Energy consumption prediction of office buildings based on echo state networks
Shi, Guang1; Liu, Derong2; Wei, Qinglai1
2016-12-05
发表期刊NEUROCOMPUTING
卷号216期号:n/a页码:478-488
文章类型Article
摘要In 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.
关键词Energy Consumption Time-series Prediction Office Buildings Echo State Networks Reservoir Topologies
WOS标题词Science & Technology ; Technology
DOI10.1016/j.neucom.2016.08.004
关键词[WOS]RECURRENT NEURAL-NETWORK ; TIME-SERIES PREDICTION ; INTRINSIC PLASTICITY ; RESERVOIRS ; OPTIMIZATION ; RECOGNITION
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61273140 ; 61374105 ; 61503377 ; 61503379 ; 61533017 ; U1501251)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000388777400046
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/13356
专题复杂系统管理与控制国家重点实验室_平行控制
作者单位1.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
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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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