Variable refrigerant flow (VRF) air-conditioning systems are widely used in various public buildings. If a fault occurs

it will result in reducing comfort and increasing energy consumption. The refrigerant charging level is an important parameter affecting the efficient operation of the air-conditioning system. In this paper

a fault diagnosis model based on Boosting integrated algorithm is proposed by taking refrigerant charge fault as the research object. Five basic classifiers

such as logistic regression

decision tree

random forest

support vector machine and BP neural network

are integrated. Chi-square test was used for feature selection

and the diagnostic model was established with experimental data for cooling and heating modes. The results show that the Boosting-based integrated model can efficiently detect the fault of VRF refrigerant charge

and the accuracy rate of the model is up to 96.8%. Compared with the traditional fault detection method

the proposed model greatly improves the response speed

accuracy and practicability of the diagnostic model.