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Energy consumption prediction of office buildings based on echo state networks
Shi, Guang1; Liu, Derong2; Wei, Qinglai1
Source PublicationNEUROCOMPUTING
2016-12-05
Volume216Issue:n/aPages:478-488
SubtypeArticle
AbstractIn this paper, energy consumption of an office building is predicted based on echo state networks (ESNs). Energy consumption of the office building is divided into consumptions from sockets, lights and air conditioners, which are measured in each room of the office building by three ammeters installed inside, respectively. On the other hand, an office building generally consists of several types of rooms, i.e., office rooms, computer rooms, storage rooms, meeting rooms, etc., the energy consumption of which varies in accordance with different working routines in each type of rooms. In this paper, several novel reservoir topologies of ESNs are developed, the performance of ESNs with different reservoir topologies in predicting the energy consumption of rooms in the office building is compared, and the energy consumption of all the rooms in the office building is predicted with the developed topologies. Moreover, parameter sensitivity of ESNs with different reservoir topologies is analyzed. A case study shows that the developed simplified reservoir topologies are sufficient to achieve outstanding performance of ESNs in the prediction of building energy consumption. (C) 2016 Elsevier B.V. All rights reserved.
KeywordEnergy Consumption Time-series Prediction Office Buildings Echo State Networks Reservoir Topologies
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.neucom.2016.08.004
WOS KeywordRECURRENT NEURAL-NETWORK ; TIME-SERIES PREDICTION ; INTRINSIC PLASTICITY ; RESERVOIRS ; OPTIMIZATION ; RECOGNITION
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61273140 ; 61374105 ; 61503377 ; 61503379 ; 61533017 ; U1501251)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000388777400046
Citation statistics
Cited Times:19[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13356
Collection复杂系统管理与控制国家重点实验室_平行控制
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
First 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,Liu, Derong,Wei, Qinglai. Energy consumption prediction of office buildings based on echo state networks[J]. NEUROCOMPUTING,2016,216(n/a):478-488.
APA Shi, Guang,Liu, Derong,&Wei, Qinglai.(2016).Energy consumption prediction of office buildings based on echo state networks.NEUROCOMPUTING,216(n/a),478-488.
MLA Shi, Guang,et al."Energy consumption prediction of office buildings based on echo state networks".NEUROCOMPUTING 216.n/a(2016):478-488.
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