An air balancing method using support vector machine for a ventilation system

作者：Jing, Gang; Cai, Wenjian^*; Chen, Haoran; Zhai, Deqing; Cui, Can; Yin, Xiaohong

来源：Building and Environment, 2018, 143: 487-495.

DOI：10.1016/j.buildenv.2018.07.037

• Ventilation system
• Air balancing
• Support vector machine (SVM)
• Machine learning
• Model-based method
• Parameter identification
• Optimization
• Experiment

In this paper, support vector machine method is introduced to solve air balancing problem for ventilation systems and a novel non-iterative air balancing method is presented. The proposed method provides a well-formulated form of air balancing problem for air ventilation system by i) building a mathematical model for duct system based on steady-state pressure balance; ii) constructing a pressure prediction model based on the system model and theory of support vector machine; iii) identifying the significant model parameters by support vector machine regression supervised machine lsseaming; and iv) determining the damper positions based on the formulated model. The performance of the method is validated experimentally in a duct testbed with five terminals. The results demonstrate that the method can achieve the desired values within 4.6% relative error and is a promising approach for air balancing.

• 出版日期2018-10-1
• 单位南阳理工学院; 青岛科技大学; 山东交通学院