# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 3 Methodology  
This section establishes a CNN-LSTM network based on a multi-modal attention mechanism for accurate power grid load forecasting, combining the advantages of Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and various attention mechanisms. The model's structure is shown in Figure 1. First, the input data are processed by the CNN module, where the convolutional layers are used to extract local features and capture the spatial dependencies of the data. The pooling layers reduce the dimensionality of the features, lowering the computational complexity while retaining key features. Next, the features pass through the multi-head self-attention mechanism module, which captures long-range dependencies between features through global modeling, further enhancing the feature representation ability. After extracting spatial features, the data are input into the LSTM layer to model the dependencies in the time series; here, they learn the temporal characteristics of load variations. Then, the data pass through the global attention mechanism module, where the time-series features are weighted, dynamically adjusting the importance of different time steps, thereby highlighting the time-domain features that are crucial for load forecasting. Finally, the features pass through the channel attention module, where the Squeeze-and-Excitation (SE) module re-weights the features of different channels, identifying and emphasizing the feature channels most relevant to the forecasting task while suppressing redundant information. The load forecasting result is generated through the output layer. Spatial characteristics refer to the spatial distribution patterns and relationships between load data from different locations or regions in power grid load forecasting. Power grid loads typically vary across different geographical locations, and these differences may be related to factors such as the local climate, electricity demand, population density, and industrial distribution. Capturing spatial characteristics helps the model understand the dependencies and relationships between different regions, leading to more accurate load predictions. Spatial characteristics are crucial in power grid load forecasting because they reflect the interactions between nodes in the power system and also reflect regional differences. If the model can effectively extract and utilize these spatial features, it will have a better understanding of the load fluctuations and relationships between different regions, improving the accuracy of predictions. For example, in load forecasting, the load in some areas may be influenced by climate changes, while other areas may be driven by industrial activities. Therefore, by modeling spatial characteristics, the model can more comprehensively capture the load variation patterns in the power grid, providing more reliable predictions in a dynamic and complex environment. The innovation of this model lies in its effective integration of spatial feature extraction, time-series modeling, and multiple attention mechanisms. It can capture both local and global features and dynamically adjust the weights of temporal and channel features, significantly improving the accuracy and stability of power grid load forecasting. Through multi-level analysis and processing of complex load data, this model shows significant advantages in terms of adaptability, generalization ability, and forecasting accuracy.