Combined models for day‐ahead electricity price forecasting based on improved gray correlation methodology

Purpose To combine the forecasting by single method using influence information fully, other than regular combined methods only focusing on historical forecasting errors. Design/methodology/approach To combine the single methods based on the analysis of improved gray correlation, with more related information being considered to enhance the price forecasting precision, such as the trend of the prices, the historical forecasting errors, and the temporal influence factors on prices. Findings A case of PJM market of USA shows that the proposed method has better performance than any other combined methods, and all single models as well. Research limitations/implications The combined performance depends on the forecasting precision of single methods, and the correlation between the single methods, as well as the number of single method that to be combined. Practical implications It is a novel idea for combined method to forecasting the time series data, such as electricity prices, electric power loads. Originality/value The proposed method considers all the following factors: the similarity between the trends of the single forecasting, the errors of the single models and the temporal influence.