An echo state network based approach to room classification of office buildings

doi:10.1016/j.neucom.2018.12.033

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 复杂系统智能机理与平行控制团队

	An echo state network based approach to room classification of office buildings
	Shi, Guang 1; Zhao, Bo2 ; Li, Chao 1; Wei, Qinglai2 ; Liu, Derong 3
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2019-03-14
卷号	333 页码:319-328
通讯作者	Wei, Qinglai(qinglai.wei@ia.ac.cn)
摘要	Office buildings commonly contain such room types as office rooms, server rooms, storage rooms, meeting rooms, etc., while the power consumption inside the rooms generally comes from appliances, lights and air-conditioners. Based on the features of power consumption in different rooms, the aim of this study is to classify the rooms into different types by proposing an echo state network (ESN) based approach. Given the data on power consumption, the proposed approach is divided into two steps, where the first step is to establish three ESNs to model the three categories of power consumption, and the second step is to establish a fourth ESN to determine the type of a room by using the outputs of the first three ESNs. The practical performance of the proposed approach is displayed by a detailed experimental study, where the proposed approach achieves high classification accuracies and shows great superiority to several classical algorithms. (C) 2019 Elsevier B.V. All rights reserved.
关键词	Power consumption Room classification Echo state networks Neural networks
DOI	10.1016/j.neucom.2018.12.033
关键词[WOS]	PARTICLE SWARM OPTIMIZATION ; ENERGY-CONSUMPTION ; NEURAL-NETWORKS ; PREDICTION ; ALGORITHMS ; MODEL
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61533017] ; National Natural Science Foundation of China[61603387] ; National Natural Science Foundation of China[61722312] ; National Natural Science Foundation of China[61773075] ; National Natural Science Foundation of China[61601458] ; National Key Research and Development Program of China[2016QY01W0103] ; National Natural Science Foundation of China[61533017] ; National Natural Science Foundation of China[61603387] ; National Natural Science Foundation of China[61722312] ; National Natural Science Foundation of China[61773075] ; National Natural Science Foundation of China[61601458] ; National Key Research and Development Program of China[2016QY01W0103]
项目资助者	National Natural Science Foundation of China ; National Key Research and Development Program of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000456834100029
出版者	ELSEVIER SCIENCE BV
七大方向——子方向分类	决策智能理论与方法
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/25348
专题	多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队
通讯作者	Wei, Qinglai
作者单位	1.Coordinat Ctr China, Natl Comp Network Emergency Response Tech Team, Beijing 100029, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Shi, Guang,Zhao, Bo,Li, Chao,et al. An echo state network based approach to room classification of office buildings[J]. NEUROCOMPUTING,2019,333:319-328.
APA	Shi, Guang,Zhao, Bo,Li, Chao,Wei, Qinglai,&Liu, Derong.(2019).An echo state network based approach to room classification of office buildings.NEUROCOMPUTING,333,319-328.
MLA	Shi, Guang,et al."An echo state network based approach to room classification of office buildings".NEUROCOMPUTING 333(2019):319-328.