Research on Electricity Price Forecast Model Based on Electricity Price Formation Mechanism and XGBoost and Highly Automated Optuna Algorithm

This paper proposes an electricity price forecasting model based on electricity price formation mechanism and XGBoost algorithm. Firstly, according to the marginal clearing price formation mechanism and unique bidding rules adopted in the monorail spot market, the unified cumulative bidding curve of the whole network is fitted by piecewise function, and the unified clearing price prediction model of the whole network is established by combining the bidding strategy of power generation enterprises. Secondly, according to the relevant data published in the monorail power spot market, the XGBoost algorithm is used to select features and solve the different daily ladder bidding strategies of power generation enterprises. Finally, the hyper-parameters of the model are optimized by the highly automated Optuna algorithm. The experimental results show that the electricity price prediction model in this paper has stronger interpretability and accuracy than the XGBoost algorithm directly substituted into the data, and proves that the XGBoost algorithm has higher prediction accuracy for the bidding strategy than the gradient boosting regression tree algorithm and the random forest algorithm, thus verifying the superiority and effectiveness of the model in the electricity price prediction of the monorail power spot market.