Short-term power load forecasting in China: A Bi-SATCN neural network model based on VMD-SE.

ABSTRACT: This study focuses on improving short-term power load forecasting, a critical aspect of power system planning, control, and operation, especially within the context of China's "dual-carbon" policy. The integration of renewable energy under this policy has introduced complexities such as nonlinearity and instability. To enhance forecasting accuracy, the VMD-SE-BiSATCN prediction model is proposed. This model improves computational efficiency and reduces prediction errors by analyzing and reconstructing sequence component complexity using sample entropy (SE) following variational mode decomposition (VMD). Additionally, a self-attention mechanism is integrated into the temporal convolutional network (TCN) to overcome the traditional TCN's limitations in capturing long-term dependencies. The m

SUBMITTER: Huang Y 

PROVIDER: S-EPMC11441686 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature