## 3 Methodology
### Accuracy Metrics
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{5}{c}{**Statistical Parameters**} \\ \cline{2-6}  & & **Means** & **Standard Deviation** & **Minimum** & **Maximum** \\ \hline \multirow{2}{*}{COP} & Training & 2.90 & 0.71 & 1.17 & 5.46 \\  & Testing & 2.76 & 0.55 & 1.22 & 3.77 \\ \multirow{2}{*}{Load side inlet temperature} & Training & 17.29 & 1.71 & 11.60 & 25.00 \\  & Testing & 17.30 & 1.75 & 12.10 & 24.20 \\ \multirow{2}{*}{Load side outlet temperature} & Training & 16.02 & 2.12 & 9.00 & 24.40 \\  & Testing & 15.99 & 2.16 & 9.10 & 23.60 \\ \multirow{2}{*}{Source side inlet temperature} & Training & 33.52 & 3.81 & 24.20 & 43.50 \\  & Testing & 33.56 & 3.74 & 24.60 & 43.00 \\ \multirow{2}{*}{Source side outlet temperature} & Training & 37.52 & 3.81 & 28.20 & 47.50 \\  & Testing & 37.56 & 3.74 & 28.60 & 47.00 \\ \multirow{2}{*}{Outside temperature} & Training & 25.43 & 2.21 & 22.90 & 33.70 \\  & Testing & 2530 & 2.22 & 23.00 & 33.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Statistical analysis of ANN model. The performance of the initial prediction model was evaluated based on the coefficient of determination, CvRMSE, and calculation time by comparing the predicted heat pump performance coefficient and the measured heat pump performance coefficient by inputting the test data into the developed prediction model. Table 15 presents the performance evaluation result, and Table 16 lists the calculation time analysis result. The ANN model satisfied the decision coefficient criterion exceeding 0.8 and the CvRMSE criterion. For the SVM, the CvRMSE was 15.9, which satisfied the criterion, but the coefficient of determination did not. The prediction accuracy of the initial model was 17.8% for the ANN model and 15.9% for the SVM, showing that the SVM was higher. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & \multicolumn{2}{c}{**Contents**} \\ \hline Type & Eps-regression \\ Kernel & Polynomial \\ Epsilon & 0.1 \\ Cost & 1 \\ Gamma & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Performance analysis of initial ANN model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{4}{c}{**Accuracy**} \\ \cline{2-5}  & \(\mathbf{R^{2}}\) & **CvRMSE** & **Error** \\ \hline ANN & 0.89 & 17.8 & \(-\)12–11 \\ SVM & 0.65 & 15.9 & \(-\)14–16 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Performance analysis of initial ANN model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{4}{c}{**Statistical Parameters**} \\ \cline{3-6}  & & **Means** & **Standard Deviation** & **Minimum** & **Maximum** \\ \hline COP & Training & 2.90 & 0.71 & 1.17 & 5.46 \\  & Testing & 2.76 & 0.55 & 1.22 & 3.77 \\ Load side inlet temperature & Training & 17.29 & 1.71 & 11.60 & 25.00 \\  & Testing & 17.30 & 1.75 & 12.10 & 24.20 \\ Load side outlet temperature & Training & 16.02 & 2.12 & 9.00 & 24.40 \\  & Testing & 15.99 & 2.16 & 9.10 & 23.60 \\ Source side inlet temperature & Training & 33.52 & 3.81 & 24.20 & 43.50 \\  & Testing & 33.56 & 3.74 & 24.60 & 43.00 \\ Source side outlet temperature & Training & 37.52 & 3.81 & 28.20 & 47.50 \\  & Testing & 37.56 & 3.74 & 28.60 & 47.00 \\ Outside temperature & Training & 25.43 & 2.21 & 22.90 & 33.70 \\  & Testing & 2530 & 2.22 & 23.00 & 33.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Structure of initial ANN model. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Category** & \multicolumn{2}{c}{**Contents**} \\ \hline  & Activation & Sigmoid \\  & Loss & Mean squared error \\ Function & Optimization algorithm & Adam \\  & Epoch & 5000 \\  & Input layer & Number of layer & 1 \\  & Number of neuron & 5 \\ Structure & Hidden layer & Number of layer & 1 \\  & Number of neuron & 2 \\  & Output layer & Number of layer & 1 \\  & Number of neuron & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Structure of initial SVM model.