With the continuous deepening of China’s power system reform

significant progress has been made in the construction of the power market. Electricity price is a key influencing factor in the electricity market and each participant conducts electricity transactions based on electricity prices. Therefore

improving the accuracy of electricity price forecasts is very important for every participant in the electricity market. Most of the previous electricity price forecasts used single - layer neural network forecasts

and the accuracy of the forecasts was limited. To this end

according to the accuracy of machine learning in forecasting

the deep be-lief network method is used to predict the day - a - day electricity price. In the calculation example

the real data of the US PJM pow-er market is used for simulation prediction and compared with other neural network prediction models. The results of calculation examples show that the prediction accuracy of the deep belief net-work model is higher. The use of deep belief networks can providean effective method for electricity price forecasting for China’s electricity sales companies.