Research on Cost Determination Technology for Power Grid Engineering Based on Bayesian Deep Learning Network Potential Impact Factor Mining

DOI

10.2991/978-94-6463-256-9_84How to use a DOI?

Keywords

Power grid engineering; Bayesian deep learning network; cost determination technology

Abstract

The cost of power grid project is a multivariable and highly nonlinear problem. With the continuous expansion of the investment scale, the factors affecting the project cost are complex, diversified, volatility and other characteristics, and the single prediction model is often not comprehensive enough. In view of this, this paper excavates out the potential impact factor of project cost based on artificial neural network learning, which has a certain self-learning, adaptive ability, is a high accuracy, wide applicability of power grid engineering cost determination model, has high value, can further improve the efficiency of power grid enterprises.

Copyright

© 2024 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  - Tianmina Wu
PY  - 2023
DA  - 2023/10/09
TI  - Research on Cost Determination Technology for Power Grid Engineering Based on Bayesian Deep Learning Network Potential Impact Factor Mining
BT  - Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023)
PB  - Atlantis Press
SP  - 840
EP  - 847
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-256-9_84
DO  - 10.2991/978-94-6463-256-9_84
ID  - Wu2023
ER  -