Research on the application of domestic BIM technology in the lifecycle management of power grid projects

Lizhong Qi; Jingguo Rong; Su Zhang; Haibo Liu; Fangrong Xu

doi:10.1117/12.3050992

27 September 2024 Research on the application of domestic BIM technology in the lifecycle management of power grid projects

Lizhong Qi, Jingguo Rong, Su Zhang, Haibo Liu, Fangrong Xu

Author Affiliations +

Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 1328115 (2024) https://doi.org/10.1117/12.3050992
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China

Abstract

The accelerated construction of smart grids has imposed higher demands on the lifecycle management of power grid projects. Addressing the issues of low efficiency, high costs, and data inconsistency in the design, construction, and operation phases of current power grid projects, this paper proposes an optimized method based on Domestic BIM Technology. This method integrates Principal Component Analysis (PCA) and Monte Carlo simulation. By utilizing PCA to extract key indicators and best practices, and consolidating design, construction, and operational data throughout the entire lifecycle, the consistency and accuracy of the data are ensured, and construction processes are optimized. Furthermore, Monte Carlo simulation is employed to simulate and optimize different load distribution schemes. The methods discussed in this paper significantly enhance the management efficiency and quality across all stages of the lifecycle management of power grid projects, providing robust technical support and assurance for the intelligent management of power grid projects.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lizhong Qi, Jingguo Rong, Su Zhang, Haibo Liu, and Fangrong Xu "Research on the application of domestic BIM technology in the lifecycle management of power grid projects", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 1328115 (27 September 2024); https://doi.org/10.1117/12.3050992

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