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

Shuo Dai () and Yitong Chen ()
Additional contact information
Shuo Dai: Kunming University of Science and Technology, Faculty of Management and Economics
Yitong Chen: Kunming University of Science and Technology, Faculty of Management and Economics

A chapter in Proceedings of 2025 2nd International Conference on Applied Economics, Management Science and Social Development (AEMSS 2025), 2025, pp 197-207 from Springer

Abstract: 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.

Keywords: Artificial neural network; Green energy; High quality development; Path decision (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-752-6_21

Ordering information: This item can be ordered from
http://www.springer.com/9789464637526

DOI: 10.2991/978-94-6463-752-6_21

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().