## 4 Data-Driven Framework for Building Energy Consumption Prediction
### Model for Prediction
#### 4.4.8 Tree-Based Models  
Tree-based models are widely used in building energy consumption prediction, especially in the field of energy load forecasting dealing with datasets with a large amount of zero inflation.  
The decision tree is the basis of tree-based models. It constructs a tree structure for prediction by recursively dividing data features. In building energy consumption prediction, the decision tree can divide the data space into different regions according to various factors such as meteorological information (such as temperature and humidity) and building features (such as building orientation and area), and each region corresponds to a specific energy consumption prediction value.  
As an ensemble extension of the decision tree, the random forest shows powerful performance in building energy consumption prediction. It is composed of multiple decision trees, and the final prediction is obtained by synthesizing the prediction results of multiple decision trees (usually by voting or averaging). During the training process, the random forest randomly samples multiple subsets with replacement from the original training dataset, and each subset is used to train a decision tree. Moreover, in the process of node splitting when constructing a decision tree, instead of considering all features, only a part of the features is randomly selected to find the best splitting point. This randomization strategy makes the random forest have good generalization ability and can effectively avoid overfitting.  
For example, [PERSON] et al. [32] found that the random forest showed certain advantages in handling complex data relationships when comparing the performance of various building energy consumption prediction models, and it can capture the non-linear relationships among various factors affecting building energy consumption. It has a certain degree of robustness to outliers and noisy data because the wrong prediction of a single decision tree will not dominate the final result. At the same time, the random forest hasa relatively fast training speed and can be processed in parallel, which is beneficial for the analysis of large-scale building energy consumption data. In practical applications, by adjusting hyperparameters such as the number of decision trees and the number of features considered during node splitting, the performance of the random forest model in building energy consumption prediction tasks can be further optimized.  
The gradient boosting tree (GBT) is also an important tree-based model. It iteratively trains a series of weak learners (usually decision trees), and each iteration learns based on the residuals of the previous model, thus gradually improving the prediction ability of the model. In building energy consumption prediction, GBT can continuously fit the complex patterns in the data. For datasets with a large amount of zero inflation, it can improve the prediction accuracy by gradually optimizing the data in different regions.  
For example, when dealing with a large number of zero-energy consumption periods (such as during holidays or when equipment is shut down) in some building energy consumption data, GBT can specifically learn the relationship between the features of these periods and energy consumption so as to more accurately predict future energy consumption situations.