A robust air balancing method for dedicated outdoor air system

Abstract

Air balancing is an important means to reduce energy consumption and improve thermal comfort and indoor air quality for dedicated outdoor air system (DOAS). It effectively distributes the required amount of air to each conditioned zone through dynamic regulation of terminal/branch dampers and fan speed. Up to now, most of the existing air balancing methods are modeled by quadratic models that greatly rely on the accuracy of the collected data and are therefore difficult to obtain satisfactory forecasting results. Noise, outlier and model approximation error are likely to cause large prediction errors in air balancing, leading to increased demand for new methods to overcome these problems. In this paper, a robust air balancing method based on data-driven model is proposed to produce a more accurate prediction of pressure difference. With precise prediction, the proposed method obtains a more accurate balancing result and is more robust against noise and outlier. The proposed method includes three parts: building mathematical model for duct system based on steady-state pressure balance; training model parameters by robust air balancing learning algorithm; estimating damper positions by the damper characteristic curve. The performance of the proposed method is validated on a laboratory duct test-bed with five terminals. The relative error can be controlled with 5.5% in the test cases. Compared with the existing air balancing methods, the proposed method is more accurate in prediction and more robust in the balancing results. The proposed method offers the possibility to obtain a superior balancing performance when sensor accuracy cannot be guaranteed in practical applications.