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Urban Water Supply Systems To Optimize The Scheduling

Posted on:2010-02-13Degree:MasterType:Thesis
Country:ChinaCandidate:T Z ZhouFull Text:PDF
GTID:2192360278469491Subject:Control Science and Engineering
Abstract/Summary:PDF Full Text Request
Based on the ChangSha water supply system, the paper made a in-depth research and discussion in water consumption forecast, network modeling, optimal scheduling of urban water supply scheduling system.Urban water consumption forecast is an important basis and premise of urban water supply optimal scheduling system. By analysing the time distribution of ChangSha water consumption, the paper established a sub-period water consumption forecast model, while making a forecast.The paper used the Adaptive-Network-based Fuzzy Inference (ANFI) to do the water consumption forecast, it not only can avoid directly inspecting the interaction between the factors and quantity, but also reduce the data random noise and improve accuracy.Of the parameters of the water supply optimal scheduling system,the pressure the pressure points is was a contraints,and then waterworks water supply was the parameter variables of objective function.So, using the ANFI, the paper established a macroscopic pressure points modle,a macroscopical relationship model between water supply and outlet pressure of every waterworks, and then laid the foundation for water supply optimal scheduling system.Based on waterworks outlet pressure regarding as decision variables, regarding mini-consuming power of waterworks as optimization target,the paper established a two grades optimal scheduling model,and used the genetic algorithm to solve the first grade model, to get the optimal water supply and the optimal outlet pressure of every waterworks.And then the paper used the method of Case-based reasoning(CBR) to solve the second grade optimal scheduling model, to get the the optimal switching state of pumps in every waterworks and met the requirements of optimal water supply and the optimal outlet pressure of every waterworks.
Keywords/Search Tags:ANFI, optimal scheduling, Genetic algorithm, CBR
PDF Full Text Request
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