Short-term Power Demand Forecasting using the Differential Polynomial Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F15%3A86090324" target="_blank" >RIV/61989100:27740/15:86090324 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.tandfonline.com/eprint/QI5qIq6CaFEFGT4bkx2h/full" target="_blank" >http://www.tandfonline.com/eprint/QI5qIq6CaFEFGT4bkx2h/full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/18756891.2015.1001952" target="_blank" >10.1080/18756891.2015.1001952</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Short-term Power Demand Forecasting using the Differential Polynomial Neural Network

Popis výsledku v původním jazyce

Power demand forecasting is important for economically efficient operation and effective control of power systems and enables to plan the load of generating unit. The purpose of the short-term electricity demand forecasting is to forecast in advance thesystem load, represented by the sum of all consumers load at the same time. 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 capacitypreparation 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. 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 ord

Název v anglickém jazyce

Short-term Power Demand Forecasting using the Differential Polynomial Neural Network

Popis výsledku anglicky

Power demand forecasting is important for economically efficient operation and effective control of power systems and enables to plan the load of generating unit. The purpose of the short-term electricity demand forecasting is to forecast in advance thesystem load, represented by the sum of all consumers load at the same time. 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 capacitypreparation 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. 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 ord

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

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

2015

Kód důvěrnosti údajů

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

Název periodika

International Journal of Computational Intelligence Systems

ISSN

1875-6891

e-ISSN

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Svazek periodika

Vol. 8

Číslo periodika v rámci svazku

No. 2 (2015)

Stát vydavatele periodika

FR - Francouzská republika

Počet stran výsledku

10

Strana od-do

297-306

Kód UT WoS článku

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EID výsledku v databázi Scopus

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