# Electric Load Forecasting Based on Deep Ensemble Learning
## 3 Results
Table 4 illustrates that the proposed method reduced RMSE by 4.96% and MAPE by 17.33% compared to the best results of the five models mentioned in the introduction. Table 5 illustrates that the proposed method realized a 12.31% reduction in MSE for three-month interval predictions, and the power load predictions using the DCC-EL framework generally outperform the five models. The analysis of the control experiment results is as follows:  
(1) Compared with [PERSON]'s model [10] in a control experiment, the DCC-EL model proposed in this study reduced the RMSE and MSE for predicting power loads over ten days and three months by 6.06% and 17.97%, respectively. The discrepancy in the experimental results might arise from the process of model integration, especially transitioning from Prophet to LSTM and then to BPNN. The handling and transformation of data during these stages could lead to loss or distortion of information. Such a loss can stem from various factors. For instance, each model may interpret data differently and assign varying weights to them. Minor inconsistencies might also emerge during the data conversion process between models. More critically, for such a model setup, if the outputs from the different models are not appropriately amalgamated or aligned, it could adversely impact the final prediction, especially when significant differences exist between these outputs. (2) Compared with [PERSON]'s model [11] in a control experiment, the DCC-EL model reduced the RMSE and MSE for predicting power loads over ten days and three months by 7.16% and 23.66%, respectively. The reason for the discrepancies in the experimental results may be that the combination of multiple models makes the debugging of the model more complicated. When there are issues with the model or its predictive performance is unsatisfactory, it is challenging to determine which part is problematic, increasing the difficulty of debugging. The complex relationships between the various models might also result in the model performing well on the training data but poorly on unseen data. Hence, this combination of models might elevate the risk of overfitting. (3) Compared with [PERSON]'s model [14] in a control experiment, the DCC-EL model reduced the RMSE and MSE for predicting power loads over ten days and three \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MSE** & **RMSE** & **MAE** & **MAPE** \\ \hline [PERSON] [10] & 0.0291 & 0.1707 & 0.1242 & 13.2469 \\ [PERSON] [11] & 0.033 & 0.1817 & 0.062 & 13.1549 \\ [PERSON] [14] & 0.0355 & 0.1884 & 0.07 & 14.6417 \\ [PERSON] [15] & 0.027 & 0.1643 & 0.054 & 11.0954 \\ [PERSON] [16] & 0.0255 & 0.1597 & 0.045 & 8.1457 \\ DCC & 0.0347 & 0.1863 & 0.0315 & 16.1546 \\ DCC-EL & **0.0121** & **0.1101** & **0.0206** & **7.9724** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy of electricity load forecast by models for the next ten days. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MSE** & **RMSE** & **MAE** & **MAPE** \\ \hline [PERSON] [10] & 0.7118 & 0.8443 & 0.6701 & 21.6826 \\ [PERSON] [11] & 0.7687 & 0.8763 & 0.6595 & 25.8729 \\ [PERSON] [14] & 0.7856 & 0.8862 & 0.7015 & 28.9389 \\ [PERSON] [15] & 0.689 & 0.8116 & 0.6073 & 24.6917 \\ [PERSON] [16] & 0.6552 & 0.8093 & 0.6073 & 20.1958 \\ DCC & 0.9482 & 0.9738 & 0.5494 & 28.1497 \\
**DCC-EL** & **0.5321** & **0.7295** & **0.3567** & **18.4589** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy of electricity load forecast by models for the next three months. months by 7.83% and 25.35%, respectively. The reasons for the discrepancies in the experimental results may be that while RF can assign importance to features, if the GRU relies heavily on certain features, simply reducing dimensions using RF might result in the loss of crucial information. Additionally, with new data, while random forest might adapt quickly, the GRU might require retraining, potentially leading to challenges in updating the model.