# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 1 Introduction
At the same time, by incorporating Multi-Head Self-Attention and Channel Attention mechanisms, the model further optimizes the weight allocation of key features, enhances the expression of important information, and reduces the interference of redundant features, thereby improving overall forecasting performance. To address the randomness and non-stationarity of power grid load data, the model designs an end-to-end feature processing and forecasting framework, ensuring higher adaptability and robustness in scenarios involving data diversity and long-sequence modeling. The experimental results show that this model outperforms existing mainstream methods in several key metrics, including short-term load forecasting accuracy, robustness, and computational efficiency, providing a more scientific load forecasting solution for smart grids. This study not only expands the technological boundaries in the field of load forecasting but also offers innovative solutions for complex environments in practical power grid scheduling. Furthermore, it lays the foundation for future exploration of multi-modal fusion, privacy protection, and distributed computing approaches in load forecasting. The main contributions of this paper are as follows:  
1. A comprehensive load forecasting model combining CNN, LSTM, Multi-Head Self-Attention (MHSA), Global Attention Mechanism (GAM), and Channel Attention Mechanism (CAM) is proposed. By integrating multiple deep learning techniques, this model fully explores the complex spatial features and temporal dependencies of power grid load data, effectively enhancing the model's forecasting accuracy and adaptability. An end-to-end efficient forecasting framework is designed, integrating the entire process from high-dimensional feature extraction to time series modeling, and attention optimization. This framework builds a seamless load forecasting system from input to output, ensuring the model's efficiency and practicality. 2. We innovatively introduce multi-head self-attention, global attention mechanism, and channel attention mechanism: The multi-head self-attention mechanism is used to model the global dependencies of long-sequence data, while the global attention mechanism enables the model to focus more specifically on important temporal features. The channel attention mechanism weights and optimizes different feature channels, highlighting important information and reducing redundant interference, significantly enhancing the model's feature extraction capability. 3. Experimental validation of the model's superiority: Through experiments on three real-world power grid load datasets, it is demonstrated that the model proposed in this paper outperforms existing mainstream methods in multiple key metrics, including forecasting accuracy, robustness, and computational efficiency, providing reliable support for the optimized scheduling and load management of smart grids. The remainder of this paper is organized as follows: In Section 2, we provide a brief review of related work. Section 3 presents the details of the proposed model. In Section 4, we comprehensively evaluate the performance of the proposed model through experiments. Finally, Section 5 summarizes the work conducted in this paper.