Optimization algorithms of operative control in water distribution systems

Klempous, Ryszard; Kotowski, Jerzy; Nikodem, Jan; Ułasiewicz, Jędrej

doi:10.1016/s0377-0427(97)00113-1

Cited by 14 publications

(4 citation statements)

References 2 publications

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“…However, to satisfy safe operation and management of their systems and make right decisions about valve and pump manoeuvres, water utilities need to be acquainted with real (or near real) 5 time end-users behaviour regarding water consumption. In addition, having a deep knowledge on water demand helps identify and control possible leakages in the network, when observed consumption and demand prediction diverge far from the expected uncertainty [2,3].…”

Section: Introductionmentioning

confidence: 99%

Hybrid regression model for near real-time urban water demand forecasting

Brentan¹,

Luvizotto²,

Herrera³

et al. 2017

Journal of Computational and Applied Mathematics

159

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Elsevier Brentan, BM.; Luvizotto, E.; Herrera Fernández, AM.; Izquierdo Sebastián, J.; Pérez García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. AbstractThe most important factor in planning and operating water distribution systems is satisfying consumer demand. This means continuously providing users with quality water in adequate volumes at reasonable pressure, thus ensuring reliable water distribution. During the last years, the application of Statistical, Machine Learning and Artificial Intelligence methodologies has been fostered for water demand forecasting. However, there is still room for improvement and new challenges concerning to on-line predictive models for water demand have aroused. This work proposes applying support vector regression, as one of the currently better Machine Learning options for short-term water demand forecasting, to build a base prediction. Over this model, a Fourier time series process is built to improve the base prediction. This addition produces a tool able to get rid of part of the errors and bias inherent to a fixed regression structure in response to new incoming time series data. The final hybrid process is validated using demand data from a water utility in Franca, Brazil. Our model, being a near real-time model for water demand, may be directly exploited in water management decision-making processes.

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Section: Introductionmentioning

confidence: 99%

Hybrid regression model for near real-time urban water demand forecasting

Brentan¹,

Luvizotto²,

Herrera³

et al. 2017

Journal of Computational and Applied Mathematics

159

View full text Add to dashboard Cite

show abstract

“…In their turn, short-term water demand forecasting models can help to suitable define the operation and management of the water systems, with the aim of supplying water to costumers with maximum efficiency [1,3]. As a result, working with water predictive models is central to suitably establish the set of variables involved in the model.…”

Section: Introductionmentioning

confidence: 99%

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

Brentan

Lima

Herrera

et al. 2017

Mathematical Problems in Engineering

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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 SelfOrganizing 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.

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“…Operative planning in water networks is difficult: a sound mathematical model leads to nonlinear mixedinteger optimization, which is currently impractical for large water supply networks as in Berlin. Because of the enormous complexity of the task, early mathematical approaches typically rely on substantially simplified network hydraulics (by dropping all nonlinearities or addressing the static case, for instance) [1][2][3][4][5][6][7][8], which is often unacceptable in practice. Other authors employ discrete dynamic programming [9][10][11][12][13][14], which is mathematically sound but only applicable to small networks unless specific properties can be exploited to increase efficiency.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Programming Techniques for Operative Planning in Large Drinking Water Networks

Burgschweiger¹,

Gnädig²,

Steinbach³

2009

TOAMJ

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Mathematical decision support for operative planning in water supply systems is highly desirable; it leads, however, to very difficult optimization problems. We propose a nonlinear programming approach that yields practically satisfactory operating schedules in acceptable computing time even for large networks. Based on a carefully designed model supporting gradient-based optimization algorithms, this approach employs a special initialization strategy for convergence acceleration, special minimum up and down time constraints together with pump aggregation to handle switching decisions, and several network reduction techniques for further speed-up. Results for selected application scenarios at Berliner Wasserbetriebe demonstrate the success of the approach.

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Optimization algorithms of operative control in water distribution systems

Cited by 14 publications

References 2 publications

Hybrid regression model for near real-time urban water demand forecasting

Hybrid regression model for near real-time urban water demand forecasting

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

Nonlinear Programming Techniques for Operative Planning in Large Drinking Water Networks

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