Non-intrusive energy estimation using random forest based multi-label classification and integer linear programming

Home energy management system is proposed to reduce the influences caused by the high ratio penetration of renewable energy generation, through managing and dispatching the residential power and energy consumption in the demand side. Being aware of how the electric energy is consumed is a key step o...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yu Liu, Congxiao Liu, Yiwen Shen, Xin Zhao, Shan Gao, Xueliang Huang
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Energy estimation Non-intrusive load monitoring Random k-labelset Random forest Integer linear programming Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/28cdc5dcba1f4728b545c03d89c05301
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:28cdc5dcba1f4728b545c03d89c05301
record_format	dspace
spelling	oai:doaj.org-article:28cdc5dcba1f4728b545c03d89c053012021-11-26T04:33:17ZNon-intrusive energy estimation using random forest based multi-label classification and integer linear programming2352-484710.1016/j.egyr.2021.08.045https://doaj.org/article/28cdc5dcba1f4728b545c03d89c053012021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721006478https://doaj.org/toc/2352-4847Home energy management system is proposed to reduce the influences caused by the high ratio penetration of renewable energy generation, through managing and dispatching the residential power and energy consumption in the demand side. Being aware of how the electric energy is consumed is a key step of this system. Non-intrusive Load Monitoring is regarded as the most potential method to address this problem, which aims to separate individual appliances in households by decomposing the total power consumption. In recent years, NILM is framed as a multi-label classification problem and many researches has been investigated in this field. In this paper, a non-intrusive method which can identify appliances power usage information from the total power consumption is proposed and thoroughly investigated. Firstly, the random k-labelset multi-label classification algorithm is enhanced by introducing random forest algorithm as base classifier. Then, grid search method and cross validation method are integrated to determine the optimal paraments set. This algorithm is used to achieve the appliances identification. Finally, based on the identification result, the integer linear programming is employed for power estimation of each appliance, especially multi-state appliances. Experimental results on low voltage networks simulator demonstrate that the proposed method has a high identification accuracy compared with the traditional random k-labelset multi-label classification methods with other base classifiers, and it is capable of identifying the power usages of different appliances accurately. The desirable performance of power estimation has broadened the applications of machine learning based non-intrusive energy monitoring.Yu LiuCongxiao LiuYiwen ShenXin ZhaoShan GaoXueliang HuangElsevierarticleEnergy estimationNon-intrusive load monitoringRandom k-labelsetRandom forestInteger linear programmingElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 283-291 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Energy estimation Non-intrusive load monitoring Random k-labelset Random forest Integer linear programming Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Energy estimation Non-intrusive load monitoring Random k-labelset Random forest Integer linear programming Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yu Liu Congxiao Liu Yiwen Shen Xin Zhao Shan Gao Xueliang Huang Non-intrusive energy estimation using random forest based multi-label classification and integer linear programming
description	Home energy management system is proposed to reduce the influences caused by the high ratio penetration of renewable energy generation, through managing and dispatching the residential power and energy consumption in the demand side. Being aware of how the electric energy is consumed is a key step of this system. Non-intrusive Load Monitoring is regarded as the most potential method to address this problem, which aims to separate individual appliances in households by decomposing the total power consumption. In recent years, NILM is framed as a multi-label classification problem and many researches has been investigated in this field. In this paper, a non-intrusive method which can identify appliances power usage information from the total power consumption is proposed and thoroughly investigated. Firstly, the random k-labelset multi-label classification algorithm is enhanced by introducing random forest algorithm as base classifier. Then, grid search method and cross validation method are integrated to determine the optimal paraments set. This algorithm is used to achieve the appliances identification. Finally, based on the identification result, the integer linear programming is employed for power estimation of each appliance, especially multi-state appliances. Experimental results on low voltage networks simulator demonstrate that the proposed method has a high identification accuracy compared with the traditional random k-labelset multi-label classification methods with other base classifiers, and it is capable of identifying the power usages of different appliances accurately. The desirable performance of power estimation has broadened the applications of machine learning based non-intrusive energy monitoring.
format	article
author	Yu Liu Congxiao Liu Yiwen Shen Xin Zhao Shan Gao Xueliang Huang
author_facet	Yu Liu Congxiao Liu Yiwen Shen Xin Zhao Shan Gao Xueliang Huang
author_sort	Yu Liu
title	Non-intrusive energy estimation using random forest based multi-label classification and integer linear programming
title_short	Non-intrusive energy estimation using random forest based multi-label classification and integer linear programming
title_full	Non-intrusive energy estimation using random forest based multi-label classification and integer linear programming
title_fullStr	Non-intrusive energy estimation using random forest based multi-label classification and integer linear programming
title_full_unstemmed	Non-intrusive energy estimation using random forest based multi-label classification and integer linear programming
title_sort	non-intrusive energy estimation using random forest based multi-label classification and integer linear programming
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/28cdc5dcba1f4728b545c03d89c05301
work_keys_str_mv	AT yuliu nonintrusiveenergyestimationusingrandomforestbasedmultilabelclassificationandintegerlinearprogramming AT congxiaoliu nonintrusiveenergyestimationusingrandomforestbasedmultilabelclassificationandintegerlinearprogramming AT yiwenshen nonintrusiveenergyestimationusingrandomforestbasedmultilabelclassificationandintegerlinearprogramming AT xinzhao nonintrusiveenergyestimationusingrandomforestbasedmultilabelclassificationandintegerlinearprogramming AT shangao nonintrusiveenergyestimationusingrandomforestbasedmultilabelclassificationandintegerlinearprogramming AT xuelianghuang nonintrusiveenergyestimationusingrandomforestbasedmultilabelclassificationandintegerlinearprogramming
_version_	1718409830082805760

Non-intrusive energy estimation using random forest based multi-label classification and integer linear programming

Ejemplares similares