Abnormal energy identification of variable refrigerant flow air-conditioning systems based on data mining techniques

Identifying abnormal energy consumption in the variable refrigerant flow (VRF) system is important for its energy efficiency enhancement and energy saving. The energy performances of the VRF system are extremely distinct under various operation conditions. In order to evaluate the VRF's energy performance, this study proposes a data-mining-based method to identify the abnormal energy consumption under different operation conditions. The local outlier factor (LOF) algorithm is used to distinguish transient data and three operation conditions are partitioned through clustering analysis. Besides, the correlation analysis is employed for key variables extraction. Then, the energy consumption is forecasted by support vector regression (SVR). The residuals of the measured energy values and the predicted values are applied in support vector data description (SVDD) algorithm to develop the responding D statistic as abnormal energy identification threshold. Finally, experiment data of various refrigerant charge level (RCL) are used to validate the proposed method. The methodology is sensitive to identifying abnormal energy consumption in VRFs at various RCLs. Nearly 100% of the abnormal energy consumption data can be accurately identified at some refrigerant charge levels. Operation condition partitioning can definitely enhance the identification efficiency.