## 2 Problem Description  
The current building-energy management system (BEMS) in shopping malls faces multiple challenges in load management, especially in the context of increasing demand for electric vehicle (EV) charging and discharging. The main task of the BEMS is to collect, predict, and optimize loads through systematic steps to achieve more efficient and economical energy management. This process aims to cope with increasingly complex fluctuations in load demand while maximizing the utilization of electricity resources. Firstly, the BEMS collects real-time data on the overall load of the shopping mall and EV charging and discharging through a data acquisition module. These real-time data are the foundation for subsequent load forecasting and can provide key basis for the system to understand and analyze future changes in electricity demand. With the increasing impact of EV charging and discharging behavior on the load demand of shopping malls, it has become particularly important for the BEMS to continuously and accurately collect these data in dynamic environments. Only when the immediacy and accuracy of data are guaranteed can the rationality of system forecasting and scheduling be ensured and scheduling errors caused by information loss or delay be avoided. After completing data collection, the core task of the BEMS is to perform load forecasting. Based on the collected EV charging and discharging data and the basic needs of the shopping mall, the BEMS can predict future load changes. Accurate load forecasting enables the system to have stronger foresight and flexibility in power dispatch decisions. For example, through load forecasting, the BEMS can prioritize EV charging during periods of low electricity prices, avoiding high electricity consumption during periods of high electricity prices. During periods of high electricity prices, the BEMS can reasonably schedule the discharge of EVs and use the stored electricity as an auxiliary power source for shopping malls, thereby reducing dependence on the grid, saving electricity costs, and improving electricity efficiency. The scenario is illustrated in Figure 1.  
The load \(P_{\text{grid}}\) of the mall is determined by the sum of the building's basic demand \(P_{\text{demand}}\) and the charging and discharging of the electric vehicle \(P_{\text{EVi}}\), and the Equation is as follows:  
\[P_{\text{grid}}=P_{\text{demand}}+\sum_{i=1}^{N}P_{\text{EVi}} \tag{1}\]  
Figure 1: Scenario diagram. The primary purpose of the cognitive control system designed within BEMS is to ensure the integrity and reliability of data transmission, particularly in cases where communication instability, data loss, or interference may occur during data collection and transmission. In this context, cyber-physical systems (CPSs) act as a critical infrastructure, providing real-time data acquisition, processing, and feedback mechanisms that further enhance BEMS's data management capabilities. Since BEMS heavily relies on precise real-time data for load forecasting and scheduling optimization, any data loss or incompleteness can directly affect the system's forecasting accuracy and scheduling performance. Therefore, the integration of CPS with the cognitive control system is essential for the efficient operation of the BEMS. Specifically, the CPS can monitor changes in EV charging and discharging as well as the load of shopping centers in real-time, collecting data through its distributed sensor network and transmitting it promptly to the BEMS. On this basis, the CPS continuously monitors the status of data transmission, detecting and transmitting issues such as packet loss, transmission delays, or interference to the cognitive control system in BEMS for resolution. The cognitive control system mitigates the impact of data loss on forecasting and decision-making through methods like retransmission, selective compensation, or inferred completion based on historical data.