## 4 Analysis of Results

Table 7: Optimized Hyperparameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Location Category** & **Optimization Algorithm** & **Learning Algorithm** & **Executing Time (in seconds)** \\ \hline \multirow{6}{*}{\begin{tabular}{c} Energy \\ Instructions \\ \end{tabular} } & \multirow{2}{*}{Grid} & RF & 546-25 \\ \cline{3-5}  & & & MCB & 291-22 \\ \cline{3-5}  & & RF & 6.68 \\ \cline{3-5}  & & & MCB & 4.50 \\ \cline{3-5}  & & RF & 100-45 \\ \cline{3-5}  & & RF & 27.08 \\ \hline \multirow{6}{*}{\begin{tabular}{c} Retail \\ Business \\ \end{tabular} } & \multirow{2}{*}{Grid} & RF & 542.43 \\ \cline{3-5}  & & MCB & 345.77 \\ \cline{3-5}  & & RF & 8.33 \\ \cline{3-5}  & & MCB & 5.82 \\ \cline{3-5}  & & RF & 143.21 \\ \cline{3-5}  & & MCB & 18.48 \\ \hline \multirow{6}{*}{\begin{tabular}{c} Service \\ Industry \\ \end{tabular} } & \multirow{2}{*}{Grid} & RF & 551.15 \\ \cline{3-5}  & & MCB & 188.99 \\ \cline{3-5}  & & RF & 9.06 \\ \cline{3-5}  & & MCB & 0.91 \\ \cline{3-5}  & & RF & 135.79 \\ \cline{3-5}  & & MCB & 18.23 \\ \hline \multirow{6}{*}{\begin{tabular}{c} Retaurant \\ \end{tabular} } & \multirow{2}{*}{Grid} & RF & 57.68 \\ \cline{3-5}  & & MCB & 37.68 \\ \cline{3-5}  & & MCB & 297.58 \\ \cline{3-5}  & & RF & 5.39 \\ \cline{3-5}  & & MCB & 4.92 \\ \cline{3-5}  & & RF & 142.56 \\ \cline{3-5}  & & MCB & 23.84 \\ \hline \multirow{6}{*}{\begin{tabular}{c} Pub \\ \end{tabular} } & \multirow{2}{*}{Grid} & RF & 62.4 \\ \cline{3-5}  & & MCB & 4.85 \\ \cline{3-5}  & & RF & 299.76 \\ \cline{3-5}  & & MCB & 31.11 \\ \cline{3-5}  & & GF & 542.57 \\ \cline{3-5}  & & MCB & 32.10 \\ \cline{3-5}  & & RF & 3.53 \\ \cline{3-5}  & & MCB & 1.83 \\ \cline{3-5}  & & RF & 148.80 \\ \cline{3-5}  & & MCB & 13.14 \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} Twitter and \\ Content Hall \\ \end{tabular} } & \multirow{2}{*}{Grid} & RF & 50.68 \\ \cline{3-5}  & & MCB & 211.75 \\ \cline{3-5}  & & RF & 7.57 \\ \cline{3-5}  & & MCB & 5.22 \\ \cline{3-5}  & & RF & 152.43 \\ \cline{3-5}  & & MCB & 30.92 \\ \cline{3-5}  & & RF & 571.96 \\ \cline{3-5}  & & MCB & 122.84 \\ \cline{3-5}  & & RF & 7.88 \\ \cline{3-5}  & & MCB & 1.59 \\ \cline{3-5}  & & RF & 176.90 \\ \cline{3-5}  & & MCB & 36.61 \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Hospital \\ Hospital \\ \end{tabular} } & \multirow{2}{*}{Grid} & RF & 326.95 \\ \cline{3-5}  & & MCB & 181.21 \\ \cline{3-5}  & & RF & 5.54 \\ \cline{3-5}  & & MCB & 4.79 \\ \cline{3-5}  & & RF & 169.42 \\ \cline{3-5}  & & MCB & 29.93 \\ \cline{3-5}  & & RF & 580.27 \\ \cline{3-5}  & & MCB & 260.24 \\ \cline{3-5}  & & RF & 7.54 \\ \cline{3-5}  & & RF & 4.88 \\ \cline{3-5}  & & RF & 161.72 \\ \cline{3-5}  & & MCB & 21.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Hyperparameter Optimization Execution Time. Figure 4: Execution Time Graph of Random Forest. Figure 5: Execution Time Graph of XGBoost. The total representation of accuracy of three different models can be found in Tables 9-11. Figures 1-3 aforementioned shows the accuracy of each experimental condition. For the Stacking, hyperparameter optimization cannot be made due to the nature of Stacking, meta learning. Once hyperparameter optimization is applied to Stacking, every model in level 1 of Stacking must contain hyperparameter optimization which will result in drastic increase of execution time. Actually, Stacking with feature selection shows the prediction accuracy as high as RF and XGB with hyperparameter optimization. Figure 6 shows prediction accuracy for each location category. The cases of foreign institutions and hospital are somewhat incredible due to low accuracy, and due to smaller number of raw data. For other location categories, prediction accuracy is in the range of 50% to 80%. As aforementioned, too various subcategories shows somewhat low accuracy while ramification of subcategories could lead to higher prediction accuracy. For instance, distinct location categories such as restaurant, pub, beverage store, theater and concert hall show high accuracy.