Power grid peak load storage and intelligence

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.

After gradually incorporating these attention mechanisms, key performance indicators (MAE, RMSE, and Max Error) showed significant improvements. This demonstrates that the proposed attention mechanisms work synergistically to significantly enhance the accuracy and robustness of power grid load forecasting.

Power grid load data exhibit complex spatial and temporal dependencies, requiring robust models with strong expressive power. The proposed model integrates CNN, LSTM, and multiple attention mechanisms to explore load data from different dimensions.

Therefore, combining CNN with LSTM allows the strengths of CNN in local feature extraction to be integrated with LSTMs' strengths in temporal modeling, enabling the model to effectively capture both local features and long-term dependencies in load data. This enhances the accuracy and robustness of power grid 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.

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.