Real-Time Electricity Price Forecasting with Dynamic Graph Convolution and Adaptive Temporal Fusion

INTRODUCTION: Real-time electricity price forecasting is critical for real-time dispatch, automatic generation control, spot trading and risk management in electricity markets. OBJECTIVES: Since large-scale wind and photovoltaic renewable energy grid integration brings strong random fluctuation of renewable power output, real-time electricity prices exhibit stronger volatility, higher randomness and tighter time coupling than day-ahead prices; the randomness of renewable power is one core factor causing abrupt price jumps, leading conventional forecasting models struggle to capture dynamic spatial–temporal dependencies and sudden fluctuation patterns. METHODS: To address these issues, this paper proposes a real-time electricity price forecasting model based on dynamic graph convolution (DGC), adaptive gated temporal fusion, and Transformer–BiGRU hybrid structure. The model uses a dynamic graph convolution module to model real-time changing topological correlations among power system features, introduces an adaptive gated fusion mechanism to enhance the weighting of real-time key features, and integrates Transformer and bidirectional GRU to capture both long-range global dependencies and high-frequency local fluctuations. RESULTS: Experiments are conducted on real-time electricity price and real-time operation data from Shandong electricity market. Results show that the proposed model achieves average prediction accuracy of 88.2%, with MAE and MSE reduced to 0.071 and 0.009 respectively, which outperforms all baseline methods significantly. CONCLUSION: The proposed method provides high-precision support for real-time market transactions, real-time scheduling and intelligent decision-making.