赵天怡

个人信息Personal Information

副教授

博士生导师

硕士生导师

主要任职:Deputy Director, Department of Civil Engineering, Dalian University of Technology

其他任职:APEC Energy Working Group Member, Member of APEC Expert Group on Energy Efficiency and Conservation (EGEE&C)

性别:男

毕业院校:哈尔滨工业大学

学位:博士

所在单位:土木工程系

学科:供热、供燃气、通风及空调工程

办公地点:厚兴楼417房间

联系方式:0411-84707734

电子邮箱:zhaotianyi@dlut.edu.cn

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Predication control for indoor temperature time-delay using Elman neural network in variable air volume system

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论文类型:期刊论文

发表时间:2017-11-01

发表刊物:ENERGY AND BUILDINGS

收录刊物:Scopus、SCIE、EI

卷号:154

页面范围:545-552

ISSN号:0378-7788

关键字:Prediction control; VAV terminal; Temperature time-delay; Elmanneural network

摘要:Aiming at the prediction control for indoor temperature time-delay in variable air volume (VAV) air conditioning system, this paper presents an indoor temperature prediction control method based on Elman neural network multi-step prediction model. Firstly, this paper introduces basic control principles of pressure-dependent and pressure-independent VAV terminal through comparable analysis and points out significance of indoor temperature prediction control based on pressure-dependent VAV terminal. Then, Elman neural network multi-step prediction model and corresponding indoor temperature prediction control method for pressure-dependent VAV terminal are proposed based on the fundamental principle of periodic prediction control for time-delay system. Finally, the effect of proposed prediction control method is validated by the experimental study according to the test data of supply air volume regulating process, provided that the supply air volume control loop adopts constant static pressure control method. Experimental results indicate the proposed indoor temperature prediction control method based on pressure-dependent VAV terminal could change the conventional regulating mode of the VAV air conditioning system, which will be benefit for improving the control stability of indoor temperature control loop and other corresponding control loops. (C) 2017 Published by Elsevier B.V.