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Energy consumption prediction of office buildings based on echo state networks
Dec 26, 2016Author:
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Title: Energy consumption prediction of office buildings based on echo state networks
Authors: Shi, G; Liu, DR; Wei, QL
Author Full Names: Shi, Guang; Liu, Derong; Wei, Qinglai
Source: NEUROCOMPUTING, 216 478-488; 10.1016/j.neucom.2016.08.004 DEC 5 2016
Language: English
Abstract: 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.
ISSN: 0925-2312
eISSN: 1872-8286
IDS Number: ED3VU
Unique ID: WOS:000388777400046
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