An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity

Acting as a virtual sensor network for household appliance energy use monitoring, non-intrusive load monitoring is emerging as the technical basis for refined electricity analysis as well as home energy management. Aiming for robust and reliable monitoring, the ensemble approach has been expected in...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yu Liu, Qianyun Shi, Yan Wang, Xin Zhao, Shan Gao, Xueliang Huang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence energy disaggregation ensemble method heterogeneous design non-intrusive load monitoring Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/c68633e83c65499eab7a181071aed56f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c68633e83c65499eab7a181071aed56f
record_format	dspace
spelling	oai:doaj.org-article:c68633e83c65499eab7a181071aed56f2021-11-25T18:59:00ZAn Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity10.3390/s212277501424-8220https://doaj.org/article/c68633e83c65499eab7a181071aed56f2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7750https://doaj.org/toc/1424-8220Acting as a virtual sensor network for household appliance energy use monitoring, non-intrusive load monitoring is emerging as the technical basis for refined electricity analysis as well as home energy management. Aiming for robust and reliable monitoring, the ensemble approach has been expected in load disaggregation, but the obstacles of design difficulty and computational inefficiency still exist. To address this, an ensemble design integrated with multi-heterogeneity is proposed for non-intrusive energy use disaggregation in this paper. Firstly, the idea of utilizing a heterogeneous design is presented, and the corresponding ensemble framework for load disaggregation is established. Then, a sparse coding model is allocated for individual classifiers, and the combined classifier is diversified by introducing different distance and similarity measures without consideration of sparsity, forming mutually heterogeneous classifiers. Lastly, a multiple-evaluations-based decision process is fine-tuned following the interactions of multi-heterogeneous committees, and finally deployed as the decision maker. Through verifications on both a low-voltage network simulator and a field measurement dataset, the proposed approach is demonstrated to be effective in enhancing load disaggregation performance robustly. By appropriately introducing the heterogeneous design into the ensemble approach, load monitoring improvements are observed with reduced computational burden, which stimulates research enthusiasm in investigating valid ensemble strategies for practical non-intrusive load monitoring implementations.Yu LiuQianyun ShiYan WangXin ZhaoShan GaoXueliang HuangMDPI AGarticleartificial intelligenceenergy disaggregationensemble methodheterogeneous designnon-intrusive load monitoringChemical technologyTP1-1185ENSensors, Vol 21, Iss 7750, p 7750 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence energy disaggregation ensemble method heterogeneous design non-intrusive load monitoring Chemical technology TP1-1185
spellingShingle	artificial intelligence energy disaggregation ensemble method heterogeneous design non-intrusive load monitoring Chemical technology TP1-1185 Yu Liu Qianyun Shi Yan Wang Xin Zhao Shan Gao Xueliang Huang An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity
description	Acting as a virtual sensor network for household appliance energy use monitoring, non-intrusive load monitoring is emerging as the technical basis for refined electricity analysis as well as home energy management. Aiming for robust and reliable monitoring, the ensemble approach has been expected in load disaggregation, but the obstacles of design difficulty and computational inefficiency still exist. To address this, an ensemble design integrated with multi-heterogeneity is proposed for non-intrusive energy use disaggregation in this paper. Firstly, the idea of utilizing a heterogeneous design is presented, and the corresponding ensemble framework for load disaggregation is established. Then, a sparse coding model is allocated for individual classifiers, and the combined classifier is diversified by introducing different distance and similarity measures without consideration of sparsity, forming mutually heterogeneous classifiers. Lastly, a multiple-evaluations-based decision process is fine-tuned following the interactions of multi-heterogeneous committees, and finally deployed as the decision maker. Through verifications on both a low-voltage network simulator and a field measurement dataset, the proposed approach is demonstrated to be effective in enhancing load disaggregation performance robustly. By appropriately introducing the heterogeneous design into the ensemble approach, load monitoring improvements are observed with reduced computational burden, which stimulates research enthusiasm in investigating valid ensemble strategies for practical non-intrusive load monitoring implementations.
format	article
author	Yu Liu Qianyun Shi Yan Wang Xin Zhao Shan Gao Xueliang Huang
author_facet	Yu Liu Qianyun Shi Yan Wang Xin Zhao Shan Gao Xueliang Huang
author_sort	Yu Liu
title	An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity
title_short	An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity
title_full	An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity
title_fullStr	An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity
title_full_unstemmed	An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity
title_sort	enhanced ensemble approach for non-intrusive energy use monitoring based on multidimensional heterogeneity
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c68633e83c65499eab7a181071aed56f
work_keys_str_mv	AT yuliu anenhancedensembleapproachfornonintrusiveenergyusemonitoringbasedonmultidimensionalheterogeneity AT qianyunshi anenhancedensembleapproachfornonintrusiveenergyusemonitoringbasedonmultidimensionalheterogeneity AT yanwang anenhancedensembleapproachfornonintrusiveenergyusemonitoringbasedonmultidimensionalheterogeneity AT xinzhao anenhancedensembleapproachfornonintrusiveenergyusemonitoringbasedonmultidimensionalheterogeneity AT shangao anenhancedensembleapproachfornonintrusiveenergyusemonitoringbasedonmultidimensionalheterogeneity AT xuelianghuang anenhancedensembleapproachfornonintrusiveenergyusemonitoringbasedonmultidimensionalheterogeneity AT yuliu enhancedensembleapproachfornonintrusiveenergyusemonitoringbasedonmultidimensionalheterogeneity AT qianyunshi enhancedensembleapproachfornonintrusiveenergyusemonitoringbasedonmultidimensionalheterogeneity AT yanwang enhancedensembleapproachfornonintrusiveenergyusemonitoringbasedonmultidimensionalheterogeneity AT xinzhao enhancedensembleapproachfornonintrusiveenergyusemonitoringbasedonmultidimensionalheterogeneity AT shangao enhancedensembleapproachfornonintrusiveenergyusemonitoringbasedonmultidimensionalheterogeneity AT xuelianghuang enhancedensembleapproachfornonintrusiveenergyusemonitoringbasedonmultidimensionalheterogeneity
_version_	1718410488650399744

An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity

Ejemplares similares