An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system. (1st April 2019)

• Record Type: Journal Article
• Authors: Jing, Gang; Cai, Wenjian; Zhang, Xin; Cui, Can; Yin, Xiaohong; Xian, Huacai
• Is Part Of: Energy. Volume 172(2019)
• Page Start: 1053
• Page End: 1065
• Publication Date: 2019-04-01
• Subjects: Ventilation -- Air balancing -- Energy saving -- Model based -- Demand-controlled ventilation -- Experimental assessment; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.02.044
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: For addressing the energy waste resulted by over-ventilation or under-ventilation in conventional demand-controlled ventilation system, an air balancing strategy is proposed to solve the over-ventilation and under-ventilation problems of the multi-zone demand-controlled ventilation system. In this study, an energy-saving oriented mathematical model is constructed to simulate the non-linear behavior of the multi-zone ventilation system and Bayesian linear regression supervised machine learning algorithm is used to estimate the unknown parameters of the constructed model. On the basis of the developed model, the damper control method is established to determine the position of the damper according to the desired airflow rate to ensure the system well-balanced. Therefore, with the constructed system model and the damper control method, the system can be well-balanced to overcome the disadvantages of over-ventilation and under-ventilation, and consumes less energy compared to the system that are not balanced. The performance of the proposed air balancing strategy for demand-controlled ventilation system is practically tested in an experimental rig with five terminals and validated by comparing to the demand-controlled ventilation strategy without air balancing. The experimental results demonstrate that the proposed strategy achieved the desired airflow rate within 4.6% maximum absolute percentage error, and also achieved a maximum value 14.3% for fan power reduction compared to conventional the strategy without air balancing. Highlights: A model is built to simulate the non-linear relations of the ventilation system. Bayesian linear regression algorithm is used to train the developed model. Damper positon control method is developed to obtain the damper operating angle.