# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 1 Introduction
[30] proposed a short-term power load forecasting method based on GRU neural networks, achieving high-accuracy next-day load forecasting through systematic preprocessing and lightweight recurrent neural network models. [PERSON] et al. [11] proposed a framework based on Long Short-Term Memory (LSTM) recurrent neural networks to address the high volatility and uncertainty problems associated with short-term load forecasting. [PERSON] et al. [31] proposed a short-term load forecasting method based on deep neural network LSTM layers, combining LSTM layers with fully connected layers. By separately inputting historical data and forecast data (such as weather information), the method improves forecasting accuracy, with LSTM layers modeling relationships in historical data and fully connected layers combining forecast data to enhance accuracy. Furthermore, numerous studies [32; 33; 34] have focused on the development and optimization of machine learning, deep learning, and probabilistic models, which have attracted significant attention due to their powerful modeling capabilities and forecasting performance. However, despite the tremendous potential of AI technologies in load forecasting, existing research still faces several limitations. Firstly, traditional methods struggle to address the non-linear characteristics and complex temporal dependencies of power load data, failing to effectively capture the changing patterns of both long-term and short-term dynamic features in the load data. This results in the models having low adaptability to non-stationary data and load characteristics with high volatility, which affects the accuracy and robustness of the predictions. Secondly, many current studies overlook the interrelationships between the internal features of the load data in feature extraction, failing to fully explore and leverage the complementarity between features. This insufficient use of information may lead to improper weight allocation for important features, thus limiting the further enhancement of model performance. Moreover, when processing long sequence data, existing methods often struggle to balance the completeness of data representation with the elimination of redundant features, which may result in information loss or the accumulation of ineffective features, thereby reducing the model's ability to capture complex time series patterns. Furthermore, these studies generally face challenges in terms of model optimization. Many methods are prone to overfitting or underfitting when dealing with data diversity, diminishing the model's generalization ability. Additionally, the high computational complexity of the models restricts their practical application in power grid scheduling, particularly in resource-constrained scenarios, where their high computational demands may lead to decreased operational efficiency. In summary, current research in load forecasting faces several bottlenecks in both theory and practice, including limitations in modeling capabilities, feature extraction, data processing, and model optimization. These limitations indicate that the field of load forecasting urgently requires more advanced and efficient forecasting methods to comprehensively improve prediction accuracy, model robustness, and practical applicability, thereby providing reliable support for the intelligent scheduling and efficient operation of modern power grids. Based on the requirements for accurate, safe, and reliable forecasting, this study focuses on constructing a comprehensive framework that integrates multiple advanced deep learning techniques to achieve both complex feature extraction and key information capture capabilities. Compared to traditional methods, the model proposed in this paper combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) to fully explore the spatial and temporal features of load data, effectively capturing its non-linear dynamic characteristics and temporal dependencies.