# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 2 Related Work  
Electric load forecasting is a critical component of energy management, significantly impacting the efficiency and reliability of power systems. With the rapid development of smart grids, the demand for accurate load forecasting has grown at an increasing rate, driving continuous innovation and advancement in related models and methods. In recent years, research has focused on deep learning models, federated learning approaches, and the integration of various hybrid models, providing new ideas and tools to enhance the accuracy and applicability of load forecasting. Supported by a variety of forecasting models and methods, significant progress has been made in electric load forecasting, including traditional forecasting methods and artificial intelligence-based forecasting methods, as well as hybrid forecasting models, which have been proposed in recent years. Single artificial intelligence forecasting methods, such as CNNs and LSTMs, often exhibit certain limitations in power grid load forecasting. Due to their fixed model structures, these methods may only perform well on specific types of load data and poorly predict complex, nonlinear load data. Additionally, single models are more susceptible to data noise and outliers, leading to unstable forecasting results and overfitting risks, particularly when training data are limited or data features are complex. In contrast, hybrid forecasting models combine multiple different forecasting methods or algorithms, integrating the advantages of each model to reduce biases and errors from single models, thereby significantly improving forecasting accuracy and robustness. Hybrid models are better suited to different types of input data and effectively consider the impact of various factors on power grid load, providing more comprehensive and reliable forecasting results. [PERSON] et al. [35] proposed a short-term load forecasting method based on a combination of multivariatelinear regression and LSTMs, which can accurately predict load behavior, capturing both global trends and local details. [PERSON] et al. (2018) proposed a novel hybrid model combining Variational Mode Decomposition (VMD) and LSTMs, which decomposes complex power load data into components with different frequencies using VMD and then uses LSTMs to forecast low-frequency and high-frequency components separately, further improving forecasting accuracy and robustness. [PERSON] et al. (2018) combined Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Networks (CNNs), and Grey Wolf Optimization (GWO) to propose a new optimal hybrid strategy for accurate power load forecasting. [PERSON] et al. (2018) proposed a stacking ensemble algorithm for short-term power load forecasting based on Convolutional Neural Networks-Bidirectional LSTM-Attention and Extreme Gradient Boosting Trees (XGBoost). This method first uses the Adaptive Hierarchical Clustering (AHC) algorithm to select datasets with similar daily features, then combines influencing factors and uses the CNN-BiLSTM-Attention and XGBoost stacking ensemble algorithm for short-term load forecasting, significantly improving forecasting accuracy. It is evident that hybrid forecasting models are the mainstream approach used for power grid load forecasting. However, despite some studies exploring the combination of different deep learning models, integrating these models with attention mechanisms remains an area in need of further exploration. The proposed method effectively addresses the limitations identified in other studies by integrating a multi-modal attention mechanism with a CNN-LSTM network. The method extracts local spatial features through the CNN module and captures temporal dependencies using the LSTM layer, achieving dual extraction of spatial and temporal features. Compared to hybrid models that solely rely on LSTM or CNN, this approach can more comprehensively capture the complex characteristics of load data.