skip to main content
10.1145/3674225.3674342acmotherconferencesArticle/Chapter ViewAbstractPublication PagespeaiConference Proceedingsconference-collections
research-article

Digital Grid Investment Risk Prediction and Identification under PSO-FAHP Based Intelligent Algorithm

Published: 31 July 2024 Publication History

Abstract

Combined with the actual situation of power grid enterprise investment, this paper analyzes the main steps of investment risk management of power grid enterprise, and carries out several researches from risk identification, risk prediction, risk assessment and early warning. First of all, data mining technology was used to analyze the event from the perspective of the whole system, to find out each risk factor affecting the occurrence of the event and classify it, and to construct an index system. Improved gray correlation analysis was used to analyze the influencing factors of power grid investment risk, obtain the indicator evaluation system, analyze the role of the relationship between the influencing factors, and provide ideas for investment decision-making. Secondly, a new grid investment risk prediction method is proposed, combining qualitative and quantitative methods to comprehensively assess the risk of grid investment. The indicators are divided into qualitative and quantitative indicators, and the investment risk prediction library is constructed through a technical method with strong objectivity. A combination model of intelligent algorithms based on particle swarm, support vector machine, and neural network is established to predict the indicators such as economic rationality, and the prediction accuracy of the model is verified, so as to prepare for the subsequent assessment of risks. This report sets three levels of warning for indicators and five levels of warning for projects and enterprises; sets warning intervals and determines the risk values of different risk indicators. Finally, the use of data integration technology, through the cluster intelligence technology of intelligent algorithms summarized to form a database, and then effective identification, prediction, assessment and early warning of power grid investment risk. And relying on the sound organization, clear functions, clear division of powers and responsibilities, flexible and efficient grid investment risk management organization, relying on intelligent management and control technology, clarifying the powers and responsibilities of each grid investment risk management function by means of legalization, realizing the efficient and synergistic operation between each function when the grid investment risk occurs, and establishing the risk management organization framework for the project and the power grid enterprise.

References

[1]
Liu Dong-hai, Gao Xing-fu and Pan Yao, "Fuzzy comprehensive optimization of hydropower project schedule with consideration of investment and risk", Journal of Tianjin University, vol. 41, no. 9, pp. 1109-1124, 2008.
[2]
Wang Shu-qiang, "Design of investment risk valuation model on subentry project of light track construction-investment risk analysis based on BP neural nets [J]", Computer Engineering and Applications, vol. 44, no. 1, pp. 219-221, 2008.
[3]
Peter J. Cmsbie and Jeffrey R. Bohn, "Modeling Default Risk" in, SAN FRANCISCO:KMV. LLC, pp. 5-8, 2000.
[4]
Valerie J. Davidson and Joanne Ryks, "Fuzzy risk assessment tool for microbial hazards in food systems", Fuzzy Sets and Systems, vol. 157, no. 9, pp. 1201-1210, 2006.
[5]
J. Taboada, J. Matlas and A. Saavedra, "Risk communications: around the world neural network models for assessing road suitability for dangerous goods transport", Human and Ecological Risk Assessment, vol. 12, no. 1, pp. 174-191, 2006.
[6]
O. Tietjen, M. Pahle and B. Fuss, Investment Risks in Power Generation: A Comparison of Fossil Fuel and Renewable Energy Dominated Markets, Postdam:Postdam Institute for Climate Impact Research, 2015.
[7]
C. Burja and V Burja, The Risk Analysis for Investments Project Decision, Annales Universitatis Apulensis Series Economica, 2009.
[8]
M. Merkova, J. Drabek and D. Jelacic, Application of Risk Analysis in Business Investment Decision-Making, Postdam: University of Zagreb, 2013.
[9]
J.H. Hall, Risk Analysis and Evaluation of Capital Investment Project, Pretoria:University of Pretoria, 2001.
[10]
David Moser, Matteo Del Buono, Ulrike Jahn, Magnus Herz, Mauricio Richter, Karel De Brabandere, "Identification of technical risks in the photovoltaic value chain and quantification of the economic impact", Progress in Photovoltaics: Research and Applications, vol.25, no.7, pp.592, 2017.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
January 2024
969 pages
ISBN:9798400716638
DOI:10.1145/3674225
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 July 2024

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

PEAI 2024

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 15
    Total Downloads
  • Downloads (Last 12 months)15
  • Downloads (Last 6 weeks)1
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media