Research on Power Grid Engineering Cost Analysis and Investment Forecasting Technology based on Probability Statistics

DOI

10.2991/978-94-6463-992-6_12How to use a DOI?

Keywords

distribution network equipment; data value mining; health code; intelligent detection technology

Abstract

The cost analysis and investment forecasting of power grid projects are critical for enhancing the efficiency of asset management in power grid enterprises. As the scale of power grids continues to expand and equipment complexity increases, traditional empirical asset management methods are no longer sufficient to meet the demands of refined management. This study, grounded in probability and statistical methods, proposes an investment analysis framework that integrates failure rate prediction with health index evaluation. By constructing failure rate models based on time parameters and health indices, it achieves accurate predictions of equipment failure rates. The model parameters are optimized using the least squares method, maximum likelihood estimation, and Monte Carlo simulation, and the validity of the models is confirmed through empirical analysis. The findings indicate a strong correlation between the health condition of equipment and its failure rate. The proposed failure rate models can effectively evaluate the operational costs and maintenance needs of equipment throughout its life cycle, providing a scientific foundation for cost optimization and investment planning in power grid projects. This research offers valuable guidance for power grid enterprises in asset allocation, cost control, and investment decision-making.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Biao Shen
AU  - Weiwei Bao
AU  - Junfeng Hu
AU  - Hao Zheng
AU  - Xiaolai Li
PY  - 2026
DA  - 2026/02/20
TI  - Research on Power Grid Engineering Cost Analysis and Investment Forecasting Technology based on Probability Statistics
BT  - Proceedings of the 2025 4th International Conference on Mathematical Statistics and Economic Analysis (MSEA 2025)
PB  - Atlantis Press
SP  - 110
EP  - 118
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-992-6_12
DO  - 10.2991/978-94-6463-992-6_12
ID  - Shen2026
ER  -