## 1 Introduction
With the continuous fluctuation of charging demand, it is difficult for CNNs to deal with long-term complex dependencies, and the accuracy and timeliness provided in high-dimensional dynamic load data scenarios are insufficient. In addition, although CNN provides a certain amount of computational efficiency in parallel processing of large-scale data, there are still problems of high computational cost and insufficient response speed in high-dimensional dynamic load data scenarios [7]. Although machine learning methods have accelerated the load prediction process to a certain extent, the basic network is still limited in capturing complex nonlinear features, long-term and short-term dependencies, and dynamic changes in load data, which limits its practical application. In order to achieve efficient and accurate prediction of the load after EV-charging and -discharging scheduling, it is necessary to introduce more advanced deep learning models to deal with the dynamic dependence and nonlinear characteristics in the load demand, so as to improve the real time and accuracy of load forecasting. The BiLSTM-Transformer model combines the time-processing capabilities of BiLSTM and the global modeling capabilities of Transformer, which can effectively capture the complex nonlinear and long-term dependencies in charging demand and significantly improve the timeliness and accuracy of load forecasting. BiLSTM is able to process past and future information in time series data, improving forecast accuracy and better capturing the dynamic changes in EV-charging demand compared to traditional one-way LSTMs. At the same time, the self-attention mechanism of Transformer is extremely efficient in processing large-scale parallel data and can capture global dependencies without relying on fixed sequences, which enhances the robustness of the model in complex charging data scenarios [8; 9]. Therefore, compared with other networks, the BiLSTM-Transformer model not only reduces the interference of abnormal data but also significantly improves the accuracy and computational efficiency of prediction, providing a real-time and stable solution for grid load management. However, predictive models alone are still not enough to fully solve the problem of sudden load fluctuations and data loss caused by EV charging and discharging. During transmission, unstable communication or data loss can directly affect the real-time monitoring and scheduling efficiency of the building-energy management system (BEMS), resulting in load imbalances, scheduling deviations, and potential energy loss. Therefore, a cognitive control model is introduced in this study, which combines the prediction results of BiLSTM-Transformer with a real-time adjustment mechanism to cope with the uncertainty caused by data loss and transmission anomalies. In this process, the cyber-physical system (CPS) monitors and analyzes data changes in real time by supporting the cognitive control system to predict the load after the mall is dispatched. Through the integrated information transmission, data processing, and feedback mechanism, the CPS enables the cognitive control system to detect and solve information loss or delay problems in a timely manner, ensuring the accuracy of load prediction even in the case of incomplete data. Specifically, the user-side cognitive control (UACC) in the cognitive control system aims to enhance the resilience of the BEMS in the event of unstable wireless communication or data loss. Each charging station acts as an independent UACC agent and can autonomously adjust the charging and discharging operations according to its own historical data and current communication conditions. UACC quantifies the information gap and makes decisions through the instantaneous reward function and adjusts the charging and discharging strategy according to the weight of the information gap and the communication retransmission cost when the information gap exceeds the set threshold.