• DocumentCode
    507263
  • Title

    To Forecast Short-Term Load in Electric Power System Based on FNN

  • Author

    Hu, Yueli ; Ji, Huijie ; Song, Xiaolong

  • Author_Institution
    Key Lab. of Adv. Display & Syst. Applic., Shanghai Univ., Shanghai, China
  • Volume
    6
  • fYear
    2009
  • fDate
    14-16 Aug. 2009
  • Firstpage
    474
  • Lastpage
    478
  • Abstract
    Electric power system load forecasting plays an important part in the Energy Management System (EMS), which has a great effect on the operating, controlling and planning of power system. Accurate load forecasting, especially short-term load forecasting, results in cost saving and guarantees secure operation condition in power system. Therefore, it is of great concern to develop an appropriate model to improve accuracy of load forecasting. In this paper, we employed the algorithm named fuzzy-neural network (FNN) and developed a prediction model for short-term forecasting. Experimental results demonstrate the effectiveness of the FNN model, and could be applied to short-term forecasting for better prediction.
  • Keywords
    electric power generation; energy management systems; fuzzy neural nets; load forecasting; power engineering computing; power system control; power system planning; prediction theory; Energy Management System; FNN; electric power system; fuzzy-neural network; power system control; power system planning; prediction model; short-term load forecasting; Control systems; Costs; Energy management; Fuzzy control; Load forecasting; Medical services; Power system management; Power system modeling; Power system planning; Predictive models;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-0-7695-3735-1
  • Type

    conf

  • DOI
    10.1109/FSKD.2009.63
  • Filename
    5359897