# Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM
## 4 Experiments
In addition, this study also reports the training time and predicting time of the experimental methods for minutely dataset in the last two columns in Table 3. The training time of the proposed method (3950 s) is double that of CNN-LSTM (2070 s) because EECP-CBL analyzes both directions including forward and backward directions in the sequence. In the other side, EECP-CBL reduced nearly 30% of the predicting time compared with CNN-LSTM. Note that in machine learning, the predicting time is more important than the training time. Trained model needs to be trained once and stored it for many predicting later. Next, the performance results for hourly dataset are introduced in Table 4. For MSE and RMSE, our approach is slightly better than Linear Regression, LSTM and CNN-LSTM. For details, our approach got 0.298 and 0.546 of MSE and RMSE while CNN-LSTM, LSTM and Linear Regression obtain (0.355, 0.596), (0.515, 0.717) and (0.425, 0.652) of MSE and RMSE respectively. For MAE, our approach just \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \#No & **Model** & **MSE** & **RMSE** & **MAE** & **MAPE** & **Training** & **Predicting** \\  & & & & & & **Time (s)** & **Time (s)** \\ \hline \multirow{2}{*}{\(1\)} & Linear & \multirow{2}{*}{0.405} & \multirow{2}{*}{0.636} & \multirow{2}{*}{0.418} & \multirow{2}{*}{74.52} & \multirow{2}{*}{1028} & \multirow{2}{*}{37.48} \\  & Regression & & & & & & \\ \multirow{2}{*}{\(2\)} & LSTM & 0.748 & 0.865 & 0.628 & 51.45 & 6880 & 114.26 \\  & CNN-LSTM & 0.374 & 0.611 & 0.349 & 34.84 & 2070 & 62.99 \\  & EECP-CBL & 0.051 & 0.225 & 0.098 & 11.66 & 3950 & 43.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performances of the experimental methods for minutely dataset. stands in second place with 0.392 while the first place is CNN-LSTM with 0.332. The gap of the first and second approach in terms of MAE is quite small. In addition, our approach stands the third place after CNN-LSTM (the first place) and LSTM (the second place) in terms of MAPE. Therefore, our approach is best approach in terms of MSE and RMSE while CNN-LSTM archived the best values in terms of MAE and MAPE for hourly dataset. In terms of processing time, our method increases 50% in training time and decrease 20% in predicting time for hourly dataset compared with CNN-LSTM model. The proposed method also achieves the best results for daily dataset which are shown in Table 5. For MSE, our approach improves nearly 50% (0.065) compared with CNN-LSTM (0.104). Meanwhile, LSTM and Linear Regression obtained the high values in MSE at 0.241 and 0.253 respectively for daily dataset. In addition, EECP-CBL also reaches the best values of RMSE, MAE and MAPE with 0.255, 0.191 and 19.15 respectively. These values are significantly better than other methods for daily dataset. Therefore, EECP-CBL is the best method for electric energy consumption prediction in medium-term timespans. Moreover, the training time of EECP-CBL for daily dataset is 61.36 s while that of CNN-LSTM is 42.35 s. Similar to the previous dataset, the proposed approach only requires 0.71 s for predicting time that is only 37% of the predicting time required by CNN-LSTM model. Finally, Table 6 shows the performances of the experimental methods for weekly dataset. Similar to daily dataset, the proposed method improves nearly 50% for MSE and 30% for RMSE, MAE and MAPE compared with the second place (CNN-LSTM). In the meantime, the results of LSTM and Linear Regression are not good in terms of all the performance metrics for weekly dataset. Therefore, our approach is the best method for electric energy consumption prediction in long-term timespans. The training time and predicting time in the last experiment are the same with the previous experiments. The training times of the proposed approach and CNN-LSTM are 20.7 s and 14.12 s respectively. However, the predicting time of our approach is only 20% of predicting time required by CNN-LSTM model.