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- W2190458623 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." @default.
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- W2190458623 date "2016-01-01" @default.
- W2190458623 modified "2023-10-12" @default.
- W2190458623 title "Extending the virtual refrigerant charge sensor (VRC) for variable refrigerant flow (VRF) air conditioning system using data-based analysis methods" @default.
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- W2190458623 doi "https://doi.org/10.1016/j.applthermaleng.2015.10.050" @default.
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