Key Risk Index Identification of UHV Project Construction Based on Improved Rough Set Model

Sen Guo; Huiru Zhao

doi:doi:10.11648/j.sjee.s.2015030401.12

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Key Risk Index Identification of UHV Project Construction Based on Improved Rough Set Model

Sen Guo, Huiru Zhao

Published in Science Journal of Energy Engineering (Volume 3, Issue 4-1)

Received: 14 December 2014 Accepted: 15 December 2014 Published: 27 December 2014

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Abstract

Compared with the conventional power grid project, the UHV project construction faces more challenges and risks. Identifying the key risk indicators of UHV project construction can improve the level of project risk management and reduce risk-related loss. Taken the data missing of risk indicators into consideration, an improved rough set model for key risk indicators identification of UHV project construction is employed with the introduction of information content. Firstly, the information content of conditional attributes set and information significance of each conditional attribute are calculated; Secondly, the reduced core attribute matrix is formed; Then, the discernibility matrix is built; Finally, the final core attribute set is determined. After building the risk index system, the key risk indicators identification of a certain UHV project construction is performed. The calculation result shows that “land requisition and logging policy risk”, “project security management risk” and “land requisition, removing and crop compensation risk” are the key risk indicators.

Published in	Science Journal of Energy Engineering (Volume 3, Issue 4-1) This article belongs to the Special Issue Soft Computing Techniques for Energy Engineering
DOI	10.11648/j.sjee.s.2015030401.12
Page(s)	6-13
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

UHV Project Construction, Key Risk Indicators, Index Identification, Improved Rough Set Model

References

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Sen Guo, Huiru Zhao. (2014). Key Risk Index Identification of UHV Project Construction Based on Improved Rough Set Model. Science Journal of Energy Engineering, 3(4-1), 6-13. https://doi.org/10.11648/j.sjee.s.2015030401.12

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ACS Style

Sen Guo; Huiru Zhao. Key Risk Index Identification of UHV Project Construction Based on Improved Rough Set Model. Sci. J. Energy Eng. 2014, 3(4-1), 6-13. doi: 10.11648/j.sjee.s.2015030401.12

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AMA Style

Sen Guo, Huiru Zhao. Key Risk Index Identification of UHV Project Construction Based on Improved Rough Set Model. Sci J Energy Eng. 2014;3(4-1):6-13. doi: 10.11648/j.sjee.s.2015030401.12

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@article{10.11648/j.sjee.s.2015030401.12,
  author = {Sen Guo and Huiru Zhao},
  title = {Key Risk Index Identification of UHV Project Construction Based on Improved Rough Set Model},
  journal = {Science Journal of Energy Engineering},
  volume = {3},
  number = {4-1},
  pages = {6-13},
  doi = {10.11648/j.sjee.s.2015030401.12},
  url = {https://doi.org/10.11648/j.sjee.s.2015030401.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjee.s.2015030401.12},
  abstract = {Compared with the conventional power grid project, the UHV project construction faces more challenges and risks. Identifying the key risk indicators of UHV project construction can improve the level of project risk management and reduce risk-related loss. Taken the data missing of risk indicators into consideration, an improved rough set model for key risk indicators identification of UHV project construction is employed with the introduction of information content. Firstly, the information content of conditional attributes set and information significance of each conditional attribute are calculated; Secondly, the reduced core attribute matrix is formed; Then, the discernibility matrix is built; Finally, the final core attribute set is determined. After building the risk index system, the key risk indicators identification of a certain UHV project construction is performed. The calculation result shows that “land requisition and logging policy risk”, “project security management risk” and “land requisition, removing and crop compensation risk” are the key risk indicators.},
 year = {2014}
}

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TY  - JOUR
T1  - Key Risk Index Identification of UHV Project Construction Based on Improved Rough Set Model
AU  - Sen Guo
AU  - Huiru Zhao
Y1  - 2014/12/27
PY  - 2014
N1  - https://doi.org/10.11648/j.sjee.s.2015030401.12
DO  - 10.11648/j.sjee.s.2015030401.12
T2  - Science Journal of Energy Engineering
JF  - Science Journal of Energy Engineering
JO  - Science Journal of Energy Engineering
SP  - 6
EP  - 13
PB  - Science Publishing Group
SN  - 2376-8126
UR  - https://doi.org/10.11648/j.sjee.s.2015030401.12
AB  - Compared with the conventional power grid project, the UHV project construction faces more challenges and risks. Identifying the key risk indicators of UHV project construction can improve the level of project risk management and reduce risk-related loss. Taken the data missing of risk indicators into consideration, an improved rough set model for key risk indicators identification of UHV project construction is employed with the introduction of information content. Firstly, the information content of conditional attributes set and information significance of each conditional attribute are calculated; Secondly, the reduced core attribute matrix is formed; Then, the discernibility matrix is built; Finally, the final core attribute set is determined. After building the risk index system, the key risk indicators identification of a certain UHV project construction is performed. The calculation result shows that “land requisition and logging policy risk”, “project security management risk” and “land requisition, removing and crop compensation risk” are the key risk indicators.
VL  - 3
IS  - 4-1
ER  -

