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

Result code in 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>

Alternative codes found

RIV/61989100:27740/16:86099104

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

IN - Informatics

OECD FORD branch

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Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2016

Confidentiality

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

Name of the periodical

Electric Power Systems Research

ISSN

0378-7796

e-ISSN

—

Volume of the periodical

137

Issue of the periodical within the volume

137

Country of publishing house

CH - SWITZERLAND

Number of pages

11

Pages from-to

113-123

UT code for WoS article

000376806100014

EID of the result in the Scopus database

2-s2.0-84964345705