The accurate prediction of spot market electricity prices plays a crucial role in protecting the interests of participants in the electricity market. Both raw material prices and climate factors can affect the fluctuation of electricity prices in the spot market. In addition

a large amount of wind and solar energy is currently involved in spot market transactions

making electricity price forecasting in the spot market more challenging. Therefore

a spot market electricity price prediction model that integrates CEEMDAN

BERT

and LSTM is proposed. Firstly

the CEEMDAN algorithm is used to decompose the original electricity price data；Subsequently

the BERT algorithm is used to process the text data of three exogenous features：raw material prices

climate conditions

and renewable energy

in order to improve the prediction accuracy of the model；Next

the electricity price decomposition subsequence is combined with the results of exogenous feature processing

and LSTM is used to predict the model. The predicted results are then overlaid to obtain the final electricity price. Finally

the effectiveness of the proposed method was verified through simulation

and the results show that the CEEMDAN-BERT-LSTM prediction model improved the accuracy of electricity price prediction significantly.