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Prediction the data consumption for power demands by elman neural network

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 9 No. 5 (2019)

DOI
10.11591/ijece.v9i5.pp4003-4009
ISSN
2722-2578, 2088-8708
Published
2019-10-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The load forecasting consider as part important in power system operation. The exact prediction for power demand is important for planning how much need extra power generation to cover extra load to keep without happen shutdown. Neural networks stay frequently designed for modeling dynamic processes. The Multi-Layer Perceptron (MLP) with Radial Basis Functions (RBF) network is static approximations used fewer frequently in the discrete-time domain. In this paper proposed predict method for daily peak load by Elman Neural Network (ENN) with using data power demand for 2 years collected from National Control Center (NCC) and comparing the result. The result show the proposal is evaluated and followed the power demand.

Collaboration network of the authors

4 authors · 4 co-authorship links

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  • University of Diyala
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Lafta E. J. AlkurawyUniversity of Diyala1
Khalid G. MohammedUniversity of Diyala1

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
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