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:	J. Pahasa, N. Theera-Umpon
Format:	Conference Proceeding
Published:	2018
Subjects:	Energy Engineering
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=51349163996&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/61041
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Institution:	Chiang Mai University

id	th-cmuir.6653943832-61041
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spelling	th-cmuir.6653943832-610412018-09-10T04:03:39Z Short-term load forecasting using wavelet transform and support vector machines J. Pahasa N. Theera-Umpon Energy Engineering 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. 2018-09-10T04:03:11Z 2018-09-10T04:03:11Z 2007-12-01 Conference Proceeding 2-s2.0-51349163996 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=51349163996&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/61041
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
topic	Energy Engineering
spellingShingle	Energy Engineering J. Pahasa N. Theera-Umpon Short-term load forecasting using wavelet transform and support vector machines
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 Proceeding
author	J. Pahasa N. Theera-Umpon
author_facet	J. Pahasa N. Theera-Umpon
author_sort	J. Pahasa
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	2018
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=51349163996&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/61041
_version_	1681425547329011712

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

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