Research on Day-Ahead Electricity Price Forecasting Based on VMD-LSTM Model

Under the global low-carbon transition background, electricity markets play a significant role in promoting efficient utilization of electric energy and optimizing resource allocation. As a core indicator of the electricity market, accurate electricity price forecasting is of great importance for market stability and participant decision-making. Based on operational data from a provincial electricity spot market in China, this paper analyzes its price characteristics and proposes the application of variational mode decomposition for noise reduction and volatility suppression in price data. The processed subsequences are then used to construct a long short-term memory forecasting model, with prediction accuracy further improved through parameter optimization. Experimental results demonstrate that the proposed method outperforms other comparative models in electricity spot market price forecasting, verifying its effectiveness and applicability, while offering a novel approach for electricity price forecasting in China's electricity market.