ICT for Efficient Water Resources Management: The ICeWater Energy Management and Control Approach

Fantozzi, Marco; Popescu, Ioana; Farnham, Tim; Archetti, Francesco; Mogre, Parag S.; Tsouchnika, E.; Chiesa, Carlo; Tsertou, Athanasia; Castro-Gama, Mario; Bimpas, Matthaios

doi:10.1016/j.proeng.2014.02.069

Cited by 28 publications

(9 citation statements)

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“…Optimization of Water Distribution Networks (WDNs) operations has been a very important field for the Operation Research (OR) community at least in the last 40 years [1] and many tools from mathematical programming as well as metaheuristics have been proposed. An updated review on optimal water distribution is given in [2] where several classes of existing solutions, including linear programming, nonlinear programming, dynamic programming, metamodeling, heuristics, and metaheuristics are deeply analyzed and referenced.…”

Section: Introductionmentioning

confidence: 99%

Bayesian optimization of pump operations in water distribution systems

2018

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Bayesian optimization has become a widely used tool in the optimization and machine learning communities. It is suitable to problems as simulation/optimization and/or with an objective function computationally expensive to evaluate. Bayesian optimization is based on a surrogate probabilistic model of the objective whose mean and variance are sequentially updated using the observations and an "acquisition" function based on the model, which sets the next observation at the most "promising" point. The most used surrogate model is the Gaussian Process which is the basis of well-known Kriging algorithms. In this paper, the authors consider the pump scheduling optimization problem in a Water Distribution Network with both ON/OFF and variable speed pumps. In a global optimization model, accounting for time patterns of demand and energy price allows significant cost savings. Nonlinearities, and binary decisions in the case of ON/OFF pumps, make pump scheduling optimization computationally challenging, even for small Water Distribution Networks. The well-known EPANET simulator is used to compute the energy cost associated to a pump schedule and to verify that hydraulic constraints are not violated and demand is met. Two Bayesian Optimization approaches are proposed in this paper, where the surrogate model is based on a Gaussian Process and a Random Forest, respectively. Both approaches are tested with different acquisition functions on a set of test functions, a benchmark Water Distribution Network from the literature and a large-scale real-life Water Distribution Network in Milan, Italy.

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Section: Introductionmentioning

confidence: 99%

Bayesian optimization of pump operations in water distribution systems

2018

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“…Abbiategrasso is a pressure management zone in Milano with an associated graph of 1213 nodes and 1391 edges and it was also a pilot in the European project ICeWater [20]. Figure 17 displays the hypervolume per generation of the two considered algorithms.…”

Section: Abbiategrassomentioning

confidence: 99%

A New Evolutionary Approach to Optimal Sensor Placement in Water Distribution Networks

Ponti

Candelieri

Archetti

2021

Water

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The sensor placement problem is modeled as a multi-objective optimization problem with Boolean decision variables. A new multi objective evolutionary algorithm (MOEA) is proposed for approximating and analyzing the set of Pareto optimal solutions. The evaluation of the objective functions requires the execution of a hydraulic simulation model of the network. To organize the simulation results a data structure is proposed which enables the dynamic representation of a sensor placement and its fitness as a heatmap. This allows the definition of information spaces, in which the fitness of a placement can be represented as a matrix or, in probabilistic terms as a histogram. The key element in the new algorithm is this probabilistic representation which is embedded in a space endowed with a metric based on a specific notion of distance. Among several distances between probability distributions the Wasserstein (WST) distance has been selected: WST has enabled to derive new genetic operators, indicators of the quality of the Pareto set and criteria to choose among the Pareto solutions. The new algorithm has been tested on a benchmark water distribution network with two objective functions showing an improvement over NSGA-II, in particular for low generation counts, making it a good candidate for expensive black-box multi-objective optimization

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“…Microcontroller-based automated water level sensing system [12] uses the water level indicator that comprises of sensor unit and control unit to monitor the supply flow in the water pump system. Information and Communication Technology (ICT) for efficient water resource management [13] uses sensors, network infrastructure, middleware, and control module to regulate the flow of water and save the resources in the environment. Model calibration [14] in real-time distributed water network system monitors the water pressure and flow in the water pipe system.…”

Section: Proposed Mobile Middleware Application For Real-time Watmentioning

confidence: 99%

Mobile middleware application and services for real-time water pump system

Krishna

Goyal

2015

2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)

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Mobile Middleware layer functions between the application layer and network layer, and uses the system services of mobile operating system. Mobile middleware provides adaptable and application specific services between the mobile devices, heterogeneous computing devices and the internet connected application services. Several applications such as mHealth, SmartHome, mCommerce, Smart Shopping, sensor-based traffic controller, agriculture monitoring and structural sensor building use mobile middleware to enhance the application services. Pervasive middleware aims at providing context manager services for high-level application requirements and underlying heterogeneous device connectivity in the network. In this article, we propose Mobile Middleware Application and Services for Real-time Water Pump System. The proposed middleware application senses the flow of water supply in the pipe, receives the control signals from the Water Level Sensor (WLS) and stops the water pump system when the tank gets filled. SMS alert messages are sent to the registered user. The proposed mobile middleware applications save the energy and water resource respectively.

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ICT for Efficient Water Resources Management: The ICeWater Energy Management and Control Approach

Cited by 28 publications

References 1 publication

Bayesian optimization of pump operations in water distribution systems

Bayesian optimization of pump operations in water distribution systems

A New Evolutionary Approach to Optimal Sensor Placement in Water Distribution Networks

Mobile middleware application and services for real-time water pump system

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