Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7048799 | Applied Thermal Engineering | 2016 | 12 Pages |
Abstract
A proper refrigerant charge amount (RCA) prediction algorithm is important to air conditioning systems. In variable refrigerant flow (VRF) systems, the traditional virtual refrigerant charge (VRC) sensor models perform well at undercharge situations but produce large prediction errors at overcharge situations. When the refrigerant charge level (RCL) is over 90%, the correlation coefficient data-based method was introduced to select the additional input variables to modify the VRC models. Two data-based algorithms, multiple linear regression (MLR) and non-linear support vector regression (SVR), were used to improve the prediction performance. The prediction performance of the pure SVR model was also compared. Results reveal that the overall prediction errors for SVR based modified VRC model (SVR-VRC) is 5.53%, the minimum among the four models. The SVR-VRC model improves the VRC models and extends the application in the VRF system when only the system self-provided sensor measurements are used.
Keywords
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Fluid Flow and Transfer Processes
Authors
Guannan Li, Yunpeng Hu, Huanxin Chen, Limei Shen, Haorong Li, Jiong Li, Wenju Hu,