## 4 BILSTM-Transformer Based Bems Model
### Microgrid Optimization Method Based on Cognitive Control
#### 4.5.3 Quantification of Information Gap
In the case of data loss, Table 2 shows the comparison of the performance parameters of the BiLSTM-Transformer model with or without the support of a cognitive control system. When the model does not have a cognitive control system (BT + data loss), the MSE is as high as 37,413.696, and the RMSE is 193.426, showing significant prediction errors, indicating that the model is difficult to accurately capture load fluctuations in the environment with missing data. When the cognitive control system (BT + data loss + cognitive control system) is added, the MSE is significantly reduced to 546.603, and the RMSE is also reduced to 23.380, indicating that the cognitive control system effectively improves the prediction ability of the model in the environment of incomplete data. This significant error reduction shows that the cognitive control system can compensate for the impact of missing data in the case of incomplete information, so as to improve the stability and prediction accuracy of the model and ensure the reliability of grid dispatching. ## 6 Conclusions

Based on the above experimental results, this study draws the following conclusions. First, the BiLSTM-Transformer model demonstrates outstanding performance in load-forecasting tasks, effectively capturing the complex patterns and long-range dependencies of load variations in commercial buildings, achieving extremely high prediction accuracy under complete data conditions. Additionally, although the model's prediction accuracy decreases in the presence of data loss, the introduction of a cognitive control system and CPS significantly enhances the robustness of the BiLSTM-Transformer model in cases of incomplete data, ensuring more stable prediction performance. CPS plays a critical role in data collection, transmission, and feedback, further improving the system's responsiveness and adaptability in dynamic environments. Compared with traditional LSTM models, the BiLSTM-Transformer model exhibits greater computational efficiency and accuracy when handling large-scale and dynamic load data, helping to improve the system's response speed in complex load scenarios. In conclusion, the BiLSTM-Transformer model, combined with a cognitive control system and CPS, provides a practical and reliable solution to address uncertainties and data loss in commercial building load forecasting, offering more accurate data support and decision-making basis for load management and power scheduling in smart grids. Conceptualization, X.X., Z.H. and J.S. ; Methodology, X.X. and Z.H. ; Software, X.X. and Z.H. ; Verification, X.X., Z.H. and Y.C. ; Formal analysis, X.X. ; Investigation, Z.H. ; Resources, X.X. ; Data management, X.X. and Z.H. ; Writing--First draft preparation, X.X. and Z.H. ; Writing--Review and Editing, J.S. ; Visualization, X.X. and Z.H. ; Supervision, J.S. ; Project management, X.X. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data are contained within the article. The authors declare no conflicts of interest. ## References

* (1) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Decentralized privacy-preserving fair exchange scheme for V2G based on blockchain. _IEEE Trans. Dependable Secur. Comput._**2022**, _19_, 2442-2456. [CrossRef]
* (2) [PERSON]; [PERSON]; [PERSON] Research progress on thermal runaway warning and protection of lithium-ion battery in energy storage power station. _High Volt. Appar._**2024**, _60_, 87-99. [CrossRef]
* (3) [PERSON]; [PERSON]; [PERSON]; [PERSON] Optimal operation of plug-in electric vehicles in power systems with high wind power penetrations. _IEEE Trans. Sustain. Energy_**2013**, \(4\), 577-585. [CrossRef]
* (4) [PERSON]; [PERSON] A conceptual framework for the vehicle-to-grid (V2G) implementation. _Energy Policy_**2009**, _37_, 4379-4390. [CrossRef]
* (5) [PERSON]; [PERSON]; [PERSON] Probabilistic assessment of impact of flexible loads under network tariffs in low-voltage distribution networks. _J. Mod. Power Syst.