Modeling the Photovoltaic Output Power using the Differential Polynomial Network and Evolutional Fuzzy Rules

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A86099463" target="_blank" >RIV/61989100:27240/17:86099463 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27730/17:86099463 RIV/61989100:27740/17:86099463

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Modeling the Photovoltaic Output Power using the Differential Polynomial Network and Evolutional Fuzzy Rules

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Modeling the Photovoltaic Output Power using the Differential Polynomial Network and Evolutional Fuzzy Rules

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Mathematical Modelling and Analysis

ISSN

1392-6292

e-ISSN

—

Svazek periodika

22

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

LT - Litevská republika

Počet stran výsledku

17

Strana od-do

78-94

Kód UT WoS článku

000393085600006

EID výsledku v databázi Scopus

2-s2.0-85009186295