## 2 Methodology
### Accuracy Analysis  
The proposed refrigeration cycle measurement method was evaluated by measuring the difference between pressure and enthalpy according to the measured data. In addition, the reliability of the proposed refrigeration cycle measurement method was checked by comparing the difference between the two points and the maximum error range of the pressure and enthalpy measured values. The pressure of the refrigeration cycle in the pressure-enthalpy diagram was approximately 0 to 4 MPa, and the enthalpy was approximately 150 to 450 kJ/kg. Preprocessing was performed to evaluate the comparative analysis of pressure and enthalpy between the two points. The data were preprocessed using normalization. Normalization converts the data intervals into a range of 0 to 1, so the variables in different ranges have the same importance. The formula for data normalization is shown in Equation (12).  
\[X_{normal}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{12}\]  
where \(X_{normal}\) is the normalized values, \(X\) is the measurement values, \(X_{min}\) is the minimum of measurement values, \(X_{max}\) is the maximum of measurement values.  
The statistics, data mining, and machine learning commonly use distance functions to quantify the similarity between data. The most common distance function is the Euclidean distance function. The closer the value of the distance function is to zero, the more similar the data used in the analysis are to each other, and it can be defined as Equation (13).  
\[d(h_{i},P_{i})=\sqrt{\sum_{i=1}^{d}(h_{i}-P_{i})^{2}} \tag{13}\]  
where \(d(h,P)\) is the distance between two points \(h\) and \(P\), \(h\) is the enthalpy of refrigeration cycle in kJ/kg, and \(P\) is the pressure of refrigeration cycle in Pa.  
The evaluation was performed using the Mean Bias Error (\(MBE\)) and the Coefficient of variation of root-mean-square error (\(Cv(RMSE)\)) proposed by ASHRAE Guideline 14. \(MBE\) and \(Cv(RMSE)\) can be calculated using Equations (14)-(16) [31].  
\[MBE=\frac{\sum_{i=1}^{n}(P_{i}-M_{i})}{n} \tag{14}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(P_{i}-M_{i})^{2}}{n}} \tag{15}\]  
\[Cv(RMSE)=\frac{RMSE}{\overline{M}}\times 100 \tag{16}\]where \(P\) is the predicted values, \(M\) is the measurement values, \(n\) is the number of data, \(\overline{M}\) is the average of measurements.  
Standard uncertainty and synthetic standard uncertainty analyses were performed to verify the reliability of the experimental data and computational results. When a measurement result is obtained from several different inputs, the standard uncertainty of this measurement result is called the synthetic standard uncertainty, and the synthesis is obtained using the law of propagation of uncertainty Equation (17) [32].  
\[u_{c}^{2}(y)=\sum_{i=1}^{N}\left(\frac{\partial f}{\partial T}\right)^{2} \delta^{2}(T)+\sum_{j=1}^{M}\left(\frac{\partial f}{\partial\varphi}\right)^{ 2}\delta^{2}(\varphi)+\sum_{k=1}^{L}\left(\frac{\partial f}{\partial q}\right) ^{2}\delta^{2}(q) \tag{17}\]  
where \(u_{c}(y)\) is the combined standard uncertainty, \(f\) is the function, \(T\) is the measured temperature, \(\varphi\) is the: measured relative humidity, \(q\) is the measured airflow rate, \(\delta(T)\) is the Uncertainty of the measured temperature, \(\delta(\varphi)\) is the Uncertainty of the measured airflow rate.