## 5 Discussion
### Comparative Analysis and Application Considerations of Prediction Models
Taking the polynomial regression as an example, first, historical data are used to predict the energy consumption value of the first future time step, and then this predicted value is used as new historical data and combined with the original historical data to use the polynomial regression model to predict the energy consumption of the next time step, and so on. When the data are relatively stable and the noise is small, the polynomial regression using this strategy can gradually update the prediction and effectively track the energy consumption change trend to a certain extent. However, the prediction error of each step will continuously accumulate. As the number of prediction steps increases, the error may gradually increase, resulting in the prediction result deviating from the actual value. Under the recursive/rolling strategy, SVR re-trains the model each time using the previous-step prediction result and historical data to predict the energy consumption of the next time step. Given SVR's good ability to handle non-linear relationships, when facing building energy consumption data with certain non-linear change laws, this strategy can adapt to data changes by continuously updating the model. However, frequent model training will increase the computational cost, and when the previous-step prediction error is large, it will have a significant impact on subsequent predictions. When MLP adopts the recursive/rolling strategy, it takes the previous-step prediction result as a new input feature and inputs it into the network together with other historical data to make the prediction for the next time step. The non-linear approximation ability of MLP helps to capture the dynamic changes in data during the recursive process. However, there is also the problem of error accumulation, and the network structure is complex. Each step of prediction requires re-calculating the network weights, resulting in a large amount of computation, which may affect the prediction efficiency. Under the recursive/rolling strategy, LSTM can effectively integrate the previous-step prediction information into the next-step prediction by using the memory cell and gating mechanism. When dealing with building energy consumption time-series data with long-term dependencies, this strategy can continuously track the energy consumption change trend by continuously updating the memory cell. However, due to its complex model structure and large amount of computation, the error accumulation during the recursive process may also have a significant impact on the final prediction result. Under the recursive/rolling strategy, Transformer focuses on the relationship between the previous-step prediction result and historical data through the self-attention mechanism and then makes the next-step prediction. The powerful global information capture ability of Transformer enables it to better use historical information to correct the prediction during the recursive process. However, due to its high computational complexity, the repeated calculations during the recursive process will greatly increase the computational cost, and the limitations of computational resources need to be carefully considered in practical applications. The sequential strategy is to train a separate model for each time step and then use these models in sequence according to the time order for prediction. In polynomial regression, polynomial regression models are constructed separately for different future time steps, and each model is trained only using the historical data before a specific time step. The advantage of this strategy is that each model is relatively simple, with a small amount of computation, and there is no problem of error accumulation. However, it is necessary to train a model for each time step, resulting in a large number of models, and the cost of model management and maintenance is high. Moreover, since each model is trained independently, it is impossible to fully utilize the correlation information between different time steps.