scholarly journals WATER DEMAND PREDICTION USING ARTIFICIAL NEURAL NETWORK FOR SUPERVISORY CONTROL

2016 ◽  
Vol 36 (1) ◽  
pp. 148-154
Author(s):  
BI Gwaivangmin ◽  
JD Jiya
Keyword(s):  
Neural Network ◽  
Supervisory Control ◽  
Water Demand ◽  
Water Distribution ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Optimal Operation ◽  
Demand Prediction ◽  
The Neural Network ◽  
Water Demand Prediction

With increase in population growth, industrial development and economic activities over the years, water demand could not be met in a water distribution network.  Thus, water demand forecasting becomes necessary at the demand nodes.  This paper presents Hourly water demand prediction at the demand nodes of a water distribution network using NeuNet Pro 2.3 neural network software and the monitoring and control of water distribution using supervisory control.  The case study is the Laminga Water Treatment Plant and its water distribution network, Jos.  The proposed model will be developed based on historic records of water demand in the 15 selected demand nodes for 60 days, 24 hours run. The data set is categorized into two set, one for training the neural network and the other for testing, with a learning rate of 50 and hidden nodes of 10 of the neural network model.  The prediction results revealed a satisfactory performance of the neural network prediction of the water demand. The predictions are then used for supervisory control to remotely control and monitor the hydraulic parameters of the water demand nodes. The practical application in the plant will cut down the cost of water production and even to a large extend provide optimal operation of the distribution networks solving the perennial problem of water scarcity in Jos. http://dx.doi.org/10.4314/njt.v36i1.19

scholarly journals Proposal of optimal operation strategy applied to water distribution network with statistical approach

2020 ◽  
Vol 15 (2) ◽  
pp. 1
Author(s):  
Alex Takeo Yasumura Lima Silva ◽  
Fernando Das Graças Braga da Silva ◽  
André Carlos da Silva ◽  
José Antonio Tosta dos Reis ◽  
Claudio Lindemberg de Freitas ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Water Distribution ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Distribution Networks ◽  
Optimal Operation ◽  
Optimization Techniques ◽  
Experimental Techniques ◽  
Computational Optimization ◽  
Depth Analysis

 Inefficiency of sanitation companies’ operation procedures threatens the population’s future supplies. Thus, it is essential to increase water and energy efficiency in order to meet future demand. Optimization techniques are important tools for the analysis of complex problems, as in distribution networks for supply. Currently, genetic algorithms are recognized by their application in literature. In this regard, an optimization model of water distribution network is proposed, using genetic algorithms. The difference in this research is a methodology based on in-depth analysis of results, using statistics and the design of experimental tools and software. The proposed technique was applied to a theoretical network developed for the study. Preliminary simulations were accomplished using EPANET, representing the main causes of water and energy inefficiency in Brazilian sanitation companies. Some parameters were changed in applying this model, such as reservoir level, pipe diameter, pumping pressures, and valve-closing percentage. These values were established by the design of experimental techniques. As output, we obtained the equation of response surface, optimized, which resulted in values of established hydraulic parameters. From these data, the obtained parameters in computational optimization algorithms were applied, resulting in losses of 26.61%, improvement of 16.19 p.p. with regard to the network without optimization, establishing an operational strategy involving three pumps and a pressure-reducing valve.  We conclude that the association of optimization and the planning of experimental techniques constitutes an encouraging method to deal with the complexity of water-distribution network optimization.

Water Demand Forecasting in Hubei Province with BP Neural Network Model

2012 ◽  
Vol 599 ◽  
pp. 701-704
Author(s):  
Zhen Quan Tang ◽  
Gang Liu ◽  
Wen Nian Xu ◽  
Zhen Yao Xia ◽  
Hai Xiao
Keyword(s):  
Neural Network ◽  
Water Resources ◽  
Bp Neural Network ◽  
Water Demand ◽  
Demand Forecasting ◽  
Hubei Province ◽  
Demand Prediction ◽  
Water Demand Prediction ◽  
Basic Link ◽  
Bp Neural Network Model

Prediction of water demand is a basic link in water resources plan and management. Reasonable and accurate prediction of storage helps to develop the plan of water resources the next year, which is very favorable to improve the utilization ratio of water resources and reduce the waste of water resources. This paper uses BP neural network to simulate and predict the water content based on the data of water in recent ten years in Hubei province and evaluates the forecast results. The results show that BP neural network for water demand prediction is feasible.

Adaptable urban water demand prediction system

2015 ◽  
Vol 15 (5) ◽  
pp. 958-964 ◽  
Author(s):  
G. Banjac ◽  
M. Vašak ◽  
M. Baotić
Keyword(s):  
Mutual Information ◽  
Water Demand ◽  
Distribution Systems ◽  
Water Distribution ◽  
Meteorological Data ◽  
Model Parameters ◽  
Prediction System ◽  
Prediction Ability ◽  
Demand Prediction ◽  
Water Demand Prediction

In this work, identification of 24-hours-ahead water demand prediction model based on historical water demand data is considered. As part of the identification procedure, the input variable selection algorithm based on partial mutual information is implemented. It is shown that meteorological data on a daily basis are not relevant for the water demand prediction in the sense of partial mutual information for the analysed water distribution systems of the cities of Tavira, Algarve, Portugal and Evanton East, Scotland, UK. Water demand prediction system is modelled using artificial neural networks, which offer a great potential for the identification of complex dynamic systems. The adaptive tuning procedure of model parameters is also developed in order to enable the model to adapt to changes in the system. A significant improvement of the prediction ability of such a model in relation to the model with fixed parameters is shown when a certain trend is present in the water demand profile.

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