support Tool

Groundwater is the main source of drinking water supply in Jordan. Over the past 30 years, many wellfields have been drilled and expanded to cover increasing drinking water demand caused by natural population growth, development of life standards and as a result of the influx of refugees to Jordan. In particular, northern Jordan groundwater resources have been severely depleted. Therefore, water suppliers and utilities have been increasingly challenged to meet water demand and deliver water of adequate quality and quantity to households in a timely manner. Meeting these objectives requires good data management, proper maintenance of groundwater wells, and effective wellfield management plans. We developed a novel monitoring strategy that allows the collection of relevant data for wellfield managers (e.g., yield, static and dynamic water level, as well as energy consumption). The new monitoring system, implemented in 2017, has greatly enhanced data availability in comparison to the situation between 2012 and 2016. The data are used in an operational decision support tool based on simple interpretation of the field observations. The implementation of the project was done using both bottom-up and top-down approaches for the Wadi Al Arab wellfield. Our results evidence that (i) simple strategies can lead to a significant improvement of wellfield management, reducing the maintenance time of the wells though appropriate monitoring (from an average of four days/maintenance/well in 2012 to less than one day/maintenance/well in 2017); (ii) the joint combination of bottom-up and top-down approaches leads to an effective implementation of the monitoring system; (iii) the simplicity of the proposed monitoring strategy makes it suitable for further implementation in other wellfields in Jordan and countries in a similar situation of both data and water scarcity.

scholarly journals A bottom-up approach of stochastic demand allocation in water quality modelling

2010 ◽  
Vol 3 (1) ◽  
pp. 1-24
Author(s):  
E. J. M. Blokker ◽  
J. H. G. Vreeburg ◽  
H. Beverloo ◽  
M. Klein Arfman ◽  
J. C. van Dijk
Keyword(s):  
Water Demand ◽  
Distribution System ◽  
Water Distribution ◽  
Test Area ◽  
Travel Times ◽  
Top Down ◽  
Flow Measurements ◽  
Bottom Up ◽  
Demand Node ◽  
Demand Allocation

Abstract. An "all pipes" hydraulic model of a DMA-sized drinking water distribution system was constructed with two types of demand allocations. One is constructed with the conventional top-down approach, i.e. a demand multiplier pattern from the booster station is allocated to all demand nodes with a correction factor to account for the average water demand on that node. The other is constructed with a bottom-up approach of demand allocation, i.e., each individual home is represented by one demand node with its own stochastic water demand pattern. The stochastic water demand patterns are constructed with an end-use model on a per second basis and per individual home. The flow entering the test area was measured and a tracer test with sodium chloride was performed to measure travel times. The two models were evaluated on the predicted sum of demands and travel times, compared with what was measured in the test area. The new bottom-up approach performs at least as well as the conventional top-down approach with respect to total demand and travel times, without the need for any flow measurements or calibration measurements. The bottom-up approach leads to a stochastic method of hydraulic modelling and gives insight into the variability of travel times as an added feature beyond the conventional way of modelling.

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 A bottom-up approach of stochastic demand allocation in a hydraulic network model: a sensitivity study of model parameters

2011 ◽  
Vol 13 (4) ◽  
pp. 714-728 ◽  
Author(s):  
E. J. M. Blokker ◽  
H. Beverloo ◽  
A. J. Vogelaar ◽  
J. H. G. Vreeburg ◽  
J. C. van Dijk
Keyword(s):  
Water Demand ◽  
Spatial Aggregation ◽  
Model Parameters ◽  
Residence Times ◽  
Time Step ◽  
Bottom Up ◽  
Demand Pattern ◽  
Demand Patterns ◽  
Water Demand Patterns ◽  
Demand Allocation

An “all pipes” hydraulic model of a drinking water distribution system was constructed with a bottom-up approach of demand allocation. This means that each individual home is represented by one demand node with its own stochastic water demand pattern. These water demand patterns were constructed with the end-use model SIMDEUM. A sensitivity test with respect to the resulting residence times was performed for several model parameters: time step, spatial aggregation, spatial correlation, demand pattern and number of simulation runs. The bottom-up approach of demand allocation was also compared to the conventional top-down approach, i.e. a single demand multiplier pattern is allocated to all demand nodes with the base demand to account for the average water demand on that node. The models were compared to measured flows and residence times in a small network. The study showed that the bottom-up approach leads to realistic water demand patterns and residence times, without the need for any flow measurements. The stochastic approach of hydraulic modelling, with a 15 minute time step, some spatial aggregation and 10 simulation runs, gives insight into the variability of residence times as an added feature beyond the conventional way of modelling.

scholarly journals District Drinking Water Planning for Sustainability in Maharashtra: Between Local and Global Scales

2021 ◽  
Vol 13 (15) ◽  
pp. 8288
Author(s):  
James L. Wescoat, Jr. ◽  
Jonnalagadda V. R. Murty
Keyword(s):  
Drinking Water ◽  
Water Demand ◽  
Special Role ◽  
Top Down ◽  
Water Planning ◽  
Bottom Up ◽  
Groundwater Levels ◽  
Operations And Maintenance ◽  
Planning Methods

