With the expansion and deployment of smart metering in power grid management and control, the need for security protection in the power system is continuously growing. However, the current construction of a comprehensive defense system for terminal data is inadequate. In this paper, we report a study on power loads to address the security challenges facing grid management, using the protection detection response recovery (PDRR) network security model as the basis. Firstly, we design an end-to-end security perception architecture using IoT technology and develop an optimization model for monitoring sensor information. In addition, we construct a data aggregation model that improves adversarial domain adaptation and incorporates deep convolutional neural networks to extract features. The proposed model enhances short-term load forecasting by combining linear predictions from autoregressive models with the nonlinear trend analysis capabilities of deep learning models. The performance of the proposed method is compared with those of the Adam and stochastic gradient descent (SGD) optimizers. Experimental results confirm that the proposed method ensures reliable data transmission, facilitates effective classification aggregation of heterogeneous data, and yields fast and accurate load forecasting results. Furthermore, the proposed method enhances the robustness of the model.