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dc.contributor.authorJing, Gangen_US
dc.contributor.authorCai, Wenjianen_US
dc.contributor.authorZhang, Xinen_US
dc.contributor.authorCui, Canen_US
dc.contributor.authorLiu, Hongwuen_US
dc.contributor.authorWang, Chengen_US
dc.identifier.citationJing, 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-.
dc.description.abstractA 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.en_US
dc.description.sponsorshipBuilding and Construction Authority (BCA)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.relationNRF2011 NRF-CRP001-090en_US
dc.rights© 2020 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleAn energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing controlen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.subject.keywordsVentilation Systemen_US
dc.subject.keywordsAir Balanceen_US
dc.description.acknowledgementThis work was partially funded by National Research Foundation of Singapore (NRF2014EWT-EIRP003-014, NRF2011 NRF-CRP001- 090), Building and Construction Authority (BCA) project (, A Project of Shandong Province Higher Educational Science and Technology Program (J17KA073) and Focus on Research and Development Plan in Shandong Province (2018GGX105011, 2019GGX101055).en_US
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