Optimizing short-term load forecasting performance is a challenge due to the non-linearity and randomness of electrical load, as well as the variability of system operating patterns. Existing methods often fail to consider how to effectively combine their complementary advantages and fail to fully capture the internal information in the load sequence, leading to a decrease in accuracy. To achieve accurate and efficient short-term load forecasting, this study proposes a novel power grid load forecasting model that integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), Multi-Head Self-Attention Mechanism (MHSA), Global Attention Mechanism (GAM), and Channel Attention Mechanism (CAM) to achieve efficient and precise short-term load forecasting. This model aims to address the issue in traditional methods where complex temporal features and important information in power grid load data are not fully captured. Firstly, the CNN module is used to extract high-dimensional spatial features from the load data, and a pooling layer is applied to reduce dimensionality while retaining key information. Then, the Multi-Head Self-Attention mechanism is employed to model the long-range dependencies of the sequence data, enhancing the ability to extract temporal features. Next, the LSTM layer further captures the time dependencies in the load sequence. Subsequently, the Global Attention mechanism helps the model focus more on the most relevant parts of the input sequence, improving the model’s performance and generalization ability. The Channel Attention module is then applied to weight different feature channels, highlighting important information and reducing redundancy. Finally, the flattened output layer produces the forecast results. Experimental validation shows that the proposed CNN-MHSA-LSTM-GAM-CAM model outperforms existing mainstream methods in terms of load forecasting accuracy, providing effective support for the optimized scheduling of smart grids.

State Grid Corporation of China implements the digital transformation strategy, integrates the resources of various data islands in various power fields under the traditional model, and quickly forms the capabilities of data governance and data services. The implementation process will encounter problems such as difficulty in obtaining big data metadata, data quality, and data traceability. This paper proposes a power grid data lineage analysis method, which performs metadata management, data extraction, data transformation, data calculation, etc. on the data at the source data end (especially for power grid big data) to generate bloodline data. See the data source link for the model object. The results show that the blood relationship analysis function can be beneficial to the tracking and positioning of data problems, to the diversified analysis of data, to the data governance of the big data platform, and to effectively solve the pain points of data display in the national network data center.