## 5 Discussion
### Comparative Analysis and Application Considerations of Prediction Models
Taking the polynomial regression model as an example, it constructs a polynomial equation with multiple future time-step outputs, integrates historical energy consumption and relevant influencing factor data, and can predict the energy consumption at multiple future time points at once. When the building energy consumption data shows a stable trend and the data correlation does not change significantly in multi-step prediction, the polynomial regression using this strategy can demonstrate the advantages of simplicity, intuitiveness, and strong interpretability and can predict the future energy consumption trend relatively accurately. However, when facing complex non-linear fluctuations or sudden change data, due to its limited ability to depict complex non-linear relationships, the prediction error may increase significantly. When the SVR adopts the direct strategy, it uses an appropriate kernel function to map the input space to a high-dimensional feature space and constructs a regression model that can simultaneously predict the energy consumption for multiple future time steps. SVR has an excellent ability to handle non-linear relationships. When the building energy consumption data presents complex non-linear characteristics, this strategy can utilize its powerful non-linear mapping ability to predict the multi-step future energy consumption relatively accurately. However, this strategy is extremely sensitive to the selection of kernel functions and parameter adjustment. Improper settings are likely to cause the model to overfit or underfit, affecting the accuracy of multi-step prediction. Under the direct strategy, the MLP constructs a multi-hidden-layer neural network, takes historical energy consumption and relevant influencing factor data as inputs, and directly outputs the energy consumption prediction values for multiple future time steps. MLP has a powerful non-linear function approximation ability. Theoretically, when facing complex building energy consumption data, it can learn complex patterns by adjusting the network structure and weights to achieve relatively accurate multi-step prediction. However, MLP is prone to falling into local optimal solutions and may encounter problems such as gradient disappearance or gradient explosion during training, which have a negative impact on its multi-step prediction performance under the direct strategy. When the LSTM uses the direct strategy, it relies on its unique gating mechanism to effectively capture the long-term dependencies in time-series data. After inputting the historical energy consumption time-series data, LSTM can directly predict the energy consumption values for multiple future time steps. Given the significant time-series characteristics of building energy consumption data, the gating mechanism of LSTM enables it to retain historical information well in multi-step prediction and has certain advantages in predicting future energy consumption trends. However, the LSTM model has a complex structure, takes a long time to train, and requires a large amount of historical data. When the data volume is insufficient, it may affect the accuracy of multi-step prediction. Based on the self-attention mechanism, the Transformer can efficiently capture the complex dependencies between vectors at any position in the input sequence. Under the direct strategy, the Transformer can simultaneously predict the building energy consumption for multiple future time steps. It has significant advantages in processing long-sequence data, can fully utilize the global information in historical energy consumption data, and can predict the multi-step future energy consumption relatively accurately. However, the Transformer model has a high computational complexity and requires a large amount of computational resources, which may be limited in practical applications. The recursive/rolling strategy is based on the prediction result of the previous step to make the next-step prediction.