# Electric Load Forecasting Based on Deep Ensemble Learning
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Business** & **Big Industry** & **Non-General Industry** & **General Industry** \\ \hline Maximum temperature & \(-\)0.00751 & 0.0279 & 0.0978 & 0.0835 \\  & (0.0756) & (0.0746) & (0.0793) & (0.0889) \\ \hline Minimum temperature & \(-\)0.0748 & \(-\)0.106 & \(-\)0.233 ** & \(-\)0.345 *** \\  & (0.0850) & (0.0806) & (0.0921) & (0.108) \\ \hline Daytime wind direction & 0.0958 & \(-\)0.0900 & 0.0848 & 0.0584 \\  & (0.153) & (0.115) & (0.154) & (0.158) \\ \hline Start\_weather & \(-\)0.107 ** & \(-\)0.00316 & 0.00849 & \(-\)0.00196 \\  & (0.0476) & (0.0495) & (0.0497) & (0.0477) \\ \hline End\_weather & 0.0216 & 0.0987 * & \(-\)0.0525 & \(-\)0.0663 \\  & (0.0482) & (0.0575) & (0.0497) & (0.0477) \\ \hline Constant & 4.927 *** & 3.950 *** & 5.129 *** & 8.500 *** \\  & (0.914) & (0.873) & (0.951) & (1.360) \\ \hline \hline \end{tabular} Standard errors in parentheses, *** \(p<\) 0.01, ** \(p<\) 0.05, * \(p<\) 0.1.  
\end{table} 
Table 2: Results of logistic regression of panel data for different industry load change. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Filters & 32 \\ Kernel\_size & 3 \\ Dilation\_rate & 2 \\ Batch\_size & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: DCC-EL model parameter settings. The proposed DCC-EL model's prediction results are compared to existing integrated learning methods. The performance of the model predictions is measured using error metrics, including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean fundamental percentage error (MAPE). The calculation methods for these four evaluation metrics are provided in Equations (38)-(41):  
\[\text{MSE}=\frac{1}{n}\underset{i=1}{\overset{n}{\underset{i=1}{\sum}}}(\hat{y }_{i}-y_{i})^{2} \tag{38}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\underset{i=1}{\sum}}} (\hat{y}_{i}-y_{i})^{2}} \tag{39}\]  
\[\text{MAE}=\frac{1}{n}\underset{i=1}{\overset{n}{\underset{i=1}{\sum}}}|\hat{ y}_{i}-y_{i}| \tag{40}\]  
\[\text{MAPE}=\frac{100\%}{n}\underset{i=1}{\overset{n}{\underset{i=1}{\sum}}} \frac{\left|\hat{y}_{i}-y_{i}\right.}{y_{i}}\left|\right. \tag{41}\]  
The normalized power load data were input into the DCC-EL framework for prediction, and the experimental results are illustrated in Figure 7. These charts display the power load prediction results for time intervals of ten days and three months, respectively. The horizontal axis indicates dates, while the vertical axis represents the normalized power load data. The red line depicts the predicted values derived from the DCC-EL framework, and the blue line showcases the actual power load values. For the ten-day prediction, the visual representation of the predicted values aligns closely with the actual values. However, in the three-month forecast, some variances are noticeable between the predicted and actual values. This discrepancy arises from the time sensitivity of the DCC-EL framework in mid-term load forecasting, which occasionally results in instability. Therefore, it necessitates periodic model adjustments to accommodate data fluctuations. \begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Active function & Relu \\ Learning-rate & 0.005 \\ Dropout & 0.2 \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 7: DCC-EL model future ten-day and three-month forecasting chart. Tables 4 and 5 present the error measurements for power load predictions across six models compared to our proposed framework for time intervals of ten days and three months, respectively. Notably, the DCC model represents the dilated causal convolutional model that has not undergone feature extraction or secondary integration by the ensemble model, explicitly used for ablation studies. The other five models, referenced in the related work section of this paper, serve as benchmarks for our control experiments.