关键词: Mamba模型, 状态空间模型, 深度学习, 电价预测

Abstract: To address the need for improving the accuracy and efficiency of electricity price forecasting in power markets, a regional electricity price prediction method based on S-Mamba2 model was proposed. This method aims to resolve high computational resource consumption and low efficiency in complex electricity price forecasting scenarios. Building upon Mamba-2, the proposed algorithm incorporates a bidirectional Mamba-2 module and a feedforward neural network encoding layer. These enhancements effectively capture spiky characteristics, seasonal patterns, inter-variate correlations (VC), and temporal dependencies (TD) in historical electricity price data. Experiments were conducted using real-world electricity price datasets from Australian Energy Market Operator (AEMO) and 2014 Global Energy Forecasting Competition (GEFCom2014). Results demonstrate that S-Mamba2 outperforms benchmark models including iTransformer and TimeDiffusion in prediction performance, achieving a maximum prediction accuracy of 97.88%. The proposed method provides robust technical support for enhancing market efficiency, reducing trading risks, and optimizing resource allocation in power markets.

Key words: Mamba model, state space model, deep learning, electricity price forecasting