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

Shuwei Liu, Jianyan Tian, Zhengxiong Ji, Yuanyuan Dai, Hengkuan Guo and Shengqiang Yang

Energy, 2024, vol. 292, issue C

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.

Keywords: Multi-digital twin; Synergistic operation mechanism; Single metric dynamic preference method; Multi-metrics dynamic fusion method; Wind power forecasting (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224000409
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:292:y:2024:i:c:s0360544224000409

DOI: 10.1016/j.energy.2024.130269

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().