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Energy consumption prediction of office buildings based on echo state networks

文献类型：期刊论文

作者Shi, Guang1; Liu, Derong2; Wei, Qinglai1
刊名NEUROCOMPUTING
出版日期2016-12-05
卷号216期号:n/a页码:478-488
关键词Energy Consumption Time-series Prediction Office Buildings Echo State Networks Reservoir Topologies
DOI10.1016/j.neucom.2016.08.004
文献子类Article
英文摘要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.
WOS关键词RECURRENT NEURAL-NETWORK ; TIME-SERIES PREDICTION ; INTRINSIC PLASTICITY ; RESERVOIRS ; OPTIMIZATION ; RECOGNITION
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000388777400046
资助机构National Natural Science Foundation of China(61273140 ; 61374105 ; 61503377 ; 61503379 ; 61533017 ; U1501251)
源URL[http://ir.ia.ac.cn/handle/173211/13356]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队
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
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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