• DocumentCode
    174252
  • Title

    Short term power prediction of the photovoltaic power station based on comparison of power profile sequences using F-Score computation

  • Author

    Radvansky, M. ; Kudelka, M. ; Snasel, V.

  • Author_Institution
    Dept. of Comput. Sci., VrB - Tech. Univ. of Ostrava, Poruba, Czech Republic
  • fYear
    2014
  • fDate
    5-8 Oct. 2014
  • Firstpage
    3497
  • Lastpage
    3502
  • Abstract
    Due to the annual increase in energy prices, photovoltaic power stations (PVPS) are often used as a primary source of power for smart off-grid houses. Integration of this kind of energy source is challenging because it is a source of variably generated power due to meteorological uncertainty, but the cost of this energy source rapidly decreases. In this paper, we present results of the short term prediction method of generated power for small PVPS based on self-organizing maps, previously introduced power profiles, their sequences and computing F-Score as an alternative to commonly used algorithms.
  • Keywords
    building integrated photovoltaics; prediction theory; pricing; self-adjusting systems; smart power grids; F-score computation; energy prices; energy source cost; meteorological uncertainty; photovoltaic power station; power primary source; power profile sequence comparison; short term power prediction method; small PVPS based on self-organizing maps; smart off-grid houses; variably generated power; Meteorology; Neurons; Photovoltaic systems; Power measurement; Vectors; photovoltaic power station; power profile; prediction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on
  • Conference_Location
    San Diego, CA
  • Type

    conf

  • DOI
    10.1109/SMC.2014.6974471
  • Filename
    6974471