Research on multi-digital twin and its application in wind power forecasting

Abstract

Digital twins are digital models that operate within the digital space to perform specific functions. It can positively impact the physical counterpart in terms of improved efficiency, reduced costs, increased safety, and reliability. However, most existing research has limited digital twins to a single digital twin model, and the inherent characteristics and practical applications of single digital twin models make it challenging to fully utilize their advantages in complex and dynamic environments. Therefore, this paper proposes the concept of a multi-digital twin (MDT), as well as its synergistic operation mechanism, and designs two specific implementation methods suitable for processing time series-related tasks: the single metric dynamic preference method and the multi-metrics dynamic fusion method both based on a time window. Then, the results of two experiments show that the average relative improvement of two methods in the MAE, RMSE, and R2 metrics were 7.83 %, 5.01 %, and 1.24 % in 2015, and 4.98 %, 2 %, and 0.29 % in 2017. This means that the proposed methods can improve the accuracy of wind power forecasting. The effectiveness of the MDT synergy operation mechanism is verified, providing new research ideas for digital twins to adapt to complex system changes.