## 3 Results
### Metamodel Selection  
In ensemble deep models, the meta-model is a higher-level model that uses the predictions of basic models as features for training, rather than directly using the original data. By learning from the predictions of the basic models, the meta-model can integrate the strengths of multiple basic models to achieve more accurate and robust predictions, potentially reducing the risk of overfitting and improving the model's generalization ability. Meta models typically choose simpler models, which are easier to understand and interpret, helping to reveal the underlying patterns and mechanisms of the data. To find the most suitable meta-model for the ensemble model proposed in this paper, one to two models were selected from linear models, machine learning models, and neural network models for experimentation. Linear models included the commonly used Ridge Regression and Lasso Regression models; machine learning models included the Support Vector Machine Regression model (SVR) and Random Forest model; and neural network models selected the Deep Neural Network model (DNN). The specific experimental results are presented in Table 5.  
In office group one, the Ridge Regression model had an RMSE of 3.809 and an MAE of 2.655, performing the best among all meta-models. The performance of SVR was slightly inferior to that of Ridge Regression, while Random Forest and Lasso performed worse. In residential group four, the Ridge Regression model also performed the best among all meta-models, with an RMSE of 17.907 and an MAE of 14.688. SVR and DNN slightly lagged behind Ridge Regression, and Random Forest and Lasso showed poorer performances. In both groups, the Ridge Regression model demonstrated a relatively better performance, with lower RMSE and MAE values, indicating its better generalization ability and stability in the predictive tasks of these two groups. Although Ridge Regression is a linear model, it achieved the best results. This may be because the basic models were all based on LSTM models, which likely presented multicollinearity in their predictions, with high correlations among them. Ridge Regression can effectively handle such multicollinearity, enhancing the predictive performance and stability of the ensemble model. \begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Metamodel** & **RMSE** & **MAE** \\ \hline \multirow{4}{*}{Office Group 1} & Ridge Regression & **3.809** & **2.655** \\  & Lasso & 6.392 & 4.905 \\  & Random Forest & 6.883 & 3.012 \\  & SVR & 4.317 & 2.978 \\  & DNN & 5.755 & 3.938 \\ \hline \multirow{4}{*}{Residential Group 4} & Ridge Regression & **17.907** & **14.688** \\  & Lasso & 19.940 & 15.794 \\ \cline{1-1}  & Random Forest & 20.775 & 16.316 \\ \cline{1-1}  & SVR & 19.595 & 14.228 \\ \cline{1-1}  & DNN & 19.593 & 14.981 \\ \hline \end{tabular}
\end{table} 
Table 5: Metamodel prediction error table. Observing Figures 8 and 9, the Original LSTM model generally performs poorly across most groups, with its predictions exhibiting relatively large errors. However, in residential group five, the Original LSTM's predictions slightly surpass those of LSTM models incorporating trend or seasonal feature components, performing just below the proposed ensemble model. This indicates the necessity of including the Original LSTM model among the basic models. From the RMSE metric, the Trend LSTM shows the best predictions in residential groups one and six compared to the other models, even outperforming the proposed model in some cases, and it ranks second to the proposed model in residential groups three and four but overall performs better than the Original LSTM model. From the MAE metric, the Trend LSTM achieves the best predictions in residential groups one and two, again outperforming the proposed model in some cases, and it is second only to the proposed model in residential groups three and four and office group two but overall performs better than the Original LSTM model.