## 4 Simulation and Experimental Analysis
### Method Comparison  
In order to show the high performance of the multi-layer stacked bidirectional LSTM neural network in short-term load forecasting, different methods that contain a BP neural network, ELM, traditional LSTM, and multi-layer stacked bidirectional LSTM model are discussed in this paper. The prediction results all the methods tested in this paper followed the same trend with the real load power shown in Figure 7. It can be seen that the multi-layer stacked bidirectional LSTM neural network will be more competitive, and the error comparison of those methods is shown in Table 3, where the MAPE, RMSE, and MAE index are calculated and compared for one day over 24 h. From Table 3, the average MAPE of the proposed method prediction model is 0.4137%; however, the average MAPE values of the BP, LSTM, and ELM models are 1.485%, 1.030%, and 0.77%, respectively. The average RMSE of the proposed method prediction model is 0.706, and those of the BP, LSTM, and ELM models are 2.95, 1.921, and 1.369, respectively. Different time interval errors are calculated between ELM, LSTM, BP, and the proposed method in Table 4. The two-hour interval forecasting results fluctuate in different evaluation indexes. However, the total evaluation indexes of the proposed method are at the minimum in one day, and the quantitate analysis forecasting errors are calculated and shown in Figure 8. Here, it can be seen that proposed method based on the multi-layer bidirectional LSTM prediction model better grasps the prediction sample information and has a more competitive forecasting performance. The multi-layer stacked bidirectional LSTM neural network model can retain the original characteristics of the load sequences and reduce \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bi-LSTM Layers** & 1 & 2 & 3 & 4 & 5 \\ \hline
**MAPE (\%)** & 0.51 & 0.465 & 0.405 & 0.41 & 0.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Error of different stacked layers of bidirectional LSTM. Figure 7: Load power of different forecasting methods. the data error by incorporating errors in unsupervised training, which can enhance the robustness of the predictive model. Different samples of training data will significantly affect the robustness of the load forecasting model. The load will be more accurate with a smaller sample in the training dataset. With 48 or 24 measurement points, the training dataset will be more random, which will increase the difficulty of the load prediction. In this paper, a sample dataset with 48 measurement points is used for the training of the proposed method to verify the robustness of the proposed method and compare it with other methods. Figure 9 shows the comparison of the load forecasting results with a half-hour training dataset; the proposed method is accurate enough to track the load profiles based on deep learning, which can extract the internal characteristics of the discrete sample load data and improve the robustness of the proposed method with the multi-layer bidirectional training mechanism. The MAPE of the proposed method is 2.39%, as shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{ \begin{tabular}{c} **Forecast** \\ **Time Interval** \\ \end{tabular}  } & \multicolumn{3}{c}{**BP [6]**} & \multicolumn{3}{c}{**LSTM [21]**} & \multicolumn{3}{c}{**ELM [8]**} & \multicolumn{3}{c}{**Proposed Method**} \\ \cline{2-13}  & **MAPE\%** & **RMSE** & **MAE** & **MAPE\%** & **RMSE** & **MAE** & **MAPE\%** & **RMSE** & **MAE** & **MAPE\%** & **RMSE** & **MAE** \\ \hline
0–2 h & 0.73 & 28.53 & 14.76 & 0.59 & 15.94 & 11.79 & 0.58 & 14.21 &  
Furthermore, in order to verify the generalization ability of the proposed method for the more complex environments on special days such as the weekend or a holiday, study cases are tested with the historical day based on different measurement points; these are shown in Figures 10 and 11.