# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 1 Introduction  
Power load forecasting is a core component in the operation and planning of power systems, playing a critical role in ensuring the safe and stable operation of the grid, improving energy efficiency, and optimizing resource allocation. It is not only a key method for achieving power supply and demand balance but also a vital foundation for supporting the economy, safety, and sustainability of the power system [1, 2, 3]. With the continuousexpansion of modern power systems, increasing complexity, and the ongoing development of intelligence, the importance of load forecasting has become more prominent. This is especially true in the context of large-scale integration of renewable energy sources and the accelerated reform of electricity markets, where the operational environment of power systems is facing unprecedented challenges and uncertainties. Short-term load forecasting (such as hourly to weekly forecasting), as a fundamental element for power system scheduling and energy efficiency management, plays a crucial role in all stages of grid operation. It is widely used in areas such as demand-side management, energy storage system optimization, grid regulation strategy development, and power generation planning [4; 5; 6]. High-precision load forecasting not only helps power companies optimize operational scheduling, allocate power generation resources efficiently, and reduce energy losses, but also effectively lowers grid operating costs and enhances economic benefits. At the same time, accurate forecasting helps to address potential load fluctuations and unexpected situations in grid operations, ensuring the safety and reliability of the grid and providing strong support for the stable operation of the power system [7; 8; 9]. However, power load data are influenced by various complex factors, including climate change, economic development, policy adjustments, and fluctuations in user behavior, leading to significant non-linearity, randomness, and complex temporal characteristics. This makes traditional statistical methods and linear models limited when it comes to addressing complex load features, and their prediction accuracy is insufficient to meet the needs of modern power systems. In particular, with the increasing proportion of renewable energy, the intermittent and random characteristics of wind and solar power further increase the difficulty of load forecasting [10; 11; 12]. Therefore, leveraging advanced artificial intelligence technologies and deep learning models to fully mine the potential patterns within power load data and improve forecasting accuracy has become a key focus of current research. In previous research on power load forecasting, traditional forecasting methods dominated the field. These methods typically include regression analysis [13], time series models [14], and gray prediction models [15]. [PERSON] et al. [16] proposed a comprehensive probabilistic density forecasting method--Gaussian process quantile regression--which can systematically handle uncertainty in power load data and achieve statistical modeling of the probability distribution of power load data. [PERSON] et al. [17] proposed a short-term load forecasting method based on multivariate linear regression (MLR), which improves linear regression for scenarios with a small number of variables in large datasets and applies multi-core parallel processing techniques to handle unlimited amounts of historical big data and forecasting scenarios within an acceptable execution time. [PERSON] et al. [15] proposed a hybrid gray prediction model combining the Ant Lion Optimization algorithm and rolling mechanism, significantly improving the accuracy and reliability of annual power load forecasting. [PERSON] et al. [18] proposed a hybrid forecasting method by combining ARIMA and ANN models, effectively addressing the linear and nonlinear characteristics of power load data and achieving high-accuracy daily load forecasting.