CASIA OpenIR
An echo state network based approach to room classification of office buildings
Shi, Guang1; Zhao, Bo2; Li, Chao1; Wei, Qinglai2; Liu, Derong3
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2019-03-14
Volume333Pages:319-328
Corresponding AuthorWei, Qinglai(qinglai.wei@ia.ac.cn)
AbstractOffice 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.
KeywordPower consumption Room classification Echo state networks Neural networks
DOI10.1016/j.neucom.2018.12.033
WOS KeywordPARTICLE SWARM OPTIMIZATION ; ENERGY-CONSUMPTION ; NEURAL-NETWORKS ; PREDICTION ; ALGORITHMS ; MODEL
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Natural Science Foundation of China ; National Key Research and Development Program of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000456834100029
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25348
Collection中国科学院自动化研究所
Corresponding AuthorWei, Qinglai
Affiliation1.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
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
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.
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