## 1 Introduction
Accurate load forecasting not only enables smart grids to allocate resources in advance and optimize power supply strategies but also mitigates grid overload pressures that can be triggered by centralized charging and discharging peaks. With accurate forecasting, smart grids can more efficiently distribute power and respond to demand, ensuring stable power consumption in commercial buildings while meeting the demand for EV charging. In addition, load forecasting provides a scientific basis for building energy management, making energy use more intelligent and flexible, reducing unnecessary waste, and enhancing the sustainability and resilience of the power system. In the load forecasting of commercial building clusters, traditional algorithms are difficult to meet the needs of real-time prediction in dynamic environments due to long computing time and slow response speed. With the increase in the demand for charging and discharging of electric vehicles, the frequency and amplitude of load fluctuations have increased significantly, which puts forward higher requirements for the real-time performance and flexibility of load prediction models. However, traditional load-forecasting methods often rely on large amounts of data processing and complex calculations, which make it difficult to respond in a timely manner due to prediction delays in rapidly changing scenarios. This increased computation time affects the timeliness of forecasts, limiting the ability of smart grids to allocate resources and optimize power supply. This limitation can lead to under- or over-allocation of resources, which can affect grid efficiency and the power stability of building clusters. In addition, the complexity of traditional algorithms leads to prediction delays, making it difficult to quickly respond to sudden load changes, hindering refined dynamic load management. Therefore, in complex and dynamic commercial building power scenarios, the low computational efficiency of traditional algorithmsmakes it unable to meet the needs of real-time load forecasting. In particular, with the increase in the scheduling frequency of electric vehicles, the fluctuation of the overall load of the shopping mall is more significant, which puts forward higher requirements for the real-time performance and computing power of the prediction model. In contrast, machine learning methods, especially deep learning models, are able to automatically learn features from large-scale data, significantly reducing computation time and better meeting the real-time needs of dynamic load forecasting. However, existing basic machine learning models still have significant limitations when dealing with complex workload data. Some common network architectures perform poorly in dealing with high-frequency nonlinear load fluctuations and long-term and short-term dependencies, and it is difficult to fully meet the dynamic needs of load forecasting in shopping malls. For example, the traditional one-way LSTM model can only make one-way predictions based on historical data, which is difficult to effectively capture the two-way information dependence in the load fluctuation caused by the charging and discharging of electric vehicles. This limitation reduces the flexibility of the model in a rapidly changing load environment, making it difficult to accurately predict future load fluctuations. In addition, the LSTM model also has difficulties in capturing long-term dependencies and nonlinear features, which limits its performance in complex data scenarios. Similarly, while convolutional neural networks (CNNs) have advantages in feature extraction and are suitable for working with static data such as images, they do not perform well when dealing with dynamic, nonlinear load data. CNN models are usually good at extracting local features, and the time dependence and global characteristics of EV charging and discharging load requirements pose a challenge for CNNs to capture these sequence data features.