## 4 Results and Discussion  
### Performance of Prediction Model  
Table 8 shows the results of analyzing the developed performance prediction model with the aforementioned accuracy metric. Figure 5 shows the results of comparing the predicted performance with the actual performance using the ANN models. The ANN \begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Contents** \\ \hline Number of trees & 400 \\ Number of samples at each decision split & 6 \\ Minimum number of samples at leaf & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameter configuration of RF model. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Category** & **Contents** \\ \hline \multirow{4}{*}{Structure} & Input layer & Number of layers & 1 \\  & Number of neurons & 4 \\  & Hidden layer & Number of layers & 5 \\  & Number of neurons & 2 \\  & Output layer & Number of layers & 1 \\  & Number of neurons & 1 \\ Function & Activation & Sigmoid \\  & Optimization algorithm & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameter configuration of ANN model. \begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Contents** \\ \hline Number of trees & 400 \\ Number of samples at each decision split & 6 \\ Minimum number of samples at leaf & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Parameter configuration of KNN model. \begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Contents** \\ \hline Number of neighbors & 3 \\ Distance metric & Euclidean \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameter configuration of ANN model. \begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Contents** \\ \hline Number of trees & 400 \\ Number of samples at each decision split & 6 \\ Minimum number of samples at leaf & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameter configuration of SVM model. model showed a relative error of \(-7.8\)-\(9\), MBE \(-3.6\), CvRMSE 5.4, which satisfies the ASHRAE Guideline standard and showed excellent performance. The SVM model showed a relative error of \(-11\) to \(11\), and met the ASHRAE Guideline standard with MBE \(-5\) and CvRMSE 6. Figure 6 shows the results of comparing the predicted performance with the actual performance using the SVM models. Figure 7 shows the results of comparing the predicted performance with the actual performance using the RF models. The RF model showed an error of \(-14\) to \(16\), and the MBE \(-7.7\) and CvRMSE 6.9 showed performance satisfying the ASHRAE Guideline criteria. The SVM model showed an error of \(-12\) to \(12\), and satisfies the ASHRAE Guideline standard with MBE \(-8.3\) and CvRMSE 8.1. Figure 8 shows the results of comparing the predicted performance with the actual performance using the KNN models. The accuracy analysis result of each prediction model is shown in Table 5, and the error analysis result ANN is \(-7.8\)-\(9\), SVM is \(-11\)-\(11\), RF is \(-14\)-\(16\) and KNN is \(-12\)-\(12\). All machine-learning models satisfied the ASHRAE Guideline standard, but the performance prediction model using artificial neural networks showed the best performance. Figure 5: Comparison of predicted and measured performance using ANN model. Figure 6: Comparison of predicted and measured performance using SVM model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Output**} & \multirow{2}{*}{**Prediction Model**} & \multicolumn{3}{c}{**Accuracy [\%]**} \\ \cline{3-5}  & & **MBE** & **CvRMSE** & **Error** \\ \hline \multirow{4}{*}{Coefficient of Performance} & ANN & \(-3.6\) & \(5.4\) & \(-7.8\)-\(9\) \\  & SVM & \(-5.0\) & \(6.0\) & \(-11\)-\(11\) \\ \cline{1-1}  & RF & \(-7.7\) & \(6.9\) & \(-14\)-\(16\) \\ \cline{1-1}  & KNN & \(-8.3\) & \(8.1\) & \(-12\)-\(12\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy of prediction model. Table 9 shows the computation time required to build a machine-learning model. The computational speed of a machine-learning model depends on the number of data and the configuration of the model.