## 4 BILSTM-Transformer Based Bems Model
### Microgrid Optimization Method Based on Cognitive Control
#### 4.5.3 Quantification of Information Gap
As can be seen from Table 1, among the load-forecasting performance of the four models, the BiLSTM-Transformer model stands out with the lowest MSE (43.290) and RMSE (6.579), showing extremely high prediction accuracy and being able to effectively track the actual load fluctuation closely. This outstanding performance is attributed to the combination of bidirectional time series processing capabilities of bidirectional LSTM and the attention mechanism of Transformer, which is able to better capture short-term and long-term dependencies. In contrast, although LSTM-Transformer performs well in capturing the overall trend, the one-way LSTM limits its utilization of bidirectional dependent information, resulting in MSE and RMSE of 336.820 and 18.352, respectively. The RNN model has a medium performance, and its simple structure makes it inferior to the LSTM series in long-term dependence processing, and the error is further increased (MSE is 489.220 and RMSE is 22.118). The MSE of the CNN-LSTM model is 505.710 and the RMSE is 22.488, which is the highest error among all models. Overall, the BiLSTM-Transformer model performs best in prediction accuracy and is best suited for complex tasks of load forecasting. #### 5.3.2 Performance Analysis of BiLSTM-Transformer with Cognitive Control Under Data Loss Conditions

In Figure 6(a), BiLSTM Transformer with Data Loss: Although there were some time periods where the predicted load closely matched the actual load, the curve trend remained basically synchronized, indicating that the model symmetrically captured the changing trend in actual load during this period. However, it can be clearly seen that during the time step from 400 to 700, especially around 500, the predicted curve exhibits significant fluctuations and spikes. This phenomenon indicates that the model failed to symmetrically reflect the actual changes in load during this period, resulting in significant differences between forecasts and reality. From a mathematical perspective of symmetry, an ideal forecasting model should capture signal features symmetrically at different time periods along the timeline, i.e., maintain consistent responses in various rising and falling trends. However, in this figure, the predicted curve exhibits irregular spikes at multiple locations, indicating a local asymmetry that suggests the model has failed to maintain stable signal capture capabilities when dealing with data loss. Especially in the high-frequency variation region, the predicted values exhibit overreaction, leading to a significant deviation from the actual load trend. During the scheduling period, there is a significant symmetry imbalance between the predicted load and the actual load of the model, especially with frequent and drastic fluctuations occurring between time steps 400 and 700, resulting in a high mean-squared error. Figure 7: Comparison of BiLSTM–Transformer with and without cognitive control under data loss. (**a**) BiLSTM Transformer with Data Loss; (**b**) BiLSTM–Transformer combined with cognitive control system and data loss. In Figure 7b, BiLSTM-Transformer combined with cognitive control system and data loss: However, between the 300 and 700 time steps, slight local biases and fluctuations in the forecasting curves can be observed, and although these fluctuations are small, the forecasts are slightly overestimated or underestimated at some peak points compared to the smoothing trend in actual load. This local asymmetry, while not significant, still reflects a subtle inadequacy in the model's ability to capture complex power changes. The results show that the predicted load of the model generally shows good symmetry during the scheduling period, especially in the middle and early part of the upward trend, the trend in the forecasting curve and the actual curve is basically the same. However, small local fluctuations reflect a slight asymmetry in the model's response to complex signals, which manifests as a slight deviation from the predicted curve.