Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)

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Digital Investment Risk Evaluation Model of Power Grid Enterprises Based on FAHP-AOA-LSSVM

Authors
Xinyi Lan1, *, Xinping Wu2, Qiuzhe Ma2, Wenqing Liu1, Jinchao Li1
1Institute of Economic and Management, North China Electric Power University, Beijing, China
2Engineering Data Center Digital Planning Division, State Power Economic Research Institute, Beijing, China
*Corresponding author. Email: 905806800@qq.com
Corresponding Author
Xinyi Lan
Available Online 28 August 2023.
DOI
10.2991/978-94-6463-222-4_13How to use a DOI?
Keywords
digital project investment risk; fuzzy hierarchical analysis; Archimedean optimization algorithm; least squares support vector machine
Abstract

The digital transformation of the economy represents the general trend. In order to effectively control the investment risk of grid digitization projects and adopt risk-coping strategies with foresight, construct an investment risk evaluation model for grid digitization projects by optimizing the kernel function parameters and regularization parameters of least squares support vector machines through the Archimedes algorithm. Questionnaires and expert judgment are used to analyze the risk factors facing digitization projects’ investment environment and establish an investment risk evaluation system. A fuzzy hierarchical analysis method is applied to evaluate the investment risk of 40 completed projects according to the actual engineering situation, and the evaluation results are normalized and processed as the input vector of the evaluation model for training. The results show that the Archimedes optimization algorithm improves the least squares support vector machine model prediction with an average absolute percentage error of 3.5026%, which can more accurately assess the riskiness of digital projects and provide a reference basis for digital project investment risk control.

Copyright
© 2023 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 the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
28 August 2023
ISBN
10.2991/978-94-6463-222-4_13
ISSN
2589-4919
DOI
10.2991/978-94-6463-222-4_13How to use a DOI?
Copyright
© 2023 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  - Xinyi Lan
AU  - Xinping Wu
AU  - Qiuzhe Ma
AU  - Wenqing Liu
AU  - Jinchao Li
PY  - 2023
DA  - 2023/08/28
TI  - Digital Investment Risk Evaluation Model of Power Grid Enterprises Based on FAHP-AOA-LSSVM
BT  - Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)
PB  - Atlantis Press
SP  - 136
EP  - 150
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-222-4_13
DO  - 10.2991/978-94-6463-222-4_13
ID  - Lan2023
ER  -