## 5 Discussion
### Comparative Analysis and Application Considerations of Prediction Models  
In building energy consumption prediction, polynomial regression shows its advantages with its simple and intuitive model form. Whether it is the univariate form that focuses on historical energy consumption data to predict trends or the multivariate form that incorporates various influencing factors such as temperature and humidity, it enables decision-makers to easily understand the relationship between variables and energy consumption prediction values. It has good generalization ability in small samples and is efficient in training. However, this model has a limited ability to capture complex non-linear relationships, and it is difficult to select the model order. If the order is too high, overfitting is likely to occur, and if it is too low, underfitting will happen. It is suitable for scenarios where the building structure and usage pattern are simple, the data features are approximately linear or low-degree non-linear, and high interpretability of the model is required, such as energy consumption prediction for small-scale conventional buildings. SVR maps the input space to a high-dimensional space with kernel functions. Its powerful non-linear processing ability enables it to effectively deal with complex non-linear problems in building energy consumption. It performs well in small-sample learning. By maximizing the margin to find the optimal hyperplane, it endows the model with good generalization performance. However, it has the problem of high computational complexity. When calculating in a high-dimensional space and processing large-scale data, the training time is long, and the requirements for computing resources are high. At the same time, it is sensitive to parameter selection and requires a large number of experiments to determine the optimal parameters. It is suitable for building energy consumption prediction where the data are non-linear, the samples are limited, and high prediction accuracy is required, such as the energy consumption prediction scenarios for buildings with unique designs or complex usage patterns. As an important branch of artificial neural networks, MLP has a strong non-linear approximation ability. Without the need for complex explicit model construction, it can simulate the dynamic behavior of complex systems such as building energy consumption with high precision through multi-layer neurons and weight adjustments and deeply mine the hidden patterns in the input data to achieve accurate prediction. However, it is prone to overfitting, especially when the training data are insufficient, the parameter setting is improper, or the model structure is complex. Moreover, the training time is long, and the adjustment of a large number of weight parameters involves a large amount of calculation. At the same time, as a black-box model, it lacks interpretability. It is suitable for scenarios with large amounts of data, complex building energy consumption characteristics, high requirements for prediction accuracy, and low requirements for interpretability, such as energy consumption prediction for large commercial complexes or comprehensive office buildings. LSTM has significant advantages in processing building energy consumption time series data. Its innovative input gate, forget gate, and output gate mechanisms successfully solve the problems of gradient vanishing or explosion in traditional recurrent neural networks and can accurately capture the long-term dependencies in time series. In scenarios such as predicting the daily energy consumption of buildings, it can dynamically adjust the memory and forgetting of information at different time steps based on historical energy consumption and current influencing factors, significantly improving the prediction accuracy and reducing indicators such as root mean square error. Its variants have also been widely applied and verified in practice.