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Djamel Labed
Laboratoire de genie électrique de Constantine, Departement of electrical engineering, University of Constantine 1.
Computer Science & IT Electrical & Electronics Engineering

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A Neuro-fuzzy Approach for Predicting Load Peak Profile Abdellah Draidi; Djamel Labed
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 6: December 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (401.995 KB) | DOI: 10.11591/ijece.v5i6.pp1304-1310

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.
Co-Authors Abdellah Draidi