Short-term load forecasting using least squares support vector machine / Abdul Zamer Afiq Abd Razak

In the electrical industry, accurate forecast of electricity load has been highlighted as most of the important issues. This paper proposes a model for short-term load forecasting using least-square support vector machines. The collected data are from Dayton, Ohio, United State. This collected data...

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Main Author:	Abd Razak, Abdul Zamer Afiq
Format:	Thesis
Language:	English
Published:	2014
Online Access:	https://ir.uitm.edu.my/id/eprint/84804/1/84804.pdf
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id	my-uitm-ir.84804
record_format	uketd_dc
spelling	my-uitm-ir.848042024-01-12T09:16:19Z Short-term load forecasting using least squares support vector machine / Abdul Zamer Afiq Abd Razak 2014 Abd Razak, Abdul Zamer Afiq In the electrical industry, accurate forecast of electricity load has been highlighted as most of the important issues. This paper proposes a model for short-term load forecasting using least-square support vector machines. The collected data are from Dayton, Ohio, United State. This collected data are analyzed and suitable features are selected for the model. Last 24 hour load demands are used to the features of load forecasting in combination with days of the week and hours of the day. The suitable data set is used for the model training, and then forecasting of day ahead hourly load demands is performed. The experimental results, obtained from a real-life benchmarks, showing that the proposed model is effective and accurate. 2014 Thesis https://ir.uitm.edu.my/id/eprint/84804/ https://ir.uitm.edu.my/id/eprint/84804/1/84804.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Mat Yasin, Zuhaila
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Mat Yasin, Zuhaila
description	In the electrical industry, accurate forecast of electricity load has been highlighted as most of the important issues. This paper proposes a model for short-term load forecasting using least-square support vector machines. The collected data are from Dayton, Ohio, United State. This collected data are analyzed and suitable features are selected for the model. Last 24 hour load demands are used to the features of load forecasting in combination with days of the week and hours of the day. The suitable data set is used for the model training, and then forecasting of day ahead hourly load demands is performed. The experimental results, obtained from a real-life benchmarks, showing that the proposed model is effective and accurate.
format	Thesis
qualification_level	Bachelor degree
author	Abd Razak, Abdul Zamer Afiq
spellingShingle	Abd Razak, Abdul Zamer Afiq Short-term load forecasting using least squares support vector machine / Abdul Zamer Afiq Abd Razak
author_facet	Abd Razak, Abdul Zamer Afiq
author_sort	Abd Razak, Abdul Zamer Afiq
title	Short-term load forecasting using least squares support vector machine / Abdul Zamer Afiq Abd Razak
title_short	Short-term load forecasting using least squares support vector machine / Abdul Zamer Afiq Abd Razak
title_full	Short-term load forecasting using least squares support vector machine / Abdul Zamer Afiq Abd Razak
title_fullStr	Short-term load forecasting using least squares support vector machine / Abdul Zamer Afiq Abd Razak
title_full_unstemmed	Short-term load forecasting using least squares support vector machine / Abdul Zamer Afiq Abd Razak
title_sort	short-term load forecasting using least squares support vector machine / abdul zamer afiq abd razak
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2014
url	https://ir.uitm.edu.my/id/eprint/84804/1/84804.pdf
_version_	1794192052843446272

Short-term load forecasting using least squares support vector machine / Abdul Zamer Afiq Abd Razak

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