Short-Term Electricity Price Forecasting Based on Grey Prediction GM(1,3) and Wavelet Neural Network

摘要

Electricity price is a core index that reflects the operation status of the power market, evaluates the efficiency of market competition, and is the basis for decision-making in the electricity market. Electricity price forecasting is of great significance to guide investment, allocate market resources spontaneously, achieve a basic balance of power supply and demand, and meet various service goals. In this paper, a short-term electricity price forecasting method based on a grey forecasting GM(1,3) and wavelet neural network combination model is adopted. Firstly, the power price sequence is decomposed and reconstructed by using the famous MALLAT algorithm of multi-resolution analysis based on wavelet transform theory, and then the final predictive electricity price sequence is obtained by using the BP neural network model. Then the predicted electricity price sequence is used as a relevant factor affecting the future daily electricity price and input to the grey GM(1,3) forecasting model for electricity price forecasting to obtain the final forecasting result. The model training and forecasting based on the 2012 load and price data published by the PJM power market in the United States show that the prediction model established by this method has higher prediction accuracy. Thus, it has important research significance for electricity market price forecasting.