An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control

A data-driven energy-saving control strategy applied to balance the multi-zone demand controlled ventilation system is presented. The proposed strategy consists of two steps: system model construction and air balancing control. Based on observed datasets, a multi-layer perceptron structure is employ...

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Main Authors: Jing, Gang, Cai, Wenjian, Zhang, Xin, Cui, Can, Liu, Hongwu, Wang, Cheng
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/155509
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1555092022-03-03T07:41:42Z An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control Jing, Gang Cai, Wenjian Zhang, Xin Cui, Can Liu, Hongwu Wang, Cheng School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Ventilation System Air Balance A data-driven energy-saving control strategy applied to balance the multi-zone demand controlled ventilation system is presented. The proposed strategy consists of two steps: system model construction and air balancing control. Based on observed datasets, a multi-layer perceptron structure is employed to model the multi-zone ventilation system. The model is used to predict the pressure differences of each damper based on the static pressure of the main duct and the desired airflow rates of each damper. Air balancing control approach is implemented based on the empirical formula of the damper. This approach is use to predict the operating positions of each damper based on the predicted pressure differences of the developed model. An experimental apparatus consisting of original components of ventilation system is set up to collect the training and testing data, and simultaneously used to validate the performance of the proposed control strategy. Experimental results demonstrate that the issue of over-ventilation and under-ventilation of demand controlled ventilation system is eliminated, and energy savings of fan power can be obtained with the proposed control strategy. Building and Construction Authority (BCA) National Research Foundation (NRF) This work was partially funded by National Research Foundation of Singapore (NRF2014EWT-EIRP003-014, NRF2011 NRF-CRP001- 090), Building and Construction Authority (BCA) project (94.23.1.3), A Project of Shandong Province Higher Educational Science and Technology Program (J17KA073) and Focus on Research and Development Plan in Shandong Province (2018GGX105011, 2019GGX101055). 2022-03-03T07:41:42Z 2022-03-03T07:41:42Z 2020 Journal Article Jing, G., Cai, W., Zhang, X., Cui, C., Liu, H. & Wang, C. (2020). An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control. Energy, 199, 117328-. https://dx.doi.org/10.1016/j.energy.2020.117328 0360-5442 https://hdl.handle.net/10356/155509 10.1016/j.energy.2020.117328 2-s2.0-85082177340 199 117328 en NRF2014EWT-EIRP003-014 NRF2011 NRF-CRP001-090 94.23.1.3 Energy © 2020 Elsevier Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Ventilation System
Air Balance
spellingShingle Engineering::Electrical and electronic engineering
Ventilation System
Air Balance
Jing, Gang
Cai, Wenjian
Zhang, Xin
Cui, Can
Liu, Hongwu
Wang, Cheng
An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control
description A data-driven energy-saving control strategy applied to balance the multi-zone demand controlled ventilation system is presented. The proposed strategy consists of two steps: system model construction and air balancing control. Based on observed datasets, a multi-layer perceptron structure is employed to model the multi-zone ventilation system. The model is used to predict the pressure differences of each damper based on the static pressure of the main duct and the desired airflow rates of each damper. Air balancing control approach is implemented based on the empirical formula of the damper. This approach is use to predict the operating positions of each damper based on the predicted pressure differences of the developed model. An experimental apparatus consisting of original components of ventilation system is set up to collect the training and testing data, and simultaneously used to validate the performance of the proposed control strategy. Experimental results demonstrate that the issue of over-ventilation and under-ventilation of demand controlled ventilation system is eliminated, and energy savings of fan power can be obtained with the proposed control strategy.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Jing, Gang
Cai, Wenjian
Zhang, Xin
Cui, Can
Liu, Hongwu
Wang, Cheng
format Article
author Jing, Gang
Cai, Wenjian
Zhang, Xin
Cui, Can
Liu, Hongwu
Wang, Cheng
author_sort Jing, Gang
title An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control
title_short An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control
title_full An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control
title_fullStr An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control
title_full_unstemmed An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control
title_sort energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control
publishDate 2022
url https://hdl.handle.net/10356/155509
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