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Short-term forecast of generation of electric energy in photovoltaic systems

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  • Bugała, A.
  • Zaborowicz, M.
  • Boniecki, P.
  • Janczak, D.
  • Koszela, K.
  • Czekała, W.
  • Lewicki, A.

Abstract

The paper presents the use of classical statistical methods and methods based on neural modeling in short-term forecasting of electric energy from photovoltaic conversion. A detailed analysis of the input data measured in central Poland (Poznań, 52°25′ N, 16°56′ E) showed that some variables like air pressure and the length of the day are statistically insignificant. The values of kurtosis, skewness and results of applied tests, to check the normality of the distribution of dependent variable in the form of daily electricity production, indicate that the linear regression models should not be the only method in forecast process. The result of neural modeling using implemented network designer is RBF 6: 6-5-1: 1 model with quality test approximately 93% and the RMS error of 0.02%. The input parameters necessary for the operation of proposed ANN model are: number of sunny hours, length of the day, air pressure, maximum air temperature, daily insolation and cloudiness.

Suggested Citation

  • Bugała, A. & Zaborowicz, M. & Boniecki, P. & Janczak, D. & Koszela, K. & Czekała, W. & Lewicki, A., 2018. "Short-term forecast of generation of electric energy in photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 306-312.
    • Handle: RePEc:eee:rensus:v:81:y:2018:i:p1:p:306-312
    DOI: 10.1016/j.rser.2017.07.032
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