Energy financial risk early warning model based on Bayesian network

Wei, Lin; Yu, Hanyue; Li, Bin

doi:10.1016/j.egyr.2022.12.151

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…Some scholars have delved into risk prediction in various projects. For instance, Wei et al (2023) introduced prediction models based on BP neural networks and Bayesian networks, focusing on early warnings for financial risks in non-renewable energy, particularly within the petroleum industry. Thaler et al (2005) leveraged neural networks to create a risk prediction model for distribution system risks in electricity consumption and excess demand.…”

Section: Exploring Risk Prediction In Diverse Projectsmentioning

confidence: 99%

“…As the proportion of investment in renewable energy projects increases, concerns regarding investment risk grow. Many scholars have explored risk assessment (Ghimire and Kim, 2018;Mostafaeipour et al, 2021), and macro-environmental risk prediction studies have also emerged (Thaler et al, 2005;Wei et al, 2023). Nevertheless, existing research suffers from subjective and incomplete risk indicators and evaluation methods, emphasizing the need for a comprehensive, systematic integrated analysis framework.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Framework for Evaluating and Predicting Investment Risks in Renewable Energy Projects: A Case Study of Maynak Hydropower Station

Xu,

Wan,

et al. 2024

Preprint

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This study introduces a holistic analysis framework designed to evaluate and predict the investment risks associated with foreign renewable energy initiatives. The primary objective of this framework is to address the inherent uncertainties that often accompany such projects. To achieve this, we employ the variable weight matter-element extension model to establish the project's fundamental reliability function. Subsequently, we enhance this model using evidence theory to determine the project's risk level and generate risk index measurement results. Additionally, we utilize the GM model for forecasting future project risks. To illustrate the practicality of our approach, we provide a case study focused on the risk assessment and prediction for the Maynak Hydropower Station. Our findings indicate that during 2008, 2014, 2020, and 2022, the project faced a high level of investment risk. Key risk indicators included political instability, policy changes, legislative gaps, cultural risks, exchange rate fluctuations, technical challenges, and management risks. Moreover, from 2023 to 2027, the project's investment risk level moderated, with risk measurement results aligning closely with actual circumstances, thus validating the efficacy and applicability of our model.

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Section: Exploring Risk Prediction In Diverse Projectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comprehensive Framework for Evaluating and Predicting Investment Risks in Renewable Energy Projects: A Case Study of Maynak Hydropower Station

Xu,

Wan,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In illiquid markets, liquidity risk can occur, and measures for risk management might be impacted by legislative changes. Technological malfunctions and other operational hazards increase complexity, and because risks are dynamic, they must always be adjusted [9]. And in the event that the predicted danger never comes to pass, the expense of risk mitigation techniques could be viewed as an opportunity cost.…”

Section: Introductionmentioning

confidence: 99%

Early Warning of Financial Risk Management using AGRNN-COA Approach

Xuwei Tang

2024

jes

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Financial risk management was used to protect an entity's financial performance and stability, entails detecting, evaluating, and reducing potential risks, such as credit defaults and market swings. It includes tactics including hedging, diversification, and risk limitation. Financial authorities must immediately responsive for financial risk management system that works with the current economic development. This paper proposes a hybrid approach for early pre-alarm warning of financial risk management system and economic development. The proposed hybrid approach is the combined performance of both the Alternating Graph-Regularized Neural Network (AGRNN) and Crayfish Optimization Algorithm (COA).Commonly it is named as AGRNN-COA technique. The major objective of the proposed approach is to give early pre-alarm warning of financial risk management. AGRNN is designed to give early pre-alarm warning of the financial system of the country. The financial risk management from the AGRNN are optimized by using the COA. By then, the proposed model is implemented in the MATLAB/Simulink working platform and the execution is calculated with the present procedures. The proposed method shows better results in all existing like Financial Risk Management Supervised Algorithm (FRM-SVM), Financial Risk Management Deep Neural Network (FRM-DNN) and Financial Risk Management Heap Based Optimization (FRM-HBO). The accuracy level of proposed FRM-AGRNN-COA approach is 98% that is higher than the other existing methods. The specificity and the F-score of the proposed FRM-AGRNN-COA approach is 99% and 97%. The error rate of the proposed FRM-AGRNN-COA approach is 1.8%, which is very less compared to other existing techniques. From the result, it is conclude that the proposed approach based error is less compared to existing techniques.

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Research of Dempster-Shafer’s Theory and Ensemble Classifier Financial Risk Early Warning Model Based on Benford’s Law

Liu,

2024

Comput Econ

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Energy financial risk early warning model based on Bayesian network

Cited by 7 publications

References 17 publications

A Comprehensive Framework for Evaluating and Predicting Investment Risks in Renewable Energy Projects: A Case Study of Maynak Hydropower Station

A Comprehensive Framework for Evaluating and Predicting Investment Risks in Renewable Energy Projects: A Case Study of Maynak Hydropower Station

Early Warning of Financial Risk Management using AGRNN-COA Approach

Research of Dempster-Shafer’s Theory and Ensemble Classifier Financial Risk Early Warning Model Based on Benford’s Law

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