Power Output Models of Ordinary Differential Equations by Polynomial and Recurrent Neural Networks

  • Ladislav Zjavka
  • Václav Snášel
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 237)


The production of renewable energy sources is unstable, influenced a weather frame. Photovoltaic power plant output is primarily dependent on the solar illuminance of a locality, which is possible to predict according to meteorological forecasts (Aladin). Wind charger power output is induced mainly by a current wind speed, which depends on several weather standings. Presented time-series neural network models can define incomputable functions of power output or quantities, which direct influence it. Differential polynomial neural network is a new neural network type, which makes use of data relations, not only absolute interval values of variables as artificial neural networks do. Its output is formed by a sum of fractional derivative terms, which substitute a general differential equation, defining a system model. In the case of time-series data application an ordinary differential equation is created with time derivatives. Recurrent neural network proved to form simple solid time-series models, which can replace the ordinary differential equation description.


power plant output solar illuminance wind charger differential polynomial neural network recurrent neural network 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.IT4 InnovationsVŠB-Technical University of OstravaOstravaCzech Republic

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