Forecasting Method of Monthly Clearing Price Under the Background of Continuous Adjustment of Power Market Supply and Demand

The monthly clearing price forecasting method is commonly used. The model input is used to construct a monthly electricity clearing price data set. The collected electricity monthly clearing price time series characteristics are too single, resulting in a large average absolute percentage error of the monthly clearing price forecast. For this reason Propose a method for forecasting monthly clearing prices in the context of the continuous adjustment of supply and demand in the electricity market. To study the impact of the continuous adjustment of power market supply and demand on the monthly clearing price, design the electricity monthly clearing price forecasting process, and use the normalization method to preprocess the data; extract time information and load information to construct a training sample set for the monthly clearing price of electricity; The non-linear mapping relationship between electricity price and various influencing factors, using BP neural network, establishes a monthly electricity clearing price prediction model, and predicts the monthly electricity clearing price. The experimental results show that the average absolute percentage error of the research method predicting the monthly electricity clearing price is smaller than the two commonly used methods, and it has better prediction accuracy of the monthly electricity clearing price.