Research on Electricity Load Forecasting Based on Attention Model

This study aims to explore an attention-based method for power load forecasting with the goal of enhancing the accuracy and efficiency of load forecasting in the power system. Power load forecasting plays a crucial role in power system operation and planning. Precise load forecasting can facilitate decision-making processes such as power dispatch, energy procurement, and grid planning. Traditional power load forecasting methods typically rely on statistical models or machine learning algorithms. However, these methods often struggle to effectively capture complex relationships and nonlinear trends in time series data. To address this issue, this study proposes a power load forecasting method based on the attention mechanism. The proposed method utilizes the attention mechanism to automatically learn the correlation and importance between different time points in the time series data. Additionally, through time series encoding and decoding operations, the method enables feature mining of critical data, ultimately leading to high-precision power prediction. Experimental results demonstrate that the attention-based power load forecasting method has achieved a significant improvement in load forecasting tasks.