Energy diagnosis of variable refrigerant flow (VRF) systems: Data mining technique and statistical quality control approach

Abstract

The variable refrigerant flow (VRF) system has changeable energy performance due to shifty meteorological conditions and unstable internal factors (e.g. complex control strategies, various part load ratios). It is difficult to determine the causes of drastic energy consumption variations are normal factors or faults. This study therefore proposed an energy diagnosis method for VRF systems based on data mining techniques and statistical quality control approaches. The correlation analysis is employed to select key factors and the DBSCAN method is used to remove the transient data and outliers. Besides, the system power consumption is predicted using the SVR algorithm. The EWMA control chart is used to diagnosis the system energy performance and it is comparatively analyzed with the x¯-R and CUSUM control charts. The reliability of the proposed method is verified by diagnosing the system energy in refrigerant undercharge and overcharge conditions, respectively. Results show that the SVR method is reliable to predict the system energy consumption with a R2 value of 0.9974. In addition, the EWMA control chart can significantly improve the VRF energy diagnosis performance compared to the x¯-R and CUSUM control charts. It achieves high correctly diagnosis ratios at both refrigerant undercharge and overcharge conditions.