Artificial neural networks in short term load forecasting / Zulkifle Ahmad

The artificial neuron network (ANN) technique for short-term load forecasting (STLF) has been proposed by several authors in order to evaluate ANN as a viable technique for STLF. Once we have to evaluate the performance of ANN methodology for practical considerations of STLF problem. This paper will...

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Bibliographic Details
Main Author:	Ahmad, Zulkifle
Format:	Thesis
Language:	English
Published:	2003
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/67083/2/67083.pdf
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id	my-uitm-ir.67083
record_format	uketd_dc
spelling	my-uitm-ir.670832023-01-05T03:58:08Z Artificial neural networks in short term load forecasting / Zulkifle Ahmad 2003 Ahmad, Zulkifle Neural networks (Computer science) Computer networks. General works. Traffic monitoring The artificial neuron network (ANN) technique for short-term load forecasting (STLF) has been proposed by several authors in order to evaluate ANN as a viable technique for STLF. Once we have to evaluate the performance of ANN methodology for practical considerations of STLF problem. This paper wills presents the results of a study to look the effectiveness of next 1 hour ANN model in 24-hour load profile at the one time was compared with the previous load on 3 months load. Data from utilities were used in modeling and forecasting. In this thesis, the back propagation will be applied as the most popular technique in Artificial Neural Network. This model is propagated forward and the error between the actual and desired output is back propagated to obtain a minimize error closer to zero. From this study we can also find that whether the Short Term Load Forecasting, Artificial Neural Network is sensitive load forecast or not. 2003 Thesis https://ir.uitm.edu.my/id/eprint/67083/ https://ir.uitm.edu.my/id/eprint/67083/2/67083.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Abd. Hadi, Razali
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Abd. Hadi, Razali
topic	Neural networks (Computer science) Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Neural networks (Computer science) Ahmad, Zulkifle Artificial neural networks in short term load forecasting / Zulkifle Ahmad
description	The artificial neuron network (ANN) technique for short-term load forecasting (STLF) has been proposed by several authors in order to evaluate ANN as a viable technique for STLF. Once we have to evaluate the performance of ANN methodology for practical considerations of STLF problem. This paper wills presents the results of a study to look the effectiveness of next 1 hour ANN model in 24-hour load profile at the one time was compared with the previous load on 3 months load. Data from utilities were used in modeling and forecasting. In this thesis, the back propagation will be applied as the most popular technique in Artificial Neural Network. This model is propagated forward and the error between the actual and desired output is back propagated to obtain a minimize error closer to zero. From this study we can also find that whether the Short Term Load Forecasting, Artificial Neural Network is sensitive load forecast or not.
format	Thesis
qualification_level	Bachelor degree
author	Ahmad, Zulkifle
author_facet	Ahmad, Zulkifle
author_sort	Ahmad, Zulkifle
title	Artificial neural networks in short term load forecasting / Zulkifle Ahmad
title_short	Artificial neural networks in short term load forecasting / Zulkifle Ahmad
title_full	Artificial neural networks in short term load forecasting / Zulkifle Ahmad
title_fullStr	Artificial neural networks in short term load forecasting / Zulkifle Ahmad
title_full_unstemmed	Artificial neural networks in short term load forecasting / Zulkifle Ahmad
title_sort	artificial neural networks in short term load forecasting / zulkifle ahmad
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2003
url	https://ir.uitm.edu.my/id/eprint/67083/2/67083.pdf
_version_	1783735658277240832

Artificial neural networks in short term load forecasting / Zulkifle Ahmad

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