Research on Day-Ahead Electricity Price Forecasting Method of New Energy Based on Multi-Information Fusion

Aiming at the strong volatility of the day-ahead electricity price series in the electricity spot market, a day-ahead electricity market price forecasting model based on complementary ensemble empirical mode decomposition-improved sparrow search algorithm-least squares support vector machine is proposed. Firstly, the grey correlation analysis method is used to screen the similar day set of the forecast day, and then the historical electricity price sequence of the similar day is decomposed by the complementary ensemble empirical mode decomposition method. Secondly, the improved sparrow search algorithm is used to optimize the least squares support vector machine to obtain the improved sparrow search algorithm-least squares support vector machine prediction model, and the decomposition results are predicted respectively, and the prediction results are superimposed to finally obtain the day-ahead electricity price prediction value. The simulation results show that the proposed method has higher prediction accuracy than other prediction models for electricity price prediction.