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Short Term Electric Load Forecasting based on Artificial Neural Networks for Weekends of Baghdad Power Grid

by Ibraheem K. Ibraheem, Mohammed Omar Ali
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 89 - Number 3
Year of Publication: 2014
Authors: Ibraheem K. Ibraheem, Mohammed Omar Ali
10.5120/15484-4263

Ibraheem K. Ibraheem, Mohammed Omar Ali . Short Term Electric Load Forecasting based on Artificial Neural Networks for Weekends of Baghdad Power Grid. International Journal of Computer Applications. 89, 3 ( March 2014), 30-37. DOI=10.5120/15484-4263

@article{ 10.5120/15484-4263,
author = { Ibraheem K. Ibraheem, Mohammed Omar Ali },
title = { Short Term Electric Load Forecasting based on Artificial Neural Networks for Weekends of Baghdad Power Grid },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 89 },
number = { 3 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 30-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume89/number3/15484-4263/ },
doi = { 10.5120/15484-4263 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:09:50.735427+05:30
%A Ibraheem K. Ibraheem
%A Mohammed Omar Ali
%T Short Term Electric Load Forecasting based on Artificial Neural Networks for Weekends of Baghdad Power Grid
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 3
%P 30-37
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This work presents proposed methodsfor short term power load forecasting (STPLF) for the governorate of Baghdad using two different models of Artificial Neural Networks (ANNs). The two models used in this work are the multi-layer perceptron (MLP) model trained with Levenberg-Marquardt Back Propagation (BP) algorithm and Radial Basis Function (RBF) neural network. Inputs to the ANN are thepast loadsvalues and the output of the ANN is the load forecast for the weekends of certain months for Baghdad governorate. The data is divided into two parts where half of them was used for training and the other half was used for testing the ANN. Simulations were achieved by MATLAB software with the aid of Neural networks toolbox, where the data obtained for the Iraqi national grid were rearranged and preprocessed. Finally, the simulations results showed that the forecasted load values for the Baghdad governorate by the proposed methods were very close to actual ones as compared with the traditional methods.

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Index Terms

Computer Science
Information Sciences

Keywords

Load forecasting multilayer perceptron radial basis neural networks (RBF) Back Propagation.

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