Short Term Power Prediction of the Photovoltaic Power Station Based on Power Profiles

  • Martin Radvanský
  • Miloš Kudělka
  • Václav Snášel
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 303)


The use of solar energy has undergone rapid development in recent years. Photovoltaic power stations (PVPS) are often used as a 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. In this paper, we present results of the short term prediction method of generated power for small PVPS based on self–organizing maps and previously introduced power profiles.


prediction photovoltaic power station power profiles 


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  1. 1.
    Bien, T., Musikowski, H.: Forecasting photovoltaic energy using a fourier series based method. In: 23rd European Photovoltaic Solar Energy Conference, pp. 3088–3091 (2008)Google Scholar
  2. 2.
    Brinkworth, B.: Forecasting photovoltaic energy using a fourier series based method. In: Solar Energy, vol. 19, pp. 343–347 (1977)Google Scholar
  3. 3.
    Chowdhury, B., Rahman, S.: Forecasting sub-hourly solar irradiance for prediction of photovoltaic output. In: Phtovoltaic Specialist Conference, pp. 171–176 (1987)Google Scholar
  4. 4.
    Glahn, H., Lowry, D.: The use of model output statistics (mos) in objective weather forecasting. Journal of Applied Meteorology 11, 1203–1211 (1972)CrossRefGoogle Scholar
  5. 5.
    Hashimoto, T., Nagakura, Y.: Prediction of output power variation of solar power plant by image measurement of cloud movement. Journal of Advanced Research in Physics 2(2), 1–6 (2011)Google Scholar
  6. 6.
    Heinemann, D., Lorentz, E., Girodo, M.: Forecasting of solar radiation. In: Solar Energy Resource Management for Electricity Generation from Local Level to Global Scale. Nova Science (2006)Google Scholar
  7. 7.
    Kohonen, T.: Self–organized formation of topologically correct feature maps. Biological Cybernetics 43, 59–69 (1982)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Kohonen, T.: Self–organizing maps. Springer, Berlin (1995)CrossRefGoogle Scholar
  9. 9.
    Mayer, D., Wald, L., Poissant, Y., Pelland, S.: Performance prediction of grid-connected photovoltaic systems using remote sensing (2008)Google Scholar
  10. 10.
    Puri, V.: Estimation of half-hour solar radiation values from hourly values. In: Solar Energy, vol. 21, pp. 409–414 (1978)Google Scholar
  11. 11.
    Radvanský, M., Kudělka, M., Snášel, V.: Identifying power profiles in the photovoltaic power station data by self-organizing maps and dimension reduction by sammon’s projection. In: Fifth International Conference of Soft Computing and Pattern Recognition (SoCPaR 2013), pp. 316–321. IEEE (2013)Google Scholar
  12. 12.
    Reickard, G.: Predicting solar radiation at high resolutions: A comparison of time series forecast. In: Solar Energy, vol. 83, pp. 342–349 (2009)Google Scholar
  13. 13.
    Sfetsos, A., Coonick, A.H.: Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. In: Solar Energy, vol. 68, pp. 169–178 (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Martin Radvanský
    • 1
  • Miloš Kudělka
    • 1
  • Václav Snášel
    • 1
  1. 1.VSB Technical University OstravaOstravaCzech Republic

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