Sustainable rural drinking water is a widespread aim in India, and globally, from the household to district, state, and national scales. Sustainability issues in the rural drinking water sector range from increasing water demand to declining groundwater levels, premature deterioration of village schemes and services, inadequate revenues for operations and maintenance, weak capacity of water operators, frequently changing state and national policies, and destabilizing effects of climate change. This paper focuses on the special role of district-scale drinking water planning, which operates at the intersection between bottom-up water demand and top-down water programs. After surveying the challenges associated with bottom-up and top-down planning approaches, we present recent efforts to strengthen district and block drinking water planning in the state of Maharashtra. A combination of district interviews, institutional history, village surveys, GIS visualization, and planning workshops were used to advance district planning goals and methods. Results assess bottom-up processes of water demand; top-down water programs and finance; and intermediate-level planning at the district and block scales. Discussion focuses on potential improvements in district planning methods in Maharashtra.

scholarly journals Forecasting Hierarchical Time Series in Power Generation

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3722 ◽  
Author(s):  
Tiago Silveira Gontijo ◽  
Marcelo Azevedo Costa
Keyword(s):  
Power Generation ◽  
Time Series ◽  
Hierarchical Structure ◽  
Moving Average ◽  
The South ◽  
Top Down ◽  
Bottom Up ◽  
Autoregressive Integrated Moving Average ◽  
Electrical Sector

Academic attention is being paid to the study of hierarchical time series. Especially in the electrical sector, there are several applications in which information can be organized into a hierarchical structure. The present study analyzed hourly power generation in Brazil (2018–2020), grouped according to each of the electrical subsystems and their respective sources of generating energy. The objective was to calculate the accuracy of the main measures of aggregating and disaggregating the forecasts of the Autoregressive Integrated Moving Average (ARIMA) and Error, Trend, Seasonal (ETS) models. Specifically, the following hierarchical approaches were analyzed: (i) bottom-up (BU), (ii) top-down (TD), and (iii) optimal reconciliation. The optimal reconciliation models showed the best mean performance, considering the primary predictive windows. It was also found that energy forecasts in the South subsystem presented greater inaccuracy compared to the others, which signals the need for individualized models for this subsystem.

scholarly journals Multiannual changes of CO<sub>2</sub> emissions in China: indirect estimates derived from satellite measurements of tropospheric NO<sub>2</sub> columns

2013 ◽  
Vol 13 (1) ◽  
pp. 255-309 ◽  
Author(s):  
E. V. Berezin ◽  
I. B. Konovalov ◽  
P. Ciais ◽  
A. Richter ◽  
S. Tao ◽  
...  
Keyword(s):  
Time Series ◽  
Co2 Emissions ◽  
Co2 Emission ◽  
Emission Inventory ◽  
Emission Inventories ◽  
Satellite Measurements ◽  
Top Down ◽  
Bottom Up ◽  
Tropospheric No2 ◽  
Emission Changes

Abstract. Multi-annual satellite measurements of tropospheric NO2 columns are used for evaluation of CO2 emission changes in China in the period from 1996 to 2008. Indirect annual top-down estimates of CO2 emissions are derived from the satellite NO2 columns measurements by means of a simple inverse modeling procedure involving simulations performed with the CHIMERE mesoscale chemistry transport model and the CO2 to NOx emission ratios from the Emission Database for Global Atmospheric Research version 4.2 (EDGAR v4.2) global anthropogenic emission inventory. Exponential trends in the normalized time series of annual emission are evaluated separately for the periods from 1996 to 2001 and from 2001 to 2008. The results indicate that the both periods manifest strong positive trends in the CO2 emissions, and that the trend in the second period was significantly larger than the trend in the first period. Specifically, the trends in the first and second periods are estimated to be in the range from 3.7 to 8.0 and from 9.5 to 13.0 percent per year, respectively, taking into account both statistical and probable systematic uncertainties. Comparison of our top-down estimates of the CO2 emission changes with the corresponding bottom-up estimates provided by EDGAR v4.2 and Global Carbon Project (GCP) emission inventories reveals that while acceleration of the CO2 emission growth in the considered period is a common feature of the both kinds of estimates, nonlinearity in the CO2 emission changes may be strongly exaggerated in the emission inventories. Specifically, the atmospheric NO2 observations do not confirm the existence of a sharp bend in the emission inventory data time series in the period from 2000 to 2002. A significant quantitative difference is revealed between the bottom-up and top-down estimates of the CO2 emission trend in the period from 1996 to 2001 (specifically, the trend was not positive according to the emission inventories, but is strongly positive in our estimates). These results confirm the findings of earlier studies which indicated probable large uncertainties in the energy production and other activity data from international energy statistics used as the input information in the emission inventories for China. For the period from 2001 to 2008, the different kinds of estimates agree within the uncertainty range. In general, satellite measurements of tropospheric NO2 are shown to be a useful source of information on CO2 sources colocated with sources of nitrogen oxides; the corresponding potential of these measurements should be exploited further in future studies.

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.

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