Risk Assessment of Integrated Electricity--Heat Energy System with Cross Entropy and Objective Entropy Weight Method

2020 ◽  
Author(s):  
Xingxing Liao ◽  
Mingfeng Xue ◽  
Xiaobo Mao ◽  
Yongtao Pan ◽  
Guoqiang Sun ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Energy System ◽  
Heat Energy ◽  
Cross Entropy ◽  
Entropy Weight ◽  
Entropy Weight Method ◽  
Weight Method

scholarly journals The Risk Assessment of Debris Flow Hazards in Banshanmen Gully Based on the Entropy Weight-Normal Cloud Method

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
X. B. Gu ◽  
S. T. Wu ◽  
X. J. Ji ◽  
Y. H. Zhu
Keyword(s):  
Risk Assessment ◽  
Debris Flow ◽  
Cloud Model ◽  
Risk Estimation ◽  
Assessment Method ◽  
Risk Level ◽  
Entropy Weight ◽  
Entropy Weight Method ◽  
Weight Method ◽  
Index Weight

The debris flow is one of the geological hazards; its occurrence is complex, fuzzy, and random. And it is affected by many indices; a new multi-index assessment method is proposed to analyze the risk level of debris flow based on the entropy weight-normal cloud model in Banshanmen gully. The index weight is calculated by using the entropy weight method. Then, the certainty degree of each index belonging to the corresponding cloud is obtained by using the cloud model. The final risk level of debris flow is determined according to the synthetic certainty degree. The conclusions are drawn that the method is feasible and accurate rate of risk estimation for debris flow is very high, so a new method and thoughts for the risk assessment of debris flow can be provided in the future.

scholarly journals Research on the establishment and method of comprehensive benefit evaluation system for smart energy systems

2020 ◽  
Vol 213 ◽  
pp. 02036
Author(s):  
Nan Xu ◽  
Zihao Zhao ◽  
Bo Zhou ◽  
Ningning Shi ◽  
Yongli Wang ◽  
...  
Keyword(s):  
Evaluation System ◽  
Energy Systems ◽  
Energy System ◽  
Energy Regulation ◽  
Entropy Weight ◽  
Benefit Evaluation ◽  
Smart Energy Systems ◽  
Comprehensive Benefit ◽  
Entropy Weight Method ◽  
Weight Method

The smart energy system is the new direction of the current energy development. It plays a vital role in the low-carbon development of my country’s energy industry and in promoting the transformation and upgrading of the energy sector. For the construction and long-term development of smart energy systems, this paper constructs a comprehensive benefit evaluation index system for smart energy systems that can cover all aspects based on the economic, environmental protection, and reliability of energy regulation and other target benefits, and proposes a system based on AHP -Improved evaluation method of entropy weight method, aiming at the hierarchical structure of smart energy evaluation system, adopts analytic hierarchy process and improved entropy weight method to combine subjective weight and objective weight, and scientifically, reasonably and objectively the construction level of smart energy system evaluation of.

The Risk Assessment of Tunnels Based on Grey Correlation and Entropy Weight Method

2017 ◽  
Vol 36 (3) ◽  
pp. 1621-1631 ◽  
Author(s):  
Cheng-lu Gao ◽  
Shu-cai Li ◽  
Jing Wang ◽  
Li-ping Li ◽  
Peng Lin
Keyword(s):  
Risk Assessment ◽  
Entropy Weight ◽  
Grey Correlation ◽  
Entropy Weight Method ◽  
Weight Method

Research on money laundering risk assessment of customers – based on the empirical research of China

2016 ◽  
Vol 19 (3) ◽  
pp. 249-263 ◽  
Author(s):  
Yao-Wen Xue ◽  
Yan-Hua Zhang
Keyword(s):  
Risk Assessment ◽  
Decision Tree ◽  
Money Laundering ◽  
Decision Tree Model ◽  
Tree Model ◽  
Entropy Weight ◽  
Content Type ◽  
Regulatory Approach ◽  
Entropy Weight Method ◽  
Weight Method

Purpose To implement a risk-based regulatory approach, this paper aims to make an assessment on customers' money laundering risk and conducts some applications. Design/methodology/approach During the transition of a regulatory approach from “rule-based” to “risk-based”, this paper considers that the area of a customer, types of business and the industries to which the customer belongs are the main indicators to judge money laundering risk of a customer. Based on the statistical analysis of 221 typical money laundering cases, first-class index weights are given by using the entropy weight method and then by combining with the membership function, this paper determines a customer’s money laundering risk levels. On the basis of the entropy weight method, this paper uses the C5.0 algorithm to construct a decision tree model and then carries out application research on customer money laundering risk assessment to verify the effectiveness of the entropy weight method and the decision tree model. Findings This empirical research found the weights of three key money laundering indicators: customer areas, business types and corresponding industries. Originality/value Asserting that current money laundering risk assessments of customers are marginal at best, this paper contends from the perspective of practice, and applies the entropy weight method and the decision tree model for money laundering risk assessment of customers.

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