# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 1 Introduction
Although traditional load forecasting methods have been able to meet the needs of early-stage predictions to a certain extent, with the increasing complexity of power systems, these methods have gradually revealed limitations in handling highly nonlinear and dynamic characteristics. In recent years, data-driven analytical methods, such as phase space reconstruction and compressed sensing, have been widely applied to address these challenges. Phase space reconstruction, by uncovering the intrinsic dynamic structures within time series data, can effectively capture the nonlinear and complex dynamic characteristics of power systems, thereby being applied to the classification and prediction of transient phenomena in power grids. [PERSON] et al. [19] utilized the phase space domain to detect and characterize these transient phenomena in power grids. [PERSON] et al. [20] proposed an efficient method for pro cessing and classifying early discharge signals in transformers by combining compressed sensing technology with machine learning classifiers (such as LSTM), providing innovative solutions for signal processing and fault detection in power system engineering. Compressed sensing technology, by extracting key features from a small amount of data, can maintain high prediction accuracy while reducing the demand for data volume and computational resources. When combined with machine learning classifiers, these methods have demonstrated significant advantages in power grid load forecasting and transient event identification. Therefore, future research could consider integrating these data-driven technologies into load forecasting models to overcome the limitations of traditional methods in handling nonlinear and dynamic characteristics. In recent years, with the rapid development of science and technology and continuous innovation in forecasting techniques, the application of artificial intelligence (AI) in load forecasting has gradually become a research hotspot. AI technologies, with their powerful data processing capabilities and pattern recognition abilities, have significantly outperformed traditional methods in terms of prediction accuracy, opening up new directions for power load forecasting. Among these, neural network methods [21; 22], due to their strong non-linear mapping capabilities and self-learning characteristics, have been widely applied; fuzzy logic methods [23; 24], by handling uncertainty and fuzziness, have provided effective tools for modeling complex load variations; and support vector machine methods [25; 26], with their excellent adaptability to small samples and high-dimensional data, have demonstrated outstanding performance in load forecasting. It is worth noting that AI methods not only surpass traditional methods in terms of prediction accuracy but also significantly enhance the ability to uncover hidden patterns in load data, especially when it comes to handling complex temporal characteristics, multi-dimensional input variables, and non-linear relationships. The rapid development of these technologies has provided strong support for addressing the diversity and uncertainty of power systems, while also promoting the shift in power load forecasting from an experience-driven approach to a data-driven paradigm. In contemporary research, many scholars have explored the application of artificial intelligence (AI) in power load forecasting to address the challenges posed by complex and dynamic forecasting environments [27; 28]. [PERSON] et al. [29] proposed a short-term load forecasting method based on deep learning (DLSF), which converts numerical forecasting problems into image processing tasks and combines Convolutional Neural Networks (CNNs) and multi-layer neural networks to effectively improve the accuracy and efficiency of power load forecasting. However, CNN lacks the ability to capture long-term dependencies when dealing with time-series data. Gated Recurrent Unit (GRU) can process time-correlated sequence data and capture contextual information through its recursive structure. [PERSON] et al.