Power output models of Ordinary Differential Equations by Polynomial and Recurrent Neural Networks
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Power output models of Ordinary Differential Equations by Polynomial and Recurrent Neural Networks
Original language description
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
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/EE2.3.30.0016" target="_blank" >EE2.3.30.0016: Opportunities for young researchers</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Intelligent Systems and Computing. Volume 237
ISBN
978-3-319-01780-8
ISSN
2194-5357
e-ISSN
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Number of pages
11
Pages from-to
1-11
Publisher name
Springer
Place of publication
Berlin
Event location
Ostrava
Event date
Aug 22, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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