Short-term load forecasting by wavelet transform and evolutionary extreme learning machine

This paper proposes a novel short-term load forecasting (STLF) method based on wavelet transform, extreme learning machine (ELM) and modified artificial bee colony (MABC) algorithm. The wavelet transform is used to decompose the load series for capturing the complicated features at different frequen...

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Main Authors:	Li, Song, Wang, Peng, Goel, Lalit
其他作者:	School of Electrical and Electronic Engineering
格式:	Article
語言:	English
出版:	2015
主題:	DRNTU::Engineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radio
在線閱讀:	https://hdl.handle.net/10356/107399 http://hdl.handle.net/10220/25475 http://dx.doi.org/10.1016/j.epsr.2015.01.002
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Nanyang Technological University
語言:	English

id	sg-ntu-dr.10356-107399
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spelling	sg-ntu-dr.10356-1073992019-12-06T22:30:10Z Short-term load forecasting by wavelet transform and evolutionary extreme learning machine Li, Song Wang, Peng Goel, Lalit School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radio This paper proposes a novel short-term load forecasting (STLF) method based on wavelet transform, extreme learning machine (ELM) and modified artificial bee colony (MABC) algorithm. The wavelet transform is used to decompose the load series for capturing the complicated features at different frequencies. Each component of the load series is then separately forecasted by a hybrid model of ELM and MABC (ELM-MABC). The global search technique MABC is developed to find the best parameters of input weights and hidden biases for ELM. Compared to the conventional neuro-evolution method, ELM-MABC can improve the learning accuracy with fewer iteration steps. The proposed method is tested on two datasets: ISO New England data and North American electric utility data. Numerical testing shows that the proposed method can obtain superior results as compared to other standard and state-of-the-art methods. Accepted version 2015-04-30T01:40:07Z 2019-12-06T22:30:09Z 2015-04-30T01:40:07Z 2019-12-06T22:30:09Z 2015 2015 Journal Article Li, S., Wang, P., & Goel, L. (2015). Short-term load forecasting by wavelet transform and evolutionary extreme learning machine. Electric power systems research, 122, 96-103. 0378-7796 https://hdl.handle.net/10356/107399 http://hdl.handle.net/10220/25475 http://dx.doi.org/10.1016/j.epsr.2015.01.002 en Electric power systems research © 2015 Elsevier B.V. This is the author created version of a work that has been peer reviewed and accepted for publication by Electric Power Systems Research, Elsevier B.V. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [Article DOI: http://dx.doi.org/10.1016/j.epsr.2015.01.002]. 22 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radio
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radio Li, Song Wang, Peng Goel, Lalit Short-term load forecasting by wavelet transform and evolutionary extreme learning machine
description	This paper proposes a novel short-term load forecasting (STLF) method based on wavelet transform, extreme learning machine (ELM) and modified artificial bee colony (MABC) algorithm. The wavelet transform is used to decompose the load series for capturing the complicated features at different frequencies. Each component of the load series is then separately forecasted by a hybrid model of ELM and MABC (ELM-MABC). The global search technique MABC is developed to find the best parameters of input weights and hidden biases for ELM. Compared to the conventional neuro-evolution method, ELM-MABC can improve the learning accuracy with fewer iteration steps. The proposed method is tested on two datasets: ISO New England data and North American electric utility data. Numerical testing shows that the proposed method can obtain superior results as compared to other standard and state-of-the-art methods.
author2	School of Electrical and Electronic Engineering
author_facet	School of Electrical and Electronic Engineering Li, Song Wang, Peng Goel, Lalit
format	Article
author	Li, Song Wang, Peng Goel, Lalit
author_sort	Li, Song
title	Short-term load forecasting by wavelet transform and evolutionary extreme learning machine
title_short	Short-term load forecasting by wavelet transform and evolutionary extreme learning machine
title_full	Short-term load forecasting by wavelet transform and evolutionary extreme learning machine
title_fullStr	Short-term load forecasting by wavelet transform and evolutionary extreme learning machine
title_full_unstemmed	Short-term load forecasting by wavelet transform and evolutionary extreme learning machine
title_sort	short-term load forecasting by wavelet transform and evolutionary extreme learning machine
publishDate	2015
url	https://hdl.handle.net/10356/107399 http://hdl.handle.net/10220/25475 http://dx.doi.org/10.1016/j.epsr.2015.01.002
_version_	1681048332119572480

Short-term load forecasting by wavelet transform and evolutionary extreme learning machine

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