## 3 Case Analysis
### Evaluation and Analysis of Model Prediction Results
#### 3.4.2 Comparative Analysis of Single Model  
In order to verify the contribution of each module in the combined model, this paper designs a comparative experiment between the combined model (iTransformer-BiLSTM) and the single models (iTransformer, Transformer, BiLSTM, and LSTM). The main parameters of the sub-model are consistent with the main model, and the optimal results are obtained through multiple experiments (see Table 4). The prediction curve and error distribution curve are shown in Figures 10-12 (among them, the prediction curve takes 288  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & & **Parameter Settings** \\ \hline iTransformer-BiLSTM & iTransformer & Number of encoder layers: 2, number of multi-head attention heads: 4, number of channels: 128. \\ \hline iTransformer-BiLSTM & BiLSTM & Number of BiLSTM layers: 2, number of hidden units per layer: 64/32, Dropout rate: 0.05. \\ \hline other & Epoch: 256, learning rate: 0.002, optimizer: [PERSON], loss function: RMSE. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main Parameter Settings of the iTransformer-BiLSTM Model.  
The data presented in the table indicate that the iTransformer-BiLSTM hybrid model performs better than all standalone models across all evaluation metrics. Specifically, it achieves the lowest RMSE (93.963) and MAE (62.788) values, reducing RMSE and MAE by 30.5% and 23.2%, respectively, compared to the best-performing standalone model, iTransformer (RMSE: 135.271, MAE: 81.733). Furthermore, compared to BiLSTM (RMSE: 196.028, MAE: 134.015), the reductions are 52.1% and 53.3%, respectively, indicating that the hybrid model effectively integrates both global and local features to significantly reduce prediction errors.  
Among standalone models, iTransformer (RMSE: 135.271, MAE: 81.733) outperforms the traditional Transformer (RMSE: 153.629, MAE: 111.194). This improvement is attributed to iTransformer's independent token encoding strategy, which treats each variable's entire time series as an independent input unit, thereby mitigating noise interference caused by variable coupling in traditional Transformers and enhancing the learning efficiency of multivariate time-series interactions. Additionally, BiLSTM (RMSE: 196.028) reduces the prediction error by 4.8% compared to LSTM (RMSE: 205.868), validating that the bidirectional propagation mechanism enhances the modeling of temporal dependencies by integrating historical and future contextual information.  
In terms of error control, the iTransformer-BiLSTM model achieves the lowest SMAPE (4.936%) in the entire table, reducing SMAPE by 17.1% and 43.9% compared to iTransformer (5.953%) and BiLSTM (8.799%), respectively. This result indicates that the hybrid model maintains stable percentage error control even in highly fluctuating industrial scenarios. Although the standalone models exhibit strong error control capabilities, the hybrid model further enhances fine-grained fitting accuracy by utilizing a projection layer to align the scale of global features (e.g., the nonlinear interaction between outdoor humidity and supply-return air temperature difference) with local temporal dynamics (e.g., the sudden load drop during lunch breaks). This optimization enables superior performance under complex operating conditions. However, the computational complexity of the hybrid model is higher than that of individual models, necessitating a trade-off between accuracy improvement and hardware resource consumption in practical deployment. Experimental statistics show that the training time of the primary model, iTransformer-BiLSTM, is approximately 683 s, which is longer than that of iTransformer (442 s) and BiLSTM (57 s),  
Figure 12: Comparison error analysis between the main model and the single model (take the first day above).  
indicating that the performance enhancement comes at the cost of increased computational overhead. In the future, engineering applicability could be further optimized through model lightweighting strategies.