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Author Information

Sen Guo

School of Economics and Management, North China Electric Power University, Changping District, Beijing 102206, China
Huiru Zhao

School of Economics and Management, North China Electric Power University, Changping District, Beijing 102206, China

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Plain Text BibTeX RIS

APA Style

Sen Guo, Huiru Zhao. (2014). Key Risk Index Identification of UHV Project Construction Based on Improved Rough Set Model. Science Journal of Energy Engineering, 3(4-1), 6-13. https://doi.org/10.11648/j.sjee.s.2015030401.12

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ACS Style

Sen Guo; Huiru Zhao. Key Risk Index Identification of UHV Project Construction Based on Improved Rough Set Model. Sci. J. Energy Eng. 2014, 3(4-1), 6-13. doi: 10.11648/j.sjee.s.2015030401.12

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AMA Style

Sen Guo, Huiru Zhao. Key Risk Index Identification of UHV Project Construction Based on Improved Rough Set Model. Sci J Energy Eng. 2014;3(4-1):6-13. doi: 10.11648/j.sjee.s.2015030401.12

Copy | Download

@article{10.11648/j.sjee.s.2015030401.12,
  author = {Sen Guo and Huiru Zhao},
  title = {Key Risk Index Identification of UHV Project Construction Based on Improved Rough Set Model},
  journal = {Science Journal of Energy Engineering},
  volume = {3},
  number = {4-1},
  pages = {6-13},
  doi = {10.11648/j.sjee.s.2015030401.12},
  url = {https://doi.org/10.11648/j.sjee.s.2015030401.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjee.s.2015030401.12},
  abstract = {Compared with the conventional power grid project, the UHV project construction faces more challenges and risks. Identifying the key risk indicators of UHV project construction can improve the level of project risk management and reduce risk-related loss. Taken the data missing of risk indicators into consideration, an improved rough set model for key risk indicators identification of UHV project construction is employed with the introduction of information content. Firstly, the information content of conditional attributes set and information significance of each conditional attribute are calculated; Secondly, the reduced core attribute matrix is formed; Then, the discernibility matrix is built; Finally, the final core attribute set is determined. After building the risk index system, the key risk indicators identification of a certain UHV project construction is performed. The calculation result shows that “land requisition and logging policy risk”, “project security management risk” and “land requisition, removing and crop compensation risk” are the key risk indicators.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Key Risk Index Identification of UHV Project Construction Based on Improved Rough Set Model
AU  - Sen Guo
AU  - Huiru Zhao
Y1  - 2014/12/27
PY  - 2014
N1  - https://doi.org/10.11648/j.sjee.s.2015030401.12
DO  - 10.11648/j.sjee.s.2015030401.12
T2  - Science Journal of Energy Engineering
JF  - Science Journal of Energy Engineering
JO  - Science Journal of Energy Engineering
SP  - 6
EP  - 13
PB  - Science Publishing Group
SN  - 2376-8126
UR  - https://doi.org/10.11648/j.sjee.s.2015030401.12
AB  - Compared with the conventional power grid project, the UHV project construction faces more challenges and risks. Identifying the key risk indicators of UHV project construction can improve the level of project risk management and reduce risk-related loss. Taken the data missing of risk indicators into consideration, an improved rough set model for key risk indicators identification of UHV project construction is employed with the introduction of information content. Firstly, the information content of conditional attributes set and information significance of each conditional attribute are calculated; Secondly, the reduced core attribute matrix is formed; Then, the discernibility matrix is built; Finally, the final core attribute set is determined. After building the risk index system, the key risk indicators identification of a certain UHV project construction is performed. The calculation result shows that “land requisition and logging policy risk”, “project security management risk” and “land requisition, removing and crop compensation risk” are the key risk indicators.
VL  - 3
IS  - 4-1
ER  -

Copy | Download