A model-based air balancing method of a ventilation system

被引：30

作者：

Jing, Gang ^{[1
]}

Cai, Wenjian ^{[2
]}

Zhai, Deqing ^{[2
]}

Liu, Shuai ^{[3
]}

Cui, Can ^{[2
]}

机构：

[1] Shandong Jiaotong Univ, Sch Tansportat & Logist Engn, Jinan 250023, Shandong, Peoples R China

[2] Nanyang Technol Univ, EXQUISITUS, Ctr E City, Sch Elect & Elect Engn, 50 Nanyang Ave, Singapore 639798, Singapore

[3] Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Shandong, Peoples R China

来源：

ENERGY AND BUILDINGS | 2018年 / 174卷

基金：

新加坡国家研究基金会;

关键词：

Ventilation system; Air balancing; Model-based method; Parameter identification; Optimization; Experiment; PREDICTIVE CONTROL; ALGORITHMS;

D O I：

10.1016/j.enbuild.2018.06.044

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

In this paper we propose a novel and easily implementable model-based air balancing method for a ventilation system. The proposed method provides a well-defined form of balancing for a ventilation system, which can be effectively solved. The method follows three procedures: (i) mathematical modeling for duct system which is based on steady-state pressure balance; (ii) model parameters identification by supervised machine learning; (iii) determination of damper positions based on the model. The performance of this method is validated through testing in a duct testbed with five terminals. The results show that the final flow rates in all terminals match with the desired values with no more than 6.8% relative error. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：506 / 512

页数：7

共 32 条

[11] Lighted-weighted model predictive control for hybrid ventilation operation based on clusters of neural network models [J].

Chen, Jianli ;

Augenbroe, Godfried ;

Song, Xinyi .

AUTOMATION IN CONSTRUCTION, 2018, 89 :250-265

[12] Airflow measurements using averaging Pitot tube under restricted conditions [J].

Cui, Can ;

Cai, Wenjian ;

Chen, Haoran .

BUILDING AND ENVIRONMENT, 2018, 139 :17-26

[13] A review on time series forecasting techniques for building energy consumption [J].

Deb, Chirag ;

Zhang, Fan ;

Yang, Junjing ;

Lee, Siew Eang ;

Shah, Kwok Wei .

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 74 :902-924

[14]

F. ASHRAE, 2013, FUND HDB

[15] A goal method and a target method for balancing exhaust ventilation duct systems with dampers [J].

Guffey, Steven .

JOURNAL OF OCCUPATIONAL AND ENVIRONMENTAL HYGIENE, 2007, 4 (03) :224-235

[16]

Homod R.Z., 2013, Journal of Energy, V2013, DOI DOI 10.1155/2013/768632

[17] Artificial Neural Networks and Genetic Algorithms in Energy Applications in Buildings [J].

Kalogirou, Soteris A. .

ADVANCES IN BUILDING ENERGY RESEARCH, 2009, 3 (01) :83-119

[18] A data-driven approach for steam load prediction in buildings [J].

Kusiak, Andrew ;

Li, Mingyang ;

Zhang, Zijun .

APPLIED ENERGY, 2010, 87 (03) :925-933

[19] A novel and dynamic demand-controlled ventilation strategy for CO2 control and energy saving in buildings [J].

Lu, Tao ;

Lue, Xiaoshu ;

Viljanen, Martti .

ENERGY AND BUILDINGS, 2011, 43 (09) :2499-2508

[20] Energy efficient demand controlled ventilation in single family houses [J].

Nielsen, Toke Rammer ;

Drivsholm, Christian .

ENERGY AND BUILDINGS, 2010, 42 (11) :1995-1998

← 1 2 3 4 →