Machine learning based diagnosis strategy for refrigerant charge amount malfunction of variable refrigerant flow system

Malfunctions would occur in a variable refrigerant flow (VRF) system after years of operation or inappropriate maintenance, thus causing unnecessary energy waste and even occupant discomfort. This study presents a machine learning based malfunction diagnosis strategy that combines the recursive feature elimination algorithm (RFE) and the classification algorithms for the typical malfunctions of VRF system. RFE based on Random Forest (RF) model firstly serves as the feature selection process to evaluate variables importance, thus acquiring the key variables related to malfunction. Then five kinds of machine learning classification models are trained using the chosen key variables to diagnosis refrigerant leakage malfunction. By comparison, the AdaBoost.M1 (ABM) model shows the most desirable performance on the all nine malfunction severity levels. The results show that the RFR-RF based feature selection method can select the most six critical variables and the ABM model established based on the six variables achieves admirable diagnostic accuracy and AUC value for faults corresponding to nine severity levels.