Research on machine learning prediction model for improving operating efficiency of electricity market

This paper focuses on the use of machine learning techniques to enhance the operational efficiency of electricity markets. In particular, an innovative forecasting model is developed by integrating long Short-Term memory (LSTM) and Gradient Boosted decision tree (GBDT). At the same time, GBDT is used to process the high-dimensional combination of LSTM processed features or original features to further refine advanced features and nonlinear relationships. Finally, the output of LSTM is combined with the GBDT processed features to form the final prediction model through weighted fusion or stacked integration. The combination of the two complements their respective advantages. The accuracy of prediction is significantly improved.