## 3 Case Analysis
### Evaluation and Analysis of Model Prediction Results
#### 3.4.3 Comparative Analysis with Other Combined Models  
To verify the scientific validity and effectiveness of the proposed iTransformer-BiLSTM hybrid model, this study conducted comparative experiments against mainstream load forecasting models, including BPNN-Adaboost, CNN-BiLSTM, and Transformer-BiLSTM, using the same test samples. The BPNN-Adaboost model, which combines traditional neural networks with ensemble algorithms, possesses a certain degree of nonlinear modeling capability [37; 38]. The CNN-BiLSTM model integrates local feature extraction (CNN) with temporal sequence modeling (BiLSTM), achieving favorable performance in certain short-term load forecasting tasks [39; 40]. The Transformer-BiLSTM model combines the global dependency learning capability of Transformer with the temporal dynamic analysis capability of BiLSTM, demonstrating strong performance in multi-step forecasting [41]. Therefore, these three hybrid models are selected as benchmarks for comparison, as they are structurally representative and align well with current research practices. The key parameter settings for each model are listed in Table 5, and the optimal results were selected from multiple experiments to ensure experimental stability. The evaluation metrics of the prediction results, fitting curves, and error distributions are presented in Table 6 and Figure 13, Figure 14, and Figure 15, respectively. The experimental results indicate that the iTransformer-BiLSTM model outperforms other benchmark models in terms of predictive performance. Compared to the Transformer-BiLSTM, CNN-BiLSTM, and BPNN-Adaboost models, its Root Mean Square Error (RMSE) is reduced by 20.554, 80.424, and 131.997, respectively. The Mean Absolute Error (MAE) decreases by 19.522, 53.243, and 82.308, while the Symmetric Mean Absolute Percentage Error (SMAPE) is lowered by 1.084%, 3.638%, and 5.980%, respectively. These results indicate that the iTransformer-BiLSTM model demonstrates better error control and predictive accuracy. For air conditioning systems in manufacturing facilities, a decrease in the SMAPE metric reflects enhanced cooling load prediction accuracy. This, in turn, facilitates more precise demand-driven cooling, mitigates energy waste associated with overcooling, and consequently improves both system operational efficiency and indoor thermal comfort. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAE** & **SMAPE (\%)** \\ \hline iTransforme-BiLSTM & 93.963 & 62.788 & 4.936 \\ Transformer-BiLSTM & 114.517 & 82.310 & 6.020 \\ CNN-BiLSTM & 174.387 & 116.031 & 8.574 \\ BPNN-Adaboost & 225.960 & 145.096 & 10.916 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Prediction Evaluation Results of the Main Model and Other Combined Models. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Main Parameter Settings** \\ \hline BPNN-Adaboost & \begin{tabular}{c} Number of weak classifiers: 5, BPNN network layers: 2, \\ Neurons per layer: 64/32 \\ \end{tabular} \\ \hline CNN-BiLSTM & \
\begin{tabular}{c} CNN layers: 2, Kernel size: 2 \(\times\) 2, Pooling layer size: 2 \(\times\) 2, \\ Channels: 128, BiLSTM settings: Same as the proposed \\ main mode \\ \end{tabular} \\ \hline Transformer-BiLSTM &
\begin{tabular}{c} Encoder layers: 2, Attention heads: 4, Channels: 128, \\ BiLSTM settings: Same as the proposed main model \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Main Parameter Settings of Different Models. Further examination of Figures 12 and 15 reveals that the peak prediction errors are primarily concentrated during periods of significant fluctuations in the actual load, such as sudden surges and drops. This indicates that the magnitude of the errors is largely influenced by abrupt load variations. In contrast, traditional models exhibit insufficient responsiveness during such periods, leading to larger deviations.