A novel data-driven air balancing method with energy-saving constraint strategy to minimize the energy consumption of ventilation system

Fanyong Cheng, Can Cui, Wenjian Cai, Xin Zhang, Yuan Ge and Bingxu Li

Energy, 2022, vol. 239, issue PB

Abstract: Air balancing is a key technology to reduce energy consumption of ventilation system and improve the quality of indoor living environment. So far, most of the existing data-driven non-iterative air balancing methods only focus on the prediction of terminal damper angle to supply appropriate airflow, but they do not pay attention to the energy-saving constraint of fan voltage and terminal damper. Therefore, their energy efficiencies are not high enough. In this paper, energy-saving constraint strategy of low fan voltage and small damper friction resistance is considered and a novel data-driven non-iterative air balancing model with energy-saving constraint strategy is proposed. The model parameters can be trained by the proposed optimization algorithm inputting acquisition data. Then, given a design airflow rate, the required fan voltage and terminal damper angle can be predicted by the trained model to achieve accurate air balancing control with high energy efficiency. The performance validation of the proposed method is executed on our experimental duct system with five terminals. Compared with the current air balancing method, the proposed method can improve energy saving potential up to 13.7%, while keeping accurate air balancing within 10% relative error standard.

Keywords: Ventilation system; Air balancing; Energy-efficiency; Energy-saving constraint strategy; Machine learning (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:239:y:2022:i:pb:s036054422102394x

DOI: 10.1016/j.energy.2021.122146

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