Power Demand Daily Predictions using the Combined Differential Polynomial Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F14%3A86090330" target="_blank" >RIV/61989100:27740/14:86090330 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-08156-4_8#page-1" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-08156-4_8#page-1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-08156-4_8" target="_blank" >10.1007/978-3-319-08156-4_8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Power Demand Daily Predictions using the Combined Differential Polynomial Network

Popis výsledku v původním jazyce

Power demand prediction is important for the economically efficient operation and effective control of power systems and enables to plan the load of generating unit. A precise load forecasting is required to avoid high generation cost and the spinning reserve capacity. Under-prediction of the demands leads to an insufficient reserve capacity preparation and can threaten the system stability, on the other hand, over-prediction leads to an unnecessarily large reserve that leads to a high cost preparations. Cooperation on the electricity grid requires from all providers to foresee the load within a sufficient accuracy. Differential polynomial neural network is a new neural network type, which forms and resolves an unknown general partial differential equation of an approximation of a searched function, described by data observations. It generates convergent sum series of relative polynomial derivative terms, which can substitute for the ordinary differential equation, describing 1-paramet

Název v anglickém jazyce

Power Demand Daily Predictions using the Combined Differential Polynomial Network

Popis výsledku anglicky

Power demand prediction is important for the economically efficient operation and effective control of power systems and enables to plan the load of generating unit. A precise load forecasting is required to avoid high generation cost and the spinning reserve capacity. Under-prediction of the demands leads to an insufficient reserve capacity preparation and can threaten the system stability, on the other hand, over-prediction leads to an unnecessarily large reserve that leads to a high cost preparations. Cooperation on the electricity grid requires from all providers to foresee the load within a sufficient accuracy. Differential polynomial neural network is a new neural network type, which forms and resolves an unknown general partial differential equation of an approximation of a searched function, described by data observations. It generates convergent sum series of relative polynomial derivative terms, which can substitute for the ordinary differential equation, describing 1-paramet

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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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

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Soft Computing. Volume 303

ISBN

978-3-319-08155-7

ISSN

1615-3871

e-ISSN

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Počet stran výsledku

10

Strana od-do

73-82

Název nakladatele

Springer Verlag

Místo vydání

London

Místo konání akce

Ostrava

Datum konání akce

23. 6. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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