The application of self-organizing methods in kohonen network for prediction of the profiles of load in a small power system

Tomasz Ciechulski , Stanisław Osowski

Abstract

The paper shows the application of self-organizing methods in Kohonen network for prediction of the profiles of load in a small power system in Poland. Four learning methods were used: WTA, CWTA, Gaussian WTM and neural gas. The prediction of power consumption has been limited to the profile of load. The vector profile prognosis is equal to the average of vectors of the winning neurons in the appropriate days of the week and month.

Author Tomasz Ciechulski - [Wojskowa Akademia Techniczna]
Tomasz Ciechulski,,
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, Stanisław Osowski (FoEE / ITEEMIS)
Stanisław Osowski,,
- The Institute of the Theory of Electrical Engineering, Measurement and Information Systems
Journal seriesPrzegląd Elektrotechniczny, ISSN 0033-2097
Issue year2016
Vol92
Pages209-213
Publication size in sheets0.5
ASJC Classification2208 Electrical and Electronic Engineering
DOIDOI:10.15199/48.2016.10.48
Languageen angielski
Score (nominal)14
Score sourcejournalList
ScoreMinisterial score = 14.0, 29-06-2020, ArticleFromJournal
Ministerial score (2013-2016) = 14.0, 29-06-2020, ArticleFromJournal
Publication indicators Scopus Citations = 1; Scopus SNIP (Source Normalised Impact per Paper): 2016 = 0.453; WoS Impact Factor: 2013 = 0.0 (2)
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