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Artificial neural network approach for electric load forecasting in power distribution company / Hambali M. A ... [et al.]

In recent years, there have been extensive researches seeking the best methods of improving the load forecast accuracy. Many of these methods are statistical based methods which include time series, regression, Box-Jenkins model, exponential smoothing and so on. However, the statistical models offer...

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Main Authors: M. A., Hambali, Y. K, Saheed, M. D, Gbolagade, M, Gaddafi
Format: Article
Language:English
Published: 2017
Subjects:
Internet Protocol multimedia subsystem. Multimedia communications
Online Access:https://ir.uitm.edu.my/id/eprint/83551/1/83551.pdf
https://ir.uitm.edu.my/id/eprint/83551/
https://e-ajuitmct.uitm.edu.my/v3/
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spelling my.uitm.ir.835512023-09-27T04:54:15Z https://ir.uitm.edu.my/id/eprint/83551/ Artificial neural network approach for electric load forecasting in power distribution company / Hambali M. A ... [et al.] eaj M. A., Hambali Y. K, Saheed M. D, Gbolagade M, Gaddafi Internet Protocol multimedia subsystem. Multimedia communications In recent years, there have been extensive researches seeking the best methods of improving the load forecast accuracy. Many of these methods are statistical based methods which include time series, regression, Box-Jenkins model, exponential smoothing and so on. However, the statistical models offer limpidity in data interpretation and sensible accuracy in load forecasting but characterized by the problems of limited modeling and hefty computational effort which makes them less desirable than the intelligent techniques. Recently, Artificial Intelligence (AI) has been a better substitute. Among the AI methods, artificial neural networks (ANNs) have got some attention from a lot of researchers in this area due to its flexibility in data modeling. In this paper, ANN for electric load forecasting is proposed. The historical data were collected for three months from Yola power transmission company office along Numan road Jimeta/Yola, Adamawa State, Nigeria. Researchers then performed data preprocessing on the data. Afterwards, data mining algorithms were applied in order to forecast electric load. In doing this, two ANN algorithms (MLP and RBF) and SMO algorithm were employed and compared. The results were then interpreted; the obtained models were analyzed to determine the pattern in load forecasting model. The experimental analysis was performed on WEKA version 3.6.10 environment. Also, 10-fold cross validation test option was used to carry out the experiments. Results obtained showed that multilayer-Perceptron model (MLP) gives an accuracy of 86% with Mean Absolute error (MAE) of 0.016, Radial basis function (RBF) had an accuracy of 76% with MAE of 0.030 and Sequential Minimal Optimization (SMO) accuracy of 85% with MAE of 0.090 which indicated a promising level of electric load forecast. 2017 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/83551/1/83551.pdf Artificial neural network approach for electric load forecasting in power distribution company / Hambali M. A ... [et al.]. (2017) e-Academia Journal <https://ir.uitm.edu.my/view/publication/e-Academia_Journal/>, 6 (2). pp. 80-90. ISSN 2289 - 6589 https://e-ajuitmct.uitm.edu.my/v3/
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Internet Protocol multimedia subsystem. Multimedia communications
spellingShingle Internet Protocol multimedia subsystem. Multimedia communications
M. A., Hambali
Y. K, Saheed
M. D, Gbolagade
M, Gaddafi
Artificial neural network approach for electric load forecasting in power distribution company / Hambali M. A ... [et al.]
description In recent years, there have been extensive researches seeking the best methods of improving the load forecast accuracy. Many of these methods are statistical based methods which include time series, regression, Box-Jenkins model, exponential smoothing and so on. However, the statistical models offer limpidity in data interpretation and sensible accuracy in load forecasting but characterized by the problems of limited modeling and hefty computational effort which makes them less desirable than the intelligent techniques. Recently, Artificial Intelligence (AI) has been a better substitute. Among the AI methods, artificial neural networks (ANNs) have got some attention from a lot of researchers in this area due to its flexibility in data modeling. In this paper, ANN for electric load forecasting is proposed. The historical data were collected for three months from Yola power transmission company office along Numan road Jimeta/Yola, Adamawa State, Nigeria. Researchers then performed data preprocessing on the data. Afterwards, data mining algorithms were applied in order to forecast electric load. In doing this, two ANN algorithms (MLP and RBF) and SMO algorithm were employed and compared. The results were then interpreted; the obtained models were analyzed to determine the pattern in load forecasting model. The experimental analysis was performed on WEKA version 3.6.10 environment. Also, 10-fold cross validation test option was used to carry out the experiments. Results obtained showed that multilayer-Perceptron model (MLP) gives an accuracy of 86% with Mean Absolute error (MAE) of 0.016, Radial basis function (RBF) had an accuracy of 76% with MAE of 0.030 and Sequential Minimal Optimization (SMO) accuracy of 85% with MAE of 0.090 which indicated a promising level of electric load forecast.
format Article
author M. A., Hambali
Y. K, Saheed
M. D, Gbolagade
M, Gaddafi
author_facet M. A., Hambali
Y. K, Saheed
M. D, Gbolagade
M, Gaddafi
author_sort M. A., Hambali
title Artificial neural network approach for electric load forecasting in power distribution company / Hambali M. A ... [et al.]
title_short Artificial neural network approach for electric load forecasting in power distribution company / Hambali M. A ... [et al.]
title_full Artificial neural network approach for electric load forecasting in power distribution company / Hambali M. A ... [et al.]
title_fullStr Artificial neural network approach for electric load forecasting in power distribution company / Hambali M. A ... [et al.]
title_full_unstemmed Artificial neural network approach for electric load forecasting in power distribution company / Hambali M. A ... [et al.]
title_sort artificial neural network approach for electric load forecasting in power distribution company / hambali m. a ... [et al.]
publishDate 2017
url https://ir.uitm.edu.my/id/eprint/83551/1/83551.pdf
https://ir.uitm.edu.my/id/eprint/83551/
https://e-ajuitmct.uitm.edu.my/v3/
_version_ 1779442783716638720
score 13.211869

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