Research on Substation Engineering Cost Prediction Technology Based on Variable Bayes Deep Learning Optimization

DOI

10.2991/978-94-6463-736-6_14How to use a DOI?

Keywords

substation engineering; variational Bayes; cost prediction technology

Abstract

The cost of power grid projects is a multivariable and highly nonlinear problem, characterized by complex uncertainties and significant variability. As investment scales expand, the interplay of technical, environmental, and operational factors further complicates cost prediction. Accurate modeling requires a combination of advanced statistical methods and machine learning techniques to address these challenges. This paper integrates variational Bayes deep learning with engineering-specific data preprocessing, including normalization and principal component analysis (PCA), to ensure precise predictions and robust decision-making. The factors affecting the cost of power grid engineering are complicated. When the engineering situation is complex and changeable, it is difficult to obtain the reliable prediction results of a single project through the experience estimation of technical personnel. Therefore, this paper proposes a substation engineering cost prediction technology based on variational Bayes deep learning. This method is obviously better than other models in terms of prediction skills and prediction reliability, and can provide effective uncertainty estimation and prediction results.

Copyright

© 2025 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  - Xiao Zeng
AU  - Xiaoling Peng
AU  - Haiyan Tong
AU  - Xiu Xu
PY  - 2025
DA  - 2025/05/22
TI  - Research on Substation Engineering Cost Prediction Technology Based on Variable Bayes Deep Learning Optimization
BT  - Proceedings of the 2025 4th International Conference on Engineering Management and Information Science (EMIS 2025)
PB  - Atlantis Press
SP  - 105
EP  - 112
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-736-6_14
DO  - 10.2991/978-94-6463-736-6_14
ID  - Zeng2025
ER  -