Proceedings of 2025 2nd International Conference on Applied Economics, Management Science and Social Development (AEMSS 2025)

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Green Energy High-quality Development Path Decision Algorithm Based on Artificial Neural Network

Authors
Shuo Dai1, *, Yitong Chen1
1Faculty of Management and Economics, Kunming University of Science and Technology, Kunming, Yunnan, 650500, People’s Republic of China
*Corresponding author. Email: soniaday@163.com
Corresponding Author
Shuo Dai
Available Online 7 June 2025.
DOI
10.2991/978-94-6463-752-6_21How to use a DOI?
Keywords
Artificial neural network; Green energy; High quality development; Path decision
Abstract

The development of green energy involves multi-party decision-making, and a single indicator is difficult to fully reflect, which affects the accuracy of decision-making and has a low coefficient of variation. Therefore, a high-quality green energy development path decision algorithm based on artificial neural network is designed. The Mapstd function and Mapminmax function of Matlab were applied for data normalization processing, and a high-quality green energy development level measurement model containing multiple measurement objects and indicators was constructed. Relative entropy of connection number and combined weighting model were introduced, and the difference degree within indicators and correlation degree among indicators were comprehensively considered. Constructed a three-layer neural network and designed a comprehensive optimal value path decision-making method to select the best from finite paths. The experiment shows that this method improves the coefficient of variation of green energy development, with an error of ± 20% and a correlation of > 0.84.

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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Volume Title
Proceedings of 2025 2nd International Conference on Applied Economics, Management Science and Social Development (AEMSS 2025)
Series
Advances in Economics, Business and Management Research
Publication Date
7 June 2025
ISBN
978-94-6463-752-6
ISSN
2352-5428
DOI
10.2991/978-94-6463-752-6_21How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - Shuo Dai
AU  - Yitong Chen
PY  - 2025
DA  - 2025/06/07
TI  - Green Energy High-quality Development Path Decision Algorithm Based on Artificial Neural Network
BT  - Proceedings of 2025 2nd International Conference on Applied Economics, Management Science and Social Development (AEMSS 2025)
PB  - Atlantis Press
SP  - 197
EP  - 207
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-752-6_21
DO  - 10.2991/978-94-6463-752-6_21
ID  - Dai2025
ER  -