Power output models of Ordinary Differential Equations by Polynomial and Recurrent Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F13%3A86087491" target="_blank" >RIV/61989100:27740/13:86087491 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-01781-5_1" target="_blank" >http://dx.doi.org/10.1007/978-3-319-01781-5_1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-01781-5_1" target="_blank" >10.1007/978-3-319-01781-5_1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Power output models of Ordinary Differential Equations by Polynomial and Recurrent Neural Networks

Popis výsledku v původním jazyce

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-ser

Název v anglickém jazyce

Power output models of Ordinary Differential Equations by Polynomial and Recurrent Neural Networks

Popis výsledku anglicky

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-ser

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.30.0016" target="_blank" >EE2.3.30.0016: Příležitost pro mladé výzkumníky</a><br>

Návaznosti

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

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 237

ISBN

978-3-319-01780-8

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

11

Strana od-do

1-11

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Ostrava

Datum konání akce

22. 8. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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