• DocumentCode
    2075350
  • Title

    An improved load forecasting method of warship based on GA-SVR

  • Author

    Li DongLiang ; Zhang Xiaofeng ; Qiao Mingzhong ; Cheng Gang

  • Author_Institution
    Inst. of Simulationmachine, Naval Univ. of Eng., Wuhan, China
  • fYear
    2011
  • fDate
    16-18 Dec. 2011
  • Firstpage
    1496
  • Lastpage
    1499
  • Abstract
    An improved forecasting method base on genetic algorithm and support vector machine for warship short-term load forecasting was presented and tested. The new influencing factors of warship power load were used in modeling which is different with the land grid and civilian vessels grid. Theory of genetic algorithm and Support vector machine was discussed first, and the method of genetic algorithm was improved to have the ability of adaptive parameter optimization. and the method of support vector machine was improved by the adaptive GA optimizational method. then a new adaptive short-term load forecasting model was established by the adaptive GA-SVM method. finally Through simulation results show that the adaptive GA-SVM method is highly feasible to predict with high accuracy and high generalization capability.
  • Keywords
    genetic algorithms; load forecasting; marine power systems; military vehicles; naval engineering; parameter estimation; regression analysis; ships; support vector machines; GA-SVR; adaptive GA optimizational method; adaptive parameter optimization; civilian vessels grid; generalization capability; genetic algorithm; land grid; support vector machine; warship power load; warship short-term load forecasting method; Genetic algorithms; Load forecasting; Load modeling; Optimization; Predictive models; Support vector machines; Training; genetic algorithm; load forecasting; support vector machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Transportation, Mechanical, and Electrical Engineering (TMEE), 2011 International Conference on
  • Conference_Location
    Changchun
  • Print_ISBN
    978-1-4577-1700-0
  • Type

    conf

  • DOI
    10.1109/TMEE.2011.6199491
  • Filename
    6199491