## 5 Discussion
### Comparative Analysis and Application Considerations of Prediction Models
It is suitable for scenarios where building energy consumption data has obvious local features and patterns and has low requirements for long-term dependencies in time series, such as short-term prediction of building energy consumption within a specific time period or energy consumption feature analysis and prediction for a group of buildings with similar building structures and usage patterns. Tree-based models represented by Random Forest in building energy consumption prediction can effectively capture the non-linear relationships among various influencing factors. By training multiple decision trees through sampling with replacement from the original training dataset and randomly selecting some features during node splitting, they have good generalization ability, effectively avoid overfitting, have a certain degree of robustness to outliers and noisy data, and the error of a single decision tree does not dominate the final result. Moreover, the training speed is relatively fast and can be processed in parallel, which is suitable for large-scale building energy consumption data analysis. However, as a black-box model, it lacks interpretability. Although the importance of features can be analyzed to understand the influence degree of factors, it is still difficult to intuitively explain the decision-making process. At the same time, it is relatively dependent on data feature selection and data balance. Unreasonable feature selection or severely unbalanced data will affect the model performance. It is suitable for processing large-scale and complex building energy consumption datasets and scenarios with high requirements for model generalization ability and robustness, such as energy consumption prediction for large communities or industrial parks with a mixture of different types of buildings. As an ensemble model strategy, Bagging generates multiple sub-datasets through sampling with replacement from the original dataset to train different models and then synthesizes the prediction results. It can effectively reduce the model variance and improve stability and generalization ability. Each sub-model can be trained in parallel, greatly shortening the training time. It is suitable for building energy consumption prediction scenarios where the variance of the basic model is large and high requirements for model stability and generalization ability are required, such as the comprehensive prediction of energy consumption of various different types of buildings. Boosting, on the other hand, trains multiple models in sequence. Each model is improved based on the errors of the previous model, gradually improving the prediction ability. For example, Adaboost can adaptively adjust the sample weights to improve the overall prediction performance. However, it is sensitive to noise, and noisy data may be amplified during iteration. Moreover, the serial training method leads to a long training time. It is suitable for building energy consumption prediction scenarios with extremely high requirements for prediction accuracy, high data quality, and little noise, such as the energy consumption prediction for special buildings (such as key areas of hospitals, data centers, etc.) with strict control over energy consumption and extremely high accuracy requirements. Multi-Step Prediction Strategies and Performance Analysis in Different Building Energy Consumption Prediction Models  
In the field of building energy consumption prediction, multi-step prediction strategies play a crucial role in accurately grasping the energy consumption change trends in the future for a certain period. Common multi-step prediction strategies include direct, recursive/rolling, and sequential strategies, and different prediction models exhibit diverse performance under these strategies. (1) Direct strategy  
The direct strategy means that the model directly predicts the building's energy consumption for multiple future time steps.