• DocumentCode
    2831318
  • Title

    Research on Prediction Model of Natural Gas Consumption Based on Grey Modeling Optimized by Genetic Algorithm

  • Author

    Xie, Yan ; Li, Mu

  • Author_Institution
    Dept. of Electr. & Inf. Eng., Wuhan Polytech. Univ., Wuhan, China
  • fYear
    2009
  • fDate
    11-12 July 2009
  • Firstpage
    335
  • Lastpage
    337
  • Abstract
    Natural gas consumption is an important index to reflect living and spending levels of residents. Carries on the prediction to it, to ensure network capacity, carry out the optimization of network scheduling, equipment maintenance and so on, is of great significance. In this paper it introduces grey modelling method optimized by genetic algorithm. The grey prediction model of natural gas consumption is given. An example is given to verify the model and contrast the actual consumption. The result indicates that the method predicting natural gas consumption is simple and accurate, that the model has good adaptability and accuracy, and that the method is important to optimize the operation and united dispatch management of the transmission and distribution pipeline network.
  • Keywords
    flow control; genetic algorithms; grey systems; natural gas technology; pipelines; prediction theory; scheduling; distribution pipeline network; genetic algorithm; grey modeling; natural gas consumption; network capacity; network scheduling; optimization; prediction model; transmission pipeline network; united dispatch management; Accuracy; Control system synthesis; Differential equations; Genetic algorithms; Genetic engineering; Natural gas; Optimization methods; Power generation economics; Predictive models; Production; genetic algorithm; grey modeling; natural gas consumption; optimization; prediction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control, Automation and Systems Engineering, 2009. CASE 2009. IITA International Conference on
  • Conference_Location
    Zhangjiajie
  • Print_ISBN
    978-0-7695-3728-3
  • Type

    conf

  • DOI
    10.1109/CASE.2009.101
  • Filename
    5194459