# Construction of Ensemble Learning Model for Home Appliance Demand Forecasting
## 3 Experiment and Discussion
### Experimental Analysis
RF excels at dealing with complicated features and mitigating overfitting, improving the model's stability and generalization capacity. Furthermore, XGBoost's ensemble learning strategy further addresses high demand volatility. To better evaluate the prediction performance of each model, we plotted a comparison, as shown in Figure 6. The predicted and actual demand values of all models were near the perfect prediction line, indicating that these models fit the data well. Notably, the blending model's predictions were generally close to the perfect prediction line, highlighting its effectiveness in handling high demand and periodicity issues, outperforming the other models. The LSTM, RF, and XGBoost models also showed predictions closer to the actual values, demonstrating high prediction accuracies. In contrast, the SVR and K-NN models exhibited a more scattered distribution of prediction results and deviated further from the perfect prediction line, indicating relatively poor prediction performances. To further validate the performances of the dominant models in predicting actual demand, we plotted the fitting curves of each model on the test set, as shown in Figure 7. Figure 7 illustrates the comparison between the actual and predicted demand values on the test set for the LSTM, RF, XGBoost and blending models, respectively. These curves not only visualize the prediction ability of each model, but also provide a detailed analysis of each model's performance under different time periods and demand fluctuations. Figure 7 illustrates the fit of the LSTM model on the test set. The figure shows that the LSTM model performed well in capturing long-term trends and seasonal variations in the time series data. In contrast, Figure 7 demonstrates that the RF model could better fit certain periods with abnormal fluctuations in real demand, highlighting its advantages in dealing with complex non-linear relationships and high-dimensional data. Figure 7 represents the prediction results of the XGBoost model, which combines the strengths of gradient boosting and excels in handling a wide range of features and high-dimensional data. The model's prediction curves on the test set reasonably matched the actual demand values, particularly during periods of high demand variation. Lastly, Figure 7 displays the prediction results of the blending model. By integrating the strengths of LSTM, RF, and XGBoost, the blending model showed outstanding performance in addressing high \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Prediction Model** & \(MAE\) & \(RMSE\) & \(R^{2}\) \\ \hline SVR & 0.0797 & 0.1863 & 0.8010 \\ k-NN & 0.0852 & 0.2001 & 0.7810 \\ LSTM & 0.0758 & 0.1738 & 0.8161 \\ RF & 0.0776 & 0.1853 & 0.8018 \\ XGBoost & 0.0723 & 0.1695 & 0.8296 \\ Blending & 0.0625 & 0.1337 & 0.9116 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison table for assessment of model indicators. demand and periodic issues. Its prediction curves exhibited the highest fit to the actual demand values, further validating the effectiveness of this combined model. The synergistic integration of these algorithms enables the hybrid fusion model to outperform individual techniques, demonstrating its effectiveness and utility in practical demand forecasting applications. While single models perform well in specific scenarios, their predictive effectiveness is limited in more complex demand environments. This underscores the rationale for employing a composite model in this study. The blending model  
Figure 6: Scatterplot comparing the predictive effects of the models. Figure 7: (a) Fitting curve of LSTM test set; (b) fitting curve of RF test set; (c) fitting curve of XGBoost test set; and (d) blending model training set fitting curve. surpassed the single models in terms of its prediction accuracy and data representation, particularly when handling multiple features and complex data relationships.