SHS Web Conf.
Volume 151, 20223rd International Symposium on Economics, Management, and Sustainable Development (EMSD 2022)
|Number of page(s)||4|
|Published online||16 December 2022|
Grid investment capability prediction based on path analysis and BP neural network
Chongqing Urban Power Supply Branch, State Grid Chongqing Electric Power Company, Chongqing 400015, China
2 North China Electric Power University, Beijing, 102206, China
With the more complex investment environment of China’s power grid, the accurate prediction of the investment ability of power grid enterprises has become an important prerequisite for managers to make precise investment decisions. This paper first selects the factors affecting the investment capacity of the power grid from the internal and external environment, and establishes the index system of the factors affecting the investment capacity. Secondly, the path analysis is used to deeply explore the interaction relationship and influence degree of each index and investment capacity. Finally, the maximum investment capacity of the power network can be predicted based on the BP neural network prediction model. The results show that the BP neural network prediction model can achieve higher prediction accuracy when predicting the power grid investment capability.
Key words: Index system / path analysis / BP neural network / power grid investment capability
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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