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A Neuro-fuzzy Approach for Predicting Load Peak Profile

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 5 No. 6 (2015)

DOI
10.11591/ijece.v5i6.pp1304-1310
ISSN
2088-8708, 2088-8708
Published
2015-12-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Load forecasting has many applications for power systems, including energy purchasing and generation, load switching, contract evaluation, and infrastructure development. Load forecasting is a complex mathematical process characterized by random data and a multitude of input variables.To solve load forecasting, two different approaches are used, the traditional and the intelligent one.Intelligent systems have proved their efficiency in load forecasting domain. Adaptive neuro-fuzzy inference systems (ANFIS) are a combination of two intelligent techniques where we can get neural networks and fuzzy logics advantages simultaneously. In this paper, we will forecast night load peak of Algerian power system using multivariate input adaptive neuro-fuzzy inference system (ANFIS) introducing the effect of the temperature and type of the day as input variables.

Collaboration network of the authors

16 authors · 30 co-authorship links · 3 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University Frères Mentouri Constantine 1
  • Mohamed-Cherif Messaadia University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Adel BouchahedUniversity Frères Mentouri Constantine 13
Mohamed AssabaaUniversity Frères Mentouri Constantine 13
Ahmed BelhaniUniversity Frères Mentouri Constantine 12
Boucetta Abdou AbdallahAffiliation unknown1
Boucetta Abd AllahUniversity Frères Mentouri Constantine 11
Fetissi SelwaUniversity Frères Mentouri Constantine 11
Labed ImenUniversity Frères Mentouri Constantine 11
Hocine SekhaneUniversity Frères Mentouri Constantine 11
Omar FeddaouiMohamed-Cherif Messaadia University1
Riad ToufoutiMohamed-Cherif Messaadia University1
Salima MezianeMohamed-Cherif Messaadia University1
Fateh MakhloufiUniversity Frères Mentouri Constantine 11
Boudjemaa MehimmedetsiAffiliation unknown1
Boudjema MehimmedetsiAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>A Neuro-fuzzy Approach for Predicting Load Peak Profile</dc:title><dc:creator>Draidi, Abdellah</dc:creator><dc:creator>Labed, Djamel</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;p&amp;gt;Load forecasting has many applications for power systems, including energy purchasing and generation, load switching, contract evaluation, and infrastructure development.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;Load forecasting is a complex mathematical process characterized by random data and a multitude of input variables.To solve load forecasting, two different approaches are used, the traditional and the intelligent one.Intelligent systems have proved their efficiency in load forecasting domain.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;Adaptive neuro-fuzzy inference systems (ANFIS) are a combination of two intelligent techniques where we can get neural networks and fuzzy logics advantages simultaneously.&amp;lt;/p&amp;gt; In this paper, we will forecast night load peak of Algerian power system using multivariate input adaptive neuro-fuzzy inference system (ANFIS) introducing the effect of the temperature and type of the day as input variables.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2015-12-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v5i6.pp1304-1310</dc:identifier><dc:identifier>10.11591/ijece.v5i6.pp1304-1310</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 5 No 6 (2015); 1304</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/5753/4446</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>