Abstract:Accurate forecasting of day-ahead electricity price is of great significance for safeguarding the interests of electricity market participants. This paper proposes a day-ahead electricity price forecasting method based on dynamic data and similar day approach with multi-dimensional impact factors, aiming to improve forecasting accuracy and adaptability to dynamic market changes. Firstly, by comprehensively considering the feature importance and the Pearson correlation coefficient, key features that affect the price of electricity spot markets are screened out. Derived features such as delay features, historical similar day features, and comprehensive weighted features are designed to enrich the input information for electricity price forecasting. Secondly, to adapt to the dynamic changes of the electricity market and improve the applicability of historical data, a rolling training mechanism is introduced to regularly update the dataset and retrain the forecasting model. Simulation analysis is carried out using data from the Guangdong electricity spot market. The experimental results show that reasonable feature construction, hyperparameter optimization, and the selection and application of rolling training strategies can effectively improve the forecasting performance of the electricity price forecasting model.