## 4 Case Study
### Superiority Analysis of QGCT-BiLSTM for Load Forecasting  
The proposed QGCT-BiLSTM method is rigorously evaluated against other load forecasting techniques, including Back Propagation (BP) Neural Network [34], Convolutional Neural Networks (CNN) [35], Long Short-Term Memory (LSTM) [36] and Random Forest (RF) [37], to validate its superiority in both regression and time-series forecasting frameworks. The evaluation of errors in load forecasting results of different methods are shown in Table 6. Comparative analyses are conducted under two distinct scenarios: regression forecasting with filtered/unfiltered key factors and pure time-series forecasting without external influencing variables. This dual approach ensured a comprehensive assessment of the QGCT-BiLSTM's adaptability.  
The significant advancements in load forecasting accuracy are demonstrated by the proposed QGCT-BiLSTM method, as evidenced by the superior performance across critical error metrics compared to conventional approaches. Under the regression load forecasting framework with filtered key factors, QGCT-BiLSTM achieves remarkably low errors, outperforming all baseline methods including BP, CNN, LSTM and RF. Moreover, The RMSE and MAE values of QGCT-BiLSTM are 20.3% and 16.8% lower than the next best method (LSTM) in the same category, respectively. This substantial improvement underscores the  
Figure 3: Load forecasting results for filtered key factors: (**a**) training set load curve; (**b**) test set load curve; (**c**) scatterplot of training set; (**d**) scatterplot of test set.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Evaluation Metrics**} & \multicolumn{2}{c}{**Unfiltered**} & \multicolumn{2}{c}{**Filtered**} \\ \cline{2-5}  & **Training Set** & **Test Set** & **Training Set** & **Test Set** \\ \hline RMSE & 187.91 & 205.15 & 105.81 & 145.27 \\ MAE & 136.05 & 162.49 & 87.60 & 113.65 \\ MBE & 1.89 & \(-\)95.56 & 7.49 & \(-\)61.61 \\ MAPE & 0.0432 & 0.0469 & 0.0366 & 0.0255 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation metrics for evaluating load forecasting results before and after filtering key influencing factors.  
efficacy of integrating bidirectional long short-term memory (BiLSTM) with the QGCT mechanism.  
Furthermore, the analysis reveals that QGCT-BiLSTM benefits significantly from the utilization of filtered key factors. When compared to the performance of QGCT-BiLSTM under unfiltered conditions, the adoption of filtered factors reduces prediction errors by 29.2% (RMSE), 30.1% (MAE) and 45.6% (MAPE), respectively, highlighting the critical role of feature selection in eliminating noise and irrelevant variables. In contrast, traditional methods such as BP and RF exhibit limited sensitivity to factor filtering, with error reductions of only 11.6-12.8% (RMSE) and 0.3-1.5% (MAPE). This QGCT-BiLSTM's robustness is emphasized by the disparity in leveraging refined input data to optimize model generalizability.  
Compared to the results of forecasting using only load data, BP, CNN, LSTM and RF compared to QGCT reduced the average of all errors by 60.32%, 69.82%, 63.81% and 57.63%, respectively, which proves the importance of introducing climatic influences for forecasting load changes.