Short-term load forecasting using wavelet transform and support vector machines

This paper presents a new technique in short-term load forecasting (STLF.) The proposed method consists of the discrete wavelet transform (DWT) and support vector machines (SVMs.) The DWT splits up load time series into low and high frequency components to be the features for the SVMs. The SVMs then...

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Main Authors:	Pahasa J., Theera-Umpon N.
Format:	Conference or Workshop Item
Language:	English
Published:	2014
Online Access:	http://www.scopus.com/inward/record.url?eid=2-s2.0-51349163996&partnerID=40&md5=30dd0babb7310b105c5383385dd2f937 http://cmuir.cmu.ac.th/handle/6653943832/1319
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Institution:	Chiang Mai University
Language:	English

id	th-cmuir.6653943832-1319
record_format	dspace
spelling	th-cmuir.6653943832-13192014-08-29T09:29:08Z Short-term load forecasting using wavelet transform and support vector machines Pahasa J. Theera-Umpon N. This paper presents a new technique in short-term load forecasting (STLF.) The proposed method consists of the discrete wavelet transform (DWT) and support vector machines (SVMs.) The DWT splits up load time series into low and high frequency components to be the features for the SVMs. The SVMs then forecast each component separately. At the end we sum up all forecasted components to produce a final forecasted load. The data from Bangkok-Noi area in Bangkok, Thailand, is used to verify on the one-day ahead load forecasting. The performance of the algorithm is compared with that of the SVM without DWT, and neural networks with and without DWT. The experimental results show that the proposed algorithm yields more accuracy in the STLF than the others. © 2007 RPS. 2014-08-29T09:29:08Z 2014-08-29T09:29:08Z 2007 Conference Paper 9789810594237 73164 http://www.scopus.com/inward/record.url?eid=2-s2.0-51349163996&partnerID=40&md5=30dd0babb7310b105c5383385dd2f937 http://cmuir.cmu.ac.th/handle/6653943832/1319 English
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
language	English
description	This paper presents a new technique in short-term load forecasting (STLF.) The proposed method consists of the discrete wavelet transform (DWT) and support vector machines (SVMs.) The DWT splits up load time series into low and high frequency components to be the features for the SVMs. The SVMs then forecast each component separately. At the end we sum up all forecasted components to produce a final forecasted load. The data from Bangkok-Noi area in Bangkok, Thailand, is used to verify on the one-day ahead load forecasting. The performance of the algorithm is compared with that of the SVM without DWT, and neural networks with and without DWT. The experimental results show that the proposed algorithm yields more accuracy in the STLF than the others. © 2007 RPS.
format	Conference or Workshop Item
author	Pahasa J. Theera-Umpon N.
spellingShingle	Pahasa J. Theera-Umpon N. Short-term load forecasting using wavelet transform and support vector machines
author_facet	Pahasa J. Theera-Umpon N.
author_sort	Pahasa J.
title	Short-term load forecasting using wavelet transform and support vector machines
title_short	Short-term load forecasting using wavelet transform and support vector machines
title_full	Short-term load forecasting using wavelet transform and support vector machines
title_fullStr	Short-term load forecasting using wavelet transform and support vector machines
title_full_unstemmed	Short-term load forecasting using wavelet transform and support vector machines
title_sort	short-term load forecasting using wavelet transform and support vector machines
publishDate	2014
url	http://www.scopus.com/inward/record.url?eid=2-s2.0-51349163996&partnerID=40&md5=30dd0babb7310b105c5383385dd2f937 http://cmuir.cmu.ac.th/handle/6653943832/1319
_version_	1681419648405340160

Short-term load forecasting using wavelet transform and support vector machines

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