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

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 237)

Abstract

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.

Keywords

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Atsalakis, G., Nezis, D., Zopounidis, C.: Neuro-Fuzzy Versus Traditional Models for Forecasting Wind Energy Production. In: Advances in Data Analysis, Statistics for Industry and Technology, pp. 275–287 (2010)Google Scholar
  2. 2.
    Cococcioni, M., D’Andrea, E., Lazzerini, B.: 24-hour-ahead forecasting of energy production in solar PV systems. In: 11th International Conference on Intelligent Systems Design and Applications (2011)Google Scholar
  3. 3.
    Hippert, H.S., Pedreira, C.E., Souza, R.C.: Neural Networks for Short-Term Load Forecasting: A Review and Evaluation. IEEE Transactions on Power Systems 16(1) (2001)Google Scholar
  4. 4.
    Kuneš, J., Vavroch, O., Franta, V.: Essentials of modeling. SNTL Praha (1989) (in Czech)Google Scholar
  5. 5.
    Nikolaev, N.Y., Iba, H.: Adaptive Learning of Polynomial Networks. Springer (2006)Google Scholar
  6. 6.
    Nikolaev, N.Y., Iba, H.: Polynomial harmonic GMDH learning networks for time series modelling. Neural Networks 16, 1527–1540 (2003)CrossRefGoogle Scholar
  7. 7.
    Prokop, L., Mišák, S., Novosad, T., Kromer, P., Platoš, J., Snášel, V.: Artificially Evolved Soft Computing Models for Photovoltaic Power Plant Output Estimation. In: IEEE International Conference on Systems, COEX Seoul, Korea (2012)Google Scholar
  8. 8.
    Prokop, L., Mišák, S., Novosad, T., Kromer, P., Platoš, J., Snášel, V.: Photovoltaic Power Plant Output Estimation by Neural Networks and Fuzzy Inference. In: Intelligent Data Engineering and Automated Learning - 13th International Conference, Natal, Brazil (2012)Google Scholar
  9. 9.
    Zjavka, L.: Recognition of Generalized Patterns by a Differential Polynomial Neural Network. Engineering, Technology & Applied Science Research 2(1) (2012)Google Scholar
  10. 10.
    National Climatic Data Center of National Oceanic and Atmospheric Administration (NOAA), http://cdo.ncdc.noaa.gov/qclcd_ascii/

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

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

Personalised recommendations