# Energy Analysis in Green Building via Machine Learning: A Case Study in a Hospital
## 3 Results and Discussion
The values were plotted in order to better observe the similarity between the results with vanilla LSTM and the actual values. Figure 3 shows the forecasting results of the vanilla LSTM model. In this figure, the TPF change between day and night hours can be clearly seen. At the same time, since the data are very frequent, the actual and estimated lines are mixed together. But this confusion is also an indicator of how close the prediction is to reality. Figure 4 shows the same results for a slightly narrower time period. In this figure, the closeness between the predicted and actual values of TPF value can be observed. In this study, the error rate was calculated as 0.00049273 with the MAE method. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Actual Values** & **Vanilla** & **Stacked** & **Bidirectional** & **CNN** & **Encoder-Decoder** \\ \hline
0.989418 & 0.989703 & 0.989936 & 0.989856 & 0.989017 & 0.989758 \\ \hline
0.989418 & 0.989789 & 0.989983 & 0.989926 & 0.989048 & 0.989863 \\ \hline
0.989418 & 0.989888 & 0.990264 & 0.989917 & 0.989163 & 0.989981 \\ \hline
0.992353 & 0.992545 & 0.992633 & 0.992589 & 0.992106 & 0.992625 \\ \hline
0.992353 & 0.992535 & 0.992621 & 0.992652 & 0.992058 & 0.992611 \\ \hline
0.992353 & 0.992521 & 0.992608 & 0.992634 & 0.992073 & 0.992598 \\ \hline \end{tabular}
\end{table} 
Table 4: Actual and predicted values. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Error Rate** & **Vanilla LSTM** & **Stacked LSTM** & \ \begin{tabular}{c} **Bidirectional** \\ **LSTM** \\ \end{tabular}  & **CNN-LSTM** & \begin{tabular}{c} **Encoder-** \\ **Decoder LSTM** \\ \end{tabular}  \\ \hline MAE & 0.00049273 & 0.00050961 & 0.00058058 & 0.0005544 & 0.00054516 \\ \hline MSE & 0.00000045 & 0.00000047 & 0.00000052 & 0.00000048 & 0.00000049 \\ \hline RMSE & 0.00067232 & 0.000685565 & 0.00072111 & 0.00069282 & 0.00070089 Figure 3: Forecasting results by using vanilla LSTM. Figure 2: Error rates for different methods of LSTM. Based on previous studies, it was deemed appropriate to compare LSTM models for the green building energy analysis process in this article. Additionally, the best LSTM model detected in this study, vanilla LSTM, was compared with other machine learning models. In this comparison, frequently used methods, SVM, Random Forests models, and the vanilla LSTM model were compared. Error rates showing the difference between the real value and the estimated value were calculated. The relevant comparison table can be seen in Table 6. Calculations were made using the MAE error rate method. Since the building studied in the article is a green building and has many optimization systems, low error rates were calculated for each model, as expected. However, as seen in Table 6, when the relevant data are analyzed with the specified methods, the lowest error rate is still in vanilla LSTM. The LSTM model is frequently used in time series forecasting. It stands out in detecting long-term time dependencies and performs well with consecutive data. The SVM model is generally suitable for small-to-medium-sized data sets. As data sets become smaller, the accuracy rate increases. However, as the data titles increase, the error rate increases. The Random Forests model is generally used in regression operations. Despite the advantage of being able to work even in cases of missing and incorrect data, the error rate increases like SVM when the data titles increase. In this study, a very high-scale data set was used. A data set consisting of 779,280 rows and 102 columns was used in this study, and as can \begin{table}
\begin{tabular}{c c c c} \hline
**Error Rate** & **Vanilla LSTM** & **SVM** & **Random Forests** \\ \hline MAE & 0.00049273 & 0.00126247 & 0.00389523 \\ \hline \end{tabular}
\end{table} 
Table 6: MAE error rates for Vanilla LSTM, SVM, and Random Forests methods. Figure 4: Forecasting results by using vanilla LSTM. (limited time period). be seen from the results, the LSTM model obtained more efficient results.