Research on Site Selection Optimization of Hydrogen Refueling Station Based on GBDT Method

DOI

10.2991/978-94-6463-570-6_13How to use a DOI?

Keywords

hydrogen refueling station location; GBDT model; sales forecast; economic benefits; hydrogen production methods

Abstract

This study aims to provide a scientific method for private investors to invest in the construction of hydrogen refueling stations with the support of government subsidies. Firstly, the Gradient Boosting Decision Tree (GBDT) model was used to predict the future sales of hydrogen fuel vehicles in different areas of the city, and accordingly, the high-sales areas were identified as the priority candidate locations for the construction of hydrogen refueling stations. On the basis of the above, a site selection model for hydrogen refueling stations with the best economic benefits was further established. The model comprehensively considers the impact of geographical constraints, spatial constraints and the cost of hydrogen production on economic benefits. The study shows that this strategy not only helps private investors maximize their investment returns, but also helps to promote the rational layout and rapid development of hydrogen energy infrastructure, thereby supporting the growth of the hydrogen fuel vehicle industry.

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  - Yaqin Li
AU  - Liying Li
PY  - 2024
DA  - 2024/11/22
TI  - Research on Site Selection Optimization of Hydrogen Refueling Station Based on GBDT Method
BT  - Proceedings of the 2024 5th International Conference on Management Science and Engineering Management (ICMSEM 2024)
PB  - Atlantis Press
SP  - 114
EP  - 121
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-570-6_13
DO  - 10.2991/978-94-6463-570-6_13
ID  - Li2024
ER  -