Short-term power load forecasting with ordinary differential equation substitutions of polynomial networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099104" target="_blank" >RIV/61989100:27240/16:86099104 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/16:86099104

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S0378779616301092" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0378779616301092</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.epsr.2016.04.003" target="_blank" >10.1016/j.epsr.2016.04.003</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Short-term power load forecasting with ordinary differential equation substitutions of polynomial networks

Popis výsledku v původním jazyce

The purpose of the short-term electricity demand forecasting is to forecast in advance the system load, represented by the sum of all consumers load at the same time. Power load forecasting is important for an economically efficient operation and effective control of power systems and enables to plan the load of generating units. A precise load forecasting is required to avoid high generation costs 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 high cost preparations. Differential polynomial neural network is a new neural network type, which decomposes and solves the selective general partial differential equation, which can model a searched function on the bases of observed data samples. It produces an output sum combination of convergent series of selected relative polynomial derivative terms, which can substitute for an ordinary differential equation solution to describe and forecast real data time-series. Partial derivative terms of several time-point variables substitute for the time derivatives of the converted general ordinary differential equation. The operating principles of the proposed method differ significantly from other conventional neural network techniques. (C) 2016 Elsevier B.V. All rights reserved.

Název v anglickém jazyce

Short-term power load forecasting with ordinary differential equation substitutions of polynomial networks

Popis výsledku anglicky

The purpose of the short-term electricity demand forecasting is to forecast in advance the system load, represented by the sum of all consumers load at the same time. Power load forecasting is important for an economically efficient operation and effective control of power systems and enables to plan the load of generating units. A precise load forecasting is required to avoid high generation costs 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 high cost preparations. Differential polynomial neural network is a new neural network type, which decomposes and solves the selective general partial differential equation, which can model a searched function on the bases of observed data samples. It produces an output sum combination of convergent series of selected relative polynomial derivative terms, which can substitute for an ordinary differential equation solution to describe and forecast real data time-series. Partial derivative terms of several time-point variables substitute for the time derivatives of the converted general ordinary differential equation. The operating principles of the proposed method differ significantly from other conventional neural network techniques. (C) 2016 Elsevier B.V. All rights reserved.

Druh

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

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Electric Power Systems Research

ISSN

0378-7796

e-ISSN

—

Svazek periodika

137

Číslo periodika v rámci svazku

137

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

11

Strana od-do

113-123

Kód UT WoS článku

000376806100014

EID výsledku v databázi Scopus

2-s2.0-84964345705