Temporal feature adaptive non-intrusive load monitoring via unsupervised probability density evolution

Toward the smart power and energy consumption, non-intrusive load monitoring is emerging as the promising technical assistance of intelligent energy user. The load behaviors of individual power users are distinct, that is potential to enhance the monitoring performance if effectively addressed. In t...

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Autores principales:	Yu Liu, Tiancheng E. Song, Xiaolong Sun, Shan Gao, Xueliang Huang
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Dictionary learning Energy consumption disaggregation Risk analysis Time of use probability Unsupervised learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/456d7cc932dd4a348efc563edad40b5c
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spelling	oai:doaj.org-article:456d7cc932dd4a348efc563edad40b5c2021-11-26T04:33:37ZTemporal feature adaptive non-intrusive load monitoring via unsupervised probability density evolution2352-484710.1016/j.egyr.2021.08.054https://doaj.org/article/456d7cc932dd4a348efc563edad40b5c2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721006570https://doaj.org/toc/2352-4847Toward the smart power and energy consumption, non-intrusive load monitoring is emerging as the promising technical assistance of intelligent energy user. The load behaviors of individual power users are distinct, that is potential to enhance the monitoring performance if effectively addressed. In this paper, the customized temporal behaviors are thoroughly investigated and utilized for load disaggregation from the view of time characteristics. At the first stage, the temporal features of appliance usage are formularized via customized time of use probability, and the model is adaptive for the specific user habit via unsupervised probability density evolution method. Then, a generic two-stage load disaggregation framework is proposed, where the primary stage is formulized by dictionary learning and for basic load disaggregation, and the secondary stage is integrated with probabilistic temporal weights and for optimal disaggregation decision. Lastly, the sparse coding principle and risk analysis theory are employed for the robust problem solution. By comprehensive verifications on low voltage networks simulator, it is demonstrated that the proposed approach is effective in temporal load feature modeling, and thereby achieving the higher accuracy and flexibility for the non-intrusive load monitoring problem.Yu LiuTiancheng E. SongXiaolong SunShan GaoXueliang HuangElsevierarticleDictionary learningEnergy consumption disaggregationRisk analysisTime of use probabilityUnsupervised learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 209-217 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Dictionary learning Energy consumption disaggregation Risk analysis Time of use probability Unsupervised learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Dictionary learning Energy consumption disaggregation Risk analysis Time of use probability Unsupervised learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yu Liu Tiancheng E. Song Xiaolong Sun Shan Gao Xueliang Huang Temporal feature adaptive non-intrusive load monitoring via unsupervised probability density evolution
description	Toward the smart power and energy consumption, non-intrusive load monitoring is emerging as the promising technical assistance of intelligent energy user. The load behaviors of individual power users are distinct, that is potential to enhance the monitoring performance if effectively addressed. In this paper, the customized temporal behaviors are thoroughly investigated and utilized for load disaggregation from the view of time characteristics. At the first stage, the temporal features of appliance usage are formularized via customized time of use probability, and the model is adaptive for the specific user habit via unsupervised probability density evolution method. Then, a generic two-stage load disaggregation framework is proposed, where the primary stage is formulized by dictionary learning and for basic load disaggregation, and the secondary stage is integrated with probabilistic temporal weights and for optimal disaggregation decision. Lastly, the sparse coding principle and risk analysis theory are employed for the robust problem solution. By comprehensive verifications on low voltage networks simulator, it is demonstrated that the proposed approach is effective in temporal load feature modeling, and thereby achieving the higher accuracy and flexibility for the non-intrusive load monitoring problem.
format	article
author	Yu Liu Tiancheng E. Song Xiaolong Sun Shan Gao Xueliang Huang
author_facet	Yu Liu Tiancheng E. Song Xiaolong Sun Shan Gao Xueliang Huang
author_sort	Yu Liu
title	Temporal feature adaptive non-intrusive load monitoring via unsupervised probability density evolution
title_short	Temporal feature adaptive non-intrusive load monitoring via unsupervised probability density evolution
title_full	Temporal feature adaptive non-intrusive load monitoring via unsupervised probability density evolution
title_fullStr	Temporal feature adaptive non-intrusive load monitoring via unsupervised probability density evolution
title_full_unstemmed	Temporal feature adaptive non-intrusive load monitoring via unsupervised probability density evolution
title_sort	temporal feature adaptive non-intrusive load monitoring via unsupervised probability density evolution
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/456d7cc932dd4a348efc563edad40b5c
work_keys_str_mv	AT yuliu temporalfeatureadaptivenonintrusiveloadmonitoringviaunsupervisedprobabilitydensityevolution AT tianchengesong temporalfeatureadaptivenonintrusiveloadmonitoringviaunsupervisedprobabilitydensityevolution AT xiaolongsun temporalfeatureadaptivenonintrusiveloadmonitoringviaunsupervisedprobabilitydensityevolution AT shangao temporalfeatureadaptivenonintrusiveloadmonitoringviaunsupervisedprobabilitydensityevolution AT xuelianghuang temporalfeatureadaptivenonintrusiveloadmonitoringviaunsupervisedprobabilitydensityevolution
_version_	1718409838318321664

Temporal feature adaptive non-intrusive load monitoring via unsupervised probability density evolution

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