Multivariate Extreme Learning Machine Based AutoEncoder for Electricity Consumption Series Clustering

Multivariate electricity consumption series clustering can reflect the trend of power consumption changes in the past time period, which can provide reliable guidance for electricity production. The dimensionality reduction-based method is an effective technology to address this problem, which obtai...

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Autores principales:	Kaihong Zheng, Jingfeng Yang, Qihang Gong, Shangli Zhou, Lukun Zeng, Sheng Li
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Extreme learning machine representation learning multivariate time series clustering electricity consumption clustering Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/f8311cedd9ce472588441afd33766417
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id	oai:doaj.org-article:f8311cedd9ce472588441afd33766417
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spelling	oai:doaj.org-article:f8311cedd9ce472588441afd337664172021-11-18T00:02:03ZMultivariate Extreme Learning Machine Based AutoEncoder for Electricity Consumption Series Clustering2169-353610.1109/ACCESS.2021.3124009https://doaj.org/article/f8311cedd9ce472588441afd337664172021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9592769/https://doaj.org/toc/2169-3536Multivariate electricity consumption series clustering can reflect the trend of power consumption changes in the past time period, which can provide reliable guidance for electricity production. The dimensionality reduction-based method is an effective technology to address this problem, which obtains the low-dimensional features of each variate or all variates for multivariate time series clustering. However, most existing dimensionality reduction-based methods ignore the joint learning of the common representations and the variable-based representations. In this paper, we build a multivariate extreme learning machine based autoencoder model for electricity consumption clustering (MELM-EC), which performs common representation learning and variable-based representation learning simultaneously. MELM-EC maps the common representation and multiple variable-based representations to the original multivariate time series and computes the common output weights within a few iterations. Experimental results on realistic multivariate time series datasets and multivariate electricity consumption series datasets demonstrate the effectiveness of the proposed MELM-EC model.Kaihong ZhengJingfeng YangQihang GongShangli ZhouLukun ZengSheng LiIEEEarticleExtreme learning machinerepresentation learningmultivariate time series clusteringelectricity consumption clusteringElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 148665-148675 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Extreme learning machine representation learning multivariate time series clustering electricity consumption clustering Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Extreme learning machine representation learning multivariate time series clustering electricity consumption clustering Electrical engineering. Electronics. Nuclear engineering TK1-9971 Kaihong Zheng Jingfeng Yang Qihang Gong Shangli Zhou Lukun Zeng Sheng Li Multivariate Extreme Learning Machine Based AutoEncoder for Electricity Consumption Series Clustering
description	Multivariate electricity consumption series clustering can reflect the trend of power consumption changes in the past time period, which can provide reliable guidance for electricity production. The dimensionality reduction-based method is an effective technology to address this problem, which obtains the low-dimensional features of each variate or all variates for multivariate time series clustering. However, most existing dimensionality reduction-based methods ignore the joint learning of the common representations and the variable-based representations. In this paper, we build a multivariate extreme learning machine based autoencoder model for electricity consumption clustering (MELM-EC), which performs common representation learning and variable-based representation learning simultaneously. MELM-EC maps the common representation and multiple variable-based representations to the original multivariate time series and computes the common output weights within a few iterations. Experimental results on realistic multivariate time series datasets and multivariate electricity consumption series datasets demonstrate the effectiveness of the proposed MELM-EC model.
format	article
author	Kaihong Zheng Jingfeng Yang Qihang Gong Shangli Zhou Lukun Zeng Sheng Li
author_facet	Kaihong Zheng Jingfeng Yang Qihang Gong Shangli Zhou Lukun Zeng Sheng Li
author_sort	Kaihong Zheng
title	Multivariate Extreme Learning Machine Based AutoEncoder for Electricity Consumption Series Clustering
title_short	Multivariate Extreme Learning Machine Based AutoEncoder for Electricity Consumption Series Clustering
title_full	Multivariate Extreme Learning Machine Based AutoEncoder for Electricity Consumption Series Clustering
title_fullStr	Multivariate Extreme Learning Machine Based AutoEncoder for Electricity Consumption Series Clustering
title_full_unstemmed	Multivariate Extreme Learning Machine Based AutoEncoder for Electricity Consumption Series Clustering
title_sort	multivariate extreme learning machine based autoencoder for electricity consumption series clustering
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/f8311cedd9ce472588441afd33766417
work_keys_str_mv	AT kaihongzheng multivariateextremelearningmachinebasedautoencoderforelectricityconsumptionseriesclustering AT jingfengyang multivariateextremelearningmachinebasedautoencoderforelectricityconsumptionseriesclustering AT qihanggong multivariateextremelearningmachinebasedautoencoderforelectricityconsumptionseriesclustering AT shanglizhou multivariateextremelearningmachinebasedautoencoderforelectricityconsumptionseriesclustering AT lukunzeng multivariateextremelearningmachinebasedautoencoderforelectricityconsumptionseriesclustering AT shengli multivariateextremelearningmachinebasedautoencoderforelectricityconsumptionseriesclustering
_version_	1718425262413053952

Multivariate Extreme Learning Machine Based AutoEncoder for Electricity Consumption Series Clustering

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