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Modelling the Photovoltaic Output Power using the Differential Polynomial Network and Evolutionary Fuzzy Rules

    Ladislav Zjavka Affiliation
    ; Pavel Kromer Affiliation
    ; Stanislav Mišak Affiliation
    ; Vaclav Snašel Affiliation

Abstract

The unstable production of renewable energy sources, which is difficult to model using conventional computational techniques, may be predicted to advantage by means of biologically inspired soft-computing methods. The photovoltaic output power is primarily dependent on the solar direct or global radiation, which short-term numerical forecasts are possible to apply for daily power predictions. The study compares two methods, which can successfully model dynamic fluctuant variances of the solar irradiance and corresponding output power time-series. Differential polynomial network is a new neural network class, which defines and substitutes for the general partial differential equation to model an unknown system function. Its total output is composed from selected neurons, i.e. relative polynomial substitution terms, formed in all network layers of a multi-layer structure. The proposed derivative polynomial regression using relative dimensionless fraction units, formed according to the Similarity analysis, can describe and generalize data relations on a wider range of values than defined by the training interval when using standard soft-computing composing techniques that apply only absolute data. 1-variable time-series observations are possible to model by time derivatives of a converted ordinary differential equation, solved analogously with partial derivative substitution terms of several time-point variables.

Keyword : photovoltaic output power, solar irradiation, differential polynomial neural network, evolution fuzzy rules

How to Cite
Zjavka, L., Kromer, P., Mišak, S., & Snašel, V. (2017). Modelling the Photovoltaic Output Power using the Differential Polynomial Network and Evolutionary Fuzzy Rules. Mathematical Modelling and Analysis, 22(1), 78-94. https://doi.org/10.3846/13926292.2017.1269025
Published in Issue
Jan 11, 2017
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This work is licensed under a Creative Commons Attribution 4.0 International License.