A novel system for multi-step electricity price forecasting for electricity market management

Electricity price forecasting is an important and challenging issue for all participants in the power market because of the wide application of electricity in our society and its inherent features. In this context, some current forecasting systems use data preprocessing and optimization for theoretical and practical achievements. However, some limitations to these systems exist which need to be urgently solved. First, future information is overdrawn in the data preprocessing stage of these forecasting systems, which is actually unknown in practical applications. The crucial question, therefore, is how to develop a forecasting system without using any future information. Second, the complex features of original nonlinear and nonstationary electricity price have a negative influence on the generalization ability of these previously developed models. To decrease the negative effects on management, a method to develop a forecasting system to improve the model’s generalization ability is required. Therefore, in this study, we developed an adaptive deterministic and probabilistic interval forecasting system for multi-step electricity price forecasting, which can present more valuable information to power market decision makers. Two cases and one comparative study are provided and analyzed to validate the performance of the developed system in multi-step electricity price forecasting. Furthermore, further discussions are presented to illustrate the significance of this study, thus proving that the results of the present study fill the present knowledge gap and provide some new future directions for related studies.