• DocumentCode
    3101231
  • Title

    Application of Recurrent Neural Network to Short-Term-Ahead Generating Power Forecasting for Photovoltaic System

  • Author

    Yona, Atsushi ; Senjyu, Tomonobu ; Funabashi, Toshihisa

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Ryukyus Univ., Okinawa
  • fYear
    2007
  • fDate
    24-28 June 2007
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    In recent years, there have been focus on environmental pollution issue resulting from consumption of fuel, e.g., coal and oil. Thus, introduction of an alternative energy source such as solar energy is expected. However, insolation is not constant and output of photovoltaic (PV) system is influenced by weather conditions. In order to predict the power output for PV system as accurate as possible, it requires method of insolation estimation. In this paper, a technique consider the insolation of each month, and confirm the validity of using neural network to predict insolation by computer simulations. The proposed method in this paper does not require complicated calculation and mathematical model with only use weather data..
  • Keywords
    photovoltaic power systems; power engineering computing; recurrent neural nets; alternative energy source; insolation estimation; mathematical model; photovoltaic system; recurrent neural network; short-term-ahead generating power forecasting; solar energy; Fuels; Neural networks; Oil pollution; Petroleum; Photovoltaic systems; Power generation; Recurrent neural networks; Solar energy; Solar power generation; Weather forecasting; insolation forecasting; neural network; power output for PV system; short-term-ahead forecasting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Engineering Society General Meeting, 2007. IEEE
  • Conference_Location
    Tampa, FL
  • ISSN
    1932-5517
  • Print_ISBN
    1-4244-1296-X
  • Electronic_ISBN
    1932-5517
  • Type

    conf

  • DOI
    10.1109/PES.2007.386072
  • Filename
    4275838