• DocumentCode
    1988650
  • Title

    Application of SVM based on rough set in smart grid energy-saving prediction

  • Author

    Wang, Ting ; Qin, Lijuan

  • Author_Institution
    Dept. of Economic Manage., North China Electr. Power Univ., Baoding, China
  • Volume
    2
  • fYear
    2010
  • fDate
    17-18 July 2010
  • Firstpage
    313
  • Lastpage
    316
  • Abstract
    With the global resources being scarce, it is the common goal of global power industry to build a reasonable energy-saving smart grid system. It combines rough set (RS) with the SVM in this paper, reduces performance of SVM input space and the dimension of input space with the reduction of RS, so as to improve SVM predict accuracy. The experimental results prove this method of RS-SVM is to high predict accuracy and of great prospect compare to the BP method.
  • Keywords
    energy conservation; power engineering computing; rough set theory; smart power grids; support vector machines; BP method; RS-SVM; global power industry; rough set; smart grid energy-saving prediction; Analytical models; Support vector machines; Eenerg-Saving effectiveness; Prediction; Rough Set; Smart Grid; Support Vector Machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Environmental Science and Information Application Technology (ESIAT), 2010 International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-7387-8
  • Type

    conf

  • DOI
    10.1109/ESIAT.2010.5567355
  • Filename
    5567355